Repository: OpenIntroOrg/openintro-statistics
Branch: master
Commit: fee25091fb24
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Directory structure:
gitextract_luzkuarl/

├── .gitignore
├── LICENSE.md
├── README.md
├── ch_distributions/
│   ├── TeX/
│   │   ├── binomial_distribution.tex
│   │   ├── ch_distributions.tex
│   │   ├── geometric_distribution.tex
│   │   ├── negative_binomial_distribution.tex
│   │   ├── normal_distribution.tex
│   │   ├── poisson_distribution.tex
│   │   └── review_exercises.tex
│   └── figures/
│       ├── 6895997/
│       │   └── 6895997.R
│       ├── amiIncidencesOver100Days/
│       │   └── amiIncidencesOver100Days.R
│       ├── between59And62/
│       │   └── between59And62.R
│       ├── eoce/
│       │   ├── GRE_intro/
│       │   │   └── gre_intro.R
│       │   ├── area_under_curve_1/
│       │   │   └── area_under_curve_1.R
│       │   ├── area_under_curve_2/
│       │   │   └── area_under_curve_2.R
│       │   ├── college_fem_heights/
│       │   │   └── college_fem_heights.R
│       │   └── stats_scores/
│       │       └── stats_scores.R
│       ├── fcidMHeights/
│       │   ├── fcidMHeights-helpers.R
│       │   └── fcidMHeights.R
│       ├── fourBinomialModelsShowingApproxToNormal/
│       │   └── fourBinomialModelsShowingApproxToNormal.R
│       ├── geometricDist35/
│       │   └── geometricDist35.R
│       ├── geometricDist70/
│       │   └── geometricDist70.R
│       ├── height40Perc/
│       │   └── height40Perc.R
│       ├── height82Perc/
│       │   └── height82Perc.R
│       ├── mikeAndJosePercentiles/
│       │   └── mikeAndJosePercentiles.R
│       ├── nbaNormal/
│       │   ├── nbaNormal-helpers.R
│       │   └── nbaNormal.R
│       ├── normApproxToBinomFail/
│       │   └── normApproxToBinomFail.R
│       ├── normalExamples/
│       │   ├── normalExamples-helpers.R
│       │   └── normalExamples.R
│       ├── normalQuantileExer/
│       │   ├── QQNorm.R
│       │   ├── normalQuantileExer-data.R
│       │   ├── normalQuantileExer.R
│       │   └── normalQuantileExerAdditional.R
│       ├── normalTails/
│       │   └── normalTails.R
│       ├── pokerNormal/
│       │   └── pokerNormal.R
│       ├── satAbove1190/
│       │   └── satAbove1190.R
│       ├── satActNormals/
│       │   └── satActNormals.R
│       ├── satBelow1030/
│       │   └── satBelow1030.R
│       ├── satBelow1300/
│       │   └── satBelow1300.R
│       ├── simpleNormal/
│       │   └── simpleNormal.R
│       ├── smallNormalTails/
│       │   └── smallNormalTails.R
│       ├── standardNormal/
│       │   └── standardNormal.R
│       ├── subtracting2Areas/
│       │   └── subtracting2Areas.R
│       ├── subtractingArea/
│       │   └── subtractingArea.R
│       ├── twoSampleNormals/
│       │   └── twoSampleNormals.R
│       └── twoSampleNormalsStacked/
│           └── twoSampleNormalsStacked.R
├── ch_foundations_for_inf/
│   ├── TeX/
│   │   ├── ch_foundations_for_inf.tex
│   │   ├── confidence_intervals.tex
│   │   ├── hypothesis_testing.tex
│   │   ├── one_sided_tests.tex
│   │   ├── review_exercises.tex
│   │   └── variability_in_estimates.tex
│   └── figures/
│       ├── 95PercentConfidenceInterval/
│       │   └── 95PercentConfidenceInterval.R
│       ├── ARCHIVE/
│       │   └── sampling_10k_prop_56p/
│       │       └── sampling_10k_prop_56p.R
│       ├── arrayOfFigureAreasForChiSquareDistribution/
│       │   ├── chiSquareAreaAbove10WithDF4/
│       │   │   └── chiSquareAreaAbove10WithDF4.R
│       │   ├── chiSquareAreaAbove11Point7WithDF7/
│       │   │   └── chiSquareAreaAbove11Point7WithDF7.R
│       │   ├── chiSquareAreaAbove4Point3WithDF2/
│       │   │   └── chiSquareAreaAbove4WithDF2.R
│       │   ├── chiSquareAreaAbove5Point1WithDF5/
│       │   │   └── chiSquareAreaAbove5Point1WithDF5.R
│       │   ├── chiSquareAreaAbove6Point25WithDF3/
│       │   │   └── chiSquareAreaAbove6Point25WithDF3.R
│       │   └── chiSquareAreaAbove9Point21WithDF3/
│       │       └── chiSquareAreaAbove9Point21WithDF3.R
│       ├── bladesTwoSampleHTPValueQC/
│       │   └── bladesTwoSampleHTPValueQC.R
│       ├── business_one_sided_20_21-p_value/
│       │   └── business_one_sided_20_21-p_value.R
│       ├── chiSquareDistributionWithInceasingDF/
│       │   └── chiSquareDistributionWithInceasingDF.R
│       ├── choosingZForCI/
│       │   └── choosingZForCI.R
│       ├── clt_prop_grid/
│       │   └── clt_prop_grid.R
│       ├── communityCollegeClaimedHousingExpenseDistribution/
│       │   └── communityCollegeClaimedHousingExpenseDistribution.R
│       ├── eoce/
│       │   ├── adult_heights/
│       │   │   └── adult_heights.R
│       │   ├── age_at_first_marriage_intro/
│       │   │   └── age_at_first_marriage_intro.R
│       │   ├── assisted_reproduction_one_sample_randomization/
│       │   │   └── assisted_reproduction_one_sample_randomization.R
│       │   ├── cflbs/
│       │   │   └── cflbs.R
│       │   ├── college_credits/
│       │   │   └── college_credits.R
│       │   ├── egypt_revolution_one_sample_randomization/
│       │   │   └── egypt_revolution_one_sample_randomization.R
│       │   ├── exclusive_relationships/
│       │   │   ├── exclusive_relationships.R
│       │   │   └── survey.csv
│       │   ├── gifted_children_ht/
│       │   │   └── gifted_children_ht.R
│       │   ├── gifted_children_intro/
│       │   │   └── gifted_children_intro.R
│       │   ├── identify_dist_ls_pop/
│       │   │   └── identify_dist_ls_pop.R
│       │   ├── identify_dist_symm_pop/
│       │   │   └── identify_dist_symm_pop.R
│       │   ├── pennies_ages/
│       │   │   ├── penniesAges.Rda
│       │   │   └── pennies_ages.R
│       │   ├── penny_weights/
│       │   │   └── penny_weights.R
│       │   ├── social_experiment_two_sample_randomization/
│       │   │   └── social_experiment_two_sample_randomization.R
│       │   ├── songs_on_ipod/
│       │   │   └── songs_on_ipod.R
│       │   ├── thanksgiving_spending_intro/
│       │   │   └── thanksgiving_spending_intro.R
│       │   └── yawning_two_sample_randomization/
│       │       └── yawning_two_sample_randomization.R
│       ├── geomFitEvaluationForSP500For1990To2011/
│       │   └── geomFitEvaluationForSP500For1990To2011.R
│       ├── geomFitPValueForSP500For1990To2011/
│       │   └── geomFitPValueForSP500For1990To2011.R
│       ├── googleHTForDiffAlgPerformancePValue/
│       │   └── googleHTForDiffAlgPerformancePValue.R
│       ├── helpers.R
│       ├── jurorHTPValueShown/
│       │   └── jurorHTPValueShown.R
│       ├── mammograms/
│       │   └── mammograms.R
│       ├── normal_dist_mean_500_se_016/
│       │   └── normal_dist_mean_500_se_016.R
│       ├── nuclearArmsReduction/
│       │   └── nuclearArmsReduction.R
│       ├── p-hat_from_53_and_59-not-used/
│       │   └── p-hat_from_53_and_59.R
│       ├── p-hat_from_53_and_59_computation/
│       │   ├── NormTailsCalc.R
│       │   └── p-hat_from_53_and_59_computation.R
│       ├── p-hat_from_867_and_907-not-used/
│       │   └── p-hat_from_867_and_907.R
│       ├── p-hat_from_86_and_90/
│       │   └── p-hat_from_86_and_90.R
│       ├── quadcopter/
│       │   └── quadcopter_attribution.txt
│       ├── sampling_100_prop_X/
│       │   └── sampling_100_prop_X.R
│       ├── sampling_10_prop_25p/
│       │   ├── sampling_10_prop_25p - one figure.R
│       │   └── sampling_10_prop_25p.R
│       ├── sampling_10k_prop_887p/
│       │   └── sampling_10k_prop_887p.R
│       ├── sampling_10k_prop_88p/
│       │   └── sampling_10k_prop_88p.R
│       ├── sampling_5k_prop_50p/
│       │   └── sampling_5k_prop_50p.R
│       ├── sampling_X_prop_56p/
│       │   └── sampling_X_prop_56p.R
│       ├── sulphStudyFindPValueUsingNormalApprox/
│       │   └── sulphStudyFindPValueUsingNormalApprox.R
│       └── whyWeWantPValue/
│           └── whyWeWantPValue.R
├── ch_inference_for_means/
│   ├── TeX/
│   │   ├── ch_inference_for_means.tex
│   │   ├── comparing_many_means_with_anova.tex
│   │   ├── difference_of_two_means.tex
│   │   ├── one-sample_means_with_the_t-distribution.tex
│   │   ├── paired_data.tex
│   │   ├── power_calculations_for_a_difference_of_means.tex
│   │   └── review_exercises.tex
│   └── figures/
│       ├── babySmokePlotOfTwoGroupsToExamineSkew/
│       │   └── babySmokePlotOfTwoGroupsToExamineSkew.R
│       ├── cbrRunTimesMenWomen/
│       │   └── cbrRunTimesMenWomen.R
│       ├── classData/
│       │   └── classData.R
│       ├── distOfDiffOfSampleMeansForBWOfBabySmokeData/
│       │   └── distOfDiffOfSampleMeansForBWOfBabySmokeData.R
│       ├── eoce/
│       │   ├── adult_heights/
│       │   │   └── adult_heights.R
│       │   ├── age_at_first_marriage_intro/
│       │   │   └── age_at_first_marriage_intro.R
│       │   ├── anova_exercise_1/
│       │   │   └── anova_exercise_1.R
│       │   ├── chick_wts_anova/
│       │   │   └── chick_wts.R
│       │   ├── chick_wts_linseed_horsebean/
│       │   │   └── chick_wts.R
│       │   ├── child_care_hours/
│       │   │   ├── child_care_hours.R
│       │   │   └── china.csv
│       │   ├── cleveland_sacramento/
│       │   │   └── cleveland_sacramento.R
│       │   ├── college_credits/
│       │   │   └── college_credits.R
│       │   ├── diamonds_1/
│       │   │   └── diamonds.R
│       │   ├── exclusive_relationships/
│       │   │   ├── exclusive_relationships.R
│       │   │   └── survey.csv
│       │   ├── friday_13th_accident/
│       │   │   └── friday_13th_accident.R
│       │   ├── friday_13th_traffic/
│       │   │   └── friday_13th_traffic.R
│       │   ├── fuel_eff_city/
│       │   │   ├── fuel_eff.csv
│       │   │   └── fuel_eff_city.R
│       │   ├── fuel_eff_hway/
│       │   │   ├── fuel_eff.csv
│       │   │   └── fuel_eff_hway.R
│       │   ├── gifted_children/
│       │   │   └── gifted_children.R
│       │   ├── gifted_children_ht/
│       │   │   └── gifted_children_ht.R
│       │   ├── gifted_children_intro/
│       │   │   └── gifted_children_intro.R
│       │   ├── global_warming_v2_1/
│       │   │   └── global_warming_v2_1.R
│       │   ├── gpa_major/
│       │   │   ├── gpa_major.R
│       │   │   └── survey.csv
│       │   ├── hs_beyond_1/
│       │   │   └── hs_beyond.R
│       │   ├── oscar_winners/
│       │   │   └── oscar_winners.R
│       │   ├── prison_isolation_T/
│       │   │   ├── prison_isolation.R
│       │   │   └── prison_isolation.csv
│       │   ├── prius_fuel_efficiency/
│       │   │   └── prius_fuel_efficiency.R
│       │   ├── prius_fuel_efficiency_update/
│       │   │   └── prius_fuel_efficiency.R
│       │   ├── t_distribution/
│       │   │   └── t_distribution.R
│       │   ├── torque_on_rusty_bolt/
│       │   │   ├── torque_on_rusty_bolt (Autosaved).R
│       │   │   └── torque_on_rusty_bolt.R
│       │   └── work_hours_education/
│       │       ├── gss2010.Rda
│       │       └── work_hours_education.R
│       ├── fDist2And423/
│       │   └── fDist2And423.R
│       ├── fDist3And323/
│       │   └── fDist3And323.R
│       ├── mlbANOVA/
│       │   └── mlbANOVA.R
│       ├── outliers_and_ss_condition/
│       │   └── outliers_and_ss_condition.R
│       ├── pValueOfTwoTailAreaOfExamVersionsWhereDFIs26/
│       │   └── pValueOfTwoTailAreaOfExamVersionsWhereDFIs26.R
│       ├── pValueShownForSATHTOfOver100PtGain/
│       │   └── pValueShownForSATHTOfOver100PtGain.R
│       ├── power_best_sample_size/
│       │   └── power_best_sample_size.R
│       ├── power_curve/
│       │   └── power_curve.R
│       ├── power_null_0_0-76/
│       │   └── power_null_0_0-76.R
│       ├── power_null_0_1-7/
│       │   └── power_null_0_1-7.R
│       ├── rissosDolphin/
│       │   └── ReadMe.txt
│       ├── run10SampTimeHistogram/
│       │   └── run10SampTimeHistogram.R
│       ├── satImprovementHTDataHistogram/
│       │   └── satImprovementHTDataHistogram.R
│       ├── stemCellTherapyForHearts/
│       │   └── stemCellTherapyForHearts.R
│       ├── stemCellTherapyForHeartsPValue/
│       │   └── stemCellTherapyForHeartsPValue.R
│       ├── tDistAppendixTwoEx/
│       │   └── tDistAppendixTwoEx.R
│       ├── tDistCompareToNormalDist/
│       │   └── tDistCompareToNormalDist.R
│       ├── tDistConvergeToNormalDist/
│       │   └── tDistConvergeToNormalDist.R
│       ├── tDistDF18LeftTail2Point10/
│       │   └── tDistDF18LeftTail2Point10.R
│       ├── tDistDF20RightTail1Point65/
│       │   └── tDistDF20RightTail1Point65.R
│       ├── textbooksF18/
│       │   ├── diffInTextbookPricesF18.R
│       │   └── textbooksF18HTTails.R
│       ├── textbooksS10/
│       │   ├── diffInTextbookPricesS10.R
│       │   └── textbooksS10HTTails.R
│       ├── textbooks_scatter/
│       │   └── textbooks_scatter.R
│       └── toyANOVA/
│           └── toyANOVA.R
├── ch_inference_for_props/
│   ├── TeX/
│   │   ├── ch_inference_for_props.tex
│   │   ├── difference_of_two_proportions.tex
│   │   ├── inference_for_a_single_proportion.tex
│   │   ├── review_exercises.tex
│   │   ├── testing_for_goodness_of_fit_using_chi-square.tex
│   │   └── testing_for_independence_in_two-way_tables.tex
│   └── figures/
│       ├── arrayOfFigureAreasForChiSquareDistribution/
│       │   ├── chiSquareAreaAbove10WithDF4/
│       │   │   └── chiSquareAreaAbove10WithDF4.R
│       │   ├── chiSquareAreaAbove11Point7WithDF7/
│       │   │   └── chiSquareAreaAbove11Point7WithDF7.R
│       │   ├── chiSquareAreaAbove4Point3WithDF2/
│       │   │   └── chiSquareAreaAbove4WithDF2.R
│       │   ├── chiSquareAreaAbove5Point1WithDF5/
│       │   │   └── chiSquareAreaAbove5Point1WithDF5.R
│       │   ├── chiSquareAreaAbove6Point25WithDF3/
│       │   │   └── chiSquareAreaAbove6Point25WithDF3.R
│       │   └── chiSquareAreaAbove9Point21WithDF3/
│       │       └── chiSquareAreaAbove9Point21WithDF3.R
│       ├── bladesTwoSampleHTPValueQC/
│       │   └── bladesTwoSampleHTPValueQC.R
│       ├── chiSquareDistributionWithInceasingDF/
│       │   └── chiSquareDistributionWithInceasingDF.R
│       ├── eoce/
│       │   ├── assisted_reproduction_one_sample_randomization/
│       │   │   └── assisted_reproduction_one_sample_randomization.R
│       │   ├── egypt_revolution_one_sample_randomization/
│       │   │   └── egypt_revolution_one_sample_randomization.R
│       │   ├── social_experiment_two_sample_randomization/
│       │   │   └── social_experiment_two_sample_randomization.R
│       │   └── yawning_two_sample_randomization/
│       │       └── yawning_two_sample_randomization.R
│       ├── geomFitEvaluationForSP500/
│       │   ├── geomFitEvaluationForSP500.R
│       │   └── sp500_1950_2018.csv
│       ├── geomFitPValueForSP500/
│       │   └── geomFitPValueForSP500.R
│       ├── iPodChiSqTail/
│       │   └── iPodChiSqTail.R
│       ├── jurorHTPValueShown/
│       │   └── jurorHTPValueShown.R
│       ├── mammograms/
│       │   └── mammograms.R
│       ├── paydayCC_norm_pvalue/
│       │   └── paydayCC_norm_pvalue.R
│       └── quadcopter/
│           └── quadcopter_attribution.txt
├── ch_intro_to_data/
│   ├── TeX/
│   │   ├── case_study_using_stents_to_prevent_strokes.tex
│   │   ├── ch_intro_to_data.tex
│   │   ├── data_basics.tex
│   │   ├── experiments.tex
│   │   ├── review_exercises.tex
│   │   └── sampling_principles_and_strategies.tex
│   └── figures/
│       ├── county_fed_spendVsPoverty/
│       │   └── county_fed_spendVsPoverty.R
│       ├── eoce/
│       │   ├── air_quality_durham/
│       │   │   ├── air_quality_durham.R
│       │   │   └── pm25_2011_durham.csv
│       │   ├── airports/
│       │   │   ├── airports.R
│       │   │   └── data/
│       │   │       └── cb_2013_us_state_20m/
│       │   │           ├── cb_2013_us_state_20m.dbf
│       │   │           ├── cb_2013_us_state_20m.prj
│       │   │           ├── cb_2013_us_state_20m.shp
│       │   │           ├── cb_2013_us_state_20m.shp.iso.xml
│       │   │           ├── cb_2013_us_state_20m.shp.xml
│       │   │           ├── cb_2013_us_state_20m.shx
│       │   │           └── state_20m.ea.iso.xml
│       │   ├── antibiotic_use_children/
│       │   │   └── antibiotic_use_children.R
│       │   ├── association_plots/
│       │   │   └── association_plots.R
│       │   ├── cleveland_sacramento/
│       │   │   └── cleveland_sacramento.R
│       │   ├── county_commute_times/
│       │   │   ├── countyMap.R
│       │   │   └── county_commute_times.R
│       │   ├── county_hispanic_pop/
│       │   │   ├── countyMap.R
│       │   │   └── county_hispanic_pop.R
│       │   ├── county_income_education/
│       │   │   └── county_income_education.R
│       │   ├── dream_act_mosaic/
│       │   │   └── dream_act_mosaic.R
│       │   ├── estimate_mean_median_simple/
│       │   │   └── estimate_mean_median_simple.R
│       │   ├── gpa_study_hours/
│       │   │   ├── gpa_study_hours.R
│       │   │   ├── gpa_study_hours.csv
│       │   │   └── gpa_study_hours.rda
│       │   ├── hist_box_match/
│       │   │   └── hist_box_match.R
│       │   ├── hist_vs_box/
│       │   │   └── hist_vs_box.R
│       │   ├── income_coffee_shop/
│       │   │   └── income_coffee_shop.R
│       │   ├── infant_mortality_rel_freq/
│       │   │   ├── factbook.rda
│       │   │   └── infant_mortality.R
│       │   ├── internet_life_expactancy/
│       │   │   ├── factbook.rda
│       │   │   └── internet_life_expactancy.R
│       │   ├── internet_life_expectancy/
│       │   │   ├── factbook.rda
│       │   │   └── internet_life_expectancy.R
│       │   ├── mammal_life_spans/
│       │   │   └── mammal_life_spans.R
│       │   ├── marathon_winners/
│       │   │   └── marathon_winners.R
│       │   ├── office_productivity/
│       │   │   └── office_productivity.R
│       │   ├── oscar_winners/
│       │   │   └── oscar_winners.R
│       │   ├── raise_taxes_mosaic/
│       │   │   └── raise_taxes_mosaic.R
│       │   ├── randomization_avandia/
│       │   │   └── randomization_avandia.R
│       │   ├── randomization_heart_transplants/
│       │   │   ├── inference.RData
│       │   │   └── randomization_heart_transplants.R
│       │   ├── reproducing_bacteria/
│       │   │   └── reproducing_bacteria.R
│       │   ├── seattle_pet_names/
│       │   │   └── seattle_pet_names.R
│       │   ├── stats_scores_box/
│       │   │   └── stats_scores_box.R
│       │   └── unvotes/
│       │       └── unvotes.R
│       ├── expResp/
│       │   └── expResp.R
│       ├── figureShowingBlocking/
│       │   └── figureShowingBlocking.R
│       ├── interest_rate_vs_income/
│       │   └── interest_rate_vs_loan_amount.R
│       ├── interest_rate_vs_loan_amount/
│       │   └── interest_rate_vs_loan_amount.R
│       ├── interest_rate_vs_loan_income_ratio/
│       │   └── interest_rate_vs_loan_income_ratio.R
│       ├── loan_amount_vs_income/
│       │   └── loan_amount_vs_income.R
│       ├── mnWinter/
│       │   └── ReadMe.txt
│       ├── multiunitsVsOwnership/
│       │   └── multiunitsVsOwnership.R
│       ├── popToSample/
│       │   ├── popToSampleGraduates.R
│       │   ├── popToSubSampleGraduates.R
│       │   └── surveySample.R
│       ├── pop_change_v_med_income/
│       │   └── pop_change_v_med_income.R
│       ├── pop_change_v_per_capita_income/
│       │   └── pop_change_v_per_capita_income.R
│       ├── samplingMethodsFigure/
│       │   ├── SamplingMethodsFunctions.R
│       │   ├── samplingMethodsFigure.R
│       │   └── samplingMethodsFigures.R
│       └── variables/
│           ├── sunCausesCancer.R
│           └── variables.R
├── ch_probability/
│   ├── TeX/
│   │   ├── ch_probability.tex
│   │   ├── conditional_probability.tex
│   │   ├── continuous_distributions.tex
│   │   ├── defining_probability.tex
│   │   ├── random_variables.tex
│   │   ├── review_exercises.tex
│   │   └── sampling_from_a_small_population.tex
│   └── figures/
│       ├── BreastCancerTreeDiagram/
│       │   ├── BreastCancerTreeDiagram.R
│       │   └── Mammogram Research.txt
│       ├── bookCostDist/
│       │   └── bookCostDist.R
│       ├── bookWts/
│       │   └── bookWts.R
│       ├── cardsDiamondFaceVenn/
│       │   └── cardsDiamondFaceVenn.R
│       ├── changeInLeonardsStockPortfolioFor36Months/
│       │   └── changeinleonardsstockportfoliofor36months.R
│       ├── complementOfD/
│       │   └── complementOfD.R
│       ├── contBalance/
│       │   └── contBalance.R
│       ├── diceSumDist/
│       │   └── diceSumDist.R
│       ├── dieProp/
│       │   └── dieProp.R
│       ├── disjointSets/
│       │   └── disjointSets.R
│       ├── eoce/
│       │   ├── cat_weights/
│       │   │   └── cat_weights.R
│       │   ├── poverty_language/
│       │   │   ├── poverty_language.R
│       │   │   └── poverty_language.tiff
│       │   ├── swing_voters/
│       │   │   ├── swing_voters.R
│       │   │   └── swing_voters.tiff
│       │   ├── tree_drawing_box_plots/
│       │   │   └── tree_drawing_box_plots.R
│       │   ├── tree_exit_poll/
│       │   │   └── tree_exit_poll.R
│       │   ├── tree_hiv_swaziland/
│       │   │   └── tree_hiv_swaziland.R
│       │   ├── tree_lupus/
│       │   │   └── tree_lupus.R
│       │   ├── tree_thrombosis/
│       │   │   └── tree_thrombosis.R
│       │   └── tree_twins/
│       │       └── tree_twins.R
│       ├── fdicHeightContDist/
│       │   └── fdicHeightContDist.R
│       ├── fdicHeightContDistFilled/
│       │   └── fdicHeightContDistFilled.R
│       ├── fdicHistograms/
│       │   ├── fdicHistograms.R
│       │   └── fdicHistograms.rda
│       ├── indepForRollingTwo1s/
│       │   └── indepForRollingTwo1s.R
│       ├── loans_app_type_home_venn/
│       │   └── loans_app_type_home_venn.R
│       ├── photoClassifyVenn/
│       │   └── photoClassifyVenn.R
│       ├── smallpoxTreeDiagram/
│       │   └── smallpoxTreeDiagram.R
│       ├── testTree/
│       │   └── testTree.R
│       ├── treeDiagramAndPass/
│       │   └── treeDiagramAndPass.R
│       ├── treeDiagramGarage/
│       │   └── treeDiagramGarage.R
│       ├── usHeightsHist180185/
│       │   └── usHeightsHist180185.R
│       └── usHouseholdIncomeDistBar/
│           └── usHouseholdIncomeDistBar.R
├── ch_regr_mult_and_log/
│   ├── TeX/
│   │   ├── ch_regr_mult_and_log.tex
│   │   ├── checking_model_assumptions_using_graphs.tex
│   │   ├── introduction_to_logistic_regression.tex
│   │   ├── introduction_to_multiple_regression.tex
│   │   ├── model_selection.tex
│   │   ├── mult_regr_case_study.tex
│   │   └── review_exercises.tex
│   └── figures/
│       ├── eoce/
│       │   ├── absent_from_school_mlr/
│       │   │   └── absent_from_school_mlr.R
│       │   ├── absent_from_school_model_select_backward/
│       │   │   └── absent_from_school_model_select_backward.R
│       │   ├── absent_from_school_model_select_forward/
│       │   │   └── absent_from_school_model_select_forward.R
│       │   ├── baby_weights_conds/
│       │   │   ├── babies.csv
│       │   │   └── baby_weights_conds.R
│       │   ├── baby_weights_mlr/
│       │   │   ├── babies.csv
│       │   │   └── baby_weights_mlr.R
│       │   ├── baby_weights_model_select_backward/
│       │   │   ├── babies.csv
│       │   │   └── baby_weights_model_select_backward.R
│       │   ├── baby_weights_model_select_forward/
│       │   │   ├── babies.csv
│       │   │   └── baby_weights_model_select_backward.R
│       │   ├── baby_weights_parity/
│       │   │   ├── babies.csv
│       │   │   └── baby_weights_parity.R
│       │   ├── baby_weights_smoke/
│       │   │   ├── babies.csv
│       │   │   └── baby_weights_smoke.R
│       │   ├── challenger_disaster_predict/
│       │   │   ├── challenger_disaster_predict.R
│       │   │   └── orings.rda
│       │   ├── gpa/
│       │   │   ├── gpa.R
│       │   │   └── gpa_survey.csv
│       │   ├── gpa_iq_conds/
│       │   │   ├── gpa_iq.csv
│       │   │   └── gpa_iq_conds.R
│       │   ├── log_regr_ex/
│       │   │   └── log_regr_ex.R
│       │   ├── movie_returns_altogether/
│       │   │   ├── horror_movies_conds.R
│       │   │   └── movie_profit.csv
│       │   ├── movie_returns_by_genre/
│       │   │   ├── horror_movies_conds.R
│       │   │   └── movie_profit.csv
│       │   ├── possum_classification_model_select/
│       │   │   └── possum_classification_model_select.R
│       │   ├── spam_filtering_model_sel/
│       │   │   └── spam_filtering_model_sel.R
│       │   └── spam_filtering_predict/
│       │       └── spam_filtering_predict.R
│       ├── loansDiagnostics/
│       │   └── loans_analysis.R
│       ├── loansSingles/
│       │   ├── intRateVsPastBankrScatter.R
│       │   └── intRateVsVerIncomeScatter.R
│       ├── logisticModel/
│       │   └── logisticModel.R
│       ├── logitTransformationFigureHoriz/
│       │   └── logitTransformationFigureHoriz.R
│       ├── marioKartDiagnostics/
│       │   └── marioKartAnalysis.R
│       └── marioKartSingle/
│           └── marioKartSingle.R
├── ch_regr_simple_linear/
│   ├── TeX/
│   │   ├── ch_regr_simple_linear.tex
│   │   ├── fitting_a_line_by_least_squares_regression.tex
│   │   ├── inference_for_linear_regression.tex
│   │   ├── line_fitting_residuals_and_correlation.tex
│   │   ├── review_exercises.tex
│   │   └── types_of_outliers_in_linear_regression.tex
│   └── figures/
│       ├── brushtail_possum/
│       │   └── ReadMe.txt
│       ├── elmhurstPlots/
│       │   └── elmhurstScatterW2Lines.R
│       ├── eoce/
│       │   ├── beer_blood_alcohol_inf/
│       │   │   ├── beer_blood_alcohol.txt
│       │   │   └── beer_blood_alcohol_inf.R
│       │   ├── body_measurements_hip_weight_corr_units/
│       │   │   └── body_measurements_hip_weight.R
│       │   ├── body_measurements_shoulder_height_corr_units/
│       │   │   └── body_measurements_shoulder_height.R
│       │   ├── body_measurements_weight_height_inf/
│       │   │   └── body_measurements_weight_height_inf.R
│       │   ├── cat_body_heart_reg/
│       │   │   └── cat_body_heart_reg.R
│       │   ├── coast_starlight_corr_units/
│       │   │   ├── coast_starlight.R
│       │   │   └── coast_starlight.txt
│       │   ├── crawling_babies_corr_units/
│       │   │   ├── crawling_babies.R
│       │   │   └── crawling_babies.csv
│       │   ├── exams_grades_correlation/
│       │   │   ├── exam_grades.txt
│       │   │   └── exams_grades_correlation.R
│       │   ├── full_lin_regr_1/
│       │   │   ├── prof_evals_beauty.csv
│       │   │   └── rate_my_prof.R
│       │   ├── full_lin_regr_2/
│       │   │   ├── prof_evals_beauty.csv
│       │   │   └── rate_my_prof.R
│       │   ├── helmet_lunch/
│       │   │   └── helmet_lunch.R
│       │   ├── husbands_wives_age_inf/
│       │   │   ├── husbands_wives.txt
│       │   │   └── husbands_wives_age_inf.R
│       │   ├── husbands_wives_correlation/
│       │   │   ├── husbands_wives.txt
│       │   │   └── husbands_wives_correlation.R
│       │   ├── husbands_wives_height_inf/
│       │   │   ├── husbands_wives.txt
│       │   │   └── husbands_wives_height_inf.R
│       │   ├── husbands_wives_height_inf_2s/
│       │   │   ├── husbands_wives.txt
│       │   │   └── husbands_wives_height_inf_2s.R
│       │   ├── identify_relationships_1/
│       │   │   └── identify_relationships_1.R
│       │   ├── identify_relationships_2/
│       │   │   └── identify_relationships_2.R
│       │   ├── match_corr_1/
│       │   │   └── match_corr_1.R
│       │   ├── match_corr_2/
│       │   │   └── match_corr_2.R
│       │   ├── match_corr_3/
│       │   │   ├── match_corr_2.R
│       │   │   └── match_corr_3.R
│       │   ├── murders_poverty_reg/
│       │   │   ├── murders.csv
│       │   │   └── murders_poverty.R
│       │   ├── outliers_1/
│       │   │   └── outliers_1.R
│       │   ├── outliers_2/
│       │   │   └── outliers_2.R
│       │   ├── rate_my_prof/
│       │   │   ├── prof_evals_beauty.csv
│       │   │   └── rate_my_prof.R
│       │   ├── speed_height_gender/
│       │   │   ├── speed_height_gender.R
│       │   │   └── speed_survey.csv
│       │   ├── starbucks_cals_carbos/
│       │   │   ├── starbucks.csv
│       │   │   └── starbucks_cals_carbos.R
│       │   ├── starbucks_cals_protein/
│       │   │   ├── starbucks.csv
│       │   │   └── starbucks_cals_protein.R
│       │   ├── tourism_spending_reg_conds/
│       │   │   ├── tourism_spending.csv
│       │   │   └── tourism_spending_reg_cond.R
│       │   ├── trees_volume_height_diameter/
│       │   │   └── trees_volume_height_diameter.R
│       │   ├── trends_in_residuals/
│       │   │   └── trends_in_residuals.R
│       │   ├── urban_homeowners_cond/
│       │   │   ├── urban_homeowners_cond.R
│       │   │   └── urban_state_data.csv
│       │   ├── urban_homeowners_outlier/
│       │   │   ├── urban_homeowners_outlier.R
│       │   │   └── urban_state_data.csv
│       │   └── visualize_residuals/
│       │       └── visualize_residuals.R
│       ├── identifyingInfluentialPoints/
│       │   └── identifyingInfluentialPoints.R
│       ├── imperfLinearModel/
│       │   └── imperfLinearModel.R
│       ├── marioKartNewUsed/
│       │   └── marioKartNewUsed.R
│       ├── notGoodAtAllForALinearModel/
│       │   └── notGoodAtAllForALinearModel.R
│       ├── outlierPlots/
│       │   └── outlierPlots.R
│       ├── pValueMidtermUnemp/
│       │   └── pValueMidtermUnemp.R
│       ├── perfLinearModel/
│       │   └── perfLinearModel.R
│       ├── posNegCorPlots/
│       │   ├── CorrelationPlot.R
│       │   ├── corForNonLinearPlots.R
│       │   └── posNegCorPlots.R
│       ├── sampleLinesAndResPlots/
│       │   └── sampleLinesAndResPlots.R
│       ├── scattHeadLTotalL/
│       │   └── scattHeadLTotalL.R
│       ├── scattHeadLTotalLLine/
│       │   └── scattHeadLTotalLLine.R
│       ├── scattHeadLTotalLResidualPlot/
│       │   └── scattHeadLTotalLResidualPlot.R
│       ├── scattHeadLTotalLSex/
│       │   └── scattHeadLTotalLSex.R
│       ├── scattHeadLTotalLTube/
│       │   └── scattHeadLTotalLTube.R
│       ├── unemploymentAndChangeInHouse/
│       │   └── unemploymentAndChangeInHouse.R
│       └── whatCanGoWrongWithLinearModel/
│           ├── makeTubeAdv.R
│           └── whatCanGoWrongWithLinearModel.R
├── ch_summarizing_data/
│   ├── TeX/
│   │   ├── case_study_malaria_vaccine.tex
│   │   ├── ch_summarizing_data.tex
│   │   ├── considering_categorical_data.tex
│   │   ├── examining_numerical_data.tex
│   │   └── review_exercises.tex
│   └── figures/
│       ├── boxPlotLayoutNumVar/
│       │   └── boxPlotLayoutNumVar.R
│       ├── carsPriceVsWeight/
│       │   └── carsPriceVsWeight.R
│       ├── countyIncomeSplitByPopGain/
│       │   └── countyIncomeSplitByPopGain.R
│       ├── countyIntensityMaps/
│       │   ├── countyIntensityMaps.R
│       │   └── countyMap.R
│       ├── county_pop_change_v_pop_transform/
│       │   └── county_pop_change_v_pop_transform.R
│       ├── county_pop_transformed/
│       │   └── county_pop_transformed.R
│       ├── discRandDotPlot/
│       │   └── discRandDotPlot.R
│       ├── email50LinesCharacters/
│       │   └── email50LinesCharacters.R
│       ├── email50LinesCharactersMod/
│       │   └── email50LinesCharactersMod.R
│       ├── email50NumCharDotPlotRobustEx/
│       │   └── email50NumCharDotPlotRobustEx.R
│       ├── email50NumCharHist/
│       │   └── email50NumCharHist.R
│       ├── emailCharactersDotPlot/
│       │   └── emailCharactersDotPlot.R
│       ├── emailNumberBarPlot/
│       │   └── emailNumberBarPlot.R
│       ├── emailNumberPieChart/
│       │   └── emailNumberPieChart.R
│       ├── emailSpamNumberMosaicPlot/
│       │   └── emailSpamNumberMosaicPlot.R
│       ├── emailSpamNumberSegBar/
│       │   └── emailSpamNumberSegBar.R
│       ├── eoce/
│       │   ├── air_quality_durham/
│       │   │   ├── air_quality_durham.R
│       │   │   └── pm25_2011_durham.csv
│       │   ├── antibiotic_use_children/
│       │   │   └── antibiotic_use_children.R
│       │   ├── association_plots/
│       │   │   └── association_plots.R
│       │   ├── cleveland_sacramento/
│       │   │   └── cleveland_sacramento.R
│       │   ├── county_commute_times/
│       │   │   ├── countyMap.R
│       │   │   └── county_commute_times.R
│       │   ├── county_hispanic_pop/
│       │   │   ├── countyMap.R
│       │   │   └── county_hispanic_pop.R
│       │   ├── dream_act_mosaic/
│       │   │   └── dream_act_mosaic.R
│       │   ├── estimate_mean_median_simple/
│       │   │   └── estimate_mean_median_simple.R
│       │   ├── hist_box_match/
│       │   │   └── hist_box_match.R
│       │   ├── hist_vs_box/
│       │   │   └── hist_vs_box.R
│       │   ├── income_coffee_shop/
│       │   │   └── income_coffee_shop.R
│       │   ├── infant_mortality_rel_freq/
│       │   │   ├── factbook.rda
│       │   │   └── infant_mortality.R
│       │   ├── mammal_life_spans/
│       │   │   └── mammal_life_spans.R
│       │   ├── marathon_winners/
│       │   │   └── marathon_winners.R
│       │   ├── office_productivity/
│       │   │   └── office_productivity.R
│       │   ├── oscar_winners/
│       │   │   └── oscar_winners.R
│       │   ├── raise_taxes_mosaic/
│       │   │   └── raise_taxes_mosaic.R
│       │   ├── randomization_avandia/
│       │   │   └── randomization_avandia.R
│       │   ├── randomization_heart_transplants/
│       │   │   ├── inference.RData
│       │   │   └── randomization_heart_transplants.R
│       │   ├── reproducing_bacteria/
│       │   │   └── reproducing_bacteria.R
│       │   └── stats_scores_box/
│       │       └── stats_scores_box.R
│       ├── histMLBSalaries/
│       │   └── histMLBSalaries.R
│       ├── loan50IncomeHist/
│       │   └── loan50IncomeHist.R
│       ├── loan50IntRateHist/
│       │   └── loan50IntRateHist.R
│       ├── loan50LoanAmountHist/
│       │   └── loan50LoanAmountHist.R
│       ├── loan50_amt_vs_income/
│       │   └── loan50_amt_vs_income.R
│       ├── loan50_amt_vs_interest/
│       │   └── loan50_amt_vs_interest.R
│       ├── loan_amount_dot_plot/
│       │   └── loan_amount_dot_plot.R
│       ├── loan_app_type_home_mosaic_plot/
│       │   └── loan_app_type_home_mosaic_plot.R
│       ├── loan_app_type_home_seg_bar/
│       │   └── loan_app_type_home_seg_bar.R
│       ├── loan_homeownership_bar_plot/
│       │   └── loan_homeownership_bar_plot.R
│       ├── loan_homeownership_pie_chart/
│       │   └── loan_homeownership_pie_chart.R
│       ├── loan_int_rate_box_plot_layout/
│       │   └── loan_int_rate_box_plot_layout.R
│       ├── loan_int_rate_dot_plot/
│       │   └── loan_int_rate_dot_plot.R
│       ├── loan_int_rate_robust_ex/
│       │   └── loan_int_rate_robust_ex.R
│       ├── malaria_rand_dot_plot/
│       │   └── malaria_rand_dot_plot.R
│       ├── medianHHIncomePoverty/
│       │   └── medianHHIncomePoverty.R
│       ├── sdAsRuleForEmailNumChar/
│       │   └── sdAsRuleForEmailNumChar.R
│       ├── sdRuleForIncome/
│       │   └── sdRuleForIncome.R
│       ├── sdRuleForIntRate/
│       │   └── sdRuleForIntRate.R
│       ├── sdRuleForLoanAmount/
│       │   └── sdRuleForLoanAmount.R
│       ├── severalDiffDistWithSdOf1/
│       │   └── severalDiffDistWithSdOf1.R
│       ├── singleBiMultiModalPlots/
│       │   └── singleBiMultiModalPlots.R
│       └── total_income_dot_plot/
│           └── total_income_dot_plot.R
├── eoce.bib
├── extraTeX/
│   ├── data/
│   │   └── data.tex
│   ├── eoceSolutions/
│   │   └── eoceSolutions.tex
│   ├── index/
│   │   └── index.tex
│   ├── preamble/
│   │   ├── copyright.tex
│   │   ├── copyright_derivative.tex
│   │   ├── preface.tex
│   │   ├── review_copy.tex
│   │   ├── title.tex
│   │   └── title_derivative.tex
│   ├── style/
│   │   ├── colorsV1.tex
│   │   ├── hardcover.tex
│   │   ├── headers.tex
│   │   ├── headers_simple.tex
│   │   ├── style.tex
│   │   ├── style_appendices.tex
│   │   ├── style_simple.tex
│   │   ├── tablet.tex
│   │   └── video.tex
│   └── tables/
│       ├── TeX/
│       │   ├── chiSquareTable.tex
│       │   ├── tTable.tex
│       │   └── zTable.tex
│       ├── code/
│       │   ├── chiSquareProbTable.R
│       │   └── normalProbTable.R
│       └── figures/
│           ├── chiSquareTail/
│           │   └── chiSquareTail.R
│           ├── normalTails/
│           │   ├── normalTails.R
│           │   └── subtractingArea/
│           │       └── subtractingArea.R
│           └── tTails/
│               └── tTails.R
├── fullminipage.sty
├── main.tex
└── openintro-statistics.Rproj

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
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main-blx.bib
main.bbl
main.blg
main.idx
main.ilg
main.ind
main.out
main.pdf
main.run.xml
main.synctex.gz
main.toc
main.upa
*.DS_Store
*gitignore~
*.Rapp.history
*~
Icon[^/]
\#*
*.dropbox
_README
*-deprecated*
*.Rhistory
OS4-201[89]-[01][0-9]-[0-3][0-9] [A-Z].pdf
main.synctex(busy)
.Rproj.user


================================================
FILE: LICENSE.md
================================================

OpenIntro Statistics is available at http://www.openintro.org under a Creative Commons Attribution-ShareAlike 3.0 Unported license (CC BY-SA):

http://creativecommons.org/licenses/by-sa/3.0/

This `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.

You may contact us if you would like to request an alternative licensing option at

https://www.openintro.org/contact

1. Communication obligation. Any derivative work must communicate that it is licensed under a CC BY-SA license.

2. 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).

3. 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.

Use 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."

4. Below are other suggested guidelines for attribution.

- 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.

- 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.


================================================
FILE: README.md
================================================
Project Organization
--------------------

- 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.
- 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.
- Exercise figures may be found in [chapter folder] > figures > eoce > [exercise figure folders]. "EOCE" means end-of-chapter exercises.
- 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.

- - -

Typesetting the Textbook
------------------------

The 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

- LaTeX 3 times.
- MakeIndex once.
- BibTeX once.
- LaTeX once.
- MakeIndex once.
- LaTeX once.

This 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.

- - -

Learning LaTeX
--------------

If 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

https://github.com/OpenIntroOrg/mini-course-materials

PDFs:

[Basics of LaTeX](https://github.com/OpenIntroOrg/mini-course-materials/raw/master/LaTeX_Basics/basicsOfLatex.pdf)

[Math and BibTeX](https://github.com/OpenIntroOrg/mini-course-materials/raw/master/LaTeX_Math_and_BibTeX/bibtexMathInLatex.pdf)

For a more authoritative review, the book "Guide to LaTeX" is an excellent resource.

Also, see the branches of [this repo](https://github.com/statkclee/mini-course-materials) by Kwangchun Lee for Korean translations of these mini-course materials.


================================================
FILE: ch_distributions/TeX/binomial_distribution.tex
================================================
\exercisesheader{}

% 17

\eoce{\qt{Underage drinking, Part I\label{underage_drinking_intro}}
Data collected by the Substance Abuse and Mental Health
Services Administration (SAMSHA) suggests that 69.7\% of
18-20 year olds consumed alcoholic beverages in any given
year.\footfullcite{webpage:alcohol}
\begin{parts}
\item Suppose a random sample of ten 18-20 year olds is taken. Is the use 
of the binomial distribution appropriate for calculating the probability that 
exactly six consumed alcoholic beverages? Explain.
\item Calculate the probability that exactly 6 out of 10 randomly sampled 18-
20 year olds consumed an alcoholic drink.
\item What is the probability that exactly four out of ten 18-20 year 
olds have \textit{not} consumed an alcoholic beverage?
\item What is the probability that at most 2 out of 5 randomly sampled 18-20 
year olds have consumed alcoholic beverages?
\item What is the probability that at least 1 out of 5 randomly sampled 18-20 
year olds have consumed alcoholic beverages?
\end{parts}
}{}

% 18

\eoce{\qt{Chickenpox, Part I\label{chicken_pox_intro}} Boston Children's
Hospital estimates that 90\% of Americans have had chickenpox by 
the time they reach adulthood. \footfullcite{bostonchildrenshospital:chickenpox}
\begin{parts}
\item Suppose we take a random sample of 100 American adults. Is the use of 
the binomial distribution appropriate for calculating the probability that exactly 97 
out of 100 randomly sampled American adults had chickenpox during childhood? Explain.
\item Calculate the probability that exactly 97 out of 100 randomly sampled 
American adults had chickenpox during childhood.
\item What is the probability that exactly 3 out of a new sample of 100 
American adults have \textit{not} had chickenpox in their childhood?
\item What is the probability that at least 1 out of 10 randomly sampled 
American adults have had chickenpox?
\item What is the probability that at most 3 out of 10 randomly sampled 
American adults have \textit{not} had chickenpox?
\end{parts}
}{}

% 19

\eoce{\qt{Underage drinking, Part II\label{underage_drinking_normal_approx}}
We learned in Exercise~\ref{underage_drinking_intro}
that about 70\% of 18-20 year olds consumed alcoholic
beverages in any given year. We now consider a random 
sample of fifty 18-20 year olds.
\begin{parts}
\item How many people would you expect to have consumed alcoholic beverages? 
And with what standard deviation?
\item Would you be surprised if there were 45 or more people who have 
consumed alcoholic beverages?
\item What is the probability that 45 or more people in this sample have 
consumed alcoholic beverages? How does this probability relate to your answer 
to part (b)?
\end{parts}
}{}

% 20

\eoce{\qt{Chickenpox, Part II\label{chicken_pox_normal_approx}} We learned in 
Exercise~\ref{chicken_pox_intro} that about 90\% of American adults had 
chickenpox before adulthood. We now consider a random sample of 120 American 
adults.
\begin{parts}
\item How many people in this sample would you expect to have had chickenpox 
in their childhood? And with what standard deviation?
\item Would you be surprised if there were 105 people who have had chickenpox 
in their childhood?
\item What is the probability that 105 or fewer people in this sample have 
had chickenpox in their childhood? How does this probability relate to your 
answer to part (b)?
\end{parts}
}{}

% 21

\eoce{\qt{Game of dreidel\label{dreidel}} A dreidel is a four-sided spinning top 
with the Hebrew letters \textit{nun}, \textit{gimel}, \textit{hei}, and 
\textit{shin}, one on each side. Each side is equally likely to come up in a 
single spin of the dreidel. Suppose you spin a dreidel three times. Calculate 
the probability of getting

\noindent\begin{minipage}[c]{0.45\textwidth}
\begin{parts}
\item at least one \textit{nun}? 
\item exactly 2 \textit{nun}s? 
\item exactly 1 \textit{hei}? 
\item at most 2 \textit{gimel}s? \vspace{3mm}
\end{parts}
\end{minipage}%
\begin{minipage}[c]{0.25\textwidth}
\ \vspace{2mm}

\Figures[An image of two wooden dreidels.]{0.95}{eoce/dreidel}{dreidel.jpg}\vspace{2mm}
\end{minipage}%
\begin{minipage}[c]{0.28\textwidth}%
{\footnotesize Photo by Staccabees, cropped \\
  (\oiRedirect{textbook-flickr_staccabees_dreidels}{http://flic.kr/p/7gLZTf}) \\
  \oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}} \\
\end{minipage}
}{}

\D{\newpage}

% 22

\eoce{\qt{Arachnophobia\label{arachnophobia}}
A Gallup Poll found that 7\% of teenagers (ages 13 to 17)
suffer from arachnophobia and are extremely afraid of spiders.
At a summer camp there are 10 teenagers sleeping in each tent.
Assume that these 10 teenagers are independent of each other.%
\footfullcite{webpage:spiders}
\begin{parts}
\item Calculate the probability that at least one of them suffers from 
arachnophobia.
\item Calculate the probability that exactly 2 of them suffer from 
arachnophobia.
\item Calculate the probability that at most 1 of them suffers from 
arachnophobia. 
\item If the camp counselor wants to make sure no more than 1 teenager in 
each tent is afraid of spiders, does it seem reasonable for him to randomly 
assign teenagers to tents?
\end{parts}
}{}

% 23

\eoce{\qt{Eye color, Part II\label{eye_color_binomial}} 
Exercise~\ref{eye_color_geometric} introduces a husband and wife with brown 
eyes who have 0.75 probability of having children with brown eyes, 0.125 
probability of having children with blue eyes, and 0.125 probability of 
having children with green eyes.
\begin{parts}
\item What is the probability that their first child will have green eyes and 
the second will not?
\item What is the probability that exactly one of their two children will 
have green eyes?
\item If they have six children, what is the probability that exactly two 
will have green eyes?
\item If they have six children, what is the probability that at least one 
will have green eyes?
\item What is the probability that the first green eyed child will be the 
$4^{th}$ child? 
\item Would it be considered unusual if only 2 out of their 6 children had 
brown eyes?
\end{parts}
}{}

% 24

\eoce{\qt{Sickle cell anemia\label{sickle_cell_anemia}} Sickle cell anemia is a 
genetic blood disorder where red blood cells lose their flexibility and 
assume an abnormal, rigid, ``sickle" shape, which results in a risk of 
various complications. If both parents are carriers of the disease, then a 
child has a 25\% chance of having the disease, 50\% chance of being a 
carrier, and 25\% chance of neither having the disease nor being a carrier. 
If two parents who are carriers of the disease have 3 children, what is the 
probability that 
\begin{parts}
\item two will have the disease?
\item none will have the disease?
\item at least one will neither have the disease nor be a carrier?
\item the first child with the disease will the be $3^{rd}$ child?
\end{parts}
}{}

% 25

\eoce{\qt{Exploring permutations\label{explore_combinations}} The formula for the 
number of ways to arrange $n$ objects is $n! = n\times(n-1)\times \cdots 
\times 2 \times 1$. This exercise walks you through the derivation of this 
formula for a couple of special cases.

\indent A small company has five employees: Anna, Ben, Carl, Damian, and 
Eddy. There are five parking spots in a row at the company, none of which are 
assigned, and each day the employees pull into a random parking spot. That 
is, all possible orderings of the cars in the row of spots are equally likely.
\begin{parts}
\item On a given day, what is the probability that the employees park in 
alphabetical order?
\item If the alphabetical order has an equal chance of occurring relative to 
all other possible orderings, how many ways must there be to arrange the five 
cars?
\item Now consider a sample of 8 employees instead. How many possible ways 
are there to order these 8 employees' cars?
\end{parts}
}{}

% 26

\eoce{\qt{Male children\label{male_children}} While it is often assumed that the 
probabilities of having a boy or a girl are the same, the actual probability 
of having a boy is slightly higher at 0.51. Suppose a couple plans to have 3 
kids. 
\begin{parts}
\item Use the binomial model to calculate the probability that two of them 
will be boys.
\item Write out all possible orderings of 3 children, 2 of whom are boys. Use 
these scenarios to calculate the same probability from part (a) but using the 
addition rule for disjoint outcomes. Confirm that your answers from parts (a) 
and (b) match.
\item If we wanted to calculate the probability that a couple who plans to 
have 8 kids will have 3 boys, briefly describe why the approach from part (b) 
would be more tedious than the approach from part (a).
\end{parts}
}{}


================================================
FILE: ch_distributions/TeX/ch_distributions.tex
================================================
\begin{chapterpage}{Distributions of random variables}
  \chaptertitle[30]{Distributions of random \titlebreak{} variables}
  \label{ch_distributions}
  \chaptersection{normalDist}
  %\chaptersection{assessingNormal}
  \chaptersection{geomDist}
  \chaptersection{binomialModel}
  \chaptersection{negativeBinomial}
  \chaptersection{poisson}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_distributions}


\chapterintro{In this chapter,
  we discuss statistical distributions that frequently
  arise in the context of data analysis or statistical
  inference.
  We start with the normal distribution in the first section,
  which is used frequently in later chapters of this book.
  The remaining sections will occasionally be referenced
  but may be considered optional for the content in this
  book.}

%_________________
\section{Normal distribution}
\label{normalDist}

\index{distribution!normal|(}
\index{normal distribution|(}

Among all the distributions we see in practice,
one is overwhelmingly the most common.
The symmetric, unimodal, bell curve is ubiquitous
throughout statistics.
Indeed it is so common, that people often know it as the
\termsub{normal curve}{normal distribution} or
\term{normal distribution}\index{distribution!normal|textbf}%
,\footnote{It
  is also introduced as the Gaussian distribution after Frederic
  Gauss, the first person to formalize its mathematical
  expression.}
shown in Figure~\ref{simpleNormal}.
Variables such as SAT scores and heights of US adult males
closely follow the normal distribution.

\begin{figure}[h]
  \centering
  \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}
  \caption{A normal curve.}
  \label{simpleNormal}
\end{figure}

\begin{onebox}{Normal distribution facts}
  Many variables are nearly normal, but none are exactly normal.
  Thus the normal distribution, while not perfect for any single
  problem, is very useful for a variety of problems.
  We will use it in data exploration and to solve important
  problems in statistics.
\end{onebox}


\subsection{Normal distribution model}

The \term{normal distribution} always describes a symmetric,
unimodal, bell-shaped curve.
However, these curves can look different depending on the
details of the model.
Specifically, the normal distribution model can be adjusted
using two parameters: mean and standard deviation.
As you can probably guess, changing the mean shifts the bell
curve to the left or right, while changing the standard deviation
stretches or constricts the curve.
Figure~\ref{twoSampleNormals} shows the normal distribution
with mean $0$ and standard deviation $1$ in the left panel
and the normal distributions with mean $19$ and standard
deviation $4$ in the right panel.
Figure~\ref{twoSampleNormalsStacked} shows these distributions
on the same axis.

\begin{figure}[h]
  \centering
  \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}
  \caption{Both curves represent the normal distribution.
      However, they differ in their center and spread.}
  \label{twoSampleNormals}
\end{figure}

\begin{figure}[h]
  \centering
  \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}
  \caption{The normal distributions shown in
      Figure~\ref{twoSampleNormals} but plotted together
      and on the same scale.}
  \label{twoSampleNormalsStacked}
\end{figure}

If a normal distribution has mean $\mu$ and standard deviation
$\sigma$, we may write the distribution as $N(\mu, \sigma)$.
The two distributions in Figure~\ref{twoSampleNormalsStacked}
may be written as
\begin{align*}
N(\mu=0,\sigma=1)
  \quad \text{and} \quad
  N(\mu=19,\sigma=4)
\end{align*}
Because the mean and standard deviation describe a normal
distribution exactly, they are called the distribution's
\termsub{parameters}{parameter}.
The normal distribution with mean $\mu = 0$ and
standard deviation $\sigma = 1$ is called the
\term{standard normal distribution}%
\index{normal distribution!standard|textbf}.

\begin{exercisewrap}
\begin{nexercise}
Write down the short-hand for a normal distribution
with\footnotemark{} \\
%\begin{enumerate}[(a)]
%\setlength{\itemsep}{0mm}
%\item
(a)
    mean~5 and standard deviation~3, \\
%\item
(b)
    mean~-100 and standard deviation~10, and \\
%\item
(c)
    mean~2 and standard deviation~9.
%\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~$N(\mu=5,\sigma=3)$.
  (b)~$N(\mu=-100, \sigma=10)$.
  (c)~$N(\mu=2, \sigma=9)$.}


\subsection{Standardizing with Z-scores}

\noindent%
We often want to put data onto a standardized scale,
which can make comparisons more reasonable.

\newcommand{\satmean}{1100}
\newcommand{\satsd}{200}
\newcommand{\actmean}{21}
\newcommand{\actsd}{6}
\newcommand{\annsatscore}{1300}
\newcommand{\annsatzscore}{1}
\newcommand{\tomsatscore}{24}
\newcommand{\tomsatzscore}{0.5}

\begin{examplewrap}
\begin{nexample}{Table~\vref{satACTstats} shows the mean
    and standard deviation for total scores on the SAT and ACT.
    The distribution of SAT and ACT scores are both nearly normal.
    Suppose Ann scored \annsatscore{} on her SAT and Tom scored
    \tomsatscore{} on his ACT.
    Who performed better?}
  \label{actSAT}%
  We use the standard deviation as a guide.
  Ann is \annsatzscore{} standard deviation above average
  on the SAT: $\satmean{} + \satsd{} = \annsatscore{}$.
  Tom is \tomsatzscore{} standard deviations above the mean
  on the ACT:
  $\actmean{} + \tomsatzscore{} \times \actsd{} = \tomsatscore{}$.
  In Figure~\ref{satActNormals}, we can see that Ann tends
  to do better with respect to everyone else than Tom did,
  so her score was better.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
\centering
\begin{tabular}{l r r}
  \hline
  & SAT & ACT \\
  \hline
  Mean \hspace{0.3cm} & \satmean{} & \actmean{} \\
  SD & \satsd{} & \actsd{} \\
  \hline
\end{tabular}
\caption{Mean and standard deviation for the SAT and ACT.}
\label{satACTstats}
\end{figure}

\begin{figure}
  \centering
  \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}
  \caption{Ann's and Tom's scores shown against
      the SAT and ACT distributions.}
  \label{satActNormals}
\end{figure}

Example~\ref{actSAT} used a standardization technique called
a Z-score, a method most commonly employed for nearly normal
observations but that may be used with any distribution.
The \term{Z-score}\index{Z@$Z$} of an observation is defined
as the number of standard deviations it falls above or below
the mean.
If the observation is one standard deviation above the mean,
its Z-score is~1.
If it is 1.5 standard deviations \emph{below} the mean,
then its Z-score is -1.5.
If $x$ is an observation from a distribution $N(\mu, \sigma)$,
we define the Z-score mathematically as
\begin{align*}
Z = \frac{x - \mu}{\sigma}
\end{align*}
Using $\mu_{SAT} = \satmean{}$, $\sigma_{SAT} = \satsd{}$,
and $x_{_{\text{Ann}}} = \annsatscore{}$, we find Ann's Z-score:
\begin{align*}
Z_{_{\text{Ann}}}
  = \frac{x_{_{\text{Ann}}} - \mu_{_{\text{SAT}}}}
      {\sigma_{_{\text{SAT}}}}
  = \frac{\annsatscore{} - \satmean{}}{\satsd{}}
  = \annsatzscore{}
\end{align*}

\begin{onebox}{The Z-score}
  The Z-score of an observation is the number of standard
  deviations it falls above or below the mean.
  We compute the Z-score for an observation $x$ that follows
  a distribution with mean $\mu$ and standard deviation
  $\sigma$ using
  \begin{align*}
  Z = \frac{x - \mu}{\sigma}
  \end{align*}
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
Use Tom's ACT score, \tomsatscore{}, along with the ACT mean and
standard deviation to find his Z-score.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{$Z_{Tom}
  = \frac{x_{\text{Tom}} - \mu_{\text{ACT}}}
      {\sigma_{\text{ACT}}}
  = \frac{\tomsatscore{} - \actmean{}}{\actsd{}}
  = \tomsatzscore{}$}

Observations above the mean always have positive Z-scores,
while those below the mean always have negative Z-scores.
If an observation is equal to the mean,
such as an SAT score of \satmean{}, then the Z-score is $0$.

\begin{exercisewrap}
\begin{nexercise}
Let $X$ represent a random variable from $N(\mu=3, \sigma=2)$,
and suppose we observe $x=5.19$. \\
%\begin{enumerate}[(a)]
%\setlength{\itemsep}{0mm}
%\item
(a)
    Find the Z-score of $x$. \\
%\item
(b)
    Use the Z-score to determine how many standard deviations
    above or below the mean $x$ falls.\footnotemark{}
%\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a) Its Z-score is given by
    $Z
      = \frac{x-\mu}{\sigma}
      = \frac{5.19 - 3}{2}
      = 2.19/2
      = 1.095$.
    (b)~The observation $x$ is 1.095 standard deviations
    \emph{above} the mean.
    We know it must be above the mean since $Z$ is positive.}

\begin{exercisewrap}
\begin{nexercise} \label{headLZScore}
Head lengths of brushtail possums follow a normal
distribution with mean 92.6 mm and standard deviation 3.6 mm.
Compute the Z-scores for possums with head lengths of 95.4 mm
and 85.8~mm.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{For $x_1=95.4$ mm:
    $Z_1
      = \frac{x_1 - \mu}{\sigma}
      = \frac{95.4 - 92.6}{3.6}
      = 0.78$.
    For $x_2=85.8$ mm:
    $Z_2 = \frac{85.8 - 92.6}{3.6} = -1.89$.}

We can use Z-scores to roughly identify which observations
are more unusual than others.
An observation $x_1$ is said to be more unusual than another
observation $x_2$ if the absolute value of its Z-score is larger
than the absolute value of the other observation's Z-score:
$|Z_1| > |Z_2|$.
This technique is especially insightful when a distribution
is symmetric.

%\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
Which of the observations in Guided Practice~\ref{headLZScore}
is more unusual?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Because the \emph{absolute value} of Z-score
  for the second observation is larger than that of the first,
  the second observation has a more unusual head length.}


\subsection{Finding tail areas}

It's very useful in statistics to be able to identify tail areas
of distributions.
For instance, what fraction of people have an SAT score below
Ann's score of 1300?
This is the same as the \term{percentile} Ann is at, which is
the percentage of cases that have lower scores than Ann.
We can visualize such a tail area like the curve and shading
shown in Figure~\ref{satBelow1300}.

\begin{figure}[h]
  \centering
  \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}
  \caption{The area to the left of $Z$ represents the
      fraction of people who scored lower than Ann.}
  \label{satBelow1300}
\end{figure}

There are many techniques for doing this, and we'll discuss
three of the options.
\begin{enumerate}
\item
    The most common approach in practice is to use
    statistical software.
    For example, in the program \R{}, we could find the area
    shown in Figure~\ref{satBelow1300} using the
    following command, which takes in the Z-score
    and returns the lower tail area: \\
    {\color{white}.....}%
        \texttt{> pnorm(1)} \\
    {\color{white}.....}%
        \texttt{[1] 0.8413447} \\
    According to this calculation,
    the region shaded that is below 1300
    represents the proportion 0.841 (84.1\%) of SAT test
    takers who had Z-scores below $Z = 1$.
    More generally, we can also specify the cutoff explicitly
    if we also note the mean and standard deviation: \\
    {\color{white}.....}%
        \texttt{> pnorm(1300, mean = 1100, sd = 200)} \\
    {\color{white}.....}%
        \texttt{[1] 0.8413447} %\\
    %\Add{More examples for using \R{} are provided
    %  at the end of the section.}


    There are many other software options, such as Python or SAS;
    even spreadsheet programs such as
    Excel and Google Sheets support these calculations.
\item
    A common strategy in classrooms is to use a graphing
    calculator, such as a TI or Casio calculator.
    These calculators require a series of button presses
    that are less concisely described.
    You can find instructions on using these calculators
    for finding tail areas of a normal distribution in the
    OpenIntro video library:
    \begin{center}
    \oiRedirect{textbook-openintro_videos}
        {www.openintro.org/videos}
    \end{center}
\item
    The last option for finding tail areas is to use
    what's called a \term{probability table};
    these are occasionally used in classrooms
    but rarely in practice.
    Appendix~\ref{normalProbabilityTable}
    contains such a table and a guide for how to use it.
\end{enumerate}
We will solve normal distribution problems in this section
by always first finding the Z-score.
The reason is that we will encounter close parallels
called \indexthis{test statistics}{test statistic}
beginning in Chapter~\ref{ch_foundations_for_inf};
these are, in many instances, an equivalent of a Z-score.

%No matter the approach you choose,
%try the Guided Practice exercises in this section
%using your preferred method.


\D{\newpage}

\subsection{Normal probability examples}
\label{normal_probability_examples}

\noindent%
Cumulative SAT scores are approximated well by a normal model,
$N(\mu = \satmean{}, \sigma = \satsd{})$.

\newcommand{\shannonsat}{1190}
\newcommand{\shannonsatz}{0.45}
\begin{examplewrap}
\begin{nexample}{Shannon is a randomly selected SAT taker,
    and nothing is known about Shannon's SAT aptitude.
    What is the probability Shannon scores at least
    \shannonsat{} on her SATs?}
  \label{satAbove1190Exam}%
  First, always draw and label a picture of the normal
  distribution.
  (Drawings need not be exact to be useful.)
  We are interested in the chance she scores above
  \shannonsat{}, so we shade this upper tail:
  \begin{center}
  \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}
  \end{center}
  The picture shows the mean and the values at
  2~standard deviations above and below the mean.
  The simplest way to find the shaded area under
  the curve makes use of the Z-score of the cutoff value.
  With $\mu = \satmean{}$, $\sigma = \satsd{}$,
  and the cutoff value $x = \shannonsat{}$,
  the Z-score is computed as
  \begin{align*}
  Z = \frac{x - \mu}{\sigma}
    = \frac{\shannonsat{} - \satmean{}}{\satsd{}}
    = \frac{90}{\satsd{}}
    = \shannonsatz{}
  \end{align*}
  Using statistical software (or another preferred method),
  we can find the area left of $Z = \shannonsatz{}$ as 0.6736.
  %This is Shannon's \term{percentile},
  %which is the fraction of folks who scored below her score
  %of \shannonsat{}.
  To find the area \emph{above} $Z = \shannonsatz{}$,
  we compute one minus the area of the lower tail:
  \begin{center}
  \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}
  \end{center}
  The probability Shannon scores at least 1190 on the SAT
  is 0.3264.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Always draw a picture first,
    and find the Z-score second}
  For any normal probability situation,
  \emph{always always always} draw and label the
  normal curve and shade the area of interest first.
  The picture will provide an estimate of the probability.
  After drawing a figure to represent the situation,
  identify the Z-score for the value of interest.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
If the probability of Shannon scoring at least \shannonsat{}
is 0.3264, then what is the probability she scores less than
\shannonsat{}?
Draw the normal curve representing this exercise,
shading the lower region instead of the upper one.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We found this probability in
  Example~\ref{satAbove1190Exam}: 0.6736. \\
  \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}}

\D{\newpage}

\newcommand{\edwardsat}{1030}
\newcommand{\edwardsatz}{-0.35}
\newcommand{\edwardsatlower}{0.3632}
\begin{examplewrap}
\begin{nexample}{Edward earned a \edwardsat{} on his SAT.
    What is his percentile?}
  \label{edwardSatBelow\edwardsat{}}%
  First, a picture is needed.
  Edward's \hiddenterm{percentile} is the proportion of people
  who do not get as high as a \edwardsat{}.
  These are the scores to the left of \edwardsat{}.
\begin{center}
\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}
\end{center}
Identifying the mean $\mu=\satmean{}$, the standard
deviation $\sigma=\satsd{}$, and the cutoff for the tail
area $x=\edwardsat{}$ makes it easy to compute the Z-score:
\begin{align*}
Z
  = \frac{x - \mu}{\sigma}
  = \frac{\edwardsat{} - \satmean{}}{\satsd{}}
  = \edwardsatz{}
\end{align*}
Using statistical software, we get a tail area of 0.3632.
Edward is at the $36^{th}$ percentile.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Use the results of Example~\ref{edwardSatBelow\edwardsat{}}
to compute the proportion of SAT takers who did better than
Edward.
Also draw a new picture.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{If Edward did better than 36\% of SAT takers,
  then about 64\% must have done better than him. \\
  \Figures{0.25}{satBelow1030}{satAbove1030}}

\begin{onebox}{Finding areas to the right}
  Many software programs return the area to the left
  when given a Z-score.
  If you would like the area to the right, first find the
  area to the left and then subtract this amount from~one.
\end{onebox}

\newcommand{\stuartsat}{1500}
\newcommand{\stuarsatz}{2}
\begin{exercisewrap}
\begin{nexercise}
Stuart earned an SAT score of \stuartsat{}.
Draw a picture for each part. \\
(a)~What is his percentile? \\
(b)~What percent of SAT takers did better than
  Stuart?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We leave the drawings to you.
  (a) $Z = \frac{\stuartsat{} - \satmean{}}{\satsd{}}
         = \stuarsatz{}
         \to 0.9772$.
  (b) $1 - 0.9772 = 0.0228$.}

Based on a sample of 100 men, the heights of male adults
in the US is nearly normal with mean 70.0''
and standard deviation 3.3''.

\begin{exercisewrap}
\begin{nexercise}
Mike is 5'7'' and Jose is 6'4'', and they both live in the US. \\
(a) What is Mike's height percentile? \\
(b) What is Jose's height percentile? \\
Also draw one picture for each~part.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{First put the heights into inches:
  67 and 76 inches.
  Figures are shown below. \\
  (a) $Z_{\text{Mike}} = \frac{67 - 70}{3.3} = -0.91\ \to\ 0.1814$.
  (b) $Z_{\text{Jose}} = \frac{76 - 70}{3.3} = 1.82\ \to\ 0.9656$.
  \\
  \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}}

\D{\newpage}

The last several problems have focused on finding the
percentile (lower tail) or the upper tail for a particular observation.
What if you would like to know the observation corresponding
to a particular percentile?

\begin{examplewrap}
\begin{nexample}{Erik's height is at the $40^{th}$ percentile.
    How tall is he?}\label{normalExam40Perc}
  As always, first draw the picture.\vspace{-4mm}
  \begin{center}
  \Figure{0.3}{height40Perc}\vspace{-1mm}
  \end{center}
  In this case, the lower tail probability is known (0.40),
  which can be shaded on the diagram.
  We want to find the observation that corresponds to this value.
  As a first step in this direction, we determine the Z-score
  associated with the $40^{th}$ percentile.
  Using software, we can obtain the corresponding Z-score
  of about -0.25.

  Knowing $Z_{Erik} = -0.25$ and the population parameters
  $\mu = 70$ and $\sigma = 3.3$ inches, the Z-score formula can be
  set up to determine Erik's unknown height, labeled
  $x_{_{\text{Erik}}}$:
  \begin{align*}
  -0.25
    = Z_{_{\text{Erik}}}
    = \frac{x_{_{\text{Erik}}} - \mu}{\sigma}
    = \frac{x_{_{\text{Erik}}} - 70}{3.3}
  \end{align*}
  Solving for $x_{_{\text{Erik}}}$ yields a height of 69.18 inches.
  That is, Erik is about 5'9''.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{What is the adult male height at the
    $82^{nd}$ percentile?}
  Again, we draw the figure first.\vspace{-3mm}
  \begin{center}
  \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}
  \end{center}
  Next, we want to find the Z-score at the $82^{nd}$ percentile,
  which will be a positive value and can be found using software
  as $Z = 0.92$.
  Finally, the height $x$ is found using the Z-score formula
  with the known mean $\mu$, standard deviation $\sigma$,
  and Z-score $Z = 0.92$:
  \begin{align*}
  0.92 = Z = \frac{x-\mu}{\sigma} = \frac{x - 70}{3.3}
  \end{align*}
  This yields 73.04 inches or about 6'1'' as the height
  at the $82^{nd}$ percentile.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
The SAT scores follow $N(\satmean{}, \satsd{})$.\footnotemark{} \\
(a) What is the $95^{th}$ percentile for SAT scores? \\
(b) What is the $97.5^{th}$ percentile for SAT scores?
\end{nexercise}
\end{exercisewrap}
\footnotetext{Short answers:
  (a) $Z_{95} = 1.6449 \to 1429$ SAT score.
  (b) $Z_{97.5} = 1.96 \to 1492$ SAT score.}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}\label{more74Less69}
Adult male heights follow $N(70.0$''$, 3.3$''$)$.\footnotemark{} \\
(a)~What is the probability that a randomly selected male
    adult is at least 6'2'' (74 inches)? \\
(b)~What is the probability that a male adult is shorter
    than 5'9'' (69 inches)?
\end{nexercise}
\end{exercisewrap}
\footnotetext{Short answers:
  (a) $Z = 1.21 \to 0.8869$, then subtract this value
      from 1 to get 0.1131.
  (b) $Z = -0.30 \to 0.3821$.}

\begin{examplewrap}
\begin{nexample}{What is the probability that a random adult
    male is between 5'9'' and 6'2''?}
  These heights correspond to 69 inches and 74 inches.
  First, draw the figure.
  The area of interest is no longer an upper or lower
  tail.\vspace{-2mm}
  \begin{center}
  \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}
  \end{center}
  The total area under the curve is~1.
  If we find the area of the two tails that are not shaded
  (from Guided Practice~\ref{more74Less69}, these areas are
  $0.3821$ and $0.1131$), then we can find the middle
  area:\vspace{-2mm}
  \begin{center}
  \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}
  \end{center}
  That is, the probability of being between 5'9'' and 6'2''
  is 0.5048.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
SAT scores follow $N(\satmean{}, \satsd{})$.
What percent of SAT takers get between \satmean{} and
1400?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{This is an abbreviated solution.
  (Be sure to draw a figure!)
  First find the percent who get below \satmean{}
  and the percent that get above 1400:
  $Z_{\satmean{}} = 0.00 \to 0.5000$ (area below),
  $Z_{1400} = 1.5 \to 0.0668$ (area above).
  Final answer: $1.0000 - 0.5000 - 0.0668 = 0.4332$.}

\begin{exercisewrap}
\begin{nexercise}
Adult male heights follow $N(70.0$''$, 3.3$''$)$.
What percent of adult males are between 5'5''
and 5'7''?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{5'5'' is 65 inches ($Z = -1.52$).
  5'7'' is 67 inches ($Z = -0.91$).
  Numerical solution: $1.000 - 0.0643 - 0.8186 = 0.1171$,
  i.e. 11.71\%.}


\D{\newpage}

\subsection{68-95-99.7 rule}

Here, 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.

\begin{figure}[hht]
\centering
\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}
\caption{Probabilities for falling within 1, 2, and 3 standard deviations of the mean in a normal distribution.}
\label{6895997}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
Use software, a calculator, or a probability table
to confirm that about 68\%, 95\%, and 99.7\%
of observations fall within 1, 2, and 3, standard deviations
of the mean in the normal distribution, respectively.
For instance, first find the area that falls between $Z=-1$
and $Z=1$, which should have an area of about 0.68.
Similarly there should be an area of about 0.95 between
$Z=-2$ and $Z=2$.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{First draw the pictures.
  Using software, we get 0.6827 within 1~standard deviation,
  0.9545 within 2~standard deviations,
  and 0.9973 within 3~standard deviations.}

It is possible for a normal random variable to fall 4,~5,
or~even more standard deviations from the mean.
However, these occurrences are very rare if the data are
nearly normal.
The probability of being further than 4 standard deviations
from the mean is about 1-in-15,000.
For 5 and 6 standard deviations, it is about 1-in-2 million
and 1-in-500 million, respectively.

\begin{exercisewrap}
\begin{nexercise}
SAT scores closely follow the normal model with mean
$\mu = \satmean{}$ and standard deviation
$\sigma = \satsd{}$.\footnotemark{} \\
(a) About what percent of test takers score 700 to 1500? \\
(b) What percent score between \satmean{} and 1500?
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a) 700 and 1500 represent two standard deviations
  below and above the mean, which means about 95\% of test takers
  will score between 700 and 1500.
  (b)~We found that 700 to 1500 represents about 95\% of test
  takers.
  These test takers would be evenly split by the center of
  the distribution, \satmean{},
  so $\frac{95\%}{2} = 47.5\%$ of all test takers
  score between \satmean{} and 1500.}


{\input{ch_distributions/TeX/normal_distribution.tex}}




%%_________________
%\section{Evaluating the normal approximation}
%\label{assessingNormal}
%
%Many processes can be well approximated by the normal distribution.
%We have already seen two good examples:
%SAT scores and the heights of US adult males.
%While using a normal model can be extremely convenient
%and helpful, it is important to remember normality is
%always an approximation.
%Evaluating the appropriateness of the normal assumption
%is a key step in many data analyses.
%
%\index{normal probability plot|(}
%
%Example~\ref{normalExam40Perc} in Section~\ref{normalDist}
%suggested the distribution of heights of US males is well
%approximated by the normal model.
%We are interested in proceeding under the assumption that
%the data are normally distributed, but first we must check
%to see if this is reasonable.
%
%There are two visual methods for checking the assumption of
%normality, which can be implemented and interpreted quickly.
%The first is a simple histogram with the best fitting normal
%curve overlaid on the plot, as shown in the left panel of
%Figure~\ref{fcidMHeights}.
%The sample mean $\bar{x}$ and standard deviation $s$ are used
%as the parameters of the best fitting normal curve.
%The closer this curve fits the histogram, the more reasonable
%the normal model assumption.
%Another common method is examining a
%\term{normal probability plot},\footnote{Also commonly
%  called a \term{quantile-quantile plot}.}
%shown in the right panel of Figure~\ref{fcidMHeights}.
%The closer the points are to a perfect straight line,
%the more confident we can be that the data follow the
%normal model.
%
%\begin{figure}[h]
%  \centering
%  \Figure{0.7}{fcidMHeights}
%  \caption{A sample of 100 male heights.
%      The observations are rounded to the nearest whole inch,
%      explaining why the points appear to jump in increments
%      in the normal probability plot.}
%  \label{fcidMHeights}
%\end{figure}
%
%\begin{examplewrap}
%\begin{nexample}{Three data sets of 40, 100, and 400
%    samples were simulated from a normal distribution,
%    and the histograms and normal probability plots
%    of the data sets are shown in Figure~\ref{normalExamples}.
%    These will provide a benchmark for what to look for
%    in plots of real data.}
%  \label{normalExamplesExample}%
%  The left panels show the histogram (top) and normal
%  probability plot (bottom) for the simulated data set
%  with 40 observations.
%  The data set is too small to really see clear structure
%  in the histogram.
%  The normal probability plot also reflects this,
%  where there are some deviations from the line.
%  We should expect deviations of this amount for
%  such a small data set.
%
%  The middle panels show diagnostic plots for the
%  data set with 100 simulated observations.
%  The histogram shows more normality and the normal
%  probability plot shows a better fit.
%  While there are a few observations that deviate
%  noticeably from the line, they are not particularly
%  extreme.
%
%  The data set with 400 observations has a histogram
%  that greatly resembles the normal distribution,
%  while the normal probability plot is nearly a perfect
%  straight line.
%  Again in the normal probability plot there is one
%  observation (the largest) that deviates slightly from
%  the line.
%  If that observation had deviated 3 times further from
%  the line, it would be of greater importance in a real
%  data set.
%  Apparent outliers can occur in normally distributed
%  data but they are rare.
%
%  Notice the histograms look more normal as the sample
%  size increases, and the normal probability plot becomes
%  straighter and more stable.
%\end{nexample}
%\end{examplewrap}
%
%\begin{figure}
%  \centering
%  \Figure{0.9}{normalExamples}
%  \caption{Histograms and normal probability plots for
%      three simulated normal data sets; $n=40$ (left),
%      $n=100$ (middle), $n=400$ (right).}
%  \label{normalExamples}
%\end{figure}
%
%\begin{examplewrap}
%\begin{nexample}{Are NBA player heights normally distributed?
%    Consider all 494 NBA players presented in
%    Figure~\ref{nbaNormal}.}
%  We first create a histogram and normal probability plot
%  of the NBA player heights.
%  The histogram in the left panel appears to have too few
%  observations at the upper end since the curve is notably
%  above the histogram.
%  The points in the normal probability plot
%  follow a straight line for much of the center of the
%  distribution, and then deviates more at the upper values.
%  We can compare these characteristics to the sample of
%  400 normally distributed observations in
%  Example~\ref{normalExamplesExample} and see that they
%  represent much stronger deviations from the normal model.
%  NBA player heights do not appear to come from a normal
%  distribution.
%\end{nexample}
%\end{examplewrap}
%
%\begin{examplewrap}
%\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}.}
%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.
%\end{nexample}
%\end{examplewrap}
%
%\begin{figure}
%  \centering
%  \Figure{0.8}{nbaNormal}
%  \caption{Histogram and normal probability plot
%      for the NBA heights from the 2008-9 season.}
%  \label{nbaNormal}
%\end{figure}
%
%\begin{figure}
%  \centering
%  \Figure{0.9}{pokerNormal}
%  \caption{A histogram of poker data with the best
%      fitting normal plot and a normal probability plot.}
%  \label{pokerNormal}
%\end{figure}
%
%\begin{exercisewrap}
%\begin{nexercise}\label{normalQuantileExercise}%
%Determine which data sets represented in
%Figure~\ref{normalQuantileExer} plausibly come from
%a nearly normal distribution.
%Are you confident in all of your conclusions?
%There are 100 (top left), 50 (top right), 500 (bottom left),
%and 15 points (bottom right) in the four plots.\footnotemark{}
%\end{nexercise}
%\end{exercisewrap}
%\footnotetext{Answers may vary a little.
%  The top-left plot shows some deviations in the smallest values
%  in the data set;
%  specifically, the left tail of the data set has some outliers
%  we should be wary of.
%  The top-right and bottom-left plots do not show any obvious
%  or extreme deviations from the lines for their respective
%  sample sizes, so a normal model would be reasonable for these
%  data sets.
%  The bottom-right plot has a consistent curvature that suggests
%  it is not from the normal distribution.
%  If we examine just the vertical coordinates of these
%  observations, we see that there is a lot of data between
%  -20 and 0, and then about five observations scattered
%  between 0 and 70.
%  This describes a distribution that has a strong right skew.}
%
%\begin{figure}
%  \centering
%  \Figure{0.7}{normalQuantileExer}
%  \caption{Four normal probability plots for
%      Guided Practice~\ref{normalQuantileExercise}.}
%  \label{normalQuantileExer}
%\end{figure}
%
%\begin{exercisewrap}
%\begin{nexercise}
%\label{normalQuantileExerciseAdditional}%
%Figure~\ref{normalQuantileExerAdditional} shows normal
%probability plots for two distributions that are skewed.
%One distribution is skewed to the low end (left skewed)
%and the other to the high end (right skewed).
%Which is which?\footnotemark{}
%\end{nexercise}
%\end{exercisewrap}
%\footnotetext{Examine where the points fall along the
%  vertical axis.
%  In the first plot, most points are near the low end
%  with fewer observations scattered along the high end;
%  this describes a distribution that is skewed to the
%  high end.
%  The second plot shows the opposite features,
%  and this distribution is skewed to the low end.}
%
%\begin{figure}[h]
%  \centering
%  \Figures{0.8}{normalQuantileExer}{normalQuantileExerAdditional}
%  \caption{Normal probability plots for
%      Guided Practice~\ref{normalQuantileExerciseAdditional}.}
%  \label{normalQuantileExerAdditional}
%\end{figure}
%
%\index{normal probability plot|)}
\index{normal distribution|)}
\index{distribution!normal|)}




%_________________
\section{Geometric distribution}
\label{geomDist}

How 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.

\subsection{Bernoulli distribution}
\label{bernoulli}

\newcommand{\insureSprob}{0.7}
\newcommand{\insureSperc}{70\%}
\newcommand{\insureFprob}{0.3}
\newcommand{\insureFperc}{30\%}
\newcommand{\insureDistA}{0.7}
\newcommand{\insureDistB}{0.21}
\newcommand{\insureDistC}{0.063}
\newcommand{\insureDistD}{0.019}
\newcommand{\insureDistE}{0.006}
\newcommand{\insureCDistA}{0.7}
\newcommand{\insureCDistB}{0.91}
\newcommand{\insureCDistC}{0.973}
\newcommand{\insureCDistCComplement}{0.027}
\newcommand{\insureCDistD}{0.992}
\newcommand{\insureCDistE}{0.998}
\newcommand{\insureGeomMean}{1.43}

\index{distribution!Bernoulli|(}

Many health insurance plans in the United States have
a deductible, where the insured individual is responsible
for costs up to the deductible, and then the costs above
the deductible are shared between the individual and
insurance company for the remainder of the year.

Suppose a health insurance company found that \insureSperc{} of the
people they insure stay below their deductible in any given year.
Each of these people can be thought of as a \term{trial}.
We label a person a \term{success} if her healthcare costs
do not exceed the deductible.
We label a person a \term{failure} if she does exceed her
deductible in the year.
Because 70\% of the individuals will not hit their deductible,
we denote the \term{probability of a success} as
$p = \insureSprob{}$.
The probability of a failure is sometimes denoted with
$q = 1 - p$, which would be \insureFprob{} for the insurance
example.

When an individual trial only has two possible outcomes, often
labeled as \resp{success} or \resp{failure}, it is called a
\termsub{Bernoulli random variable}{distribution!Bernoulli}.
We chose to label a person who does not hit her deductible
as a ``success'' and all others as ``failures''.
However, we could just as easily have reversed these labels.
The mathematical framework we will build does not depend
on which outcome is labeled a success and which a failure,
as long as we are consistent.

Bernoulli random variables are often denoted as \resp{1}
for a success and \resp{0} for a failure.
In addition to being convenient in entering data,
it is also mathematically handy.
Suppose we observe ten trials:
\begin{center}
\resp{1} \resp{1} \resp{1} \resp{0} \resp{1} \resp{0} \resp{0} \resp{1} \resp{1} \resp{0}
\end{center}
Then the \term{sample proportion}, $\hat{p}$, is the
sample mean of these observations:
\begin{align*}
\hat{p} = \frac{\text{\# of successes}}{\text{\# of trials}}
    = \frac{1+1+1+0+1+0+0+1+1+0}{10} = 0.6
\end{align*}%
This mathematical inquiry of Bernoulli random variables can
be extended even further.
%\Comment{Maybe the next footnote should instead be an EOCE?}
Because \resp{0} and \resp{1} are numerical outcomes,
we can define the {mean} and {standard deviation}
of a Bernoulli random variable.
(See Exercises~\ref{bernoulli_mean_derivation}
and~\ref{bernoulli_sd_derivation}.)

\begin{onebox}{Bernoulli random variable}
%  A Bernoulli random variable has exactly two possible
%  outcomes, often labeled \resp{1} for the ``success''
%  outcome and \resp{0} for the ``failure'' outcome.\vspace{3mm}
  If $X$ is a random variable that takes value 1 with
  probability of success $p$ and 0 with probability $1-p$,
  then $X$ is a Bernoulli random variable with mean
  and standard deviation
  \begin{align*}
  \mu &= p
      &\sigma&= \sqrt{p(1-p)}
  \end{align*}
\end{onebox}

In 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.

\index{distribution!Bernoulli|)}


\D{\newpage}

\subsection{Geometric distribution}

\index{distribution!geometric|(}

The \termsub{geometric distribution}{distribution!geometric}
is used to describe how
many trials it takes to observe a success.
Let's first look at an example.

\begin{examplewrap}
\begin{nexample}{Suppose we are working at the insurance
    company and need to find a case where the person did
    not exceed her (or his) deductible as a case study.
    If the probability a person will not exceed her
    deductible is \insureSprob{} and we are drawing people
    at random, what are the chances that the first person
    will not have exceeded her deductible, i.e. be a success?
    The second person?
    The third?
    What about we pull $n - 1$ cases before we find
    the first success, i.e. the first success is the
    $n^{th}$ person?
    (If the first success is the fifth person, then we say $n=5$.)}
  \label{waitForDeductible}%
  The probability of stopping after the first person is just
  the chance the first person will not hit her (or his)
  deductible:~\insureSprob{}.
  The probability the second person is the first to hit
  her deductible is
  \begin{align*}
  &P(\text{second person is the first to not hit deductible}) \\
  &\quad
    = P(\text{the first will, the second won't})
    = (\insureFprob{})(\insureSprob{})
    = \insureDistB{}
  \end{align*}
  Likewise, the probability it will be the third case is
  $(\insureFprob{})(\insureFprob{})(\insureSprob{})
    = \insureDistC$.

  If the first success is on the $n^{th}$ person,
  then there are $n-1$ failures and finally 1 success,
  which corresponds to the probability
  $(\insureFprob{})^{n-1}(\insureSprob{})$.
  This is the same as
  $(1-\insureSprob{})^{n-1}(\insureSprob{})$.
\end{nexample}
\end{examplewrap}

Example~\ref{waitForDeductible} illustrates what the
\termsub{geometric distribution}{distribution!geometric},
which describes the waiting
time until a success for
\term{independent and identically distributed (iid)}
Bernoulli random variables.
In this case, the \emph{independence} aspect just means
the individuals in the example don't affect each other,
and \emph{identical} means they each have the same probability
of success.

The geometric distribution from Example~\ref{waitForDeductible} is shown in Figure~\ref{geometricDist70}. In general, the probabilities for a geometric distribution decrease \term{exponentially} fast.

\begin{figure}[h]
  \centering
  \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}
  \caption{The geometric distribution when the probability
      of success is $p = \insureSprob{}$.}
  \label{geometricDist70}
\end{figure}

While 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.

\begin{onebox}{Geometric Distribution}
  \index{distribution!geometric|textbf}%
  If the probability of a success in one trial is $p$
  and the probability of a failure is $1-p$, then the
  probability of finding the first success in the
  $n^{th}$ trial is given by\vspace{-1.5mm}
  \begin{align*}
  (1-p)^{n-1}p
  \end{align*}
  The mean (i.e. expected value), variance,
  and standard deviation of this wait time are given by
  \begin{align*}
  \mu &= \frac{1}{p}
      &\sigma^2 &=\frac{1-p}{p^2}
      &\sigma &= \sqrt{\frac{1-p}{p^2}}
  \end{align*}
\end{onebox}

It 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.

It 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.

\begin{exercisewrap}
\begin{nexercise}
The probability that a particular case would not exceed their
deductible is said to be \insureSprob{}.
If we were to examine cases until we found one that where
the person did not hit her deductible, how many cases should
we expect to check?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We would expect to see about
    $1 / \insureSprob{} \approx \insureGeomMean{}$
    individuals to find the first success.}

\begin{examplewrap}
\begin{nexample}{What is the chance that we would find
    the first success within the first 3 cases?}
  \label{insureFirstSuccessInLT4}%
  This is the chance it is the first ($n=1$), second ($n=2$),
  or third ($n=3$) case is the first success, which are three
  disjoint outcomes.
  Because the individuals in the sample are randomly sampled
  from a large population, they are independent.
  We compute the probability of each case and add the separate
  results:
  \begin{align*}
  &P(n=1, 2, \text{ or }3) \\
    & \quad = P(n=1)+P(n=2)+P(n=3) \\
    & \quad = (\insureFprob{})^{1-1}(\insureSprob{})
        + (\insureFprob{})^{2-1}(\insureSprob{})
        + (\insureFprob{})^{3-1}(\insureSprob{}) \\
    & \quad = \insureCDistC{}
  \end{align*}
  There is a probability of \insureCDistC{} that we would
  find a successful case within 3 cases.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Determine a more clever way to solve Example~\ref{insureFirstSuccessInLT4}.
Show that you get the same result.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{First find the probability of the complement:
  $P($no success in first 3~trials$)
      = \insureFprob{}^3 = \insureCDistCComplement{}$.
  Next, compute one minus this probability:
  $1 - P($no success in 3 trials$)
      = 1 - \insureCDistCComplement{}
      = \insureCDistC{}$.}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{Suppose a car insurer has determined
    that 88\% of its drivers will not exceed their deductible
    in a given year.
    If someone at the company were to randomly draw
    driver files until they found one that had not exceeded
    their deductible, what is the expected number of drivers
    the insurance employee must check?
    What is the standard deviation of the number of driver files
    that must be drawn?}
  \label{carInsure08DrawOne}%
  In this example, a success is again when someone will not
  exceed the insurance deductible, which has probability
  $p = 0.88$.
  The expected number of people to be checked is
  $1 / p = 1 / 0.88 = 1.14$ and the standard deviation is
  $\sqrt{(1-p)/p^2} = 0.39$.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Using the results from Example~\ref{carInsure08DrawOne},
$\mu = 1.14$ and $\sigma = 0.39$, would it be appropriate
to use the normal model to find what proportion
of experiments would end in 3 or fewer trials?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{No. The geometric distribution is always
  right skewed and can never be well-approximated by the
  normal model.}

The independence assumption is crucial to the geometric
distribution's accurate description of a scenario.
Mathematically, we can see that to construct the probability
of the success on the $n^{th}$ trial, we had to use the
Multiplication Rule for Independent Processes.
It is no simple task to generalize the geometric model
for dependent trials.

\index{distribution!geometric|)}


{\input{ch_distributions/TeX/geometric_distribution.tex}}





\section{Binomial distribution}
\label{binomialModel}

\index{distribution!binomial|(}

The \termsub{binomial distribution}{distribution!binomial}
is used to describe
the number of successes in a fixed number of trials.
%,
%and this distribution is occasionally used in statistics,
%especially when doing more careful analysis of samples
%of data where simpler tools are not helpful.
This is different from the geometric distribution,
which described the number of trials we must wait before
we observe a success.


\subsection{The binomial distribution}

%\newcommand{\insureSprob}{0.7}
%\newcommand{\insureSperc}{70\%}
%\newcommand{\insureFprob}{0.3}
%\newcommand{\insureFperc}{30\%}
%\newcommand{\insureDistA}{0.7}
%\newcommand{\insureDistB}{0.21}
%\newcommand{\insureDistC}{0.063}
%\newcommand{\insureDistD}{0.019}
%\newcommand{\insureDistE}{0.006}
%\newcommand{\insureCDistA}{0.7}
%\newcommand{\insureCDistB}{0.91}
%\newcommand{\insureCDistC}{0.973}
%\newcommand{\insureCDistCComplement}{0.027}
%\newcommand{\insureCDistD}{0.992}
%\newcommand{\insureCDistE}{0.998}
%\newcommand{\insureGeomMean}{1.43}
\newcommand{\insureS}{\resp{not}}
\newcommand{\insureF}{\resp{exceed}}
% Doesn't consider binomial coefficient in next calculated value.
\newcommand{\insureBinomCinDSingleScenario}{0.103}
\newcommand{\insureBinomCinD}{0.412}
\newcommand{\insureBinomEinHSingleScenario}{0.00454}
\newcommand{\insureBinomEinH}{0.254}
\newcommand{\insureBinomFourtyExpValue}{28}
\newcommand{\insureBinomFourtySD}{2.9}
\newcommand{\insureBinomFourtyLower}{22}
\newcommand{\insureBinomFourtyUpper}{34}

\noindent%
Let's again imagine ourselves back at the insurance agency
where \insureSperc{} of individuals do not exceed their
deductible.

\begin{examplewrap}
\begin{nexample}{Suppose the insurance agency is considering
    a random sample of four individuals they insure.
    What is the chance exactly one of them will exceed
    the deductible and the other three will not?
    Let's call the four people
    Ariana ($A$),
    Brittany ($B$),
    Carlton ($C$),
    and Damian ($D$)
    for convenience.}
  \label{insureOneOfFourExceedsDeductible}%
  Let's consider a scenario where one person exceeds
  the deductible:
  \begin{align*}
  &P(A=\text{\insureF{}},
      \text{ }B=\text{\insureS{}},
      \text{ }C=\text{\insureS{}},
      \text{ }D=\text{\insureS{}}) \\
    &\quad = P(A=\text{\insureF{}})\ 
        P(B=\text{\insureS{}})\ 
        P(C=\text{\insureS{}})\ 
        P(D=\text{\insureS{}}) \\
    &\quad =  (\insureFprob{})
        (\insureSprob{})
        (\insureSprob{})
        (\insureSprob{}) \\
    &\quad = (\insureSprob{})^3 (\insureFprob{})^1 \\
    &\quad = \insureBinomCinDSingleScenario{}
  \end{align*}
  But there are three other scenarios: Brittany, Carlton,
  or Damian could have been the one to exceed the deductible.
  In each of these cases, the probability is again
  $(\insureSprob{})^3 (\insureFprob{})^1$.
  These four scenarios exhaust all the possible ways that
  exactly one of these four people could have exceeded
  the deductible, so the total probability is
  $4 \times (\insureSprob{})^3 (\insureFprob{})^1
      = \insureBinomCinD{}$.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Verify that the scenario where Brittany is the only one
to exceed the deductible has probability
$(\insureSprob{})^3 (\insureFprob{})^1$.~\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{
  $P(A=\text{\insureS{}},
      \text{ }B=\text{\insureF{}},
      \text{ }C=\text{\insureS{}},
      \text{ }D=\text{\insureS{}})
    = (\insureSprob{})(\insureFprob{})
        (\insureSprob{})(\insureSprob{})
    = (\insureSprob{})^3 (\insureFprob{})^1$.}


The scenario outlined in Example~\ref{insureOneOfFourExceedsDeductible} is an
example of a binomial distribution scenario.
The \termsub{binomial distribution}{distribution!binomial}
describes the probability of having exactly $k$ successes
in $n$ independent Bernoulli trials with probability
of a success $p$
(in Example~\ref{insureOneOfFourExceedsDeductible},
$n=4$, $k=3$, $p=\insureSprob{}$).
We would like to determine the probabilities associated
with the binomial distribution more generally,
i.e. we want a formula where we can use $n$, $k$, and $p$
to obtain the probability.
To do this, we reexamine each part of
Example~\ref{insureOneOfFourExceedsDeductible}.

There were four individuals who could have been the one
to exceed the deductible, and each of these four scenarios
had the same probability.
Thus, we could identify the final probability as
\begin{align*}
[\text{\# of scenarios}] \times P(\text{single scenario})
\end{align*}
The first component of this equation is the number of ways
to arrange the $k=3$ successes among the $n=4$ trials.
The second component is the probability of any of the four
(equally probable) scenarios.

\D{\newpage}

Consider $P($single scenario$)$ under the general case of
$k$ successes and $n-k$ failures in the $n$ trials.
In any such scenario, we apply the Multiplication Rule
for independent events:
\begin{align*}
p^k (1 - p)^{n - k}
\end{align*}
This is our general formula for $P($single scenario$)$.

Secondly, we introduce a general formula for the number
of ways to choose $k$ successes in $n$ trials,
i.e. arrange $k$ successes and $n - k$ failures:
\begin{align*}
{n\choose k} = \frac{n!}{k! (n - k)!}
\end{align*}
The quantity ${n\choose k}$ is read
\term{n choose k}.\footnote{Other notation for
  $n$ choose $k$ includes $_nC_k$, $C_n^k$, and $C(n,k)$.}
The exclamation point notation (e.g. $k!$) denotes
a \term{factorial} expression.\label{factorial_defined}
\begin{align*}
& 0! = 1 \\
& 1! = 1 \\
& 2! = 2\times1 = 2 \\
& 3! = 3\times2\times1 = 6 \\
& 4! = 4\times3\times2\times1 = 24 \\
& \vdots \\
& n! = n\times(n-1)\times...\times3\times2\times1
\end{align*}
Using the formula, we can compute the number of ways
to choose $k = 3$ successes in $n = 4$ trials:
\begin{align*}
{4 \choose 3} = \frac{4!}{3!(4-3)!}
  = \frac{4!}{3!1!} 
  = \frac{4\times3\times2\times1}{(3\times2\times1) (1)}
  = 4
\end{align*}
This result is exactly what we found by carefully thinking
of each possible scenario in
Example~\ref{insureOneOfFourExceedsDeductible}.

Substituting $n$ choose $k$ for the number of scenarios
and $p^k(1-p)^{n-k}$ for the single scenario probability
yields the general binomial formula.

\begin{onebox}{Binomial distribution}
  Suppose the probability of a single trial being
  a success is $p$.
  Then the probability of observing exactly $k$ successes
  in $n$ independent trials is given by\vspace{-1mm}
  \begin{align*}
  {n\choose k}p^k(1-p)^{n-k} = \frac{n!}{k!(n-k)!}p^k(1-p)^{n-k}
  \end{align*}
  The mean, variance, and standard deviation
  of the number of observed successes are\vspace{-2mm}
  \begin{align*}
  \mu &= np
  &\sigma^2 &= np(1-p)
  &\sigma&= \sqrt{np(1-p)}
  \end{align*}
\end{onebox}

\begin{onebox}{Is it binomial? Four conditions to check.}
  \label{isItBinomialTipBox}%
  (1) The trials are independent. \\
  (2) The number of trials, $n$, is fixed. \\
  (3) Each trial outcome can be classified as a \emph{success}
      or \emph{failure}. \\
  (4) The probability of a success, $p$, is the same for
      each trial.
\end{onebox}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{What is the probability that 3 of 8 randomly
    selected individuals will have exceeded the insurance
    deductible, i.e. that 5 of 8 will not exceed the deductible?
    Recall that 70\% of individuals will not exceed the
    deductible.}
  We would like to apply the binomial model,
  so we check the conditions.
  The number of trials is fixed ($n = 8$) (condition 2)
  and each trial outcome can be classified as a success
  or failure (condition 3).
  Because the sample is random, the trials are independent
  (condition~1) and the probability of a success is the same
  for each trial (condition~4).

  In the outcome of interest, there are $k = 5$ successes
  in $n = 8$ trials (recall that a success is an individual
  who does \emph{not} exceed the deductible), and the
  probability of a success is $p = \insureSprob{}$.
  So the probability that 5 of 8 will not exceed the
  deductible and 3 will exceed the deductible is given by
  \begin{align*}
  { 8 \choose 5}(\insureSprob{})^5
  (1-\insureSprob{})^{8-5}
    &= \frac{8!}{5!(8-5)!}
        (\insureSprob{})^5(1-\insureSprob{})^{8-5} \\
    &= \frac{8!}{5!3!}
        (\insureSprob{})^5(\insureFprob{})^3
  \end{align*}
  Dealing with the factorial part:
  \begin{align*}
  \frac{8!}{5!3!}
    = \frac{8\times7\times6\times5\times4\times3\times2\times1}
        {(5\times4\times3\times2\times1)(3\times2\times1)}
    = \frac{8\times7\times6}{3\times2\times1}
    = 56
  \end{align*}
  Using $(\insureSprob{})^5(\insureFprob{})^3
    \approx \insureBinomEinHSingleScenario{}$,
  the final probability is about
  $56 \times \insureBinomEinHSingleScenario{}
    \approx \insureBinomEinH{}$.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Computing binomial probabilities}
  The first step in using the binomial model is to check
  that the model is appropriate.
  The second step is to identify $n$, $p$, and $k$.
  As the last stage use software or the formulas
  to determine the probability, then interpret the results.%
  \vspace{3mm}

  If you must do calculations by hand, it's often useful
  to cancel out as many terms as possible in the top and
  bottom of the binomial coefficient.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
If we randomly sampled 40 case files from the insurance agency
discussed earlier, how many of the cases would you expect to not
have exceeded the deductible in a given year?
What is the standard deviation of the number that would not
have exceeded the deductible?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We are asked to determine the expected number
  (the mean) and the standard deviation, both of which can
  be directly computed from the formulas:
  $\mu = np = 40 \times \insureSprob{}
    = \insureBinomFourtyExpValue$
  and $\sigma = \sqrt{np(1-p)}
    = \sqrt{40\times \insureSprob{}\times \insureFprob{}}
    = \insureBinomFourtySD{}$.
  Because very roughly 95\% of observations fall within
  2~standard deviations of the mean
  (see Section~\ref{variability}), we would probably observe
  at least \insureBinomFourtyLower{}
  but fewer than \insureBinomFourtyUpper{} individuals
  in our sample who would not exceed the deductible.}

\begin{exercisewrap}
\begin{nexercise}
The probability that a random smoker will develop a severe
lung condition in his or her lifetime is about $0.3$.
If you have 4 friends who smoke, are the conditions for the
binomial model satisfied?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{One possible answer:
  if the friends know each other, then the independence
  assumption is probably not satisfied.
  For example, acquaintances may have similar smoking habits,
  or those friends might make a pact to quit together.}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
\label{noMoreThanOneFriendWSevereLungCondition}%
Suppose these four friends do not know each other
and we can treat them as if they were a random sample
from the population.
Is the binomial model appropriate?
What is the probability that\footnotemark{}
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item
    None of them will develop a severe lung condition?
\item
    One will develop a severe lung condition?
\item
    That no more than one will develop a severe lung condition?
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{To check if the binomial model is appropriate,
  we must verify the conditions.
  (i)~Since we are supposing we can treat the friends
  as a random sample, they are independent.
  (ii)~We have a fixed number of trials ($n=4$).
  (iii)~Each outcome is a success or failure.
  (iv)~The probability of a success is the same for each
  trials since the individuals are like a random sample
  ($p=0.3$ if we say a ``success'' is someone getting
  a lung condition, a morbid choice).
  Compute parts~(a) and~(b) using the binomial formula:
  $P(0)
    = {4 \choose 0} (0.3)^0 (0.7)^4
    = 1\times1\times0.7^4
    = 0.2401$,
  $P(1)
    = {4 \choose 1} (0.3)^1(0.7)^{3}
    = 0.4116$.
  Note: $0!=1$.
  Part~(c) can be computed as the sum of parts~(a) and~(b):
  $P(0) + P(1) = 0.2401 + 0.4116 = 0.6517$.
  That is, there is about a 65\% chance that no more than
  one of your four smoking friends will develop a severe
  lung condition.}

\begin{exercisewrap}
\begin{nexercise}
What is the probability that at least 2 of your 4 smoking
friends will develop a severe lung condition in their
lifetimes?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The complement (no more than one will develop
  a severe lung condition) as computed in Guided
  Practice~\ref{noMoreThanOneFriendWSevereLungCondition}
  as 0.6517, so we compute one minus this value:~0.3483.}

\begin{exercisewrap}
\begin{nexercise}
Suppose you have 7 friends who are smokers and they can
be treated as a random sample of smokers.\footnotemark{}
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item
    How many would you expect to develop a severe lung
    condition, i.e. what is the mean?
\item
    What is the probability that at most 2 of your 7
    friends will develop a severe lung condition.
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~$\mu=0.3\times7 = 2.1$.
  (b)~$P($0, 1, or 2 develop severe lung condition$)
      = P(k=0) + P(k=1)+P(k=2) = 0.6471$.}

Next we consider the first term in the binomial probability,
$n$ choose $k$ under some special scenarios.

\begin{exercisewrap}
\begin{nexercise}
Why is it true that ${n \choose 0}=1$ and ${n \choose n}=1$
for any number $n$?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Frame these expressions into words.
  How many different ways are there to arrange 0 successes
  and $n$ failures in $n$ trials?
  (1 way.)
  How many different ways are there to arrange $n$ successes
  and 0 failures in $n$ trials?
  (1 way.)}

\begin{exercisewrap}
\begin{nexercise}
How many ways can you arrange one success and $n-1$ failures
in $n$ trials?
How many ways can you arrange $n-1$ successes and one failure
in $n$ trials?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{One success and $n-1$ failures:
  there are exactly $n$ unique places we can put
  the success, so there are $n$ ways to arrange one
  success and $n-1$ failures.
  A~similar argument is used for the second question.
  Mathematically, we show these results by verifying
  the following two equations:
  \begin{align*}
  {n \choose 1} = n,
    \qquad {n \choose n-1} = n
  \end{align*}}


\newpage


\subsection{Normal approximation to the binomial distribution}
\label{normalApproxBinomialDistSubsection}

\index{distribution!binomial!normal approximation|(}

The 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.

\newcommand{\smokeprop}{0.15}
\newcommand{\smokeperc}{15\%}
\newcommand{\smokepropcomp}{0.85}
\newcommand{\smokeperccomp}{85\%}
\newcommand{\smokex}{42}
\newcommand{\smokexplusone}{43}
\newcommand{\smoken}{400}
\newcommand{\smokelowertailbinom}{0.0054}
\newcommand{\smokemean}{60}
\newcommand{\smokemeancomp}{340}
\newcommand{\smokesd}{7.14}
\newcommand{\smokez}{-2.52}
\newcommand{\smokelowertailnormal}{0.0059}

\begin{examplewrap}
\begin{nexample}{Approximately \smokeperc{} of the
    US population smokes cigarettes.
    A local government believed their community had
    a lower smoker rate and commissioned a survey of
    400 randomly selected individuals.
    The survey found that only \smokex{} of the
    \smoken{} participants smoke cigarettes.
    If the true proportion of smokers in the community
    was really \smokeperc{}, what is the probability
    of observing \smokex{} or fewer smokers in a sample
    of \smoken{} people?}
  \label{exactBinomSmokerExSetup}%
  We leave the usual verification that the four conditions
  for the binomial model are valid as an exercise.

  The question posed is equivalent to asking,
  what is the probability of observing
  $k=0$, 1, 2, ..., or \smokex{} smokers in a sample of
  $n = \smoken{}$ when $p=\smokeprop{}$?
  We can compute these \smokexplusone{} different
  probabilities and add them together to find the answer:
  \begin{align*}
  &P(k=0\text{ or }k=1\text{ or }\cdots\text{ or } k=\smokex{}) \\
	&\qquad = P(k=0) + P(k=1) + \cdots + P(k=\smokex{}) \\
	&\qquad = \smokelowertailbinom{}
  \end{align*}
  If the true proportion of smokers in the community
  is $p=\smokeprop{}$, then the probability of observing
  \smokex{} or fewer smokers in a sample of $n=\smoken{}$
  is \smokelowertailbinom{}.
\end{nexample}
\end{examplewrap}

The computations in Example~\ref{exactBinomSmokerExSetup}
are tedious and long.
In general, we should avoid such work if an alternative method
exists that is faster, easier, and still accurate.
Recall that calculating probabilities of a range of values
is much easier in the normal model.
We might wonder, is it reasonable to use the normal model
in place of the binomial distribution?
Surprisingly, yes, if certain conditions are met.

\begin{exercisewrap}
\begin{nexercise}
Here we consider the binomial model when the probability
of a success is $p = 0.10$.
Figure~\ref{fourBinomialModelsShowingApproxToNormal}
shows four hollow histograms for simulated samples from
the binomial distribution using four different sample sizes:
$n = 10, 30, 100, 300$.
What happens to the shape of the distributions as the sample
size increases?
What distribution does the last hollow histogram
resemble?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The distribution is transformed from a blocky
  and skewed distribution into one that rather resembles the
  normal distribution in last hollow histogram.}

\begin{figure}[h]
  \centering
  \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}
  \caption{Hollow histograms of samples from the binomial
      model when $p = 0.10$.
      The sample sizes for the four plots are
      $n = 10$, 30, 100, and 300, respectively.}
  \label{fourBinomialModelsShowingApproxToNormal}
\end{figure}

\begin{onebox}{Normal approximation of the binomial distribution}
  The binomial distribution with probability of success
  $p$ is nearly normal when the sample size $n$ is
  sufficiently large that $np$ and $n(1-p)$ are both
  at least 10.
  The approximate normal distribution has parameters
  corresponding to the mean and standard deviation of
  the binomial distribution:\vspace{-1.5mm}
  \begin{align*}
  \mu &= np
      &\sigma& = \sqrt{np(1 - p)}
  \end{align*}
\end{onebox}

The normal approximation may be used when computing
the range of many possible successes.
For instance, we may apply the normal distribution to
the setting of Example~\ref{exactBinomSmokerExSetup}.

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{How can we use the normal approximation
    to estimate the probability of observing \smokex{} or
    fewer smokers in a sample of \smoken{}, if the true
    proportion of smokers is $p = \smokeprop{}$?}
  \label{approxNormalForSmokerBinomEx}
  Showing that the binomial model is reasonable was a
  suggested exercise in Example~\ref{exactBinomSmokerExSetup}.
  We also verify that both $np$ and $n(1-p)$ are at least 10:
  \begin{align*}
  np &= \smoken{} \times \smokeprop{} = \smokemean{}
  &n (1 - p) &= \smoken{} \times \smokepropcomp{}
      = \smokemeancomp{}
  \end{align*}
  With these conditions checked, we may use the normal
  approximation in place of the binomial distribution
  using the mean and standard deviation from the binomial
  model:
  \begin{align*}
  \mu &= np = \smokemean{}
  &\sigma &= \sqrt{np(1 - p)} = \smokesd{}
  \end{align*}
  We want to find the probability of observing
  \smokex{} or fewer smokers using this model.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Use the normal model $N(\mu = \smokemean{}, \sigma = \smokesd{})$
to estimate the probability of observing \smokex{} or fewer
smokers.
Your answer should be approximately equal to the solution
of Example~\ref{exactBinomSmokerExSetup}:%
~\smokelowertailbinom{}.~\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Compute the Z-score first:
  $Z = \frac{\smokex{} - \smokemean{}}{\smokesd{}} = \smokez{}$.
  The corresponding left tail area is \smokelowertailnormal{}.}



\newpage


\subsection{The normal approximation breaks down on small intervals}

The 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.

\newcommand{\smokeA}{49}
\newcommand{\smokeB}{50}
\newcommand{\smokeC}{51}
\newcommand{\smokeABCBinom}{0.0649}
\newcommand{\smokeABCNormal}{0.0421}
\newcommand{\smokeABCNormalFixed}{0.0633}

Suppose we wanted to compute the probability of observing
\smokeA{}, \smokeB{}, or \smokeC{} smokers in \smoken{}
when $p = \smokeprop{}$.
With such a large sample, we might be tempted to apply
the normal approximation and use the range \smokeA{} to \smokeC{}.
However, we would find that the binomial solution and the normal
approximation notably differ:
\begin{align*}
\text{Binomial:}&\ \smokeABCBinom{}
&\text{Normal:}&\ \smokeABCNormal{}
\end{align*}
We can identify the cause of this discrepancy using
Figure~\ref{normApproxToBinomFail}, which shows the areas
representing the binomial probability (outlined) and normal
approximation (shaded).
Notice that the width of the area under the normal
distribution is 0.5 units too slim on both sides of
the interval.

\begin{figure}[h]
  \centering
  \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}
  \caption{A normal curve with the area between
      \smokeA{} and \smokeC{} shaded.
      The outlined area represents the exact binomial
      probability.}
  \label{normApproxToBinomFail}
\end{figure}

\begin{onebox}{Improving the normal approximation
    for the binomial distribution}
  The normal approximation to the binomial distribution
  for intervals of values is usually improved if cutoff
  values are modified slightly.
  The cutoff values for the lower end of a shaded region
  should be reduced by 0.5, and the cutoff value for the
  upper end should be increased by 0.5.
\end{onebox}

The tip to add extra area when applying the normal
approximation is most often useful when examining
a range of observations.
In the example above, the revised normal distribution
estimate is \smokeABCNormalFixed{}, much closer to the
exact value of \smokeABCBinom{}.
While it is possible to also apply this correction when
computing a tail area, the benefit of the modification
usually disappears since the total interval is typically
quite wide.

\index{distribution!binomial!normal approximation|)}
\index{distribution!binomial|)}


{\input{ch_distributions/TeX/binomial_distribution.tex}}




%_________________
\section{Negative binomial distribution}
\label{negativeBinomial}

\index{distribution!negative binomial|(}

The 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.

\begin{examplewrap}
\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?}
We 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.
\end{nexample}
\end{examplewrap}

To identify a negative binomial case, we check 4 conditions. The first three are common to the binomial distribution.

\begin{onebox}{Is it negative binomial? Four conditions to check}
(1) The trials are independent. \\
(2) Each trial outcome can be classified as a success or failure. \\
(3) The probability of a success ($p$) is the same for each trial. \\
(4) The last trial must be a success.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
Suppose 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
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{examplewrap}
\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}
Because 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.
\end{nexample}
\end{examplewrap}

\begin{figure}[ht]
\newcommand{\succObs}[1]{{\color{oiB}$\stackrel{#1}{S}$}}
\centering
\begin{tabular}{c|c ccc cl | r}
\multicolumn{8}{c}{\hspace{10mm}Kick Attempt} \\
& & 1 & 2 & 3 & 4 & \multicolumn{2}{l}{5\hfill6} \\
\hline
1&& $F$ & $F$ & \succObs{1} & \succObs{2} & \succObs{3} & \succObs{4} \\
2&& $F$ & \succObs{1} & $F$ & \succObs{2} & \succObs{3} & \succObs{4} \\
3&& $F$ & \succObs{1} & \succObs{2} & $F$ & \succObs{3} & \succObs{4} \\
4&& $F$ & \succObs{1} & \succObs{2} & \succObs{3} & $F$ & \succObs{4} \\
5&& \succObs{1} & $F$ & $F$ & \succObs{2} & \succObs{3} & \succObs{4} \\
6&& \succObs{1} & $F$ & \succObs{2} & $F$ & \succObs{3} & \succObs{4} \\
7&& \succObs{1} & $F$ & \succObs{2} & \succObs{3} & $F$ & \succObs{4} \\
8&& \succObs{1} & \succObs{2} & $F$ & $F$ & \succObs{3} & \succObs{4} \\
9&& \succObs{1} & \succObs{2} & $F$ & \succObs{3} & $F$ & \succObs{4} \\
10&& \succObs{1} & \succObs{2} & \succObs{3} & $F$ & $F$ & \succObs{4} \\
\end{tabular}
\caption{The ten possible sequences when the fourth successful kick is on the sixth attempt.}
\label{successFailureOrdersForBriansFieldGoals}
\end{figure}

\begin{exercisewrap}
\begin{nexercise} \label{probOfEachSeqOfSixTriesToGetFourSuccesses}
Each 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{The first sequence:
  $0.2 \times 0.2 \times 0.8 \times
      0.8 \times 0.8 \times 0.8
    = 0.0164$.}

\D{\newpage}

If 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
{\small\begin{align*}
&P(\text{it takes Brian six tries to make four field goals}) \\
& \quad = P(\text{Brian makes three of his first five field goals, and he makes the sixth one}) \\
& \quad = P(\text{$1^{st}$ sequence OR $2^{nd}$ sequence OR ... OR $10^{th}$ sequence})
\end{align*}
}where the sequences are from Figure~\ref{successFailureOrdersForBriansFieldGoals}. We can break down this last probability into the sum of ten disjoint possibilities:
{\small\begin{align*}
&P(\text{$1^{st}$ sequence OR $2^{nd}$ sequence OR ... OR $10^{th}$ sequence}) \\
&\quad = P(\text{$1^{st}$ sequence}) + P(\text{$2^{nd}$ sequence}) + \cdots + P(\text{$10^{th}$ sequence})
\end{align*}
}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.

The 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:
\begin{align*}
&P(\text{it takes Brian six tries to make four field goals}) \\
&= [\text{Number of possible sequences}] \times P(\text{Single sequence})
\end{align*}
Each part is examined separately, then we multiply to get the final result.

We 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:
\begin{align*}
&P(\text{Single sequence}) \\
&= P(\text{$n-k$ failures and then $k$ successes}) \\
&= (1-p)^{n-k} p^{k}
\end{align*}

\D{\newpage}

We 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:
\begin{align*}
{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)!}
\end{align*}
This 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}.

\begin{onebox}{Negative binomial distribution}
  The negative binomial distribution describes the
  probability of observing the $k^{th}$ success on
  the $n^{th}$ trial, where all trials are independent:
  \begin{align*}
  P(\text{the $k^{th}$ success on the $n^{th}$ trial})
      = {n-1 \choose k-1} p^{k}(1-p)^{n-k}
  \end{align*}
  The value $p$ represents the probability that
  an individual trial is a success.
\end{onebox}

\begin{examplewrap}
\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.}
The 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 $n=6$.
\begin{align*}
{n-1 \choose k-1}p^k(1-p)^{n-k}\ 
	=\ \frac{5!}{3!2!} (0.8)^4 (0.2)^2\ 
	=\ 10\times 0.0164\ 
	=\ 0.164
\end{align*}
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
The 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
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise}
Assume Brian's kick attempts are independent. What is the probability that Brian will kick his fourth field goal within 5 attempts?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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:
\begin{align*}
& P(n=4\text{ OR }n=5) = P(n=4) + P(n=5) \\
&\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
\end{align*}}

\D{\newpage}

\begin{onebox}{Binomial versus negative binomial}
  In the binomial case, we typically have a fixed number
  of trials and instead consider the number of successes.
  In the negative binomial case, we examine how many trials
  it takes to observe a fixed number of successes and
  require that the last observation be a success.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
On 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
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item What is the probability the hospital will admit
    a heart attack patient on exactly three days this week?

\item What is the probability the second day with a heart
    attack patient will be the fourth day of the week?

\item What is the probability the fifth day of next month
    will be the first day with a heart attack patient?
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\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.}

\index{distribution!negative binomial|)}


{\input{ch_distributions/TeX/negative_binomial_distribution.tex}}





%_________________
\section{Poisson distribution}
\label{poisson}

\index{distribution!Poisson|(}

\begin{examplewrap}
\begin{nexample}{There are about 8 million individuals
    in New York City.
    How many individuals might we expect to be hospitalized
    for acute myocardial infarction (AMI), i.e. a heart attack,
    each day?
    According to historical records, the average number is
    about 4.4 individuals.
    However, we would also like to know the approximate
    distribution of counts.
    What would a histogram of the number of AMI occurrences
    each day look like if we recorded the daily counts over
    an entire year?}
  \label{amiIncidencesEachDayOver1YearInNYCExample}%
  A histogram of the number of occurrences of AMI on 365 days
  for NYC is shown in
  Figure~\ref{amiIncidencesOver100Days}.\footnotemark{}
  The sample mean (4.38) is similar to the historical average
  of~4.4.
  The sample standard deviation is about 2, and the histogram
  indicates that about 70\% of the data fall between 2.4 and~6.4.
  The distribution's shape is unimodal and skewed to the right.
\end{nexample}
\end{examplewrap}
\footnotetext{These data are simulated. In practice, we should check for an association between successive days.}

\begin{figure}[h]
  \centering
  \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}
  \caption{A histogram of the number of occurrences
      of AMI on 365 separate days in NYC.}
  \label{amiIncidencesOver100Days}
\end{figure}

The \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:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item having a heart attack,
\item getting married, and
\item getting struck by lightning.
\end{itemize}
The 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.

The 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 ($\lambda$)}
(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.

\D{\newpage}

\begin{onebox}{Poisson distribution}
  Suppose we are watching for events and the number
  of observed events follows a Poisson distribution
  with rate $\lambda$.
  Then
  \begin{align*}
  P(\text{observe $k$ events})
      = \frac{\lambda^{k} e^{-\lambda}}{k!}
  \end{align*}
  where $k$ may take a value 0, 1, 2, and so on,
  and $k!$ represents $k$-factorial, as described on
  page~\pageref{factorial_defined}.
  The letter $e\approx2.718$ is the base of the natural
  logarithm.
  The mean and standard deviation of this distribution
  are $\lambda$ and $\sqrt{\lambda}$, respectively.
\end{onebox}

We 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.

A 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.

Even when events are not really independent --
for instance, Saturdays and Sundays are especially
popular for weddings --
a Poisson model may sometimes still be reasonable
if we allow it to have a different rate for different
times.
In the wedding example, the rate would be modeled as
higher on weekends than on weekdays.
The idea of modeling rates for a Poisson distribution
against a second variable such as the day of week forms
the foundation of some more advanced methods that fall
in the realm of \termsub{generalized linear models}
    {generalized linear model}.
In Chapters~\ref{linRegrForTwoVar}
and~\ref{multipleAndLogisticRegression},
we will discuss a foundation of linear models.

\index{distribution!Poisson|)}


{\input{ch_distributions/TeX/poisson_distribution.tex}}


================================================
FILE: ch_distributions/TeX/geometric_distribution.tex
================================================
\exercisesheader{}

% 11

\eoce{\qtq{Is it Bernoulli\label{is_it_bernouilli}} Determine if each trial can be 
considered an independent Bernoulli trial for the following situations.
\begin{parts}
\item Cards dealt in a hand of poker.
\item Outcome of each roll of a die.
\end{parts}
}{}

% 12

\eoce{\qt{With and without replacement\label{with_without_replacement}} In the 
following situations assume that half of the specified population is male and 
the other half is female.
\begin{parts}
\item Suppose you're sampling from a room with 10 people. What is the 
probability of sampling two females in a row when sampling with replacement? 
What is the probability when sampling without replacement?
\item Now suppose you're sampling from a stadium with 10,000 people. What is 
the probability of sampling two females in a row when sampling with 
replacement? What is the probability when sampling without replacement?
\item We often treat individuals who are sampled from a large population as 
independent. Using your findings from parts~(a) and~(b), explain whether or 
not this assumption is reasonable.
\end{parts}
}{}

% 13

\eoce{\qt{Eye color, Part I\label{eye_color_geometric}} A husband and wife both 
have brown eyes but carry genes that make it possible for their children to 
have brown eyes (probability 0.75), blue eyes (0.125), or green eyes (0.125).
\begin{parts}
\item What is the probability the first blue-eyed child they have is their 
third child? Assume that the eye colors of the children are independent of 
each other.
\item On average, how many children would such a pair of parents have before 
having a blue-eyed child? What is the standard deviation of the number of 
children they would expect to have until the first blue-eyed child?
\end{parts}
}{}

% 14

\eoce{\qt{Defective rate\label{defective_rate}} A machine that produces a special 
type of transistor (a component of computers) has a 2\% defective rate. The 
production is considered a random process where each transistor is 
independent of the others.
\begin{parts}
\item What is the probability that the $10^{th}$ transistor produced is the 
first with a defect?
\item What is the probability that the machine produces no defective 
transistors in a batch of 100?
\item On average, how many transistors would you expect to be produced before 
the first with a defect? What is the standard deviation?
\item Another machine that also produces transistors has a 5\% defective rate 
where each transistor is produced independent of the others. On average how 
many transistors would you expect to be produced with this machine before the 
first with a defect? What is the standard deviation?
\item Based on your answers to parts (c) and (d), how does increasing the 
probability of an event affect the mean and standard deviation of the wait 
time until success?
\end{parts}
}{}

% 15

\eoce{\qt{Bernoulli, the mean\label{bernoulli_mean_derivation}}
Use the probability rules from
Section~\ref{randomVariablesSection}
to derive the mean of a Bernoulli random variable,
i.e. a random variable $X$ that takes value 1
with probability $p$ and value 0 with probability $1 - p$.
That is, compute the expected value of a generic
Bernoulli random variable.
}{}

% 16

\eoce{\qt{Bernoulli, the standard deviation\label{bernoulli_sd_derivation}}
Use the probability rules from
Section~\ref{randomVariablesSection}
to derive the standard deviation of a Bernoulli random variable,
i.e. a random variable $X$ that takes value 1
with probability $p$ and value 0 with probability $1 - p$.
That is, compute the square root of the variance of a generic
Bernoulli random variable.
}{}


================================================
FILE: ch_distributions/TeX/negative_binomial_distribution.tex
================================================
\exercisesheader{}

% 27

\eoce{\qt{Rolling a die\label{roll_die}} Calculate the 
following probabilities and indicate which probability distribution model is 
appropriate in each case. You roll a fair die 5 times. What is the 
probability of rolling
\begin{parts}
\item the first 6 on the fifth roll?
\item exactly three 6s?
\item the third 6 on the fifth roll?
\end{parts}
}{}

% 28

\eoce{\qt{Playing darts\label{play_darts}} Calculate the following probabilities 
and indicate which probability distribution model is appropriate in each 
case. A very good darts player can hit the bull's eye (red circle in the 
center of the dart board) 65\% of the time. What is the probability that he
\begin{parts}
\item hits the bullseye for the $10^{th}$ time on the $15^{th}$ try?
\item hits the bullseye 10 times in 15 tries?
\item hits the first bullseye on the third try?
\end{parts}
}{}

% 29

\eoce{\qt{Sampling at school\label{sampling_at_school}} For a sociology class 
project you are asked to conduct a survey on 20 students at your school. You 
decide to stand outside of your dorm's cafeteria and conduct the survey on a 
random sample of 20 students leaving the cafeteria after dinner one evening. 
Your dorm is comprised of 45\% males and 55\% females.
\begin{parts}
\item Which probability model is most appropriate for calculating the 
probability that the $4^{th}$ person you survey is the $2^{nd}$ female? 
Explain.
\item Compute the probability from part (a).
\item The three possible scenarios that lead to $4^{th}$ person you survey 
being the $2^{nd}$ female are
\[ \{M, M, F, F\}, \{M, F, M, F\}, \{F, M, M, F\} \]
One common feature among these scenarios is that the last trial is always 
female. In the first three trials there are 2 males and 1 female. Use the 
binomial coefficient to confirm that there are 3 ways of ordering 2 males and 
1 female. 
\item Use the findings presented in part (c) to explain why the formula for 
the coefficient for the negative binomial is ${n-1 \choose k-1}$ while the 
formula for the binomial coefficient is ${n \choose k}$.
\end{parts}
}{}

% 30

\eoce{\qt{Serving in volleyball\label{serving_volleyball}} A not-so-skilled 
volleyball player has a 15\% chance of making the serve, which involves 
hitting the ball so it passes over the net on a trajectory such that it will 
land in the opposing team's court. Suppose that her serves are independent of 
each other.
\begin{parts}
\item What is the probability that on the $10^{th}$ try she will make her 
$3^{rd}$ successful serve?
\item Suppose she has made two successful serves in nine attempts. What is 
the probability that her $10^{th}$ serve will be successful?
\item Even though parts (a) and (b) discuss the same scenario, the 
probabilities you calculated should be different. Can you explain the reason 
for this discrepancy?
\end{parts}
}{}


================================================
FILE: ch_distributions/TeX/normal_distribution.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Area under the curve, Part I\label{area_under_curve_1}} What percent of a 
standard normal distribution $N(\mu=0, \sigma=1)$ is found in each region? 
Be sure to draw a graph. \vspace{-3mm}
\begin{multicols}{4}
\begin{parts}
\item $Z < -1.35$
\item $Z > 1.48$
\item $-0.4 < Z < 1.5$
\item $|Z| > 2$
\end{parts}
\end{multicols}
}{}

% 2

\eoce{\qt{Area under the curve, Part II\label{area_under_curve_2}} What percent of 
a standard normal distribution $N(\mu=0, \sigma=1)$ is found in each region? 
Be sure to draw a graph. \vspace{-3mm}
\begin{multicols}{4}
\begin{parts}
\item $Z > -1.13$
\item $Z < 0.18$
\item $Z > 8$
\item $|Z| < 0.5$
\end{parts}
\end{multicols}
}{}

% 3

\eoce{\qt{GRE scores, Part I\label{GRE_intro}} Sophia who took the Graduate Record 
Examination (GRE) scored 160 on the Verbal Reasoning section and 157 on the 
Quantitative Reasoning section. The mean score for Verbal Reasoning section 
for all test takers was 151 with a standard deviation of 7, and the mean 
score for the Quantitative Reasoning was 153 with a standard deviation of 
7.67. Suppose that both distributions are nearly normal. 
\begin{parts}
\item Write down the short-hand for these two normal distributions.
\item What is  Sophia's Z-score on the Verbal Reasoning section? On the 
Quantitative Reasoning section? Draw a standard normal distribution curve and 
mark these two Z-scores.
\item What do these Z-scores tell you?
\item Relative to others, which section did she do better on?
\item Find her percentile scores for the two exams.
\item What percent of the test takers did better than her on the Verbal 
Reasoning section? On the Quantitative Reasoning section?
\item Explain why simply comparing raw scores from the two sections could lead 
to an incorrect conclusion as to which section a student did better on.
\item If the distributions of the scores on these exams are not nearly 
normal, would your answers to parts (b) - (f) change? Explain your reasoning.
\end{parts}
}{}

% 4

\eoce{\qt{Triathlon times, Part I\label{triathlon_times_intro}} In triathlons, it 
is common for racers to be placed into age and gender groups. Friends Leo and 
Mary both completed the Hermosa Beach Triathlon, where Leo competed in the 
\textit{Men, Ages 30 - 34} group while Mary competed in the \textit{Women, 
Ages 25 - 29} group. Leo completed the race in 1:22:28 (4948 seconds), while 
Mary completed the race in 1:31:53 (5513 seconds). Obviously Leo finished 
faster, but they are curious about how they did within their respective 
groups. Can you help them? Here is some information on the performance of 
their groups:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item The finishing times of the \textit{Men, Ages 30 - 34} group has a mean 
of 4313 seconds with a standard deviation of 583 seconds.
\item The finishing times of the \textit{Women, Ages 25 - 29} group has a 
mean of 5261 seconds with a standard deviation of 807 seconds.
\item The distributions of finishing times for both groups are approximately 
Normal.
\end{itemize}
Remember: a better performance corresponds to a faster finish.
\begin{parts}
\item Write down the short-hand for these two normal distributions.
\item What are the Z-scores for Leo's and Mary's finishing times? What do 
these Z-scores tell you?
\item Did Leo or Mary rank better in their respective groups? Explain your 
reasoning.
\item What percent of the triathletes did Leo finish faster than in his group?
\item What percent of the triathletes did Mary finish faster than in her 
group?
\item If the distributions of finishing times are not nearly normal, would 
your answers to parts (b)~-~(e) change? Explain your reasoning.
\end{parts}
}{}

% 5

\eoce{\qt{GRE scores, Part II\label{GRE_cutoffs}} In Exercise~\ref{GRE_intro} we 
saw two distributions for GRE scores: $N(\mu=151, \sigma=7)$ for the verbal 
part of the exam and $N(\mu=153, \sigma=7.67)$ for the quantitative part. Use 
this information to compute each of the following:
\begin{parts}
\item The score of a student who scored in the $80^{th}$ percentile on the 
Quantitative Reasoning section.
\item The score of a student who scored worse than 70\% of the test takers in 
the Verbal Reasoning section.
\end{parts}
}{}

\D{\newpage}

% 6

\eoce{\qt{Triathlon times, Part II\label{triathlon_times_cutoffs}} In 
Exercise~\ref{triathlon_times_intro} we saw two distributions for triathlon 
times: $N(\mu=4313, \sigma=583)$ for \emph{Men, Ages 30 - 34} and 
$N(\mu=5261, \sigma=807)$ for the \emph{Women, Ages 25 - 29} group. Times are 
listed in seconds. Use this information to compute each of the following:
\begin{parts}
\item The cutoff time for the fastest 5\% of athletes in the men's group, i.e. those 
who took the shortest 5\% of time to finish. 
\item The cutoff time for the slowest 10\% of athletes in the women's group. 
\end{parts}
}{}

% 7

\eoce{\qt{LA weather, Part I\label{la_weather_intro}} The average daily high 
temperature in June in LA is 77\degree F with a standard deviation of 
5\degree F. Suppose that the temperatures in June closely follow a normal 
distribution. 
\begin{parts}
\item What is the probability of observing an 83\degree F temperature or 
higher in LA during a randomly chosen day in June?
\item How cool are the coldest 10\% of the days (days with lowest 
high temperature) during June in LA?
\end{parts}
}{}

% 8

\eoce{\qt{CAPM\label{CAPM}} The Capital Asset Pricing Model (CAPM) is a financial 
model that assumes returns on a portfolio are normally distributed. Suppose a 
portfolio has an average annual return of 14.7\% (i.e. an average gain of 
14.7\%) with a standard deviation of 33\%. A return of 0\% means the value of 
the portfolio doesn't change, a negative return means that the portfolio 
loses money, and a positive return means that the portfolio gains money.
\begin{parts}
\item What percent of years does this portfolio lose money, i.e. have a 
return less than 0\%?
\item What is the cutoff for the highest 15\% of annual returns with this 
portfolio?
\end{parts}
}{}

% 9

\eoce{\qt{LA weather, Part II\label{la_weather_unit_change}} 
Exercise~\ref{la_weather_intro} states that average daily high temperature in 
June in LA is 77\degree F with a standard deviation of 5\degree F, and it can 
be assumed that they to follow a normal distribution. We use the following 
equation to convert \degree F (Fahrenheit) to \degree C (Celsius):
\[ C = (F - 32) \times \frac{5}{9}. \]
\begin{parts}
\item Write the probability model for the distribution of temperature in 
\degree C in June in LA.
\item What is the probability of observing a 28\degree C (which roughly 
corresponds to 83\degree F) temperature or higher in June in LA? Calculate 
using the \degree C model from part (a).
\item Did you get the same answer or different answers in part (b) of this 
question and part (a) of Exercise~\ref{la_weather_intro}? Are you surprised? Explain.
\item Estimate the IQR of the temperatures (in \degree C) in June in LA.
\end{parts}
}{}

% 10

\eoce{\qt{Find the SD\label{find_sd_cholesterol}}
Cholesterol levels for women aged 20 to 34 follow an
approximately normal distribution with mean 185 milligrams
per deciliter (mg/dl).
Women with cholesterol levels above 220 mg/dl are considered
to have high cholesterol and about 18.5\% of women fall into
this category.
What is the standard deviation of the 
distribution of cholesterol levels for women aged 20 to~34?
}{}


================================================
FILE: ch_distributions/TeX/poisson_distribution.tex
================================================
\exercisesheader{}

% 31

\eoce{\qt{Customers at a coffee shop\label{coffee_shop_customers}} A coffee shop 
serves an average of 75 customers per hour during the morning rush.
\begin{parts}
\item
  Which distribution have we studied that is most appropriate
  for calculating the probability of a given number of customers
  arriving within one hour 
  during this time of day?
\item What are the mean and the standard deviation of the number of customers 
this coffee shop serves in one hour during this time of day?
\item Would it be considered unusually low if only 60 customers showed up to 
this coffee shop in one hour during this time of day?
\item Calculate the probability that this coffee shop serves 70 customers in 
one hour during this time of day.
\end{parts}
}{}

% 32

\eoce{\qt{Stenographer's typos\label{stenographer_typos}} A very skilled 
court stenographer makes one typographical error (typo) per hour on average.
\begin{parts}
\item What probability distribution is most appropriate for calculating the 
probability of a given number of typos this stenographer makes in an hour?
\item What are the mean and the standard deviation of the number of typos 
this stenographer makes?
\item Would it be considered unusual if this stenographer made 4 typos in a 
given hour? 
\item Calculate the probability that this stenographer makes at most 2 typos 
in a given hour.
\end{parts}
}{}

% 33

\eoce{\qtq{How many cars show up\label{cars_in_parking_lot}}
For Monday through Thursday when there isn't a holiday,
the average number of vehicles that visit a particular
retailer between 2pm and 3pm each afternoon is 6.5,
and the number of cars that show up on any given day
follows a Poisson distribution.
\begin{parts}
\item
    What is the probability that exactly
    5 cars will show up next Monday?
\item
    What is the probability that
    0, 1, or 2 cars will show up next Monday
    between 2pm and 3pm?
\item
    There is an average of 11.7 people who visit during
    those same hours from vehicles.
    Is it likely that the number of people visiting
    by car during this hour is also Poisson?
    Explain.
\end{parts}
}{}

% 34

\eoce{\qt{Lost baggage\label{lost_baggage}}
Occasionally an airline will lose a bag.
Suppose a small airline has found it can reasonably
model the number of bags lost each weekday using a
Poisson model with a mean of 2.2 bags.
\begin{parts}
\item
    What is the probability that the airline
    will lose no bags next Monday?
\item
    What is the probability that the airline
    will lose 0, 1, or 2 bags on next Monday?
\item
    Suppose the airline expands over the course
    of the next 3 years, doubling the number of
    flights it makes, and the CEO asks you if
    it's reasonable for them to continue
    using the Poisson model with a mean of~2.2.
    What is an appropriate recommendation?
    Explain.
\end{parts}
}{}


================================================
FILE: ch_distributions/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 35

\eoce{\qt{Roulette winnings\label{roulette_winnings}} In the game of roulette, a 
wheel is spun and you place bets on where it will stop. One popular bet is 
that it will stop on a red slot; such a bet has an 18/38 chance of winning. 
If it stops on red, you double the money you bet. If not, you lose the money 
you bet. Suppose you play 3 times, each time with a \$1 bet. Let Y represent 
the total amount won or lost. Write a probability model for Y.
}{}

% 36

\eoce{\qt{Speeding on the I-5, Part I\label{speeding_i5_intro}} The distribution of 
passenger vehicle speeds traveling on the Interstate 5 Freeway (I-5) in 
California is nearly normal with a mean of 72.6 miles/hour and a standard 
deviation of 4.78 miles/hour.\footfullcite{Johnson+Murray:2010}
\begin{parts}
\item What percent of passenger vehicles travel slower than 80 miles/hour?
\item What percent of passenger vehicles travel between 60 and 80 miles/hour?
\item How fast do the fastest 5\% of passenger vehicles travel?
\item The speed limit on this stretch of the I-5 is 70 miles/hour. 
Approximate what percentage of the passenger vehicles travel above the speed 
limit on this stretch of the I-5.
\end{parts}
}{}

% 37

\eoce{\qt{University admissions\label{university_admissions}} Suppose a university 
announced that it admitted 2,500 students for the following year's freshman 
class. However, the university has dorm room spots for only 1,786 freshman 
students. If there is a 70\% chance that an admitted student will decide to 
accept the offer and attend this university, what is the approximate 
probability that the university will not have enough dormitory room spots for 
the freshman class?
}{}

% 38

\eoce{\qt{Speeding on the I-5, Part II\label{speeding_i5_geometric}} 
Exercise~\ref{speeding_i5_intro} states that the distribution of speeds of 
cars traveling on the Interstate 5 Freeway (I-5) in California is nearly 
normal with a mean of 72.6 miles/hour and a standard deviation of 4.78 
miles/hour. The speed limit on this stretch of the I-5 is 70 miles/hour.
\begin{parts}
\item A highway patrol officer is hidden on the side of the freeway. What is 
the probability that 5~cars pass and none are speeding? Assume that the 
speeds of the cars are independent of each other.
\item On average, how many cars would the highway patrol officer expect to 
watch until the first car that is speeding? What is the standard deviation of 
the number of cars he would expect to watch?
\end{parts}
}{}

% 39

\eoce{\qt{Auto insurance premiums\label{auto_insurance_premiums}} Suppose a 
newspaper article states that the distribution of auto insurance premiums for 
residents of California is approximately normal with a mean of \$1,650. The 
article also states that 25\% of California residents pay more than \$1,800. 
\begin{parts}
\item What is the Z-score that corresponds to the top 25\% (or the $75^{th}$ 
percentile) of the standard normal distribution?
\item What is the mean insurance cost? What is the cutoff for the 75th 
percentile?
\item Identify the standard deviation of insurance premiums in California.
\end{parts}
}{}

% 40

\eoce{\qt{SAT scores\label{sat_scores}}
SAT scores (out of 1600) are distributed 
normally with a mean of 1100 and a standard deviation of 200.
Suppose a school council awards a certificate of excellence
to all students who score at least 1350 on the SAT,
and suppose we pick one of the recognized students at random.
What is the probability this student's score will be
at least 1500?
(The material covered in
Section~\ref{conditionalProbabilitySection}
on conditional probability
would be useful for this question.)
}{}

% 41

\eoce{\qt{Married women} \label{married_women} The American Community Survey 
estimates that 47.1\% of women ages 15 years and over are married.
\footfullcite{marWomenACS}
\begin{parts}
\item We randomly select three women between these ages. What is the 
probability that the third woman selected is the only one who is married?
\item What is the probability that all three randomly selected women are 
married?
\item On average, how many women would you expect to sample before selecting 
a married woman? What is the standard deviation?
\item If the proportion of married women was actually 30\%, how many women 
would you expect to sample before selecting a married woman? What is the 
standard deviation?
\item Based on your answers to parts (c) and (d), how does decreasing the 
probability of an event affect the mean and standard deviation of the wait 
time until success?
\end{parts}
}{}

\D{\newpage}

% 42

\eoce{\qt{Survey response rate\label{survey_response_rate}} Pew Research reported 
that the typical response rate to their surveys is only 9\%. If for a 
particular survey 15,000 households are contacted, what is the probability 
that at least 1,500 will agree to respond? \footfullcite{surveysPew}
}{}

% 43

\eoce{\qt{Overweight baggage\label{overweight_baggage}} Suppose weights of the 
checked baggage of airline passengers follow a nearly normal distribution 
with mean 45 pounds and standard deviation 3.2 pounds. Most airlines charge a 
fee for baggage that weigh in excess of 50 pounds. Determine what percent of 
airline passengers incur this fee.
}{}

% 44

\eoce{\qt{Heights of 10 year olds, Part I\label{heights_10_yrs}}
Heights of 10 year olds, regardless of gender, closely follow
a normal distribution with mean 55 inches and standard deviation
6~inches.
\begin{parts}
\item
    What is the probability that a randomly chosen 10 year old
    is shorter than 48 inches?
\item
    What is the probability that a randomly chosen 10 year old
    is between 60 and 65 inches?
\item
    If the tallest 10\% of the class is considered
    ``very tall'',
    what is the height cutoff for ``very tall"?
\end{parts}
}{}

% 45

\eoce{\qt{Buying books on Ebay\label{buy_boooks_ebay}}
Suppose you're considering buying your expensive chemistry
textbook on Ebay.
Looking at past auctions suggests that the 
prices of this textbook follow an approximately normal
distribution with mean \$89 and standard deviation \$15.
\begin{parts}

\item What is the probability that a randomly selected auction for this book 
closes at more than \$100?

\item Ebay allows you to set your maximum bid price so that if someone 
outbids you on an auction you can automatically outbid them, up to the 
maximum bid price you set. If you are only bidding on one auction, what are 
the advantages and disadvantages of setting a bid price too high or too low? 
What if you are bidding on multiple auctions?

\item If you watched 10 auctions, roughly what percentile might you use for a 
maximum bid cutoff to be somewhat sure that you will win one of these ten 
auctions? Is it possible to find a cutoff point that will ensure that you win 
an auction?

\item If you are willing to track up to ten auctions closely, about what 
price might you use as your maximum bid price if you want to be somewhat sure 
that you will buy one of these ten books?

\end{parts}
}{}

% 46

\eoce{\qt{Heights of 10 year olds, Part II\label{heights_10_yrs_prob}}
Heights of 10 year olds, regardless of gender, closely follow
a normal distribution with mean 55 inches and standard deviation
6~inches.
\begin{parts}
\item
    The height requirement for \textit{Batman the Ride} at
    Six Flags Magic Mountain is 54 inches.
    What percent of 10 year olds cannot go on this ride?
\item
    Suppose there are four 10 year olds.
    What is the chance that at least two of them
    will be able to ride \emph{Batman the Ride}?
\item
    Suppose you work at the park to help them better
    understand their customers' demographics, and
    you are counting people as they enter
    the park.
    What is the chance that the first 10 year old
    you see who can ride \emph{Batman the Ride} is
    the 3rd 10 year old who enters the park?
\item
    What is the chance that the fifth 10 year old
    you see who can ride \emph{Batman the Ride} is
    the 12th 10 year old who enters the park?
\end{parts}
}{}

% 47

\eoce{\qt{Heights of 10 year olds, Part III\label{heights_10_yrs_dist}}
Heights of 10 year olds, regardless of gender, closely follow
a normal distribution with mean 55 inches and standard deviation
6~inches.
\begin{parts}
\item
    What fraction of 10 year olds are taller than
    76 inches?
\item\label{heights_10_yrs_dist_76_inches}
    If there are 2,000 10 year olds entering
    Six Flags Magic Mountain in a single day,
    then compute the expected number of
    10 year olds who are at least 76 inches tall.
    (You may assume the heights of the 10-year olds
    are independent.)
\item
    Using the binomial distribution,
    compute the probability that 0 of the 2,000
    10 year olds will be at least 76 inches tall.
\item
    The number of 10 year olds who enter
    Six Flags Magic Mountain and are
    at least 76 inches tall in a given day
    follows a Poisson distribution with
    mean equal to the value found in
    part~(\ref{heights_10_yrs_dist_76_inches}).
    Use the Poisson distribution to identify
    the probability no 10 year old will enter
    the park who is 76 inches or taller.
\end{parts}
}{}

% 48

\eoce{\qt{Multiple choice quiz\label{mc_quiz}} In a multiple choice quiz there are 
5 questions and 4 choices for each question (a, b, c, d). Robin has not 
studied for the quiz at all, and decides to randomly guess the answers. What 
is the probability that
\begin{parts}
\item the first question she gets right is the $3^{rd}$ question?
\item she gets exactly 3 or exactly 4 questions right?
\item she gets the majority of the questions right?
\end{parts}
}{}


================================================
FILE: ch_distributions/figures/6895997/6895997.R
================================================
library(openintro)
data(COL)

myPDF("6895997.pdf", 5, 2.5,
      mar = c(2, 0, 0, 0))
X <- seq(-4, 4, 0.01)
Y <- dnorm(X)
plot(X, Y,
     type = 'n',
     axes = FALSE,
     xlim = c(-3.2, 3.2),
     ylim = c(0, 0.4))
abline(h = 0, col = COL[6])
at <- -3:3
labels <- expression(mu - 3 * sigma,
                     mu - 2 * sigma,
                     mu - sigma,
                     mu,
                     mu + sigma,
                     mu + 2 * sigma,
                     mu + 3 * sigma)
axis(1, at, labels)
for (i in 3:1) {
  these <- (i - 1 <= X & X <= i)
  polygon(c(i - 1, X[these], i),
          c(0, Y[these], 0),
          col = COL[i],
          border = COL[i])
  these <- (-i <= X & X <= -i + 1)
  polygon(c(-i, X[these], -i + 1),
          c(0, Y[these], 0),
          col = COL[i],
          border = COL[i])
}

# _____ Label 99.7 _____ #
arrows(-3, 0.03,
       3, 0.03,
       code = 3,
       col = '#444444',
       length = 0.15)
text(0, 0.02, '99.7%', pos = 3)

# _____ Label 95 _____ #
arrows(-2, 0.13,
       2, 0.13,
       code = 3,
       col = '#444444',
       length = 0.15)
text(0, 0.12, '95%', pos = 3)

# _____ Label 68 _____ #
arrows(-1, 0.23,
       1, 0.23,
       code = 3,
       col = '#444444',
       length = 0.15)
text(0, 0.22, '68%', pos = 3)

lines(X, Y, col = '#888888')
abline(h = 0, col = '#AAAAAA')
dev.off()


================================================
FILE: ch_distributions/figures/amiIncidencesOver100Days/amiIncidencesOver100Days.R
================================================
library(openintro)

x <- ami.occurrences$ami

myPDF("amiIncidencesOver100Days.pdf", 5, 2.4,
       mar = c(3, 3.5, 0.5, 1))
histPlot(x,
         breaks = (0:max(2 * x + 1)) / 2 - 0.25,
         axes = FALSE,
         col = COL[1],
         xlab = "",
         ylab = "")
at     <- 0:1000
labels <- rep("", length(at))
axis(1, at = at, labels = labels, tcl = -0.18)
axis(1, at = seq(0, 1000, 5), tcl = -0.35)
axis(2, at = seq(0, 1000, 20))
par(las = 0)
mtext("AMI Events (by Day)", 1, 1.8)
mtext("Frequency", 2, 2.4)
dev.off()


================================================
FILE: ch_distributions/figures/between59And62/between59And62.R
================================================
library(openintro)
data(COL)

myPDF('between59And62.pdf', 2.5, 0.9,
      mar = c(1.4, 0, 0, 0),
      mgp = c(3, 0.45, 0))
normTail(70, 3.3,
         M = c(69, 74),
         col = COL[1],
         axes = FALSE)
labels <- round(70 + 3.3 * c(-2, 0, 2), 2)
axis(1, labels, cex.axis = 0.8)
dev.off()


================================================
FILE: ch_distributions/figures/eoce/GRE_intro/gre_intro.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set input data ----------------------------------------------------

mean_v = 151
sd_v = 7
sophia_v = 160
sophia_v_Z = (sophia_v - mean_v) / sd_v

mean_q = 153
sd_q = 7.67
sophia_q = 157  
sophia_q_Z = (sophia_q - mean_q) / sd_q

# gre_intro ---------------------------------------------------------

pdf("gre_intro.pdf", height = 3, width = 5)

par(mar = c(0,0,0,0), las = 1, mgp = c(3,1,0))

m = 0
s = 1

X <- m + s*seq(-3.2,3.2,0.01)
Y <- dnorm(X, m, s)

plot(X, Y, type='l', axes = F, 
     xlim = c(-3.4,3.4), ylim = c(-0.11, 0.4), 
     ylab = "")
lines(X, rep(0,length(X)))

lines(c(0,0), dnorm(0)*c(0.01,0.99), col = COL[6], lty=3)

z = sophia_v_Z
text(x = z+0.1, dnorm(z)*1.05, "VR", pos=3, col= COL[1], cex = 1.5)
text(x = z + 0.5, y = -0.03, paste("Z =", round(sophia_v_Z, 2)), 
     col = COL[1], cex = 1.5)
lines(c(z,z), dnorm(z, m, s)*c(0.01,0.99), lty=2, col= COL[1])

z = sophia_q_Z
text(x = z+0.1, dnorm(z)*1.05, "QR", pos=3, col= COL[4], cex = 1.5)
text(x = z - 0.5, y = -0.03, paste("Z =", round(sophia_q_Z, 2)), 
     col = COL[4], cex = 1.5)
lines(c(z,z), dnorm(z, m, s)*c(0.01,0.99), lty=2, col= COL[4])

dev.off()

# gre_intro_VR ---------------------------------------------------------

pdf("gre_intro_VR.pdf", height = 2, width = 4)

par(mar = c(2,0,0,0), las = 1, mgp = c(3,1,0), 
    cex.lab = 1.25, cex.axis = 0.9)

normTail(m = mean_v, s = sd_v, L = sophia_v, col = COL[1])

dev.off()

# gre_intro_QR ---------------------------------------------------------

pdf("gre_intro_QR.pdf", height = 2, width = 4)

par(mar = c(2,0,0,0), las = 1, mgp = c(3,1,0), 
    cex.lab = 1.25, cex.axis = 0.9)

normTail(m = mean_q, s = sd_q, L = sophia_q, col = COL[1])

dev.off()

================================================
FILE: ch_distributions/figures/eoce/area_under_curve_1/area_under_curve_1.R
================================================
# load packages -----------------------------------------------------

library(openintro)

# Z < -1.35 ---------------------------------------------------------

pdf("zltNeg.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = -1.35
u = NA

normTail(m = m, s = s, L = l, U = u, 
         axes = FALSE, col = COL[1], 
         xlab = "(a)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()

# Z > 1.48 ----------------------------------------------------------

pdf("zgtPos.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = NA
u = 1.48

normTail(m = m, s = s, L = l, U = u, 
         axes = FALSE, col = COL[1], 
         xlab = "(b)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()

# -0.4 < Z < 1.5-----------------------------------------------------

pdf("zBet.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = NA
u = NA
M = c(-0.4,1.5)

normTail(m = m, s = s, L = l, U = u, M = M,
         axes = FALSE, col = COL[1], 
         xlab = "(c)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()

# -2 < Z < 2---------------------------------------------------------

pdf("zgtAbs.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = -2
u = 2
M = NA

normTail(m = m, s = s, L = l, U = u, M = M,
         axes = FALSE, col = COL[1], 
         xlab = "(d)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()


================================================
FILE: ch_distributions/figures/eoce/area_under_curve_2/area_under_curve_2.R
================================================
# load packages -----------------------------------------------------

library(openintro)

# Z > -1.13 ---------------------------------------------------------

pdf("zgtNeg.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = NA
u = -1.13
M = NA

normTail(m = m, s = s, L = l, U = u, M = M,
         axes = FALSE, col = COL[1], 
         xlab = "(a)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()

# Z < 0.18 ----------------------------------------------------------

pdf("zltPos.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = 0.18
u = NA
M = NA

normTail(m = m, s = s, L = l, U = u, 
         axes = FALSE, col = COL[1], 
         xlab = "(b)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()

# Z > 8 -------------------------------------------------------------

pdf("zgt8.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = NA
u = 8
M = NA

normTail(m = m, s = s, L = l, U = u, M = M,
         axes = FALSE, col = COL[1], 
         xlab = "(c)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()

# -0.5 < Z < 0.5 ----------------------------------------------------

pdf("zgtAbs.pdf", height = 3, width = 5)

par(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))

m = 0
s = 1
l = NA
u = NA
M = c(-0.5,0.5)

normTail(m = m, s = s, L = l, U = u, M = M,
         axes = FALSE, col = COL[1], 
         xlab = "(d)", cex.lab = 2)
axis(1, at = c(m - 3*s, l, m, u, m + 3*s), 
     label = c(NA,l,m,u,NA), cex.axis = 2)

dev.off()


================================================
FILE: ch_distributions/figures/eoce/college_fem_heights/college_fem_heights.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------

heights = c(54, 55, 56, 56, 57, 58, 58, 59, 60, 60, 60, 61, 
            61, 62, 62, 63, 63, 63, 64, 65, 65, 67, 67, 69, 73)

# format data for including in text ---------------------------------

cat(paste("\\stackrel{", 1:25, "}{", sort(heights), "}", sep=""), sep=", ")

# plot histogram of heights -----------------------------------------

pdf("heightsFcoll_hist.pdf", height = 4, width = 6)

par(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)

histPlot(heights, col = COL[1], xlab = "Heights", ylab = "", probability = TRUE, axes = FALSE, ylim = c(0,0.085))
axis(1)
#axis(2, labels = NA)

x = heights
xfit = seq(min(x)-5, max(x)+5, length = 400)
yfit = dnorm(xfit, mean = mean(x), sd = sd(x))
lines(xfit, yfit, col = COL[4], lwd = 2)

dev.off()

# normal probability plot of heights --------------------------------

pdf("heightsFcoll_qq.pdf", height = 4, width = 6)

par(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)

qqnorm(heights, col = COL[1], pch = 19, main = "", axes = FALSE)
axis(1)
axis(2)
qqline(heights, col = COL[1])

dev.off()

================================================
FILE: ch_distributions/figures/eoce/stats_scores/stats_scores.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------

scores = c(79, 83, 57, 82, 94, 83, 72, 74, 73, 71, 
           66, 89, 78, 81, 78, 81, 88, 69, 77, 79)

# format data for including in text ---------------------------------

cat(paste("\\stackrel{", 1:20, "}{", sort(scores), "}", sep=""), sep=", ")

# plot histogram of scores  -----------------------------------------

pdf("scores_hist.pdf", height = 4, width = 6)

par(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)

histPlot(scores, col = COL[1], 
         xlab = "Scores", ylab = "", 
         probability = TRUE, 
         axes = FALSE)
axis(1)
#axis(2, labels = NA)

x = scores
xfit = seq(min(x)-5, max(x)+5, length = 400)
yfit = dnorm(xfit, mean = mean(x), sd = sd(x))
lines(xfit, yfit, col = COL[4], lwd = 2)

dev.off()

# normal probability plot of scores  --------------------------------

pdf("scores_qq.pdf", height = 4, width = 6)

par(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)

qqnorm(scores, col = COL[1], 
       pch = 19, main = "", 
       axes = FALSE)
axis(1)
axis(2)
qqline(scores, col = COL[1])

dev.off()

================================================
FILE: ch_distributions/figures/fcidMHeights/fcidMHeights-helpers.R
================================================

QQNorm <- function(x, M, SD, col) {
  qqnorm(x,
         cex = 0.7,
         main = '',
         axes = FALSE,
         ylab = 'male heights (in.)',
         col = col)
  axis(1)
  axis(2)
  abline(M, SD)
}

NormalHist <- function(obs, hold, M, SD, col) {
  plot(0, 0,
       type = 'n',
       xlab = 'Male heights (inches)',
       ylab = '',
       axes = FALSE,
       main = '',
       xlim = M + c(-3, 3) * SD,
       ylim = c(0, max(hold$density)))
  for (i in 1:length(hold$counts)) {
    rect(hold$breaks[i], 0,
         hold$breaks[i + 1], hold$density[i],
         col = col)
  }
  axis(1)
  x <- seq(min(obs) - 2, max(obs) + 2, 0.01)
  y <- dnorm(x, M, SD)
  lines(x, y, lwd = 1.5)
}

================================================
FILE: ch_distributions/figures/fcidMHeights/fcidMHeights.R
================================================
library(openintro)

obs <- male_heights_fcid$height_inch
source("fcidMHeights-helpers.R")

hold <- hist(obs, plot = FALSE)

myPDF("fcidMHeights.pdf", 6, 2.7,
      mfrow = c(1, 2),
      mgp = c(2, 0.7, 0),
      mar = c(3, 0.2, 1, 0.8))
NormalHist(obs, hold, mean(obs), sd(obs), COL[1])

par(mar = c(3,4,1,0))
qqnorm(obs,
       cex = 0.7,
       main = '',
       axes = FALSE,
       ylab = 'Male Heights (inches)',
       col = COL[1])
axis(1)
axis(2)
qqline(obs)
dev.off()


================================================
FILE: ch_distributions/figures/fourBinomialModelsShowingApproxToNormal/fourBinomialModelsShowingApproxToNormal.R
================================================
library(openintro)
data(COL)

k  <- -50:500
p  <- 0.1
n  <- c(10, 30, 100, 300)
xl <- c(0, 0, 0, 10) - 1
xu <- c(7, 11, 24, 50) - 1
axis1 <- list()
axis1[[1]] <- seq(0, 6, 2)
axis1[[2]] <- seq(0, 10, 2)
axis1[[3]] <- seq(0, 20, 5)
axis1[[4]] <- seq(10, 50, 10)

myPDF('fourBinomialModelsShowingApproxToNormal.pdf', 5.5, 4.1,
      mfrow = c(2, 2),
      mar = c(3.9, 1, 0.5, 1),
      mgp = c(2.2, 0.6, 0))

for (i in 1:4) {
  plot(k - 0.05, dbinom(k, n[i], p),
       type = 's',
       xlim = c(xl[i], xu[i]),
       axes = FALSE,
       xlab = paste("n  = ", n[i]),
       ylab = "",
       col = COL[1],
       lwd = 2)
  axis(1, axis1[[i]])
  abline(h = 0)
  if (i == 2) {
  	par(mar = c(3.25, 1, 0.9, 1))
  }
}

dev.off()


================================================
FILE: ch_distributions/figures/geometricDist35/geometricDist35.R
================================================
library(openintro)
data(COL)

p <- 0.35
x <- 1:100
y <- (1 - p)^(x - 1) * p
myPDF('geometricDist35.pdf', 6, 3.1,
      mar = c(2.6, 3.6, 0.5, 0.5),
      mgp = c(2.5, 0.34, 0))
plot(x, y,
     xlim = c(0.5, 14.5),
     type = 'n',
     axes = FALSE,
     xlab = '',
     ylab = 'Probability')
mtext('Number of Trials', line = 1.5, side = 1)
axis(1, at = seq(2, 14, 2))
par(mgp = c(2.25, 0.5, 0))
axis(2, seq(0, 0.3, 0.1))
for (i in 1:14) {
  rect(x[i] - 0.4, 0,
       x[i] + 0.4, y[i],
       col = COL[1])
}
abline(h = 0)
text(14.7, 0.003, '...', col = '#444444')
dev.off()


================================================
FILE: ch_distributions/figures/geometricDist70/geometricDist70.R
================================================
library(openintro)
data(COL)

p <- 0.7
x <- 1:100
y <- (1 - p)^(x - 1) * p
myPDF('geometricDist70.pdf', 6, 3.1,
      mar = c(2.6, 3.6, 0.5, 0.5),
      mgp = c(2.5, 0.34, 0))
plot(x, y,
     xlim = c(0.5, 8.5),
     type = 'n',
     axes = FALSE,
     xlab = '',
     ylab = 'Probability')
mtext(paste('Number of Trials Until a Success for p =', p),
    line = 1.5, side = 1)
axis(1, at = seq(1, 20, 1))
par(mgp = c(2.25, 0.5, 0))
axis(2, seq(0, 0.6, 0.2))
axis(2, seq(0, 0.7, 0.1), rep("", 8), tcl = -0.15)
for (i in 1:14) {
  rect(x[i] - 0.4, 0,
       x[i] + 0.4, y[i],
       col = COL[1])
}
abline(h = 0)
text(14.7, 0.003, '...', col = '#444444')
dev.off()


================================================
FILE: ch_distributions/figures/height40Perc/height40Perc.R
================================================
library(openintro)
data(COL)

myPDF('height40Perc.pdf', 2.15, 0.95,
      mar = c(1.31, 0, 0.01, 0),
      mgp = c(3, 0.45, 0))
X <- seq(-4, 4, 0.01)
Y <- dnorm(X)
plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.1, 3.1))
axis(1,
     at = c(-2, 0, 2),
     labels = round(70 + 3.3 * c(-2, 0, 2), 2),
     cex.axis = 0.8)
these <- which(X <= -0.25)
polygon(c(X[these[1]], X[these], X[rev(these)[1]]),
        c(0, Y[these], 0),
        col = COL[1])

text(-2, 0.24, '  40%\n(0.40)', cex = 0.8, col = COL[1])

lines(X, Y)
abline(h = 0)

dev.off()


================================================
FILE: ch_distributions/figures/height82Perc/height82Perc.R
================================================
library(openintro)
data(COL)

myPDF('height82Perc.pdf', 2.15, 1,
      mar = c(1.31, 0, 0.01, 0),
      mgp = c(3, 0.45, 0))
X <- seq(-4, 4, 0.01)
Y <- dnorm(X)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 3.4))
axis(1,
     at = c(-2, 0, 2),
     labels = round(70 + 3.3 * c(-2, 0, 2), 2),
     cex.axis = 0.8)
these <- which(X <= 0.92)
polygon(c(X[these[1]], X[these], X[rev(these)[1]]),
        c(0, Y[these], 0), col = COL[1])

text(-2, 0.23, '  82%\n(0.82)', cex = 0.8, col = COL[1])

arrows(2, 0.2, 1.45, 0.07, length = 0.07)
text(2.1, 0.18, '  18%\n(0.18)', cex = 0.8, pos = 3)

lines(X, Y)
abline(h = 0)
dev.off()


================================================
FILE: ch_distributions/figures/mikeAndJosePercentiles/mikeAndJosePercentiles.R
================================================
library(openintro)
data(COL)

myPDF("mikeAndJosePercentiles.pdf", 7, 1.3,
      mar = c(2, 0.2, 0.2, 0.2),
      mgp = c(3, 0.8, 0),
      tcl = -0.4)
layout(matrix(0:2, 1), c(0.5, 2, 2), 1)

normTail(70, 3.3,
         L = 67,
         axes = FALSE,
         col = COL[1])
axis(1,
     at = c(-100, 67, 70, 1000),
     cex.axis = 1.7)
text(62, 0.07, "Mike", cex = 2)

normTail(70, 3.3,
         L = 76,
         axes = FALSE,
         col = COL[1])
axis(1,
     at = c(-100, 70, 76, 1000),
     cex.axis = 1.7)
text(62, 0.07, "Jose", cex = 2)

dev.off()

================================================
FILE: ch_distributions/figures/nbaNormal/nbaNormal-helpers.R
================================================

QQNorm <- function(x, M, SD, col) {
  qqnorm(x,
         cex = 0.7,
         main = '',
         axes = FALSE,
         ylab = 'Observed',
         col = col)
  axis(1)
  axis(2)
  qqline(x)
}

NormalHist <- function(obs, hold, M, SD, col) {
  x <- seq(min(obs) - 2, max(obs) + 2, 0.01)
  y <- dnorm(x, M, SD)
  plot(0, 0,
       type = 'n',
       xlab = 'Height (inches)',
       ylab = '',
       axes = FALSE,
       main = '',
       xlim = M + c(-3, 3) * SD,
       ylim = c(0, max(hold$density, y)))
  for (i in 1:length(hold$counts)) {
    rect(hold$breaks[i], 0,
         hold$breaks[i + 1], hold$density[i],
         col = col)
  }
  axis(1)
  lines(x, y, lwd = 1.5)
}

================================================
FILE: ch_distributions/figures/nbaNormal/nbaNormal.R
================================================
library(openintro)
dim(nba_players_19)
head(nba_players_19)

source("nbaNormal-helpers.R")

obs <- nba_players_19$height
M  <- mean(obs)
SD <- sd(obs)
hold <- hist(obs, plot = FALSE)

myPDF("nbaNormal.pdf", 6, 2.5,
      mfrow = c(1, 2),
      mgp = c(2, 0.5, 0),
      mar = c(3, 0.5, 0.5, 2),
      cex.axis = 0.8)
NormalHist(obs, hold, M, SD, COL[1])
par(mar = c(3, 4, 0.5, 0.5))
QQNorm(obs, M, SD, COL[1])
dev.off()


================================================
FILE: ch_distributions/figures/normApproxToBinomFail/normApproxToBinomFail.R
================================================
library(openintro)
data(COL)

k <- 0:400
p <- 0.15
n <- 400
x1 <- 49
x2 <- 51
m <- n * p
s <- sqrt(n * p * (1 - p))

myPDF('normApproxToBinomFail.pdf', 7.5, 2.6,
      mar = c(1.9, 1, 0.3, 1),
      mgp = c(2.2, 0.6, 0),
      tcl = -0.35)

X <- seq(0, 100, 0.01)
Y <- dnorm(X, m, s)
plot(X, Y,
     type = "l",
     xlim = c(37, 83),
     axes = FALSE,
     xlab = "",
     ylab = "")
polygon(c(x1, x1, x2, x2),
        dnorm(c(-1000, x1, x2, -1000), m, s),
        col = COL[1])
polygon(rep(c(x1 - 1.1, x1, x1 + 1, x2 + 0.1), rep(2, 4)) + 0.5,
        dbinom(c(-1000, x1, x1, x1 + 1, x1 + 1, x2, x2, -1000),
            n, p),
        border = COL[4],
        lwd = 2)
axis(1)
axis(1,
     1:200,
     rep("", 200),
     tcl = -0.12)
abline(h = 0)

dev.off()


================================================
FILE: ch_distributions/figures/normalExamples/normalExamples-helpers.R
================================================

QQNorm <- function(x, M, SD, col) {
  qqnorm(x,
         cex = 0.7,
         main = '',
         axes = FALSE,
         ylab = 'observed',
         col = col)
  axis(1, cex.axis = 1.2)
  axis(2, cex.axis = 1.2)
  qqline(x)
}

NormalHist <- function(obs, hold, M, SD, col) {
  plot(0, 0,
       type = 'n',
       xlab = '',
       ylab = '',
       axes = FALSE,
       main = '',
       xlim = c(-3, 3),
       ylim = c(0, max(hold$density)))
  for (i in 1:length(hold$counts)) {
    rect(hold$breaks[i], 0,
         hold$breaks[i + 1], hold$density[i],
         col = col)
  }
  axis(1, cex.axis = 1.2)
  x <- seq(min(obs) - 2, max(obs) + 2, 0.01)
  y <- dnorm(x, M, SD)
  lines(x, y, lwd = 1.5)
}

================================================
FILE: ch_distributions/figures/normalExamples/normalExamples.R
================================================
library(openintro)
data(COL)

obs1 <- simulated_normal$n40
obs2 <- simulated_normal$n100
obs3 <- simulated_normal$n400

hold1 <- hist(obs1, plot=FALSE)
M1    <- mean(obs1)
SD1   <- sd(obs1)

hold2 <- hist(obs2, breaks=10, plot=FALSE)
M2    <- mean(obs2)
SD2   <- sd(obs2)

hold3 <- hist(obs3, breaks=12, plot=FALSE)
M3    <- mean(obs3)
SD3   <- sd(obs3)

source("normalExamples-helpers.R")

myPDF("normalExamples.pdf", 7.3, 4.4,
      mfrow = c(2, 3),
      mgp = c(2, 0.7, 0),
      mar = c(3, 0, 1, 1))
NormalHist(obs1, hold1, M1, SD1, COL[1])
NormalHist(obs2, hold2, M2, SD2, COL[2])
NormalHist(obs3, hold3, M3, SD3, COL[3])

par(mar = c(3,2.85,1,1.8))
QQNorm(obs1, M1, SD1, COL[1])
QQNorm(obs2, M2, SD2, COL[2])
QQNorm(obs3, M3, SD3, "#B09A00")

dev.off()


================================================
FILE: ch_distributions/figures/normalQuantileExer/QQNorm.R
================================================

QQNorm <- function(obs, at  =  pretty(obs), lwd = 2) {
  qqnorm(obs,
         cex = 0.9,
         main = '',
         axes = FALSE,
         ylab = 'Observed',
         xlab = "",
         col = COL[1],
         lwd = lwd)
  mtext("Theoretical quantiles",
        1,
        1.8,
        cex = 0.8)
  axis(1, cex.axis = 1.1)
  axis(2, at = at, cex.axis = 1.1)
}


================================================
FILE: ch_distributions/figures/normalQuantileExer/normalQuantileExer-data.R
================================================


================================================
FILE: ch_distributions/figures/normalQuantileExer/normalQuantileExer.R
================================================
library(openintro)
data(COL)


obs1 <- simulated_dist$d1
obs2 <- simulated_dist$d2
obs3 <- simulated_dist$d3
obs4 <- simulated_dist$d4

source("QQNorm.R")

myPDF("normalQuantileExer.pdf", 6, 5.3,
      mfrow = c(2,2),
      mgp = c(2.4,.55,0),
      mar = c(3.5,3.45,1,1),
      cex.lab = 1.1)
QQNorm(obs1, seq(0, 120, 40), lwd = 1.5)
QQNorm(obs2, lwd = 1.5)
QQNorm(obs3, seq(-3, -1, 1), lwd = 1.5)
QQNorm(obs4, lwd = 1.5)
dev.off()


================================================
FILE: ch_distributions/figures/normalQuantileExer/normalQuantileExerAdditional.R
================================================
library(openintro)
data(COL)

source("QQNorm.R")

obs1 <- simulated_dist$d5
obs2 <- simulated_dist$d6


myPDF("normalQuantileExerAdditional.pdf", 7.2, 3.18,
      mfrow = c(1, 2),
      mgp = c(2.4, 0.55, 0),
      mar = c(3.5, 3.45, 1, 1),
      cex.lab = 1.1)

QQNorm(obs1, 0:2, lwd = 2)
QQNorm(obs2, seq(5, 15, 5), lwd = 2)
dev.off()


================================================
FILE: ch_distributions/figures/normalTails/normalTails.R
================================================
library(openintro)
data(COL)

myPDF("normalTails.pdf", 4.3, 1,
      mar = c(0.81, 1, 0.3, 1),
      mgp = c(3, -0.2, 0),
      mfrow = c(1,2))
normTail(0, 1,
         -0.8,
         col = COL[1],
         axes = FALSE)
at <- c(-5, 0, 5)
labels <- c(-5, 'Negative Z', 5)
cex.axis <- 0.7
tick <- FALSE
axis(1, at, labels, cex.axis = cex.axis, tick = tick)
lines(c(0, 0),
      dnorm(0) * c(0.01, 0.99),
      col = COL[6],
      lty = 3,
      lwd = 1.5)

normTail(0, 1,
         0.8,
         col = COL[1],
         axes = FALSE)
labels <- c(-5, 'Positive Z', 5)
axis(1, at, labels, cex.axis = cex.axis, tick = tick)
lines(c(0, 0),
      dnorm(0) * c(0.01, 0.99),
      col = COL[6],
      lty = 3,
      lwd = 1.5)
dev.off()


================================================
FILE: ch_distributions/figures/pokerNormal/pokerNormal.R
================================================
library(openintro)
data(COL)

obs <- c(-110, -9, -60, 316, -200, -196,
         320, -160, 31, 331, 1731, 21,
         -926, -475, 914, -300, -15, 1,
         -29, 829, 761, 227, -141, -672,
         352, 385, 24, 103, -826, 95,
         115, 39, -9, -1000, -35, -200,
         -200, 235, 70, 307, 135, 60,
         -100, -295, -1000, 361, -95,
         337, 3712, -255)

M  <- mean(obs)
SD <- sd(obs)
x <- seq(min(obs) - 3000,
         max(obs) + 3000,
         1)
y <- dnorm(x, M, SD)
myPDF("pokerNormal.pdf", 6.5, 2.7,
      mfrow = 1:2,
      mgp = c(2, 0.5, 0),
      mar = c(3, 0.5, 0.5, 2))
histPlot(obs,
         xlab = 'Poker earnings (US$)',
         ylab = '',
         axes = FALSE,
         main = '',
         xlim = c(-2000, 4000),
         probability = TRUE,
         col = COL[1])
axis(1,
     cex.axis = 0.7,
     mgp = c(2, 0.35, 0))
lines(x, y,
      lwd = 1.5)

par(mar = c(3, 4, 0.5, 0.5),
    mgp = c(2.8, 0.5, 0),
    cex.axis = 0.8)
qqnorm(obs,
       cex = 0.8, col = COL[1], lwd = 2,
       main = '',
       axes = FALSE,
       xlab = '',
       ylab = 'Observed')
mtext('Theoretical Quantiles',
      line = 2,
      side = 1)
axis(1)
axis(2)
dev.off()


================================================
FILE: ch_distributions/figures/satAbove1190/satAbove1190.R
================================================
library(openintro)
data(COL)

myPDF("satAbove1190.pdf", 3, 1.4,
      mar = c(1.2, 0, 0, 0),
      mgp = c(3, 0.17, 0))
normTail(1100, 200,
         U = 1190,
         axes = FALSE,
         col = COL[1])
axis(1, at = c(700, 1100, 1500),
     cex.axis = 0.8)
dev.off()


================================================
FILE: ch_distributions/figures/satActNormals/satActNormals.R
================================================
library(openintro)
data(COL)

set.seed(1)

pdf('satActNormals.pdf', 6, 3.5)
par(mfrow = c(2, 1),
    las = 1,
    mar = c(2.5, 0, 0.5, 0))

# _____ Curve 1 _____ #
m <- 1100
s <- 200
X <- m + s * seq(-6, 6, 0.01)
Y <- dnorm(X, m, s)
plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = m + s * 2.7 * c(-1, 1))
axis(1, at = m + s * (-3:3))
abline(h = 0)
lines(c(m, m),
      dnorm(m, m, s) * c(0.01, 0.99),
      lty = 2,
      col = '#EEEEEE')
lines(c(m, m) + s,
      dnorm(m + s, m, s) * c(0.01, 1.25),
      lty = 2, col = COL[1])
text(m + s,
     dnorm(m + s, m, s) * 1.25,
     'Ann',
     pos = 3,
     col = COL[1])


# _____ Curve 2 _____ #
par(mar = c(2, 0, 1, 0))
m <- 21
s <- 6
X <- m + s * seq(-6, 6, 0.01)
Y <- dnorm(X, m, s)
plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = m + s * 2.7 * c(-1, 1))
axis(1, at = m + s * (-3:3))
abline(h = 0)
lines(c(m, m),
      dnorm(m, m, s) * c(0.01, 0.99),
      lty = 2,
      col = '#EEEEEE')
lines(c(m, m) + 3,
      dnorm(m + 3, m, s) * c(0.01, 1.2),
      lty = 2,
      col = COL[1])
text(m + 3,
     dnorm(m + 3, m, s) * 1.05,
     'Tom',
     pos = 4,
     col = COL[1])

dev.off()


================================================
FILE: ch_distributions/figures/satBelow1030/satBelow1030.R
================================================
library(openintro)
data(COL)


myPDF('satBelow1030.pdf', 2.875, 1,
      mar = c(1.5, 0, 0, 0),
      mgp = c(3, 0.45, 0))
normTail(1100, 200, 1030,
         axes = FALSE,
         col = COL[1])
axis(1, at = c(700, 1100, 1500))
dev.off()


myPDF('satAbove1030.pdf', 3, 1,
      mar = c(1.5, 4, 0, 0),
      mgp = c(3, 0.45, 0))
normTail(1100, 200,
         U = 1030,
         axes = FALSE,
         col = COL[1])
axis(1, at = c(700, 1100, 1500))
dev.off()


================================================
FILE: ch_distributions/figures/satBelow1300/satBelow1300.R
================================================
library(openintro)
data(COL)

#===> plot <===#
myPDF("satBelow1300.pdf", 2.25, 1,
      mar = c(1.2, 0, 0, 0),
      mgp = c(3, 0.17, 0))
normTail(1100, 200,
         L = 1300,
         col = COL[1],
         cex.axis = 0.6)
dev.off()


================================================
FILE: ch_distributions/figures/simpleNormal/simpleNormal.R
================================================
library(openintro)
data(COL)

myPDF("simpleNormal.pdf", 4.3, 1.5,
      mar = 0.1 * rep(1, 4))

X <- seq(-5,5,0.01)
Y <- dnorm(X)
plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-4, 4),
     lwd = 2,
     col = COL[5])
#axis(1, at = -3:3)
abline(h = -0.002, col = COL[5])

dev.off()


================================================
FILE: ch_distributions/figures/smallNormalTails/smallNormalTails.R
================================================
library(openintro)

myPDF("smallNormalTails.pdf", 4.56, 1.2,
      mar = c(1.3, 1, 0.5, 1),
      mgp = c(3, 0.27, 0),
      mfrow = c(1, 2))

X <- seq(-4, 4, 0.01)
Y <- dnorm(X)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 3.4))
at = c(-5, -0.8, 0, 5)
labels = c(-5, '-Z', 0, 5)
axis(1, at, labels, cex.axis = 0.7)
these <- which(X < -0.799)
polygon(c(X[these[1]], X[these], X[rev(these)[1]]),
        c(0, Y[these], 0),
        col = '#CCCCCC')
lines(X, Y)
abline(h = 0)
lines(c(0, 0), c(0, dnorm(0)),
      col = '#CCCCCC',
      lty = 3)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 3.4))
axis(1,
     at = c(-5, 0.8, 0, 5),
     labels = c(-5, 'Z', 0,5),
     cex.axis = 0.7)
these <- which(X > 0.801)
polygon(c(X[these[1]], X[these],X[rev(these)[1]]),
        c(0, Y[these], 0),
        col = '#CCCCCC')
lines(X, Y)
abline(h = 0)
lines(c(0, 0),
      c(0, dnorm(0)),
      col = '#CCCCCC',
      lty = 3)

dev.off()


================================================
FILE: ch_distributions/figures/standardNormal/standardNormal.R
================================================
library(openintro)

set.seed(1)
x <- rnorm(1e5)
hold <- hist(x, breaks = 50, plot = FALSE)

myPDF("standardNormal.pdf", 1250 / 255, 650 / 255,
      mar = c(2, 0, 0.5, 0))

X <- seq(-4, 4, 0.01)
Y <- dnorm(X)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 3.4))
axis(1, at = -3:3)
for(i in 1:length(hold$counts)){
  rect(hold$breaks[i], 0,
       hold$breaks[i+1], hold$density[i],
       border = '#DDDDDD',
       col = '#F4F4F4')
}
lines(X, Y)
abline(h = 0)

dev.off()


================================================
FILE: ch_distributions/figures/subtracting2Areas/subtracting2Areas.R
================================================
library(openintro)
data(COL)

AddShadedPlot <- function(x, y, offset,
                          shade.start = -8,
                          shade.until = 8) {
  lines(x + offset, y)
  lines(x + offset, rep(0, length(x)))
  these <- which(shade.start <= x & x <= shade.until)
  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,
          c(0, y[these], 0),
          col = COL[1])
  lines(x + offset, y)
}
AddText <- function(x, text) {
  text(x, 0.549283, text)
}

pdf('subtracting2Areas.pdf', 4, 0.7)
par(las = 1,
    mar = rep(0, 4),
    mgp = c(3, 0, 0))
X <- seq(-3.2, 3.2, 0.01)
Y <- dnorm(X)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 24 + 3.4),
     ylim = c(0, 0.622))

AddShadedPlot(X, Y, 0)
AddText(0, format(c(1, 0.0001), scientific = FALSE)[1])

AddShadedPlot(X, Y, 8, -8, -0.3)
AddText(8, format(0.3821, scientific = FALSE)[1])

AddShadedPlot(X, Y, 16, 1.21, 8)
AddText(16, format(0.1131, scientific = FALSE)[1])

AddShadedPlot(X, Y, 24, -0.3, 1.21)
AddText(24, format(0.5048, scientific = FALSE)[1])

lines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)
lines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)
lines(c(8 + 3.72, 8 + 4.28), rep(0.549283, 2), lwd = 2)
lines(c(8 + 3, 2 * 8 - 3), c(0.2, 0.2), lwd = 3)

text(20, 0.549283,
     ' = ')
segments(rep(19, 2), c(0.17, 0.23), rep(21, 2), lwd = 3)
dev.off()



================================================
FILE: ch_distributions/figures/subtractingArea/subtractingArea.R
================================================
library(openintro)

AddShadedPlot <- function(x, y, offset,
                          shade.start = -8,
                          shade.until = 8) {
  lines(x + offset, y)
  lines(x + offset, rep(0, length(x)))
  these <- which(shade.start <= x & x <= shade.until)
  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,
          c(0, y[these], 0),
          col = COL[1])
  lines(x + offset, y)
}
AddText <- function(x, text) {
  text(x, 0.549283, text, cex = 2)
}

pdf('subtractingArea.pdf', 6, 1.4)
par(las = 1,
    mar = rep(0, 4),
    mgp = c(3, 0, 0))
X <- seq(-3.2, 3.2, 0.01)
Y <- dnorm(X)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 16 + 3.4),
     ylim = c(0, 0.622))

AddShadedPlot(X, Y, 0)
AddText(0, format(c(1, 0.0001), scientific = FALSE)[1])

AddShadedPlot(X, Y, 8, -8, 0.45)
AddText(8, format(0.6736, scientific = FALSE)[1])

AddShadedPlot(X, Y, 16, 0.45, 8)
AddText(16, format(0.3264, scientific = FALSE)[1])

lines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)
lines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)

text(12, 0.549283,
     ' = ',
     cex = 2)
segments(c(11, 11), c(0.17, 0.23), c(13, 13), lwd = 3)
dev.off()


pdf('subtracted.pdf', 3, 0.95)
par(las = 1,
    mar = c(1.5, 3, 0, 0),
    mgp = c(3, 0.55, 0))
normTail(1100, 200, L = 1190, col = COL[1], axes = FALSE)
axis(1, at = c(700, 1100, 1500))
dev.off()


================================================
FILE: ch_distributions/figures/twoSampleNormals/twoSampleNormals.R
================================================
library(openintro)
data(COL)

set.seed(1)
x <- rnorm(100000)
hold <- hist(x,
             breaks = 50,
             plot = FALSE)

myPDF("twoSampleNormals.pdf", 6, 2,
      mfrow = c(1,2), las = 1, mar = c(2.5,1,0.5,1))

# curve 1
X <- seq(-4,4,0.01)
Y <- dnorm(X)
plot(X, Y,
     type = 'l',
     col = COL[1],
     axes = FALSE,
     xlim = c(-3.4, 3.4))
axis(1, at = -3:3)
for (i in 1:length(hold$counts)) {
  rect(hold$breaks[i], 0,
       hold$breaks[i+1], hold$density[i],
       border = COL[5,4], col = COL[7,3])
}
lines(X, Y, col = COL[1], lwd = 2)
abline(h = 0)

# curve 2
X <- seq(3,35,0.01)
Y <- dnorm(X, 19, 4)
plot(X, Y, type = 'l', col = COL[2], axes = FALSE, xlim = c(5.4,32.6))
axis(1, at = 19+4*(-3:3))

for (i in 1:length(hold$counts)) {
  rect(19 + 4 * hold$breaks[i], 0,
       19 + 4 * hold$breaks[i + 1], hold$density[i] / 4,
       border = COL[5, 4], col = COL[7, 3])
}
lines(X, Y, col = COL[2], lwd = 2)
abline(h = 0)

dev.off()


================================================
FILE: ch_distributions/figures/twoSampleNormalsStacked/twoSampleNormalsStacked.R
================================================
library(openintro)
data(COL)

myPDF("twoSampleNormalsStacked.pdf", 4.65, 2,
      mar = c(1.7,1,0.1,1))

# curve 1
X <- seq(-4,4,0.01)
Y <- dnorm(X)
plot(X, Y,
     type = 'l',
     col = COL[1],
     axes = FALSE,
     xlim = c(-5, 35))
axis(1, at = seq(-10, 40, 10))
lines(X, Y, col = COL[1], lwd = 3)
abline(h = 0)

# curve 2
X <- seq(4, 35, 0.01)
Y <- dnorm(X, 19, 4)
lines(X, Y, col = COL[2], lwd = 3)

dev.off()


================================================
FILE: ch_foundations_for_inf/TeX/ch_foundations_for_inf.tex
================================================
\begin{chapterpage}{Foundations for inference}
  \chaptertitle{Foundations for inference}
  \label{foundationsForInference}
  \label{ch_foundations_for_inf}
  \chaptersection{pointEstimates}
  \chaptersection{confidenceIntervals}
  \chaptersection{hypothesisTesting}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_foundations_for_inf}

\chapterintro{Statistical inference is primarily
  concerned with understanding and quantifying the
  uncertainty of parameter estimates.
  While the equations and details change
  depending on the setting, the foundations for inference
  are the same throughout all of statistics. \\

  \noindent%
  We start with a familiar topic:
  the idea of using a sample proportion to estimate
  a population proportion.
  Next, we create what's called a
  \emph{\hiddenterm{confidence interval}}, which is a range
  of plausible values where we may find the true population
  value.
  Finally, we introduce the
  \emph{hypothesis testing framework},
  which allows us to formally evaluate claims about the
  population, such as whether a survey provides strong
  evidence that a candidate has the support of a majority
  of the voting population.}



%__________________
\section{Point estimates and sampling variability}
\label{pointEstimates}

\index{data!solar survey|(}

Companies such as Pew Research frequently conduct
polls as a way to understand the state of public opinion
or knowledge on many topics, including politics,
scientific understanding, brand recognition, and more.
%These polls typically reach a sample of 300 to
%10,000 people.
The ultimate goal in taking a poll is generally to use
the responses to estimate the opinion or knowledge of the
broader population.

%These polls are often based on 500 to 5000 people,
%and a polling company such as Pew would use this sample
%to estimate the opinions of the broader population.
%For example, Pew frequently conducts a poll on about
%1000 adults about their feelings about the direction
%of their country.
%In early 2019, they found that 
%Through this and future sections,
%we'll use some new notation and terminology:
%\begin{itemize}
%\item
%    For all inference problems concerning proportions,
%    the population proportion will be written as $p$.
%    When discussing a population summary such as $p$,
%    it is common to refer to the value as a population
%    \term{parameter}.
%    In the solar survey,
%    $p$ represents the proportion of \emph{all}
%    American adults who support solar energy.
%\item Using Pew Research sample, we can estimate that the proportion
%    of American adults who support expanding solar energy is
%    somewhere near \pewsolarpollpercent{}.
%    This is called the \term{sample proportion},
%    and it gets a special label of $\hat{p}$
%    (spoken as \emph{p-hat}).
%\item The size of a sample will generally
%    be denoted by $n$. In the case of this Pew Research poll,
%    the \term{sample size} is $n = \pewsolarpollsize{}$.
%\end{itemize}



%In the United States, those 1000 adults would be used
%to generalize out to a population of about \emph{250 million}
%American adults.
%A~natural question arises:
%\begin{quote}
%\em
%If the poll was based on only a thousand people,
%how reliable is it?
%\end{quote}
%For instance, if we took another poll,
%we wouldn't get the exact same answer,
%so how trustworthy is the result?
%This is the topic of this first inference section,
%where we hope to understand how variable estimates
%are from one sample to the next,
%which will give us an idea of how much trust we should
%(or shouldn't) put into such polls.


\subsection{Point estimates and error}

\index{point estimate|(}

Suppose a poll suggested the US President's approval
rating is 45\%.
We would consider 45\% to be a
\term{point estimate}\index{estimate} of the approval
rating we might see if we collected responses from the
entire population.
%\footnote{When we collect responses from the
%  entire population, it is called a \term{census}.
%  It is often expensive to conduct a census,
%  which is why we often instead take a sample.}
This entire-population response proportion is
generally referred to as the \term{parameter}
of interest.
When the parameter is a proportion,
it is often denoted by $p$,
%We typically estimate the parameter by collecting
%information from a sample of the population;
%we compute the observed proportion in the sample;
%also called a \term{point estimate},
and we often refer to the sample proportion as $\hat{p}$
(pronounced \emph{p-hat}\footnote{Not to be confused with
  \emph{phat}, the slang term used for something cool,
  like this book.}).
Unless we collect responses from every individual in the population,
$p$ remains unknown, and we use $\hat{p}$ as our estimate of~$p$.
The difference we observe from the poll versus
the parameter is called the \term{error} in the estimate.
%There are other considerations that can influence
%the error in a sample's estimate can be influenced
%by other factors, too.
%it is not the complete story.
%For this reason, we will also find it convenient to track
%the \term{sample size}, which is generally referred to using
%the letter $n$.
Generally, the error consists of two aspects:
sampling error and bias.
%Throughout the rest of this section,
%we discuss what a point estimate like
%\pewsolarpollpercent{} represents
%and the sampling uncertainty associated with such an estimate.
%If we take a simple random sample of 1000 American adults
%and ask them for their opinion about solar energy,
%will we tend to get a result close to the
%\pewsolarpollpercent{} value,
%or might we see observations far from the truth?


%
%Suppose that we know that \pewsolarpollpercent{}
%of American adults 
%
%American adults' attitudes towards different forms of energy.
%They found that \pewsolarpollpercent{} of respondents
%favored expanding
%solar energy.
%In this case, Pew Research worked to ensure
%that the sample was representative.
%However, a~natural question remains:
%\begin{quote}
%\em
%If the poll was based on only a thousand people,
%how reliable is it?
%\end{quote}
%If we took another poll, we wouldn't get the exact same answer.
%Maybe we'd get 90\%, or perhaps even 80\%.
%Ultimately, it's unlikely that the actual proportion of
%Americans who support expanding solar energy is
%\emph{exactly}~\pewsolarpollpercent{}, but the data suggest
%the actual support is close to \pewsolarpollpercent{}.
%This type of uncertainty --
%the variability in the estimate from one sample to the next --
%is called the \term{sampling error},
%and it is a major focus throughout the rest of this book.

%\footnote{Another major form
%  of error is \term{bias}, which basically is a systematic
%  tendency to over or under-estimate the true population value.
%  For instance, if we took a political poll and undersampled
%  one of the political parties, the sample would not be
%  representative and would skew in a particular direction.}
%Ultimately, it's unlikely that the actual proportion of Americans
%who support expanding solar energy is \emph{exactly}
%\pewsolarpollpercent{}, but the data suggest the actual
%support is close to \pewsolarpollpercent{}.


%The Pew Research poll is a point estimate
%of the actual proportion
%of American adults who support expanding solar energy.
%This estimate of \pewsolarpollpercent{} is unlikely
%to be perfect,
%and it's quite possible for the population proportion
%to be a little lower or a little higher than the
%sample proportion.
%The difference between a point estimate and
%the parameter is called the estimate's \term{error}.

\termsub{Sampling error}{sampling error},
sometimes called \emph{\hiddenterm{sampling uncertainty}},
describes how much an estimate will tend to vary from
one sample to the next.
For instance, the estimate from one sample might be 1\% too low
while in another it may be 3\% too high.
Much of statistics, including much of this book,
is focused on understanding and quantifying sampling error,
and we will find it useful to consider a sample's size
to help us quantify this error;
the \term{sample size} is often represented by the letter $n$.
%Intuitively, a larger sample would tend to produce a more
%accurate estimate than what we would
%obtain from a smaller sample.
%This is exactly the ref
%estimate from a smaller sample,
%and this is generally true.

\termsub{Bias}{bias} describes a systematic tendency
to over- or under-estimate the true population value.
For~example, if we were taking a student poll asking
about support for a new college stadium, we'd probably
get a biased estimate of the stadium's level of student
support by wording the question as,
\emph{Do you support your school by supporting funding
  for the new stadium?}
We try to minimize bias through thoughtful data
collection procedures, which were discussed in
Chapter~\ref{ch_intro_to_data}
and are the topic of many other books.

%While bias is an incredibly important topic,
%it's forms are so varied that 
%so vast and context-specific that we 

%\begin{onebox}{Sampling error vs bias}
%  \termsub{Sampling error}{sampling error} is uncertainty
%  in a point estimate that happens naturally from one sample
%  to the next.
%  The methods we discuss are useful for understanding,
%  quantifying, and working with sampling errors.
%  \stdvspace{}
%
%  In contrast, another common form of error is \term{bias},
%  which is a systematic tendency to over or under-estimate
%  the true population value.
%  For instance, if we took a political poll but our sample
%  didn't include a roughly representative distribution of
%  the political parties, the sample would likely skew
%  in a particular direction and be biased.
%\end{onebox}




\subsection{Understanding the variability of a point estimate}
\label{simulationForUnderstandingVariabilitySection}

\newcommand{\pewsolarpollsize}{1000}
\newcommand{\pewsolarparprop}{0.88}
\newcommand{\pewsolarparpropcomplement}{0.12}
\newcommand{\pewsolarparpercent}{88\%}
\newcommand{\pewsolarparpercentcomplement}{12\%}
\newcommand{\pewsolarpollprop}{0.887}
\newcommand{\pewsolarpollpropcomplement}{0.113}
\newcommand{\pewsolarpollpercent}{88.7\%}
\newcommand{\pewsolarpollpercentcomplement}{11.3\%}
\newcommand{\pewsolarpollcount}{887}
\newcommand{\pewsolarpollexpcount}{880}
\newcommand{\pewsolarpollcountcomplement}{113}
\newcommand{\pewsolarpollexpcountcomplement}{120}
\newcommand{\pewsolarpollse}{0.010}

Suppose the proportion of American adults who support
the expansion of solar energy is $p = \pewsolarparprop{}$,
which is our parameter of interest.\footnote{We haven't
  actually conducted a census to measure this value perfectly.
  However, a very large sample has suggested the actual
  level of support is about \pewsolarparpercent{}.}
If we were to take a poll of \pewsolarpollsize{} American adults
on this topic, the estimate would not be perfect,
but how close might we expect the sample proportion
in the poll would be to \pewsolarparpercent{}?
We want to understand, \emph{how does the
sample proportion $\hat{p}$ behave when the true population
proportion is
\pewsolarparprop{}}.\footnote{\pewsolarparpercent{}
  written as a proportion would be
  \pewsolarparprop{}.
  It is common to switch between proportion and percent.
  However, formulas presented in this book always refer
  to the proportion, not the percent.}
Let's find out!
We can simulate responses we would get from a simple
random sample of 1000 American adults,
which is only possible because we know the actual
support for expanding solar energy is \pewsolarparprop{}.
%
%
%We could
%run the survey again to see how consistent the results
%are, but who has the time and money for that? Instead,
%we can investigate the properties of $\hat{p}$ using simulations.
%
%To simulate the sample, we'll suppose that the population
%proportion is exactly \pewsolarpollpercent{}.
%Now, we know
%the population proportion isn't exactly \pewsolarpollpercent\%,
%but we do expect it to be close, so this simulation will offer
%us some insights about the property of $\hat{p}$.
%If we took a random sample
%from this population, how accurate would the point estimate be?
Here's how we might go about constructing such a simulation:
%simulate it:
\begin{enumerate}
\item There were about 250 million American adults in 2018.
    On 250 million pieces of paper, write ``support''
    on \pewsolarparpercent{} of them and ``not'' on
    the other \pewsolarparpercentcomplement{}.
\item Mix up the pieces of paper and pull out \pewsolarpollsize{}
    pieces to represent our sample of \pewsolarpollsize{}
    American adults.
\item Compute the fraction of the sample that say ``support''.
\end{enumerate}
Any volunteers to conduct this simulation? Probably not. Running
this simulation with 250 million pieces of paper would be
time-consuming and very costly, but we can simulate it
using computer code; we've written a short program in
Figure~\ref{solarPollSimulationCodeR}
in case you are curious what the computer code looks like.
In this simulation, the sample gave a point estimate of
$\hat{p}_1 = 0.894$. We~know the population proportion
for the simulation was $p = \pewsolarparprop{}$, so we know
the estimate had an error of
$0.894 - \pewsolarparprop{} = \text{+0.014}$.

%\setlength\textwidth{\officialtextwidth-10mm}
\begin{figure}[h]
\texttt{\# 1.\ Create a set of 250 million entries,
where \pewsolarparpercent{} of them are "support" \\
\#\ \ \ \ and \pewsolarparpercentcomplement{} are "not". \\
pop\us{}size <- 250000000 \\
possible\_entries <- c(rep("support", \pewsolarparprop{} * pop\us{}size), rep("not", \pewsolarparpropcomplement{} * pop\us{}size))
\\[3mm]
\# 2.\ Sample \pewsolarpollsize{} entries without replacement. \\
sampled\_entries <- sample(possible\_entries, size = \pewsolarpollsize{}) \\[3mm]
\# 3.\ Compute p-hat:~count the number that are "support",
then divide by \\
\#\ \ \ \ the sample size. \\
sum(sampled\_entries == "support") / \pewsolarpollsize{}}
\caption{For those curious, this is code for
    a single $\hat{p}$ simulation using the
    statistical software called \R{}\index{R}.
    Each line that starts with \texttt{\#} is a
    \term{code comment},
    which is used to describe in regular language what the
    code is doing.
    We've provided software labs in \R{} at
    \oiRedirect{os}{openintro.org/book/os}
    for anyone interested in learning more.}
\label{solarPollSimulationCodeR}
\end{figure}
% \setlength\textwidth{\officialtextwidth}

One simulation isn't enough to get a great sense of the
distribution of estimates we might expect in the simulation,
so we should run more simulations.
In a second simulation,
we get $\hat{p}_2 = 0.885$, which has an error of~+0.005.
In another, $\hat{p}_3 = 0.878$ for an error of -0.002.
And in another,
an estimate of $\hat{p}_4 = 0.859$ with an error of -0.021.
With the help of a computer, we've run the simulation 10,000 times
and created a histogram of the results from all 10,000 simulations
in Figure~\ref{sampling_10k_prop_88p}. This
distribution of sample proportions is called a
\term{sampling distribution}.
We can characterize this sampling distribution as follows:
\begin{description}
\setlength{\itemsep}{0mm}
\item[Center.]
    The center of the distribution is
    $\bar{x}_{\hat{p}} = \pewsolarparprop{}0$,
    which is the same as the parameter.
    Notice that the simulation mimicked a simple random sample
    of the population, which is a straightforward sampling
    strategy that helps avoid sampling bias.
%    That~is, we see that the sample proportion is an
%    \termsub{unbiased estimate}{unbiased}
%    of the population proportion.
\item[Spread.]
    The standard deviation of the distribution
    is $s_{\hat{p}} = \pewsolarpollse{}$.
    When we're talking about
    a sampling distribution or the variability of
    a point estimate, we typically use the term
    \termsub{standard error}{standard error (SE)}
    rather than \emph{standard deviation},
    and the notation $SE_{\hat{p}}$ is used for the standard
    error associated with the sample proportion.
\item[Shape.]
    The distribution is symmetric and bell-shaped,
    and it \emph{resembles a normal distribution}.
\end{description}
These findings are encouraging!
When the population
proportion is $p = \pewsolarparprop{}$ and the sample size is
$n = \pewsolarpollsize{}$,
the sample proportion $\hat{p}$ tends to give
a pretty good estimate
of the population proportion.
We also have the interesting observation
that the histogram resembles a normal distribution.

\begin{figure}[h]
   \centering
   \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}
   %\Figure{0.8}{sampling_10k_prop_887p}
   \caption{A histogram of 10,000 sample proportions,
       where each sample is taken from a population
       where the population proportion is
       \pewsolarparprop{} and the sample size
       is $n = \pewsolarpollsize{}$.}
   \label{sampling_10k_prop_88p}
   %\label{sampling_10k_prop_887p}
\end{figure}

\begin{onebox}{Sampling distributions are
    never observed, but we keep them in mind}
  In real-world applications, we never actually observe the
  sampling distribution, yet it is useful to always think of
  a point estimate as coming from such a hypothetical
  distribution.
  \mbox{Understanding} the sampling distribution will help us
  characterize and make sense of the point estimates that we
  do observe.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{If we used a much smaller sample size of $n = 50$,
would you guess that the standard error for $\hat{p}$ would be larger
or smaller than when we used $n = \pewsolarpollsize{}$?}
\label{smallerSampleWhatHappensToPropErrorExercise}
Intuitively, it seems like more data is better
than less data, and generally that is correct! The typical error
when $p = \pewsolarparprop{}$ and $n = 50$ would be larger
than the error we would expect when $n = \pewsolarpollsize{}$.
\end{nexample}
\end{examplewrap}

%\noindent
Example~\ref{smallerSampleWhatHappensToPropErrorExercise}
highlights an important property we will see again and again:
a bigger sample tends to provide a more precise point estimate
than a smaller sample.

\index{point estimate|)}


\subsection{Central Limit Theorem}

The distribution in
Figure~\ref{sampling_10k_prop_88p} looks an awful lot like
a normal distribution. That is no anomaly; it~is the result
of a general principle called the
\index{Central Limit Theorem!proportion|textbf}
\term{Central Limit Theorem}.

\begin{onebox}{Central Limit Theorem and the success-failure condition}
  When observations are independent and the sample size is
  sufficiently large, the sample proportion $\hat{p}$ will tend
  to follow a normal distribution with the following mean and
  standard error:%\footnotemark{}
  \begin{align*}
    \mu_{\hat{p}} &= p
    &SE_{\hat{p}} &= \sqrt{\frac{p (1 - p)}{n}}
  \end{align*}
  In order for the Central Limit Theorem to hold,
  the sample size is typically considered sufficiently large
  when $np \geq 10$ and $n(1-p) \geq 10$, which is called the
  \term{success-failure condition}.
\end{onebox}
%\footnotetext{Some statisticians will say what we
%  have written for $SE_{\hat{p}}$ should be called
%  the \emph{standard deviation of $\hat{p}$}
%  and the standard error is a term for
%  an estimated version (that we'll first encounter
%  in Section~\ref{apply_clt_real_world_setting}).
%  We adhere to simpler terminology in this book
%  that is also accepted,
%  where the listed formula also can be called the
%  \emph{standard error}.}

The Central Limit Theorem is incredibly important, and it provides
a foundation for much of statistics.
As we begin applying
the Central Limit Theorem, be mindful of the two
technical conditions:
the observations must be independent, and the sample size must
be sufficiently large such that $np \geq 10$ and $n(1-p) \geq 10$.

\begin{examplewrap}
\begin{nexample}{Earlier we estimated the mean and standard
error of $\hat{p}$ using simulated data when
$p = \pewsolarparprop{}$ and $n = \pewsolarpollsize{}$.
Confirm that the Central Limit Theorem applies
and the sampling  distribution is approximately
normal.}\label{sample_p88_n1000_confirm_normal}
\begin{description}
\item[Independence.] There are $n = \pewsolarpollsize{}$
    observations for each
    sample proportion $\hat{p}$, and each of those observations
    are independent draws. \emph{The most common way for
    observations to be considered independent is if they are from
    a simple random sample.}
    \index{independent}
    \index{independence}
    \index{Central Limit Theorem!independence}
\item[Success-failure condition.] We can confirm the sample size
    is sufficiently large by checking the success-failure condition
    and confirming the two calculated values are greater than~10:
    \begin{align*}
    np &= \pewsolarpollsize{} \times \pewsolarparprop{}
        = \pewsolarpollexpcount{}
        \geq 10
    &n(1-p) &= \pewsolarpollsize{} \times (1 - \pewsolarparprop{})
        = \pewsolarpollexpcountcomplement{}
        \geq 10
    \end{align*}
\end{description}
The independence and success-failure conditions are both
satisfied, so the Central Limit Theorem applies, and it's
reasonable to model $\hat{p}$ using a normal distribution.
\end{nexample}
\end{examplewrap}

\begin{onebox}{How to verify sample observations are independent}
  Subjects in an experiment are considered independent
  if they undergo random assignment to the treatment
  groups.\stdvspace{}

  If the observations are from a simple random sample,
  then they are independent.\stdvspace{}

  If a sample is from a seemingly random process,
  e.g. an occasional error on an assembly line,
  checking independence is more difficult. In~this case,
  use your best judgement.
\end{onebox}

An additional condition that is sometimes added for samples
from a population is that they are no larger than 10\% of
the population.
When the sample exceeds 10\% of the population size,
the methods we discuss tend to overestimate the sampling error
slightly versus what we would get using more advanced
methods.\footnote{For example, we could use what's called the
  \term{finite population correction factor}:
  if the sample is of size $n$ and the population size is $N$,
  then we can multiply the typical standard error formula by
  $\sqrt{\frac{N-n}{N-1}}$
  to obtain a smaller, more precise estimate of the
  actual standard error.
  When $n < 0.1 \times N$, this correction factor is
  relatively small.}
This is very rarely an issue, and when it is an issue,
our methods tend to be conservative, so we consider this
additional check as optional.

\begin{examplewrap}
\begin{nexample}{Compute the theoretical mean and standard error
of $\hat{p}$ when
$p = \pewsolarparprop{}$ and $n = \pewsolarpollsize{}$,
according to the
Central Limit Theorem.}\label{sample_p88_n1000_mean_se}
The mean of the $\hat{p}$'s is simply the population proportion:
$\mu_{\hat{p}} = \pewsolarparprop{}$.

The calculation of the standard error of $\hat{p}$ uses
the following formula:
\begin{align*}
SE_{\hat{p}}
    = \sqrt{\frac{p (1 - p)}{n}}
    = \sqrt{\frac{\pewsolarparprop{} (1 - \pewsolarparprop{})}
        {\pewsolarpollsize{}}}
    = \pewsolarpollse{}
\end{align*}
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Estimate how frequently the sample proportion
$\hat{p}$ should be within 0.02 (2\%) of the population value,
$p = \pewsolarparprop{}$. Based on
Examples~\ref{sample_p88_n1000_confirm_normal}
and~\ref{sample_p88_n1000_mean_se},
we know that the distribution is approximately
$N(\mu_{\hat{p}} = \pewsolarparprop{}, SE_{\hat{p}} = \pewsolarpollse{})$.}
\label{sampling_10k_prop_887p-prop_from_867_to_907}
After so much practice in Section~\ref{normalDist},
this normal distribution example will hopefully feel familiar!
We would like to understand the fraction of $\hat{p}$'s
between 0.86 and 0.90:
\begin{center}
\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}
\end{center}
With $\mu_{\hat{p}} = \pewsolarparprop{}$ and
$SE_{\hat{p}} = \pewsolarpollse{}$,
we can compute the Z-score for both the left and right cutoffs:
\begin{align*}
Z_{0.86}
  &= \frac{0.86 - \pewsolarparprop{}}{\pewsolarpollse{}}
  = -2
&Z_{0.90}
  &= \frac{0.90 - \pewsolarparprop{}}{\pewsolarpollse{}}
  = 2
\end{align*}
We can use either statistical software, a graphing calculator,
or a table to find the areas to the tails, and in any case we
will find that they are each 0.0228. The total tail areas are
$2 \times 0.0228 = 0.0456$, which leaves the shaded area of
0.9544. That is, about 95.44\% of the sampling distribution
in Figure~\ref{sampling_10k_prop_88p} is within $\pm0.02$
of the population proportion, $p = \pewsolarparprop{}$.
\end{nexample}
\end{examplewrap}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
In Example~\ref{smallerSampleWhatHappensToPropErrorExercise}
we discussed how a smaller sample would tend
to produce a less reliable estimate. Explain how this intuition
is reflected in the formula for
$SE_{\hat{p}} = \sqrt{\frac{p (1 - p)}{n}}$.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Since the
  sample size $n$ is in the denominator
  (on the bottom) of the fraction,
  a bigger sample size means the entire
  expression when calculated will tend to be smaller.
  That is, a larger sample size would correspond to
  a smaller standard error.}


\subsection{Applying the Central Limit Theorem to
    a real-world setting}
\label{apply_clt_real_world_setting}

We do not actually know the population proportion
unless we conduct an expensive poll of all individuals
in the population.
Our earlier value of $p = 0.88$ was based on poll
conducted by Pew Research of \pewsolarpollsize{}
American adults that found
$\hat{p} = \pewsolarpollprop{}$ of them favored
expanding solar energy.
The researchers might have wondered:
does the sample proportion from the poll approximately
follow a normal distribution?
We can check the conditions from the Central Limit Theorem:
\begin{description}
\item[Independence.] The poll is a simple random sample of
    American adults, which means that the observations are
    independent.
\item[Success-failure condition.] To check this condition,
    we need the population proportion, $p$, to check if both
    $np$ and $n(1-p)$ are greater than 10.
    However, we do not actually know $p$, which
    is exactly why the pollsters would take a sample!
    In cases like these, we often use $\hat{p}$
    as our next best way to check the success-failure condition:
    \begin{align*}
    n\hat{p}
        &= \pewsolarpollsize{} \times \pewsolarpollprop{}
        = \pewsolarpollcount{}
    &n (1 - \hat{p})
        &= \pewsolarpollsize{} \times (1 - \pewsolarpollprop{})
        = \pewsolarpollcountcomplement{}
    \end{align*}
    The sample proportion $\hat{p}$ acts as
    a reasonable substitute for $p$ during this check,
    and each value in this case is well above the minimum of 10.
\end{description}

This \term{substitution approximation} of using $\hat{p}$ in
place of $p$ is also useful when computing the standard error
of the sample proportion:
\begin{align*}
SE_{\hat{p}}
    = \sqrt{\frac{p (1 - p)}{n}}
    \approx \sqrt{\frac{\hat{p} (1 - \hat{p})}{n}}
    = \sqrt{\frac{\pewsolarpollprop{}
        (1 - \pewsolarpollprop{})}{\pewsolarpollsize{}}}
    = \pewsolarpollse{}
\end{align*}
This substitution technique is sometimes
referred to as the ``\hiddenterm{plug-in principle}''.
In this case, $SE_{\hat{p}}$ didn't change enough to
be detected using only 3 decimal places
versus when we completed the calculation with
\pewsolarparprop{} earlier.
The computed standard error tends to be reasonably stable
even when observing slightly different proportions in one
sample or another.


\D{\newpage}

\subsection{More details regarding the Central Limit Theorem}

\noindent%
We've applied the Central Limit Theorem in numerous examples
so far this chapter:
\begin{quote}{\em
When observations are independent and the sample size is
sufficiently large, the distribution of $\hat{p}$ resembles
a normal distribution with
\begin{align*}
  \mu_{\hat{p}} &= p
  &SE_{\hat{p}} &= \sqrt{\frac{p (1 - p)}{n}}
\end{align*}
The sample size is considered sufficiently large
when $n p \geq 10$ and $n (1 - p) \geq 10$.
}\end{quote}
In this section, we'll explore the success-failure
condition and seek to better understand the
Central Limit Theorem.

An interesting question to answer is, \emph{what happens when
$np < 10$ or $n(1-p) < 10$?} As we did in
Section~\ref{simulationForUnderstandingVariabilitySection},
we can simulate drawing samples of different sizes where,
say, the true proportion is $p = 0.25$.
Here's a sample of size~10:
\begin{center}
% paste(sample(c("yes", "no"), 10, TRUE, c(.25, .75)), collapse = ", ")
no, no, yes, yes, no, no, no, no, no, no
\end{center}
In this sample, we observe a sample proportion of yeses
of $\hat{p} = \frac{2}{10} = 0.2$. We can simulate many such
proportions to understand the sampling distribution of
$\hat{p}$ when $n = 10$ and $p = 0.25$, which we've plotted
in Figure~\ref{sampling_10_prop_25p}
alongside a normal distribution with the
same mean and variability.
These distributions have a number of important differences.

\begin{figure}[h]
   \centering
   \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}
   \caption{Left: simulations of $\hat{p}$ when the sample size
       is $n = 10$ and the population proportion is $p = 0.25$.
       Right: a normal distribution with the same mean (0.25)
       and standard deviation (0.137).}
   \label{sampling_10_prop_25p}
\end{figure}

\begin{figure}
  \centering
  \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}
  \caption{Sampling distributions for several scenarios
      of $p$ and $n$. \\
      Rows: $p = 0.10$, $p = 0.20$, $p = 0.50$,
      $p = 0.80$, and $p = 0.90$. \\
      Columns: $n = 10$ and $n = 25$.}
  \label{clt_prop_grid_1}
\end{figure}

\begin{figure}
  \centering
  \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}
  \caption{Sampling distributions for several scenarios
      of $p$ and $n$. \\
      Rows: $p = 0.10$, $p = 0.20$, $p = 0.50$,
      $p = 0.80$, and $p = 0.90$. \\
      Columns: $n = 50$, $n = 100$, and $n = 250$.}
  \label{clt_prop_grid_2}
\end{figure}

\begin{center}
\begin{tabular}{lccc}
\hline
    &  Unimodal?  &  Smooth?  &  Symmetric? \\
\hline
Normal: $N(0.25, 0.14)$  &
    \highlightO{Yes}  &
    \highlightO{Yes}  &
    \highlightO{Yes} \\
$n = 10$, $p = 0.25$  &
    \highlightO{Yes}  &
    \highlightT{No}  &
    \highlightT{No} \\
\hline
\end{tabular}
\end{center}
Notice that the success-failure condition
was not satisfied when $n = 10$ and $p = 0.25$:
\begin{align*}
n p = 10 \times 0.25 = 2.5 &&
    n (1 - p) = 10 \times 0.75 = 7.5
\end{align*}
This single sampling distribution does not show that
the success-failure condition is the perfect guideline,
but we have found that the guideline did correctly
identify that a normal distribution might not be appropriate.

We can complete several additional simulations,
shown in
Figures~\ref{clt_prop_grid_1}
and~\ref{clt_prop_grid_2},
and we can see some trends:
\begin{enumerate}
\item When either $np$ or $n(1 - p)$ is small, the
    distribution is more \term{discrete},
    i.e. \emph{not continuous}.
\item When $np$ or $n(1-p)$ is smaller than~10,
    the skew in the distribution is more noteworthy.
\item The larger both $np$ \emph{and} $n(1 - p)$,
    the more normal the distribution.
    This may be a little harder to see for the larger
    sample size in these plots as the variability
    also becomes much smaller.
\item When $np$ and $n(1 - p)$ are both very large,
    the distribution's discreteness is hardly evident,
    and the distribution looks much more
    like a normal distribution.
\end{enumerate}

\D{\newpage}

So far we've only focused on the skew and discreteness
of the distributions.
We haven't considered how the mean and standard error
of the distributions change.
Take a moment to look back at the graphs,
and pay attention to three things:
\begin{enumerate}
\item The centers of the distribution are always at
    the population proportion, $p$, that was used to
    generate the simulation. Because the sampling
    distribution of $\hat{p}$ is always centered at
    the population parameter $p$, it means the sample
    proportion $\hat{p}$ is \term{unbiased} when
    the data are independent and drawn from such
    a population.
\item For a particular population proportion $p$,
    the variability in the sampling distribution
    decreases as the sample size~$n$ becomes larger.
    This will likely align with your intuition:
    an estimate based on a larger sample size will
    tend to be more accurate.
\item For a particular sample size, the variability
    will be largest when $p = 0.5$. The differences
    may be a little subtle, so take a close look.
    This reflects the role of the proportion
    $p$ in the standard error formula:
    $SE = \sqrt{\frac{p (1 - p)}{n}}$.
    The standard error is largest when $p = 0.5$.
\end{enumerate}

At no point will the distribution of $\hat{p}$ look
\emph{perfectly} normal, since $\hat{p}$ will always
take discrete values ($x / n$).
It is always a matter of degree, and we will use
the standard success-failure condition with minimums
of 10 for $np$ and $n (1 - p)$ as our guideline
within this~book.


\subsection{Extending the framework for other statistics}

The strategy of using a sample statistic to estimate
a parameter is quite common, and it's a strategy that
we can apply to other statistics besides a proportion.
For instance, if we want to estimate the average salary
for graduates from a particular college, we could
survey a random sample of recent graduates;
in that example, we'd be using a sample mean $\bar{x}$
to estimate the population mean~$\mu$ for all graduates.
As another example, if we want to estimate the
difference in product prices for two websites,
we might take a random sample of products available
on both sites, check the prices on each,
and then compute the average difference;
this strategy certainly would give us some idea
of the actual difference through a point estimate.

While this chapter emphasizes a single proportion
context, we'll encounter many different contexts
throughout this book where these methods will be
applied.
The principles and general ideas are the same,
even if the details change a little.
We've also sprinkled some other contexts into
the exercises to help you start thinking about
how the ideas generalize.


{\input{ch_foundations_for_inf/TeX/variability_in_estimates.tex}}





%__________________
\section{Confidence intervals for a proportion}
\label{confidenceIntervals}

\index{confidence interval|(}

The sample proportion $\hat{p}$ provides a single plausible value
for the population proportion $p$. However, the sample proportion
isn't perfect and will have some \emph{standard error}
associated with it.
When stating an estimate for the population  proportion,
it is better practice to provide a plausible
\emph{range of values} instead of supplying just the point
estimate.


\subsection{Capturing the population parameter}

Using only a point estimate is like fishing in a murky
lake with a spear. We can throw a spear where we
saw a fish, but we will probably miss. On the other hand,
if we toss a net in that area, we have a good chance of
catching the fish.
A \term{confidence interval} is like fishing with a net,
and it represents a range of plausible values where we
are likely to find the population parameter.

If we report a point estimate $\hat{p}$, we probably
will not hit the exact population proportion. On the
other hand, if we report a range of plausible values,
representing a confidence interval,
we have a good shot at capturing the parameter.

\begin{exercisewrap}
\begin{nexercise}
If we want to be very certain we capture the population
proportion in an interval, should we use a wider interval
or a smaller interval?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{If we want to be more
    certain we will capture the fish, we might use a
    wider net. Likewise, we use a wider confidence interval
    if we want to be more certain that we capture the
    parameter.}

\subsection{Constructing a 95\% confidence interval}

Our sample proportion $\hat{p}$ is the most plausible
value of the population proportion, so it makes sense
to build a confidence interval around this point estimate.
The standard error\index{standard error (SE)|textbf}
provides a guide for how
large we should make the confidence interval.

The standard error represents the standard deviation
of the point estimate, and when the Central
Limit Theorem conditions are satisfied,
the point estimate closely follows a normal distribution.
In a normal distribution, 95\% of
the data is within 1.96~standard deviations of the mean.
Using this principle, we can construct a confidence
interval that extends 1.96~standard errors from the sample
proportion to be \termsub{95\% confident}
    {confident!95\% confident}\index{confident|textbf}
that the interval captures the population proportion:
\begin{align*}
\text{point estimate}\ &\pm\ 1.96 \times SE \\
\hat{p}\ &\pm\ 1.96 \times \sqrt{\frac{p (1 - p)}{n}}
%\label{95PercentConfidenceIntervalFormula}
\end{align*}
But what does ``95\% confident'' mean? Suppose we took
many samples and built a 95\% confidence interval from
each. Then about 95\% of those intervals would
contain the parameter,~$p$.
Figure~\ref{95PercentConfidenceInterval} shows the
process of creating 25 intervals from 25 samples
from the simulation in
Section~\ref{simulationForUnderstandingVariabilitySection},
where 24 of the resulting confidence intervals contain
the simulation's population proportion of
$p = \pewsolarparprop{}$, and one interval does not.

\D{\newpage}

\begin{figure}
  \centering
  \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}
  \caption{Twenty-five point estimates and confidence
      intervals from the simulations in
      Section~\ref{simulationForUnderstandingVariabilitySection}.
      These intervals are shown relative to the population
      proportion $p = \pewsolarparprop{}$.
      Only~1 of these~25
      intervals did not capture the population
      proportion, and this interval has been bolded.}
  \label{95PercentConfidenceInterval}
\end{figure}

\begin{examplewrap}
\begin{nexample}{In Figure~\ref{95PercentConfidenceInterval},
one interval does not contain $p = \pewsolarparprop{}$.
Does this imply that the population proportion used
in the simulation could not have been
$p = \pewsolarparprop{}$?}
Just as some observations naturally
occur more than 1.96~standard deviations
from the mean, some point estimates will be more than
1.96~standard errors from the parameter of interest.
A confidence interval only provides a plausible range
of values.
While we might say other values are implausible
based on the data, this does not mean they are impossible.
\end{nexample}
\end{examplewrap}

\begin{onebox}{95\% confidence interval for a parameter}
  \index{confidence interval!95\%}
  When the distribution of a point estimate qualifies for
  the Central Limit Theorem and
  therefore closely follows a normal distribution,
  we can construct a 95\% confidence interval as
  \begin{align*}
  \text{point estimate} &\pm 1.96 \times SE
  \end{align*}
  % This confidence interval only accounts for sampling error,
  % not bias.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{In Section~\ref{pointEstimates} we learned about
    a Pew Research poll where
    \pewsolarpollpercent{} of a random sample of
    \pewsolarpollsize{} American adults
    supported expanding the role of solar power.
    Compute and
    interpret a 95\% confidence interval for the population
    proportion.} \label{95p_ci_for_pew_solar_support}
  We earlier confirmed that $\hat{p}$ follows a normal
  distribution and has a standard error of
  $SE_{\hat{p}} = \pewsolarpollse{}$.
  To compute the 95\% confidence interval, plug the
  point estimate $\hat{p} = \pewsolarpollprop{}$ and
  standard error into the 95\% confidence interval formula:
  \begin{align*}
  \hat{p} \pm 1.96 \times SE_{\hat{p}}
  \quad\to\quad
  \pewsolarpollprop{} \pm 1.96 \times \pewsolarpollse{}
  \quad\to\quad
  (0.8674, 0.9066)
  \end{align*}
  We are 95\% confident that the actual proportion of
  American adults who support expanding solar power is
  between 86.7\% and 90.7\%.
  (It's common to round to the nearest percentage point
  or nearest tenth of a percentage point when reporting
  a confidence interval.)
\end{nexample}
\end{examplewrap}


\D{\newpage}

\subsection{Changing the confidence level}
\label{changingTheConfidenceLevelSection}

\index{confidence interval!confidence level|(}

Suppose we want to consider confidence intervals where the confidence
level is higher than 95\%, such as a confidence
level of~99\%. Think back to the analogy about trying to catch a fish:
if~we want to be more sure that we will catch the fish, we should use
a wider net. To create a 99\% confidence level, we must also widen our
95\% interval. On the other hand, if we want an interval with lower
confidence, such as 90\%, we could use a slightly narrower
interval than our original 95\% interval.

The 95\% confidence interval structure provides guidance in
how to make intervals with different confidence levels.
The general 95\% confidence interval for a point estimate
that follows a normal distribution is
\begin{eqnarray*}
\text{point estimate}\ \pm\ 1.96 \times SE
\end{eqnarray*}
There are three components to this interval: the point estimate,
``1.96'', and the standard error. The choice of $1.96\times SE$ was
based on capturing 95\% of the data since the estimate is within
1.96 standard errors of the parameter about 95\% of the time.
The choice of 1.96 corresponds to a 95\% confidence level. 

\begin{exercisewrap}
\begin{nexercise} \label{leadInForMakingA99PercentCIExercise}
If $X$ is a normally distributed random variable, what is the
probability of the value $X$ being
within 2.58~standard deviations of the mean?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{This is equivalent to asking how often the
    Z-score will be larger than -2.58 but less than 2.58.
    For a picture, see Figure~\ref{choosingZForCI}.
    To determine this probability, we can use statistical software,
    a calculator, or a table to look up -2.58 and 2.58 for
    a normal distribution: 0.0049 and 0.9951.
    Thus, there is a $0.9951-0.0049 \approx 0.99$ probability
    that an unobserved normal random variable
    $X$ will be within 2.58~standard deviations of $\mu$.}

Guided Practice~\ref{leadInForMakingA99PercentCIExercise} highlights
that 99\% of the time a normal random variable will be within
2.58~standard deviations of the mean.
To create a 99\% confidence interval, change 1.96 in the 95\%
confidence interval formula to be $2.58$.
That is, the formula
for a 99\% confidence interval is
\begin{align*}
\text{point estimate}\ \pm\ 2.58 \times SE
%\label{99PercCIForProp}
\end{align*}

\begin{figure}[h]
  \centering
  \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}
  \caption{The area between -$z^{\star}$ and $z^{\star}$ increases as
      $z^{\star}$ becomes larger. If the confidence level is 99\%,
      we choose $z^{\star}$ such that 99\% of a normal
      normal distribution is between -$z^{\star}$ and $z^{\star}$,
      which corresponds to 0.5\%
      in the lower tail and 0.5\% in the upper tail:
      $z^{\star}=2.58$.}
\label{choosingZForCI}
\index{confidence interval!confidence level|)}
\end{figure}

\D{\newpage}

This approach -- using the Z-scores in the
normal model to compute confidence levels --
is appropriate when a point estimate such as $\hat{p}$
is associated with a normal distribution.
%For the context of sample proportions, the
%normal distribution is reasonable when the sample
%observations are independent and the success-failure condition
%holds ($np$ and $n(1-p)$ are both at least 10).
For some other point estimates, a normal model is not a good fit;
in these cases, we'll use alternative distributions that better
represent the sampling distribution.

\begin{onebox}{Confidence interval using any confidence level}
  If a point estimate closely follows a normal model
  with standard error $SE$, then a confidence interval
  for the population parameter is
  \begin{align*}
  \text{point estimate}\ \pm\ z^{\star} \times SE
  \end{align*}
  where $z^{\star}$ corresponds to the confidence
  level selected.
\end{onebox}

Figure~\ref{choosingZForCI} provides a picture of how to identify
$z^{\star}$ based on a confidence level. We~select $z^{\star}$
so that the area between -$z^{\star}$ and $z^{\star}$ in the
standard normal distribution\index{standard normal distribution}\index{normal distribution!standard}\index{distribution!normal!standard},
$N(0, 1)$, corresponds to the confidence level.

\begin{onebox}{Margin of error}
  \label{marginOfErrorTermBox}%
  In a confidence interval, $z^{\star}\times SE$ is called the
  \term{margin of error}.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{Use the data in
    Example~\ref{95p_ci_for_pew_solar_support} to
    create a 90\% confidence interval for the proportion of American
    adults that support expanding the use of solar power.
    We have already verified conditions for normality.}
  We first find $z^{\star}$ such that 90\% of the distribution falls
  between -$z^{\star}$ and $z^{\star}$ in the
  \index{standard normal distribution}%
  \index{normal distribution!standard}%
  \index{distribution!normal!standard}%
  standard normal distribution, $N(\mu = 0, \sigma = 1)$.
  We can do this using a graphing calculator,
  statistical software, or a probability table by looking for an
  upper tail of 5\% (the other 5\% is in the lower tail):
  $z^{\star}=1.65$.
  The 90\% confidence interval can then be computed as
  \begin{align*}
  \hat{p}\ \pm\ 1.6449 \times SE_{\hat{p}}
      \quad\to\quad 0.887\ \pm\ 1.65 \times 0.0100
      \quad\to\quad (0.8705, 0.9034)
  \end{align*}
  That is, we are 90\% confident that 87.1\% to 90.3\% of American
  adults supported the expansion of solar power in 2018.
\end{nexample}
\end{examplewrap}

\newcommand{\onepropconfintsummary}[0]{
\begin{onebox}{Confidence interval for a single proportion}
  Once you've determined a one-proportion confidence interval
  would be helpful for an application,
  there are four steps to constructing the interval:
  \begin{description}
  \item[Prepare.]
      Identify $\hat{p}$ and $n$, and determine what
      confidence level you wish to use.
  \item[Check.]
      Verify the conditions to ensure $\hat{p}$
      is nearly normal.
      For one-proportion confidence intervals,
      use $\hat{p}$ in place of $p$ to check
      the success-failure condition.
  \item[Calculate.]
      If the conditions hold, compute $SE$ using $\hat{p}$,
      find $z^{\star}$, and construct the interval.
  \item[Conclude.]
      Interpret the confidence interval in the context
      of the problem.
  \end{description}
\end{onebox}
}
\onepropconfintsummary{}


\D{\newpage}

\subsection{More case studies}

\index{data!Ebola poll|(}

\newcommand{\wsjebolapollsize}{1042}
\newcommand{\wsjebolapollsizecomma}{1,042}
\newcommand{\wsjebolapollprop}{0.82}
\newcommand{\wsjebolapollpropcomplement}{0.18}
\newcommand{\wsjebolapollpercent}{82}
\newcommand{\wsjebolapollpercentcomplement}{18}
\newcommand{\wsjebolapollcount}{854}
\newcommand{\wsjebolapollcountcomplement}{188}
\newcommand{\wsjebolapollse}{0.012}


In New York City on October 23rd, 2014, a doctor who had recently been
treating Ebola patients in Guinea went to the hospital with a slight fever
and was subsequently diagnosed with Ebola. Soon thereafter,
an NBC~4 New York/The Wall Street Journal/Marist Poll found that
\wsjebolapollpercent{}\% of New Yorkers favored a ``mandatory 21-day
quarantine for anyone who has come in contact with an Ebola
patient''. This poll included responses
of \wsjebolapollsizecomma{} New York adults between
Oct 26th and~28th, 2014.
%\footnote{This survey, like the others
%  you'll see in this book, ...}
%We may want a confidence interval for the proportion of New York
%adults who favored a mandatory quarantine of anyone who had been in
%contact with an Ebola patient.

\begin{examplewrap}
\begin{nexample}{What is the point estimate in this case,
    and is it reasonable to
    use a normal distribution to model that point estimate?}
  The point estimate, based on a sample of size $n = \wsjebolapollsize{}$,
  is $\hat{p} = \wsjebolapollprop{}$.
  To check whether $\hat{p}$ can be reasonably
  modeled using a normal distribution, we check independence
  (the poll is based on a simple random sample) and the
  success-failure condition
  ($\wsjebolapollsize{} \times \hat{p} \approx \wsjebolapollcount{}$
  and $\wsjebolapollsize{} \times (1 - \hat{p})
      \approx \wsjebolapollcountcomplement{}$,
  both easily greater than~10).
  With the conditions met, we are assured
  that the sampling distribution of $\hat{p}$ can be
  reasonably modeled using a normal distribution.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Estimate the standard error of
    $\hat{p} = \wsjebolapollprop{}$ from the Ebola survey.}
  \label{seOfPropOfNYEbolaSurvey}%
  We'll use the substitution approximation of
  $p \approx \hat{p} = \wsjebolapollprop{}$ to compute
  the standard error:
  \begin{align*}
  SE_{\hat{p}}
    = \sqrt{\frac{p(1-p)}{n}}
    \approx \sqrt{\frac{\wsjebolapollprop{}
        (1 - \wsjebolapollprop{})}{\wsjebolapollsize{}}}
    = \wsjebolapollse{}
  \end{align*}
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Construct a 95\% confidence interval for $p$,
    the proportion of New York adults who supported a quarantine
    for anyone who has come into contact with an Ebola patient.}
  \label{ex_ci_ny_ebola_quarantine}%
  Using the standard error $SE = 0.012$ from
  Example~\ref{seOfPropOfNYEbolaSurvey},
  the point estimate \wsjebolapollprop{}, and $z^{\star} = 1.96$
  for a 95\% confidence level, the confidence interval is
  \begin{eqnarray*}
  \text{point estimate} \ \pm\ z^{\star} \times SE
    \quad\to\quad \wsjebolapollprop{} \ \pm\ 1.96\times \wsjebolapollse{}
    \quad\to\quad (0.796, 0.844)
  \end{eqnarray*}
  We are 95\% confident that the proportion of New York adults
  in October 2014 who supported a quarantine for anyone who had come
  into contact with an Ebola patient was between 0.796 and 0.844.
\index{data!Ebola poll|)}
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Answer the following two questions about the confidence interval
from Example~\ref{ex_ci_ny_ebola_quarantine}:\footnotemark{}
\begin{enumerate}[(a)]
\item
    What does 95\% confident mean in this context?
\item
    Do you think the confidence interval is still valid
    for the opinions of New Yorkers today?
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~If we took many such samples and computed
  a 95\% confidence interval for each, then about 95\% of those
  intervals would contain the actual proportion of New York
  adults who supported a quarantine for anyone who has come into
  contact with an Ebola patient. \\
  (b)~Not necessarily. The poll was taken at a
  time where there was a huge public safety concern.
  Now that people have had some time to step back,
  they may have changed their opinions.
  We would need to run a new poll if we wanted to get an
  estimate of the current proportion of New York adults who
  would support such a quarantine period.}

\D{\newpage}

\index{data!wind turbine survey|(}

\newcommand{\pewwindpollsize}{\pewsolarpollsize}
\newcommand{\pewwindpollprop}{0.848}
\newcommand{\pewwindpollpropcomplement}{0.152}
\newcommand{\pewwindpollpercent}{84.8}
\newcommand{\pewwindpollpercentcomplement}{15.2}
\newcommand{\pewwindpollcount}{848}
\newcommand{\pewwindpollcountcomplement}{152}
\newcommand{\pewwindpollse}{0.0114}

\begin{exercisewrap}
\begin{nexercise}
\label{pew_wind_turbine_support_normal_dist_gp}%
In the Pew Research poll about solar energy, they
also inquired about other forms of energy,
and \pewwindpollpercent{}\% of the \pewwindpollsize{}
respondents supported expanding the use of wind
turbines.\footnotemark{}
\begin{enumerate}[(a)]
\item
    Is it reasonable to model the proportion
    of US adults who support expanding wind turbines
    using a normal distribution?
\item
    Create a 99\% confidence interval for the level of American
    support for expanding the use of wind turbines for power
    generation.
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~The survey was a random sample
  and counts are both $\geq 10$
  ($\pewwindpollsize{} \times \pewwindpollprop{}
    = \pewwindpollcount{}$
  and $\pewwindpollsize{} \times \pewwindpollpropcomplement{}
    = \pewwindpollcountcomplement$),
  so independence and the success-failure condition
  are satisfied, and
  $\hat{p} = \pewwindpollprop{}$ can be
  modeled using a normal distribution. \\
  (b)~Guided
  Practice~\ref{pew_wind_turbine_support_normal_dist_gp}
  confirmed that $\hat{p}$ closely follows
  a normal distribution, so we can use the C.I.~formula:
  \begin{align*}
  \text{point estimate} \pm z^{\star} \times SE
  \end{align*}
  In this case, the point estimate is
  $\hat{p} = \pewwindpollprop{}$.
  For a 99\% confidence interval, $z^{\star} = 2.58$.
  Computing the standard error:
  $SE_{\hat{p}}
    = \sqrt{\frac{\pewwindpollprop{}(1 - \pewwindpollprop{})}
        {\pewwindpollsize{}}}
    = \pewwindpollse{}$.
  Finally, we compute the interval as
  $\pewwindpollprop{} \pm 2.58 \times \pewwindpollse{}
    \to (0.8186,   0.8774)$.
  It is also important to \emph{always} provide an interpretation
  for the interval: we are 99\% confident the proportion of
  American adults that support expanding the use of wind
  turbines in 2018 is between 81.9\% and 87.7\%.}

We can also construct confidence intervals for other
parameters, such as a population mean.
In these cases, a confidence interval would be computed
in a similar way to that of a single proportion:
a point estimate plus/minus some margin of error.
We'll dive into these details in later chapters.


\subsection{Interpreting confidence intervals}
\label{interpretingCIs}

\index{confidence interval!interpretation|(}

In each of the examples, we described the confidence
intervals by putting them into the context of the data and also
using somewhat formal language:
\begin{description}
  \item[Solar.] We are 90\% confident that 87.1\% to 90.4\% of
      American adults support the expansion of solar power in 2018.
  \item[Ebola.] We are 95\% confident that the proportion
      of New York adults in October 2014 who supported a quarantine
      for anyone who had come into contact with an Ebola patient was
      between 0.796 and 0.844.
  \item[Wind Turbine.] We are 99\% confident the proportion of
      Americans adults that support expanding the use of wind
      turbines is between 81.9\% and 87.7\% in 2018.
\end{description}
First, notice that the statements are always about the population
parameter, which considers \emph{all} American adults for the
energy polls or \emph{all} New York adults for the quarantine poll.

We also avoided another common mistake:
\emph{incorrect} language might try to describe the confidence interval
as capturing the population parameter with a certain probability.
Making a probability interpretation is a common error:
while it might be useful to think of it as a probability,
the confidence level only quantifies how plausible
it is that the parameter is in the given interval.

Another important consideration of confidence intervals is that they
are \emph{only about the population parameter}.
A confidence interval says nothing about individual
observations or point estimates.
Confidence intervals only provide a plausible range for
population parameters.

\index{bias|(}
Lastly, keep in mind the methods we discussed only apply
to sampling error, not to bias.
If a data set is collected in a way that will tend to
systematically under-estimate
(or over-estimate) the population parameter, the techniques
we have discussed will not address that problem.
Instead, we rely on careful data collection procedures to
help protect against bias in the examples we have considered,
which is a common practice employed by data scientists
to combat bias.
\index{bias|)}

\begin{exercisewrap}
\begin{nexercise}
Consider the 90\% confidence interval for the solar
energy survey: 87.1\% to 90.4\%.
If~we ran the survey again, can we say that we're
90\% confident that the new survey's proportion
will be between 87.1\% and 90.4\%?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{
  No, a confidence interval only provides a range of plausible
  values for a parameter,
  not future point estimates.}




\index{data!wind turbine survey|)}
\index{data!solar survey|)}
\index{confidence interval!interpretation|)}

\CalculatorVideos{confidence intervals for a single proportion}

\index{confidence interval|)}


{\input{ch_foundations_for_inf/TeX/confidence_intervals.tex}}






%__________________
\section{Hypothesis testing for a proportion}
\label{hypothesisTesting}

\index{hypothesis testing|(}

The following question comes from a book written by
Hans Rosling, Anna Rosling R{\"o}nnlund, and Ola Rosling
called \emph{\oiRedirect{amazon_factfulness}{Factfulness}}:
\begin{quote}
  {\em How many of the world's 1~year old children today
  have been vaccinated against some disease:
  \begin{enumerate}[a.]
  \setlength{\itemsep}{0mm}
  \item 20\%
  \item 50\%
  \item 80\%
  \end{enumerate}}
\end{quote}
Write down what your answer (or guess),
and when you're ready, find the answer in the
footnote.\footnote{The correct answer is (c):
  80\% of the world's 1~year olds have been vaccinated
  against some disease.}

In this section,
we'll be exploring how people with a 4-year college
degree perform on this and other world health questions
as we learn about hypothesis tests, which are
a framework used to rigorously evaluate competing
ideas and claims.

\newcommand{\roslingAsize}{50}
\newcommand{\roslingAprop}{0.24}
\newcommand{\roslingApropcomplement}{0.76}
\newcommand{\roslingApercent}{24}
\newcommand{\roslingApercentcomplement}{76}
\newcommand{\roslingAcount}{12}
\newcommand{\roslingAcountcomplement}{38}
\newcommand{\roslingAse}{0.060}
% n <- 50; x <- 12; (p <- x/n); (se <- sqrt(p * (1 - p) / n)); p + c(-1, 1) * 1.96 * se

%There's an adage in United States financial markets that
%it is better to get out of investments during the six ``summer''
%months: \emph{sell in May and go away!}\footnote{Summer in the
%northern hemisphere, anyways. \rotatebox[origin=c]{180}{(Hello
%Australia!)}} While this clever saying does rhyme, that doesn't
%mean it is sound financial advice. Let's investigate.

%so is this is a pretty strong statement, since the stock
%market has a very strong historical trend of moving upwards.
%
%To test this theory, we've retrieved the 
%
%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!

%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 

%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:

%\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.
%\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 

%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 


\subsection{Hypothesis testing framework}

We’re interested in understanding how much people know
about world health and development.
If we take a multiple choice
world health question, then we might like to understand~if
\begin{description}
\item[$\mathbf{H_0}$:]
    People never learn these particular topics and their
    responses are simply equivalent to random guesses.
\item[$\mathbf{H_A}$:]
    People have knowledge that helps them do better
    than random guessing, or perhaps, they have false knowledge
    that leads them to actually do worse than random guessing.
\end{description}
These competing ideas are called \term{hypotheses}.
We call $H_0$ the null hypothesis and $H_A$ the alternative
hypothesis.
When there is a subscript 0 like in $H_0$,
data scientists pronounce it as ``nought''
(e.g.~$H_0$ is pronounced ``H-nought'').

\begin{onebox}{Null and alternative hypotheses}
  The \term{null hypothesis ($H_0$)} often represents
  a skeptical perspective or a claim to be tested.
  The \term{alternative hypothesis ($H_A$)} represents an
  alternative claim under consideration and is often
  represented by a range of possible parameter values.
  \stdvspace{}
  
  Our job as data scientists is to play the role of a skeptic:
  before we buy into the alternative hypothesis, we need to
  see strong supporting evidence.
\end{onebox}

The null hypothesis often represents a skeptical position
or a perspective of ``no difference''.
In our first example, we'll consider whether
the typical person does any different than random guessing
on Roslings' question about infant vaccinations.

The alternative hypothesis generally represents a new
or stronger perspective. In the case of the question
about infant vaccinations,
it would certainly be interesting to learn whether
people do better than random guessing, since that would
mean that the typical person knows something about
world health statistics.
It would also be very interesting if we learned
that people do \emph{worse} than random guessing,
which would suggest people believe
incorrect information about world health.

The 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. 

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise} \label{hypTestCourtExample}
A 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{The jury considers whether the evidence is so
    convincing (strong) that there is no reasonable doubt
    regarding the person's guilt;
    in such a case, the jury rejects innocence
    (the null hypothesis) and concludes the defendant
    is guilty (alternative hypothesis).}

Jurors examine the evidence to see whether it convincingly
shows a defendant is guilty.
Even if the jurors leave unconvinced of guilt beyond
a reasonable doubt, this does not mean they believe the
defendant is innocent.
This is also the case with hypothesis testing:
\emph{even if we fail to reject the null hypothesis,
we typically do not accept the null hypothesis as true}.
Failing to find strong evidence for the alternative
hypothesis is not equivalent to accepting
the null hypothesis.

When considering Roslings' question about infant vaccination,
the null hypothesis represents the notion that the people
we will be considering -- college-educated adults --
are as accurate as random guessing.
That is, the proportion
$p$ of respondents who pick the correct
answer, that 80\% of 1~year olds have been vaccinated
against some disease, is about 33.3\%
(or 1-in-3 if wanting to be perfectly precise).
The alternative hypothesis is that this proportion is something
other than 33.3\%. While it's helpful to write these hypotheses
in words, it can be useful to write them using mathematical
notation:
\begin{description}
\item[$H_0$:] $p = 0.333$
\item[$H_A$:] $p \neq 0.333$
\end{description}
In this hypothesis setup, we want to make a conclusion about
the population parameter $p$. The value we are comparing the
parameter to is called the \term{null value}, which in this
case is 0.333. It's common to label the null value with the
same symbol as the parameter but with a subscript~`0'.
That is, in this case, the null value is $p_0 = 0.333$
(pronounced ``p-nought equals 0.333'').

\begin{examplewrap}
\begin{nexample}{It may seem impossible that the
    proportion of people who get the correct answer
    is \emph{exactly} 33.3\%. If we don't believe the
    null hypothesis, should we simply reject it?}
  No. While we may not buy into the notion that
  the proportion is exactly 33.3\%, the hypothesis testing
  framework requires that there be strong evidence before
  we reject the null hypothesis and conclude something
  more interesting.

  After all, even if we don't believe the proportion is
  \emph{exactly} 33.3\%, that doesn't really tell us anything
  useful! We would still be stuck with the original question:
  do people do better or worse than random guessing on
  Roslings' question?
  Without data that strongly
  points in one direction or the other, it is both
  uninteresting and pointless to reject $H_0$.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
  Another example of a real-world hypothesis testing situation
  is evaluating whether a new drug is better or worse
  than an existing drug at treating a particular disease.
  What should we use for the null and alternative hypotheses in
  this case?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The null hypothesis ($H_0$) in this case is
    the declaration of \emph{no difference}: the drugs are equally
    effective. The alternative hypothesis ($H_A$) is that the
    new drug performs differently than the original,
    i.e. it could perform better or worse.}


\D{\newpage}

\subsection{Testing hypotheses using confidence intervals}
\label{utilizingOurCI}

We will use the \data{rosling\us{}responses}
data set to evaluate
the hypothesis test evaluating whether college-educated adults
who get the question about infant vaccination correct is different
from 33.3\%.
This data set summarizes the answers of \roslingAsize{}
college-educated adults.
Of these \roslingAsize{} adults, \roslingApercent{}\%~of
respondents got the question correct that 80\% of 1~year olds
have been vaccinated against some disease.

Up until now, our discussion has been philosophical.
However, now that we have data, we might ask ourselves:
does the data provide strong evidence that the proportion
of all college-educated adults who would answer this
question correctly is different than 33.3\%?

We learned in Section~\ref{pointEstimates} that there is
fluctuation from one sample to another, and it is unlikely
that our sample proportion, $\hat{p}$,
will exactly equal $p$, but we want to make
a conclusion about~$p$.
We~have a nagging concern:
is this deviation of \roslingApercent{}\%
from 33.3\% simply due to chance,
or~does the data provide strong evidence that the
population proportion is different from 33.3\%?

In Section~\ref{confidenceIntervals}, we learned how to
quantify the uncertainty in our estimate using confidence
intervals. 
The same method for measuring variability can be useful
for the hypothesis test.

\begin{examplewrap}
\begin{nexample}{Check whether it is reasonable to construct
    a confidence interval for $p$ using the sample data, and
    if so, construct a 95\% confidence interval.}
  The conditions are met for $\hat{p}$ to be approximately
  normal: the data come from a simple random sample (satisfies
  independence), and $n\hat{p} = \roslingAcount$ and
  $n(1 - \hat{p}) = \roslingAcountcomplement$ are both
  at least 10 (success-failure condition).

  To construct the confidence interval, we will need to identify
  the point estimate ($\hat{p} = \roslingAprop$),
  the critical value for
  the 95\% confidence level ($z^{\star} = 1.96$), and the standard
  error of $\hat{p}$
  ($SE_{\hat{p}} = \sqrt{\hat{p}(1 - \hat{p}) / n} = \roslingAse$).
  With those pieces, the confidence interval for $p$ can be
  constructed:
  \begin{align*}
    &\hat{p} \pm z^{\star} \times SE_{\hat{p}} \\
    &\roslingAprop \pm 1.96 \times \roslingAse \\
    &(0.122, 0.358)
  \end{align*}
  We are 95\% confident that the proportion of all
  college-educated adults to correctly answer this
  particular question about infant vaccination is between
  12.2\% and 35.8\%.
\end{nexample}
\end{examplewrap}
%At a first glance, it looks like it might be. After all,
%36\% isn't that close to 50\%, so maybe this data constitutes
%\emph{strong evidence}. We need to 

Because the null value in the hypothesis test is $p_0 = 0.333$,
which falls within the range of plausible values from the
confidence interval, we cannot say the null value is
implausible.\footnote{Arguably this method is slightly imprecise.
  As we'll see in a few pages, the standard error is often
  computed slightly differently in the context of a hypothesis
  test for a proportion.}
That is, the data do not provide sufficient evidence to reject
the notion that the performance of college-educated
adults was different than random guessing,
and we do not reject the null hypothesis,~$H_0$.

\begin{examplewrap}
\begin{nexample}{Explain why we cannot conclude that
    college-educated adults simply guessed on the
    infant vaccination question.}
  While we failed to reject $H_0$, that does not
  necessarily mean the null hypothesis is true.
  Perhaps there was an actual difference,
  but we were not able to detect it with the
  relatively small sample of~\roslingAsize{}.

%  Second, we are only evaluating the proportion,
%  and if the population proportion is 0.333,
%  there are still multiple ways to arrive at that proportion.
%  For example,
%  perhaps some adults guessed but others did not.
%  And of those who didn't guess,
%  their past knowledge simply wasn't very useful on this
%  question and so most of them still got it wrong.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Double negatives can sometimes be used in statistics}
  In many statistical explanations, we use double negatives.
  For instance, we might say that the null hypothesis is
  \emph{not implausible} or we \emph{failed to reject}
  the null hypothesis.
  Double negatives are used to communicate that while we
  are not rejecting a position, we are also not saying it
  is correct.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}\label{roslingB_hypothesis_setup}%
Let's move onto a second question posed by the Roslings:
\begin{quote}{\em
  There are 2 billion children in the world today
  aged 0-15 years old, how many children will there
  be in year 2100 according to the United Nations?
  \begin{enumerate}[a.]
  \setlength{\itemsep}{0mm}
  \item 4 billion.
  \item 3 billion.
  \item 2 billion.
  \end{enumerate}
}\end{quote}
Set up appropriate hypotheses to evaluate whether
college-educated adults are better than random guessing
on this question.
Also, see if you can guess the correct answer before checking
the answer in the footnote!\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{%
The appropriate hypotheses are:

$H_0$: the proportion who get the answer correct is the same
as random guessing: 1-in-3, or $p = 0.333$.

$H_A$: the proportion who get the answer correct is different
than random guessing, $p \neq 0.333$.

The correct answer to the question is 2~billion.
While the world population is projected to increase,
the average age is also expected to rise.
That is, the majority of the population growth will
happen in older age groups, meaning people are projected
to live longer in the future across much of the world.}

% n <- 228; x <- 39; p <- x / n; n; p; 1 - p; x; n - x; sqrt(p*(1-p)/n)
\newcommand{\roslingBsize}{228}
\newcommand{\roslingBprop}{0.149}
\newcommand{\roslingBpropcomplement}{0.851}
\newcommand{\roslingBpercent}{14.9\%}
\newcommand{\roslingBpercentcomplement}{85.1\%}
\newcommand{\roslingBcount}{34}
\newcommand{\roslingBcountcomplement}{194}
\newcommand{\roslingBse}{0.024}
% n <- 228; x <- 34; (p <- x/n); (se <- sqrt(p * (1 - p) / n)); p + c(-1, 1) * 1.96 * se

\begin{exercisewrap}
\begin{nexercise}\label{roslingB_normality}%
This time we took a larger sample of
\roslingBsize{} college-educated adults,
\roslingBcount{} (\roslingBpercent{}) selected the correct
answer to the question in Guided
Practice~\ref{roslingB_hypothesis_setup}: 2~billion.
Can we model the sample proportion using a normal distribution
and construct a confidence interval?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We check both conditions, which are satisfied,
so it is reasonable to use
a normal distribution for $\hat{p}$: \\
\textbf{Independence.} Since the data are from a simple
    random sample, the observations are independent. \\
\textbf{Success-failure.} We'll use $\hat{p}$ in place of $p$
    to check: $n\hat{p} = \roslingBcount$
    and $n(1 - \hat{p}) = \roslingBcountcomplement$.
    Both are greater than 10, so the success-failure condition
    is satisfied.}

\begin{examplewrap}
\begin{nexample}{Compute a 95\% confidence interval for the
    fraction of college-educated adults who answered the
    children-in-2100 question correctly, and evaluate the
    hypotheses in Guided
    Practice~\ref{roslingB_hypothesis_setup}.}
  To compute the standard error, we'll again use
  $\hat{p}$
  in place of $p$ for the calculation:
  \begin{align*}
  SE_{\hat{p}}
      = \sqrt{\frac{\hat{p}(1 - \hat{p})}{n}}
      = \sqrt{\frac{\roslingBprop{}(1 - \roslingBprop{})}
          {\roslingBsize{}}}
      = \roslingBse{}
  \end{align*}
  In Guided Practice~\ref{roslingB_normality},
  we found that $\hat{p}$ can be modeled using
  a normal distribution,
  which ensures a 95\% confidence interval may be accurately
  constructed as
  \begin{align*}
  \hat{p}~\pm~z^{\star} \times SE
  \quad\to\quad
  \roslingBprop{}~\pm~1.96 \times \roslingBse{}
  \quad\to\quad
  (0.103, 0.195)
  \end{align*}
  Because the null value, $p_0 = 0.333$, is not in the
  confidence interval, a population proportion of 0.333
  is implausible and we reject the null hypothesis.
  That is, the data provide statistically significant
  evidence that the actual proportion of college adults
  who get the children-in-2100 question correct is
  different from random guessing. Because the entire
  95\% confidence interval
  is below 0.333, we can conclude college-educated adults
  do \emph{worse} than random guessing on this question.

  One subtle consideration is that we used a
  95\% confidence interval.
  What if we had used a 99\% confidence level?
  Or even a 99.9\% confidence level?
  It's possible to come to a different conclusion
  if using a different confidence level.
  Therefore, when we make a conclusion based
  on confidence interval, we should also be sure
  it is clear what confidence level we used.
\end{nexample}
\end{examplewrap}

The worse-than-random performance on this
last question is not a fluke:
there are many such world health questions where people
do worse than random guessing.
In general, the answers suggest that people tend to be
more pessimistic about progress than reality suggests.
This topic is discussed in much greater detail in
the Roslings' book,
\emph{\oiRedirect{amazon_factfulness}{Factfulness}}.


\D{\newpage}

\subsection{Decision errors}

\index{hypothesis testing!decision errors|(}

Hypothesis tests are not flawless: we can make an incorrect
decision in a statistical hypothesis test based on the data.
For example, in the court system innocent people are
sometimes wrongly convicted and the guilty sometimes walk free.
%Unfortunately, we never truly know if $H_0$ or $H_A$ holds true.
One key distinction with statistical hypothesis tests is that
we have the tools necessary to probabilistically quantify how
often we make errors in our conclusions.

Recall that there are two competing hypotheses:
the null and the alternative.
In a hypothesis test, we make a statement about which one might
be true, but we might choose incorrectly. There are four possible
scenarios, which are summarized in Figure~\ref{fourHTScenarios}.

\begin{figure}[ht]
\centering
\begin{tabular}{l l c c}
& & \multicolumn{2}{c}{\textbf{Test conclusion}} \\
\cline{3-4}
\vspace{-3.7mm} \\
& & do not reject $H_0$ &  reject $H_0$ in favor of $H_A$ \\
\cline{2-4}
\vspace{-3.7mm} \\
& $H_0$ true &
    okay &  \highlight{Type~1 Error} \\
\raisebox{1.5ex}{\textbf{Truth}} & $H_A$ true &
    \highlight{Type~2 Error} & okay \\
\cline{2-4}
\end{tabular}
\caption{Four different scenarios for hypothesis tests.}
\label{fourHTScenarios}
\end{figure}

A \term{Type~1 Error} is rejecting the null hypothesis when
$H_0$ is actually true.
A \term{Type~2 Error} is failing to
reject the null hypothesis when the alternative is actually
true.

\begin{exercisewrap}
\begin{nexercise} \label{whatAreTheErrorTypesInUSCourts}
In a US court, the defendant is either innocent ($H_0$) or
guilty ($H_A$).
What does a Type~1 Error represent in this context?
What does a Type~2 Error represent?
Figure~\ref{fourHTScenarios} may be useful.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{If
  the court makes a Type~1 Error, this means the defendant
  is innocent ($H_0$ true) but wrongly convicted.
  Note that a Type~1 Error is only possible if we've rejected
  the null hypothesis.

  A Type~2 Error means the court failed to reject $H_0$
  (i.e. failed to convict the person) when she was
  in fact guilty ($H_A$ true).
  Note that a Type~2 Error is only possible if we have
  failed to reject the null hypothesis.}

\begin{examplewrap}
\begin{nexample}{How could we reduce the Type~1 Error rate
    in US courts?
    What influence would this have on the Type~2 Error rate?}
    \label{howToReduceType1ErrorsInUSCourts}%
  To lower the Type~1 Error rate, we might
  raise our standard for conviction from
  ``beyond a reasonable doubt'' to
  ``beyond a conceivable doubt'' so fewer people would
  be wrongly convicted. However, this would also make
  it more difficult to convict the people who are
  actually guilty, so we would make more Type~2 Errors.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise} \label{howToReduceType2ErrorsInUSCourts}
How could we reduce the Type~2 Error rate in US courts?
What influence would this have on the Type~1 Error
rate?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{To lower the Type~2 Error rate, we want
  to convict more guilty people. We could lower the
  standards for conviction from ``beyond a reasonable
  doubt'' to ``beyond a little doubt''. Lowering the bar
  for guilt will also result in more wrongful convictions,
  raising the Type~1 Error rate.}

\index{hypothesis testing!decision errors|)}

Exercises~\ref{whatAreTheErrorTypesInUSCourts}-\ref{howToReduceType2ErrorsInUSCourts} provide
an important lesson: if we reduce how often we make
 one type of error, we generally make more of the
 other type.

Hypothesis testing is built around rejecting or failing
to reject the null hypothesis.
That is, we do not reject $H_0$ unless we have strong evidence.
But what precisely does \emph{strong evidence} mean?
As a general rule of thumb, for those cases where the null
hypothesis is actually true, we do not want to incorrectly
reject $H_0$ more than 5\% of the time.
This corresponds to a \term{significance level}%
\index{hypothesis testing!significance level} of 0.05.
That is, if the null hypothesis is true,
the significance level indicates how often
the data lead us to incorrectly reject $H_0$.
We often write the significance level using $\alpha$
(the Greek letter \emph{alpha}\index{Greek!alpha@alpha ($\alpha$)}):
$\alpha = 0.05$.
We discuss the appropriateness of different significance
levels in Section~\ref{significanceLevel}.

\D{\newpage}

If we use a 95\% confidence interval to evaluate a
hypothesis test and the null hypothesis happens to be true,
we will make an error whenever the point estimate is
at least 1.96 standard errors away from the population
parameter.
This happens about 5\% of the time (2.5\% in each tail).
Similarly, using a 99\% confidence interval to evaluate
a hypothesis is equivalent to a significance level of
$\alpha = 0.01$.

A confidence interval is very helpful in determining
whether or not to reject the null hypothesis.
However, the confidence interval approach isn't always
sustainable.
In several sections, we will encounter situations where
a confidence interval cannot be constructed.
For example, if we wanted to evaluate the hypothesis
that several proportions are equal, it isn't clear how
to construct and compare many confidence intervals
altogether.

Next we will introduce a statistic called the \emph{p-value}
to help us expand our statistical toolkit, which will
enable us to both better understand the strength of
evidence and work in more complex data scenarios in
later sections.



\subsection{Formal testing using p-values}

\label{pValue}

\index{hypothesis testing!p-value|(}

The p-value is a way of quantifying the strength of the
evidence against the null hypothesis and in favor of the
alternative hypothesis.
Statistical hypothesis testing typically uses the
p-value method rather than making a decision based
on  confidence intervals.

\begin{onebox}{p-value}
  The \term{p-value}\index{hypothesis testing!p-value|textbf}
  is the probability of observing data at least as favorable
  to the alternative hypothesis as our current data set,
  if the null hypothesis were true. We typically use a summary
  statistic of the data, in this section the sample proportion,
  to help compute the p-value and evaluate the hypotheses.
\end{onebox}

%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.

\index{data!coal power support|(}

\newcommand{\pewcoalpollsize}{1000}
\newcommand{\pewcoalpollprop}{0.37}
\newcommand{\pewcoalpollpropcomplement}{0.63}
\newcommand{\pewcoalpollpercent}{37\%}
\newcommand{\pewcoalpollpercentcomplement}{63\%}
\newcommand{\pewcoalpollcount}{370}
\newcommand{\pewcoalpollcountcomplement}{630}
\newcommand{\pewcoalpollse}{0.0153}
\newcommand{\pewcoalpollnullvalue}{0.5}
\newcommand{\pewcoalpollnullse}{0.016}

\begin{examplewrap}
\begin{nexample}{Pew Research asked a random sample of
    \pewcoalpollsize{} American
    adults whether they supported the increased usage of coal to
    produce energy.
    Set up hypotheses to evaluate whether
    a majority of American adults support or oppose
    the increased usage of coal.}
  The uninteresting result is that there is no majority either way:
  half of Americans support and the other half oppose expanding the
  use of coal to produce energy. The alternative hypothesis would
  be that there is a majority support or oppose
  (though we do not known which one!) expanding the
  use of coal. If $p$ represents the proportion supporting, then
  we can write the hypotheses as
  \begin{description}
    \item[$H_0$:] $p = 0.5$
    \item[$H_A$:] $p \neq 0.5$
  \end{description}
  In this case, the null value is $p_0 = 0.5$.
\end{nexample}
\end{examplewrap}

%\begin{examplewrap}
%\begin{nexample}{Suppose the null value, $p_0 = 0.5$,
%    was the actual level of support for coal usage.
%    Describe how we could simulate a survey of
%    \pewcoalpollsize{} responses when $p_0 = 0.5$.}
%  \label{simOnePropExample}%
%  If we pick a random person to participate in the survey,
%  then \emph{under the null hypothesis},
%  the chances they would support coal usage is $p_0 = 0.5$.
%  If this were true, then it's the same as flipping a fair coin.
%  That is, we can simulate an individual person's response by
%  flipping a coin;
%  if it's heads, we say \resp{support},
%  and if it's tails, \resp{oppose}.
%  To simulate \pewcoalpollsize{} independent responses,
%  we can flip the coin a total of 1000 times and compute the
%  fraction of instances that were heads as the observed
%  proportion.
%  We did this and observed 487 heads for a proportion
%  of $\hat{p}_{\text{sim, 1}} = 0.487$.
%\end{nexample}
%\end{examplewrap}
%
%Example~\ref{simOnePropExample} described how we could
%simulate a survey result under the null hypothesis that
%the population proportion is equal to $p_0$.
%In this way, we check what kind of sample observations
%we might expect to see \emph{if the null hypothesis were true}.
%Of course, a single simulation is interesting, but not that
%informative.
%If we run the simulation again, we get a value of
%$\hat{p}_{\text{sim, 2}} = 0.502$.
%And again: $\hat{p}_{\text{sim, 3}} = 0.523$.
%We can do this many times on a computer,
%just like we did for a population proportion of 0.88
%in Section~\ref{pointEstimates}.
%The results of 5,000 simulated surveys are summarized
%in a histogram in Figure~\ref{sampling_5k_prop_50p}.
%
%\begin{figure}[h]
%  \centering
%  \Figure{0.8}{sampling_5k_prop_50p}
%  \caption{
%      Simulated surveys proportion
%      \emph{if} the population proportion
%      were equal to the null value, $p_0 = 0.5$.
%      All 5,000 simulated sample proportions
%      lie between 0.44 and 0.56.}
%  \label{sampling_5k_prop_50p}
%\end{figure}
%
%\begin{examplewrap}
%\begin{nexample}{The actual Pew Research survey found that
%    \pewcoalpollpercent{} of the \pewcoalpollsize{}
%    respondents supported increasing the use of coal.
%    Use Figure~\ref{sampling_5k_prop_50p}
%    to estimate how frequently we might observe a proportion
%    of \pewcoalpollprop{} if the null hypothesis that
%    the population proportion is 0.5 were actually true.
%    What might you conclude from this finding?}
%  Not one of the 5,000 simulations yielded a sample proportion
%  of \pewcoalpollpercent{} or further from 0.5.
%  That is, \emph{if} the actually population proportion is
%  actually 0.5, then we observed something so rare that we
%  wouldn't necessarily see it if we repeated the process
%  5,000 times.
%  Ultimately, the observed sample result is nearly
%  impossible (extremely improbable!) if we believe that
%  the population proportion is 0.5.
%  This evidence casts significant doubt on the notion that
%  $p = 0.5$, and we should reject the null hypothesis,~$H_0$.
%\end{nexample}
%\end{examplewrap}

When evaluating hypotheses for proportions using the
p-value method,
we will slightly modify how we check the success-failure
condition and compute the standard error for the
single proportion case.
These changes aren't dramatic, but pay close attention
to how we use the null value, $p_0$.

\begin{examplewrap}
\begin{nexample}{Pew Research's sample show that
    \pewcoalpollpercent{}
    of American adults support increased usage of coal.
    We now wonder, does \pewcoalpollpercent{} represent
    a real difference from the null hypothesis of 50\%?
    What would the sampling distribution of $\hat{p}$
    look like if the null hypothesis were true?}
  If the null hypothesis were true, the population proportion
  would be the null value, 0.5.
  We~previously learned that
  the sampling distribution of $\hat{p}$ will be normal when
  two conditions are~met:
  \begin{description}
    \item[Independence.]
        The poll was based on a simple random sample,
        so independence is satisfied.
    \item[Success-failure.]
        Based on the poll's sample size of
        $n = \pewcoalpollsize{}$,
        the success-failure condition is met, since
        \begin{align*}
        np ~ \stackrel{H_0}{=}
            ~ \pewcoalpollsize{} \times \pewcoalpollnullvalue{}
            = 500
        \qquad\qquad
        n (1 - p) ~ \stackrel{H_0}{=}
            ~ \pewcoalpollsize{} \times
                (1 - \pewcoalpollnullvalue{})
            = 500
        \end{align*}
        are both at least 10.
        Note that the success-failure condition was checked
        using the null value, $p_0 = 0.5$;
        this is the first procedural difference from
        confidence intervals.
  \end{description}
  If the null hypothesis were true, the sampling distribution
  indicates that a sample proportion based on
  $n = \pewcoalpollsize{}$ observations
  would be normally distributed. Next, we can compute the standard
  error, where we will again use the null value $p_0 = 0.5$ in the
  calculation:
  \begin{align*}
  SE_{\hat{p}}
    = \sqrt{\frac{p (1 - p)}{n}}
    \quad \stackrel{H_0}{=} \quad
        \sqrt{\frac{\pewcoalpollnullvalue{} \times
            (1 - \pewcoalpollnullvalue{})}{\pewcoalpollsize{}}}
    = \pewcoalpollnullse{}
  \end{align*}
  This marks the other procedural difference from confidence
  intervals: since the sampling distribution is determined
  under the null proportion, the null value $p_0$ was used for
  the proportion in the calculation rather than $\hat{p}$.

  Ultimately, if the null hypothesis were true, then the sample
  proportion should follow a normal distribution with mean
  \pewcoalpollnullvalue{}
  and a standard error of \pewcoalpollnullse{}.
  This distribution is shown in
  Figure~\ref{normal_dist_mean_500_se_016}.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
  \caption{
      If the null hypothesis were true,
      this normal distribution describes the
      distribution of $\hat{p}$.}
  \label{normal_dist_mean_500_se_016}
\end{figure}

\begin{onebox}{Checking success-failure and computing
      $\mathbf{SE_{\hat{p}}}$
      for a hypothesis test}
  When using the p-value method to evaluate a hypothesis test,
  we check the conditions for $\hat{p}$ and construct the
  standard error using the null value, $p_0$, instead of using
  the sample proportion. \stdvspace{}

  In a hypothesis test with a p-value, we are supposing the
  null hypothesis is true,
  which is a different mindset than when we compute
  a confidence interval.
  This is why we use $p_0$ instead of $\hat{p}$
  when we check conditions and compute the standard error
  in this context.
\end{onebox}

When we identify the sampling distribution under the null hypothesis,
it has a special name: the \term{null distribution}.
The p-value represents the probability of the observed $\hat{p}$,
or a $\hat{p}$ that is more extreme,
if the null hypothesis were true.
To find the p-value, we generally find the null distribution,
and then we find a tail area in that distribution corresponding
to our point estimate.
%In some cases, as in this particular instance,
%the null distribution is a normal distribution.

\begin{examplewrap}
\begin{nexample}{If the null hypothesis were true,
    determine the chance of finding $\hat{p}$ at least
    as far into the tails as \pewcoalpollprop{}
    under the null distribution,
    which is a normal distribution with mean
    $\mu = \pewcoalpollnullvalue{}$
    and $SE = \pewcoalpollnullse{}$.}
%  When we compute the p-value, we think about the chance
%  of our observation, if the null hypothesis were true.
%
  This is a normal probability problem where
  $x = \pewcoalpollprop{}$.
  First, we draw a simple graph to represent the situation,
  similar to what is shown in
  Figure~\ref{normal_dist_mean_500_se_016}.
  Since $\hat{p}$ is so far out in the tail, we know the
  tail area is going to be very small. To find it, we start
  by computing the Z-score using the mean of 0.5 and the
  standard error of \pewcoalpollnullse{}:
  \begin{align*}
  Z = \frac{\pewcoalpollprop{} - 0.5}{\pewcoalpollnullse{}} = -8.125 
  \end{align*}
  We can use software to find the tail area:
  $2.2 \times 10^{-16}$
  (0.00000000000000022).
  If using the normal probability table in
  Appendix~\ref{normalProbabilityTable},
  we'd find that $Z = -8.125$ is off the table,
  so we would use the smallest area listed: 0.0002.

  The potential $\hat{p}$'s in the upper tail beyond
  \pewcoalpollpropcomplement{}, which are shown
  in Figure~\ref{normal_dist_mean_500_se_016_with_upper},
  also represent observations at least as extreme as
  the observed value of \pewcoalpollprop{}.
  To account for these values that are also more
  extreme under the hypothesis setup,
  we double the lower tail to get an estimate
  of the p-value: $4.4 \times 10^{-16}$
  (or if using the table method, 0.0004).

  The p-value represents the probability of observing
  such an extreme sample proportion by chance, if the null
  hypothesis were true.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
      {normal_dist_mean_500_se_016_with_upper}
  \caption{
      If $H_0$ were true, then the values above
      \pewcoalpollpropcomplement{} are just
      as unlikely as values below \pewcoalpollprop{}.}
  \label{normal_dist_mean_500_se_016_with_upper}
\end{figure}




\begin{examplewrap}
\begin{nexample}{How should we evaluate the hypotheses using the
    p-value of $4.4 \times 10^{-16}$?
    Use the standard significance level of $\alpha = 0.05$.}
  If the null hypothesis were true, there's only an incredibly
  small chance of observing such an extreme deviation of
  $\hat{p}$ from 0.5.
  This means one of the following must be true:
  \begin{enumerate}
    \item The null hypothesis is true, and we just happened
        to observe something so extreme that it only happens
        about once in every 23 quadrillion times
        (1~quadrillion = 1~million $\times$ 1~billion).
    \item The alternative hypothesis is true,
        which would be consistent
        with observing a sample proportion far from 0.5.
  \end{enumerate}
  The first scenario is laughably improbable,
  while the second scenario seems much more plausible.

  Formally, when we evaluate a hypothesis test,
  we compare the p-value to the significance level,
  which in this case is $\alpha = 0.05$.
  Since the p-value is less than $\alpha$,
  we reject the null hypothesis.
  That is, the data provide strong evidence against $H_0$.
  The data indicate the direction of the difference:
  a majority of Americans do not support
  expanding the use of coal-powered energy.
\end{nexample}
\end{examplewrap}

\index{data!coal power support|)}

\begin{onebox}{Compare the p-value to $\pmb{\alpha}$ to
      evaluate $\pmb{H_0}$}
  When the p-value is less than the significance level, $\alpha$,
  reject $H_0$. We would report a conclusion that the data provide
  strong evidence supporting the alternative hypothesis. \\[2mm]
  When the p-value is greater than $\alpha$, do not reject $H_0$,
  and report that we do not have sufficient evidence to reject the
  null hypothesis. \\[2mm]
  In either case, it is important to describe the conclusion
  in the context of the data.
\end{onebox}







\index{data!nuclear arms reduction|(}

\begin{exercisewrap}
\begin{nexercise}
Do a majority of Americans support or oppose nuclear arms
reduction? Set up hypotheses to evaluate this
question.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{We would like to understand if a majority
  supports or opposes, or ultimately, if there is no difference.
  If $p$ is the proportion of Americans who support nuclear
  arms reduction, then
  $H_0$: $p = 0.50$ and $H_A$: $p \neq 0.50$.}

\newcommand{\gallupnucleararmspollsize}{1028}
\newcommand{\gallupnucleararmspollprop}{0.56}
\newcommand{\gallupnucleararmspollpropcomplement}{0.44}
\newcommand{\gallupnucleararmspollpercent}{56}
\newcommand{\gallupnucleararmspollpercentcomplement}{44}
\newcommand{\gallupnucleararmspollnullcount}{514}
\newcommand{\gallupnucleararmspollse}{0.0155}
\newcommand{\gallupnucleararmspollnullvalue}{0.5}
\newcommand{\gallupnucleararmspollnullse}{0.0156}

\begin{examplewrap}
\begin{nexample}{A simple random sample of
    \gallupnucleararmspollsize{} US adults
    in March 2013 show that
    \gallupnucleararmspollpercent{}\% support nuclear arms
    reduction.
    Does this provide convincing evidence that a majority
    of Americans supported nuclear arms reduction at the
    5\% significance level?} \label{NuclearArmsInferenceExample}
  First, check conditions:
  \begin{description}
  \item[Independence.] The poll was of a simple random sample
      of US adults, meaning the observations are independent.
  \item[Success-failure.] In a one-proportion hypothesis test,
      this condition is checked using the null proportion,
      which is $p_0 = \gallupnucleararmspollnullvalue{}$
      in this context:
      $n p_0 = n (1 - p_0)
          = \gallupnucleararmspollsize{} \times
              \gallupnucleararmspollnullvalue{}
          = \gallupnucleararmspollnullcount{} \geq 10$.
  \end{description}
  With these conditions verified,
  we can model $\hat{p}$ using a normal model.

  Next the standard error can be computed.
  The null value $p_0$ is used again here,
  because this is a hypothesis test for a single proportion.
  \begin{align*}
  SE_{\hat{p}}
      = \sqrt{\frac{p_0 (1 - p_0)}{n}}
      = \sqrt{\frac{\gallupnucleararmspollnullvalue{}
          (1 - \gallupnucleararmspollnullvalue{})}
          {\gallupnucleararmspollsize{}}}
      = \gallupnucleararmspollnullse{}
  \end{align*}
  Based on the normal model, the test statistic can be
  computed as the Z-score of the point estimate:
  \begin{align*}
  Z = \frac{\text{point estimate} - \text{null value}}{SE}
      = \frac{\gallupnucleararmspollprop{} - 0.50}
          {\gallupnucleararmspollnullse{}}
      = 3.85
  \end{align*}
  It's generally helpful to draw null distribution and
  the tail areas of interest for computing the p-value:
  \begin{center}
  \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}
  \end{center}
  The upper tail area is about 0.0001,
  and we double this tail area to get the p-value: 0.0002.
  Because the p-value is smaller than 0.05, we reject $H_0$.
  The poll provides convincing evidence that a majority
  of Americans supported nuclear arms reduction efforts
  in March 2013.
\end{nexample}
\end{examplewrap}

\index{data!nuclear arms reduction|)}

\D{\newpage}

\newcommand{\oneprophtsummary}{
\begin{onebox}{Hypothesis testing for a single proportion}
  Once you've determined a one-proportion hypothesis test is the
  correct procedure, there are four steps to completing the
  test:
  \begin{description}
  \item[Prepare.]
      Identify the parameter of interest,
      list hypotheses,
      identify the significance level,
      and identify $\hat{p}$ and $n$.
  \item[Check.]
      Verify conditions
      to ensure $\hat{p}$ is nearly normal under $H_0$.
      For one-proportion hypothesis tests, use the null
      value to check the success-failure condition.
  \item[Calculate.]
      If the conditions hold, compute the standard
      error, again using $p_0$, compute the Z-score,
      and identify the p-value.
  \item[Conclude.]
      Evaluate the hypothesis test by comparing the p-value
      to $\alpha$, and provide a conclusion in the context
      of the problem.
  \end{description}
\end{onebox}
}
\oneprophtsummary{}

\CalculatorVideos{hypothesis tests for a single proportion}


\subsection{Choosing a significance level}
\label{significanceLevel}

\index{hypothesis testing!significance level|(}
\index{significance level|(}

Choosing a significance level for a test is important in
many contexts, and the traditional level is $\alpha = 0.05$.
However, it can be helpful to adjust the significance level
based on the application. We may select a level that is
smaller or larger than 0.05 depending on the consequences
of any conclusions reached from the test.

If making a Type~1 Error is dangerous or especially costly,
we should choose a small significance level (e.g. 0.01).
Under this scenario we want to be very cautious about
rejecting the null hypothesis, so we demand very strong
evidence favoring $H_A$ before we would reject $H_0$.

If a Type~2 Error is relatively more dangerous or much more
costly than a Type~1 Error, then we might choose a higher
significance level (e.g. 0.10). Here we want to be cautious
about failing to reject $H_0$ when the alternative hypothesis
is actually true.

Additionally, if the cost of collecting data is small relative
to the cost of a Type~2 Error, then it may also be a good
strategy to collect more data.
Under this strategy, the Type~2 Error can be reduced
while not affecting the Type~1 Error rate.
Of course, collecting extra data is often costly,
so~there is typically a cost-benefit analysis to be considered.
%We'll discuss this topic a bit more in
%Sections~\ref{} and~\ref{}.
%\Comment{Fix this reference.}

\newcommand{\doorhingeflawrate}{0.2}

\begin{examplewrap}
\begin{nexample}{A car manufacturer is considering switching
    to a new, higher quality piece of equipment that constructs
    vehicle door hinges.
    They figure that they will save money in the long run
    if this new machine produces hinges
    that have flaws less than
    \doorhingeflawrate{}\% of the time.
    However, if the hinges are flawed more than
    \doorhingeflawrate{}\% of
    the time, they wouldn't get a good enough
    return-on-investment from the new piece of equipment,
    and they would lose money.
    Is there good reason to modify the significance level
    in such a hypothesis test?}
  The null hypothesis would be that the rate of flawed
  hinges is \doorhingeflawrate{}\%,
  while the alternative is that it the rate
  is different than \doorhingeflawrate{}\%.
  This decision is just one of many that have a marginal
  impact on the car and company.
  A significance level of 0.05 seems reasonable since
  neither a Type~1 or Type~2 Error should be dangerous
  or (relatively) much more expensive.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{The same car manufacturer is considering
    a slightly more expensive supplier for parts related
    to safety, not door hinges.
    If the durability of these
    safety components is shown to be better than the
    current supplier, they will switch manufacturers.
    Is there good reason to modify the significance level
    in such an evaluation?}
  The null hypothesis would be that the suppliers' parts
  are equally reliable. Because safety is involved,
  the car company should be eager to switch to the slightly
  more expensive manufacturer (reject $H_0$), even if the
  evidence of increased safety is only moderately strong.
  A slightly larger significance level,
  such as $\alpha=0.10$, might be appropriate.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
A part inside of a machine is very expensive to replace.
However, the machine usually functions properly even if
this part is broken, so the part is replaced only if we
are extremely certain it is broken based on a series of
measurements.
Identify appropriate hypotheses for this test
(in plain language) and suggest an appropriate significance
level.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Here
  the null hypothesis is that the part is not broken,
  and the alternative is that it is broken.
  If we don't have sufficient evidence to reject $H_0$,
  we would not replace the part.
  It sounds like failing to fix the part if it is broken
  ($H_0$ false, $H_A$ true) is not very problematic,
  and replacing the part is expensive.
  Thus, we should require very strong evidence against
  $H_0$ before we replace the part.
  Choose a small significance level, such as $\alpha=0.01$.}

\begin{onebox}{Why is 0.05 the default?}
  The $\alpha = 0.05$ threshold is most common. But why?
  Maybe the standard level should be smaller, or perhaps larger.
  If you're a little puzzled, you're reading with an
  extra critical eye -- good job!
  We've made a 5-minute task to help clarify \emph{why 0.05}:
  \begin{center}
  \oiRedirect{textbook-why05}{www.openintro.org/why05}
  \end{center}
\end{onebox}


\index{significance level|)}
\index{hypothesis testing!significance level|)}
\index{hypothesis testing|)}


\subsection{Statistical significance versus practical significance}

When the sample size becomes larger,
point estimates become more precise and any real differences
in the mean and null value become easier to detect and recognize.
Even a very small difference would likely be detected if we took
a large enough sample.
Sometimes researchers will take such large samples that even
the slightest difference is detected, even differences where
there is no practical value.
In such cases, we still say the difference is
\term{statistically significant},
but it is not \term{practically significant}.
For example, an online experiment might identify that placing
additional ads on a movie review website statistically
significantly increases viewership of a TV show by 0.001\%,
but this increase might not have any practical value.

%Statistically significant differences are sometimes
%so minor that they are not practically relevant.
%This is especially important to research:
%if we conduct a study, we want to focus on finding
%a meaningful result.
%We don't want to spend lots of money finding results
%that hold no practical value.

One role of a data scientist in conducting a study often
includes planning the size of the study.
The data scientist might first consult experts or scientific
literature to learn what would be the smallest meaningful
difference from the null value.
She also would obtain other information,
such as a very rough estimate of the true proportion $p$,
so that she could roughly estimate the standard error.
From here, she can suggest a sample size that is sufficiently
large that, if there is a real difference that is meaningful,
we could detect it.
While larger sample sizes may still be used,
these calculations are especially helpful when considering
costs or potential risks, such as possible health impacts
to volunteers in a medical study.


\D{\newpage}

\subsection{One-sided hypothesis tests (special topic)}

So far we've only considered what are called \term{two-sided
hypothesis tests}, where we care about detecting whether $p$
is either above or below some null value $p_0$.
There is a second type of hypothesis test called a
\term{one-sided hypothesis test}.
For a one-sided hypothesis test,
the hypotheses take one of the following forms:
\begin{enumerate}
\item There's only value in detecting if the population
    parameter is \emph{less than} some value~$p_0$.
    In~this case, the alternative hypothesis is written
    as $p < p_0$ for some null value $p_0$.
\item There's only value in detecting if the population
    parameter is \emph{more than} some value~$p_0$:
    In~this case, the alternative hypothesis is written
    as $p > p_0$.
\end{enumerate}
While we adjust the form of the alternative hypothesis,
we continue to write the null hypothesis using an equals-sign
in the one-sided hypothesis test case.

In the entire hypothesis testing procedure,
there is only one difference in evaluating a one-sided
hypothesis test vs a two-sided hypothesis test:
how to compute the p-value.
In a one-sided hypothesis test, we compute the p-value as
the tail area in the \emph{direction of the alternative
hypothesis only}, meaning it is represented by a single
tail area. Herein lies the reason why one-sided tests
are sometimes interesting: if we don't have to double
the tail area to get the p-value, then the p-value is
smaller and the level of evidence required to identify
an interesting finding in the direction of the
alternative hypothesis goes down.
However, one-sided tests aren't all sunshine and rainbows:
the heavy price paid is that any interesting findings
in the opposite direction must be disregarded.

\begin{examplewrap}
\begin{nexample}{
    In Section~\ref{basicExampleOfStentsAndStrokes},
    we encountered an example where doctors were interested
    in determining whether stents would help people who had
    a high risk of stroke.
    The researchers believed the stents would help.
    Unfortunately, the data showed the opposite:
    patients who received stents actually did worse.
    Why was using a two-sided test so important in
    this context?}
    \label{basicExampleOfStentsAndStrokesOneSided}
  Before the study, researchers had reason to believe
  that stents would help patients since existing research
  suggested stents helped in patients with heart attacks.
  It would surely have been tempting to use a one-sided
  test in this situation, and had they done this,
  they would have limited their ability to identify
  potential harm to patients.
\end{nexample}
\end{examplewrap}

Example~\ref{basicExampleOfStentsAndStrokesOneSided}
highlights that using a one-sided hypothesis creates
a risk of overlooking data supporting the opposite
conclusion.
We could have made a similar error when reviewing
the Roslings' question data this section;
if we had a pre-conceived notion that
college-educated people wouldn't do worse than random
guessing and so used a one-sided test,
we would have missed the really interesting finding
that many people have incorrect knowledge about
global public health.
%Here are a few other situations where it has been,
%or would have been, very useful to have an open mind
%and consider the contrarian view:
%\begin{itemize}
%\item The 2008 financial crisis. There were warning signs,
%    but few people recognized them.
%    In fact, some financial firms essentially bought into
%    the notion that housing prices could only rise, not fall.
%\item 
%    
%\end{itemize}

When might a one-sided test be appropriate to use?
\emph{Very rarely.}
Should you ever find yourself considering using a
one-sided test, carefully answer the following question:
\begin{quote}{\em
  What would I, or others, conclude if the data happens
  to go clearly in the opposite direction than my
  alternative hypothesis?
}\end{quote}
If you or others would find any value in making
a conclusion about the data that goes in the opposite
direction of a one-sided test, then a two-sided hypothesis
test should actually be used.
These considerations can be subtle, so exercise caution.
We will only apply two-sided tests in the rest of
this book.

\begin{examplewrap}
\begin{nexample}{
    Why can't we simply run a one-sided
    test that goes in the direction of the data?}
  We've been building a careful framework that
  controls for the Type~1 Error, which is the
  significance level $\alpha$ in a hypothesis test.
  We'll use the $\alpha = 0.05$ below to keep
  things simple.

  Imagine we could pick the one-sided test after
  we saw the data. What will go wrong?
  \begin{itemize}
  \item If $\hat{p}$ is \emph{smaller} than
      the null value,
      then a one-sided test where $p < p_0$ would
      mean that any observation in the
      \emph{lower} 5\% tail of the null distribution
      would lead to us rejecting $H_0$.
  \item If $\hat{p}$ is \emph{larger} than
      the null value,
      then a one-sided test where $p > p_0$ would
      mean that any observation in the
      \emph{upper} 5\% tail of the null distribution
      would lead to us rejecting $H_0$.
  \end{itemize}
  Then if $H_0$ were true, there's a 10\% chance of
  being in one of the two tails, so our testing error
  is actually $\alpha = 0.10$, not 0.05.
  That is,
  not being careful about when to use one-sided tests
  effectively undermines the methods we're working
  so hard to develop and utilize.
\end{nexample}
\end{examplewrap}

\index{hypothesis testing|)}


{\input{ch_foundations_for_inf/TeX/hypothesis_testing.tex}}


================================================
FILE: ch_foundations_for_inf/TeX/confidence_intervals.tex
================================================
\exercisesheader{}

% 7

\eoce{\qt{Chronic illness, Part I\label{chronic_illness_intro}} 
In 2013, the Pew Research Foundation reported that ``45\% of U.S. adults report 
that they live with one or more chronic conditions''.
\footfullcite{data:pewdiagnosis:2013} However, this value was based on a sample, 
so it may not be a perfect estimate for the population parameter of interest on 
its own. The study reported a standard error of about 1.2\%, and a normal model 
may reasonably be used in this setting. Create a 95\% confidence interval for 
the proportion of U.S. adults who live with one or more chronic conditions. Also 
interpret the confidence interval in the context of the study.
}{}

% 8

\eoce{\qt{Twitter users and news, Part I\label{twitter_users_intro}} 
A poll conducted in 2013 found that 52\% of U.S. adult Twitter users 
get at least some news on Twitter.\footfullcite{data:pewtwitternews:2013}. 
The standard error for this estimate was 2.4\%, and a normal distribution 
may be used to model the sample proportion. Construct a 99\% confidence 
interval for the fraction of U.S. adult Twitter users who get some 
news on Twitter, and interpret the confidence interval in context.
}{}

% 9

\eoce{\qt{Chronic illness, Part II\label{chronic_illness_tf}} In 2013, the Pew Research Foundation reported that 
``45\% of U.S. adults report that they live with one or more chronic 
conditions'', and the standard error for this estimate is 1.2\%. Identify each 
of the following statements as true or false. Provide an explanation to justify 
each of your answers.
\begin{parts}
\item We can say with certainty that the confidence interval from 
Exercise~\ref{chronic_illness_intro} contains the true percentage of U.S. adults who 
suffer from a chronic illness.
\item If we repeated this study 1,000 times and constructed a 95\% confidence 
interval for each study, then approximately 950 of those confidence intervals 
would contain the true fraction of U.S. adults who suffer from chronic illnesses.
\item The poll provides statistically significant evidence (at the 
$\alpha = 0.05$ level) that the percentage of U.S. adults who suffer from 
chronic illnesses is below 50\%.
\item Since the standard error is 1.2\%, only 1.2\% of people in the study 
communicated uncertainty about their answer.
\end{parts}
}{}

% 10

\eoce{\qt{Twitter users and news, Part II\label{twitter_users_tf}} A poll conducted in 2013 found that 52\% of 
U.S. adult Twitter users get at least some news on Twitter, and the standard 
error for this estimate was 2.4\%. Identify each of the following statements as 
true or false. Provide an explanation to justify each of your answers.
\begin{parts}
\item The data provide statistically significant evidence that more than half of 
U.S. adult Twitter users get some news through Twitter. Use a significance level 
of $\alpha = 0.01$.
(This part uses concepts from Section~\ref{hypothesisTesting} and will be
corrected in a future edition.)
\item Since the standard error is 2.4\%, we can conclude that 97.6\% of all U.S. 
adult Twitter users were included in the study.
\item If we want to reduce the standard error of the estimate, we should collect 
less data.
\item If we construct a 90\% confidence interval for the percentage of U.S. 
adults Twitter users who get some news through Twitter, this confidence interval 
will be wider than a corresponding 99\% confidence interval.
\end{parts}
}{}

\D{\newpage}

% 11

\eoce{\qt{Waiting at an ER, Part I\label{er_wait_intro_prop_ok}} A hospital administrator 
hoping to improve wait times decides to estimate the average emergency 
room waiting time at her hospital. She collects a simple random sample 
of 64 patients and determines the time (in minutes) between when they 
checked in to the ER until they were first seen by a doctor. A 95\% 
confidence interval based on this sample is (128 minutes, 147 minutes), 
which is based on the normal model for the mean. Determine whether the 
following statements are true or false, and explain your reasoning.
\begin{parts}
\item We are 95\% confident that the average waiting time of these 64 emergency 
room patients is between 128 and 147 minutes.
\item We are 95\% confident that the average waiting time of all patients at 
this hospital's emergency room is between 128 and 147 minutes.
\item 95\% of random samples have a sample mean between 128 and 147 minutes.
\item A 99\% confidence interval would be narrower than the 95\% confidence 
interval since we need to be more sure of our estimate.
\item The margin of error is 9.5 and the sample mean is 137.5.
\item In order to decrease the margin of error of a 95\% confidence interval to 
half of what it is now, we would need to double the sample size.
(Hint: the margin of error for a mean scales in the same way with sample size
as the margin of error for a proportion.)
\end{parts}
}{}

% 12

\eoce{\qt{Mental health\label{mental_health}}
The General Social Survey asked the question:
``For how many days during the past 30 days was your 
mental health, which includes stress, depression,
and problems with emotions, not good?"
Based on responses from 1,151 US residents,
the survey reported a 95\% confidence interval of
3.40 to 4.24 days in 2010.
\begin{parts}
\item
    Interpret this interval in context of the data.
\item
    What does ``95\% confident" mean? Explain in the
    context of the application.
\item
    Suppose the researchers think a 99\% confidence level
    would be more appropriate for this interval.
    Will this new interval be smaller or wider than the
    95\% confidence interval?
\item
    If a new survey were to be done with 500 Americans,
    do you think the standard error of the estimate be
    larger, smaller, or about the same.
\end{parts}
}{}

% 13

\eoce{\qt{Website registration\label{website_registration_design_prop}}
A website is trying to increase registration for first-time visitors,
exposing 1\% of these visitors to a new site design.
Of 752 randomly sampled visitors over a month who saw the
new design, 64 registered.
\begin{parts}
\item
    Check any conditions required for constructing a confidence
    interval.
\item
    Compute the standard error.
\item
    Construct and interpret a 90\% confidence interval for the
    fraction of first-time visitors of the site who would register
    under the new design
    (assuming stable behaviors by new visitors over time).
\end{parts}
}{}

% 14

\eoce{\qt{Coupons driving visits\label{store_coupon_prop}}
A store randomly samples 603 shoppers over the course of a year
and finds that 142 of them made their visit because of a coupon
they'd received in the mail.
Construct a 95\% confidence interval for the fraction of all shoppers
during the year whose visit was because of a coupon they'd received
in the mail.
}{}


================================================
FILE: ch_foundations_for_inf/TeX/hypothesis_testing.tex
================================================
\exercisesheader{}

% 15

\eoce{\qt{Identify hypotheses, Part I\label{
}}
Write the null and alternative hypotheses in words and then symbols
for each of the following  situations.
\begin{parts}
\item
    A tutoring company would like to understand if most
    students tend to improve their grades (or not) after
    they use their services.
    They sample 200 of the students who used their service
    in the past year and ask them if their grades have
    improved or declined from the previous year.
\item
    Employers at a firm are worried about the effect of March Madness,
    a basketball championship held each spring in the US, on employee
    productivity.
    They estimate that on a regular business day employees spend on
    average 15 minutes of company time checking personal email,
    making personal phone calls, etc.
    They also collect data on how much company time employees spend
    on such non-business activities during March Madness.
    They want to determine if these data provide convincing evidence
    that employee productivity changed during March Madness.
\end{parts}
}{}

% 16

\eoce{\qt{Identify hypotheses, Part II\label{identify_hypotheses_prop_and_mean_2}} 
Write the null and alternative hypotheses in words and using symbols 
for each of the following situations.
\begin{parts}
\item
    Since 2008, chain restaurants in California have been required
    to display calorie counts of each menu item. Prior to menus
    displaying calorie counts, the average calorie intake of diners
    at a restaurant was 1100 calories.
    After calorie counts started to be displayed on menus,
    a nutritionist collected data on the number of calories consumed
    at this restaurant from a random sample of diners.
    Do these data provide convincing evidence of a difference in the
    average calorie intake of a diners at this restaurant?
\item
    The state of Wisconsin would like to understand
    the fraction of its adult residents that consumed alcohol
    in the last year,
    specifically if the rate is different from the
    national rate of 70\%.
    To help them answer this question, they conduct
    a random sample of 852 residents and ask them
    about their alcohol consumption.
\end{parts}
}{}

% 17

\eoce{\qt{Online communication\label{online_communication_prop_ht_errors}}
A study suggests that 60\% of college student spend
10~or more hours per week communicating with others online.
You believe that this is incorrect and decide to collect your 
own sample for a hypothesis test.
You randomly sample 160 students from your dorm
and find that 70\% spent 10~or more hours a week
communicating with others online.
A~friend of yours, who offers to help you with
the hypothesis test, comes up with the following
set of hypotheses.
Indicate any errors you see.
\begin{align*}
H_0&: \hat{p} < 0.6 \\
H_A&: \hat{p} > 0.7
\end{align*}
}{}

% 18

\eoce{\qt{Married at 25\label{married_at_25_prop_ht_errors}}
A study suggests that the 25\% of 25 year olds have
gotten married.
You believe that this is incorrect and decide to collect
your own sample for a hypothesis test.
From a random sample of 25 year olds in census data
with size 776,
you find that 24\% of them are married.
A friend of yours offers to help you with setting
up the hypothesis test and comes up with the following
hypotheses.
Indicate any errors you see.
\begin{align*}
H_0&: \hat{p} = 0.24 \\
H_A&: \hat{p} \neq 0.24
\end{align*}
}{}

% 19

\eoce{\qt{Cyberbullying rates\label{cyberbullying_prop_ci_ht}}
Teens were surveyed about cyberbullying, and
54\% to 64\% reported experiencing cyberbullying
(95\% confidence interval).\footfullcite{pew_cyber_bully_2018}
Answer the following questions based on this interval.
\begin{parts}
\item 
    A newspaper claims that a majority of teens
    have experienced cyberbullying.
    Is this claim supported by the confidence interval?
    Explain your reasoning.
\item\label{cyberbullying_prop_ci_ht_researcher}
    A researcher conjectured that 70\% of teens have
    experienced cyberbullying.
    Is this claim supported by the confidence interval?
    Explain your reasoning.
\item
    Without actually calculating the interval, determine
    if the claim of the researcher from
    part~(\ref{cyberbullying_prop_ci_ht_researcher})
    would be supported based on a 90\% confidence interval?
\end{parts}
}{}

\D{\newpage}

% 20

\eoce{\qt{Waiting at an ER, Part II\label{er_wait_ci_ht_prop_ok}}
Exercise~\ref{er_wait_intro_prop_ok} 
provides a 95\% confidence interval for the mean waiting
time at an emergency room (ER) of (128 minutes, 147 minutes).
Answer the following questions based on this interval.
\begin{parts}
\item
    A local newspaper claims that the average waiting time
    at this ER exceeds 3 hours.
    Is this claim supported by the confidence interval?
    Explain your reasoning.
\item\label{er_wait_ci_ht_prop_ok_dean}
    The Dean of Medicine at this hospital claims the
    average wait time is 2.2 hours.
    Is this claim supported by the confidence interval?
    Explain your reasoning.
\item
    Without actually calculating the interval,
    determine if the claim of the Dean from
    part~(\ref{er_wait_ci_ht_prop_ok_dean})
    would be supported based on a 99\% confidence interval?
\end{parts}
}{}

% 21

\eoce{\qt{Minimum wage, Part I\label{minimum_wage_prop_1}}
Do a majority of US adults believe raising
the minimum wage will help the economy,
or is there a majority who do not believe this?
A~Rasmussen Reports survey of a random sample of 1,000 US adults found
that 42\% believe it will help the
economy.\footfullcite{webpage:rasmussen-2019-raise-minimum-wage}
Conduct an appropriate hypothesis test to help
answer the research question.
}{}

% 22

\eoce{\qt{Getting enough sleep\label{univ_students_enough_sleep}}
400 students were randomly sampled from a large university,
and 289 said they did not get enough sleep.
Conduct a hypothesis test to check whether this
represents a statistically significant difference
from 50\%, and use a significance level of 0.01.
}{}

% 23

\eoce{\qt{Working backwards, Part I\label{backwards_prop_1}}
You are given the following hypotheses:
\begin{align*}
H_0&: p = 0.3 \\
H_A&: p \ne 0.3
\end{align*}
We know the sample size is 90.
For what sample proportion would the p-value be equal to 0.05?
Assume that all conditions  necessary for inference are satisfied.
}{}

% 24

\eoce{\qt{Working backwards, Part II\label{backwards_prop_2}}
You are given the following hypotheses:
\begin{align*}
H_0&: p = 0.9 \\
H_A&: p \ne 0.9
\end{align*}
We know that the sample size is 1,429.
For what sample proportion would the p-value be equal to 0.01?
Assume that all conditions necessary for inference are satisfied.
}{}

% 25

\eoce{\qt{Testing for Fibromyalgia\label{errors_fibromyalgia}} A patient named Diana 
was diagnosed with Fibromyalgia, a long-term syndrome of body pain, and was 
prescribed anti-depressants. Being the skeptic that she is, Diana didn't 
initially believe that anti-depressants would help her symptoms. However after 
a couple months of being on the medication she decides that the 
anti-depressants are working, because she feels like her symptoms are in fact 
getting better.
\begin{parts}
\item Write the hypotheses in words for Diana's skeptical position when she 
started taking the anti-depressants.
\item What is a Type~1 Error in this context?
\item What is a Type~2 Error in this context?
\end{parts}
}{}

% 26

\eoce{\qtq{Which is higher\label{prop_which_higher_found_inf}}
In each part below, there is a value of interest and two
scenarios (I and II).
For each part, report if the value of interest is larger
under scenario I, scenario II, or whether the value is
equal under the scenarios.
\begin{parts}
\item
     The standard error of $\hat{p}$ when
     (I)~$n = 125$ or (II)~$n = 500$.
\item
    The margin of error of a confidence interval
    when the confidence level is
    (I)~90\% or (II)~80\%.
\item
    The p-value for a Z-statistic of 2.5 calculated
    based on a (I)~sample with $n = 500$ or based on
    a (II)~sample with $n = 1000$.
\item
    The probability of making a Type~2 Error when the
    alternative hypothesis is true and the significance
    level is (I)~0.05 or (II)~0.10.
\end{parts}
}{}


================================================
FILE: ch_foundations_for_inf/TeX/one_sided_tests.tex
================================================
\subsection{One-sided hypothesis tests (special topic)}

\Comment{This section needs a lot of work. Maybe it shouldn't
    even be mentioned? It absolutely should not be so aggressive
    and also much shorter.}

\emph{One-sided hypothesis testing is an advanced
    topic due to the nuances around using this method.
    You need only read this section if you are ever asked
    to complete a \term{one-sided hypothesis test}.} \\

So far we've only considered what are called \term{two-sided
hypothesis tests}, where we care about detecting whether $p$
is either above or below some null value $p_0$.
There is a second type of hypothesis test called a
\term{one-sided hypothesis test}.
For a one-sided hypothesis test,
the hypotheses take the form of one of the following:
\begin{enumerate}
\item If we truly only care about detecting if the population
    parameter were \emph{less than} some value~$p_0$:
  \begin{description}
  \item[$\mathbf{H_0}$:] $p = p_0$.
  \item[$\mathbf{H_A}$:] $p < p_0$. The parameter $p$ is less
      than the null value $p_0$.
  \end{description}
\item If we truly only care about detecting if the population
    parameter were \emph{more than} some value~$p_0$:
  \begin{description}
  \item[$\mathbf{H_0}$:] $p = p_0$.
  \item[$\mathbf{H_A}$:] $p > p_0$. The parameter $p$ is more
      than the null value $p_0$.
  \end{description}
\end{enumerate}
Notice that we still write the null hypothesis using an
equality in the one-sided hypothesis test case.

While this one-sided test approach is common in many
introductory statistics textbooks, these tests create
some philosophical problems that we lightly touch on
here. In some instances, such as in clinical trials
where we might test out whether a new drug is effective,
one-sided tests are banned.

\begin{example}{Suppose we're on a business team that is
    considering whether to go into a new market. If they
    more than 20\% of the buyers would be interested in
    their product, they will move into that market. If not,
    they will not enter the market. Set up an appropriate
    one-sided hypothesis test for this situation.}
  We care about determining whether there is convincing
  evidence that the population proportion $p$ is greater
  than 20\%, so we make this our alternative hypothesis
  and use equality for the null:
  \begin{description}
  \item[$\mathbf{H_0}$:] $p = 0.20$
  \item[$\mathbf{H_A}$:] $p > 0.20$
  \end{description}
\end{example}

\begin{example}{The business runs a survey of a simple
    random sample of 400 people in the market of interest,
    and 21\% of the people express interest in the
    business' product. Will (should) the business decide
    to enter the market?}
    \label{business_one_sided_20_21}
  There is only one difference in evaluating a one-sided
  hypothesis test vs a two-sided hypothesis test: how to
  compute the p-value.
  In a one-sided hypothesis test, we compute the p-value as
  the tail area in the \emph{direction of the alternative
  hypothesis}. In this example, here we only care about
  detecting whether $p$ is greater than 20\%, so we compute
  the upper tail area and use this as the p-value.
  \begin{description}
  \item[Conditions.] The data come from a simple random sample
      and the success failure condition is satisfied
      ($n \times p_0 = 80$ and $n \times (1 - p_0) = 320$).
  \item[Compute.] Compute the standard error using the null
      value: $SE_{\hat{p}} = \sqrt{0.2 (1 - 0.8) / 400} = 0.01$.
      Next compute
      $Z = \frac{\hat{p} - p_0}{SE_{\hat{p}}}
         = \frac{0.21 - 0.20}{0.01}
         = 1.00$.
      Finally, compute the tail area where $p > 0.20$,
      we consider the upper tail:
      \begin{center}
      \includegraphics[width=0.3\textwidth]{ch_inference_for_props/figures/business_one_sided_20_21-p_value/business_one_sided_20_21-p_value}
      \end{center}
      We can find the p-value from software or using the
      normal probability table: 0.1587.
  \item[Conclude.] Since the p-value is greater than 0.05,
      we do not find convincing evidence that the fraction
      of the market that's interested in the company's
      product is greater than 20\%. In this case, the
      company would not enter the market.
  \end{description}
\end{example}

There's a piece of human behavior that we left off in
Example~\ref{business_one_sided_20_21}: the company
would not have entered the market \emph{yet}. One-sided
hypothesis tests work well in isolation. However,
they also define not just a decision but what we can
\emph{learn} from the data, if we are to be thoughtful
about our analysis. %controlling Type~1 Errors.
In the next example, we consider the hypothetical
situation where the survey data came back with
a much smaller percent.

%The one-sided hypothesis test presented in
%Example~\ref{business_one_sided_20_21}
%didn't throw up any surprises when it comes to
%questioning whether a one-sided test was reasonable
%
%In Example~\ref{business_one_sided_20_21},
%we considered some data and evaluated the
%one-sided test. However, the examples that follow will dive
%into the logic and philosophy behind one-sided tests and
%whether we can be robotic enough to apply them properly.

\begin{example}{Suppose the survey had actually come back
    with a result that only 7\% of the 400 people were
    interested in their product. In this case, the Z-score
    would have been $Z = -13$. This corresponds to a lower
    tail area of very nearly~0 and an upper tail are of
    very nearly~1.
    How would we correctly interpret this finding when using
    the one-sided alternative hypothesis that $p > 0.20$?}
    \label{business_one_sided_20_7}
  In this one-sided analysis, the p-value would be larger
  than 0.05, and we would simply conclude that we do not
  have strong evidence that the true proportion is greater
  than 20\%.
  
  This is the only conclusion we can make. Our p-value
  doesn't say \emph{anything} about that the result went
  in completely the opposite direction.
\end{example}

\begin{example}{Suppose the company board saw the
    hypothetical survey results from
    Example~\ref{business_one_sided_20_7}
    where the survey findings were that only 7\% of
    the 400 surveyed people were interested in the product.
    How do you think they would interpret those results?}
    \label{business_one_sided_20_7-exec_interpretation}
  The board is probably going to feel comfortable with
  their decision to not enter the market, as they should
  since the p-value is large. However, they may now believe
  the actual proportion to be notably \emph{less than}
  20\%. Unfortunately this is not a valid statistical
  conclusion if we are using a one-sided test: we should
  not attempt to describe or infer the magnitude of the
  difference in the opposite direction of a one-sided
  $H_A$ since this means we are actually running a
  two-sided test.
\end{example}

\emph{You can't have your cake and eat it, too.}
Using a one-sided test to get a slightly smaller p-value
\emph{if} the data goes in the direction of interest means
we cannot later change our minds and make an assertive
conclusion in the opposite direction.
Our natural human tendencies to learn from data and use that
knowledge in the future will generally undermine the validity
of a one-sided hypothesis test. That is, unless there is
an astoundingly good reason and special situation,
only use two-sided tests.
We will not present any additional one-sided scenarios
in this textbook due to the problems we've outlined here,
and because we haven't been able to outline a situation
where this arose.
%even in our
%contrived example where we attempted to set up a situation
%where a one-sided test would be appropriate, we've
%stumbled into a reason why it would actually \emph{not}
%be appropriate.

\begin{termBox}{\tBoxTitle{The risk of flipping
    a one-sided test to a two-sided test inflates
    the Type~1 Error}
  We've been working very hard to build a rigorous
  system for analyzing data. If we introduce the risk
  of flip-flopping into that system, we undermine the
  the principles we're using in statistics.}
\end{termBox}

\begin{example}{
    In Section~\ref{basicExampleOfStentsAndStrokes},
    we encountered an example where doctors were interested
    in determining whether stents would help people were at
    a high risk of stroke.
    The researchers believed the stents would help.
    Unfortunately, they did not, and the study found strong
    evidence that patients who received stents actually did
    worse.
    Why was using a two-sided test so important in
    this context?}
  Before the study, researchers strongly believed that stents
  would, at worst, help patients. Had they used a one-sided
  test, they couldn't have legitimately identified the strong
  evidence that the stents were in fact \emph{harming} the
  types of patients they considered. Without being able to
  recognize and acknowledge that there was likely harm to
  the patients, these doctors (or other doctors) might have
  instead tried to complete a larger study to try to find
  evidence that stents help -- and in the process, they would
  put patients in harm's way.
\end{example}




================================================
FILE: ch_foundations_for_inf/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 27

\eoce{\qt{Relaxing after work\label{relax_after_work}} The General Social Survey asked the question:
``After an average work day, about how many hours do you have to relax or pursue 
activities 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 
relaxing or pursuing activities they enjoy was (1.38, 1.92).
\begin{parts}
\item Interpret this interval in context of the data.
\item Suppose another set of researchers reported a confidence interval with a 
larger margin of error based on the same sample of 1,155 Americans. How does 
their confidence level compare to the confidence level of the interval stated 
above?
\item Suppose next year a new survey asking the same question is conducted, and 
this time the sample size is 2,500. Assuming that the population 
characteristics, with respect to how much time people spend relaxing after work, 
have not changed much within a year. How will the margin of error of the 95\% 
confidence interval constructed based on data from the new survey compare to the 
margin of error of the interval stated above?
\end{parts}
}{}

% 28

\eoce{\qt{Minimum wage, Part II\label{minimum_wage_prop_2}}
In Exercise~\ref{minimum_wage_prop_1},
we learned that a Rasmussen Reports survey
of 1,000 US adults found that 42\% believe
raising the minimum wage will help the economy.
Construct a 99\% confidence interval for the
true proportion of US adults who believe this.
}{}

% 29

\eoce{\qt{Testing for food safety\label{errors_food_safety}} A food safety inspector 
is called upon to investigate a restaurant with a few customer reports of poor 
sanitation practices. The food safety inspector uses a hypothesis testing 
framework to evaluate whether regulations are not being met. If he decides 
the restaurant is in gross violation, its license to serve food will be revoked.
\begin{parts}
\item Write the hypotheses in words.
\item What is a Type~1 Error in this context?
\item What is a Type~2 Error in this context?
\item Which error is more problematic for the restaurant owner? Why?
\item Which error is more problematic for the diners? Why?
\item As a diner, would you prefer that the food safety inspector requires 
strong evidence or very strong evidence of health concerns before revoking a 
restaurant's license? Explain your reasoning.
\end{parts}
}{}

% 30

\eoce{\qt{True or false\label{tf_found_inf_prop_friendly}}
Determine if the following statements are true or false, and 
explain your reasoning. If false, state how it could be corrected.
\begin{parts}
\item If a given value (for example, the null hypothesized value of a parameter) 
is within a 95\% confidence interval, it will also be within a 99\% confidence 
interval.
\item Decreasing the significance level ($\alpha$) will increase the probability 
of making a Type~1 Error.
\item Suppose the null hypothesis is $p = 0.5$ and we fail to reject $H_0$. 
Under this scenario, the true population proportion is 0.5.
\item With large sample sizes, even small differences between the null value and 
the observed point estimate, a difference often called the
effect size\index{effect size}, will be identified as statistically significant.
\end{parts}
}{}

% 31

\eoce{\qt{Unemployment and relationship problems\label{unemployment_relationship}} 
A USA Today/Gallup poll asked a group of
unemployed and underemployed Americans if they have
had major problems in their  relationships with their
spouse or another close family member as a result of
not having a job (if unemployed) or not having
a full-time job (if underemployed).
27\%~of the 1,145 unemployed respondents and
25\%~of the 675 underemployed respondents said they had
major problems in relationships as a  result of their
employment status.
\begin{parts}
\item
    What are the hypotheses for evaluating if the proportions
    of unemployed and underemployed people who had relationship
    problems were different?
\item
    The p-value for this hypothesis test is approximately 0.35.
    Explain what this means in context of the hypothesis test
    and the data.
\end{parts}
}{}

\D{\newpage}

% 32

\eoce{\qt{Nearsighted\label{nearsighted_updated}}
It is believed that nearsightedness affects about 8\% of 
all children.
In a random sample of 194 children, 21 are nearsighted.
Conduct a hypothesis test for the following question:
do these data provide evidence that the 8\% value is inaccurate?
}{}

% 33

\eoce{\qt{Nutrition labels\label{nutrition_labels}}
The nutrition label on a bag of potato chips says
that a one ounce (28~gram) serving of potato chips
has 130 calories and contains ten grams of fat,
with three grams of saturated fat.
A~random sample of 35 bags yielded
a confidence interval for the number of calories
per bag of 128.2 to 139.8 calories.
Is there evidence that the nutrition label does not 
provide an accurate measure of calories in the bags
of potato chips?
}{}

% 34

\eoce{\qt{CLT for proportions\label{CLT_prop}}
Define the term ``sampling distribution" of the sample proportion,
and describe how the shape, center, and spread of the sampling
distribution change as the sample size increases when $p = 0.1$.
}{}

% 35

\eoce{\qt{Practical vs. statistical significance\label{prac_stat_sig}}
Determine whether the following statement is true
or false, and explain your reasoning:
``With large sample sizes, even small differences
between the null value and the observed point
estimate can be statistically significant.''
}{}

% 36

\eoce{\qt{Same observation, different sample size\label{same_obs_diff_n}} Suppose you 
conduct a hypothesis test based on a sample where the sample size is $n = 50$, 
and arrive at a p-value of 0.08. You then refer back to your notes and discover 
that you made a careless mistake, the sample size should have been $n = 500$. 
Will your p-value increase, decrease, or stay the same? Explain.
}{}

% 37

\eoce{\qt{Gender pay gap in medicine\label{gender_pay_gap_medicine}}
A study examined the average pay for men and women
entering the workforce as doctors for 21 different
positions.\footfullcite{LoSassoMedicineGenderPayGap}
\begin{parts}
\item\label{gender_pay_gap_medicine_hypotheses}
    If each gender was equally paid, then we would expect
    about half of those positions to have men paid more
    than women and women would be paid more than men in
    the other half of positions.
    Write appropriate hypotheses to test this scenario.
\item
    Men were, on average, paid more in 19 of those
    21 positions.
    Supposing these 21 positions represent a simple random sample,
    complete a hypothesis test using your hypotheses
    from part~(\ref{gender_pay_gap_medicine_hypotheses}).
\end{parts}
}{}


================================================
FILE: ch_foundations_for_inf/TeX/variability_in_estimates.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Identify the parameter, Part I\label{identify_parameter_1}} For each of the following situations, state 
whether the parameter of interest is a mean or a proportion. It may be helpful 
to examine whether individual responses are numerical or categorical.
\begin{parts}
\item In a survey, one hundred college students are asked how many hours per 
week they spend on the Internet.
\item In a survey, one hundred college students are asked: ``What percentage of 
the time you spend on the Internet is part of your course work?"
\item In a survey, one hundred college students are asked whether or not they 
cited information from Wikipedia in their papers.
\item In a survey, one hundred college students are asked what percentage of 
their total weekly spending is on alcoholic beverages.
\item In a sample of one hundred recent college graduates, it is found that 85 
percent expect to get a job within one year of their graduation date.
\end{parts}
}{}

% 2

\eoce{\qt{Identify the parameter, Part II\label{identify_parameter_2}} For each of the 
following situations, state whether the parameter of interest is a mean or a 
proportion. 
\begin{parts}
\item A poll shows that 64\% of Americans personally worry a great deal about 
federal spending and the budget deficit.
\item A survey reports that local TV news has shown a 17\% increase in revenue 
within a two year period while newspaper revenues decreased by 6.4\% during this 
time period.
\item In a survey, high school and college students are asked whether or not 
they use geolocation services on their smart phones.
\item In a survey, smart phone users are asked whether or not they use a web-based taxi service.
\item In a survey, smart phone users are asked how many times they used a web-based taxi service over the last year.
\end{parts}
}{}

% 3

\eoce{\qt{Quality control\label{comp_chips_quality_ctrl_prop}}
As part of a quality control process for computer chips,
an engineer at a factory randomly samples 212 chips
during a week of production to test the current rate of
chips with severe defects.
She finds that 27 of the chips are defective.
\begin{parts}
\item
    What population is under consideration in the data set?
\item
    What parameter is being estimated?
\item\label{comp_chips_quality_ctrl_prop_pt_est}%
    What is the point estimate for the parameter?
\item\label{comp_chips_quality_ctrl_prop_se_name}%
    What is the name of the statistic we use to measure
    the uncertainty of the point estimate?
\item\label{comp_chips_quality_ctrl_prop_se_calc_w_pt_est}%
    Compute the value from
    part~(\ref{comp_chips_quality_ctrl_prop_se_name})
    for this context.
\item
    The historical rate of defects is 10\%.
    Should the engineer be surprised by the observed
    rate of defects during the current week?
\item
    Suppose the true population value was found to be 10\%.
    If we use this proportion to recompute the value in
    part~(\ref{comp_chips_quality_ctrl_prop_se_calc_w_pt_est})
    using $p = 0.1$ instead of $\hat{p}$,
    does the resulting value change much?
\end{parts}
}{}

% 4

\eoce{\qt{Unexpected expense\label{us_emergency_expense_prop}}
In a random sample 765 adults in the United States, 322 say
they could not cover a \$400 unexpected expense without borrowing
money or going into debt.
% Ref: https://www.federalreserve.gov/publications/files/2017-report-economic-well-being-us-households-201805.pdf
\begin{parts}
\item
    What population is under consideration in the data set?
\item
    What parameter is being estimated?
\item\label{us_emergency_expense_prop_pt_est}%
    What is the point estimate for the parameter?
\item\label{us_emergency_expense_prop_se_name}%
    What is the name of the statistic we use to measure
    the uncertainty of the point estimate?
\item\label{us_emergency_expense_prop_se_calc_w_pt_est}%
    Compute the value from
    part~(\ref{us_emergency_expense_prop_se_name})
    for this context.
\item
    A cable news pundit thinks the value is actually 50\%.
    Should she be surprised by the data?
\item
    Suppose the true population value was found to be 40\%.
    If we use this proportion to recompute the value in
    part~(\ref{us_emergency_expense_prop_se_calc_w_pt_est})
    using $p = 0.4$ instead of $\hat{p}$,
    does the resulting value change much?
\end{parts}
}{}

\D{\newpage}

% 5

\eoce{\qt{Repeated water samples\label{repeated_water_samples_prop}}
A nonprofit wants to understand the fraction of households that
have elevated levels of lead in their drinking water.
They expect at least 5\% of homes will have elevated levels of
lead, but not more than about 30\%.
They randomly sample 800 homes and work with the owners to retrieve
water samples, and they compute the fraction of these homes
with elevated lead levels.
They repeat this 1,000 times and build a distribution
of sample proportions.
\begin{parts}
\item
    What is this distribution called?
\item
    Would you expect the shape of this distribution to be
    symmetric, right skewed, or left skewed?
    Explain your reasoning.
\item
    If the proportions are distributed around 8\%,
    what is the variability of the distribution?
\item
    What is the formal name of the value you computed in~(c)?
\item
    Suppose the researchers' budget is reduced, and they are only
    able to collect 250 observations per sample, but they can still
    collect 1,000 samples.
    They build a new distribution of sample proportions.
    How will the variability of this new distribution compare
    to the variability of the distribution when each sample
    contained 800 observations?
\end{parts}
}{}

% 6

\eoce{\qt{Repeated student samples\label{repeated_student_samples_prop}}
Of all freshman at a large college, 16\% made the dean's list
in the current year.
As part of a class project, students randomly sample 40 students
and check if those students made the list.
They repeat this 1,000 times and build a distribution
of sample proportions.
\begin{parts}
\item
    What is this distribution called?
\item
    Would you expect the shape of this distribution to be
    symmetric, right skewed, or left skewed?
    Explain your reasoning.
\item
    Calculate the variability of this distribution.
\item
    What is the formal name of the value you computed in~(c)?
\item
    Suppose the students decide to sample again,
    this time collecting 90 students per sample,
    and they again collect 1,000 samples.
    They build a new distribution of sample proportions.
    How will the variability of this new distribution compare
    to the variability of the distribution when each sample
    contained 40 observations?
\end{parts}
}{}


================================================
FILE: ch_foundations_for_inf/figures/95PercentConfidenceInterval/95PercentConfidenceInterval.R
================================================
library(openintro)
data(COL)
data(run10)
set.seed(52)

# This still references run10, but the actual range of values
# isn't shown, so just tweaking the printed value.

myPDF('95PercentConfidenceInterval.pdf', 6, 3.4,
      mar = c(1.7, 1, 0, 1),
      mgp = c(2.7, 0.7, 0))
m <- 94.52
s <- 16.0
n <- 100
k <- 25
SE <- s/sqrt(n)

set.seed(3)
means <- c()
SE    <- c()
for(i in 1:k){
  temp <- sample(nrow(run10), n)
  d    <- run10$time[temp]
  means[i] <- mean(d, na.rm = TRUE)
  SE[i]    <- sd(d)/sqrt(n)
}
xR <- m + 4 * c(-1, 1) * s / sqrt(n)
yR <- c(0.7, 25.3)
plot(xR, yR,
     type = 'n',
     xlab = 'run time (minutes)',
     ylab = '',
     axes = FALSE)
abline(v = m, lty = 2, col = COL[5,2])
axis(1, at = m, "p = 0.88")
for(i in 1:k){
  ci <- means[i] + 2 * c(-1, 1) * SE[i]
  if(abs(means[i] - m) > 1.96 * SE[i]){
    col <- COL[4]
    points(means[i], i, cex = 1.4, col = col)
    lines(ci, rep(i, 2), col = col, lwd = 4)
  } else {
    col <- COL[1]
  }
  points(means[i], i, pch = 20, cex = 1.2, col = col)
  lines(ci, rep(i, 2), col = col)
}
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/ARCHIVE/sampling_10k_prop_56p/sampling_10k_prop_56p.R
================================================
set.seed(1)
library(openintro)
data(COL)

n.sim <- 10000
samp.size <- 1000

samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(0.44, 0.56)), n.sim)
results <- apply(samples, 1, mean)
mean(results)
sd(results)

myPDF('sampling_10k_prop_56p.pdf', 6.5, 3.2,
    mar = c(3.5, 3.5, 0.7, 0.7),
    mgp = c(2.3, 0.6, 0),
    yaxs = "i")
histPlot(results,
    col = COL[1], breaks = 25,
    xlab = "Sample Proportions",
    ylab = "",
    axes = FALSE)
at <- seq(0, 1, 0.05)
axis(1, at = seq(0, 1, 0.01), labels = rep("", 101))
axis(1, at = at)
# axis(2, at = seq(0, 1200, 100), label = rep("", 13))
axis(2, at = seq(0, 1200, 200))
# abline(v = 0.56, col = COL[4])

dev.off()


================================================
FILE: ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove10WithDF4/chiSquareAreaAbove10WithDF4.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove10WithDF4.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(10,
              4,
              c(0, 18),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove11Point7WithDF7/chiSquareAreaAbove11Point7WithDF7.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove11Point7WithDF7.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(11.7,
              7,
              c(0, 25),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove4Point3WithDF2/chiSquareAreaAbove4WithDF2.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove4Point3WithDF2.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(4.3,
              2,
              c(0, 15),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove5Point1WithDF5/chiSquareAreaAbove5Point1WithDF5.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove5Point1WithDF5.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(5.1,
              5,
              c(0, 25),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove6Point25WithDF3/chiSquareAreaAbove6Point25WithDF3.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove6Point25WithDF3.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(6.25,
              3,
              c(0, 15),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove9Point21WithDF3/chiSquareAreaAbove9Point21WithDF3.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove9Point21WithDF3.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(9.21,
              3,
              c(0, 15),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/bladesTwoSampleHTPValueQC/bladesTwoSampleHTPValueQC.R
================================================
library(openintro)
data(COL)

myPDF('bladesTwoSampleHTPValueQC.pdf', 3.04, 1.56,
      mar = c(2.4, 0, 0.5, 0),
      mgp = c(3, 0.45, 0))
normTail(U = 2.3, col = COL[1], axes = FALSE)
at <- c(-5, 0, 2.3, 5)
labels <- c(0, 0.03, 0.059, 0)
axis(1, at, labels, cex.axis = 0.9)
par(mgp = c(5, 1.3, 0))
axis(1, at = 0, '(null value)', cex.axis = 0.7)
arrows(2.5, 0.19,
       2.5, 0.05,
       length = 0.1,
       col = COL[1])
text(2.5, 0.18, "0.006",
     pos = 3,
     cex = 0.8,
     col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/business_one_sided_20_21-p_value/business_one_sided_20_21-p_value.R
================================================
library(openintro)
data(COL)

myPDF('business_one_sided_20_21-p_value.pdf', 2.15, 0.95,
      mar = c(1.31, 0, 0.01, 0),
      mgp = c(3, 0.45, 0))
X <- seq(-4, 4, 0.01)
Y <- dnorm(X)
normTail(0.20, 0.01, U = 0.21, cex.axis = 0.8, axes = FALSE, col = COL[1])
at <- c(0.18, 0.20, 0.22)
axis(1, at, cex.axis = 0.8)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/chiSquareDistributionWithInceasingDF/chiSquareDistributionWithInceasingDF.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareDistributionWithInceasingDF.pdf', 6.5, 3,
      mar = c(2, 0.5, 0.25, 0.5),
      mgp = c(2.1, 0.7, 0))
x <- c(0, seq(0.0000001, 40, 0.05))
DF <- c(2.0000001, 4, 9)
y <- list()
for (i in 1:length(DF)) {
  y[[i]] <- dchisq(x, DF[i])
}
plot(0, 0,
     type = 'n',
     xlim = c(0, 25),
     ylim = range(c(y, recursive = TRUE)),
     axes = FALSE)
for (i in 1:length(DF)) {
  lines(x, y[[i]],
        lty = i,
        col = COL[ifelse(i == 3, 4, i)],
        lwd = 1.5 + i / 2)
}
abline(h = 0)
axis(1)
legend('topright',
       lwd = 0.3 + 1:4 / 1.25,
       col = COL[c(1, 2, 4)],
       lty = 1:4,
       legend = paste(round(DF)),
       title = 'Degrees of Freedom',
       cex = 1)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/choosingZForCI/choosingZForCI.R
================================================
library(openintro)
data(COL)

myPDF('choosingZForCI.pdf', 7.56, 3.84,
      mar=c(3.3, 1, 0.5, 1),
      mgp=c(2.1, 0.6, 0))
normTail(M = c(-1.96, 1.96),
         df = 10,
         col = COL[1],
         xlim = 3.3 * c(-1, 1),
         ylim = c(0, 0.59),
         xlab='Standard Deviations from the Mean')
X <- rev(seq(-4, 4, 0.025))
Y <- dt(X, 10) # makes better visual

yMax <- 0.41

these <- (-2.58 < X & X < 2.58)
x <- c(2.58, X[these], -2.58)
y <- c(0, dt(X[these], 10), 0)
polygon(x, y, col=COL[1,3], border='#00000000')

lines(1.96*c(-1,1), rep(yMax,2), lwd=2)
lines(rep(-1.96,2), c(0,yMax), lty=2, col=COL[6])
lines(rep( 1.96,2), c(0,yMax), lty=2, col=COL[6])
text(0, yMax, '95%, extends -1.96 to 1.96', pos=3)

yMax <- 0.53
lines(2.58*c(-1,1), rep(yMax,2), lwd=2)
lines(rep(-2.58,2), c(0,yMax), lty=2, col='#00000055')
lines(rep( 2.58,2), c(0,yMax), lty=2, col='#00000055')
text(0, yMax, '99%, extends -2.58 to 2.58', pos=3)

dev.off()


================================================
FILE: ch_foundations_for_inf/figures/clt_prop_grid/clt_prop_grid.R
================================================
library(openintro)
data(COL)

props <- c(0, 0.1, 0.2, 0.50, 0.8, 0.9)
samp.size.1 <- c(0, 10, 25)
samp.size.2 <- c(50, 100, 250)

plot.width <- 7
plot.height <- 10


SetupLayout <- function(show) {
  myMat <- rbind(matrix(1:18, nrow = 6, ncol = 3, byrow = TRUE))
  if (show) {
    myW <- c(0.5, rep(1, 2))
  } else {
    myW <- rep(1, 3)
  }
  myH <- c(0.5, rep(1, 5))
  layout(myMat, myW, myH)
}

PlotSampDist <- function(n, p, main) {
  par(mar = mar)
  x <- seq(0, n)
  y <- dbinom(x, n, p)
  p.hat <- x / n
  width <- 0.2 / n
  plot(p.hat, y, type = "n", axes = FALSE,
      xlab = "", ylab = "")
  axis(1)
  rect(p.hat - width, 0, p.hat + width, y, border = COL[1], col = COL[1])
  abline(h = 0)
}

TextPlot <- function(text, cex = 2.5, vertical = FALSE) {
  plot(0:1, 0:1, axes = FALSE, type = "n", xlab = "", ylab = "")
  text(0.5, 0.5, text, cex = cex)
}

BuildGrid <- function(props, samp.size) {
  for (p in props) {
    for (n in samp.size) {
      par(mar = rep(0, 4))
      if (p == 0 && n == 0) {
        TextPlot("")
      } else if (p > 0 && n == 0) {
        TextPlot(paste("p =", p))
      } else if (p == 0 && n > 0) {
        TextPlot(paste("n =", n))
      } else {
        PlotSampDist(n, p)
      }
    }
  }
}


mar <- c(3.5, 1.5, 0.7, 1.5)

myPDF('clt_prop_grid_1.pdf', plot.width, plot.height,
    mgp = c(2.3, 0.6, 0),
    yaxs = "i",
    mfrow = c(5, 2))
SetupLayout(TRUE)
BuildGrid(props, samp.size.1)
dev.off()


myPDF('clt_prop_grid_2.pdf', plot.width, plot.height,
    mar = c(3.5, 3, 0.7, 0.2),
    mgp = c(2.3, 0.6, 0),
    yaxs = "i",
    mfrow = c(5, 2))
SetupLayout(FALSE)
BuildGrid(props, samp.size.2)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/communityCollegeClaimedHousingExpenseDistribution/communityCollegeClaimedHousingExpenseDistribution.R
================================================
library(openintro)
data(COL)

x <- student.housing$price
t.test(x, mu = 650)
mean(x)
sd(x)
length(x)

myPDF('communityCollegeClaimedHousingExpenseDistribution.pdf',
      6.5, 3.4,
      mar = c(3.2, 3.5, 1, 1),
      mgp = c(1.9, 0.7, 0))
histPlot(x,
         breaks = 20,
         xlab = 'Housing Expense (dollars)',
         ylab = '',
         col = COL[1],
         axes = FALSE)
axis(1, at = seq(400, 1200, 200))
axis(2, at = seq(0, 30, 5))
mtext('Freqency', side = 2, line = 2.3, las = 0)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/eoce/adult_heights/adult_heights.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(bdims)

# histogram of heights ----------------------------------------------

pdf("adult_heights_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.5,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(bdims$hgt, col = COL[1], xlab = "Height", ylab = "")
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/age_at_first_marriage_intro/age_at_first_marriage_intro.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(ageAtMar)

# histogram of age at first marriage --------------------------------
pdf("age_at_first_marriage_intro_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.7,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(ageAtMar$age, col = COL[1], xlab = "Age at first marriage", ylab = "")
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/assisted_reproduction_one_sample_randomization/assisted_reproduction_one_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set sample size and number of simulations -------------------------
n = 25
N = 10^4

# randomize ---------------------------------------------------------

set.seed(15)

p <- 0.31

pHat <- rbinom(N, n, p)/n
M    <- max(pHat)*n

pHatObs <- 0.4

sum(pHat >= pHatObs)/N

# plot randomization dist for question ------------------------------

pdf("assisted_reproduction_one_sample_randomization.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at = (0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

abline(h = seq(250, 1500, 250), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("assisted_reproduction_one_sample_randomization_soln.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at = (0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

histPlot(pHat[pHat >= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, 
         col = COL[1], add = TRUE)

lines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)
text(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/cflbs/cflbs.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# inputs ------------------------------------------------------------

m = 9000
s = 1000
n = 15
se = s / sqrt(n)

# plot sketch -------------------------------------------------------

pdf("cflbs_sketch.pdf", height = 3, width = 6)

par(mar = c(2,1,1,0), las = 1, mgp = c(3,1,0))

# population

X <- seq((m - 3 * s),(m + 3 * s), 1)
Y <- dnorm(X, m, s)

plot(X, Y, type = 'l', axes = FALSE, 
     xlim = c(min(X), max(X)), ylim = c(0, 0.0015))
     ylab = "", lwd = 2.5)
lines(X, rep(0, length(X)), lwd = 1.5)
axis(1, at = seq((m - 3 * s), (m + 3 * s),s), cex.axis = 1.25)

# sampling

X <- seq((m - 5 * se),(m + 5 * se), 1)
Y <- dnorm(X, m, se)

lines(X, Y, type = 'l', lty = 2, lwd = 2.5, col = COL[1])

legend("topright", c("Population","Sampling (n = 15)"), 
       lty = c(1,2), col = c("black", COL[1]), inset = 0.03, 
       cex = 1.25, lwd = c(2.5,2.5))

dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/college_credits/college_credits.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(credits)

# histogram of college credits --------------------------------------

pdf("college_credits_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(credits[,1], col = COL[1], xlab = "Number of credits", ylab = "")
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/egypt_revolution_one_sample_randomization/egypt_revolution_one_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set sample size and number of simulations -------------------------
n = 20
N = 10^4

# randomize ---------------------------------------------------------

set.seed(5)

pHat <- rbinom(N, n, 0.69)/n
M    <- max(pHat)*n

pHatObs <- 0.57

sum(pHat <= pHatObs)/N

# plot randomization dist for question ------------------------------

pdf("egypt_revolution_one_sample_randomization.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at=(0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

abline(h = seq(250,1500,250), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("egypt_revolution_one_sample_randomization_soln.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at=(0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

histPlot(pHat[pHat <= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, 
         col = COL[1], add = TRUE)

lines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)
text(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/exclusive_relationships/exclusive_relationships.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(dplyr)

# load data ---------------------------------------------------------
survey <- read.csv("survey.csv")

# sample size -------------------------------------------------------
n <- survey %>%
  filter(!is.na(excl_relation)) %>%
  nrow() # 203

# histogram ---------------------------------------------------------

pdf("exclusive_relationships_rel_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(survey$excl_relation, col = COL[1], xlab = "Number of exclusive relationships", ylab = "")
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/exclusive_relationships/survey.csv
================================================
"excl_relation"
2
4
1
4
NA
2
2
2
1
4
2
4
2
7
NA
1
NA
1
9
NA
4
1
2
4
2
1
5
1
9
1
2
1
4
4
1
8
NA
1
6
4
1
1
2
2
4
2
5
4
1
1
5
5
4
4
1
5
4
4
5
2
6
1
1
4
1
7
5
5
5
1
1
7
6
2
NA
1
2
6
1
NA
NA
4
1
2
4
1
4
NA
5
2
5
4
4
4
1
1
6
6
NA
2
2
2
5
4
2
7
1
2
5
4
1
4
6
1
4
4
1
7
5
5
7
2
5
4
1
8
5
6
1
2
2
1
1
4
2
4
1
1
NA
2
10
4
2
4
1
2
5
2
2
2
4
2
5
1
2
4
4
2
1
1
2
4
NA
5
2
1
2
NA
6
4
2
2
4
4
4
4
4
4
5
4
1
5
4
4
5
4
4
3
4
4
2
NA
2
1
2
4
2
2
1
1
1
NA
1
3
5
4
6
1
2
5
1
8
4
2
1
2
2
5


================================================
FILE: ch_foundations_for_inf/figures/eoce/gifted_children_ht/gifted_children_ht.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(gifted)

# plot mom's IQ -----------------------------------------------------
pdf("gifted_children_ht_momIQ_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(gifted$motheriq, col = COL[1], 
         xlab = "Mother's IQ", ylab = "", axes = FALSE)
axis(1)
axis(2, at = c(0,4,8,12))
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/gifted_children_intro/gifted_children_intro.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(gifted)

# plot counts -------------------------------------------------------
pdf("gifted_children_ht_count_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(gifted$count, col = COL[1], 
         xlab = "Age child first counted to 10 (in months)", ylab = "", 
         axes = FALSE)
axis(1)
axis(2, at = c(0,3,6))
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/identify_dist_ls_pop/identify_dist_ls_pop.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------
set.seed(85479)
a  = rbeta(1e6, 3.5, 2)
b = a * 94

# plot population ---------------------------------------------------
myPDF("identify_dist_ls_pop.pdf", 4.25, 1.95, mar=c(2.3,0,0,0), 
      mgp=c(2.7,0.5,0), las = 1)
densityPlot(b, bw = 1, from = 0, to = 101, col = COL[5], 
            fadingBorder = "66", histo = "faded", xlab = "", axes = FALSE, ylab = "")
axis(1)
text(x = 10, y = 0.015, "Population")
text(x = 10, y = 0.0125, expression(paste(mu, " = 60")))
text(x = 10, y = 0.01, expression(paste(sigma, " = 18")))
dev.off()

# plot sample -------------------------------------------------------
set.seed(2452)
samp = sample(b, size = 500)

myPDF("identify_dist_ls_samp.pdf", 3.2, 2, mar=c(3.3,2,0.5,0.5), mgp=c(2.1,0.5,0))
hist(samp, col = COL[1], xlab = "Plot B", ylab = "", main = "", axes=FALSE)
axis(1)
axis(2, at=c(0, 50, 100))
dev.off()

# plot sampling, n = 5 ----------------------------------------------

set.seed(24524)

sampling_18 = rep(0, 500)
n = 18

for(i in 1:500){
  temp <- sample(b, n)
  sampling_18[i] <- mean(temp)
}

myPDF("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))
hist(sampling_18, col = COL[1], xlab = "Plot C", ylab = "", main = "", axes=FALSE)
axis(1)
axis(2, at=c(0, 50, 100))
dev.off()

# plot sampling, n = 81 ---------------------------------------------

set.seed(3563)
sampling_81 = rep(0, 500)
n = 81

for(i in 1:500){
  temp <- sample(b, n)
  sampling_81[i] <- mean(temp)
}

myPDF("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))
hist(sampling_81, col = COL[1], xlab = "Plot A", ylab = "", main = "", axes=FALSE)
axis(1)
axis(2, at=c(0, 50, 100))
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/identify_dist_symm_pop/identify_dist_symm_pop.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------
set.seed(85479)
a  = rnorm(1e6, 10, 3)

# plot population ---------------------------------------------------
myPDF("identify_dist_symm_pop.pdf", 4.25, 1.95, mar=c(2.3,0,0,0), 
      mgp=c(2.7,0.5,0), las = 1)
densityPlot(a, bw = 1/4, from = -2, to = 22, col = COL[5], 
            fadingBorder = "66", histo = "faded", xlab = "", 
            axes = FALSE, ylab = "", breaks = 60, xlim=c(0, 20))
axis(1, at = seq(0,20,5), labels = seq(0,20,5))
text(x = 17, y = 0.103, "Population")
text(x = 17, y = 0.085, expression(paste(mu, " = 10")))
text(x = 17, y = 0.07, expression(paste(sigma, " = 3")))
dev.off()

# plot sample -------------------------------------------------------
set.seed(9582)
samp = sample(a, size = 100)

myPDF("identify_dist_symm_samp.pdf", 3.2, 2, mar=c(3.3,2,0.5,0.5), mgp=c(2.1,0.5,0))
hist(samp, col = COL[1], xlab = "Plot B", ylab = "", main = "", axes=FALSE)
axis(1)
axis(2, at=c(0, 10, 20))
dev.off()

# plot sampling, n = 5 ----------------------------------------------

set.seed(7793)
sampling_5 = rep(0, 100)
n = 5

for(i in 1:100){
  	temp <- sample(a, n)
   	sampling_5[i] <- mean(temp)
   	}

myPDF("identify_dist_symm_sampling_n5.pdf", 3.2, 2, mar=c(3.3,2,0.5,0.5), 
      mgp=c(2.1,0.5,0))
hist(sampling_5, col = COL[1], xlab = "Plot A", ylab = "", main = "", axes=FALSE)
axis(1)
axis(2, at=c(0, 10, 20))
dev.off()

# plot sampling, n = 25 ---------------------------------------------

set.seed(3563)
sampling_25 = rep(0, 100)
n = 25

for(i in 1:100){
  	temp <- sample(a, n)
   	sampling_25[i] <- mean(temp)
   	}

myPDF("identify_dist_symm_sampling_n25.pdf", 3.2, 2, mar=c(3.3,2,0.5,0.5), 
      mgp=c(2.1,0.5,0))
hist(sampling_25, col = COL[1], xlab = "Plot C", ylab = "", main = "", axes = FALSE)
axis(2, at=seq(0, 20, 10))
axis(1, at = 9:11, labels = 9:11)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/eoce/pennies_ages/pennies_ages.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
load("penniesAges.Rda")

# plot population ---------------------------------------------------
pdf("pennies_ages_pop.pdf", height = 3, width = 5.8)
par(mar=c(2,2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(penniesAges$age, col = COL[1], xlab = "Penny ages", 
         ylab = "", axes = FALSE)
axis(1)
dev.off()

# plot sampling, n = 5 ----------------------------------------------

set.seed(123)
xbar = c()
for(i in 1:5000){
	sub = sample(c(1:nrow(penniesAges)), size = 5, replace = TRUE)
	xbar = c(xbar, mean(penniesAges$age[sub]))
}
xbar5 = xbar

myPDF("pennies_ages_sampling_n5.pdf", 3, 2.4, 
      mar=c(3.5,0.5,0.5,0.5), las=1, mgp=c(2.1,0.4,0))
histPlot(xbar5, col = COL[1], 
         xlab = expression(bar(x)[" n = 5"]), ylab = "", 
         axes = FALSE)
axis(1)
dev.off()

# plot sampling, n = 30 ----------------------------------------------

set.seed(234)
xbar = c()
for(i in 1:5000){
  sub = sample(c(1:nrow(penniesAges)), size = 30, replace = TRUE)
  xbar = c(xbar, mean(penniesAges$age[sub]))
}
xbar30 = xbar

myPDF("pennies_ages_sampling_n30.pdf", 3, 2.4, 
      mar=c(3.5,0.5,0.5,0.5), las=1, mgp=c(2.1,0.4,0))
histPlot(xbar30, col = COL[1], 
         xlab = expression(bar(x)[" n = 30"]), ylab = "", 
         axes = FALSE)
axis(1)
dev.off()

# plot sampling, n = 100 --------------------------------------------

set.seed(345)
xbar = c()
for(i in 1:5000){
  sub = sample(c(1:nrow(penniesAges)), size = 100, replace = TRUE)
  xbar = c(xbar, mean(penniesAges$age[sub]))
}
xbar100 = xbar

myPDF("pennies_ages_sampling_n100.pdf", 3, 2.4, 
      mar=c(3.5,0.5,0.5,0.5), las=1, mgp=c(2.1,0.4,0))
histPlot(xbar100, col = COL[1], 
         xlab = expression(bar(x)[" n = 100"]), ylab = "", 
         axes = FALSE)
axis(1)
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/penny_weights/penny_weights.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# input -------------------------------------------------------------

m = 2.5
s = 0.03
n = 10
se = s / sqrt(n)

# plot sketch -------------------------------------------------------

pdf("penny_weights_sketch.pdf", height = 3, width = 6)

par(mar=c(2,0,0,0), las=1, mgp=c(3,1,0), mfrow = c(1,1))

# population

X <- seq((m - 3 * s), (m + 3 * s), 0.001)
Y <- dnorm(X, m, s)

plot(X, Y, type = 'l', axes = FALSE, 
     xlim = c(min(X), max(X)), ylim = c(0, 42), 
     ylab = "", lwd = 2.5)
lines(X, rep(0, length(X)), lwd = 1.5)
axis(1, at = seq((m - 3 * s), (m + 3 * s),s), cex.axis = 1.25)

# sampling

X <- seq((m - 5 * se), (m + 5 * se), 0.001)
Y <- dnorm(X, m, se)

lines(X, Y, type = 'l', lty = 2, lwd = 2.5, col = COL[1])

legend("topright", c("Population","Sampling (n = 10)"), 
       lty = c(1,2), col = c("black",COL[1]), 
       inset = 0.03, cex = 1.25, lwd = c(2.5,2.5))

dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/social_experiment_two_sample_randomization/social_experiment_two_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set number of simulations -----------------------------------------
N = 10^4

# randomize ---------------------------------------------------------

pHatObs = -0.35

set.seed(3)

sc <- c(rep("p", 20), rep("c",25))
int <- c(rep(c("y", "n"), c(5, 15)), rep(c("y", "n"), c(15, 10)))

d <- rep(NA, N)
for(i in 1:N){
	scf  <- sample(sc)
	p1   <- sum(int[scf == "p"] == "y") / 20
	p2   <- sum(int[scf == "c"] == "y") / 25
	d[i] <- p1 - p2
}
sum((d) <= pHatObs) / N

# plot randomization dist for question ------------------------------

pdf("social_experiment_two_sample_randomization.pdf", height = 3, width = 6)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

temp1 <- sort(unique(d))
temp2 <- diff(temp1[1:2])/2
br    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)

histPlot(d, breaks = br, col=COL[7,4], 
         main="", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*"    "), 
         ylab="", axes=FALSE)
axis(1, seq(-0.4, 0.4, 0.2))
axis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)
abline(h = 0)

abline(h = c((1:4)*N/20), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("social_experiment_two_sample_randomization_soln.pdf", height = 3, width = 6)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

temp1 <- sort(unique(d))
temp2 <- diff(temp1[1:2])/2
br    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)

histPlot(d, breaks = br, col=COL[7,4], 
         main="", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*"    "), 
         ylab="", axes=FALSE)
axis(1, seq(-0.4, 0.4, 0.2))
axis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)
abline(h = 0)

histPlot(d[d <= pHatObs], breaks=br, col=COL[1], add=TRUE)
abline(h=0)
lines(rep(pHatObs, 2), c(0, 3)*N/25, lty=3, lwd=1.7)
text(pHatObs, 3*N/25, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/songs_on_ipod/songs_on_ipod.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(ipod)

# population histogram ----------------------------------------------
pdf("songs_on_ipod_pop_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(ipod$songLength, col = COL[1], 
         xlab = "Length of song", ylab = "")
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/thanksgiving_spending_intro/thanksgiving_spending_intro.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(tgSpending)

# population histogram ----------------------------------------------
pdf("thanksgiving_spending_intro_pop_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(tgSpending$spending, col = COL[1], 
         xlab = "Spending", ylab = "")
dev.off()

================================================
FILE: ch_foundations_for_inf/figures/eoce/yawning_two_sample_randomization/yawning_two_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set number of simulations -----------------------------------------
N = 10^4

# randomize ---------------------------------------------------------

pHatObs = 0.04

set.seed(29)

gr <- c(rep("trtmt", 34), rep("ctrl",16))
yawn <- c(rep(c("y", "n"), c(10, 24)), rep(c("y", "n"), c(4, 12)))

d <- rep(NA, N)
for(i in 1:N){
  grf  <- sample(gr)
  p1   <- sum(yawn[grf == "trtmt"] == "y") / 34
  p2   <- sum(yawn[grf == "ctrl"] == "y") / 16
  d[i] <- p2 - p1
}
sum((d) >= pHatObs) / N

# plot randomization dist for question ------------------------------

pdf("yawning_two_sample_randomization.pdf", height = 3.5, width = 6.7)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

histPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], 
         main="", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*"    "), 
         ylab="", axes=FALSE)
axis(1)
axis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)
abline(h = 0)

abline(h = c((1:5)*N/20), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("yawning_two_sample_randomization_soln.pdf", height = 3.5, width = 6.7)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

histPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], 
         main="", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*"    "), 
         ylab="", axes=FALSE)
axis(1)
axis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)
abline(h = 0)

histPlot(d[d >= pHatObs], breaks=seq(-0.6, 0.7, 0.02), col=COL[1], add=TRUE)
abline(h=0)
lines(rep(pHatObs, 2), c(0, 6.1)*N/25, lty=3, lwd=1.7)
text(pHatObs, 6*N/25, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_foundations_for_inf/figures/geomFitEvaluationForSP500For1990To2011/geomFitEvaluationForSP500For1990To2011.R
================================================
library(openintro)
data(COL)
library(stockPortfolio)
gr <- getReturns("^GSPC",
                 freq = "d",
                 start = "1990-01-01",
                 end = "2011-12-31")
R  <- ifelse(gr$R[gr$R != 0] > 0, 1, 0)
CC <- table(diff(which(R == 1)))
CC[names(CC) == 7] <- sum(CC[names(CC) %in% 7:9])
CC <- CC[- which(names(CC) %in% 8:9)]
p  <- mean(R)
pr <- p * (1 - p)^(0:5)
pr <- append(pr, 1 - sum(pr))

CC <- c(CC)
C  <- rep(1:7, CC)
EE <- round(pr * sum(CC))
E  <- rep(1:7, EE)

myPDF('geomFitEvaluationForSP500For1990To2011.pdf', 7, 3.5,
      mar = c(3.2, 4.2, 0.2, 1),
      mgp = c(2.1, 0.7, 0))
histPlot(C - 0.13,
         breaks = seq(0, 8, 0.25),
         xlim = c(0.5, 7.5),
         ylim = c(0, 1600),
         xlab = 'Wait until positive day',
         ylab = '',
         axes = FALSE,
         col = COL[1])
histPlot(E + 0.13,
         breaks = seq(0, 8, 0.25),
         add = TRUE,
         col = COL[3])
axis(1, 1:7, c(1:6, "7+"))
axis(2, at = seq(0, 1200, 400))
par(las = 0)
mtext('Frequency', 2, line = 3)
legend('topright',
       fill = COL[c(1, 3)],
       legend = c('Observed', 'Expected'))
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/geomFitPValueForSP500For1990To2011/geomFitPValueForSP500For1990To2011.R
================================================
library(openintro)
data(COL)

myPDF('geomFitPValueForSP500For1990To2011.pdf', 6.6, 2.387,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.5, 0))
ChiSquareTail(15.08,
              6,
              c(0, 30),
              col = COL[1])
arrows(15.1, max(y) / 3,
       15.5, max(y) / 10,
       length = 0.1,
       col = COL[1])
text(15.1, max(y)/3, 'Area representing\nthe p-value',
     pos = 3,
     col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/googleHTForDiffAlgPerformancePValue/googleHTForDiffAlgPerformancePValue.R
================================================
library(openintro)
data(COL)

myPDF('googleHTForDiffAlgPerformancePValue.pdf', 5, 2.25,
    mar = c(2, 1, 1, 1), mgp = c(2.1, 0.7, 0))
ChiSquareTail(6.12,
              2,
              c(0, 16),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/helpers.R
================================================

RunSimulation <- function(p, n.sim, samp.size, xlim, xlab, show = "n") {

  samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - p, p)), n.sim)
  results <- apply(samples, 1, mean)
  breaks <- seq(-0.0025, 1.0025, 0.005)
  if (samp.size < 100) {
    breaks <- seq(-0.01, 1.01, 0.02)
  }
  if (missing(xlim)) {
    xlim <- range(results)
  }
  if (missing(xlab)) {
    xlab <- "Sample Proportions"
  }
  histPlot(results,
      col = COL[1], breaks = breaks,
      xlim = xlim,
      xlab = xlab,
      ylab = "",
      axes = FALSE)
  spread <- format(c(0.001, round(sqrt(p * (1 - p) / samp.size), 3)))[2]
  main <- bquote(
      "n = "*.(samp.size)~~~~~
      mu[hat(p)]*" = "*.(p)~~~~~
      sigma[hat(p)]*" = "*.(spread))
  if (show == "p") {
    main <- bquote(
        "p = "*.(p)~~~~~
        sigma[hat(p)]*" = "*.(spread))
  }
  mtext(main, line = 0.4, cex = 0.9)
  if (all(xlim == c(0, 1))) {
    at1 <- seq(0, 1, 0.1)
    at2 <- seq(0, 1, 0.2)
  } else {
    at1 <- seq(0, 1, 0.025)
    at2 <- seq(0, 1, 0.05)
  }
  axis(1, at = at1, labels = rep("", length(at1)))
  axis(1, at = at2)
  # axis(2, at = seq(0, 1200, 100), label = rep("", 13))
  # axis(2, at = seq(0, 1200, 200))
  results
}


================================================
FILE: ch_foundations_for_inf/figures/jurorHTPValueShown/jurorHTPValueShown.R
================================================
library(openintro)
data(COL)

myPDF('jurorHTPValueShown.pdf', 4.4, 1.87,
      mar = c(1.5, 1, 0.2, 1),
      mgp = c(2.1, 0.45, 0))
ChiSquareTail(5.89,
              3,
              c(0, 16),
              col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/mammograms/mammograms.R
================================================
require(openintro)
data(COL)

fn <- 'mammogramPValue.pdf'
myPDF(fn, 4, 1.2,
      mar = c(1.5, 0, 0.1, 0),
      mgp = c(3, 0.3, 0))
normTail(L = -0.17, U = 0.17,
        col = COL[1],
        axes = FALSE,
        xlim = c(-3.2, 3.2))
at <- c(-10, -2, 0, 2, 10)
labels <- c(0, -0.0014, 0, 0.0014, 0)
axis(1, at, labels, cex.axis = 0.9)
# lines(rep(0, 2), c(0, dnorm(0)), col = COL[4])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/normal_dist_mean_500_se_016/normal_dist_mean_500_se_016.R
================================================
require(openintro)
data(COL)

fn1 <- 'normal_dist_mean_500_se_016.pdf'
fn2 <- 'normal_dist_mean_500_se_016_with_upper.pdf'

GenerateGraph <- function(show.tails = FALSE) {
  normTail(0.5, 0.016, L = 0.37, U = 0.63, col = COL[1],
      xlim = c(0.32, 0.68), axes = FALSE)
  at <- c(-1, 0.37, 0.5, 0.63, 2)
  font.36 <- 1
  if (!show.tails) {
    at <- c(-1, 0.37, 0.5, 2)
    font.36 <- 2
  }
  axis(1, at, cex.axis = 0.9)
  if (show.tails) {
    lines(c(-1, 0.37), rep(0, 2), lwd = 5, col = COL[1])
    arrows(0.37, 7, 0.35, 1,
           length = 0.1,
           lwd = 2,
           col = COL[1])
    expr <- expression("Tail Area for "*hat(p))
    text(0.39, 7, expr, pos = 3, col = COL[1],
        font = font.36)
    lines(c(1, 0.63), rep(0, 2), lwd = 5, col = COL[1])
    arrows(0.63, 7, 0.65, 1,
           length = 0.1,
           lwd = 2,
           col = COL[1])
    expr <- expression("Equally unlikely if "*H[0]*" is true")
    text(0.61, 7, expr, pos = 3, col = COL[1], cex = 0.8)
  } else {
    arrows(0.38, 7, 0.371, 1,
           length = 0.1,
           lwd = 2,
           col = COL[1])
    expr <- expression("Observed "*hat(p)*" = 0.37")
    text(0.39, 7, expr, pos = 3, col = COL[1],
        font = font.36)
  }
}


myPDF(fn1, 5, 1.5,
      mar = c(1.55, 0, 0.1, 0),
      mgp = c(3, 0.5, 0))
GenerateGraph()
dev.off()

myPDF(fn2, 5, 1.5,
      mar = c(1.55, 0, 0.1, 0),
      mgp = c(3, 0.5, 0))
GenerateGraph(TRUE)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/nuclearArmsReduction/nuclearArmsReduction.R
================================================
require(openintro)
data(COL)

fn <- 'nuclearArmsReductionPValue.pdf'
myPDF(fn, 3.5, 1,
      mar = c(1.55, 0, 0.1, 0),
      mgp = c(3, 0.5, 0))
normTail(U = 3.75, col = COL[1], axes = FALSE,
         xlim = c(-6, 6))
at <- c(-10, 0, 3.75, 10)
labels <- expression(0, 0.50, 0.56, 0)
axis(1, at, labels, cex.axis = 0.9)
lines(c(3.75, 10), rep(0, 2), lwd = 5, col = COL[1])
lines(c(-3.75, -10), rep(0, 2), lwd = 5, col = COL[1])
arrows(4.3, 0.1, 4.5, 0.03,
       length = 0.1,
       lwd = 2,
       col = COL[1])
text(4.3, 0.1, "upper tail", pos = 3, col = COL[1], font = 2)
arrows(-4.3, 0.1, -4.5, 0.03,
       length = 0.1,
       lwd = 2,
       col = COL[1])
text(-4.3, 0.1, "lower tail", pos = 3, col = COL[1], font = 2)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/p-hat_from_53_and_59-not-used/p-hat_from_53_and_59.R
================================================
library(openintro)
data(COL)

myPDF('p-hat_from_53_and_59.pdf', 2.15, 0.95,
      mar = c(1.31, 0, 0.01, 0),
      mgp = c(3, 0.45, 0))
X <- seq(-4, 4, 0.01)
Y <- dnorm(X)
normTail(0.56, 0.0156, M = c(0.53, 0.59), cex.axis = 0.8, axes = FALSE, col = COL[1])
at <- c(0.53, 0.56, 0.59)
axis(1, at, cex.axis = 0.8)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/p-hat_from_53_and_59_computation/NormTailsCalc.R
================================================
NormTailsCalc <- function(z1, z2, file.name) {

  if (!missing(file.name)) {
    pdf(paste0(file.name, '.pdf'), 4, 0.7)
  }
  par(las = 1,
      mar = rep(0, 4),
      mgp = c(3, 0, 0))


  AddShadedPlot <- function(
      x, y, offset,
      shade.start = -8,
      shade.until = 8) {

    lines(x + offset, y)
    lines(x + offset, rep(0, length(x)))
    these <- which(shade.start <= x & x <= shade.until)
    polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,
            c(0, y[these], 0),
            col = COL[1])
    lines(x + offset, y)
  }

  AddText <- function(x, text) {
    text(x, 0.549283, text)
  }

  X <- seq(-3.2, 3.2, 0.01)
  Y <- dnorm(X)

  plot(X, Y,
      type = 'l',
      axes = FALSE,
      xlim = c(-3.4, 24 + 3.4),
      ylim = c(0, 0.622))

  AddShadedPlot(X, Y, 0)
  AddText(0, format(c(1, 0.0001), scientific = FALSE)[1])

  AddShadedPlot(X, Y, 8, -8, -0.3)
  AddText(8, format(0.3821, scientific = FALSE)[1])

  AddShadedPlot(X, Y, 16, 1.21, 8)
  AddText(16, format(0.1131, scientific = FALSE)[1])

  AddShadedPlot(X, Y, 24, -0.3, 1.21)
  AddText(24, format(0.5048, scientific = FALSE)[1])

  lines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)
  lines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)
  lines(c(8 + 3.72, 8 + 4.28), rep(0.549283, 2), lwd = 2)
  lines(c(8 + 3, 2 * 8 - 3), c(0.2, 0.2), lwd = 3)

  text(20, 0.549283, ' = ')
  segments(rep(19, 2), c(0.17, 0.23), rep(21, 2), lwd = 3)
  if (!missing(file.name)) {
    dev.off()
  }
}



================================================
FILE: ch_foundations_for_inf/figures/p-hat_from_53_and_59_computation/p-hat_from_53_and_59_computation.R
================================================
library(openintro)
data(COL)

AddShadedPlot <- function(x, y, offset,
                          shade.start = -8,
                          shade.until = 8) {
  lines(x + offset, y)
  lines(x + offset, rep(0, length(x)))
  these <- which(shade.start <= x & x <= shade.until)
  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,
          c(0, y[these], 0),
          col = COL[1])
  lines(x + offset, y)
}
AddText <- function(x, text) {
  text(x, 0.549283, text)
}

pdf('p-hat_from_53_and_59_computation.pdf', 4, 0.7)
par(las = 1,
    mar = rep(0, 4),
    mgp = c(3, 0, 0))
X <- seq(-3.2, 3.2, 0.01)
Y <- dnorm(X)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 24 + 3.4),
     ylim = c(0, 0.622))

AddShadedPlot(X, Y, 0)
AddText(0, format(c(1, 0.0001), scientific = FALSE)[1])

AddShadedPlot(X, Y, 8, -8, -0.3)
AddText(8, format(0.3821, scientific = FALSE)[1])

AddShadedPlot(X, Y, 16, 1.21, 8)
AddText(16, format(0.1131, scientific = FALSE)[1])

AddShadedPlot(X, Y, 24, -0.3, 1.21)
AddText(24, format(0.5048, scientific = FALSE)[1])

lines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)
lines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)
lines(c(8 + 3.72, 8 + 4.28), rep(0.549283, 2), lwd = 2)
lines(c(8 + 3, 2 * 8 - 3), c(0.2, 0.2), lwd = 3)

text(20, 0.549283,
     ' = ')
segments(rep(19, 2), c(0.17, 0.23), rep(21, 2), lwd = 3)
dev.off()



================================================
FILE: ch_foundations_for_inf/figures/p-hat_from_867_and_907-not-used/p-hat_from_867_and_907.R
================================================
library(openintro)
data(COL)

myPDF('p-hat_from_867_and_907.pdf', 2.15, 0.95,
      mar = c(1.31, 0, 0.01, 0),
      mgp = c(3, 0.45, 0))
X <- seq(-4, 4, 0.01)
Y <- dnorm(X)
normTail(0.887, 0.0100, M = c(0.867, 0.907), cex.axis = 0.8, axes = FALSE, col = COL[1])
at <- c(0.867, 0.887, 0.907)
axis(1, at, cex.axis = 0.8)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/p-hat_from_86_and_90/p-hat_from_86_and_90.R
================================================
library(openintro)
data(COL)

myPDF('p-hat_from_86_and_90.pdf', 2.15, 0.95,
      mar = c(1.31, 0, 0.01, 0),
      mgp = c(3, 0.45, 0))
X <- seq(-4, 4, 0.01)
Y <- dnorm(X)
normTail(0.88, 0.0100, M = c(0.86, 0.90), cex.axis = 0.8, axes = FALSE, col = COL[1])
at <- c(0.86, 0.88, 0.90)
axis(1, at, cex.axis = 0.8)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/quadcopter/quadcopter_attribution.txt
================================================
https://secure.flickr.com/photos/sebilden/14642916088

Photographer: David J
License: CC BY 2.0


================================================
FILE: ch_foundations_for_inf/figures/sampling_100_prop_X/sampling_100_prop_X.R
================================================
set.seed(4)
library(openintro)
data(COL)
source("../helpers.R")

p <- c(0.03, 0.20, 0.50, 0.80, 0.97)
# Must sub p's actual value into expression() below.
n.sim <- 50000
samp.size <- 100
mar <- c(3.5, 1.5, 2.3, 1.5)


myPDF('sampling_100_prop_X_12.pdf', 8, 2.8,
    mfrow = c(1, 2),
    yaxs = "i",
    mar = mar,
    mgp = c(2.3, 0.6, 0))
for (p. in p[1:2]) {
  if (p. == 0.05) {
    par(mar = c(3.5, 0.2, 2.3, 2))
  } else if (p. == 0.2) {
    par(mar = c(3.5, 2, 2.3, 0.2))
  }
  xlab <- ""
  RunSimulation(p., n.sim, samp.size, xlab = xlab, show = "p")
}
dev.off()

myPDF('sampling_100_prop_X_3.pdf', 4.5, 2.8,
    yaxs = "i",
    mar = mar,
    mgp = c(2.3, 0.6, 0))
for (p. in p[3]) {
  par(mar = c(3.5, 0.2, 2.3, 0.2))
  xlab <- ""
  RunSimulation(p., n.sim, samp.size, xlab = xlab, show = "p")
}
dev.off()

myPDF('sampling_100_prop_X_45.pdf', 8, 2.8,
    mfrow = c(1, 2),
    yaxs = "i",
    mar = mar,
    mgp = c(2.3, 0.6, 0))
for (p. in p[4:5]) {
  if (p. %in% c(0.80)) {
    par(mar = c(3.5, 0.2, 2.3, 2))
  } else {
    par(mar = c(3.5, 2, 2.3, 0.2))
  }
  xlab <- "Sample Proportion"
  RunSimulation(p., n.sim, samp.size, xlab = xlab, show = "p")
}
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/sampling_10_prop_25p/sampling_10_prop_25p - one figure.R
================================================
set.seed(3)
library(openintro)

n.sim <- 10000
samp.size <- 10  # 2541
prop <- 0.25
width <- 0.025

samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)
results <- apply(samples, 1, mean)
mean(results)
sd(results)

myPDF('sampling_10_prop_25p.pdf', 4.5, 2.4,
    mar = c(3.5, 3, 0.7, 0.2),
    mgp = c(2.3, 0.6, 0),
    xaxs = "i")
histPlot(results,
    col = COL[1],
    breaks = seq(-2 * width,
        max(results) + 2 * width, width) - width / 2,
    xlab = "Sample Proportions",
    ylab = "",
    xlim = c(-0.2, 1.05),
    axes = FALSE)
at <- seq(-0.2, 1, 0.1)
axis(1, at = seq(0, 1, 0.1), labels = rep("", 11))
axis(1, at = at)
axis(2, at = seq(0, 2000, 1000))
abline(h = 0, lwd = 2)
x <- seq(-1, 2, 0.001)
y <- dnorm(x, prop, sd(results))
bin.max <- 0.98 * max(table(results))
y <- y * bin.max / max(y)
lines(x, y)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/sampling_10_prop_25p/sampling_10_prop_25p.R
================================================
set.seed(3)
library(openintro)
data(COL)

n.sim <- 10000
samp.size <- 10  # 2541
prop <- 0.25
width <- 0.025

samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)
results <- apply(samples, 1, mean)
mean(results)
sd(results)

myPDF('sampling_10_prop_25p.pdf', 9, 2.4,
    mar = c(3.5, 4, 0.7, 0.2),
    mgp = c(2.3, 0.6, 0),
    yaxs = "i",
    mfrow = c(1, 2))
histPlot(results,
    col = COL[1], breaks = seq(0, max(results) + 2 * width, width) - width / 2,
    xlab = "Sample Proportions",
    ylab = "",
    axes = FALSE)
at <- seq(0, 1, 0.1)
axis(1, at = seq(0, 1, 0.1), labels = rep("", 11))
axis(1, at = at)
axis(2, at = seq(0, 2000, 1000))
abline(h = 0, lwd = 2)
par(las = 0)
mtext("Frequency", 2, 2.9)
par(las = 1)
# x <- seq(-1, 2, 0.001)
# y <- dnorm(x, prop, sd(results))
# bin.max <- max(table(results))
# y <- y * bin.max / max(y)
# lines(x, y)

par(yaxs = "r", mar = c(3.5, 2.5, 0.4, 0.2))
normTail(prop, sd(results), L = -1000, lwd = 2, axes = FALSE)
axis(1, seq(-1, 2, 0.2))
abline(v = 0, lty = 2)

dev.off()


================================================
FILE: ch_foundations_for_inf/figures/sampling_10k_prop_887p/sampling_10k_prop_887p.R
================================================
set.seed(4)
library(openintro)
data(COL)

n.sim <- 10000
samp.size <- 1000  # 2541
prop <- 0.887

samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)
results <- apply(samples, 1, mean)
mean(results)
sd(results)

myPDF('sampling_10k_prop_887p.pdf', 6.5, 3.2,
    mar = c(3.5, 3.8, 1.8, 0.7),
    mgp = c(2.3, 0.6, 0),
    yaxs = "i")
histPlot(results,
    col = COL[1], breaks = 50,
    xlab = "Sample Proportions",
    ylab = "",
    axes = FALSE)
at <- seq(0, 1, 0.02)
axis(1, at = seq(0, 1, 0.01), labels = rep("", 101))
axis(1, at = at)
# axis(2, at = seq(0, 1200, 100), label = rep("", 13))
axis(2, at = seq(0, 750, 250))
# abline(v = 0.89, col = COL[4])
par(las = 0)
mtext("Frequency", 2, 2.7)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/sampling_10k_prop_88p/sampling_10k_prop_88p.R
================================================
set.seed(4)
library(openintro)
data(COL)

n.sim <- 10000
samp.size <- 1000  # 2541
prop <- 0.88

samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)
results <- apply(samples, 1, mean)
mean(results)
sd(results)

myPDF('sampling_10k_prop_88p.pdf', 6.5, 3.2,
    mar = c(3.5, 3.8, 1.8, 0.7),
    mgp = c(2.3, 0.6, 0),
    yaxs = "i")
histPlot(results,
    col = COL[1], breaks = 50,
    xlab = "Sample Proportions",
    ylab = "",
    axes = FALSE)
at <- seq(0, 1, 0.02)
axis(1, at = seq(0, 1, 0.01), labels = rep("", 101))
axis(1, at = at)
# axis(2, at = seq(0, 1200, 100), label = rep("", 13))
axis(2, at = seq(0, 750, 250))
# abline(v = 0.89, col = COL[4])
par(las = 0)
mtext("Frequency", 2, 2.7)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/sampling_5k_prop_50p/sampling_5k_prop_50p.R
================================================
set.seed(3)
library(openintro)
data(COL)

n.sim <- 5000
samp.size <- 1000
prop <- 0.5

samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)
results <- apply(samples, 1, mean)
mean(results)
sd(results)

myPDF('sampling_5k_prop_50p.pdf', 6.5, 3.2,
    mar = c(3.5, 3.8, 1.8, 0.7),
    mgp = c(2.3, 0.6, 0),
    yaxs = "i")
histPlot(results,
    col = COL[1], breaks = 50,
    xlab = "Sample Proportions",
    ylab = "",
    axes = FALSE,
    xlim = c(0.35, 0.65))
at <- seq(0, 1, 0.02)
axis(1, at = seq(0, 1, 0.01), labels = rep("", 101))
axis(1, at = seq(0, 1, 0.05))
# axis(1, at = at)
# axis(2, at = seq(0, 1200, 100), label = rep("", 13))
axis(2, at = seq(0, 200, 100))
par(las = 0)
mtext("Frequency", 2, 2.7)
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/sampling_X_prop_56p/sampling_X_prop_56p.R
================================================
set.seed(4)
library(openintro)
data(COL)
source("../helpers.R")

p <- 0.56
# Must sub p's actual value into expression() below.
n.sim <- 50000
samp.size <- c(5, 25, 100) # , 1000)
mar <- c(3.5, 1.5, 2.3, 1.5)


myPDF('sampling_X_prop_56p.pdf', 4, 5,
    mfrow = c(3, 1),
    yaxs = "i",
    mar = mar,
    mgp = c(2.3, 0.6, 0))
for (ss in samp.size) {
  par(mar = c(3.5, 0.2, 2.3, 0.2))
  xlab <- ifelse(ss < 100, "", "Sample Proportion")
  RunSimulation(p, n.sim, ss, xlim = c(0, 1), xlab = xlab)
}
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/sulphStudyFindPValueUsingNormalApprox/sulphStudyFindPValueUsingNormalApprox.R
================================================
library(openintro)
data(COL)

myPDF('sulphStudyFindPValueUsingNormalApprox.pdf', 6.7, 2.4,
      mar = c(2, 0, 0.5, 0),
      mgp = c(3, 0.65, 0))
normTail(U = 1.9,
         df = 20,
         col = COL[1],
         axes = FALSE,
         xlim = c(-3.5, 3.5))
at <- c(-5, 0, 1.9, 5)
labels <- expression(0, 'null diff. = 0   ',
                     '   obs. diff. = 0.025', 0)
axis(1, at, labels)
yMax <- 0.4

text(0, yMax * 0.4, '0.973')

arrows(2.3, yMax / 2,
       2.3, yMax / 9,
       length = 0.1,
       col = COL[1],
       lwd = 1.5)
text(2.3, yMax / 2, 'p-value\n 0.027',
     pos = 3,
     col = COL[1])
dev.off()


================================================
FILE: ch_foundations_for_inf/figures/whyWeWantPValue/whyWeWantPValue.R
================================================
library(openintro)
data(COL)

BuildWhyWeWantPValuePlot <- function(
    file.name = 'whyWeWantPValue.pdf',
    expression1 = expression('Distribution of '*bar(x)),
    expression2 = expression('observed '*bar(x))) {

  myPDF(file.name, 6.3, 2.5,
        mar = c(2, 1, 0.5, 1),
        mgp = c(2.1, 0.6, 0))
  normTail(L = -5, df = 20,
           axes = FALSE, xlim = c(-6, 3),
           lwd = 2.5, curveColor = COL[5])
  at <- seq(-10, 5, 5)
  labels <- expression('', 'null value '*-5*'×SE   ',
                       'null value', '')
  axis(1, at, labels)
  yMax <- 0.4
  text(0, yMax / 2 - 0.02,
      expression1,
      cex = 1.1, col = COL[5])
  text(0, yMax / 3 - 0.01,
      expression('if '*H[0]*' was true'),
      cex = 1.1, col = COL[5])
  arrows(-5, yMax / 3, -5, yMax / 20,
      length = 0.1, lwd = 2, col = COL[1])
  text(-5, yMax / 3, expression2,
      cex = 1.1, pos = 3, col = COL[1])
  dev.off()
}

BuildWhyWeWantPValuePlot()
BuildWhyWeWantPValuePlot(
    "whyWeWantPValueProp.pdf",
    expression("Distribution of "*hat(p)*","),
    expression("Observed " *hat(p)))  # "Observed proportion")


================================================
FILE: ch_inference_for_means/TeX/ch_inference_for_means.tex
================================================
\begin{chapterpage}{Inference for numerical data}
  \chaptertitle{Inference for numerical data}
  \label{inferenceForNumericalData}
  \label{ch_inference_for_means}
  \chaptersection{oneSampleMeansWithTDistribution}
  \chaptersection{pairedData}
  \chaptersection{differenceOfTwoMeans}
  \chaptersection{PowerForDifferenceOfTwoMeans}
  \chaptersection{anovaAndRegrWithCategoricalVariables}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_inference_for_means}


\chapterintro{Chapter~\ref{ch_foundations_for_inf}
  introduced a framework for statistical inference based
  on confidence intervals and hypotheses using the
  normal distribution for sample proportions.
  In this chapter, we encounter several new point estimates
  and a couple new distributions.
  In each case, the inference ideas remain the same:
  determine which point estimate or test statistic is useful,
  identify an appropriate distribution for the point estimate
  or test statistic, and
  apply the ideas of inference.}



%__________________
\section[One-sample means with the $t$-distribution]
    {One-sample means with the
        $\pmb{\MakeLowercase{t}}$-distribution}
\label{oneSampleMeansWithTDistribution}

\noindent%
Similar to how we can model the behavior of the
sample proportion $\hat{p}$ using a normal distribution,
the sample mean $\bar{x}$ can also be modeled using
a normal distribution when certain conditions are met.
\index{point estimate!single mean}
However, we'll soon learn that a new distribution,
called the $t$-distribution,
tends to be more useful when working with the sample mean.
We'll first learn about this new distribution,
then we'll use it to construct confidence intervals
and conduct hypothesis tests for the mean.


\subsection[The distribution of $\bar{x}$]
    {The sampling distribution of $\pmb{\bar{x}}$}

The sample mean tends to follow
a normal distribution centered at the population mean,~$\mu$,
when certain conditions are met.
Additionally, we can compute a standard error for the sample
mean using the population standard deviation $\sigma$
and the sample size $n$.

\begin{onebox}{Central Limit Theorem for the sample mean}
  When we collect a sufficiently large sample of
  $n$~independent observations from a population with
  mean $\mu$ and standard deviation $\sigma$,
  the sampling distribution of $\bar{x}$ will be nearly
  normal with
  \begin{align*}
  &\text{Mean}=\mu
  &&\text{Standard Error }(SE) = \frac{\sigma}{\sqrt{n}}
  \end{align*}
\end{onebox}

\noindent%
Before diving into confidence intervals and hypothesis
tests using $\bar{x}$, we first need to cover two topics:
\begin{itemize}
\item
    When we modeled $\hat{p}$ using the normal distribution,
    certain conditions had to be satisfied.
    The conditions for working with $\bar{x}$
    are a little more complex, and we'll spend
    Section~\ref{x_bar_conditions} discussing
    how to check conditions for inference.
\item
    The standard error is dependent on the population
    standard deviation, $\sigma$.
    However, we rarely know $\sigma$, and instead
    we must estimate it.
    Because this estimation is itself imperfect,
    we use a new distribution called the
    $t$-distribution\index{t-distribution@$t$-distribution}
    to fix this problem, which we discuss in
%    While we can use the plug-in principle,
%    using the sample standard deviation $s$ in place of $\sigma$,
%    this is not quite enough to resolve the issue entirely.
%    and .
%    We'll cover this topic in
    Section~\ref{introducingTheTDistribution}.
\end{itemize}


\subsection[Evaluating the two conditions required for
    modeling $\bar{x}$]
  {Evaluating the two conditions required for
      modeling $\pmb{\bar{x}}$}
\label{x_bar_conditions}

\noindent%
Two conditions are required to apply the
Central Limit Theorem\index{Central Limit Theorem}
for a sample mean~$\bar{x}$:
\begin{description}
\item[Independence.]
    The sample observations must be independent,
    The most common way to satisfy this condition is
    when the sample is a simple random sample from the
    population.
    If the data come from a random process,
    analogous to rolling a die,
    this would also satisfy the independence condition.
\item[Normality.]
    When a sample is small,
    we also require that the sample observations
    come from a normally distributed population.
    We can relax this condition more and more
    for larger and larger sample sizes.
    This condition is obviously vague,
    making it difficult to evaluate,
    so next we introduce a couple rules of thumb
    to make checking this condition easier.
\end{description}
%%Before we get to the sample size consideration, let's
%%consider a special case of the normal distribution
%%where any sample size is sufficient.
%
%%There is also a special case of the Central Limit Theorem
%%for when the data come from a nearly normal distribution.
%%In this case the sample mean will be nearly normal
%%regardless of sample size.
%
%\begin{onebox}{Special case of the Central Limit Theorem
%    for normally distributed data}
%  The sampling distribution of $\bar{x}$ is nearly normal when
%  the sample observations are independent and come from a nearly
%  normal distribution.
%  This is true for any sample size.
%\end{onebox}
%
%%For population distributions that are not normal,
%%the sample mean $\bar{x}$ will still look normal if the sample
%%size is large enough.
%%To check what is \emph{large enough}, we ask two questions:
%%\begin{itemize}
%%\item
%%    Is the sample show evident skew or outliers?
%%    If so, then if t
%%\end{itemize}
%
%In practice, the population never exactly follows
%a normal distribution,
%and the more ``non-normal'' a population
%distribution, the larger the required sample size required for
%$\bar{x}$ to be reasonably modeled using a normal distribution.
%The rough rule of thumb is, if you don't see any clear outliers
%and we don't have reason to believe particularly extreme outliers
%are present in population, then this condition is satisfied.

\begin{onebox}{Rules of thumb:
    how to perform the normality check}
  There is no perfect way to check the normality condition,
  so instead we use two rules of thumb: %,
%  one for small samples ($n < 30$)
%  and another for large samples ($n \geq 30$):
  \begin{description}
  \setlength{\itemsep}{0mm}
  \item[$\mathbf{n < 30}$:]
      If the sample size $n$ is less than 30
      and there are no clear outliers in the data,
      then we typically assume the data come from
      a nearly normal distribution to satisfy the
      condition.
  \item[$\mathbf{n \geq 30}$:]
      If the sample size $n$ is at least 30
      and there are no \emph{particularly extreme} outliers,
      then we typically assume the sampling distribution
      of $\bar{x}$ is nearly normal, even if the underlying
      distribution of individual observations is not.
  \end{description}
\end{onebox}

In this first course in statistics, you aren't expected
to develop perfect judgement on the normality condition.
However, you are expected to be able to handle
clear cut cases based on the rules of thumb.\footnote{More
  nuanced guidelines would consider further relaxing
  the \emph{particularly extreme outlier} check when the
  sample size is very large.
  However, we'll leave further discussion here to a future course.}

\begin{examplewrap}
\begin{nexample}{Consider the following two plots
    that come from simple random samples from
    different populations.
    Their sample sizes are $n_1 = 15$ and $n_2 = 50$.
    \begin{center}
    \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}
    \end{center}
    Are the independence and normality conditions met
    in each case?}
  \label{outliers_and_ss_condition_ex}%
  Each samples is from a simple random sample of its
  respective population, so the independence condition
  is satisfied.
  Let's next check the normality condition for
  each using the rule of thumb.
  
  The first sample has fewer than 30 observations,
  so we are watching for any clear outliers.
  None are present; while there is a small gap in the
  histogram between 5 and~6, this gap is small and
  20\% of the observations in this small sample
  are represented in that far right bar of the histogram,
  so we can hardly call these clear outliers.
  With no clear outliers, the normality condition
  is reasonably~met.

  The second sample has a sample size greater than 30 and
  includes an outlier that appears to be roughly 5 times
  further from the center of the distribution than the
  next furthest observation.
  This is an example of a particularly extreme outlier,
  so the normality condition would not be satisfied.
\end{nexample}
\end{examplewrap}

In practice, it's typical to also do a mental check to evaluate
whether we have reason to believe the underlying population
would have moderate skew (if $n < 30$)
or have particularly extreme outliers ($n \geq 30$)
beyond what we observe in the data.
For example, consider the number of followers
for each individual account on Twitter,
and then imagine this distribution.
The large majority of accounts have built up
a couple thousand followers or fewer,
while a relatively tiny fraction have amassed
tens of millions of followers,
meaning the distribution is extremely skewed.
When we know the data come from such an extremely
skewed distribution,
it takes some effort to understand what sample
size is large enough for the normality condition
to be satisfied.

%if we were sampling accounts from Twitter
%and examining the distribution of followers on the sampled
%accounts, we can expect that the vast majority of accounts
%will have fewer than 1,000 followers and that there
%will be some very extreme outliers who have tens of millions
%of followers.

%Distribution of the number of subscribers for
%      anyone who has uploaded a video to YouTube.
%      Most such individuals will have built little to
%      no following, while others will have amassed tens
%      of millions of subscribers.
%Generally, we do not presume you to always know when the
%underlying population has particularly extreme outliers.
%That~is, besides looking at the data itself,
%considering the mental check for whether particularly extreme
%outliers are likely to be a sanity check, not a formal check.

%\begin{figure}[h]
%  \centering
%  \Figure{0.8}{outliers_and_ss_condition}
%  \caption{Sample observations for
%      Example~\ref{outliers_and_ss_condition_ex}.}
%  \label{outliers_and_ss_condition}
%\end{figure}

%A more thorough sample size condition assessment would
%also consider two additional aspects beyond the core
%guidance above.

%The most nuanced checks are then when the sample size
%is very small -- and we have almost no observational data
%to allow us to check the condition.
%\begin{description}
%\item[Population knowledge.]
%    If we have information about the population beyond
%    what we've observed in the sample, we would consider
%    this information as well.
%    For example, if the sample size is under 30
%    and the population is known to be moderately skewed
%    (something difficult to detect with a small sample
%    in the observed data),
%    we might still not consider the 
%    For example, if the sample size is under 30 but the
%    population is known to be moderately skewed,
%    then the sample size condition is not reasonable.
%    Likewise, if the population is known to have particularly
%    extreme observations (examples below), then we may
%    require a particularly large sample size if we want
%    to use the normal model for $\bar{x}$.
%\item[Relaxing the extreme outlier condition.]
%    When the sample size gets very large,
%    we may even be able to overcome issues with
%    particularly extreme outliers.
%    However, there isn't clear guidance, and instead,
%    custom simulations can be helpful but are beyond
%    the scope of this book.
%\end{description}
%In this first course in statistics,
%you won't (and aren't expected to) have perfect judgement
%on when the sample size condition is or is not met.
%However, you are expected to be able to handle the
%clear cut cases based on the core guidelines.

%For those wanting to do more rigorous checks
%or for the situation that the ,
%then we add a 
%below are slightly more careful checks, it's convenient
%to break down 
%\begin{description}
%\item[Sample size under 30.]
%    If the sample size is less than 30, then we simply follow
%    the rule of thumb and there isn't
%    extreme skew in the data (usually punctuated by
%    extreme outliers), then we can proceed.
%\item[Sample size at least 30.]
%    If the sample size is at least 30 and there isn't
%    extreme skew in the data (usually punctuated by
%    extreme outliers), then we can proceed.
%\end{description}
%then it's generally reasonable to consider $\bar{x}$
%as following a nearly normal distribution.

%\Comment{Check the ``99\%'' and ``hundreds'' claim in the
%  income example below.}
%
%\begin{examplewrap}
%\begin{nexample}{Describe a couple populations that you know
%    would have particularly extreme outliers.}
%  Wealth distributions in many countries have
%      particularly extreme outliers.
%      For example, over 99\% of the population
%      has fewer than \$10 million saved,
%      while there are hundreds individuals in the
%      United States with over \$1~billion and who
%      are unlikely to be captured in even a moderate-sized
%      sample.
%
%  Distribution of the number of subscribers for
%      anyone who has uploaded a video to YouTube.
%      Most such individuals will have built little to
%      no following, while others will have amassed tens
%      of millions of subscribers.
%
%%  So while we won't be quizzing you on a variety of applications
%%  in this book, when you apply these skills elsewhere it is
%%  important to keep this consideration in mind and do some
%%  research if you aren't sure about outliers.
%\end{nexample}
%\end{examplewrap}
%
%Generally, we do not presume you to always know when the
%underlying population has particularly extreme outliers.
%That~is, besides looking at the data itself,
%considering the mental check for whether particularly extreme
%outliers are likely to be a sanity check, not a formal check.

%\begin{examplewrap}
%\begin{nexample}{Suppose we randomly sampled 20 individuals
%    from the United States and considered their incomes. %,
%    % which are shown in the following distribution:
%    However, the population is known to have particularly
%    extreme outliers, e.g. some individuals with incomes
%    above \$10 million.
%    No matter what we observe in the original 20 observations,
%    can you say whether we should proceed with modeling
%    $\bar{x}$ using a normal distribution?}
%  When we know the population distribution to have particularly
%  extreme outliers, then even if we observe no outliers in our
%  sample, we should not proceed to model $\bar{x}$ using
%  a normal distribution.
%
%  Generally, we do not presume you to always know when the
%  underlying population has particularly extreme outliers.
%  So while we won't be quizzing you on a variety of applications
%  in this book, when you apply these skills elsewhere it is
%  important to keep this consideration in mind and do some
%  research if you aren't sure about outliers.
%\end{nexample}
%\end{examplewrap}

%However, if one or more of clear outliers are present are evidently present,
%the guidelines around a reasonable minimum sample become murky.
%If 
%We'll see some other examples throughout the rest of this book,
%which will help in developing some intuition around this topic,
%but in many cases, data with .

\index{Central Limit Theorem!normal data|)}



\subsection[Introducing the $t$-distribution]
    {Introducing the $\pmb{t}$-distribution}
\label{introducingTheTDistribution}

\index{t-distribution@$t$-distribution|(}
\index{distribution!t@$t$|(}

In practice, we cannot directly calculate the standard error
for $\bar{x}$ since we do not know the population standard
deviation,~$\sigma$.
We encountered a similar issue when computing the standard
error for a sample proportion, which relied on the population
proportion,~$p$.
Our solution in the proportion context was to use sample
value in place
of the population value when computing the standard error.
We'll employ a similar strategy for computing the standard
error of $\bar{x}$, using the sample
standard deviation $s$ in place of $\sigma$:
\begin{align*}
SE = \frac{\sigma}{\sqrt{n}} \approx \frac{s}{\sqrt{n}}
\end{align*}
This strategy tends to work well when we have
a lot of data and can estimate $\sigma$ using $s$ accurately.
However, the estimate is less precise with smaller samples,
and this leads to problems when using the normal
distribution to model $\bar{x}$.
% --
%when the sample size is large --
%but it is less reliable when the sample size is smaller
%than about 30. % independent observations.

We'll find it useful to use a new distribution for
inference calculations called the
\termsub{$\pmb{t}$-distribution}{t-distribution@$t$-distribution}.
A~$t$-distribution, shown as a solid line in
Figure~\ref{tDistCompareToNormalDist}, has a bell shape.
However, its tails are thicker than the normal distribution's,
meaning observations are more likely to fall beyond two
standard deviations from the mean than under the normal
distribution. %\footnote{The standard deviation of the
  %$t$-distribution is actually a little more than 1.
  %However, it is useful to always think of the $t$-distribution
  %as having a standard deviation of 1 in all of our applications.}
%This distribution is important since it accounts for
%a key challenge with modeling the sample mean:
%the standard error of the sample mean isn't as
%precise when the sample size is small.
The extra thick tails of the $t$-distribution are exactly
the correction needed to resolve the problem of using~$s$
in place of $\sigma$ in the $SE$ calculation.

\begin{figure}[h]
  \centering
  \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}
  \caption{Comparison of a $t$-distribution
      and a normal distribution.}
  \label{tDistCompareToNormalDist}
\end{figure}

The $t$-distribution is always centered at zero and
has a single parameter: degrees of freedom.
The \termsub{degrees of freedom ($\pmb{df}$)}
    {degrees of freedom ($df$)!$t$-distribution}
describes the precise form of the bell-shaped $t$-distribution.
Several $t$-distributions are shown in
Figure~\ref{tDistConvergeToNormalDist}
in comparison to the normal distribution.

In general, we'll use a $t$-distribution
with $df = n - 1$ to model the sample mean
when the sample size is $n$.
That is, when we have more observations,
the degrees of freedom will be larger and
the $t$-distribution will look more like the
standard normal distribution;
when the degrees of freedom is about 30 or more,
the $t$-distribution is nearly indistinguishable
from the normal distribution.

\begin{figure}[h]
  \centering
  \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}
  \caption{The larger the degrees of freedom, the more
      closely the $t$-distribution resembles the standard
      normal distribution.}
  \label{tDistConvergeToNormalDist}
\end{figure}

\begin{onebox}{Degrees of freedom
    ($\pmb{\MakeLowercase{df}}$)}
  The degrees of freedom describes the shape of the
  $t$-distribution.
  The larger the degrees of freedom, the more closely
  the distribution approximates the normal model. \stdvspace{}

  When modeling $\bar{x}$ using the $t$-distribution,
  use $df = n - 1$.
\end{onebox}

%\Comment{Cut this next sentence?}
%In Section~\ref{tDistSolutionToSEProblem},
%we relate degrees of freedom to sample size.

The $t$-distribution allows us greater flexibility than
the normal distribution when analyzing numerical data.
In~practice, it's common to use statistical software,
such as R, Python, or SAS for these analyses.
Alternatively, a graphing calculator or a
\termsub{$\pmb{t}$-table}{t-table@$t$-table} may be used;
the $t$-table is similar to the normal distribution table,
and it may be found in Appendix~\ref{tDistributionTable},
which includes usage instructions and examples
for those who wish to use this option.
No matter the approach you choose, apply your method
using the examples below to confirm your working
understanding of the $t$-distribution.

\begin{examplewrap}
\begin{nexample}{What proportion of the $t$-distribution
    with 18 degrees of freedom falls below -2.10?}
  Just like a normal probability problem, we first draw
  the picture in Figure~\ref{tDistDF18LeftTail2Point10}
  and shade the area below -2.10.
%  If this were a normal distribution, the area would be
%  a little less than 0.025, since about 5\% of the area
%  under a normal curve goes out beyond $\pm 1.96$ standard
%  deviations.
  Using statistical software, we can obtain a precise
  value: 0.0250.
%  The tail area below -2.10 in the $t$-distribution with
%  $df = 18$ is the same as the tail area below -1.96 in
%  the normal distribution.
\end{nexample}
\end{examplewrap}

\begin{figure}
  \centering
  \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}
  \caption{The $t$-distribution with 18 degrees of freedom.
      The area below -2.10 has been shaded.}
  \label{tDistDF18LeftTail2Point10}
\end{figure}

\begin{examplewrap}
\begin{nexample}{A $t$-distribution with 20 degrees of freedom
    is shown in the left panel of
    Figure~\ref{tDistDF20RightTail1Point65}.
    Estimate the proportion of the distribution falling
    above 1.65.}
  With a normal distribution, this would correspond to
  about~0.05, so we should expect the $t$-distribution
  to give us a value in this neighborhood.
  Using statistical software: 0.0573.
\end{nexample}
\end{examplewrap}

\begin{figure}
  \centering
  \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}
  \caption{Left: The $t$-distribution with 20 degrees
      of freedom, with the area above 1.65 shaded.
      Right:~The $t$-distribution with 2 degrees of freedom,
      with the area further than 3 units from 0 shaded.}
  \label{tDistDF20RightTail1Point65}
\end{figure}

\begin{examplewrap}
\begin{nexample}{A $t$-distribution with 2 degrees of freedom
    is shown in the right panel of
    Figure~\ref{tDistDF20RightTail1Point65}.
    Estimate the proportion of the distribution falling more
    than 3~units from the mean (above or below).}
  With so few degrees of freedom, the $t$-distribution will
  give a more notably different value than the normal
  distribution.
  Under a normal distribution, the area would be about
  0.003 using the 68-95-99.7 rule.
  For a $t$-distribution with $df = 2$, the area in both
  tails beyond 3~units totals 0.0955.
  This area is dramatically different than what
  we obtain from the normal distribution.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
What proportion of the $t$-distribution with 19 degrees
of freedom falls above -1.79 units?
Use your preferred method for finding tail areas.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We want to find the shaded area \emph{above}
  -1.79 (we leave the picture to you).
  The lower tail area has an area of 0.0447,
  so the upper area would have an area of $1 - 0.0447 = 0.9553$.}

\index{distribution!t@$t$|)}
\index{t-distribution@$t$-distribution|)}


%\subsection{Conditions for using the $\mathbf{t}$-distribution
%    for inference on a sample mean}
%\label{tDistSolutionToSEProblem}
%
%\noindent%
%To proceed with the $t$-distribution for inference about a single mean, we first check two conditions.
%\begin{description}
%\item[Independence.]
%    We verify this condition just as we did before.
%    We collect a simple random sample, or if the data are from
%    an experiment or random process, we check to the best of our
%    abilities that the observations were independent.
%\item[Sample size.]
%    We use the earlier rule of thumb to evaluate this condition:
%
%    If the sample size $n$ is less than 30
%    and there are no clear outliers in the data,
%    then the sample size condition is satisfied.
%
%    If the sample size $n$ is at least 30
%    and there are no \emph{particularly extreme} outliers,
%    then the sample size condition is satisfied.
%\end{description}
%When examining a sample mean and estimated standard error
%from a sample of $n$ independent and nearly normal observations,
%we use a $t$-distribution with $n - 1$ degrees of freedom~($df$).
%For example, if the sample size was 19, then we would use the
%$t$-distribution with $df = 19 - 1 = 18$ degrees of freedom
%and proceed in a way similar to how we worked with proportions.


\D{\newpage}

\subsection[One sample $t$-confidence intervals]
    {One sample $\pmb{t}$-confidence intervals}
\label{oneSampleTConfidenceIntervals}

\index{data!dolphins and mercury|(}

Let's get our first taste of applying the $t$-distribution
in the context of an example about the mercury content
of dolphin muscle.
%Dolphins are at the top of the oceanic food chain, which causes dangerous substances such as mercury to concentrate in their organs and muscles.
Elevated mercury concentrations are an important problem
for both dolphins
and other animals, like humans, who occasionally eat them.
\captionsetup{width=86mm}

\begin{figure}[h]
\centering
\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}  \\
\addvspace{2mm}
\begin{minipage}{\textwidth}
   \caption[rissosDolphinPic]{A Risso's dolphin.\vspace{-1mm} \\
   -----------------------------\vspace{-2mm}\\
   {\footnotesize Photo by Mike Baird (\oiRedirect{textbook-bairdphotos_com}{www.bairdphotos.com}). \oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}.}\vspace{-8mm}}
   \label{rissosDolphin}
\end{minipage}
\stdvspace{}
\end{figure}
\captionsetup{width=\mycaptionwidth}

We 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.

\begin{figure}[h]
\centering
\begin{tabular}{ccc cc}
\hline
$n$ & $\bar{x}$ & $s$ & minimum & maximum \\
19   & 4.4	  & 2.3  & 1.7	       & 9.2 \\
\hline
\end{tabular}
\caption{Summary of mercury content in the muscle of
    19 Risso's dolphins from the Taiji area.
    Measurements are in micrograms of mercury per wet gram
    of muscle ($\mu$g/wet g).}
\label{summaryStatsOfHgInMuscleOfRissosDolphins}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Are the independence and
    normality conditions satisfied for this data~set?}
  The observations are a simple random sample,
  therefore independence is reasonable.
  The summary statistics in
  Figure~\ref{summaryStatsOfHgInMuscleOfRissosDolphins}
  do not suggest any clear outliers, since
  all observations are within 2.5 standard deviations
  of the mean.
  Based on this evidence, the normality condition
  seems reasonable.
\end{nexample}
\end{examplewrap}

In 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:
\begin{align*}
&\text{point estimate} \ \pm\  t^{\star}_{df} \times SE
&&\to
&&\bar{x} \ \pm\  t^{\star}_{df} \times \frac{s}{\sqrt{n}}
\end{align*}
%The sample mean is the point estimate of interest.
%The standard error is computed using $SE = s/\sqrt{n}$.

\begin{examplewrap}
\begin{nexample}{Using the summary statistics in
    Figure~\ref{summaryStatsOfHgInMuscleOfRissosDolphins},
    compute the standard error for the average
    mercury content in the $n = 19$ dolphins.}
  We plug in $s$ and $n$ into the formula:
  $
  %\begin{align*}
  SE
    = s / \sqrt{n}
    = 2.3 / \sqrt{19}
    = 0.528
  %\end{align*}
  $.
\end{nexample}
\end{examplewrap}

The value $t^{\star}_{df}$ is a cutoff we obtain based on the
confidence level and the $t$-distribution with $df$ degrees
of freedom.
That cutoff is found in the same way as with a normal
distribution: we find $t^{\star}_{df}$ such that
the fraction of the $t$-distribution with $df$ degrees
of freedom within a distance $t^{\star}_{df}$
of 0 matches the confidence level of interest.

\begin{examplewrap}
\begin{nexample}{When $n = 19$, what is the appropriate
    degrees of freedom?
    Find $t^{\star}_{df}$ for this degrees of freedom
    and the confidence level of 95\%}
  The degrees of freedom is easy to calculate:
  $df = n - 1 = 18$.
  
  Using statistical software, we find the cutoff where
  the upper tail is equal to 2.5\%:
  $t^{\star}_{18} = 2.10$.
  The area below -2.10 will also be equal to 2.5\%.
  That is, 95\% of the $t$-distribution with $df = 18$
  lies within 2.10 units of~0.
\end{nexample}
\end{examplewrap}

%\begin{onebox}{Degrees of freedom for a single sample}
%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.
%\end{onebox}

%In our current example, we should use the $t$-distribution
%with $df=19-1=18$ degrees of freedom.
%We can generally identify $t_{18}^{\star}$
%using statistical software.
%Alternatively, we could use the $t$-table in
%Appendix~\ref{tDistributionTable}.
%Generally the value of $t^{\star}_{df}$ is slightly larger
%than what we would get under the normal model with~$z^{\star}$.

\begin{examplewrap}
\begin{nexample}{Compute and interpret the 95\% confidence interval
    for the average mercury content in Risso's dolphins.}
  We can construct the confidence interval as
  \begin{align*}
  \bar{x} \ \pm\  t^{\star}_{18} \times SE
    \quad \to \quad 4.4 \ \pm\  2.10 \times 0.528
    \quad \to \quad (3.29, 5.51)
  \end{align*}
  We are 95\% confident the average mercury content of muscles
  in Risso's dolphins is between 3.29 and 5.51 $\mu$g/wet gram,
  which is considered extremely high.
\end{nexample}
\end{examplewrap}

\index{data!dolphins and mercury|)}

\begin{onebox}{Finding a
    $\pmb{\MakeLowercase{t}}$-confidence interval
    for the mean}
  Based on a sample of $n$ independent and nearly normal
  observations, a confidence interval for the population
  mean is
  \begin{align*}
  &\text{point estimate} \ \pm\  t^{\star}_{df} \times SE
  &&\to
  &&\bar{x} \ \pm\  t^{\star}_{df} \times \frac{s}{\sqrt{n}}
  \end{align*}
  where $\bar{x}$ is the sample mean, $t^{\star}_{df}$
  corresponds to the confidence level and degrees of freedom
  $df$, and $SE$ is the standard error as estimated by
  the sample.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise} \label{croakerWhiteFishPacificExerConditions}
\index{data!white fish and mercury|(}
The FDA's webpage provides some data on mercury content of fish.
Based on a sample of 15 croaker white fish (Pacific),
a sample mean and standard deviation were computed as 0.287
and 0.069 ppm (parts per million), respectively.
The 15 observations ranged from 0.18 to 0.41 ppm.
We will assume these observations are independent.
Based on the summary statistics of the data,
do you have any objections to the normality condition
of the individual observations?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The sample size is under 30,
  so we check for obvious outliers:
  since all observations are within 2 standard deviations
  of the mean, there are no such clear outliers.}

\begin{examplewrap}
\begin{nexample}{Estimate the standard error of
    $\bar{x} = 0.287$ ppm using the data summaries in
    Guided Practice~\ref{croakerWhiteFishPacificExerConditions}.
    If we are to use the $t$-distribution to create a
    90\% confidence interval for the actual mean of the
    mercury content, identify the degrees of freedom
    and $t^{\star}_{df}$.}
  \label{croakerWhiteFishPacificExerSEDFTStar}%
  The standard error: $SE = \frac{0.069}{\sqrt{15}} = 0.0178$.

  Degrees of freedom: $df = n - 1 = 14$.

  Since the goal is a 90\% confidence interval,
  we choose $t_{14}^{\star}$ so that the two-tail area
  is 0.1:
  $t^{\star}_{14} = 1.76$.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Confidence interval for a single mean}
  Once you've determined a one-mean confidence interval
  would be helpful for an application,
  there are four steps to constructing the interval:
  \begin{description}
  \item[Prepare.]
      Identify $\bar{x}$, $s$, $n$, and determine what
      confidence level you wish to use.
  \item[Check.]
      Verify the conditions to ensure $\bar{x}$
      is nearly normal.
  \item[Calculate.]
      If the conditions hold, compute $SE$,
      find $t_{df}^{\star}$, and construct the interval.
  \item[Conclude.]
      Interpret the confidence interval in the context
      of the problem.
  \end{description}
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
\label{croakerWhiteFish90ci}
Using 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{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{
  $\bar{x} \ \pm\ t^{\star}_{14} \times SE
      \ \to\  0.287 \ \pm\  1.76 \times 0.0178
      \ \to\ (0.256, 0.318)$.
  We are 90\% confident that the average mercury content
  of croaker white fish (Pacific) is between 0.256 and 0.318 ppm.}

\begin{exercisewrap}
\begin{nexercise}
The 90\% confidence interval from
Guided Practice~\ref{croakerWhiteFish90ci}
is 0.256 ppm to 0.318 ppm.
Can we say that 90\% of croaker white fish (Pacific)
have mercury levels between 0.256 and 0.318 ppm?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{
  No, a confidence interval only provides a range
  of plausible values for a population parameter,
  in this case the population mean.
  It does not describe what we might observe
  for individual observations.}

\index{data!white fish and mercury|)}

%Now that we've whet \Comment{spelling?} your palette with confidence
%intervals for a mean, let's speed on through to
%hypothesis tests for the mean.


\subsection[One sample $t$-tests]
    {One sample $\pmb{t}$-tests}
\label{oneSampleTTests}

\newcommand{\cherryblossomn}{100}
\newcommand{\cherryblossommean}{97.32}
\newcommand{\cherryblossomnull}{93.29}
\newcommand{\cherryblossomsd}{16.98}
\newcommand{\cherryblossomse}{1.70}
\newcommand{\cherryblossomz}{2.37}

\noindent%
Is 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.

The 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.

\begin{exercisewrap}
\begin{nexercise}
What are appropriate hypotheses for this context?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise}
The data come from a simple random sample of all participants,
so the observations are independent.
However, should we be worried about the normality condition?
See Figure~\ref{run10SampTimeHistogram} for a histogram
of the differences and evaluate if we can move
forward.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{With a sample of \cherryblossomn{},
  we should only be concerned if there is are particularly
  extreme outliers.
  The histogram of the data doesn't show any outliers of concern
  (and arguably, no outliers at all).}

\begin{figure}[h]
  \centering
  \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} 
  \caption{A histogram of \var{time} for the sample
      Cherry Blossom Race data.}
  \label{run10SampTimeHistogram}
\end{figure}

When completing a hypothesis test for the one-sample mean,
the process is nearly identical to completing a hypothesis
test for a single proportion.
First, we find the Z-score using the observed value,
null value, and standard error;
however, we call it a \term{T-score} since we use
a $t$-distribution for calculating the tail area.
Then we find the p-value using the same ideas we used
previously: find the one-tail area under the sampling
distribution, and double it.

\D{\newpage}

%\begin{exampleewrap}
%\begin{nexample}{With independence satisfied and normality
%    not a concern, we can proceed with performing a hypothesis
%    test using the $t$-distribution.
%    The sample mean and sample standard deviation of the
%    sample of \cherryblossomn{} runners from the 2017 Cherry
%    Blossom Race are \cherryblossommean{} and
%    \cherryblossomsd{} minutes, respectively.
%    Recall that the sample size is 100.
%    What is the p-value for the test, and what is your
%    conclusion?}
%\end{nexercise}
%\end{exercisewrap}

\begin{examplewrap}
\begin{nexample}{With both the independence
    and normality conditions satisfied,
    we can proceed with a hypothesis test using
    the $t$-distribution.
    The sample mean and sample standard deviation
    of the sample
    of \cherryblossomn{} runners from the
    2017 Cherry Blossom Race
    are \cherryblossommean{} and \cherryblossomsd{} minutes,
    respectively.
    Recall that the sample size is 100
    and the average run time in 2006 was
    \cherryblossomnull{} minutes.
    Find the test statistic and p-value.
    What is your conclusion?}

  To find the test statistic (T-score),
  we first must determine the standard error:
  \begin{align*}
  SE
    = \cherryblossomsd{} / \sqrt{\cherryblossomn{}}
    = \cherryblossomse{}
  \end{align*}
  Now we can compute the \emph{T-score}
  using the sample mean (\cherryblossommean{}),
  null value (\cherryblossomnull{}), and $SE$:
  \begin{align*}
  T
    = \frac{\cherryblossommean{} - \cherryblossomnull{}}
        {\cherryblossomse{}}
    = \cherryblossomz{}
  \end{align*}
  For $df = \cherryblossomn{} - 1 = 99$,
  we can determine using statistical software
  (or a $t$-table) that the one-tail area is 0.01,
  which we double to get the p-value:~0.02.

  Because the p-value is smaller than 0.05,
  we reject the null hypothesis.
  That is, the data provide strong evidence that the average
  run time for the Cherry Blossom Run in 2017 is different
  than the 2006 average.
  Since the observed value is above the null value
  and we have rejected the null hypothesis, we would conclude
  that runners in the race were slower on average in 2017
  than in 2006.
\end{nexample}
\end{examplewrap}

%%\begin{onebox}{When using a $t$-distribution, we use a T-score (same as Z-score)}
%To help us remember to use the $t$-distribution,
%we use a $T$ to represent the test statistic,
%and we often call this a \term{T-score}.
%The Z-score and T-score are computed in the exact same way
%and are conceptually identical:
%each represents how many standard errors the observed value
%is from the null value.
%%\end{onebox}

\begin{onebox}{Hypothesis testing for a single mean}
  Once you've determined a one-mean hypothesis test is the
  correct procedure, there are four steps to completing the
  test:
  \begin{description}
  \item[Prepare.]
      Identify the parameter of interest,
      list out hypotheses,
      identify the significance level,
      and identify $\bar{x}$, $s$, and $n$.
  \item[Check.]
      Verify conditions to ensure $\bar{x}$ is nearly normal.
  \item[Calculate.]
      If the conditions hold, compute $SE$,
      compute the T-score, and identify the p-value.
  \item[Conclude.]
      Evaluate the hypothesis test by comparing the p-value
      to $\alpha$, and provide a conclusion in the context
      of the problem.
  \end{description}
\end{onebox}

\CalculatorVideos{confidence intervals and hypothesis tests for a single mean}


{\input{ch_inference_for_means/TeX/one-sample_means_with_the_t-distribution.tex}}





%__________________
\section{Paired data}
\label{pairedData}

\newcommand{\uclabookN}{68}
\newcommand{\uclabookDF}{67}
\newcommand{\uclabookM}{3.58}
\newcommand{\uclabookSD}{13.42}
\newcommand{\uclabookSE}{1.63}

\index{paired|(}
\index{data!textbooks|(}

\noindent%
In an earlier edition of this textbook,
we found that Amazon prices were, on average,
lower than those of the UCLA Bookstore for UCLA courses
in 2010.
It's been several years, and many stores have adapted
to the online market, so we wondered,
how is the UCLA Bookstore doing today?

We sampled 201 UCLA courses.
Of those, \uclabookN{}
required books could be found on Amazon.
A~portion of the data set from these courses
is shown in Figure~\ref{textbooksDF},
where prices are in US dollars.

\begin{figure}[h]
\centering
\begin{tabular}{r ll ccc}
  \hline
 & subject &
     course\us{}number &
     bookstore &
     amazon &
     price\us{}difference \\ 
  \hline
  1 & American Indian Studies & M10 & 47.97 & 47.45 & 0.52 \\ 
  2 & Anthropology & 2 & 14.26 & 13.55 & 0.71 \\ 
  3 & Arts and Architecture & 10 & 13.50 & 12.53 & 0.97 \\
  $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ \\
  %\uclabookDF{} & Korean & 1 & 24.96 & 23.79 & 1.17 \\ 
  \uclabookN{} & Jewish Studies & M10 & 35.96 & 32.40 & 3.56 \\
  \hline
\end{tabular}
\caption{Four cases of the \data{textbooks} data set.%
    \vspace{-3mm}}
\label{textbooksDF}
\end{figure}
% 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),])

\subsection{Paired observations}

Each textbook has two corresponding prices in the data set:
one for the UCLA Bookstore and one for Amazon.
When two sets of observations have this special
correspondence, they are said to be \term{paired}.

\begin{onebox}{Paired data}
  Two sets of observations are \emph{paired} if each
  observation in one set has a special correspondence
  or connection with exactly one observation in the other
  data set.
\end{onebox}

To analyze paired data, it is often useful to look
at the difference in outcomes of each pair of observations.
In the textbook data, we look at the differences
in prices, which is represented as the
\var{price\us{}difference} variable
in the data set.
Here the differences are taken as
\begin{align*}
\text{UCLA Bookstore price} - \text{Amazon price}
\end{align*}
%for each book.
It is important that we always subtract using
a consistent order;
here Amazon prices are always subtracted from UCLA prices.
The first difference shown in Figure~\ref{textbooksDF}
is computed as $47.97 - 47.45 = 0.52$.
Similarly, the second difference is computed as
$14.26 - 13.55 = 0.71$,
 and the third is $13.50 - 12.53 = 0.97$.
A histogram of the differences is shown in
Figure~\ref{diffInTextbookPricesF18}.
Using differences between paired observations
is a common and useful way to analyze paired data.

\begin{figure}[h]
  \centering
  \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}
  \caption{Histogram of the difference in price for
      each book sampled.}
  \label{diffInTextbookPricesF18}
\end{figure}


\subsection{Inference for paired data}

To analyze a paired data set,
we simply analyze the differences.
We can use the same $t$-distribution techniques
we applied in
Section~\ref{oneSampleMeansWithTDistribution}.

\begin{figure}[h]
\centering
\begin{tabular}{ccccc}
\hline
$n_{_{\text{\emph{diff}}}}$	&\hspace{3mm}& $\bar{x}_{_{\text{\emph{diff}}}}$	&\hspace{3mm}& $s_{_{\text{\emph{diff}}}}$ \vspace{1mm}\\
\uclabookN{}  && \uclabookM{}  && \uclabookSD{} \\
\hline
\end{tabular}
\caption{Summary statistics for the \uclabookN{} price differences.}
\label{textbooksSummaryStats}
\end{figure}

%\Comment{Consider breaking the next example into two pieces.}

\begin{examplewrap}
\begin{nexample}{Set up a hypothesis test
    to determine whether, on average, there is a difference
    between Amazon's price for a book and the UCLA
    bookstore's price.
    Also, check the conditions for whether we can move
    forward with the test using the $t$-distribution.}
  \label{htSetupTextbookPriceDiff}%
  We are considering two scenarios: there is no difference
  or there is some difference in average prices.
  \begin{itemize}
  \setlength{\itemsep}{0mm}
  \item[$H_0$:]
      $\mu_{\text{\emph{diff}}} = 0$.
      There is no difference in the average textbook price.
  \item[$H_A$:]
      $\mu_{\text{\emph{diff}}} \neq 0$.
      There is a difference in average prices.
  \end{itemize}

  Next, we check the independence and normality conditions.
  The observations are based on a simple random sample,
  so independence is reasonable.
  While there are some outliers,
  $n = \uclabookN{}$ and none of the outliers
  are particularly extreme, so the normality
  of $\bar{x}$ is satisfied.
  With these conditions satisfied,
  we can move forward with the $t$-distribution.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Complete the hypothesis test started
    in Example~\ref{htSetupTextbookPriceDiff}.}
  \label{SEAndTScoreTextbookPriceDiff}
  To compute the test  compute the standard error associated with
  $\bar{x}_{\text{\emph{diff}}}$ using the standard
  deviation of the differences
  ($s_{_{\text{\emph{diff}}}} = \uclabookSD{}$)
  and the number of differences
  ($n_{_{\text{\emph{diff}}}} = \uclabookN{}$):
  \begin{align*}
  SE_{\bar{x}_{\text{\emph{diff}}}}
    = \frac{s_{\text{\emph{diff}}}}{\sqrt{n_{\text{\emph{diff}}}}}
    = \frac{\uclabookSD{}}{\sqrt{\uclabookN{}}} = \uclabookSE{}
  \end{align*}
  The test statistic is the T-score of
  $\bar{x}_{\text{\emph{diff}}}$
  under the null condition that the actual mean
  difference is~0:
  \begin{align*}
  T
    = \frac{\bar{x}_{\text{\emph{diff}}} - 0}
        {SE_{\bar{x}_{\text{\emph{diff}}}}}
    = \frac{\uclabookM{} - 0}{\uclabookSE{}} = 2.20
  \end{align*}
  To visualize the p-value, the sampling distribution
  of $\bar{x}_{\text{\emph{diff}}}$ is drawn as though
  $H_0$ is true,
  and the p-value is represented by the two shaded tails:
  \begin{center}
  \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}
  \end{center}
  The degrees of freedom is
  $df = \uclabookN{} - 1 = \uclabookDF{}$.
  Using statistical software, we find the
  one-tail area of 0.0156.
  Doubling this area gives the p-value: 0.0312.

  Because the p-value is less than 0.05,
  we reject the null hypothesis.
  Amazon prices are, on average, lower than the
  UCLA Bookstore prices for UCLA courses.
\end{nexample}
\end{examplewrap}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
Create a 95\% confidence interval for the average
price difference between books at the UCLA bookstore
and books on Amazon.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Conditions
  have already verified and the standard error
  computed in
  Example~\ref{htSetupTextbookPriceDiff}.
  To find the interval,
  identify $t^{\star}_{\uclabookDF{}}$ using statistical software
  or the $t$-table ($t^{\star}_{\uclabookDF{}} = 2.00$),
  and plug it, the point estimate,
  and the standard error into the confidence
  interval formula:
  \begin{align*}
  \text{point estimate} \ \pm\ z^{\star} \times SE
      \quad\to\quad
          \uclabookM{} \ \pm\ 2.00 \times \uclabookSE{}
      \quad\to\quad (0.32, 6.84)
  \end{align*}
  We are 95\% confident that Amazon is, on average,
  between \$0.32 and \$6.84 less expensive
  than the UCLA Bookstore for UCLA course books.}

\begin{exercisewrap}
\begin{nexercise}
We have strong evidence that Amazon is,
on average, less expensive.
How should this conclusion affect UCLA student
buying habits?
Should UCLA students always buy their books
on Amazon?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The average price difference
  is only mildly useful for this question.
  Examine the distribution shown in
  Figure~\ref{diffInTextbookPricesF18}.
  There are certainly a handful of cases where
  Amazon prices are far below the UCLA Bookstore's,
  which suggests it is worth checking Amazon
  (and probably other online sites) before purchasing.
  However, in many cases the Amazon price is
  above what the UCLA Bookstore charges,
  and most of the time the price isn't that different.
  Ultimately, if getting a book immediately from
  the bookstore is notably more convenient,
  e.g. to get started on reading or homework,
  it's likely a good idea to go with the UCLA
  Bookstore unless the price difference on a
  specific book happens to be quite large.

  For reference, this is a very different result
  from what we (the authors) had seen in a similar
  data set from 2010.
  At that time, Amazon prices were almost uniformly
  lower than those of the UCLA Bookstore's and by
  a large margin, making the case to use Amazon over
  the UCLA Bookstore quite compelling at that time.
  Now we frequently check multiple websites
  to find the best price.}

\index{data!textbooks|)}
\index{paired|)}

{\input{ch_inference_for_means/TeX/paired_data.tex}}






%__________________
\section{Difference of two means}
\label{differenceOfTwoMeans}

\noindent%
In this section we consider a difference in
two population means, $\mu_1 - \mu_2$, under the condition
that the data are not paired.
Just as with a single sample, we identify conditions to ensure
we can use the $t$-distribution with a point estimate
of the difference, $\bar{x}_1 - \bar{x}_2$,
and a new standard error formula.
Other than these two differences, the details are almost
identical to the one-mean procedures.

We apply these methods in three contexts: determining whether
stem cells can improve heart function,
exploring the relationship between pregnant womens' smoking
habits and birth weights of newborns,
and exploring whether there is statistically significant
evidence that one variation of an exam is harder than
another variation.
This section is motivated by questions like
``Is there convincing evidence that newborns from mothers
who smoke have a different average birth weight than newborns
from mothers who don't smoke?''


\subsection{Confidence interval for a difference of means}

\index{data!stem cells, heart function|(}
\index{point estimate!difference of means|(}

Does treatment using embryonic stem cells (ESCs)
help improve heart function following a heart attack?
Figure~\ref{statsSheepEscStudy} contains summary statistics
for an experiment to test ESCs in sheep that had a heart attack.
Each of these sheep was randomly assigned to the ESC
or control group, and the change in their hearts' pumping
capacity was measured in the study.
Figure~\ref{stemCellTherapyForHearts} provides
histograms of the two data sets.
A~positive value corresponds to increased pumping capacity,
which generally suggests a stronger recovery.
Our goal will be to identify a 95\% confidence interval
for the effect of ESCs on the change in heart pumping
capacity relative to the control group.

\begin{figure}[h]
\centering
\begin{tabular}{l rrrrr}
\hline
\hspace{10mm}	& $n$	& $\bar{x}$	& $s$  \\
\hline
ESCs     & 9  & 3.50   & 5.17  \\
control  & 9  & -4.33  & 2.76  \\
\hline
\end{tabular}
\caption{Summary statistics of the embryonic stem cell study.}
\label{statsSheepEscStudy}
\end{figure}

The point estimate of the difference in the heart pumping variable
is straightforward to find: it is the difference in the sample means.
\begin{align*}
\bar{x}_{esc} - \bar{x}_{control}\ 
  =\ 3.50 - (-4.33)\ 
  =\ 7.83
\end{align*}
For the question of whether we can model this difference
using a $t$-distribution, we'll need to check new conditions.
Like the 2-proportion cases, we will require a more
robust version of independence so we are confident
the two groups are also independent.
Secondly, we also check for normality in each
group separately, which in practice is a check
for outliers.

\index{point estimate!difference of means|)}

%\begin{examplewrap}
%\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}
%We first setup the hypotheses:
%\begin{itemize}
%\setlength{\itemsep}{0mm}
%\item[$H_0$:] The stem cells do not improve heart pumping function. $\mu_{esc} - \mu_{control} = 0$.
%\item[$H_A$:] The stem cells do improve heart pumping function. $\mu_{esc} - \mu_{control} > 0$.
%\end{itemize}
%\end{nexample}
%\end{examplewrap}

\begin{onebox}{Using the
    $\pmb{\MakeLowercase{t}}$-distribution
    for a difference in means}
  \label{ConditionsForTwoSampleTDist}%
  The $t$-distribution can be used for inference when working
  with the standardized difference of two means if
  \begin{itemize}
  \setlength{\itemsep}{0mm}
  \item \emph{Independence, extended.}
    The data are independent within and between
    the two groups, e.g. the data come from
    independent random samples or from a
    randomized experiment.
  \item \emph{Normality.}
    We check the outliers rules of thumb for
    each group separately.
  \end{itemize}
  The standard error may be computed as
  \begin{align*}
  SE%_{\bar{x}_{1} - \bar{x}_{2}}
    = \sqrt{\frac{\sigma_1^2}{n_1} + \frac{\sigma_2^2}{n_2}}
    %\approx \sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}
  \index{standard error (SE)!difference in means}
  \end{align*}
  The official formula for the degrees of freedom is quite
  complex %\footnotemark{}
  and is generally computed using software,
  so instead you may use the smaller of
  $n_1 - 1$ and $n_2 - 1$ for the degrees of freedom
  if software isn't readily available.
\end{onebox}
%\footnotetext{$df = 
%    \left.
%    \left[\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}\right]^2
%    \middle/
%    \left[\frac{(s_1^2 / n_1)^2}{n_1 - 1} +
%        \frac{(s_2^2 / n_2)^2}{n_2 - 1}\right]
%    \right.$}


\D{\newpage}

\begin{examplewrap}
\begin{nexample}{Can the $t$-distribution be used to make
    inference using the point estimate,
    $\bar{x}_{esc} - \bar{x}_{control} = 7.83$?}
  First, we check for independence.
  Because the sheep were randomized into
  the groups, independence within
  and between groups is satisfied.

  Figure~\ref{stemCellTherapyForHearts}
  does not reveal any clear outliers
  in either group.
  (The ESC group does look a bit more variability,
  but this is not the same as having clear outliers.)

  With both conditions met, we can use the
  $t$-distribution to model the difference of sample means.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
  \caption{Histograms for both the embryonic stem cell
      and control group.}
  \label{stemCellTherapyForHearts}
\end{figure}

%\begin{onebox}{Conditions for applying the $t$-distribution to $\bar{x}_1 - \bar{x}_2$}
%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.
%\end{onebox}

%In addition to new conditions, we also will need an updated
%formula for the standard error for the difference of two means.
%
%\begin{onebox}{Distribution of a difference of sample means}
%  The sample difference of two means, $\bar{x}_1 - \bar{x}_2$,
%  can be modeled using the $t$-distribution and the standard error
%  \begin{align*}
%  SE%_{\bar{x}_{1} - \bar{x}_{2}}
%    = \sqrt{\frac{\sigma_1^2}{n_1} + \frac{\sigma_2^2}{n_2}}
%    %\approx \sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}
%  \end{align*}
%  when each sample mean can itself be modeled using
%  a $t$-distribution and the samples are independent.
%  The official formula for the degrees of freedom is quite
%  complex %\footnotemark{}
%  and is generally computed using software,
%  so instead you may use the smaller of
%  $n_1 - 1$ and $n_2 - 1$ for the degrees of freedom
%  if software isn't readily available.
%\end{onebox}
%%\footnotetext{$df = 
%%    \left.
%%    \left[\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}\right]^2
%%    \middle/
%%    \left[\frac{(s_1^2 / n_1)^2}{n_1 - 1} +
%%        \frac{(s_2^2 / n_2)^2}{n_2 - 1}\right]
%%    \right.$}

%We can quantify the variability in the point estimate,
%$\bar{x}_{esc} - \bar{x}_{\text{control}}$,
%using the following formula for its standard error:
%\index{standard error (SE)!difference in means}
%\begin{align*}
%SE%_{\bar{x}_{esc} - \bar{x}_{control}}
%  = \sqrt{\frac{\sigma_{esc}^2}{n_{esc}}
%      + \frac{\sigma_{control}^2}{n_{control}}}
%\end{align*}
As with the one-sample case, we always compute the
standard error using sample standard deviations rather
than population standard deviations:
\begin{align*}
SE%_{\bar{x}_{esc} - \bar{x}_{control}}
	%= \sqrt{\frac{\sigma_{esc}^2}{n_{esc}} + \frac{\sigma_{control}^2}{n_{control}}} %\\
	= \sqrt{\frac{s_{esc}^2}{n_{esc}} + \frac{s_{control}^2}{n_{control}}}
	= \sqrt{\frac{5.17^2}{9} + \frac{2.76^2}{9}} = 1.95
\end{align*}
Generally, we use statistical software to find the appropriate
degrees of freedom, or if software isn't available,
we can use the smaller
of $n_1 - 1$ and $n_2 - 1$ for the degrees of freedom,
e.g. if using a $t$-table to find tail areas.
For transparency in the Examples and Guided Practice,
we'll use the latter approach for finding $df$;
in the case of the ESC example, this means we'll use $df = 8$.

\begin{examplewrap}
\begin{nexample}{Calculate a 95\% confidence interval for the
    effect of ESCs on the change in heart pumping capacity of
    sheep after they've suffered a heart attack.}
  We will use the sample difference and the standard error that 
  we computed earlier calculations:
  \begin{align*}
  \bar{x}_{esc} - \bar{x}_{control} = 7.83
  && SE = \sqrt{\frac{5.17^2}{9} + \frac{2.76^2}{9}} = 1.95
  \end{align*}
  Using $df = 8$, we can identify the
  critical value of $t^{\star}_{8} = 2.31$
  for a 95\% confidence interval.
  Finally, we can enter the values into the confidence
  interval formula:
  \begin{align*}
  \text{point estimate} \ \pm\ t^{\star} \times SE
    \quad\rightarrow\quad 7.83 \ \pm\ 2.31\times 1.95
    \quad\rightarrow\quad (3.32, 12.34)
  \end{align*}
  We are 95\% confident that embryonic stem cells improve
  the heart's pumping function in sheep that have suffered
  a heart attack by 3.32\% to 12.34\%.
  
%  Had we used software to get a more precise degrees
%  of freedom ($df = 12.225$), the confidence interval
%  would have been slightly slimmer.
\end{nexample}
\end{examplewrap}

\index{data!stem cells, heart function|)}

\noindent%
As with past statistical inference applications,
there is a well-trodden procedure.
\begin{description}
\setlength{\itemsep}{0mm}
\item[Prepare.]
    Retrieve critical contextual information,
    and if appropriate, set up hypotheses.
\item[Check.]
    Ensure the required conditions are reasonably
    satisfied.
\item[Calculate.]
    Find the standard error, and then construct
    a confidence interval, or if conducting
    a hypothesis test, find a test statistic
    and p-value.
\item[Conclude.]
    Interpret the results in the context of the
    application.
\end{description}
The details change a little from one setting to the next,
but this general approach remain the same.


%\D{\newpage}

\subsection{Hypothesis tests for the difference of two means}

\index{data!baby\_smoke|(}

A 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.
%Figure~\ref{babySmokePlotOfTwoGroupsToExamineSkew}.

\begin{figure}[h]
\centering
\begin{tabular}{rrrrrll}
  \hline
 & fage & mage & weeks & weight & sex & smoke \\ 
  \hline
1 & NA & 13 &  37 & 5.00 & female & nonsmoker \\ 
  2 & NA & 14 &  36 & 5.88 & female & nonsmoker \\ 
  3 & 19 & 15 &  41 & 8.13 & male & smoker \\ 
  $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ \\
  150 & 45 & 50 &  36 & 9.25 & female & nonsmoker \\ 
   \hline
\end{tabular}
\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.}
\label{babySmokeDF}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Set up appropriate hypotheses to evaluate
    whether there is a relationship between a mother smoking
    and average birth weight.}
  \label{babySmokeHTForWeight}%
  The null hypothesis represents the case of no difference
  between the groups.
  \begin{itemize}
  \setlength{\itemsep}{0mm}
  \item[$H_0$:]
      There is no difference in average birth weight for
      newborns from mothers who did and did not smoke.
      In statistical notation: $\mu_{n} - \mu_{s} = 0$,
      where $\mu_{n}$ represents non-smoking mothers and
      $\mu_s$ represents mothers who smoked.
  \item[$H_A$:]
      There is some difference in average newborn weights
      from mothers who did and did not smoke
      ($\mu_{n} - \mu_{s} \neq 0$).
  \end{itemize}
\end{nexample}
\end{examplewrap}

We check the two conditions necessary to model the difference
in sample means using the $t$-distribution.
\begin{itemize}
\item
    Because the data come from a simple random sample,
    the observations are independent,
    both within and between samples.
\item
    With both data sets over 30 observations,
    we inspect the data in
    Figure~\ref{babySmokePlotOfTwoGroupsToExamineSkew}
    for any particularly extreme outliers
    and find none.
\end{itemize}
Since both conditions are satisfied, the difference
in sample means may be modeled using a $t$-distribution.

\begin{figure}[hhh]
  \centering
  \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}
  \caption{The left panel represents birth weights for infants
      whose mothers smoked.
      The right panel represents the birth weights for
      infants whose mothers who did not smoke.}
  \label{babySmokePlotOfTwoGroupsToExamineSkew}
\end{figure}

%Summary statistics are shown for each sample in Figure~\ref{SumStatsBirthWeightNewbornsSmoke}.

%\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
\label{babySmokeCalcForWeight}
The summary statistics in
Figure~\ref{SumStatsBirthWeightNewbornsSmoke} may be useful
for this Guided Practice.\footnotemark{}
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item
    What is the point estimate of the population difference,
    $\mu_{n} - \mu_{s}$?
\item
    Compute the standard error of the point estimate from
    part~(a).
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~The difference in sample means is an
  appropriate point estimate: $\bar{x}_{n} - \bar{x}_{s} = 0.40$.
  (b)~The standard error of the estimate can be
  calculated using the standard error formula:
  \begin{align*}
  SE
    = \sqrt{\frac{\sigma_n^2}{n_n} + \frac{\sigma_s^2}{n_s}}
      \approx \sqrt{\frac{s_n^2}{n_n} + \frac{s_s^2}{n_s}}
    = \sqrt{\frac{1.60^2}{100} + \frac{1.43^2}{50}}
    = 0.26
  \end{align*}}

\begin{figure}[hhh]
\centering
\begin{tabular}{lrr}
\hline
& \resp{smoker} & \resp{nonsmoker} \\
\hline
mean & 6.78 & 7.18 \\
st. dev. & 1.43 & 1.60 \\
samp. size & 50 & 100 \\
\hline
\end{tabular}
\caption{Summary statistics for the \data{ncbirths} data set.}
\label{SumStatsBirthWeightNewbornsSmoke}
\end{figure}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{Complete the
    hypothesis test started in
    Example~\ref{babySmokeHTForWeight}
    and Guided Practice~\ref{babySmokeCalcForWeight}.
    Use a significance level of $\alpha=0.05$.
    For reference, $\bar{x}_{n} - \bar{x}_{s} = 0.40$,
    $SE = 0.26$, and the sample sizes were $n_n = 100$
    and $n_s = 50$.}
  \label{babySmokeHTForWeightComputePValueAndEvalHT}%
  We can find the test statistic for this test
  using the values from
  Guided Practice~\ref{babySmokeCalcForWeight}:
  \begin{align*}
  T = \frac{\ 0.40 - 0\ }{0.26} = 1.54
  \end{align*}
  The p-value is represented by the two shaded tails
  in the following plot:
  \begin{center}
    \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}
  \end{center}
  We find the single tail area using software
  (or the $t$-table in Appendix~\ref{tDistributionTable}).
  We'll use the
  smaller of $n_n - 1 = 99$ and $n_s - 1 = 49$ as the
  degrees of freedom: $df = 49$.
  The one tail area is 0.065;
  doubling this value gives the two-tail area and p-value,
  0.135.

  The p-value is larger than the significance value, 0.05,
  so we do not reject the null hypothesis.
  There is insufficient evidence to say there is a difference
  in average birth weight of newborns from North Carolina mothers
  who did smoke during pregnancy and newborns from North Carolina
  mothers who did not smoke during pregnancy.
\end{nexample}
\end{examplewrap}

%\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
We've seen much research suggesting smoking is harmful
during pregnancy, so how could we fail to reject the null
hypothesis in
Example~\ref{babySmokeHTForWeightComputePValueAndEvalHT}?
\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{It is possible that there is a difference
  but we did not detect it.
  If there is a difference, we made a Type~2 Error.}

\begin{exercisewrap}
\begin{nexercise}
\label{babySmokeHTIDingHowToDetectDifferences}%
If we made a Type~2 Error and there is a difference,
what could we have done differently in data collection
to be more likely to detect the difference?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We could have collected more data.
  If the sample sizes are larger, we tend to have
  a better shot at finding a difference if one exists.
  In fact, this is exactly what we would find if we
  examined a larger data set!}

Public service announcement: while we have used this relatively
small data set as an example, larger data sets show that women
who smoke tend to have smaller newborns.
In~fact, some in the tobacco industry actually had the audacity
to tout that as a \emph{benefit} of~smoking:
\begin{quotation}
  \noindent%
  \emph{It's true.
  The babies born from women who smoke are smaller,
  but they're just as healthy as the babies born from
  women who do not smoke.
  And some women would prefer having smaller babies.} \\[2mm]
  \indent\indent\indent\indent\indent\indent%
    - Joseph Cullman, Philip Morris' Chairman of the Board \\
  \indent\indent\indent\indent\indent\indent%
  {\color{white}...}on CBS' \emph{Face the Nation}, Jan 3,~1971
\end{quotation}
Fact check: the babies from women who smoke are not actually
as healthy as the babies from women who do not
smoke.\footnote{You can watch an episode of John Oliver
  on \emph{Last Week Tonight} to explore the present day
  offenses of the tobacco industry.
  Please be aware that there is some adult language:
  \oiRedirect{textbook-johnoliver_tobacco}{youtu.be/6UsHHOCH4q8}.}
% Resource on this topic:
% http://archive.tobacco.org/Documents/documentquotes.html

\index{data!baby\_smoke|)}


\D{\newpage}

\subsection{Case study: two versions of a course exam}

\index{data!two exam comparison|(}

An 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.

\begin{figure}[hht]
\centering
\begin{tabular}{l rrrrr}
\hline
Version\hspace{2mm}	& $n$	& $\bar{x}$	& $s$	& min	& max  \\
\hline
A		& 30		& 79.4		& 14 	& 45		& 100 \\
B		& 27		& 74.1		& 20		& 32		& 100 \\
\hline
\end{tabular}
\caption{Summary statistics of scores for each exam version.}
\label{summaryStatsForTwoVersionsOfExams}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
\label{htSetupForEvaluatingTwoExamVersions}%
Construct hypotheses to evaluate whether the observed
difference in sample means, $\bar{x}_A - \bar{x}_B=5.3$,
is due to chance. We will later evaluate these hypotheses
using $\alpha = 0.01$.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\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. $\mu_A - \mu_B \neq 0$.}

%\D{\newpage}

\begin{exercisewrap}
\begin{nexercise} \label{conditionsForTDistForEvaluatingTwoExamVersions}%
To evaluate the hypotheses in Guided Practice~\ref{htSetupForEvaluatingTwoExamVersions} using the $t$-distribution, we must first verify conditions.\footnotemark{}
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item
    Does it seem reasonable that the scores are independent?
\item
    Any concerns about outliers?
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~Since the exams were shuffled,
  the ``treatment'' in this case was randomly assigned,
  so independence within and between groups is satisfied.
  (b)~The summary statistics suggest the data are roughly
  symmetric about the mean, and the min/max values don't
  suggest any outliers of concern.}

After 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
\begin{align*}
SE
  = \sqrt{\frac{s_A^2}{n_A} + \frac{s_B^2}{n_B}}
  = \sqrt{\frac{14^2}{30} + \frac{20^2}{27}}
  = 4.62
\end{align*}
Finally, we construct the test statistic:
\begin{align*}
T
  = \frac{\text{point estimate} - \text{null value}}{SE}
  = \frac{(79.4-74.1) - 0}{4.62}
  = 1.15
\end{align*}
If we have a computer handy, we can identify the degrees
of freedom as 45.97.
Otherwise we use the smaller of $n_1-1$ and $n_2-1$: $df=26$.

\D{\newpage}

\begin{figure}[h]
  \centering
  \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}
  \caption{The $t$-distribution with 26 degrees of freedom
      and the p-value from exam example represented
      as the shaded areas.}
  \label{pValueOfTwoTailAreaOfExamVersionsWhereDFIs26}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Identify the p-value depicted in
    Figure~\ref{pValueOfTwoTailAreaOfExamVersionsWhereDFIs26}
    using $df = 26$, and provide a conclusion in the
    context of the case study.}
  Using software, we can find the one-tail area (0.13)
  and then double this value to get the two-tail area,
  which is the p-value: 0.26.
  (Alternatively, we could use the $t$-table in
  Appendix~\ref{tDistributionTable}.)

  In Guided
  Practice~\ref{htSetupForEvaluatingTwoExamVersions},
  we specified that we would use $\alpha = 0.01$.
  Since the p-value is larger than $\alpha$,
  we do not reject the null hypothesis.
  That is, the data do not convincingly show that one exam
  version is more difficult than the other, and the teacher
  should not be convinced that she should add points to the
  Version~B exam scores.
\end{nexample}
\end{examplewrap}

\index{data!two exam comparison|)}

%\subsection{Summary for inference using the $t$-distribution}
%
%\Comment{This subsection should be heavily updated.}
%
%%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.
%
%\textbf{Hypothesis tests.} When applying the $t$-distribution for a hypothesis test, we proceed as follows:
%\begin{itemize}
%\setlength{\itemsep}{0mm}
%\item Write appropriate hypotheses.
%\item Verify conditions for using the $t$-distribution.
%\begin{itemize}
%\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.
%\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.
%\end{itemize}
%\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 $n_2 - 1$.
%\item Compute the T-score and p-value.
%\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.
%\end{itemize}
%\noindent\textbf{Confidence intervals.} Similarly, the following is how we generally computed a confidence interval using a $t$-distribution:
%\begin{itemize}
%\item Verify conditions for using the $t$-distribution. (See above.)
%\item Compute the point estimate of interest, the standard error, the degrees of freedom, and $t^{\star}_{df}$.
%\item Calculate the confidence interval using the general formula, point estimate $\pm\ t_{df}^{\star} SE$.
%\item Put the conclusions in context and in plain language so even non-data scientists can understand the results.
%\end{itemize}
%
%\CalculatorVideos{confidence intervals and hypothesis tests for a difference of means}


%\subsection{Examining the standard error formula (special topic)}
%
%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
%\begin{align*}
%SE_{\bar{x}_1} = \frac{s_1}{\ \sqrt{n_1}\ }
%\end{align*}
%where $s_1$ and $n_1$ represent the sample standard deviation and sample size.
%
%The standard error of the difference of two sample means can be constructed from the standard errors of the separate sample means:
%\begin{align*}
%SE_{\bar{x}_{1} - \bar{x}_{2}}
%	= \sqrt{SE_{\bar{x}_1}^2 + SE_{\bar{x}_2}^2}
%	= \sqrt{\frac{s_1^2}{{n_1}} + \frac{s_2^2}{{n_2}}}
%\end{align*}
%This special relationship follows from probability theory.
%
%\begin{exercisewrap}
%\begin{nexercise}
%\label{derivingSEForDiffOfTwoMeansExercise}%
%Prerequisite: Section~\ref{randomVariablesSection}.
%We can rewrite the equation above in a different way:
%\begin{align*}
%SE_{\bar{x}_{1} - \bar{x}_{2}}^2
%  = SE_{\bar{x}_1}^2 + SE_{\bar{x}_2}^2
%\end{align*}
%Explain where this formula comes from using the ideas of probability theory.\footnotemark{}
%\end{nexercise}
%\end{exercisewrap}
%\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 $SE_{\bar{x}_1}^2 + SE_{\bar{x}_2}^2$.}


%\D{\newpage}

\subsection{Pooled standard deviation estimate (special topic)}
\label{pooledStandardDeviations}

Occasionally, two populations will have standard deviations
that are so similar that they can be treated as identical.
For example, historical data or a well-understood biological
mechanism may justify this strong assumption.
In such cases, we can make the $t$-distribution approach
slightly more precise by using a pooled standard deviation.

The \term{pooled standard deviation} of two groups is a way
to use data from both samples to better estimate the standard
deviation and standard error.
If $s_1^{}$ and $s_2^{}$ are the standard deviations
of groups~1 and~2 and there are very good reasons to believe
that the population standard deviations are equal,
then we can obtain an improved estimate of the group variances
by pooling their data:
\begin{align*}
s_{pooled}^2 = \frac{s_1^2\times (n_1-1) + s_2^2\times (n_2-1)}{n_1 + n_2 - 2}
\end{align*}
where $n_1$ and $n_2$ are the sample sizes, as before.
To use this new statistic, we substitute $s_{pooled}^2$
in place of $s_1^2$ and $s_2^2$ in the standard error formula,
and we use an updated formula for the degrees of freedom:
\begin{align*}
df = n_1 + n_2 - 2
\end{align*}

The benefits of pooling the standard deviation are realized
through obtaining a better estimate of the standard deviation
for each group and using a larger degrees of freedom parameter
for the $t$-distribution.
Both of these changes may permit a more accurate model of the
sampling distribution of $\bar{x}_1 - \bar{x}_2$,
if the standard deviations of the two groups are indeed equal.

\begin{onebox}
    {Pool standard deviations only after careful consideration}
  A pooled standard deviation is only appropriate when
  background research indicates the population standard
  deviations are nearly equal.
  When the sample size is large and the condition
  may be adequately checked with data, the benefits
  of pooling the standard deviations greatly diminishes.
\end{onebox}


{\input{ch_inference_for_means/TeX/difference_of_two_means.tex}}







%__________________
\section{Power calculations for a difference of means}
\label{PowerForDifferenceOfTwoMeans}

\noindent%
Often times in experiment planning,
there are two competing considerations:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item
    We want to collect enough data that we can detect
    important effects.
\item
    Collecting data can be expensive, and in experiments
    involving people, there may be some risk to patients.
\end{itemize}
In this section, we focus on the context of a clinical trial,
which is a health-related experiment where the subject
are people, and we will determine an appropriate sample size
where we can be 80\% sure that we would detect any practically
important effects.\footnote{Even though we don't cover it
  explicitly, similar sample size planning is also helpful
  for observational studies.}


\subsection{Going through the motions of a test}

We're going to go through the motions of a hypothesis test.
This will help us frame our calculations for determining
an appropriate sample size for the study.

\begin{examplewrap}
\begin{nexample}{Suppose a pharmaceutical company has developed
    a new drug for lowering blood pressure, and they are
    preparing a clinical trial (experiment) to test the
    drug's effectiveness.
    They recruit people who are taking a particular standard
    blood pressure medication.
    People in the control group will continue to take their
    current medication through generic-looking pills to ensure
    blinding.
    Write down the hypotheses for a two-sided hypothesis test
    in this context.}
  Generally, clinical trials use a two-sided alternative
  hypothesis, so below are suitable hypotheses for this context:
  \begin{description}
  \setlength{\itemsep}{0mm}
  \item[$H_0$:]
      The new drug performs exactly as well as the
      standard medication. \\
      $\mu_{trmt} - \mu_{ctrl} = 0$.
  \item[$H_A$:]
      The new drug's performance differs from the
      standard medication. \\
      $\mu_{trmt} - \mu_{ctrl} \neq 0$.
  \end{description}
%  This two-sided test ensures we'll be alerted if either
%  the new drug works better or worse than the standard
%  medication.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{The researchers would like to run the clinical
    trial on patients with systolic blood pressures between 140
    and 180~mmHg.
    Suppose previously published studies suggest that the
    standard deviation of the patients' blood pressures will
    be about 12~mmHg and the distribution of patient blood
    pressures will be approximately symmetric.\footnotemark{}
    If~we had 100 patients per group, what would be the
    approximate standard error for
    $\bar{x}_{trmt} - \bar{x}_{ctrl}$?}
  The standard error is calculated as follows:
  \begin{align*}
  SE_{\bar{x}_{trmt} - \bar{x}_{ctrl}}
    = \sqrt{\frac{s_{trmt}^2}{n_{trmt}} +
        \frac{s_{ctrl}^2}{n_{ctrl}}}
    = \sqrt{\frac{12^2}{100} + \frac{12^2}{100}}
    = 1.70
  \end{align*}
  This may be an imperfect estimate of
  $SE_{\bar{x}_{trmt} - \bar{x}_{ctrl}}$,
  since the standard deviation estimate we used may not
  be perfectly correct for this group of patients.
  However, it is sufficient for our purposes.
\end{nexample}
\end{examplewrap}
\footnotetext{In this particular study, we'd generally measure
  each patient's blood pressure at the beginning and end
  of the study, and then the outcome measurement for
  the study would be the average change in blood pressure.
  That is, both $\mu_{trmt}$ and $\mu_{ctrl}$ would
  represent average differences.
  This is what you might think of as a 2-sample paired
  testing structure, and we'd analyze it exactly just like
  a hypothesis test for a difference in the average change
  for patients.
  In the calculations we perform here, we'll suppose
  that 12~mmHg is the predicted standard deviation of
  a patient's blood pressure difference over the course
  of the study.}

\begin{examplewrap}
\begin{nexample}{What does the null distribution of
    $\bar{x}_{trmt} - \bar{x}_{ctrl}$ look like?}
  The degrees of freedom are greater than 30, so the
  distribution of $\bar{x}_{trmt} - \bar{x}_{ctrl}$
  will be approximately normal.
  The standard deviation of this distribution
  (the standard error) would be about 1.70, and under
  the null hypothesis, its mean would be 0.
  \begin{center}
  \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}
  \end{center}
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{For what values of
    $\bar{x}_{trmt} - \bar{x}_{ctrl}$ would we reject
    the null hypothesis?}
  For $\alpha = 0.05$, we would reject $H_0$ if the difference
  is in the lower 2.5\% or upper 2.5\% tail:
  \begin{description}
  \setlength{\itemsep}{0mm}
  \item[Lower 2.5\%:]
      For the normal model, this is 1.96 standard errors
      below~0, so any difference smaller than
      $-1.96 \times 1.70 = -3.332$~mmHg.
  \item[Upper 2.5\%:]
      For the normal model, this is 1.96 standard errors
      above~0, so any difference larger than
      $1.96 \times 1.70 = 3.332$~mmHg.
  \end{description}
  The boundaries of these \term{rejection regions} are shown below:
  \begin{center}
  \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}
  \end{center}
\end{nexample}
\end{examplewrap}

Next, we'll perform some hypothetical calculations to determine
the probability we reject the null hypothesis, if the alternative
hypothesis were actually true.


\subsection%[Computing the power for a 2-sample test]
    {Computing the power for a 2-sample test}

When planning a study, we want to know how likely we are
to detect an effect we care about.
In~other words, if there is a real effect, and that effect
is large enough that it has practical value, then what's
the probability that we detect that effect?
This probability is called the \term{power}, and we can
compute it for different sample sizes or for different
\emph{effect sizes}.

We first determine what is a practically significant result.
Suppose that the company researchers care about finding any
effect on blood pressure that is 3~mmHg or larger vs the
standard medication.
Here, 3~mmHg is the minimum \term{effect size} of interest,
and we want to know how likely we are to detect this size
of an effect in the study.

\begin{examplewrap}
\begin{nexample}{Suppose we decided to move forward with
    100 patients per treatment group and the new drug reduces
    blood pressure by an additional 3~mmHg relative to the
    standard medication.
    What is the probability that we detect a drop?}
  \label{PowerFor100AtNeg3}%
  Before we even do any calculations, notice that if
  $\bar{x}_{trmt} - \bar{x}_{ctrl} = -3$~mmHg, there
  wouldn't even be sufficient evidence to reject $H_0$.
  That's not a good sign.

  To calculate the probability that we will reject $H_0$,
  we need to determine a few things:
  \begin{itemize}
  \setlength{\itemsep}{0mm}
  \item
      The sampling distribution for
      $\bar{x}_{trmt} - \bar{x}_{ctrl}$ when the true difference
      is -3~mmHg.
      This is the same as the null distribution,
      except it is shifted to the left by~3:
      \begin{center}
      \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}
          {power_null_C_0_1-7_with_alt_at_3}
      \end{center}
  \item
      The rejection regions, which are outside of the
      dotted lines above.
  \item
      The fraction of the distribution that falls in the
      rejection region.
  \end{itemize}
  In short, we need to calculate the probability that
  $x < -3.332$ for a normal distribution with mean -3
  and standard deviation~1.7.
  To do so, we first shade the area we want to calculate:
  \begin{center}
    \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}
        {power_null_D_0_1-7_with_alt_at_3_and_shaded}
  \end{center}
  We'll use a normal approximation, which is good approximation
  when the degrees of freedom is about 30 or more.
  We'll start by calculating the Z-score and find the tail area
  using either statistical software or the probability table:
  \begin{align*}
  Z = \frac{-3.332 - (-3)}{1.7} = -0.20 \qquad \to \qquad 0.42
  \end{align*}
  The power for the test is about 42\% when
  $\mu_{trmt} - \mu_{ctrl} = -3$ and each group has
  a sample size of~100.
\end{nexample}
\end{examplewrap}

In Example~\ref{PowerFor100AtNeg3}, we ignored the upper
rejection region in the calculation, which was in the
opposite direction of the hypothetical truth, i.e. -3.
The reasoning?
There wouldn't be any value in rejecting the null hypothesis
and concluding there was an increase when in fact there was
a decrease.

We've also used a normal distribution instead
of the $t$-distribution.
This is a convenience, and if the sample size is too small,
we'd need to revert back to using the $t$-distribution.
We'll discuss this a bit further at the end of this section.


\D{\newpage}

\subsection{Determining a proper sample size}

In the last example, we found that if we have a sample size
of 100 in each group, we can only detect an effect size of
3~mmHg with a probability of about 0.42.
Suppose the researchers moved forward and only used
100 patients per group, and the data did not support
the alternative hypothesis,
i.e. the researchers did not reject $H_0$.
This is a very bad situation to be in for a few reasons:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item
    In the back of the researchers' minds, they'd all be
    wondering, \emph{maybe there is a real and meaningful
    difference, but we weren't able to detect it with such
    a small sample}. 
\item
    The company probably invested hundreds of millions
    of dollars in developing the new drug, so now they
    are left with great uncertainty about its potential
    since the experiment didn't have a great shot at
    detecting effects that could still be important.
\item
    Patients were subjected to the drug, and we can't even
    say with much certainty that the drug doesn't help
    (or harm) patients.
\item
    Another clinical trial may need to be run to get a more
    conclusive answer as to whether the drug does hold any
    practical value, and conducting a second clinical trial
    may take years and many millions of dollars.
\end{itemize}
We want to avoid this situation, so we need to determine
an appropriate sample size to ensure we can be pretty
confident that we'll detect any effects that are practically
important.
As mentioned earlier, a change of 3~mmHg was deemed to be the
minimum difference that was practically important.
As~a first step, we could calculate power for several
different sample sizes.
For instance, let's try 500 patients per group.

\begin{exercisewrap}
\begin{nexercise}
Calculate the power to detect a change of -3~mmHg when using
a sample size of 500 per group.\footnotemark{}
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item
    Determine the standard error (recall that the standard
    deviation for patients was expected to be about 12~mmHg).
\item
    Identify the null distribution and rejection regions.
\item
    Identify the alternative distribution when
    $\mu_{trmt} - \mu_{ctrl} = -3$.
\item
    Compute the probability we reject the null hypothesis.
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a) The standard error is given as
  $SE = \sqrt{\frac{12^2}{500} + \frac{12^2}{500}} = 0.76$.\\
  (b)~\&~(c)~The null distribution, rejection boundaries,
  and alternative distribution are shown below: \\
  \indent%
  \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}
      {power_null_0_0-76_with_alt_at_3_and_shaded} \\
  The rejection regions are the areas on the outside of the
  two dotted lines and are at $\pm 0.76 \times 1.96 = \pm 1.49$. \\
  (d)~The area of the alternative distribution where
  $\mu_{trmt} - \mu_{ctrl} = -3$ has been shaded.
  We compute the Z-score and find the tail area:
  $Z = \frac{-1.49 - (-3)}{0.76} = 1.99 \to 0.977$.
%  (can use $df = 500$ from the minimum of the two sample
%  sizes minus 1),
%  which is the power of the test for a difference of 3~mmHg.
  With 500 patients per group, we would be about 97.7\% sure
  (or~more) that we'd detect any effects that are at least
  3~mmHg in size.}

The researchers decided 3~mmHg was the minimum difference
that was practically important, and with a sample size of~500,
we can be very certain (97.7\% or better) that we will detect
any such difference.
We now have moved to another extreme where we are exposing
an unnecessary number of patients to the new drug in the
clinical trial.
Not only is this ethically questionable, but it would also
cost a lot more money than is necessary to be quite sure
we'd detect any important effects.

The most common practice is to identify the sample size where
the power is around 80\%, and sometimes 90\%.
Other values may be reasonable for a specific context,
but 80\% and 90\% are most commonly targeted as a good
balance between high power and not exposing too many
patients to a new treatment (or wasting too much money).

We could compute the power of the test at several other
possible sample sizes until we find one that's close to~80\%,
but there's a better way.
We should solve the problem backwards.

\begin{examplewrap}
\begin{nexample}{What sample size will lead to a power of 80\%? Use $\alpha = 0.05$.}
  \label{sample_size_for_80_percent_power}%  This is referenced in EOCE.
  We'll assume we have a large enough sample that the normal
  distribution is a good approximation for the test statistic,
  since the normal distribution and the $t$-distribution
  look almost identical when the degrees of freedom are
  moderately large (e.g. $df \geq 30$).
  If that doesn't turn out to be true, then we'd need to make
  a correction.

  We start by identifying the Z-score that would give us a lower
  tail of 80\%.
  For a moderately large sample size per group,
  the Z-score for a lower tail of 80\% would be about $Z = 0.84$.
%  (If our calculations suggest a very sample size,
%  we should recalculate this part and basically do the
%  problem one more time.)
  \begin{center}
    \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}
  \end{center}
  Additionally, the rejection region extends
  $1.96\times SE$ from the center of the null distribution
  for $\alpha = 0.05$.
  This allows us to calculate the target distance between
  the center of the null and alternative distributions in
  terms of the standard error:
  \begin{align*}
  0.84 \times SE + 1.96 \times SE = 2.8 \times SE
  \end{align*}
  In our example, we want the distance between the null
  and alternative distributions' centers to equal the minimum
  effect size of interest, 3~mmHg, which allows us to set up
  an equation between this difference and the standard error:
  \begin{align*}
  3 &= 2.8 \times SE \\
  3 &= 2.8 \times \sqrt{\frac{12^2}{n} + \frac{12^2}{n}} \\
  n &= \frac{2.8^2}{3^2} \times \left( 12^2 + 12^2 \right)
    = 250.88 \\
  \end{align*}
  We should target 251 patients per group in order to achieve
  80\% power at the 0.05 significance level for this context.
\end{nexample}
\end{examplewrap}

The standard error difference of $2.8 \times SE$ is specific
to a context where the targeted power is 80\% and the
significance level is $\alpha = 0.05$.
If the targeted power is 90\% or if we use a different
significance level, then we'll use something a little
different than $2.8 \times SE$.

Had the suggested sample size been relatively small
-- roughly 30 or smaller -- it would have been a good idea
to rework the calculations using the degrees of fredom
for the smaller sample size under that initial sample size.
That is, we would have revised the 0.84 and 1.96
values based on degrees of freedom implied by the initial
sample size.
The revised sample size target would generally have then
been a little larger.

%\begin{examplewrap}
%\begin{nexample}{Suppose the suggested sample size from
%    the power calculation was 15 per group.
%    This is a relatively small sample size,
%    and the conditions about the sample size being
%    large in Example~\ref{}
%    wouldn't be valid.
%    What should we do?}
%  First, recognizing that there is \emph{something}
%  to do is already great here:
%  it's easy to forget the earlier assumption about
%  a moderately large sample size.
%  So if you catch yourself here, that is something
%  to be commended!
%
%  Next, we basically update the values of 0.84 and 1.96
%  in the calculations.
%  First, we identify the degrees of freedom
%  ($df = 14$ as a rough guide, though 
%  We'd find the values
%  corresponding to this more precise $t$-distribution.
%  For example, had the sample-size per group been suggested
%  as~15, we would have used $df = 14$;
%  this would have led to a T-score of 0.87 (in place of 0.84)
%  and a rejection region cutoff of 2.14.
%  The reworked sample size would then have been suggested
%  as about 16\% larger.
%  If we did not do this extra step, our estimated power would
%  drop from 80\% to 74\%.
%  While that would not be the end of the world, being precise
%  is part of the job of being a data scientist!
%\end{nexample}
%\end{examplewrap}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
Suppose the targeted power was 90\% and we were using
$\alpha = 0.01$.
How many standard errors should separate the centers
of the null and alternative distribution, where the
alternative distribution is centered at the minimum
effect size of interest?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{First, find the Z-score such that 90\% of the
  distribution is below it: $Z = 1.28$.
  Next, find the cutoffs for the rejection regions: $\pm 2.58$.
  Then the difference in centers should be about
  $1.28 \times SE + 2.58 \times SE = 3.86 \times SE$.}

\begin{exercisewrap}
\begin{nexercise}
What are some considerations that are important in determining
what the power should be for an experiment?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Answers will vary, but here are a few
  important considerations:
  \begin{itemize}
  \setlength{\itemsep}{0mm}
  \item Whether there is any risk to patients in the study.
  \item The cost of enrolling more patients.
  \item The potential downside of not detecting an effect
      of interest.
  \end{itemize}}

Figure~\ref{power_curve_neg-3} shows the power for sample
sizes from 20~patients to 5,000~patients when $\alpha = 0.05$
and the true difference is -3.
This curve was constructed by writing a program to compute
the power for many different sample sizes.

\begin{figure}[h]
  \centering
  \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}
  \caption{The curve shows the power for different sample
      sizes in the context of the blood pressure example when
      the true difference is~-3.
      Having more than about 250 to 350 observations doesn't
      provide much additional value in detecting an effect when
      $\alpha = 0.05$.}
  \label{power_curve_neg-3}
\end{figure}

%\begin{exercisewrap}
%\begin{nexercise}
%
%\end{nexercise}
%\end{exercisewrap}

Power calculations for expensive or risky experiments are
critical.
However, what about experiments that are inexpensive and
where the ethical considerations are minimal?
For example, if we are doing final testing on a new feature
on a popular website, how would our sample size considerations
change?
As before, we'd want to make sure the sample is big enough.
However, suppose the feature has undergone some testing and
is known to perform well
(e.g.~the website's users seem to enjoy the feature).
Then it may be reasonable to run a larger experiment
if there's value from having a more precise estimate
of the feature's effect, such as helping guide the
development of the next useful feature.


{\input{ch_inference_for_means/TeX/power_calculations_for_a_difference_of_means.tex}}





%__________________
\section{Comparing many means with ANOVA}
\label{anovaAndRegrWithCategoricalVariables}

\index{analysis of variance (ANOVA)|(}

\noindent%
Sometimes we want to compare means across many groups.
We might initially think to do pairwise comparisons.
For example, if there were three groups, we might be tempted
to compare the first mean with the second,
then with the third,
and then finally compare the second and third means for
a total of three comparisons.
However, this strategy can be treacherous.
If we have many groups and do many comparisons,
it is likely that we will eventually find a difference
just by chance, even if there is no difference in the
populations.
Instead, we should apply a holistic test to check whether
there is evidence that at least one pair groups are
in fact different, and this is where \emph{ANOVA} saves
the~day.


\subsection{Core ideas of ANOVA}

In this section, we will learn a new method called
\term{analysis of variance (ANOVA)} and a new test
statistic called $F$.
ANOVA uses a single hypothesis test to check whether
the means across many groups are equal:
\begin{itemize}
\setlength{\itemsep}{0mm}
\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 $i$.
\item[$H_A$:] At least one mean is different.
\end{itemize}
Generally we must check three conditions on the data before performing ANOVA:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item the observations are independent within and across groups,
\item the data within each group are nearly normal, and
\item the variability across the groups is about equal.
\end{itemize}
When 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.

\begin{examplewrap}
\begin{nexample}{College departments commonly run multiple
    lectures of the same introductory course each semester
    because of high demand.
    Consider a statistics department that runs three lectures
    of an introductory statistics course.
    We might like to determine whether there are statistically
    significant differences in first exam scores in these three
    classes ($A$,~$B$, and~$C$).
    Describe appropriate hypotheses to determine whether
    there are any differences between the three classes.}
  \label{firstExampleForThreeStatisticsClassesAndANOVA}%
  The hypotheses may be written in the following form:
  \begin{itemize}
  \setlength{\itemsep}{0mm}
  \item[$H_0$:]
      The average score is identical in all lectures.
      Any observed difference is due to chance.
      Notationally, we write $\mu_A=\mu_B=\mu_C$.
  \item[$H_A$:]
      The average score varies by class.
      We would reject the null hypothesis in favor of the
      alternative hypothesis if there were larger differences
      among the class averages than what we might expect
      from chance alone.
  \end{itemize}
\end{nexample}
\end{examplewrap}

Strong evidence favoring the alternative hypothesis in ANOVA
is described by unusually large differences among the group means.
We will soon learn that assessing the variability of the group
means relative to the variability among individual observations
within each group is key to ANOVA's success.

\begin{examplewrap}
\begin{nexample}{Examine Figure~\ref{toyANOVA}.
    Compare groups I, II, and III.
    Can you visually determine if the differences in the group
    centers is due to chance or not? Now compare
    groups IV, V, and~VI.
    Do these differences appear to be due to chance?}
  Any real difference in the means of groups I, II, and~III
  is difficult to discern, because the data within each group
  are very volatile relative to any differences in the
  average outcome.
  On the other hand, it appears there are differences
  in the centers of groups IV, V, and~VI.
  For instance, group~V appears to have a higher mean than
  that of the other two groups.
  Investigating groups IV, V, and~VI, we see the differences
  in the groups' centers are noticeable because those
  differences are large \emph{relative to the variability
  in the individual observations within each group}.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
  \caption{Side-by-side dot plot for the outcomes for six groups.}
  \label{toyANOVA}
\end{figure}


\subsection{Is batting performance related to player position in MLB?}

\index{data!MLB batting|(}

\newcommand{\mlbdata}{\data{bat18}}
\newcommand{\mlbN}{429}
\newcommand{\mlbK}{3}
\newcommand{\mlbMinAB}{100}
\newcommand{\mlbDFA}{2}
\newcommand{\mlbDFB}{426}
\newcommand{\mlbF}{5.077}
\newcommand{\mlbPvalue}{0.0066}

We would like to discern whether there are real differences
between the batting performance of baseball players according
to their position:
outfielder (\resp{OF}), infielder (\resp{IF}),
%designated hitter (\resp{DH}),
and catcher (\resp{C}).
We will use a data set called \mlbdata{},
which includes batting records of \mlbN{} Major League
Baseball (MLB) players from the 2018 season who had
at least \mlbMinAB{} at bats.
Six of the \mlbN{} cases represented in \mlbdata{}
are shown in Figure~\ref{mlbBat18DataMatrix},
and descriptions for each variable are provided
in Figure~\ref{mlbBat18Variables}.
The measure we will use for the player batting
performance (the outcome variable) is on-base
percentage (\var{OBP}).
The on-base percentage roughly represents the fraction
of the time a player successfully gets on base or hits
a home run.

\begin{figure}[h]
\centering
\begin{tabular}{r lll ccc ccc}
  \hline
  & name & team & position & AB & H & HR &RBI & AVG & OBP \\ 
  \hline
  1 &  Abreu, J & CWS & IF &  499 &  132 &   22 &
      78 & 0.265 & 0.325 \\
  2 &  Acuna Jr., R & ATL & OF &  433 &  127 &   26 &
      64 & 0.293 & 0.366 \\
  3 &  Adames, W & TB & IF &  288 &   80 &   10 &
      34 & 0.278 & 0.348 \\
  $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ &
      $\vdots$ & $\vdots$ & $\vdots$ \\
  427 &  Zimmerman, R & WSH & IF & 288 &   76 &
      13 &   51 & 0.264 & 0.337 \\
  428 & Zobrist, B & CHC & IF & 455 & 139 & 9 &
      58 & 0.305 & 0.378 \\
  \mlbN{} &  Zunino, M & SEA & C &  373 &   75 &   20 &
      44 & 0.201 & 0.259 \\
   \hline
\end{tabular}
\caption{Six cases from the \mlbdata{} data matrix.}
\label{mlbBat18DataMatrix}
\end{figure}

\begin{figure}[h]
\centering\small
\begin{tabular}{lp{8.5cm}}
\hline
{\bf variable} & {\bf description} \\
\hline
\var{name} & Player name \\
\var{team} & The abbreviated name of the player's team \\
\var{position} &
    The player's primary field position
    (\resp{OF}, \resp{IF}, \resp{C}) \\
\var{AB} & Number of opportunities at bat \\
\var{H} & Number of hits \\
\var{HR} & Number of home runs \\
\var{RBI} & Number of runs batted in \\
\var{AVG} &
    Batting average, which is equal to $\resp{H}/\resp{AB}$ \\
\var{OBP} &
    On-base percentage, which is roughly equal to the fraction
    of times a player gets on base or hits a home run \\
\hline
\end{tabular}
\caption{Variables and their descriptions for the
    \mlbdata{} data set.}
\label{mlbBat18Variables}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
\label{nullHypForOBPAgainstPosition}%
The null hypothesis under consideration is the following:
$\mu_{\resp{OF}} = \mu_{\resp{IF}} = %\mu_{\resp{DH}} = 
    \mu_{\resp{C}}$.
Write the null and corresponding alternative hypotheses
in plain language.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{$H_0$: The average on-base percentage is equal
  across the three positions.
  $H_A$: The average on-base percentage varies across some
  (or all) groups.}

\begin{examplewrap}
\begin{nexample}{The player positions have been divided
    into three groups: outfield (\resp{OF}), infield (\resp{IF}),
    %designated hitter (\resp{DH}),
    and catcher~(\resp{C}).
    What would be an appropriate point estimate of the on-base
    percentage by outfielders, $\mu_{\resp{OF}}$?}
  A good estimate of the on-base percentage by outfielders would
  be the sample average of \var{OBP} for just those players
  whose position is outfield: $\bar{x}_{OF} = 0.320$.
\end{nexample}
\end{examplewrap}

Figure~\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.

\begin{figure}[h]
\centering\small
\begin{tabular}{l rrr}
\hline
	& \resp{OF} & \resp{IF} & \resp{C} \\
\hline
Sample size ($n_i$) & 160 & 205 & 64 \\
Sample mean ($\bar{x}_i$) & 0.320 & 0.318 & 0.302 \\
Sample SD ($s_i$) & 0.043 & 0.038 & 0.038 \\
\hline
\end{tabular}
\caption{Summary statistics of on-base percentage, split by player position.}
\label{mlbHRPerABSummaryTable}
\end{figure}

\begin{figure}[h]
  \centering
  \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}
  \caption{Side-by-side box plot of the on-base percentage
      for \mlbN{} players across three groups.
      With over a hundred players in both the infield and
      outfield groups, the apparent outliers are not a concern.}
  \label{mlbANOVABoxPlot}
\end{figure}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{The largest difference between the sample means
    is between the catcher and the outfielder positions.
    Consider again the original hypotheses:
    \begin{itemize}
    \setlength{\itemsep}{0mm}
    \item[$H_0$:]
        $\mu_{\resp{OF}} = \mu_{\resp{IF}} = \mu_{\resp{C}}$
    \item[$H_A$:]
        The average on-base percentage ($\mu_i$) varies
        across some (or all) groups.
    \end{itemize}
    Why might it be inappropriate to run the test by simply
    estimating whether the difference of $\mu_{\var{C}}$ and
    $\mu_{\resp{OF}}$ is statistically significant at a 0.05
    significance level?}
  \label{multCompExIncDiscOfClassrooms}%
  The primary issue here is that we are inspecting the data
  before picking the groups that will be compared.
  It is inappropriate to examine all data by eye
  (informal testing) and only afterwards decide which parts
  to formally test.
  This is called \term{data snooping} or \term{data fishing}.
  Naturally, we would pick the groups with the large
  differences for the formal test, and this would leading
  to an inflation in the Type~1 Error rate.
  To understand this better, let's consider a slightly
  different problem.

  Suppose we are to measure the aptitude for students in
  20~classes in a large elementary school at the beginning
  of the year.
  In this school, all students are randomly assigned to
  classrooms, so any differences we observe between the
  classes at the start of the year are completely due
  to chance.
  However, with so many groups, we will probably observe
  a few groups that look rather different from each other.
  If we select only these classes that look so different
  and then perform a formal test,
  we will probably make the wrong conclusion that the
  assignment wasn't random.
  While we might only formally test differences
  for a few pairs of classes, we informally evaluated
  the other classes by eye before choosing the most extreme
  cases for a comparison.
\end{nexample}
\end{examplewrap}

For additional information on the ideas expressed in
Example~\ref{multCompExIncDiscOfClassrooms}, we recommend
reading about the
\term{prosecutor's fallacy}.\footnote{See, for example,
  \oiRedirect{textbook-prosecutors_fallacy}
      {statmodeling.stat.columbia.edu/2007/05/18/the\_prosecutors}.}

In the next section we will learn how to use the $F$~statistic
and ANOVA to test whether observed differences in sample means
could have happened just by chance even if there was no
difference in the respective population means.


\D{\newpage}

\subsection{Analysis of variance (ANOVA)
    and the $\pmb{F}$-test}

The method of analysis of variance in this context focuses
on answering one question:
is the variability in the sample means so large that it seems
unlikely to be from chance alone?
This question is different from earlier testing procedures
since we will \emph{simultaneously} consider many groups,
and evaluate whether their sample means differ more than
we would expect from natural variation.
We~call this variability the
\term{mean square between groups ($MSG$)},
and it has an associated degrees of freedom,
$df_{G} = k - 1$ when there are
$k$~groups.\index{degrees of freedom (df)!ANOVA}
The $MSG$ can be thought of as a scaled variance formula
for means.
If the null hypothesis is true, any variation in the sample
means is due to chance and shouldn't be too large.
Details of $MSG$ calculations are provided in the
footnote.\footnote{Let $\bar{x}$ represent the mean of
  outcomes across all groups.
  Then the mean square between groups is computed as
  \begin{align*}
  MSG
    = \frac{1}{df_{G}}SSG
    = \frac{1}{k-1}\sum_{i=1}^{k} n_{i}
        \left(\bar{x}_{i} - \bar{x}\right)^2
  \end{align*}
  where $SSG$ is called the \term{sum of squares between groups}
  and $n_{i}$ is the sample size of group $i$.}
However, we typically use software for these computations.

The mean square between the groups is, on its own, quite useless
in a hypothesis test.
We~need a benchmark value for how much variability should
be expected among the sample means if the null hypothesis is true.
To this end, we compute a pooled variance estimate,
often abbreviated as the \term{mean square error ($MSE$)},
which has an associated degrees of freedom value $df_E = n - k$.
It is helpful to think of $MSE$ as a measure of the variability
within the groups.
Details of the computations of the $MSE$ and a link to an
extra online section for ANOVA calculations are provided
in the footnote\footnote{Let $\bar{x}$ represent the mean
  of outcomes across all groups.
  Then the \term{sum of squares total ($SST$)} is computed as
  \begin{align*}
  SST = \sum_{i=1}^{n} \left(x_{i} - \bar{x}\right)^2
  \end{align*}
  where the sum is over all observations in the data set.
  Then we compute the \term{sum of squared errors ($SSE$)}
  in one of two equivalent ways:
  \begin{align*}
  SSE &= SST - SSG \\
  	&= (n_1-1)s_1^2 + (n_2-1)s_2^2 + \cdots + (n_k-1)s_k^2
  \end{align*}
  where $s_i^2$ is the sample variance (square of the standard
  deviation) of the residuals in group $i$.
  Then the $MSE$ is the standardized form of $SSE$:
  $MSE = \frac{1}{df_{E}}SSE$.
  
  \noindent%
  For additional details on ANOVA calculations, see
  \oiRedirect{stat_extra_anova_calculations}
      {www.openintro.org/d?file=stat\_extra\_anova\_calculations}}
for interested readers.

When the null hypothesis is true, any differences among the
sample means are only due to chance, and the $MSG$ and $MSE$
should be about equal.
As~a test statistic for ANOVA, we examine the fraction of $MSG$
and~$MSE$:
\begin{align*}
F = \frac{MSG}{MSE}
\end{align*}
The $MSG$ represents a measure of the between-group variability,
and $MSE$ measures the variability within each of the groups.

\begin{exercisewrap}
\begin{nexercise}
For the baseball data, $MSG = 0.00803$ and $MSE=0.00158$.
Identify the degrees of freedom associated with MSG and
MSE and verify the $F$ statistic is approximately
\mlbF{}.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{There are $k = \mlbK{}$ groups,
  so $df_{G} = k - 1 = \mlbDFA{}$.
  There are $n = n_1 + n_2 + n_3 = \mlbN{}$ total observations,
  so $df_{E} = n - k = \mlbDFB{}$.
  Then the $F$ statistic is computed as the ratio of $MSG$
  and $MSE$:
  $F
    = \frac{MSG}{MSE}
    = \frac{0.00803}{0.00158}
    = 5.082
    \approx \mlbF{}$.
  ($F = \mlbF{}$ was computed by using values for $MSG$
  and $MSE$ that were not rounded.)}

We can use the $F$ statistic to evaluate the hypotheses in
what is called an \termsub{$\pmb{F}$-test}{F-test@$F$-test}.
A p-value can be computed from the $F$ statistic using
an $F$~distribution, which has two associated parameters:
$df_{1}$ and~$df_{2}$.
For the $F$ statistic in ANOVA,
$df_{1} = df_{G}$ and $df_{2} = df_{E}$.
An $F$ distribution with \mlbDFA{} and \mlbDFB{} degrees
of freedom, corresponding to the $F$ statistic for the
baseball hypothesis test, is shown in
Figure~\ref{fDist2And423Shaded}.

\begin{figure}[h]
  \centering
  \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}
  \caption{An $F$ distribution with $df_1=2$ and $df_2=426$.}
  \label{fDist2And423Shaded}
\end{figure}

\D{\newpage}

The larger the observed variability in the sample
means ($MSG$) relative to the within-group observations ($MSE$),
the larger $F$ will be and the stronger the evidence against
the null hypothesis.
Because larger values of $F$ represent stronger evidence against
the null hypothesis, we use the upper tail of the distribution
to compute a p-value.

\begin{onebox}{The $\pmb{F}$ statistic and the
    $\pmb{F}$-test}
  Analysis of variance (ANOVA) is used to test whether
  the mean outcome differs across 2~or more groups.
  ANOVA uses a test statistic $F$, which represents
  a standardized ratio of variability in the sample means
  relative to the variability within the groups.
  If~$H_0$ is true and the model conditions are satisfied,
  the statistic $F$ follows an $F$ distribution with
  parameters $df_{1} = k - 1$ and $df_{2} = n - k$.
  The upper tail of the $F$ distribution is used to
  represent the p-value.
\end{onebox}

%\begin{exercisewrap}
%\begin{nexercise}
%\label{describePValueAreaForFDistributionInMLBOBPExample}%
%The test statistic for the baseball example is $F = \mlbF{}$.
%Shade the area corresponding to the p-value in
%Figure~\ref{fDist2And423}. \footnotemark{}
%\end{nexercise}
%\end{exercisewrap}
%\footnotetext{\ \vspace{-4mm}\\%
%  \Figures{0.5}{fDist2And423}{fDist2And423Shaded}}

\begin{examplewrap}
\begin{nexample}{The p-value corresponding to
    the shaded area in
    Figure~\ref{fDist2And423Shaded}
    is equal to about \mlbPvalue{}.
    Does this provide strong evidence against the
    null hypothesis?}
  The p-value is smaller than 0.05, indicating the evidence
  is strong enough to reject the null hypothesis
  at a significance level of 0.05.
  That is, the data provide strong evidence that the average
  on-base percentage varies by player's primary field position.
\end{nexample}
\end{examplewrap}


\subsection{Reading an ANOVA table from software}

The calculations required to perform an ANOVA by hand are
tedious and prone to human error.
For these reasons, it is common to use statistical software
to calculate the $F$ statistic and p-value.

An ANOVA can be summarized in a table very similar to that
of a regression summary, which we will see in
Chapters~\ref{linRegrForTwoVar}
and~\ref{multipleAndLogisticRegression}.
Figure~\ref{anovaSummaryTableForOBPAgainstPosition}
shows an ANOVA summary to test whether the mean of on-base
percentage varies by player positions in the MLB.
Many of these values should look familiar;
in particular, the $F$-test statistic and p-value
can be retrieved from the last two columns.

\begin{figure}[ht]
\centering
\begin{tabular}{lrrrrr}
  \hline
  & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\ 
  \hline
  position & \mlbDFA{} & 0.0161 & 0.0080 & 5.0766 & 0.0066 \\ 
  Residuals & \mlbDFB{} & 0.6740 & 0.0016 &  &  \\ 

  \hline
\multicolumn{6}{r}{$s_{pooled} = 0.040$ on $df = 423$}
\end{tabular}
\caption{ANOVA summary for testing whether the average
    on-base percentage differs across player positions.}
\label{anovaSummaryTableForOBPAgainstPosition}
\end{figure}


\D{\newpage}

\subsection{Graphical diagnostics for an ANOVA analysis}

There are three conditions we must check for an ANOVA analysis:
all observations must be independent,
the data in each group must be nearly normal,
and the variance within each group must be approximately equal.
\begin{description}
\item[Independence.]
    If the data are a simple random sample,
    this condition is satisfied.
    For processes and experiments, carefully consider whether
    the data may be independent (e.g. no pairing).
    For example, in the MLB data, the data were not sampled.
    However, there are not obvious reasons why independence
    would not hold for most or all observations.
\item[Approximately normal.]
    As with one- and two-sample testing for means,
    the normality assumption is especially important
    when the sample size is quite small when it is
    ironically difficult to check for non-normality.
    A histogram of the observations from each group
    is shown in Figure~\ref{mlbANOVADiagNormalityGroups}.
    Since each of the groups we're considering have
    relatively large sample sizes,
    what we're looking for are major outliers.
    None are apparent, so this conditions is reasonably met.
    \begin{figure}[h]
      \centering
      \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}
      \caption{Histograms of OBP for each field position.}
      \label{mlbANOVADiagNormalityGroups}
    \end{figure}
\item[Constant variance.]
    The last assumption is that the variance in the
    groups is about equal from one group to the next.
    This assumption can be checked by examining a
    side-by-side box plot of the outcomes across the
    groups, as in Figure~\vref{mlbANOVABoxPlot}.
    In this case, the variability is similar in the
    three groups but not identical.
    We see in Table~\vref{mlbHRPerABSummaryTable}
    that the standard deviation doesn't vary much
    from one group to the next.
\end{description}

\index{data!MLB batting|)}

\begin{onebox}{Diagnostics for an ANOVA analysis}
  Independence is always important to an ANOVA analysis.
  The normality condition is very important when the sample
  sizes for each group are relatively small.
  The constant variance condition is especially important
  when the sample sizes differ between groups.
\end{onebox}


\D{\newpage}

\subsection{Multiple comparisons and controlling Type~1 Error rate}
\label{multipleComparisonsAndControllingTheType1ErrorRate}

\index{significance level!multiple comparisons|(}

When we reject the null hypothesis in an ANOVA analysis,
we might wonder, which of these groups have different means?
To answer this question, we compare the means of each possible
pair of groups.
For instance, if there are three groups and there is strong
evidence that there are some differences in the group means,
there are three comparisons to make:
group~1 to group~2, group~1 to group~3, and group~2 to group~3.
These comparisons can be accomplished using
a two-sample $t$-test, but we use a modified significance level
and a pooled estimate of the standard deviation across groups.
Usually this pooled standard deviation can be found in the
ANOVA table, e.g. along the bottom of
Figure~\ref{anovaSummaryTableForOBPAgainstPosition}.

\begin{examplewrap}
\begin{nexample}{
    Example~\vref{firstExampleForThreeStatisticsClassesAndANOVA}
    discussed three statistics lectures, all taught during the
    same semester.
    Figure~\ref{summaryStatisticsForClassTestData}
    shows summary statistics for these three courses,
    and a side-by-side box plot of the data is shown
    in Figure~\ref{classDataSBSBoxPlot}.
    We would like to conduct an ANOVA for these data.
    Do you see any deviations from the three conditions
    for ANOVA?}
  In this case (like many others) it is difficult to check
  independence in a rigorous way.
  Instead, the best we can do is use common sense to consider
  reasons the assumption of independence may not hold.
  For instance, the independence assumption may not be
  reasonable if there is a star teaching assistant that only
  half of the students may access;
  such a scenario would divide a class into two subgroups.
  No such situations were evident for these particular data,
  and we believe that independence is acceptable.

  The distributions in the side-by-side box plot appear
  to be roughly symmetric and show no noticeable outliers.

  The box plots show approximately equal variability,
  which can be verified in
  Figure~\ref{summaryStatisticsForClassTestData},
  supporting the constant variance assumption.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
\centering
\begin{tabular}{lrrr}
  \hline
Class $i$	& A	& B	& C \\ 
  \hline
$n_i$		& 58	& 55	& 51 \\ 
$\bar{x}_i$	& 75.1	& 72.0	& 78.9 \\ 
$s_i$		& 13.9	& 13.8	& 13.1 \\ 
\hline
\end{tabular}
\caption{Summary statistics for the first midterm scores
    in three different lectures of the same course.}
\label{summaryStatisticsForClassTestData}
\end{figure}

\begin{figure}[h]
  \centering
  \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}
  \caption{Side-by-side box plot for the first midterm
      scores in three different lectures of the same course.}
  \label{classDataSBSBoxPlot}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
\label{exerExaminingAnovaSummaryTableForMidtermData}%
ANOVA was conducted for the midterm data,
and summary results are shown in
Figure~\ref{anovaSummaryTableForMidtermData}.
What should we conclude?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The p-value of the test is 0.0330,
  less than the default significance level of 0.05.
  Therefore, we reject the null hypothesis and conclude
  that the difference in the average midterm scores are
  not due to chance.}

\begin{figure}[h]
\centering
\begin{tabular}{lrrrrr}
  \hline
 & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\ 
  \hline
lecture & 2 & 1290.11 & 645.06 & 3.48 & 0.0330 \\ 
  Residuals & 161 & 29810.13 & 185.16 &  &  \\ 
   \hline
\multicolumn{6}{r}{$s_{pooled}=13.61$ on $df=161$}
\end{tabular}
\caption{ANOVA summary table for the midterm data.}
\label{anovaSummaryTableForMidtermData}
\end{figure}

There is strong evidence that the different means in each
of the three classes is not simply due to chance.
We might wonder, which of the classes are actually different?
As discussed in earlier chapters, a two-sample $t$-test
could be used to test for differences in each possible pair
of groups.
However, one pitfall was discussed in
Example~\vref{multCompExIncDiscOfClassrooms}:
when we run so many tests, the Type~1 Error rate increases.
This issue is resolved by using a modified significance level.

\begin{onebox}{Multiple comparisons and the Bonferroni
    correction for $\pmb{\alpha}$}
  The scenario of testing many pairs of groups is called
  \term{multiple comparisons}.
  The \term{Bonferroni correction} suggests that a more
  stringent significance level is more appropriate for
  these tests:
  \begin{align*}
  \alpha^{\star} = \alpha / K
  \end{align*}
  where $K$ is the number of comparisons being considered
  (formally or informally).
  If there are $k$ groups, then usually all possible pairs
  are compared and $K=\frac{k(k-1)}{2}$.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{In Guided
    Practice~\ref{exerExaminingAnovaSummaryTableForMidtermData},
    you found strong evidence of differences in the average
    midterm grades between the three lectures.
    Complete the three possible pairwise comparisons using
    the Bonferroni correction and report any differences.}
  \label{multipleComparisonsOfThreeStatClasses}%
  We use a modified significance level of
  $\alpha^{\star} = 0.05 / 3 = 0.0167$.
  Additionally, we use the pooled estimate of the standard
  deviation:
  $s_{pooled}=13.61$ on $df=161$,
  which is provided in the ANOVA summary table.

  Lecture A versus Lecture B:
  The estimated difference and standard error are,
  respectively,
  \begin{align*}
  \bar{x}_A - \bar{x}_{B} &= 75.1 - 72 = 3.1
  &&SE = \sqrt{\frac{13.61^2}{58} + \frac{13.61^2}{55}} = 2.56
  \end{align*}
  (See Section~\vref{pooledStandardDeviations}
  for additional details.)
  This results in a T-score of 1.21 on $df = 161$
  (we use the $df$ associated with $s_{pooled}$).
  Statistical software was used to precisely identify the two-sided
  p-value since the modified significance level of 0.0167 is not
  found in the $t$-table.
  The p-value (0.228) is larger than $\alpha^*=0.0167$,
  so there is not strong evidence of a difference in the means
  of lectures A and~B.

  Lecture A versus Lecture C: The estimated difference and
  standard error are 3.8 and 2.61, respectively.
  This results in a $T$ score of 1.46 on $df = 161$
  and a two-sided p-value of 0.1462.
  This p-value is larger than $\alpha^*$, so there is not
  strong evidence of a difference in the means of lectures
  A and~C.

  Lecture B versus Lecture C: The estimated difference
  and standard error are 6.9 and 2.65, respectively.
  This results in a $T$ score of 2.60 on $df = 161$
  and a two-sided p-value of 0.0102.
  This p-value is smaller than $\alpha^*$.
  Here we find strong evidence of a difference in the
  means of lectures B and~C.
\end{nexample}
\end{examplewrap}

\D{\newpage}

\noindent%
We might summarize the findings of the analysis from
Example~\ref{multipleComparisonsOfThreeStatClasses}
using the following notation:
\begin{align*}
\mu_A &\stackrel{?}{=} \mu_B
	&\mu_A &\stackrel{?}{=} \mu_C
	&\mu_B &\neq \mu_C
\end{align*}
The midterm mean in lecture A is not statistically
distinguishable from those of lectures B or C.
However, there is strong evidence that lectures B and~C
are different.
In~the first two pairwise comparisons, we did not have
sufficient evidence to reject the null hypothesis.
Recall that failing to reject $H_0$ does not imply $H_0$ is true.

\begin{onebox}{Reject $\pmb{H_0}$ with ANOVA
    but find no differences in group means}
  It is possible to reject the null hypothesis using ANOVA
  and then to not subsequently identify differences in the
  pairwise comparisons.
  However, \emph{this does not invalidate the ANOVA conclusion}.
  It only means we have not been able to successfully identify
  which specific groups differ in their means.
\end{onebox}

The ANOVA procedure examines the big picture:
it considers all groups simultaneously to decipher whether
there is evidence that some difference exists.
Even if the test indicates that there is strong evidence
of differences in group means, identifying with
high confidence a specific difference as statistically
significant is more difficult.

Consider the following analogy:
we observe a Wall Street firm that makes large quantities
of money based on predicting mergers.
Mergers are generally difficult to predict,
and if the prediction success rate is extremely high,
that may be considered sufficiently strong evidence
to warrant investigation by the Securities and Exchange
Commission~(SEC).
While the SEC may be quite certain that there is insider
trading taking place at the firm, the evidence against
any single trader may not be very strong.
It is only when the SEC considers all the data that they
identify the pattern.
This is effectively the strategy of ANOVA:
stand back and consider all the groups simultaneously.

\index{significance level!multiple comparisons|)}
\index{analysis of variance (ANOVA)|)}


{\input{ch_inference_for_means/TeX/comparing_many_means_with_anova.tex}}


================================================
FILE: ch_inference_for_means/TeX/comparing_many_means_with_anova.tex
================================================
\exercisesheader{}

% 35

\eoce{\qt{Fill in the blank\label{fitb_anova}} When doing an ANOVA, you observe 
large differences in means between groups. Within the ANOVA framework, this 
would most likely be interpreted as evidence strongly favoring the \underline{\hspace{20mm}} hypothesis.
}{}

% 36

\eoce{\qtq{Which test\label{which_test_anova}} We would like to test if 
students who are in the social sciences, natural sciences, arts and 
humanities, and other fields spend the same amount of time studying for 
this course. What type of test should we use? Explain your reasoning.
}{}

% 37

\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.
\begin{center}
\begin{tabular}{lrrrrr}
\hline
        & Df    & Sum Sq        & Mean Sq   & F value   & Pr($>$F) \\ 
\hline
feed        & 5     & 231,129.16    & 46,225.83     & 15.36     & 0.0000 \\ 
Residuals   & 65 & 195,556.02   & 3,008.55  &       &  \\ 
\hline
%\multicolumn{6}{r}{$s_{pooled} = 55.85$ on $df=65$}
\end{tabular}
\end{center}
Conduct 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.

\begin{minipage}[c]{0.65\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.35\textwidth}
{\footnotesize\begin{tabular}{l c c c}
\hline
            & Mean      & SD        & n \\
\hline
casein          & 323.58        & 64.43 & 12 \\
horsebean   & 160.20        & 38.63 & 10 \\
linseed         & 218.75        & 52.24 & 12 \\
meatmeal    & 276.91        & 64.90 & 11 \\
soybean         & 246.43        & 54.13 & 14 \\
sunflower       & 328.92        & 48.84 & 12 \\
\hline
\end{tabular}}
\end{minipage}
}{}

% 38

\eoce{\qt{Teaching descriptive statistics\label{teach_descriptive_stats}} A study 
compared five different methods for teaching descriptive statistics. The five 
methods were traditional lecture and discussion, programmed textbook 
instruction, programmed text with lectures, computer instruction, and computer 
instruction with lectures. 45 students were randomly assigned, 9 to each 
method. After completing the course, students took a 1-hour exam. 
\begin{parts}
\item What are the hypotheses for evaluating if the average test scores are 
different for the different teaching methods?
\item What are the degrees of freedom associated with the $F$-test for 
evaluating these hypotheses?
\item Suppose the p-value for this test is 0.0168. What is the conclusion?
\end{parts}
}{}

\D{\newpage}

% 39

\eoce{\qt{Coffee, depression, and physical activity\label{coffee_depression_phys_act}} 
Caffeine is the world's most widely used stimulant, with approximately 80\% consumed 
in the form of coffee. Participants in a study investigating the relationship between 
coffee consumption and exercise were asked to report the number of hours they spent per 
week on moderate (e.g., brisk walking) and vigorous (e.g., strenuous sports and jogging) 
exercise. Based on these data the researchers estimated the total hours of metabolic 
equivalent tasks (MET) per week, a value always greater than 0. The table below gives 
summary statistics of MET for women in this study based on the amount of coffee consumed.
\footfullcite{Lucas:2011}
 
\begin{adjustwidth}{-4em}{-4em}
\begin{center}
\begin{tabular}{l  r  r  r  r  r  r}
                & \multicolumn{5}{c}{\textit{Caffeinated coffee consumption}} \\
\cline{2-6}
                & $\le$ 1 cup/week  & 2-6 cups/week & 1 cup/day 
                                            & 2-3 cups/day  & $\ge$ 4 cups/day  & Total \\
\hline
Mean            & 18.7              & 19.6          & 19.3  
                                            & 18.9          & 17.5 \\
SD              & 21.1              & 25.5          & 22.5  
                                            & 22.0          & 22.0 \\
n               & 12,215            & 6,617         & 17,234    
                                            & 12,290        & 2,383             & 50,739 \\
\hline
\end{tabular}
\end{center}
\end{adjustwidth}

\begin{parts}

\item Write the hypotheses for evaluating if the average physical activity level 
varies among the different levels of coffee consumption.

\item Check conditions and describe any assumptions you must make to proceed with 
the test.

\item Below is part of the output associated with this test. Fill in the empty cells.

\begin{center}
\renewcommand{\arraystretch}{1.25}
\begin{tabular}{lrrrrr}
  \hline
            & Df
                        & Sum Sq
                                    & Mean Sq
                                                & F value
                                                            & Pr($>$F) \\ 
  \hline
coffee      & \fbox{\textcolor{white}{{\footnotesize XXXXX}}}    
                        & \fbox{\textcolor{white}{{\footnotesize XXXXX}}}      
                                    & \fbox{\textcolor{white}{{\footnotesize XXXXX}}}           
                                                & \fbox{\textcolor{white}{{\footnotesize XXXXX}}}
                                                            & 0.0003 \\ 
Residuals   & \fbox{\textcolor{white}{{\footnotesize XXXXX}}} 
                        & 25,564,819
                                    & \fbox{\textcolor{white}{{\footnotesize  XXXXX}}}
                                                &
                                                            &  \\ 
   \hline
Total       & \fbox{\textcolor{white}{{\footnotesize XXXXX}}} 
                        & 25,575,327
\end{tabular}
\end{center}

\item What is the conclusion of the test?

\end{parts}
}{}

% 40

\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.
\begin{center}
\begin{tabular}{rrrrrrrrr}
  \hline
            & Sec 1 & Sec 2 & Sec 3 & Sec 4 & Sec 5 & Sec 6 & Sec 7 & Sec 8 \\ 
  \hline
$n_i$       & 33 & 19 & 10 & 29 & 33 & 10 & 32 & 31 \\ 
$\bar{x}_i$ & 92.94 & 91.11 & 91.80 & 92.45 & 89.30 & 88.30 & 90.12 & 93.35 \\ 
$s_i$       & 4.21 & 5.58 & 3.43 & 5.92 & 9.32 & 7.27 & 6.93 & 4.57 \\ 
   \hline
\end{tabular}
\end{center}
The ANOVA output below can be used to test for differences between the average scores from the different discussion sections.
\begin{center}
\begin{tabular}{lrrrrr}
\hline
            & Df        & Sum Sq & Mean Sq  & F value & Pr($>$F) \\ 
\hline
section         & 7         & 525.01    & 75.00         & 1.87  & 0.0767 \\ 
Residuals   & 189   & 7584.11   & 40.13         &       &  \\ 
\hline
\end{tabular}
\end{center}
Conduct 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.
}{}

\D{\newpage}

% 41

\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.
\begin{center}
\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}
\end{center}
\begin{center}
\begin{tabular}{lrrrrr}
  \hline
            & Df    & Sum Sq    & Mean Sq   & F value   & Pr($>$F) \\ 
  \hline
major       & 2     & 0.03      & 0.015      & 0.185     & 0.8313 \\ 
Residuals   & 195   & 15.77     & 0.081      &           &  \\ 
   \hline
\end{tabular}
\end{center}
\begin{parts}
\item Write the hypotheses for testing for a difference between average GPA across majors.
\item What is the conclusion of the hypothesis test?
\item How many students answered these questions on the survey, i.e. what is the sample size?
\end{parts}
}{}

% 42

\eoce{\qt{Work hours and education\label{work_hours_education}} The General Social Survey 
collects data on demographics, education, and work, among many other characteristics 
of US residents. \footfullcite{data:gss} Using ANOVA, we can consider 
educational attainment levels for all 1,172 respondents at once. Below are the 
distributions of hours worked by educational attainment and relevant summary 
statistics that will be helpful in carrying out this analysis.
\begin{center}

\begin{tabular}{l  r  r  r  r  r  r}
                & \multicolumn{5}{c}{\textit{Educational attainment}} \\
\cline{2-6}
                & Less than HS  & HS    & Jr Coll   & Bachelor's & Graduate & Total \\
\hline
Mean            & 38.67         & 39.6  & 41.39     & 42.55     & 40.85     & 40.45 \\
SD              & 15.81         & 14.97 & 18.1      & 13.62     & 15.51     & 15.17 \\
n               & 121           & 546   & 97        & 253       & 155       & 1,172 \\
\hline
\end{tabular}

\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}
\end{center}
\begin{parts}
\item Write hypotheses for evaluating whether the average number of hours 
worked varies across the five groups.
\item Check conditions and describe any assumptions you must make to proceed 
with the test.
\item Below is part of the output associated with this test. Fill in the 
empty cells.

\begin{center}
\renewcommand{\arraystretch}{1.25}
\begin{tabular}{lrrrrr}
  \hline
            & Df    
                    & Sum Sq        
                            & Mean Sq       
                                    & F-value      
                                            & Pr($>$F) \\ 
  \hline
degree      & \fbox{\textcolor{white}{{\footnotesize XXXXX}}}       
                    & \fbox{\textcolor{white}{{\footnotesize XXXXX}}}       
                            & 501.54    
                                    & \fbox{\textcolor{white}{{\footnotesize XXXXX}}}   
                                            & 0.0682 \\ 
Residuals   & \fbox{\textcolor{white}{{\footnotesize XXXXX}}} 
                    & 267,382     
                            & \fbox{\textcolor{white}{{\footnotesize  XXXXX}}}          
                                    &       
                                            &  \\ 
   \hline
Total       & \fbox{\textcolor{white}{{\footnotesize XXXXX}}} 
                    &\fbox{\textcolor{white}{{\footnotesize XXXXX}}}
\end{tabular}
\end{center}

\item What is the conclusion of the test?

\end{parts}
}{}

% 43

\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.
\begin{parts}
\item As the number of groups increases, the modified significance level for pairwise tests increases as well.
\item As the total sample size increases, the degrees of freedom for the residuals increases as well.
\item The constant variance condition can be somewhat relaxed when the sample sizes are relatively consistent across groups.
\item The independence assumption can be relaxed when the total sample size is large.
\end{parts}
}{}

% 44

\eoce{\qt{Child care hours\label{child_care_hours}} The China Health and Nutrition 
Survey aims to examine the effects of the health, nutrition, and family planning 
policies and programs implemented by national and local governments.\footfullcite{data:china} It, for example, collects information on number of hours Chinese parents spend 
taking care of their children under age 6. The side-by-side box plots below 
show the distribution of this variable by educational attainment of the parent. 
Also provided below is the ANOVA output for comparing average hours across 
educational attainment categories.
\begin{center}
\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}
\end{center}
\begin{center}
\begin{tabular}{lrrrrr}
\hline
            & Df    & Sum Sq    & Mean Sq   & F value   & Pr($>$F) \\ 
\hline
education   & 4     & 4142.09   & 1035.52   & 1.26      & 0.2846 \\ 
Residuals   & 794   & 653047.83 & 822.48    &           &  \\ 
\hline
\end{tabular}
\end{center}
\begin{parts}
\item Write the hypotheses for testing for a difference between the average 
number of hours spent on child care across educational attainment levels.
\item What is the conclusion of the hypothesis test?
\end{parts}
}{}

% 45

\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.
The relevant ANOVA output is given below,
and we have checked for you that there are no meaningful
differences in variability across the groups.
\begin{center}
\begin{tabular}{lrrrrr}
  \hline
 & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\ 
  \hline
treatment & 2 & 639.48 & 319.74 & 3.33 & 0.0461 \\ 
  Residuals & 39 & 3740.43 & 95.91 &  &  \\ 
   \hline
\multicolumn{6}{r}{$s_{pooled} = 9.793$ on $df=39$}
\end{tabular}
\end{center}
\begin{parts}
\item What are the hypotheses?
\item\label{prison_isolation_anova_test_conclusion}
    What is the conclusion of the test?
    Use a 5\% significance level.
\item
    If in
    part~(\ref{prison_isolation_anova_test_conclusion})
    you determined that the test is significant,
    conduct pairwise tests to determine which groups
    are different from each other.
    If you did not reject the null hypothesis in
    part~(\ref{prison_isolation_anova_test_conclusion}),
    recheck your answer.
    Summary statistics for each group are provided below.
\begin{center}
\begin{tabular}{l  r  r  r  r  }
\hline
                & Tr 1  & Tr 2  & Tr 3      \\
\hline
Mean            & 6.21  & 2.86  & -3.21           \\
SD              & 12.3  & 7.94  & 8.57       \\
n               & 14        & 14        & 14     \\
\hline
\end{tabular}
\end{center}
\end{parts}
}{}

% 46

\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.

If the null hypothesis that the means of four groups are all the same is rejected using ANOVA at a 5\% significance level, then ...
\begin{parts}
\item we can then conclude that all the means are different from one another.
\item the standardized variability between groups is higher than the standardized variability within groups.
\item the pairwise analysis will identify at least one pair of means that are significantly different.
\item the appropriate $\alpha$ to be used in pairwise comparisons is 0.05 / 4 = 0.0125 since there are four groups.
\end{parts}
}{}


================================================
FILE: ch_inference_for_means/TeX/difference_of_two_means.tex
================================================
\exercisesheader{}

% 23

\eoce{\qt{Friday the 13$^{\text{th}}$, Part I\label{friday_13th_traffic}} In the 
early 1990's, researchers in the UK collected data on traffic flow, number of 
shoppers, and traffic accident related emergency room admissions on Friday the 
13$^{\text{th}}$ and the previous Friday, Friday the 6$^{\text{th}}$. The 
histograms below show the distribution of number of cars passing by a specific 
intersection on Friday the 6$^{\text{th}}$ and Friday the 13$^{\text{th}}$ for 
many such date pairs. Also given are some sample statistics, where the 
difference is the number of cars on the 6th minus the number of cars on the 13th.\footfullcite{Scanlon:1993}
\begin{center}
\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} \\
$\:$ \\
{\small
\begin{tabular}{l c c c}
\hline
        & 6$^{\text{th}}$   & 13$^{\text{th}}$  & Diff.\\
\hline  
$\bar{x}$   &128,385            & 126,550       & 1,835 \\
$s$     &7,259          & 7,664         & 1,176 \\
$n$     &10             & 10                & 10 \\
\hline
\end{tabular}
}
\end{center}
\begin{parts}
\item Are there any underlying structures in these data that should be 
considered in an analysis? Explain.
\item What are the hypotheses for evaluating whether the number of people out 
on Friday the 6$^{\text{th}}$ is different than the number out on Friday the 
13$^{\text{th}}$?
\item Check conditions to carry out the hypothesis test from part~(b).
\item Calculate the test statistic and the p-value.
\item What is the conclusion of the hypothesis test?
\item Interpret the p-value in this context.
\item What type of error might have been made in the conclusion of your test? 
Explain.
\end{parts}
}{}

% 24

\eoce{\qt{Diamonds, Part I\label{diamonds_1}} Prices of diamonds are determined by 
what is known as the 4 Cs: cut, clarity, color, and carat weight. The prices of 
diamonds go up as the carat weight increases, but the increase is not smooth. 
For example, the difference between the size of a 0.99 carat diamond and a 1 
carat diamond is undetectable to the naked human eye, but the price of a 1 
carat diamond tends to be much higher than the price of a 0.99 diamond. In this 
question we use two random samples of diamonds, 0.99 carats and 1 carat, each 
sample of size 23, and compare the average prices of the diamonds. In order to 
be able to compare equivalent units, we first divide the price for each diamond 
by 100 times its weight in carats. That is, for a 0.99 carat diamond, we divide 
the price by 99. For a 1 carat diamond, we divide the price by 100. The 
distributions and some sample statistics are shown below.\footfullcite{ggplot2} \\[1mm]
\begin{minipage}[c]{0.57\textwidth}
Conduct a hypothesis test to evaluate if there is a difference between the 
average standardized prices of 0.99 and 1 carat diamonds. Make sure to state 
your hypotheses clearly, check relevant conditions, and interpret your results 
in context of the data. \\[2mm]
\begin{tabular}{l c c }
\hline
        & 0.99 carats       & 1 carat\\
\hline  
Mean    & \$44.51          & \$56.81           \\
SD      & \$13.32          &\$16.13            \\
n       &23             & 23 \\
\hline
\end{tabular}
\end{minipage}%
\begin{minipage}[c]{0.43\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

\D{\newpage}

% 25

\eoce{\qt{Friday the 13$^{\text{th}}$, Part II\label{friday_13th_accident}}
The Friday the $13^{th}$ study reported in
Exercise~\ref{friday_13th_traffic} also provides data on traffic
accident related emergency room admissions.
The distributions of these counts from Friday the 6$^{\text{th}}$ and
Friday the 13$^{\text{th}}$ are shown below for six such paired dates
along with summary statistics.
You may assume that conditions for inference are met.
\begin{center}
\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} \\
$\:$ \\
\begin{minipage}[c]{0.32\textwidth}
\begin{tabular}{l c c c}
\hline
        & 6$^{\text{th}}$   & 13$^{\text{th}}$  & diff\\
\hline  
Mean    &7.5                & 10.83             & -3.33 \\
SD      &3.33           & 3.6               & 3.01 \\
n       &6              & 6             & 6 \\
\hline
\end{tabular}
\end{minipage}
\end{center}

\begin{parts}
\item Conduct a hypothesis test to evaluate if there is a difference between 
the average numbers of traffic accident related emergency room admissions 
between Friday the 6$^{\text{th}}$ and Friday the~13$^{\text{th}}$.
\item Calculate a 95\% confidence interval for the difference between the 
average numbers of traffic accident related emergency room admissions between 
Friday the 6$^{\text{th}}$ and Friday the 13$^{\text{th}}$.
\item The conclusion of the original study states, ``Friday 13th is unlucky for 
some. The risk of hospital admission as a result of a transport accident may be 
increased by as much as 52\%. Staying at home is recommended.'' Do you agree 
with this statement? Explain your reasoning.
\end{parts}
}{}

% 26

\eoce{\qt{Diamonds, Part II\label{diamonds_2}} In Exercise~\ref{diamonds_1}, we 
discussed diamond prices (standardized by weight) for diamonds with weights 0.
99 carats and 1 carat. See the table for summary statistics, and then construct 
a 95\% confidence interval for the average difference between the standardized 
prices of 0.99 and 1 carat diamonds. You may assume the conditions for 
inference are met.
\begin{center}
\begin{tabular}{l c c }
\hline
        & 0.99 carats       & 1 carat\\
\hline  
Mean    & \$44.51          & \$56.81           \\
SD      & \$13.32          &\$16.13            \\
n       &23             & 23 \\
\hline
\end{tabular}
\end{center}
}{}

% 27

\eoce{\qt{Chicken diet and weight,
    Part I\label{chick_wts_linseed_horsebean}}
Chicken farming is a multi-billion dollar industry,
and any methods that increase the growth rate of young
chicks can reduce consumer costs while increasing
company profits, possibly by millions of dollars.
An experiment was conducted to measure and compare
the effectiveness of various feed supplements on the
growth rate of chickens.
Newly hatched chicks were randomly allocated into six groups, 
and each group was given a different feed supplement.
Below are some summary statistics from this data set along
with box plots showing the distribution of weights by
feed type.\footfullcite{data:chickwts}

\noindent\begin{minipage}[c]{0.65\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.35\textwidth}
{\footnotesize\begin{tabular}{l c c c}
\hline
            & Mean      & SD        & n \\
\hline
casein          & 323.58        & 64.43 & 12 \\
horsebean   & 160.20        & 38.63 & 10 \\
linseed         & 218.75        & 52.24 & 12 \\
meatmeal    & 276.91        & 64.90 & 11 \\
soybean         & 246.43        & 54.13 & 14 \\
sunflower       & 328.92        & 48.84 & 12 \\
\hline
\end{tabular}}
\end{minipage} 

\begin{parts}
\item Describe the distributions of weights of chickens that were fed linseed 
and horsebean.
\item Do these data provide strong evidence that the average weights of 
chickens that were fed linseed and horsebean are different? Use a 5\% 
significance level.
\item What type of error might we have committed? Explain.
\item Would your conclusion change if we used $\alpha = 0.01$?
\end{parts}
}{}

\D{\newpage}

% 28

\eoce{\qt{Fuel efficiency of manual and automatic cars, Part I\label{fuel_eff_city}} 
Each year the US Environmental Protection Agency (EPA)
releases fuel economy data on cars manufactured in that year.
Below are summary statistics on fuel efficiency (in miles/gallon)
from random samples of cars with manual and automatic transmissions.
Do these data provide strong evidence of a difference between the
average fuel efficiency of cars with manual and automatic
transmissions in terms of their average city mileage?
Assume that conditions for inference are
satisfied. \footfullcite{data:epaMPG}

\noindent\begin{minipage}[c]{0.38\textwidth}
\begin{center}
\begin{tabular}{l c c }
\hline
        & \multicolumn{2}{c}{City MPG} \\
\hline
        & Automatic     & Manual         \\
Mean    & 16.12         & 19.85      \\
SD      & 3.58          & 4.51       \\
n       & 26            & 26 \\
\hline
& \\
& \\
\end{tabular}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.6\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

% 29

\eoce{\qt{Chicken diet and weight, Part II\label{chick_wts_casein_soybean}} Casein is 
a common weight gain supplement for humans. Does it have an effect on chickens? 
Using data provided in Exercise~\ref{chick_wts_linseed_horsebean}, test the 
hypothesis that the average weight of chickens that were fed casein is 
different than the average weight of chickens that were fed soybean. If your 
hypothesis test yields a statistically significant result, discuss whether or 
not the higher average weight of chickens can be attributed to the casein diet. 
Assume that conditions for inference are satisfied.
}{}

% 30

\eoce{\qt{Fuel efficiency of manual and automatic cars, Part II\label{fuel_eff_hway}} 
The table provides summary statistics on highway fuel economy
of the same 52 cars from Exercise~\ref{fuel_eff_city}.
Use these statistics to calculate a 98\% confidence interval
for the difference between average highway mileage of manual
and automatic cars, and interpret this interval in the context
of the data.\footfullcite{data:epaMPG}

\noindent\begin{minipage}[c]{0.38\textwidth}
\begin{center}
\begin{tabular}{l c c }
\hline
        & \multicolumn{2}{c}{Hwy MPG} \\
\hline
            & Automatic     & Manual         \\
Mean    & 22.92         & 27.88          \\
SD      & 5.29          & 5.01           \\
n       & 26            & 26 \\
\hline
& \\
& \\
\end{tabular}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.6\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

\D{\newpage}

% 31

\eoce{\qt{Prison isolation experiment, Part I\label{prison_isolation_T}}
Subjects from Central Prison in Raleigh, NC, volunteered
for an experiment involving an ``isolation'' experience.
The goal of the experiment was to find a treatment 
that reduces subjects' psychopathic deviant T scores.
This score measures a person's need for control or their rebellion against 
control, and it is part of a commonly used mental health test called the 
Minnesota Multiphasic Personality Inventory (MMPI) test. The experiment had 
three treatment groups: 
\begin{enumerate}[(1)]
\setlength{\itemsep}{0mm}
\item
    Four hours of sensory restriction plus a 15 minute
    ``therapeutic" tape advising that professional help
    is available.
\item
    Four hours of sensory restriction plus a 15 minute
    ``emotionally neutral'' tape on training hunting dogs.
\item
    Four hours of  sensory restriction but no taped message.
\end{enumerate}
Forty-two subjects were randomly assigned to these treatment groups, and an 
MMPI test was administered before and after the treatment. Distributions of the 
differences between pre and post treatment scores (pre - post) are shown below, 
along with some sample statistics. Use this information to independently test 
the effectiveness of each treatment. Make sure to clearly state your 
hypotheses, check conditions, and interpret results in the context of the data.\footfullcite{data:prison}

\begin{center}
\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} \\
$\:$ \\
\begin{tabular}{l  r  r  r  r  }
\hline
                & Tr 1  & Tr 2  & Tr 3      \\
\hline
Mean            & 6.21  & 2.86  & -3.21           \\
SD              & 12.3  & 7.94  & 8.57       \\
n               & 14        & 14        & 14     \\
\hline
\end{tabular}
\end{center}
}{}

% 32

\eoce{\qt{True / False: comparing means\label{tf_compare_means}} Determine if the 
following statements are true or false, and explain your reasoning for 
statements you identify as false.
\begin{parts}
\item When comparing means of two samples where $n_1 = 20$ and $n_2 = 40$, we 
can use the normal model for the difference in means since $n_2 \ge 30$.
\item As the degrees of freedom increases, the $t$-distribution approaches 
normality.
\item We use a pooled standard error for calculating the standard error of the 
difference between means when sample sizes of groups are equal to each other.
\end{parts}
}{}


================================================
FILE: ch_inference_for_means/TeX/one-sample_means_with_the_t-distribution.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Identify the critical $t$\label{identify_critical_t}} An independent random 
sample is selected from an approximately normal population with unknown 
standard deviation. Find the degrees of freedom and the critical $t$-value 
(t$^\star$) for the given sample size and confidence level.
%\begin{multicols}{4}
\begin{parts}
\item $n = 6$, CL = 90\%
\item $n = 21$, CL = 98\%
\item $n = 29$, CL = 95\%
\item $n = 12$, CL = 99\%
\end{parts}
%\end{multicols}
}{}

% 2

\eoce{\qt{$t$-distribution\label{t_distribution}}
The figure on the right shows three 
unimodal and symmetric curves:
the standard normal (z) distribution,
the $t$-distribution with 5 degrees of freedom,
and the $t$-distribution with 1 degree of freedom.
Determine which is which, and explain your reasoning.
\begin{center}

\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}
\end{center}
}{}

% 3

\eoce{\qt{Find the p-value, Part I\label{find_T_pval_1_2_sided}}
An independent random sample 
is selected from an approximately normal population
with an unknown standard 
deviation.
Find the p-value for the given sample size and test statistic.
Also determine if the null hypothesis would be rejected at 
$\alpha = 0.05$.
\begin{parts}
\item $n = 11$, $T = 1.91$
\item $n = 17$, $T = -3.45$
\item $n = 7$, $T = 0.83$
\item $n = 28$, $T = 2.13$
\end{parts}
}{}

% 4

\eoce{\qt{Find the p-value, Part II\label{find_T_pval_2_2_sided}}
An independent random sample 
is selected from an approximately normal population
with an unknown standard 
deviation.
Find the p-value for the given sample size and test statistic.
Also determine if the null hypothesis would be rejected at 
$\alpha = 0.01$.
\begin{parts}
\item $n = 26$, $T = 2.485$
\item $n = 18$, $T = 0.5$
\end{parts}
}{}

% 5

\eoce{\qt{Working backwards, Part I\label{work_backwards_1}} A 95\% confidence 
interval for a population mean, $\mu$, is given as (18.985, 21.015). This 
confidence interval is based on a simple random sample of 36 observations. 
Calculate the sample mean and standard deviation. Assume that all conditions 
necessary for inference are satisfied. Use the $t$-distribution in any 
calculations.
}{}

% 6

\eoce{\qt{Working backwards, Part II\label{work_backwards_2}} A 90\% confidence 
interval for a population mean is (65, 77). The population distribution is 
approximately normal and the population standard deviation is unknown. This 
confidence interval is based on a simple random sample of 25 observations. 
Calculate the sample mean, the margin of error, and the sample standard 
deviation.
}{}

\D{\newpage}

% 7

\eoce{\qt{Sleep habits of New Yorkers\label{ny_sleep_habits_2_sided}}
New York is known as 
``the city that never sleeps".
A random sample of 25 New Yorkers were asked how 
much sleep they get per night.
Statistical summaries of these data are shown 
below.
The point estimate suggests New Yorkers sleep less than
8~hours a night on average.
Is the result statistically significant?
\begin{center}
\begin{tabular}{rrrrrr}
 \hline
n   & $\bar{x}$ & s     & min   & max \\ 
 \hline
25  & 7.73      & 0.77  & 6.17  & 9.78 \\ 
  \hline
\end{tabular}
\end{center}

\begin{parts}
\item Write the hypotheses in symbols and in words.
\item Check conditions, then calculate the test statistic, $T$, and the 
associated degrees of freedom.
\item Find and interpret the p-value in this context. Drawing a picture may be 
helpful.
\item What is the conclusion of the hypothesis test?
\item If you were to construct a 90\% confidence interval that corresponded to 
this hypothesis test, would you expect 8 hours to be in the interval?
\end{parts}
}{}

% 8

\eoce{\qt{Heights of adults\label{adult_heights}}
Researchers studying anthropometry 
collected body girth measurements and skeletal diameter measurements, as well as 
age, weight, height and gender, for 507 physically active individuals. The 
histogram below shows the sample distribution of heights in centimeters. 
\footfullcite{Heinz:2003} \\
\begin{minipage}[c]{0.75\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.23\textwidth}
\begin{center}
\begin{tabular}{l|r l}
Min     & 147.2 \\
Q1      & 163.8 \\
Median  & 170.3 \\
Mean    & 171.1 \\
SD      &  9.4 \\
Q3      & 177.8 \\
Max     & 198.1 \\
\end{tabular}
\end{center}
\end{minipage}
\begin{parts}
\item What is the point estimate for the average height of active individuals? 
What about the median?
\item What is the point estimate for the standard deviation of the heights of 
active individuals? What about the IQR?
\item Is a person who is 1m 80cm (180 cm) tall considered unusually tall? And is 
a person who is 1m 55cm (155cm) considered unusually short? Explain your 
reasoning.
\item The researchers take another random sample of physically active 
individuals. Would you expect the mean and the standard deviation of this new 
sample to be the ones given above? Explain your reasoning.
\item The sample means obtained are point estimates for the mean height of all 
active individuals, if the sample of individuals is equivalent to a simple 
random sample.
What measure do we use to quantify the variability of such an estimate?
Compute 
this quantity using the data from the original sample under the condition that 
the data are a simple random sample. 
\end{parts}
}{}

% 9

\eoce{\qt{Find the mean\label{find_mean_2_sided}}
You are given the following hypotheses:
\begin{align*}
H_0&: \mu = 60 \\
H_A&: \mu \neq 60
\end{align*}
We know that the sample standard deviation is 8
and the sample size is 20.
For what sample mean would the p-value be equal to 0.05?
Assume that all conditions necessary for inference are satisfied.
}{}

\D{\newpage}

% 10

\eoce{\qt{$t^\star$ vs. $z^\star$\label{critical_t_vs_z}} For a given confidence 
level, $t^{\star}_{df}$ is larger than $z^{\star}$. Explain how $t^{*}_{df}$ 
being slightly larger than $z^{*}$ affects the width of the confidence interval.
}{}

% 11

\eoce{\qt{Play the piano\label{play_piano_2_sided}}
Georgianna claims that in a small city 
renowned for its music school, the average child takes less than 5 years of 
piano lessons. We have a random sample of 20 children from the city, with a 
mean of 4.6 years of piano lessons and a standard deviation of 2.2 years.
\begin{parts}
\item
    Evaluate Georgianna's claim (or that the opposite might be true)
    using a hypothesis test.
\item
    Construct a 95\% confidence interval for the number of years
    students in this city take piano lessons, and interpret it
    in context of the data.
\item
    Do your results from the hypothesis test and the confidence
    interval agree?
    Explain your reasoning.
\end{parts}
}{}

% 12

\eoce{\qt{Auto exhaust and
    lead exposure\label{auto_exhaust_lead_exposure_2_sided}} 
Researchers interested in lead exposure due to car exhaust
sampled the blood of 52 police officers subjected to constant
inhalation of automobile exhaust fumes while working traffic
enforcement in a primarily urban environment.
The blood samples of these officers had an average lead
concentration of 124.32 $\mu$g/l and a SD of 37.74 $\mu$g/l;
a previous study of individuals from a nearby suburb,
with no history of exposure, found an average blood level
concentration 
of 35 $\mu$g/l.\footfullcite{Mortada:2000}
\begin{parts}
\item
    Write down the hypotheses that would be appropriate for
    testing if the police officers appear to have been exposed
    to a different concentration of lead.
\item\label{auto_exhaust_lead_exposure_2_sided_cond}
    Explicitly state and check all conditions necessary for
    inference on these data.
\item
    Regardless of your answers in
    part~(\ref{auto_exhaust_lead_exposure_2_sided_cond}),
    test the hypothesis that the downtown police officers have
    a higher lead exposure than the group in the previous study.
    Interpret your results in context.
\end{parts}
}{}

% 13

\eoce{\qt{Car insurance savings\label{car_insurance_savings}}
A market researcher wants to evaluate car insurance savings
at a competing company.
Based on past studies he is assuming that the standard
deviation of savings is \$100.
He wants to collect data such that he can get a margin of
error of no more than \$10 at a 95\% confidence level.
How large of a sample should he collect?
}{}

% 14

\eoce{\qt{SAT scores\label{sat_scores_CI}}
The standard deviation of SAT scores for students at
a particular Ivy League college is 250 points.
Two statistics students, Raina and Luke, want to estimate
the average SAT score of students at this college as part
of a class project.
They want their margin of error to be no more than 25 points.
\begin{parts}
\item
    Raina wants to use a 90\% confidence interval.
    How large a sample should she collect?
\item
    Luke wants to use a 99\% confidence interval.
    Without calculating the actual sample size, determine
    whether his sample should be larger or smaller
    than Raina's, and explain your reasoning.
\item
    Calculate the minimum required sample size for Luke.
\end{parts}
}{}


================================================
FILE: ch_inference_for_means/TeX/paired_data.tex
================================================
\exercisesheader{}

% 15

\eoce{\qt{Air quality\label{air_quality_shortened}}
Air quality measurements were collected in 
a random sample of 25 country capitals in 2013, and then again in the same 
cities in 2014. We would like to use these data to compare
average air quality between the two years.
Should we use a paired or non-paired test? Explain your reasoning.
}{}

% 16

\eoce{\qt{True / False: paired\label{tf_paired}} Determine if the following 
statements are true or false. If false, explain.
\begin{parts}
\item In a paired analysis we first take the difference of each pair of observations, 
and then we do inference on these differences.
\item Two data sets of different sizes cannot be analyzed as paired data.
\item Consider two sets of data that are paired with each other.
Each observation in one data set has a natural correspondence with 
exactly one observation from the other data set.
\item Consider two sets of data that are paired with each other.
Each observation in one data set is subtracted from the average of the 
other data set's observations.
\end{parts}
}{}

% 17

\eoce{\qt{Paired or not? Part I\label{paired_or_not_1}} In each of the following 
scenarios, determine if the data are paired.
\begin{parts}
\item Compare pre- (beginning of semester) and post-test (end of semester) 
scores of students.
\item Assess gender-related salary gap by comparing salaries of randomly 
sampled men and women.
\item Compare artery thicknesses at the beginning of a study and after 2 years 
of taking Vitamin E for the same group of patients.
\item Assess effectiveness of a diet regimen by comparing the before and after 
weights of subjects.
\end{parts}
}{}

% 18

\eoce{\qt{Paired or not? Part II\label{paired_or_not_2}} In each of the following 
scenarios, determine if the data are paired.
\begin{parts}
\item We would like to know if Intel's stock and Southwest Airlines' stock have 
similar rates of return. To find out, we take a random sample of 50 days, and 
record Intel's and Southwest's stock on those same days.
\item We randomly sample 50 items from Target stores and note the price for 
each. Then we visit Walmart and collect the price for each of those same 50 
items.
\item A school board would like to determine whether there is a difference in 
average SAT scores for students at one high school versus another high school 
in the district. To check, they take a simple random sample of 100 students 
from each high school.
\end{parts}
}{}

% 19

\eoce{\qt{Global warming, Part I\label{global_warming_v2_1}}
Let's consider a limited set of climate data,
examining temperature differences in 1948 vs~2018.
We sampled 197 locations from the
National Oceanic and Atmospheric Administration's
(NOAA) historical data,
where the data was available for both years of interest.
We want to know: were there more days with temperatures
exceeding 90\textdegree{}F in 2018 or
in~1948?\footfullcite{webpage:noaa_1948_2018}
The difference in number of days exceeding 90\textdegree{}F
(number of days in 2018 - number of days in 1948) was calculated
for each of the 197 locations.
The average of these differences was 2.9 days with 
a standard deviation of 17.2 days.
We are interested in determining whether these data provide
strong evidence that there were more days in 2018 that
exceeded 90\textdegree{}F from NOAA's weather
stations.\vspace{3mm}

\noindent%
\begin{minipage}[c]{0.65\textwidth}
\begin{parts}
\item
    Is there a relationship between the observations collected
    in 1948 and 2018?
    Or are the observations in the two groups independent?
    Explain.
\item
    Write hypotheses for this research in symbols and in words.
\item
    Check the conditions required to complete this test.
    A histogram of the differences is given to the right.
\item
    Calculate the test statistic and find the p-value.
\item
    Use $\alpha = 0.05$ to evaluate the test,
    and interpret your conclusion in context.
\item
    What type of error might we have made?
    Explain in context what the error means.
\item
    Based on the results of this hypothesis test,
    would you expect a confidence interval for the
    average difference between the number of days
    exceeding 90\textdegree{}F from 1948 and 2018
    to include 0?
    Explain your reasoning.
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.02\textwidth}
\ 
\end{minipage}
\begin{minipage}[c]{0.32\textwidth}
\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}
\end{minipage}
% library(openintro); d <- climate70$dx90_2018 - climate70$dx90_1948; mean(d); sd(d); length(d); t.test(d)
}{}

\D{\newpage}

% 20

\eoce{\qt{High School and Beyond, Part I\label{hs_beyond_1}} The National Center of 
Education Statistics conducted a survey of high school seniors, collecting test 
data on reading, writing, and several other subjects. Here we examine a simple 
random sample of 200 students from this survey. Side-by-side box plots of 
reading and writing scores as well as a histogram of the differences in scores 
are shown below.
\begin{center}
\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}
\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}
\end{center}
\begin{parts}
\item Is there a clear difference in the average reading and writing scores?
\item Are the reading and writing scores of each student independent of each 
other?
\item Create hypotheses appropriate for the following research question: is 
there an evident difference in the average scores of students in the reading 
and writing exam?
% is there evidence that students on average perform differently on the reading and writing exam?
\item Check the conditions required to complete this test.
\item The average observed difference in scores is 
$\bar{x}_{read-write} = -0.545$, and the standard deviation of the differences 
is 8.887 points. Do these data provide convincing evidence of a difference 
between the average scores on the two exams?
\item What type of error might we have made? Explain what the error means in 
the context of the application.
\item Based on the results of this hypothesis test, would you expect a 
confidence interval for the average difference between the reading and writing 
scores to include 0? Explain your reasoning.
\end{parts}
}{}

% 21

\eoce{\qt{Global warming, Part II\label{global_warming_v2_2}}
We considered the change in the number of days exceeding
90\textdegree{}F from 1948 and 2018 at 197 randomly sampled
locations from the NOAA database in
Exercise~\ref{global_warming_v2_1}.
The mean and standard deviation of the reported differences
are 2.9 days and 17.2 days.
\begin{parts}
\item
    Calculate a 90\% confidence interval for the average
    difference between number of days exceeding 90\textdegree{}F
    between 1948 and 2018.
    We've already checked the conditions for you.
\item
    Interpret the interval in context.
\item
    Does the confidence interval provide convincing evidence
    that there were more days exceeding 90\textdegree{}F
    in 2018 than in 1948 at NOAA stations?
    Explain.
\end{parts}
}{}

% 22

\eoce{\qt{High school and beyond, Part II\label{hs_beyond_2}} We considered the 
differences between the reading and writing scores of a random sample of 200 
students who took the High School and Beyond Survey in Exercise~\ref{hs_beyond_1}. The 
mean and standard deviation of the differences are 
$\bar{x}_{read-write} = -0.545$ and 8.887 points.
\begin{parts}
\item Calculate a 95\% confidence interval for the average difference between 
the reading and writing scores of all students.
\item Interpret this interval in context.
\item Does the confidence interval provide convincing evidence that there is a 
real difference in the average scores? Explain.
\end{parts}
}{}


================================================
FILE: ch_inference_for_means/TeX/power_calculations_for_a_difference_of_means.tex
================================================
\exercisesheader{}

% 33

\eoce{\qt{Increasing corn yield\label{increase_corn_yield}} A large farm wants to 
try out a new type of fertilizer to evaluate whether it will improve the 
farm's corn production. The land is broken into plots that produce an 
average of 1,215 pounds of corn with a standard deviation of 94 pounds per 
plot. The owner is interested in detecting any average difference of at 
least 40 pounds per plot. How many plots of land would be needed for the 
experiment if the desired power level is 90\%?
Use $\alpha = 0.05$.
Assume each plot of land gets 
treated with either the current fertilizer or the new fertilizer.
}{}

% 34

\eoce{\qt{Email outreach efforts\label{email_outreach_efforts}} A medical research 
group is recruiting people to complete short surveys about their medical 
history. For example, one survey asks for information on a person's family 
history in regards to cancer. Another survey asks about what topics were 
discussed during the person's last visit to a hospital. So far, as people 
sign up, they complete an average of just 4~surveys, and the standard 
deviation of the number of surveys is about~2.2. The research group wants to 
try a new interface that they think will encourage new enrollees to complete 
more surveys, where they will randomize each enrollee to either get the new 
interface or the current interface. How many new enrollees do they need for 
each interface to detect an effect size of 0.5 surveys per enrollee, if the 
desired power level is 80\%?
Use $\alpha = 0.05$.
}{}


================================================
FILE: ch_inference_for_means/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 47

\eoce{\qt{Gaming and distracted eating, Part I\label{gaming_distracted_eating_intake}}
A group of researchers are interested in the possible effects of distracting 
stimuli during eating, such as an increase or decrease in the amount of food 
consumption. To test this hypothesis, they monitored food intake for a group of 
44 patients who were randomized into two equal groups. The treatment group ate 
lunch while playing solitaire, and the control group ate lunch without any 
added distractions. Patients in the treatment group ate 52.1 grams of biscuits, 
with a standard deviation of 45.1 grams, and patients in the control group ate 
27.1 grams of biscuits, with a standard deviation of 26.4 grams. Do these data 
provide convincing evidence that the average food intake (measured in amount of 
biscuits consumed) is different for the patients in the treatment group? Assume 
that conditions for inference are satisfied. \footfullcite{Oldham:2011}
}{}

% 48

\eoce{\qt{Gaming and distracted eating, Part II\label{gaming_distracted_eating_recall}} 
The researchers from Exercise~\ref{gaming_distracted_eating_intake} also 
investigated the effects of being distracted by a game on how much people eat. 
The 22 patients in the treatment group who ate their lunch while playing 
solitaire were asked to do a serial-order recall of the food lunch items they 
ate. The average number of items recalled by the patients in this group was 4.
9, with a standard deviation of 1.8. The average number of items recalled by 
the patients in the control group (no distraction) was 6.1, with a standard 
deviation of 1.8. Do these data provide strong evidence that the average number 
of food items recalled by the patients in the treatment and control groups are 
different?
}{}

% 49

\eoce{\qt{Sample size and pairing\label{sample_size_pairing}} Determine if the 
following statement is true or false, and if false, explain your reasoning: If 
comparing means of two groups with equal sample sizes, always use a paired test.
}{}

% 50

\eoce{\qt{College credits\label{college_credits}}
A college counselor is interested in 
estimating how many credits a student typically enrolls
in each semester.
The counselor decides to randomly sample 100 students
by using the registrar's 
database of students.
The histogram below shows the distribution of the number 
of credits taken by these students.
Sample statistics for this distribution are 
also provided.\\
\begin{minipage}[c]{0.1\textwidth}
\ 
\end{minipage}
\begin{minipage}[c]{0.5\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.32\textwidth}
\begin{center}
\begin{tabular}{l|r l}
Min     & 8 \\
Q1      & 13 \\
Median  & 14 \\
Mean    & 13.65 \\
SD      & 1.91 \\
Q3      & 15 \\
Max     & 18 \\
\end{tabular}
\end{center}
\end{minipage}
\begin{parts}
\item What is the point estimate for the average
number of credits taken per semester by students at this college?
What about the median?
\item What is the point estimate for the standard deviation
of the number of credits taken per semester by students at
this college?
What about the IQR?
\item Is a load of 16 credits unusually high for this college?
What about 18 credits?
Explain your reasoning.
\item The college counselor takes another
random sample of 100 students and this 
time finds a sample mean of 14.02 units.
Should she be surprised that this sample
statistic is slightly different than the
one from the original sample? 
Explain your reasoning.
\item
The sample means given above are point estimates
for the mean number of 
credits taken by all students at that college.
What measures do we use to 
quantify the variability of this estimate?
Compute this quantity using the data 
from the original sample.
\end{parts}
}{}

\D{\newpage}

% 51

\eoce{\qt{Hen eggs\label{hen_eggs}} The distribution of the number of eggs laid 
by a certain species of hen during their breeding period has a mean of 35 eggs 
with a standard deviation of 18.2. Suppose a group of researchers 
randomly samples 45 hens of this species, counts the number of eggs laid 
during their breeding period, and records the sample mean. They repeat 
this 1,000 times, and build a distribution of sample 
means. 
\begin{parts}
\item What is this distribution called? 
\item Would you expect the shape of this distribution to be symmetric, right 
skewed, or left skewed? Explain your reasoning.
\item Calculate the variability of this distribution and state the appropriate 
term used to refer to this value.
\item Suppose the researchers' budget is reduced and they are only able to 
collect random samples of 10 hens. The sample mean of the number of eggs is 
recorded, and we repeat this 1,000 times, and build a new distribution of sample 
means. How will the variability of this new distribution compare to the 
variability of the original distribution?
\end{parts}
}{}

% 52

\eoce{\qt{Forest management\label{forest_mgmt_tree_growth}}
Forest rangers wanted to better understand the rate
of growth for younger trees in the park.
They took measurements of a random sample of 50 young trees
in 2009 and again measured those same trees in 2019.
The data below summarize their measurements,
where the heights are in feet:
\begin{center}
\begin{tabular}{l c c c}
\hline
          & 2009   & 2019  & Differences\\
\hline  
$\bar{x}$ & 12.0  & 24.5  & 12.5 \\
$s$       & 3.5   & 9.5   & 7.2 \\
$n$       & 50    & 50    & 50 \\
\hline
\end{tabular}
\end{center}
Construct a 99\% confidence interval for the
average growth of (what had been) younger trees
in the park over 2009-2019.
}{}

% 53

\eoce{\qt{Experiment resizing\label{tech_exp_resizing}}
At a startup company running a new weather app, an engineering
team generally runs experiments where a random sample of 1\%
of the app's visitors in the control group and another
1\% were in the treatment group to test each new feature.
The team's core goal is to increase a metric
called \emph{daily visitors},
which is essentially the number of visitors to the app
each day.
They track this metric in each experiment arm and
as their core experiment metric.
In their most recent experiment, the team tested
including a new animation when the app started,
and the number of daily visitors in this experiment
stabilized at +1.2\% with a 95\% confidence interval
of (-0.2\%, +2.6\%).
This means if this new app start animation was launched,
the team thinks they might lose as many as 0.2\% of daily
visitors or gain as many as 2.6\% more daily visitors.
Suppose you are consulting as the team's data scientist,
and after discussing with the team,
you and they agree that they should run
another experiment that is bigger.
You also agree that this new experiment
should be able to detect a gain in the daily visitors
metric of 1.0\% or more with 80\% power.
Now they turn to you and ask,
``How big of an experiment do we need to run
to ensure we can detect this effect?''
\begin{parts}
\item\label{tech_exp_resizing_target_se}
    How small must the standard error be if
    the team is to be able to detect an effect
    of 1.0\% with 80\% power and a significance
    level of $\alpha = 0.05$?
    You may safely assume the percent change in
    daily visitors metric follows a normal distribution.
\item\label{tech_exp_resizing_original_se}
    Consider the first experiment, where
    the point estimate was +1.2\% and the
    95\% confidence interval was (-0.2\%, +2.6\%).
    If that point estimate followed a normal
    distribution, what was the standard error
    of the estimate?
\item\label{tech_exp_resizing_ratio}
    The ratio of the standard error from
    part~(\ref{tech_exp_resizing_target_se})
    vs the standard error from
    part~(\ref{tech_exp_resizing_original_se})
    should be~1.97.
    How much bigger of an experiment is needed
    to shrink a standard error by a factor of~1.97?
\item
    Using your answer from
    part~(\ref{tech_exp_resizing_ratio})
    and that the original experiment was
    a 1\% vs 1\% experiment to recommend
    an experiment size to the team.
\end{parts}
}{}

\D{\newpage}

% 54

\eoce{\qt{Torque on a rusty bolt\label{torque_on_rusty_bolt}}
Project Farm is a YouTube channel that routinely
compares different products.
In one episode, the channel evaluated different
options for loosening rusty
bolts.\footfullcite{youtube:torque_on_rusty_bolt}
Eight options were evaluated,
including a control group where no treatment was given
(``none'' in the graph),
to determine which was most effective.
For all treatments, there were four bolts tested,
except for a treatment of heat with a blow torch,
where only two data points were collected.
The results are shown in the figure below:
\begin{center}
\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}
\end{center}
\begin{parts}
\item\label{torque_on_rusty_bolt_appropriate}
    Do you think it is reasonable to apply ANOVA in this case?
\item
    Regardless of your answer in
    part~(\ref{torque_on_rusty_bolt_appropriate}),
    describe hypotheses for ANOVA in this context,
    and use the table below to carry out the test.
    Give your conclusion in the context of the data.
    \begin{center}
    \begin{tabular}{lrrrrr}
    \hline
    & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\ 
    \hline
    treatment & 7 & 3603.43 & 514.78 & 4.03 & 0.0056 \\ 
    Residuals & 22 & 2812.80 & 127.85 &  &  \\ 
    \hline
    \end{tabular}
    \end{center}
\item\label{torque_on_rusty_bolt_pvalues}
    The table below are p-values for pairwise $t$-tests
    comparing each of the different groups.
    These p-values have not been corrected for multiple
    comparisons.
    Which pair of groups appears most likely to represent
    a difference?
    \begin{center}\footnotesize
    \begin{tabular}{l ccc ccc c}
    \hline
    & AeroKroil & Heat & Liquid Wrench & none &
        PB Blaster & Royal Purple & WD-40 \\ 
    \hline
    Acetone/ATF & 0.2026 & 0.0308 & 0.0476 & 0.1542 &
        0.3294 & 0.5222 & 0.3744 \\ 
    AeroKroil &  & 0.0027 & 0.0025 & 0.8723 & 0.7551 &
        0.5143 & 0.6883 \\ 
    Heat &  &  & 0.5580 & 0.0020 & 0.0050 & 0.0096 &
        0.0059 \\ 
    Liquid Wrench &  &  &  & 0.0017 & 0.0053 &
        0.0117 & 0.0065 \\ 
    none &  &  &  &  & 0.6371 & 0.4180 & 0.5751 \\ 
    PB Blaster &  &  &  &  &  & 0.7318 & 0.9286 \\ 
    Royal Purple &  &  &  &  &  &  & 0.8000 \\
    \hline
    \end{tabular}
    \end{center}
\item
    There are 28 p-values shown in the table in
    part~(\ref{torque_on_rusty_bolt_pvalues}).
    Determine if any of them are statistically
    significant after correcting for multiple
    comparisons.
    If so, which one(s)?
    Explain your answer.
\end{parts}
}{}

% 55

\eoce{\qt{Exclusive relationships\label{exclusive_relationships}} A survey conducted 
on a reasonably random sample of 203 undergraduates asked, among many other 
questions, about the number of exclusive relationships these students have been 
in. The histogram below shows the distribution of the data from this sample. 
The sample average is 3.2 with a standard deviation of 1.97.
\begin{center}
\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}
\end{center}
Estimate the average number of exclusive relationships Duke students have been 
in using a 90\% confidence interval and interpret this interval in context. 
Check any conditions required for inference, and note any assumptions you must 
make as you proceed with your calculations and conclusions.
}{}

% 56

\eoce{\qt{Age at first marriage, Part I\label{age_at_first_marriage_intro}} 
The National Survey of Family Growth conducted by the Centers for Disease 
Control gathers information on family life, marriage and divorce, pregnancy, 
infertility, use of contraception, and men's and women's health. One of the 
variables collected on this survey is the age at first marriage. The histogram 
below shows the distribution of ages at first marriage of 5,534 randomly sampled 
women between 2006 and 2010. The average age at first marriage among these women 
is 23.44 with a standard deviation of 4.72.\footfullcite{data:nsfg:2010}
\begin{center}
\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}
\end{center}
Estimate the average age at first marriage of women using a 95\% confidence 
interval, and interpret this interval in context. Discuss any relevant 
assumptions.
}{}

% 57

\eoce{\qt{Online communication\label{online_communication}} A study suggests that the 
average college student spends 10 hours per week communicating with others 
online. You believe that this is an underestimate and decide to collect your 
own sample for a hypothesis test. You randomly sample 60 students from your 
dorm and find that on average they spent 13.5 hours a week communicating with 
others online. A friend of yours, who offers to help you with the hypothesis 
test, comes up with the following set of hypotheses. Indicate any errors you see.
\begin{align*}
H_0&: \bar{x} < 10~hours \\
H_A&: \bar{x} > 13.5~hours
\end{align*}
}{}

% 58

\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 
of a 2006 - 2010 survey showing that the average age of women at first marriage 
is 23.44.
Suppose a social scientist thinks this value has changed 
since the survey was taken.
Below is how she set up her hypotheses.
Indicate any errors you see.
\begin{align*}
H_0&: \bar{x} \neq 23.44~years~old \\
H_A&: \bar{x} = 23.44~years~old
\end{align*}
}{}


================================================
FILE: ch_inference_for_means/figures/babySmokePlotOfTwoGroupsToExamineSkew/babySmokePlotOfTwoGroupsToExamineSkew.R
================================================
library(openintro)
data(COL)
data(births)
d <- births


myPDF('babySmokePlotOfTwoGroupsToExamineSkew.pdf', 2 * 4.5, 2.3,
      mfrow = 1:2, #2:1,
      mar = c(3, 1, 2.5, 1),
      mgp = c(1.7, 0.55, 0))
xlab.start <- 'Newborn Weights (lbs)'
histPlot(d$weight[d$smoke == 'smoker'],
         xlim = c(0, 11),
         axes = FALSE,
         xlab = xlab.start,
         main = 'Mothers Who Smoked',
         col = COL[1])
axis(1)

# par(mar = c(2.8, 1, 0.5, 1))
histPlot(d$weight[d$smoke == 'nonsmoker'],
         xlim = c(0, 11),
         axes = FALSE,
         xlab = xlab.start,
         main = 'Mothers Who Did Not Smoke',
         col = COL[1])
axis(1)
dev.off()


================================================
FILE: ch_inference_for_means/figures/cbrRunTimesMenWomen/cbrRunTimesMenWomen.R
================================================
library(openintro)
data(COL)
data(run10Samp)

set.seed(1)
m <- run10Samp$time[run10Samp$gender=='M']
mean(m); sd(m)
f <- run10Samp$time[run10Samp$gender=='F']
mean(f); sd(f)

myPDF('cbrRunTimesMenWomen.pdf', 3.8, 3,
      mgp = c(2.5, 0.7, 0),
      mar = c(2, 4, 0.5, 1))
boxPlot(m,
        at = 1,
        xlim = c(0.5, 2.5),
        ylim = c(45, 150),
        axes = FALSE,
        ylab = 'run time (minutes)',
        lcol = COL[1],
        col = COL[1,3],
        lwd = 1)
boxPlot(f,
        add = 2,
        axes = FALSE,
        lcol = COL[1],
        col = COL[1, 3],
        lwd = 1)
axis(1, at = 1:2, labels = c('men', 'women'))
axis(2, at = c(50, 100, 150))
dev.off()


================================================
FILE: ch_inference_for_means/figures/classData/classData.R
================================================
library(openintro)
data(COL)
library(xtable)
data(classData)

myPDF("classDataSBSBoxPlot.pdf", 5.5, 2.7,
      mgp = c(2.3, 0.5, 0),
      mar = c(3.4, 3.2, 0.5, 0.5))
boxPlot(classData$m1, classData$lecture,
        axes = FALSE,
        xlab = "Lecture",
        ylab = "Midterm Scores",
        lcol = COL[1],
        lwd = 1.3,
        medianLwd = 2.5)
axis(1, c(-50, 1:3, 50), c("", "A", "B", "C", ""))
axis(2, seq(0, 100, 20))
dev.off()

by(classData$m1, classData$lecture, length)
by(classData$m1, classData$lecture, mean)
by(classData$m1, classData$lecture, sd)

anova(lm(m1 ~ lecture, classData))
summary(lm(m1 ~ lecture, classData))
xtable(anova(lm(m1 ~ lecture, classData)))


================================================
FILE: ch_inference_for_means/figures/distOfDiffOfSampleMeansForBWOfBabySmokeData/distOfDiffOfSampleMeansForBWOfBabySmokeData.R
================================================
library(openintro)
data(COL)
data(births)
d <- births

myPDF('distOfDiffOfSampleMeansForBWOfBabySmokeData.pdf', 3.5, 1.2,
      mar=c(1.6, 0, 0, 0),
      mgp=c(3, 0.5, 0))
normTail(0, 1,
         L = -1.54,
         U = 1.54,
         df = 20, # Aesthetics
         col = COL[1],
         axes = FALSE)
at <- c(-5, 0, 1.54, 5)
labels <- expression(0, mu[n]-mu[s]*' = 0', 'obs. diff', 0)
axis(1, at, labels, cex.axis=0.9)
# abline(h=0)
dev.off()


================================================
FILE: ch_inference_for_means/figures/eoce/adult_heights/adult_heights.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(bdims)

# histogram of heights ----------------------------------------------

pdf("adult_heights_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.5,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(bdims$hgt, col = COL[1], xlab = "Height", ylab = "")
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/age_at_first_marriage_intro/age_at_first_marriage_intro.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(ageAtMar)

# histogram of age at first marriage --------------------------------
pdf("age_at_first_marriage_intro_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.7,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(ageAtMar$age, col = COL[1], xlab = "Age at first marriage", ylab = "")
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/anova_exercise_1/anova_exercise_1.R
================================================
library(openintro)

d <- penetrating_oil

myPDF("torque_on_rusty_bolt_dot_plot.pdf", 7, 3.2,
    mar = c(3.5, 6.5, 0.1, 0.3),
    mgp = c(2.3, 0.55, 0))
dotPlot(d$torque, d$treatment,
    pch = 19, col = COL[1, 2], cex = 2,
    vertical = FALSE,
    xlab = paste(
        "Torque Required to Loosen Rusty Bolt,",
        "in Foot-Pounds"),
    ylab = "")
abline(h = 1:8, col = COL[5, 7])
dev.off()

anova(lm(d$torque ~ d$treatment))



================================================
FILE: ch_inference_for_means/figures/eoce/chick_wts_anova/chick_wts.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(dplyr)

# load data ---------------------------------------------------------
data(chickwts)

# summary stats ----------------------------------------------------
chickwts %>%
  group_by(feed) %>%
  summarise(mean = round(mean(weight), 2),
            sd = round(sd(weight), 2),
            length = n())

# side-by-side box plots of weight by feed -------------------------
pdf("chick_wts_box.pdf", height = 4, width = 8)
par(mar=c(2, 4, 0.5, 0.5), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)
boxPlot(chickwts$weight, fact = chickwts$feed, 
        h = T, col = COL[1], horiz = FALSE, 
        ylab = "Weight (in grams)",
        lwd = 1.5, medianLwd = 2.5, lcol = COL[1])
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/chick_wts_linseed_horsebean/chick_wts.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(dplyr)

# load data ---------------------------------------------------------
data(chickwts)

# summary stats ----------------------------------------------------
chickwts %>%
  group_by(feed) %>%
  summarise(mean = round(mean(weight), 2),
            sd = round(sd(weight), 2),
            length = n())

# side-by-side box plots of weight by feed -------------------------
pdf("chick_wts_box.pdf", height = 4, width = 8)
par(mar=c(2, 4, 0.5, 0.5), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)
boxPlot(chickwts$weight, fact = chickwts$feed, 
		h = T, col = COL[1], lwd = 1.5, medianLwd = 2.5, lcol = COL[1],
		horiz = FALSE, ylab = "Weight (in grams)")
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/child_care_hours/child_care_hours.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------
china <- read.csv("china.csv")

# subset and clean data ---------------------------------------------
china <- china[!is.na(china$gender) & !is.na(china$child_care) 
               & !is.na(china$edu) & china$child_care != -99 & china$edu != 9,]

china$edu[china$edu == 1] <- "Primary school"
china$edu[china$edu == 2] <- "Lower middle school"
china$edu[china$edu == 3] <- "Upper middle school"
china$edu[china$edu == 4] <- "Technical or vocational"
china$edu[china$edu == 5] <- "College"
china$edu <- factor(china$edu, 
                    levels = c("Primary school", "Lower middle school", 
                               "Upper middle school", "Technical or vocational",
                               "College"))

# summary stats -----------------------------------------------------

by(china$child_care, china$edu, mean)
by(china$child_care, china$edu, sd)
by(china$child_care, china$edu, length)

# plot --------------------------------------------------------------

pdf("child_care_hours.pdf", height = 4, width = 15)

par(mar = c(2,4,1,5), las = 1, mgp = c(2.7,0.7,0), 
    cex.lab = 1.45, cex.axis = 1.45)
boxPlot(china$child_care, fact = china$edu, ylab = "Child care hours", 
        col = COL[1,2], xlim = c(0.6, 5.4),
        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)
dev.off()

# anova -------------------------------------------------------------
xtable(anova(lm(china$child_care ~ china$edu)), digits = 2)

================================================
FILE: ch_inference_for_means/figures/eoce/child_care_hours/china.csv
================================================
gender,edu,child_care
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1,5,3
2,3,3
1,2,3
1,3,3
1,NA,3
1,NA,3
2,1,3
2,2,3
2,4,3
1,2,3
2,1,3
1,3,3
2,4,3
1,1,3
2,NA,3
1,5,3
2,NA,3
1,1,3
2,2,3
1,2,3
1,NA,3
2,1,3
2,2,3
2,3,3
2,1,3
1,NA,3
1,1,3
2,NA,3
1,1,3
1,3,3
1,1,3
2,NA,3
2,2,4
1,4,4
1,2,4
2,2,4
2,2,4
1,3,4
1,NA,4
1,5,4
2,1,4
1,4,4
1,2,4
2,2,4
2,2,4
2,4,4
2,4,4
2,2,4
1,3,4
2,NA,4
1,2,4
1,2,4
2,1,4
2,4,4
2,3,4
1,1,4
2,2,4
2,2,4
1,2,4
1,NA,4
1,1,5
2,1,5
1,5,5
2,5,5
2,2,5
2,3,5
1,4,5
1,3,5
1,3,5
1,1,5
2,2,5
2,2,5
2,2,5
1,2,5
2,2,5
2,1,5
1,2,5
2,2,5
2,3,5
1,2,5
2,2,5
2,NA,5
2,NA,5
1,NA,5
2,NA,5
1,1,5
2,NA,5
2,2,6
2,2,6
2,4,6
2,4,6
1,5,6
1,2,6
1,2,6
1,3,6
2,NA,6
2,2,6
2,2,6
1,1,6
1,2,6
1,1,6
1,2,6
1,NA,6
2,1,6
1,2,6
1,1,6
1,NA,6
2,1,6
2,1,6
1,2,6
2,1,6
2,2,6
2,NA,6
2,NA,6
2,NA,6
2,NA,6
2,5,7
1,2,7
1,2,7
2,2,7
2,4,7
1,1,7
2,1,7
1,2,7
1,2,7
2,2,7
1,5,7
1,3,7
1,1,7
1,2,7
2,NA,7
2,4,7
1,NA,7
2,2,7
2,3,7
1,2,7
2,1,7
2,1,7
2,1,7
2,2,7
1,3,7
2,5,7
2,2,7
2,NA,7
1,NA,7
2,NA,7
1,2,7
1,3,7
1,1,7
1,5,7
1,4,7
1,3,7
1,4,7
1,3,7
2,1,7
2,NA,7
2,NA,7
1,2,7
1,NA,7
1,1,7
1,4,7
2,2,7
2,NA,7
2,NA,7
2,3,7
2,1,7
2,3,7
1,2,7
1,NA,7
1,2,7
1,5,7
1,1,7
1,NA,7
1,2,7
1,1,7
2,1,7
1,2,7
1,1,7
1,1,7
2,1,7
2,NA,7
2,1,7
1,2,7
1,1,7
2,NA,7
1,2,7
2,1,7
1,NA,7
1,2,7
2,NA,7
2,NA,7
1,3,7
1,2,7
1,2,7
1,3,7
1,1,7
1,2,7
1,NA,7
2,NA,7
2,2,7
1,NA,7
1,2,7
2,NA,7
1,2,7
2,1,8
1,2,8
1,NA,8
1,2,8
1,NA,8
2,NA,8
2,3,8
1,1,8
2,NA,8
2,3,8
1,2,8
2,1,8
2,4,8
1,3,8
1,2,8
1,2,8
2,2,8
2,2,8
1,2,8
1,1,8
2,NA,8
2,1,8
1,2,8
1,1,8
1,1,8
2,1,8
1,4,8
1,4,8
1,2,8
2,2,8
2,2,8
1,2,9
2,NA,9
1,NA,9
2,3,9
1,2,9
2,NA,9
2,NA,9
2,2,10
1,2,10
1,2,10
2,2,10
1,1,10
1,2,10
1,2,10
1,2,10
1,3,10
2,NA,10
1,3,10
1,1,10
2,NA,10
2,2,10
1,2,10
2,2,10
1,4,10
2,3,10
1,1,10
2,5,10
2,4,10
1,1,10
1,3,10
2,2,10
2,NA,10
1,2,10
2,2,10
1,2,10
2,1,10
1,5,10
1,1,10
2,NA,10
2,2,10
1,2,10
1,2,10
1,2,10
2,2,10
2,NA,10
2,2,10
1,2,10
2,4,10
2,1,10
2,NA,10
2,2,10
1,1,10
2,2,10
1,5,10
2,2,10
2,2,10
1,1,10
1,2,10
1,2,10
1,2,10
2,2,10
2,1,10
2,2,10
2,2,10
2,2,10
2,NA,10
1,2,10
1,1,10
2,NA,10
1,1,10
2,1,10
2,2,10
2,NA,10
1,2,10
2,3,10
2,1,10
2,1,10
2,2,10
2,NA,10
1,5,10
2,1,10
1,NA,10
1,NA,10
1,3,10
2,NA,10
2,2,10
2,1,10
1,2,11
1,2,11
2,2,11
2,1,12
2,2,12
2,1,12
2,2,12
1,2,12
2,1,12
2,5,12
2,2,12
2,2,12
2,NA,12
2,NA,12
1,2,12
2,4,12
2,4,12
2,2,12
2,5,12
2,3,12
1,1,12
2,NA,12
2,2,12
1,4,12
2,1,13
1,5,14
2,5,14
2,2,14
2,2,14
2,2,14
2,1,14
2,3,14
1,NA,14
1,2,14
2,1,14
1,5,14
1,5,14
2,4,14
2,1,14
2,1,14
2,2,14
1,3,14
2,1,14
1,2,14
1,1,14
1,4,14
2,5,14
2,3,14
2,5,14
1,2,14
2,NA,14
1,4,14
1,1,14
1,1,14
2,NA,14
2,NA,14
2,NA,14
1,NA,14
1,2,14
1,2,14
2,2,14
2,2,14
1,5,14
2,5,14
2,NA,14
2,NA,14
1,2,14
2,NA,14
2,5,14
2,3,14
2,2,14
2,2,14
1,2,14
2,2,14
1,2,14
2,1,14
1,1,14
2,4,14
2,NA,14
2,NA,14
2,NA,14
2,2,14
2,1,14
2,1,14
2,NA,14
2,5,14
1,2,14
1,2,14
1,3,14
2,5,14
2,4,14
2,1,14
2,2,14
2,1,14
1,3,14
2,NA,14
2,4,14
2,NA,14
2,NA,14
2,1,14
1,NA,14
1,4,14
2,1,14
1,2,14
1,2,14
1,2,14
2,1,14
1,1,14
2,2,14
1,1,14
2,1,14
2,NA,14
2,NA,14
2,2,14
2,1,14
1,2,14
1,5,15
2,2,15
2,9,15
2,1,15
1,5,15
2,NA,15
1,1,15
2,NA,15
1,1,15
2,1,15
1,NA,15
2,4,15
2,2,15
1,2,15
2,2,15
2,1,15
1,NA,15
2,NA,15
1,2,15
1,NA,15
2,2,15
2,NA,16
1,3,16
2,5,16
1,1,16
1,1,16
2,1,17
2,1,18
2,2,18
2,2,18
2,4,18
1,2,18
2,NA,18
2,NA,18
1,2,18
2,1,18
2,NA,18
1,2,18
1,2,18
2,2,18
1,2,18
2,NA,18
2,1,18
1,4,20
1,2,20
2,2,20
2,2,20
2,2,20
1,NA,20
2,1,20
2,2,20
2,5,20
2,2,20
1,2,20
2,NA,20
2,4,20
1,4,20
1,5,20
1,1,20
2,1,20
2,2,20
2,2,20
2,2,20
2,2,20
2,1,20
2,2,20
1,2,20
2,NA,20
1,1,20
2,2,20
2,1,20
2,1,20
1,5,20
2,2,20
2,2,20
2,2,20
2,2,20
2,NA,20
1,5,20
2,NA,20
1,4,20
2,2,20
2,2,20
2,NA,20
2,2,20
2,NA,20
2,1,20
2,3,20
2,NA,20
2,NA,20
1,2,20
2,1,20
2,2,20
2,NA,20
1,3,20
1,2,20
2,2,20
2,1,20
2,2,20
1,2,20
2,2,20
1,1,20
2,NA,20
2,3,21
2,3,21
2,5,21
1,3,21
2,5,21
2,NA,21
2,5,21
2,2,21
2,1,21
2,4,21
2,2,21
1,2,21
2,NA,21
2,NA,21
2,2,21
2,2,21
2,NA,21
2,1,21
2,5,21
1,2,21
1,3,21
2,NA,21
2,1,21
2,4,21
2,1,21
2,NA,21
2,2,21
1,2,21
2,1,21
2,1,21
1,3,21
2,1,21
2,NA,21
2,2,21
2,2,21
1,2,21
2,NA,21
2,5,22
1,2,22
2,2,22
2,2,22
2,NA,24
2,2,24
2,1,24
2,1,24
2,NA,24
2,NA,24
1,NA,24
1,1,24
2,2,24
1,4,24
2,1,24
2,NA,24
2,3,25
2,3,25
2,NA,25
1,5,25
2,1,25
2,NA,25
2,NA,25
2,1,25
2,NA,27
1,2,27
2,5,28
2,5,28
2,2,28
1,5,28
2,2,28
2,4,28
1,2,28
2,2,28
1,5,28
2,3,28
1,5,28
2,NA,28
2,1,28
1,3,28
2,2,28
2,1,28
2,1,28
2,1,28
1,1,28
1,NA,28
1,NA,28
1,1,28
2,2,28
2,3,28
2,2,28
2,NA,28
2,2,28
2,2,28
1,NA,28
1,2,30
1,2,30
2,NA,30
2,3,30
2,2,30
2,2,30
2,1,30
2,NA,30
2,NA,30
1,2,30
2,2,30
2,NA,30
1,NA,30
2,1,30
2,NA,30
2,1,30
2,1,30
2,2,30
1,5,30
2,4,30
1,4,30
2,2,30
1,3,30
2,NA,30
1,2,30
2,1,30
2,3,30
2,3,30
2,1,30
1,2,30
2,1,30
2,NA,30
2,1,30
1,2,30
2,4,30
2,1,30
2,4,30
2,4,30
2,1,30
2,2,30
1,5,30
2,2,30
2,NA,30
2,NA,30
2,NA,30
1,1,30
2,NA,31
2,NA,35
2,NA,35
2,5,35
2,NA,35
1,NA,35
1,3,35
2,2,35
2,2,35
2,1,35
2,NA,35
2,2,35
2,4,35
2,5,35
1,4,35
2,2,35
2,2,35
1,5,35
2,NA,35
2,NA,35
2,2,35
2,2,36
2,2,36
2,1,36
2,2,36
1,2,36
2,2,36
2,1,39
2,2,40
2,1,40
2,1,40
2,2,40
2,2,40
2,1,40
2,3,40
2,NA,40
2,4,40
2,2,40
1,3,40
2,2,40
2,5,40
2,1,40
2,2,41
2,1,41
2,3,42
2,NA,42
2,NA,42
2,3,42
2,2,42
1,1,42
2,NA,42
2,1,42
2,NA,42
2,1,42
2,NA,42
2,4,42
2,NA,42
2,NA,44
2,NA,44
2,4,45
2,4,47
2,4,48
2,1,48
2,4,48
2,NA,48
1,1,48
1,NA,48
2,NA,48
2,2,49
2,NA,49
2,NA,49
2,NA,49
2,4,49
2,NA,49
2,NA,49
1,3,50
2,NA,50
2,1,50
1,2,50
1,2,50
1,2,50
1,2,50
2,1,50
2,NA,50
2,2,50
2,1,50
2,3,50
2,1,50
2,1,50
2,1,50
2,2,50
2,NA,50
2,5,50
2,2,56
2,9,56
2,2,56
2,3,56
2,1,56
1,1,56
2,1,56
2,NA,56
2,NA,56
2,1,56
2,2,56
2,1,56
2,1,56
2,1,56
2,NA,56
2,2,56
2,NA,56
2,2,60
2,2,60
2,2,60
2,4,60
1,1,60
2,4,60
2,1,60
2,3,60
1,2,60
2,2,60
1,2,60
2,1,60
2,NA,60
2,2,60
2,NA,60
2,1,60
2,1,60
2,2,60
2,1,60
1,1,60
2,2,60
1,1,60
2,1,62
2,2,62
2,1,63
2,1,63
2,1,63
2,2,63
2,2,63
2,2,65
2,2,70
2,2,70
2,2,70
2,2,70
2,4,70
2,5,70
2,2,70
2,NA,70
2,1,70
2,NA,70
1,3,70
2,2,70
2,2,70
2,NA,70
2,2,70
2,4,70
2,NA,70
2,2,70
2,NA,70
2,2,70
2,4,70
2,4,70
2,1,70
2,2,70
2,5,70
2,3,70
1,NA,70
2,NA,70
1,2,70
2,2,70
2,2,70
2,4,70
2,NA,70
2,1,70
2,4,70
2,2,70
2,4,70
2,2,72
1,2,72
2,2,77
1,1,80
2,5,80
2,2,80
1,5,80
2,2,80
2,1,80
2,2,80
2,4,80
2,2,84
1,2,84
1,4,84
2,2,84
2,2,84
2,NA,84
1,2,84
2,2,84
2,1,84
2,2,84
2,2,84
2,3,84
2,NA,88
2,1,90
2,1,90
2,NA,90
2,2,90
2,2,96
2,3,98
2,NA,98
2,2,100
2,1,100
2,2,100
2,2,102
2,4,106
1,5,107
1,5,108
2,1,110
2,4,112
2,2,120
2,2,120
2,4,121
2,4,137
1,3,140
2,4,140
2,1,140
2,2,150
2,1,160
2,NA,160
2,3,161
2,2,168
2,1,168
2,1,168
1,2,168
2,NA,168
2,1,168
1,3,NA
2,5,NA
1,2,NA
1,5,NA
2,2,NA
2,4,NA
1,2,NA
1,4,NA
2,5,NA
1,5,NA
2,2,NA
1,4,NA
1,2,NA
1,5,NA
2,3,NA
2,5,NA
1,4,NA
2,4,NA
2,5,NA
1,5,NA
2,4,NA
1,2,NA
2,5,NA
2,3,NA
1,5,NA
2,5,NA
1,2,NA
2,1,NA
1,5,NA
2,3,NA
2,5,NA
1,5,NA
2,1,NA
1,2,NA
2,2,NA
1,4,NA
2,3,NA
1,3,NA
2,3,NA
1,4,NA
2,4,NA
1,5,NA
2,5,NA
1,5,NA
2,5,NA
1,2,NA
2,5,NA
2,4,NA
1,5,NA
2,3,NA
2,NA,NA
1,NA,NA
1,4,NA
2,4,NA
1,5,NA
2,2,NA
2,2,NA
1,1,NA
2,1,NA
1,2,NA
2,2,NA
2,5,NA
1,5,NA
2,4,NA
1,5,NA
2,5,NA
2,4,NA
1,5,NA
1,5,NA
2,5,NA
1,6,NA
2,1,NA
1,5,NA
2,5,NA
2,1,NA
1,5,NA
2,2,NA
2,2,NA
1,3,NA
2,4,NA
2,3,NA
1,4,NA
2,4,NA
1,1,NA
2,NA,NA
1,NA,NA
2,NA,NA
2,3,NA
1,3,NA
2,2,NA
1,1,NA
2,NA,NA
1,3,NA
2,2,NA
2,4,NA
1,4,NA
2,2,NA
2,2,NA
2,NA,NA
1,4,NA
2,2,NA
1,2,NA
2,2,NA
1,4,NA
2,2,NA
1,4,NA
2,4,NA
1,5,NA
2,5,NA
1,NA,NA
2,1,NA
2,NA,NA
1,2,NA
2,5,NA
2,3,NA
1,2,NA
2,2,NA
1,1,NA
2,1,NA
1,1,NA
2,1,NA
1,2,NA
2,2,NA
1,2,NA
1,1,NA
2,2,NA
1,2,NA
2,2,NA
1,4,NA
1,1,NA
2,2,NA
1,4,NA
1,1,NA
2,2,NA
2,2,NA
2,NA,NA
1,3,NA
2,2,NA
1,4,NA
1,2,NA
2,NA,NA
1,2,NA
1,2,NA
2,2,NA
1,1,NA
2,2,NA
1,2,NA
2,3,NA
1,2,NA
2,NA,NA
2,2,NA
1,2,NA
2,2,NA
2,1,NA
1,3,NA
2,2,NA
1,5,NA
1,1,NA
2,2,NA
1,2,NA
2,4,NA
2,NA,NA
1,3,NA
2,2,NA
2,5,NA
1,3,NA
2,2,NA
1,2,NA
2,2,NA
1,3,NA
2,2,NA
2,5,NA
1,1,NA
2,NA,NA
1,2,NA
2,2,NA
2,2,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,2,NA
2,2,NA
2,1,NA
1,2,NA
2,3,NA
1,2,NA
2,2,NA
1,3,NA
2,3,NA
1,2,NA
2,4,NA
1,3,NA
1,4,NA
2,2,NA
1,5,NA
1,5,NA
2,5,NA
1,2,NA
1,2,NA
1,5,NA
2,3,NA
1,5,NA
2,4,NA
1,5,NA
2,5,NA
1,5,NA
2,3,NA
1,5,NA
2,2,NA
1,5,NA
1,2,NA
2,2,NA
1,5,NA
2,5,NA
1,5,NA
2,4,NA
1,5,NA
2,4,NA
1,1,NA
2,1,NA
1,5,NA
2,2,NA
1,4,NA
2,2,NA
1,2,NA
2,1,NA
1,4,NA
2,5,NA
1,NA,NA
1,5,NA
2,4,NA
1,1,NA
2,2,NA
1,2,NA
2,4,NA
1,3,NA
1,2,NA
2,2,NA
2,4,NA
1,2,NA
2,1,NA
1,2,NA
2,4,NA
2,5,NA
2,NA,NA
1,2,NA
1,4,NA
1,2,NA
2,2,NA
1,2,NA
2,1,NA
1,2,NA
2,2,NA
1,NA,NA
1,5,NA
2,2,NA
1,3,NA
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2,6,NA
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1,6,NA
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2,NA,NA
1,NA,NA
1,NA,NA
1,NA,NA
2,NA,NA
2,NA,NA
1,2,NA
1,2,NA
2,1,NA
2,NA,NA
2,3,NA
2,NA,NA
1,1,NA
2,1,NA
1,2,NA
2,NA,NA
1,2,NA
2,NA,NA
1,4,NA
1,2,NA
2,1,NA
1,2,NA
2,NA,NA
1,2,NA
1,2,NA
2,2,NA
2,NA,NA
1,3,NA
2,2,NA
2,NA,NA
1,2,NA
2,2,NA
1,2,NA
2,NA,NA
1,2,NA
1,2,NA
2,NA,NA
1,2,NA
2,NA,NA
1,2,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,2,NA
2,2,NA
1,NA,NA
1,NA,NA
1,NA,NA
2,NA,NA
1,1,NA
1,2,NA
1,1,NA
2,NA,NA
1,2,NA
1,2,NA
1,1,NA
2,2,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,NA,NA
1,4,NA
2,2,NA
1,NA,NA
2,NA,NA
2,NA,NA
1,2,NA
2,NA,NA
1,1,NA
1,2,NA
2,NA,NA
1,NA,NA
1,NA,NA
1,NA,NA
2,NA,NA
1,1,NA
1,NA,NA
1,NA,NA
2,NA,NA
2,1,NA
1,2,NA
1,1,NA
2,NA,NA
1,1,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,1,NA
2,NA,NA
2,2,NA
2,NA,NA
1,NA,NA
1,NA,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,NA,NA
2,NA,NA
1,2,NA
2,NA,NA
1,2,NA
2,3,NA
1,NA,NA
2,NA,NA
1,2,NA
2,1,NA
1,1,NA
2,NA,NA
1,2,NA
1,1,NA
1,2,NA
1,NA,NA
2,NA,NA
2,1,NA

================================================
FILE: ch_inference_for_means/figures/eoce/cleveland_sacramento/cleveland_sacramento.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# take a sample -----------------------------------------------------
cle_sac = cle_sac[!is.na(cle_sac$personal_income),]

set.seed(8957)
sac = sample(cle_sac$personal_income[cle_sac$city == "Sacramento"], 17)
cle = sample(cle_sac$personal_income[cle_sac$city == "Cleveland"], 21)

# plot of total personal income in Cle and Sac ----------------------
pdf("cleveland_sacramento_hist.pdf", height = 5, width = 7)

par(mar = c(3.7, 2, 1,1), las = 1, mgp = c(2.5, 0.7, 0), 
    mfrow = c(2,1), cex.lab = 1.25)

histPlot(cle, xlim = c(0, 180000), ylim = c(0,10),
         ylab = "", xlab = "", col = COL[1], breaks = 8, axes = FALSE)
axis(1, at = seq(0,180000,45000))
axis(2, at = seq(0,10,5))
text(x = 120000, y = 8, labels = "Cleveland, OH", pos = 4, cex = 1.25)

histPlot(sac, xlim = c(0,180000), ylim = c(0,10), 
         ylab = "", xlab = "Total personal income", col = COL[1], breaks = 8,
         axes = FALSE)
axis(1, at = seq(0,180000,45000))
axis(2, at = seq(0,10,5))
text(x = 120000, y = 8, labels = "Sacramento, CA", pos = 4, cex = 1.25)

dev.off()

# summary stats -----------------------------------------------------
mean(cle, na.rm = TRUE)
sd(cle, na.rm = TRUE)
length(cle)

mean(sac, na.rm = TRUE)
sd(sac, na.rm = TRUE)
length(sac)


================================================
FILE: ch_inference_for_means/figures/eoce/college_credits/college_credits.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(credits)

# histogram of college credits --------------------------------------

pdf("college_credits_hist.pdf", height = 2, width = 4)
par(mar=c(3.4,3.4,0.5,0.5), las=1, mgp=c(2.2,0.7,0), cex.lab = 1)
histPlot(credits$credits, col = COL[1],
    xlab = "Number of credits",
    ylab = "Frequency",
    axes = FALSE)
axis(1)
axis(2, seq(0, 30, 10))
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/diamonds_1/diamonds.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(ggplot2)

# load data ---------------------------------------------------------
data(diamonds)

# calculate ppc: price per carat ------------------------------------
diamonds$ppc <- diamonds$price / (diamonds$carat * 100)

# subset for cara = 1 or carat = 0.99 -------------------------------
diamonds_100_99 <- diamonds[diamonds$carat == 1 | diamonds$carat == 0.99,]

# take samples ------------------------------------------------------
nn <- diamonds_100_99$ppc[diamonds_100_99$carat == 0.99]

set.seed(123)
one <- sample(diamonds_100_99$ppc[diamonds_100_99$carat == 1], size = 23, replace = FALSE)

# create variables --------------------------------------------------
ppc <- c(nn, one)
carat <- c(rep("0.99 carats",23), rep("1 carat",23))

# box plots ---------------------------------------------------------
pdf("diamonds_box.pdf", height = 3, width = 4)
par(mar = c(2, 4, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)
boxPlot(ppc, fact = carat, ylab = "Point price (in dollars)", axes = FALSE,
		lcol = COL[1], lwd = 1.5, medianLwd = 2.5)
axis(1, at = c(1,2), labels = c("0.99 carats", "1 carat"))
axis(2, at = seq(20, 80, 20))
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/exclusive_relationships/exclusive_relationships.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(dplyr)

# load data ---------------------------------------------------------
survey <- exclusive_relationship

# sample size -------------------------------------------------------
n <- survey %>%
  na.omit(excl_relation) %>%
  nrow() # 203

# histogram ---------------------------------------------------------

pdf("exclusive_relationships_rel_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(survey$excl_relation, col = COL[1], xlab = "Number of exclusive relationships", ylab = "", xlim = c(0, 10))
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/exclusive_relationships/survey.csv
================================================
"excl_relation"
2
4
1
4
NA
2
2
2
1
4
2
4
2
7
NA
1
NA
1
9
NA
4
1
2
4
2
1
5
1
9
1
2
1
4
4
1
8
NA
1
6
4
1
1
2
2
4
2
5
4
1
1
5
5
4
4
1
5
4
4
5
2
6
1
1
4
1
7
5
5
5
1
1
7
6
2
NA
1
2
6
1
NA
NA
4
1
2
4
1
4
NA
5
2
5
4
4
4
1
1
6
6
NA
2
2
2
5
4
2
7
1
2
5
4
1
4
6
1
4
4
1
7
5
5
7
2
5
4
1
8
5
6
1
2
2
1
1
4
2
4
1
1
NA
2
10
4
2
4
1
2
5
2
2
2
4
2
5
1
2
4
4
2
1
1
2
4
NA
5
2
1
2
NA
6
4
2
2
4
4
4
4
4
4
5
4
1
5
4
4
5
4
4
3
4
4
2
NA
2
1
2
4
2
2
1
1
1
NA
1
3
5
4
6
1
2
5
1
8
4
2
1
2
2
5


================================================
FILE: ch_inference_for_means/figures/eoce/friday_13th_accident/friday_13th_accident.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# subset for accidents ----------------------------------------------
friday_acc <- friday[friday$type == "accident",]

# Hist of 6th vs. 13th accidents ------------------
H <- function(x, xlab) {
  tmp <- hist(x,
      col = COL[1],
      xlab = xlab,
      ylab = "",
      main = "",
      axes = FALSE)
  axis(1, at = pretty(tmp$breaks, n = 3))
  axis(2, at = seq(0, max(tmp$counts)))
  # rug(x)
  return(tmp)
}

myPDF("friday_13th_accident_hist.pdf", 7, 1.9 * 7.5 / 9,
    mar = c(3.2, 2.5, 0.5, 2.5),
    mgp = c(2, 0.7, 0),
    mfrow = c(1,3),
    cex.lab = 1.25)
H(friday_acc$sixth, "Friday the 6th")
H(friday_acc$thirteenth, "Friday the 13th")
H(friday_acc$sixth - friday_acc$thirteenth, "Difference")
dev.off()


================================================
FILE: ch_inference_for_means/figures/eoce/friday_13th_traffic/friday_13th_traffic.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(friday)

# subset for accidents ----------------------------------------------
friday_tr <- friday[friday$type == "traffic",]

# Hist of 6th vs. 13th vs. diff traffic -------------------------
H <- function(x, xlab) {
  tmp <- hist(x,
      col = COL[1],
      xlab = xlab,
      ylab = "",
      main = "",
      axes = FALSE)
  axis(1, at = pretty(tmp$breaks, n = 3))
  axis(2, at = seq(0, max(tmp$counts)))
  # rug(x)
  return(tmp)
}

myPDF("friday_13th_traffic_hist.pdf", 9, 2,
    mar = c(4, 2.5, 0.5, 2.5),
    mgp = c(2.9, 0.7, 0),
    mfrow = c(1,3),
    cex.lab = 1.25)
H(friday_tr$sixth, "Friday the 6th")
H(friday_tr$thirteenth, "Friday the 13th")
H(friday_tr$sixth - friday_tr$thirteenth, "Difference")
dev.off()



================================================
FILE: ch_inference_for_means/figures/eoce/fuel_eff_city/fuel_eff.csv
================================================
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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,

================================================
FILE: ch_inference_for_means/figures/eoce/fuel_eff_city/fuel_eff_city.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
fuel_eff <- read.csv("fuel_eff.csv")

# select a small sample ---------------------------------------------
man_rows <- which(fuel_eff$transmission == "M")
aut_rows <- which(fuel_eff$transmission == "A")

set.seed(3583)
man_rows_samp <- sample(man_rows, 26)
aut_rows_samp <- sample(aut_rows, 26)

fuel_eff_samp <- fuel_eff[c(man_rows_samp,aut_rows_samp), ]
fuel_eff_samp$transmission <- droplevels(fuel_eff_samp$transmission)

levels(fuel_eff_samp$transmission) <- c("automatic", "manual")

# plot --------------------------------------------------------------
myPDF("fuel_eff_city_box.pdf", 3.5, mar = c(3.7,2,0.3,1), mgp = c(2.5,0.55,0))
boxPlot(fuel_eff_samp$city_mpg, fact = fuel_eff_samp$transmission, ylim = c(10,37), 
        xlab = "City MPG", axes = FALSE, xlim=c(0.5, 2.5),
        lwd = 1.5, lcol = COL[1], medianLwd = 2.5)
axis(1, at = c(1,2), labels = c("automatic", "manual"))
axis(2, at = c(15,25,35))
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/fuel_eff_hway/fuel_eff.csv
================================================
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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,
2012,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
2012,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
2012,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,
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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
2012,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,
2012,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,
2012,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,
2012,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,
2012,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,

================================================
FILE: ch_inference_for_means/figures/eoce/fuel_eff_hway/fuel_eff_hway.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
fuel_eff <- read.csv("fuel_eff.csv")

# select a small sample ---------------------------------------------
man_rows <- which(fuel_eff$transmission == "M")
aut_rows <- which(fuel_eff$transmission == "A")

set.seed(3583)
man_rows_samp <- sample(man_rows, 26)
aut_rows_samp <- sample(aut_rows, 26)

fuel_eff_samp <- fuel_eff[c(man_rows_samp,aut_rows_samp), ]
fuel_eff_samp$transmission <- droplevels(fuel_eff_samp$transmission)

levels(fuel_eff_samp$transmission) <- c("automatic", "manual")

# plot --------------------------------------------------------------
myPDF("fuel_eff_hway_box.pdf", 3.5, mar = c(3.7,2,0.3,1), mgp = c(2.5,0.55,0))
boxPlot(fuel_eff_samp$hwy_mpg, fact = fuel_eff_samp$transmission, ylim = c(10, 37), 
        xlab = "Hwy MPG", axes = FALSE, xlim = c(0.5, 2.5),
        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)
axis(1, at = c(1,2), labels = c("automatic","manual"))
axis(2, at = c(15,25,35))
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/gifted_children/gifted_children.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(gifted)

# histogram of IQ scores --------------------------------------------
myPDF("gifted_children_IQ_hist.pdf", 5.5, 1.55, mar = c(3, 2, 0.2, 1), 
      mgp=c(1.8, 0.55, 0), mfrow = c(1,3))

histPlot(gifted$motheriq, col = COL[1], 
         xlab = "Mother's IQ", ylab = "", 
         axes = FALSE, xlim = c(100,140), ylim = c(0,12))
axis(1, at = seq(100,140,20))
axis(2, at = seq(0,12,4))
histPlot(gifted$fatheriq, col = COL[1], 
         xlab = "Father's IQ", ylab = "", 
         axes = FALSE, xlim = c(110,130), ylim = c(0,12))
axis(1, at = seq(100,130,10))
axis(2, at = seq(0,12,4))
histPlot(gifted$motheriq - gifted$fatheriq, col = COL[1], 
         xlab = "Diff.", ylab = "", 
         axes = FALSE, xlim = c(-20,20), ylim = c(0,12))
axis(1, at = seq(-20,20,20))
axis(2, at = seq(0,12,4))

dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/gifted_children_ht/gifted_children_ht.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(gifted)

# plot mom's IQ -----------------------------------------------------
pdf("gifted_children_ht_momIQ_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(gifted$motheriq, col = COL[1], 
         xlab = "Mother's IQ", ylab = "", axes = FALSE)
axis(1)
axis(2, at = c(0,4,8,12))
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/gifted_children_intro/gifted_children_intro.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(gifted)

# plot counts -------------------------------------------------------
pdf("gifted_children_ht_count_hist.pdf", height = 3, width = 6)
par(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)
histPlot(gifted$count, col = COL[1], 
         xlab = "Age child first counted to 10 (in months)", ylab = "", 
         axes = FALSE)
axis(1)
axis(2, at = c(0,3,6))
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/global_warming_v2_1/global_warming_v2_1.R
================================================
library(openintro)
d <- climate70$dx90_2018 - climate70$dx90_1948
mean(d)
sd(d)
length(d)
t.test(d)


myPDF("global_warming_v2_1_diffs.pdf", 4, 3,
    mar = c(3.9, 2, 0.5, 0.5))
histPlot(d, col = COL[1], 
    xlab = "Differences in Number of Days",
    ylab = "")
axis(1)
dev.off()

================================================
FILE: ch_inference_for_means/figures/eoce/gpa_major/gpa_major.R
================================================
library(openintro)
library(xtable)

survey <- read.csv("survey.csv")

# subset for meaningful gpa -----------------------------------------
survey <- subset(survey,
    !is.na(survey$gpa) &
    !is.na(survey$major) &
    survey$gpa <= 4)

# set major level names ---------------------------------------------
levels(survey$major) <- c(
    "Arts and Humanities",
    "Natural Sciences",
    "Social Sciences")

# boxplot -----------------------------------------------------------
myPDF("gpa_major.pdf", 7.2, 2.7,
    mar = c(2.2,4.7,0.5,1),
    mgp = c(3.5,0.7,0),
    cex.lab = 1.25,
    cex.axis = 1.25)

boxPlot(survey$gpa, fact = survey$major, col = COL[1], 
        ylab = "GPA", axes = FALSE,
        xlim = c(0.6, 3.4),
        ylim = c(2.5, 4),
        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)
axis(1, at = c(1,2,3), 
     labels = c("Arts and Humanities", "Natural Sciences", "Social Sciences"))
axis(2, at = seq(2.5, 4, 0.5))
dev.off()

# anova output ------------------------------------------------------
xtable(anova(lm(survey$gpa ~ survey$major)), digits = 2)

# summary stats -----------------------------------------------------
round(by(survey$gpa, survey$major, mean),2)
round(by(survey$gpa, survey$major, sd),2)
by(survey$gpa, survey$major, length)

================================================
FILE: ch_inference_for_means/figures/eoce/gpa_major/survey.csv
================================================
"gpa","major"
4,"social sciences"
3.8,"social sciences"
3.93,"social sciences"
3.4,"natural sciences"
NA,"natural sciences"
3.9,"social sciences"
NA,"natural sciences"
3.69,"social sciences"
3.2,"social sciences"
3.2,"social sciences"
3.52,"social sciences"
3.68,"social sciences"
3.4,"social sciences"
3.7,"arts and humanities"
NA,"social sciences"
3.75,"natural sciences"
3.3,"arts and humanities"
3.425,"social sciences"
3.795,"social sciences"
3.5,NA
3.83,"natural sciences"
3.3,"social sciences"
3.75,"social sciences"
4.3,"social sciences"
3.15,"natural sciences"
3.7,"social sciences"
3.8,"natural sciences"
3.63,NA
3.9,"arts and humanities"
3.294,"social sciences"
3.7,"arts and humanities"
3.4,"natural sciences"
4,"natural sciences"
3.4,"arts and humanities"
3.7,"natural sciences"
3.8,"social sciences"
3.4,"natural sciences"
3.4,"social sciences"
NA,"natural sciences"
3.4,"social sciences"
3,"social sciences"
3.6,"social sciences"
3.567,"social sciences"
3.3,"natural sciences"
3.4,"social sciences"
3.6,"arts and humanities"
3.67,"social sciences"
3.82,"social sciences"
2.9,"social sciences"
3.9,"social sciences"
3.4,"social sciences"
3.6,"social sciences"
3.1,"social sciences"
3.4,"social sciences"
3.8,"natural sciences"
3.7,"arts and humanities"
3.7,"social sciences"
3.8,"arts and humanities"
3.9,"arts and humanities"
3.92,"social sciences"
3.8,"social sciences"
3.868,"natural sciences"
3.35,"social sciences"
3.85,"arts and humanities"
3.55,NA
3.7,"social sciences"
3.65,"natural sciences"
3.125,"arts and humanities"
4,"natural sciences"
3.25,"arts and humanities"
3.86,"arts and humanities"
3.5,"social sciences"
3.45,"social sciences"
3.6,"natural sciences"
NA,"arts and humanities"
3.866,"social sciences"
3.82,"social sciences"
3.2,"arts and humanities"
3.5,"arts and humanities"
3.8,"natural sciences"
3.8,"social sciences"
3.7,"natural sciences"
3.75,"social sciences"
3.3,"natural sciences"
3.875,"social sciences"
3.7,"social sciences"
3.5,"social sciences"
NA,"natural sciences"
3.2,"natural sciences"
3.566,"social sciences"
3.5,"social sciences"
4.3,"natural sciences"
3.6,"natural sciences"
3.2,"social sciences"
NA,"natural sciences"
3.825,"social sciences"
3.85,"social sciences"
3.75,"natural sciences"
4,"social sciences"
3.4,"social sciences"
3.9,"social sciences"
3.825,"arts and humanities"
3.7,"social sciences"
3.8,"social sciences"
2.91,"social sciences"
3.559,"natural sciences"
3.69,"social sciences"
3.3,"natural sciences"
3.75,"arts and humanities"
3.9,"social sciences"
3.65,"social sciences"
3.5,"natural sciences"
3.6,"social sciences"
3.675,"social sciences"
3.9,"natural sciences"
3.6,"social sciences"
3.675,"social sciences"
3.7,"social sciences"
3.66,"social sciences"
3.733,"natural sciences"
3.7,"social sciences"
2.6,"social sciences"
4,"arts and humanities"
3.2,"arts and humanities"
3.16,"social sciences"
3.7,NA
3.5,"natural sciences"
3.65,"natural sciences"
3.9,"social sciences"
3.785,"social sciences"
3.1,"social sciences"
3.15,"social sciences"
3.61,"natural sciences"
3.3,"natural sciences"
NA,"social sciences"
3.7,"arts and humanities"
3.7,"arts and humanities"
3.75,"arts and humanities"
NA,"social sciences"
3.4,"natural sciences"
3.6,"arts and humanities"
3.5,"social sciences"
3.8,"natural sciences"
3.7,"social sciences"
3.925,"social sciences"
3.84,"natural sciences"
3.85,"arts and humanities"
3.41,"arts and humanities"
3.825,"natural sciences"
2.95,"natural sciences"
3.925,"natural sciences"
3.3,"natural sciences"
3.3,"arts and humanities"
3.6,"natural sciences"
NA,"arts and humanities"
4,"social sciences"
NA,"social sciences"
3.3,"arts and humanities"
3.89,"natural sciences"
3.2,"social sciences"
3.97,"natural sciences"
3.3,"social sciences"
3.3,"arts and humanities"
3.86,"social sciences"
3.76,"natural sciences"
3.8,"social sciences"
3.5,"social sciences"
NA,"natural sciences"
3.6,"social sciences"
3.55,"arts and humanities"
3.97,"natural sciences"
3.925,"social sciences"
3.68,"natural sciences"
3.25,"social sciences"
3.56,"social sciences"
2.85,"social sciences"
3.6,"social sciences"
3.45,"natural sciences"
3.5,"social sciences"
3.15,"natural sciences"
3.35,"social sciences"
3.5,"social sciences"
3.79,"arts and humanities"
3.022,"social sciences"
3.46,"social sciences"
3.55,"social sciences"
3.97,"social sciences"
3.925,"social sciences"
3.2,"social sciences"
3.4,"natural sciences"
3.9,"natural sciences"
NA,"natural sciences"
3.6,"arts and humanities"
3.83,"social sciences"
3.8,"natural sciences"
4,"social sciences"
3.5,"social sciences"
3.3,"arts and humanities"
4,"social sciences"
3.1,"social sciences"
3.5,"social sciences"
3.62,"social sciences"
3.6,"natural sciences"
3.8,"social sciences"
3.2,"social sciences"
3.925,"social sciences"
3.84,"social sciences"
3.1,"arts and humanities"
4,"natural sciences"
3.33,NA
3.35,"natural sciences"
3.925,"social sciences"
3,"natural sciences"
3.6,"social sciences"
3.7,"social sciences"
3.84,"social sciences"
3.8,"social sciences"
3.1,"social sciences"


================================================
FILE: ch_inference_for_means/figures/eoce/hs_beyond_1/hs_beyond.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(hsb2)

# create variables from data ----------------------------------------
scores <- c(hsb2$read, hsb2$write)
gp <- c(rep('read', nrow(hsb2)), rep('write', nrow(hsb2)))

# read vs. write side-by-side box plot ------------------------------
pdf("hs_beyond_read_write_box.pdf", height = 3, width = 5)
par(mar = c(3, 4, 0.5, 0.5), las = 1, mgp = c(2.8, 0.7, 0), 
    cex.axis = 1.1, cex.lab = 1.1)
openintro::dotPlot(scores, gp, vertical = TRUE, ylab = "scores", 
                   at=1:2+0.13, col = COL[1,3], 
                   xlim = c(0.5,2.5), ylim = c(20, 80), 
                   axes = FALSE, cex.lab = 1.25, cex.axis = 1.25)
axis(1, at = c(1,2), labels = c("read","write"), cex.lab = 1.25, cex.axis = 1.25)
axis(2, at = seq(20, 80, 20), cex.axis = 1.25)
boxplot(scores ~ gp, add = TRUE, axes = FALSE, col = NA)
dev.off()

# histogram of differences of read and write ------------------------
pdf("hs_beyond_diff_hist.pdf", height = 3, width = 5.5)
par(mar=c(3.3, 2, 0.5, 0.5), las = 1, mgp = c(2.1, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)
histPlot(hsb2$read - hsb2$write, col = COL[1], 
         xlab = "Differences in scores (read - write)", ylab = "")
dev.off()


================================================
FILE: ch_inference_for_means/figures/eoce/oscar_winners/oscar_winners.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(oscars)

# plot of oscar winner women and men ages ---------------------------
p <- oscars %>%
  mutate(award = ifelse(gender == "female", "Best Actress", "Best Actor")) %>%
  ggplot(aes(x = age)) +
    geom_histogram(binwidth = 10, fill = COL[1,1], color = COL[5,1], size = 0.3) +
    facet_wrap(~award, nrow = 2) +
    theme_minimal() +
    labs(x = "Age (in years)", y = "")

ggsave(p, file = "ch_inference_for_means/oscar_winners/figures/oscars_winners_hist.pdf",
       height = 6, width = 8)

# summary stats -----------------------------------------------------
oscars %>%
  group_by(gender) %>%
  summarise(
    mean = mean(age),
    sd = sd(age),
    n = n()
  )


================================================
FILE: ch_inference_for_means/figures/eoce/prison_isolation_T/prison_isolation.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
prison <- read.csv("prison_isolation.csv")

# calculate diffs ---------------------------------------------------

diff1 = prison$PreTrt1 - prison$PostTrt1
diff2 = prison$PreTrt2 - prison$PostTrt2
diff3 = prison$PreTrt3 - prison$PostTrt3

diff = c(diff1, diff2, diff3)
tr = c(rep("Tr 1", 14), rep("Tr 2", 14), rep("Tr 3", 14))

# hists ------------------------------------------
H <- function(x, xlab) {
  tmp <- hist(x,
      col = COL[1],
      xlab = xlab,
      ylab = "",
      main = "",
      axes = FALSE)
  axis(1, at = pretty(tmp$breaks, n = 3))
  axis(2, at = pretty(c(0, max(tmp$counts)), n = 3))
  # rug(x)
  return(tmp)
}

myPDF("prison_isolation_hist.pdf", 9, 2,
    mar = c(4, 2.5, 0.5, 2.5),
    mgp = c(2.9, 0.7, 0),
    mfrow = c(1,3),
    cex.lab = 1.25)
for (i in 1:3) {
  H(diff[tr == paste("Tr", i)], paste("Treatment", i))
}
dev.off()


================================================
FILE: ch_inference_for_means/figures/eoce/prison_isolation_T/prison_isolation.csv
================================================
PreTrt1,PostTrt1,PreTrt2,PostTrt2,PreTrt3,PostTrt3
67,74,88,79,86,90
86,50,79,81,53,53
64,64,67,83,81,102
69,76,83,74,69,67
67,64,79,76,81,76
79,81,76,69,76,81
67,74,71,71,74,69
67,50,67,75,60,60
69,60,69,64,67,69
57,57,67,64,86,83
76,62,67,64,86,107
90,76,74,71,74,71
71,71,81,74,71,71
93,76,81,64,71,81

================================================
FILE: ch_inference_for_means/figures/eoce/prius_fuel_efficiency/prius_fuel_efficiency.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------
prius <- c(54.6, 59.5, 49.5, 44.3, 63.3, 52.3, 55.4, 
           50.3, 60.3, 53.2, 52.6, 46.6, 52.1, 52.0)

# histogram ---------------------------------------------------------
pdf("prius_fuel_efficiency_hist.pdf", height = 3, width = 6)

par(mar = c(4,2,0,0), las = 1, mgp = c(2.5,1,0),
    cex.lab = 1.25, cex.axis = 1.25)

histPlot(prius, ylab = "",xlab = "Mileage (in MPG)", col = COL[1], axes = FALSE)
axis(1, at = seq(40, 65, 5))
axis(2, at = seq(0, 6, 2))

dev.off()

# summary stats -----------------------------------------------------
round(mean(prius), 1)
round(sd(prius), 1)
length(prius)

================================================
FILE: ch_inference_for_means/figures/eoce/prius_fuel_efficiency_update/prius_fuel_efficiency.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------
prius <- c(54.6, 59.5, 49.5, 44.3, 63.3, 52.3, 55.4, 
           50.3, 60.3, 53.2, 52.6, 46.6, 52.1, 52.0)

# histogram ---------------------------------------------------------
pdf("prius_fuel_efficiency_hist.pdf", height = 3, width = 6)

par(mar = c(4,2,0,0), las = 1, mgp = c(2.5,1,0),
    cex.lab = 1.25, cex.axis = 1.25)

histPlot(prius, ylab = "",xlab = "Mileage (in MPG)", col = COL[1], axes = FALSE)
axis(1, at = seq(40, 65, 5))
axis(2, at = seq(0, 6, 2))

dev.off()

# summary stats -----------------------------------------------------
round(mean(prius), 1)
round(sd(prius), 1)
length(prius)

================================================
FILE: ch_inference_for_means/figures/eoce/t_distribution/t_distribution.R
================================================
# plot --------------------------------------------------------------
pdf('t_distribution.pdf', 4.3, 2.3)

par(mar=c(2, 0, 0, 0), mgp=c(5, 0.6, 0))
plot(c(-4.2, 4.2), c(0, dnorm(0)), type='n', axes=FALSE, xlab = "", ylab = "")
axis(1)
abline(h=0)

X <- seq(-5, 5, 0.01)
Y <- dnorm(X)
lines(X, Y, lwd = 0.7)

Y <- dt(X, 1)
lines(X, Y, lty=3)

Z <- dt(X, 5)
lines(X, Z, lty=5)

legend("topright", lty = c(1,5,3), c("solid","dashed","dotted"), inset = 0.01, box.col = "white")

dev.off()


================================================
FILE: ch_inference_for_means/figures/eoce/torque_on_rusty_bolt/torque_on_rusty_bolt (Autosaved).R
================================================
library(openintro)
library(xtable)

d <- penetrating_oil

myPDF("torque_on_rusty_bolt_dot_plot.pdf", 7, 3.2,
    mar = c(3.5, 6.5, 0.1, 0.3),
    mgp = c(2.3, 0.55, 0))
dotPlot(d$torque, d$treatment,
    pch = 19, col = COL[1, 2], cex = 2,
    vertical = FALSE,
    xlab = paste(
        "Torque Required to Loosen Rusty Bolt,",
        "in Foot-Pounds"),
    ylab = "")
abline(h = 1:8, col = COL[5, 7])
dev.off()

m <- lm(torque ~ treatment, data = penetrating_oil)
anova(m)
xtable(anova(m))

xbar <- tapply(penetrating_oil$torque, penetrating_oil$treatment, mean)
n <- table(penetrating_oil$treatment)
stopifnot(all(names(xbar) == names(n)))
s <- summary(m)$sigma
df <- summary(m)$df[2]

p <- matrix("", length(n), length(n))
N <- length(n)
K <- N * (N - 1) / 2
for (i in 1:(N - 1)) {
  for (j in (i + 1):N) {
    diff <- xbar[i] - xbar[j]
    se <- s * sqrt(1 / n[i] + 1 / n[j])
    p[i, j] <- round(2 * pt(-abs(diff / se), df), 4)
  }
}
rownames(p) <-
    colnames(p) <-
    names(xbar)
xtable(p[1:7, 2:8])



================================================
FILE: ch_inference_for_means/figures/eoce/torque_on_rusty_bolt/torque_on_rusty_bolt.R
================================================
library(openintro)
library(xtable)

d <- penetrating_oil

myPDF("torque_on_rusty_bolt_dot_plot.pdf", 7, 3.2,
    mar = c(3.5, 6.5, 0.1, 0.3),
    mgp = c(2.3, 0.55, 0))
dotPlot(d$torque, d$treatment,
    pch = 19, col = COL[1, 2], cex = 2,
    vertical = FALSE,
    xlab = paste(
        "Torque Required to Loosen Rusty Bolt,",
        "in Foot-Pounds"),
    ylab = "")
abline(h = 1:8, col = COL[5, 7])
dev.off()

m <- lm(torque ~ treatment, data = penetrating_oil)
anova(m)
xtable(anova(m))

xbar <- tapply(penetrating_oil$torque, penetrating_oil$treatment, mean)
n <- table(penetrating_oil$treatment)
stopifnot(all(names(xbar) == names(n)))
s <- summary(m)$sigma
df <- summary(m)$df[2]

p <- matrix("", length(n), length(n))
N <- length(n)
K <- N * (N - 1) / 2
for (i in 1:(N - 1)) {
  for (j in (i + 1):N) {
    diff <- xbar[i] - xbar[j]
    se <- s * sqrt(1 / n[i] + 1 / n[j])
    tmp <- round(2 * pt(-abs(diff / se), df), 4)
    p[i, j] <- format(c(tmp, 0.0001), scientific = FALSE)[1]
  }
}
rownames(p) <-
    colnames(p) <-
    names(xbar)
xtable(p[1:7, 2:8])



================================================
FILE: ch_inference_for_means/figures/eoce/work_hours_education/work_hours_education.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------
load("gss2010.Rda")
gss <- gss2010

# subset & clean data -----------------------------------------------
gss_sub <- gss[which(!is.na(gss$hrs1) & !is.na(gss$degree)), ]
gss_sub <- gss_sub[, which(names(gss_sub) == "degree" | names(gss_sub) == "hrs1")]
levels(gss_sub$degree) <- c("Less than HS","HS","Jr Coll","Bachelor's","Graduate")

# plot --------------------------------------------------------------

pdf("work_hours_education.pdf", height = 2.5, width = 8)

par(mar = c(2,3.5,0.5,.5), mgp = c(2.3,0.7,0), las = 1)

boxPlot(gss_sub$hrs1, fact = gss_sub$degree, 
        col = COL[1,2], ylab = "Hours worked per week", xlim=c(0.6, 5.4),
        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)
dev.off()

# summary stats -----------------------------------------------------

round(by(gss_sub$hrs1, gss_sub$degree, mean),2)
round(by(gss_sub$hrs1, gss_sub$degree, sd),2)
by(gss_sub$hrs1, gss_sub$degree, length)

# anova -------------------------------------------------------------

xtable(anova(lm(gss_sub$hrs1 ~ gss_sub$degree)), digits = 2)


================================================
FILE: ch_inference_for_means/figures/fDist2And423/fDist2And423.R
================================================
rm(list = ls())
library(openintro)

X <- seq(0, 8, len = 300)
Y <- df(X, 2.00001, 423)

myPDF("fDist2And423.pdf", 5, 2.4,
      mar = c(2.8, 0.5, 0, 0.5),
      mgp = c(1.8, 0.4, 0))
plot(X, Y,
     type = "l",
     xlab = "F",
     ylab = "",
     axes = FALSE,
     lwd = 1.5)
lines(c(0, 8), rep(0, 2))
axis(1)
dev.off()


myPDF("fDist2And423Shaded.pdf", 5, 1.8,
      mar = c(1.6, 3.1, 0.5, 0.5),
      mgp = c(2, 0.5, 0))
plot(X, Y,
     type = "l",
     xlab = "F",
     ylab = "",
     axes = FALSE)
lines(c(0, 8), rep(0, 2))
axis(1)
temp <- which(X > 5.077)
x    <- X[c(temp, rev(temp), temp[1])]
y    <- c(Y[temp], rep(0, length(temp)), Y[temp[1]])
polygon(x, y, col = COL[4], border = COL[4], lwd = 2)
arrows(6.3, 0.3, 6.5, 0.05, length = 0.05)
text(6.3, 0.3, "Small tail area", pos = 3)
dev.off()


================================================
FILE: ch_inference_for_means/figures/fDist3And323/fDist3And323.R
================================================
rm(list = ls())
library(openintro)

X <- seq(0, 6, len = 300)
Y <- df(X, 3, 323)

myPDF("fDist3And323.pdf", 5, 2.4,
      mar = c(2.8, 0.5, 0, 0.5),
      mgp = c(1.8, 0.4, 0))
plot(X, Y,
     type = "l",
     xlab = "F",
     ylab = "",
     axes = FALSE,
     lwd = 1.5)
abline(h = 0)
axis(1)
dev.off()


myPDF("fDist3And323Shaded.pdf", 5, 1.8,
      mar = c(1.6, 3.1, 0.5, 0.5),
      mgp = c(2, 0.5, 0))
plot(X, Y,
     type = "l",
     xlab = "F",
     ylab = "",
     axes = FALSE)
abline(h = 0)
axis(1)
temp <- which(X > 1.994)
x    <- X[c(temp, rev(temp), temp[1])]
y    <- c(Y[temp], rep(0, length(temp)), Y[temp[1]])
polygon(x, y, col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_means/figures/mlbANOVA/mlbANOVA.R
================================================
rm(list = ls())
library(xtable)
library(openintro)
library(dplyr)
d   <- subset(mlb_players_18, AB >= 100)
d   <- subset(d, !position %in% c("P", "DH"))
pos <- list(c("LF", "CF", "RF"), c("1B", "2B", "3B", "SS"), "DH", "C")
POS <- c("OF", "IF", "DH", "C")

for (i in 1:length(pos)) {
  these <- which(d$position %in% pos[[i]])
  cat(length(these), "\n")
  d$position[these] <- POS[i]
}
d <- select(d, name, team, position, AB, H, HR, RBI, AVG, OBP)
d <- d[order(d$name, d$team), ]
rownames(d) <- NULL
xtable(rbind.data.frame(head(d, 3), tail(d, 3)), digits = 3)


mod <- lm(OBP ~ position, data = d)
summary(mod)
anova(mod)
xtable(summary(mod))
xtable(anova(mod), digits = 4)


myPDF("mlbANOVABoxPlot.pdf", 5.4, 3,
      mar = c(2.8, 4, 0, 1))
key <- POS[c(1, 2, 4)]
boxPlot(d$OBP, d$position,
        xlab = "",
        ylab = "On-Base Percentage",
        axes = FALSE,
        pchCex = 1,
        key = key,
        col = COL[1, 3],
        lcol = COL[1])
mtext("Position", 1, 1.5)
axis(1, 1:3, key)
axis(2)
dev.off()


tab <- rbind(
    by(d$OBP, d$position, length),
    by(d$OBP, d$position, mean),
    by(d$OBP, d$position, sd))[, c("OF", "IF", "C")]
xtable(tab, digits = 3)


g <- rep(1:3, c(10, 1000, 1000))
x <- c()
for (i in 1:3) {
  n <- sum(g == i)
  x <- c(x, rnorm(n))
}
sum(by(x, g, length) * (by(x, g, mean) - mean(x))^2) / 2
anova(lm(x ~ as.factor(g)))


# uTeams <- unique(mlb_players_18$team)
# nTeams <- length(uTeams)
# myPDF("mlbANOVADiagIndepOfTeam.pdf", 5, 4)
# dotPlot(mod$res, mlbBat10$team,
        # key = uTeams,
        # ylim = c(0, nTeams),
        # axes = FALSE,
        # ylab = "Teams",
        # xlab = "Residuals",
        # col = COL[1])
# axis(1)
# axis(2, 1:nTeams, uTeams, cex.axis = 0.5)
# abline(h = 1:nTeams, col = COL[7], lwd = 0.5)
# abline(h = seq(1, nTeams, 5), col = COL[6], lwd = 1.5)
# dev.off()


myPDF("mlbANOVADiagNormality.pdf", 5, 4,
      mar = c(3.5, 4.4, 0.5, 0.5))
qqnorm(mod$res,
       main = "",
       xlab = "",
       ylab = "",
       pch = 20,
       cex = 0.7,
       col = COL[1,3])
mtext("Theoretical Quantiles", 1, 2.2)
par(las = 0)
mtext("Residuals", 2, line = 3.3)
dev.off()


myPDF("mlbANOVADiagNormalityGroups.pdf", 6, 1.7,
      mar = c(3.4, 3.4, 2, 0.5),
      mgp = c(2.2, 0.55, 0),
      mfrow = c(1, 3))
xlim <- range(d$OBP)
at <- pretty(xlim, 3)
breaks <- pretty(xlim, 15)
HistOfOBP <- function(x, main) {
  histPlot(x,
      main = main,
      xlim = xlim,
      breaks = breaks,
      xlab = "On-Base Percentage",
      ylab = "Frequency",
      col = COL[1],
      axes = FALSE)
  axis(1, at)
  axis(2)
}
HistOfOBP(d$OBP[d$position == "OF"], "Outfielders")
HistOfOBP(d$OBP[d$position == "IF"], "Infielders")
HistOfOBP(d$OBP[d$position == "C"], "Catchers")
dev.off()


myPDF("mlbANOVADiagConstantVar.pdf", 5, 4,
      mar = c(3.5, 4.4, 0.5, 0.5))
boxPlot(mod$res, d$position,
        main = "",
        xlab = "",
        ylab = "",
        pch = 20,
        cex = 0.7,
        col = COL[1, 3],
        lcol = COL[1])
mtext("Position", 1, 2.2)
par(las = 0)
mtext("Residuals", 2, line = 3.3)
dev.off()


anova(lm(OBP ~ team + position, d))
anova(lm(OBP ~ position + team, d))



================================================
FILE: ch_inference_for_means/figures/outliers_and_ss_condition/outliers_and_ss_condition.R
================================================
library(openintro)
set.seed(2)

d1 <- rnorm(15, 3, 2)
d2 <- c(exp(rnorm(49, 0, 0.7)), 22)

myPDF('outliers_and_ss_condition.pdf', 8, 2.5,
      mar = c(3, 3, 0.5, 2),
      mgp = c(1.8, 0.5, 0),
      mfrow = c(1, 2))

histPlot(d1, axes = FALSE, # breaks = 20,
         xlab = "Sample 1 Observations (n = 15)",
         ylab = "",
         col = COL[1])
axis(1, at = seq(-10, 10, 2))
axis(2)
par(las = 0)
mtext("Frequency", 2, 1.8)

par(las = 1, mar = c(3, 4, 0.5, 0.5))
histPlot(d2, axes = FALSE, breaks = 20,
         xlab = "Sample 2 Observations (n = 50)",
         ylab = "",
         col = COL[1])
axis(1, at = seq(-10, 30, 10))
axis(2)
par(las = 0)
mtext("Frequency", 2, 2)

dev.off()


================================================
FILE: ch_inference_for_means/figures/pValueOfTwoTailAreaOfExamVersionsWhereDFIs26/pValueOfTwoTailAreaOfExamVersionsWhereDFIs26.R
================================================
library(openintro)
data(COL)

myPDF('pValueOfTwoTailAreaOfExamVersionsWhereDFIs26.pdf',
      4.8, 1.7,
      mar = c(1.6, 1, 0, 1),
      mgp = c(0, 0.45, 0))
normTail(0, 1,
         L = -1.15,
         U = 1.15,
         df = 26,
         col = COL[1])
lines(c(1.16, 1.16),
      c(dt(1.16, 26), 0.25),
      lty = 3,
      cex = 2)
text(1.55, 0.24, "T = 1.15",
     pos = 3)
dev.off()


================================================
FILE: ch_inference_for_means/figures/pValueShownForSATHTOfOver100PtGain/pValueShownForSATHTOfOver100PtGain.R
================================================
library(openintro)
data(COL)

myPDF('pValueShownForSATHTOfOver100PtGain.pdf', 4, 2,
      mar = c(1.5, 1, 0.2, 1),
      mgp = c(0, 0.45, 0))
normTail(0, 1,
         U = 2.39,
         df = 20,
         col = COL[1])
lines(c(2.4, 2.4),
      c(dt(2.4, 20), 0.1),
      lty = 3,
      lwd = 2)
text(2.73, 0.088, "T = 2.39",
     pos = 3,
     cex = 0.8)
dev.off()


================================================
FILE: ch_inference_for_means/figures/power_best_sample_size/power_best_sample_size.R
================================================
library(openintro)
data(COL)

BuildNull <- function() {
  normTail(0, 1.07, L = -1000, U = 1000,
           df = 50, lwd = 2.5, axes = FALSE,
           curveColor = COL[1],
           xlim = c(-10, 10))
  axis(1, at = seq(-15, 15, 3))
  mtext(expression(bar(x)[trmt] - bar(x)[ctrl]),
        side = 1, line = 1.5)
  text(0.6, 0.3, "Null distribution", col = COL[1], pos = 4)
  lines(rep(0, 2), c(0, dnorm(0, 0, 1.07)),
        col = COL[1,4], lwd = 0.5)
}

# _____ Null Distribution + Alternative At -3 _____ #
myPDF('power_best_sample_size.pdf',
      7, 1.5,
      mar = c(2.5, 0, 0, 0),
      mgp = c(0, 0.45, 0))
BuildNull()
normTail(-3, 1.07, L = -2.10, U = 1000,
         df = 50, lwd = 2, add = TRUE,
         curveColor = COL[2, 2],
         col = COL[2, 2], border = COL[2])
lines(rep(-3, 2), c(0, dnorm(0, 0, 1.07)),
      col = COL[2,4], lwd = 0.5)
segments(2.1 * c(-1, 1), rep(0, 2), y1 = rep(0.2, 2),
         col = COL[4, 4], lty = 3, lwd = 3)
segments(2.1 * c(-1, 1), rep(0, 2), y1 = rep(0.2, 2),
         col = COL[4, 4], lty = 3, lwd = 1.5)
text(rep(-6, 2), 1.5 * c(0.21, 0.15),
     c("Distribution with",
       expression(mu[trmt] - mu[ctrl]*" = -3")),
     col = COL[2])
arrows(-3, 0.02, -2.15, 0.02,
       col = COL[3], lwd = 2,
       length = 0.05, code = 3)
text(-2.85, 0.01, "0.84 SE", pos = 3, col = COL[3], cex = 0.75)
rect(-1.5, 0.005, 0.5, 0.1, col = "#ffffffAA", border = "#00000000")
arrows(-2.05, 0.02, 0, 0.02,
       col = COL[4], lwd = 2,
       length = 0.05, code = 3)
text(-1, 0.007, "1.96 SE", pos = 3, col = COL[4], cex = 0.75)
dev.off()




================================================
FILE: ch_inference_for_means/figures/power_curve/power_curve.R
================================================
library(openintro)
data(COL)

n <- c(10:500, seq(510, 2000, 10), seq(2100, 10000, 100))
se <- sapply(n, function(x) sqrt(2 * 12^2 / x))
left.reject <- qt(0.025, n - 1) * se
x <- (left.reject - (-3)) / se
p <- pt(x, 2 * n - 2)

myPDF('power_curve_neg-3.pdf', 7, 3)
plot(n, p,
     xlab = "Sample Size Per Group",
     ylab = "Power",
     xlim = c(20, 5000),
     ylim = 0:1,
     type = "n",
     log = "x",
     axes = FALSE)
axis(1)
axis(2)
abline(h = 0:1, lty = 2, col = COL[6,2])
lines(n, p, col = COL[1], lwd = 3)
dev.off()




================================================
FILE: ch_inference_for_means/figures/power_null_0_0-76/power_null_0_0-76.R
================================================
library(openintro)
data(COL)

BuildNull <- function() {
  normTail(0, 0.8, L = -1000, U = 1000,
           df = 50, lwd = 2.5, axes = FALSE,
           curveColor = COL[1],
           xlim = c(-10, 10))
  axis(1, at = seq(-15, 15, 3))
  mtext(expression(bar(x)[trmt] - bar(x)[ctrl]),
        side = 1, line = 1.5)
  text(0.6, 0.4, "Null distribution", col = COL[1], pos = 4)
  lines(rep(0, 2), c(0, dnorm(0, 0, 0.8)),
        col = COL[1,4], lwd = 0.5)
}

# _____ Null Distribution + Alternative At -3 _____ #
myPDF('power_null_0_0-76_with_alt_at_3_and_shaded.pdf',
      7, 1.4,
      mar = c(2.5, 0, 0, 0),
      mgp = c(0, 0.45, 0))
BuildNull()
normTail(-3, 0.8, L = -1.49, U = 1000,
         df = 50, lwd = 2.5, add = TRUE,
         curveColor = COL[2],
         col = COL[2, 3], border = COL[2])
lines(rep(-3, 2), c(0, dnorm(0, 0, 0.8)),
      col = COL[2,4], lwd = 0.5)
segments(1.5 * c(-1, 1), rep(0, 2), y1 = rep(0.3, 2),
         col = COL[4], lty = 3, lwd = 3)
segments(1.5 * c(-1, 1), rep(0, 2), y1 = rep(0.3, 2),
         col = COL[4], lty = 3, lwd = 1.5)
text(rep(-5.8, 2), 2 * c(0.21, 0.15),
     c("Distribution with",
       expression(mu[trmt] - mu[ctrl]*" = -3")),
     col = COL[2])
dev.off()




================================================
FILE: ch_inference_for_means/figures/power_null_0_1-7/power_null_0_1-7.R
================================================
library(openintro)
data(COL)

BuildNull <- function(xlim = c(-10, 10)) {
  normTail(0, 1.70, L = -1000, U = 1000,
           df = 50, lwd = 2.5, axes = FALSE,
           curveColor = COL[1],
           xlim = xlim)
  axis(1, at = seq(-15, 15, 3))
  mtext(expression(bar(x)[trmt] - bar(x)[ctrl]),
        side = 1, line = 1.8)
  text(1.2, 0.2, "Null distribution", col = COL[1], pos = 4)
  lines(rep(0, 2), c(0, dnorm(0, 0, 1.70)),
        col = COL[1,4], lwd = 0.5)
}

# _____ Null Distribution Only _____ #
myPDF('power_null_A_0_1-7.pdf',
      7, 1.9,
      mar = c(2.8, 0, 0, 0),
      mgp = c(0, 0.45, 0))
BuildNull()
dev.off()



# _____ Null Distribution + Rejection Regions _____ #
myPDF('power_null_B_0_1-7_with_rejection_region.pdf',
      7, 1.9,
      mar = c(2.8, 0, 0, 0),
      mgp = c(0, 0.45, 0))
BuildNull()
segments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),
         col = COL[4], lty = 3, lwd = 3)
segments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),
         col = COL[4], lty = 3, lwd = 1.5)
text(c(-6, 0, 0, 6), c(0.07, 0.09, 0.05, 0.07),
     c(expression("Reject " * H[0]),
       "Do not",
       expression("reject " * H[0]),
       expression("Reject " * H[0])),
     col = COL[4])
dev.off()



# _____ Null Distribution + Alternative At -3 _____ #
myPDF('power_null_C_0_1-7_with_alt_at_3.pdf',
      7, 1.9,
      mar = c(2.8, 0, 0, 0),
      mgp = c(0, 0.45, 0))
BuildNull(xlim = c(-8.8, 10))
normTail(-3, 1.70, L = -1000, U = 1000,
         df = 50, lwd = 2.5, add = TRUE,
         curveColor = COL[2])
lines(rep(-3, 2), c(0, dnorm(0, 0, 1.70)),
      col = COL[2,4], lwd = 0.5)
segments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),
         col = COL[4], lty = 3, lwd = 3)
segments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),
         col = COL[4], lty = 3, lwd = 1.5)
text(rep(-6.5, 2), c(0.21, 0.175),
     c("Distribution with",
       expression(mu[trmt] - mu[ctrl]*" = -3")),
     col = COL[2])
dev.off()



# _____ Null Distribution + Alternative At -3 + Shaded _____ #
myPDF('power_null_D_0_1-7_with_alt_at_3_and_shaded.pdf',
      7, 1.9,
      mar = c(2.8, 0, 0, 0),
      mgp = c(0, 0.45, 0))
BuildNull()
normTail(-3, 1.70, L = -3.332, U = 1000,
         df = 50, lwd = 2.5, add = TRUE,
         curveColor = COL[2],
         border = COL[2],
         col = COL[2,3])
lines(rep(-3, 2), c(0, dnorm(0, 0, 1.70)),
      col = COL[2,4], lwd = 0.5)
segments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),
         col = COL[4], lty = 3, lwd = 3)
segments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),
         col = COL[4], lty = 3, lwd = 1.5)
text(rep(-6.5, 2), c(0.21, 0.175),
     c("Distribution with",
       expression(mu[trmt] - mu[ctrl]*" = -3")),
     col = COL[2])
dev.off()



================================================
FILE: ch_inference_for_means/figures/rissosDolphin/ReadMe.txt
================================================

Photo by Mike Baird (http://www.bairdphotos.com/). Image was licensed under Creative Commons Attribution 2.0 Generic.


================================================
FILE: ch_inference_for_means/figures/run10SampTimeHistogram/run10SampTimeHistogram.R
================================================
library(openintro)
data(COL)


data(run10Samp)
d <- run10Samp

myPDF("run10SampTimeHistogram.pdf", 5, 2.8,
      mar = c(3.5, 3.5, 0.5, 1),
      mgp = c(2.2, 0.55, 0))
histPlot(d$time,
         main = "",
         xlab = "Time (Minutes)",
         ylab = "Frequency",
         col = COL[1])
dev.off()


set.seed(1)
run17 <- subset(run17, event == "10 Mile")
mean(run17$net_sec / 60)
d <- run17[sample(nrow(run17), 100), ]
myPDF("run17SampTimeHistogram.pdf", 5, 2.8,
      mar = c(3.5, 3.5, 0.5, 1),
      mgp = c(2.2, 0.55, 0))
histPlot(d$net_sec / 60,
         main = "",
         xlab = "Time (Minutes)",
         ylab = "Frequency",
         col = COL[1])
dev.off()
t.test(d$net_sec / 60, mu = 93.29)
mean(d$net_sec / 60)
sd(d$net_sec / 60)



================================================
FILE: ch_inference_for_means/figures/satImprovementHTDataHistogram/satImprovementHTDataHistogram.R
================================================
library(openintro)
data(COL)

set.seed(2)
x <- round(rnorm(30, 120, 70))
t.test(x - 100)
mean(x)
sd(x)

myPDF('satImprovementHTDataHistogram.pdf', 3.9, 2.2,
      mar = c(1.6, 2, 0.2, 1),
      mgp = c(0, 0.45, 0))
histPlot(x,
         xlab = '',
         ylab = '',
         main = '',
         axes = FALSE,
         col = COL[1])
axis(1)
axis(2, at = seq(0, 10, 5))
dev.off()


================================================
FILE: ch_inference_for_means/figures/stemCellTherapyForHearts/stemCellTherapyForHearts.R
================================================
library(openintro)
data(COL)
data(stem.cells)
d <- stem.cells

change <- d$after - d$before
t.test(change ~ d[,1])

myPDF('stemCellTherapyForHearts.pdf', 4.8, 4.2,
      mar=c(3.2, 1.8, 1.7, 0.7),
      mgp=c(2, 0.3, 0),
      mfrow=c(2, 1))
histPlot(change[d[,1] == 'esc'],
         xlim=c(-10, 15),
         axes=FALSE,
         xlab='',
         main='',
         breaks=seq(-10, 15, 2.5),
         col=COL[1])
x.axis.at <- seq(-10, 15, 5)
x.axis.labels <- paste0(seq(-10, 15, 5), "%")
cex.axis <- 0.85
axis(1, x.axis.at, x.axis.labels, cex.axis=cex.axis)
mtext('Embryonic stem cell transplant', line=0.5, cex=1.1)
mtext('Change in heart pumping function', 1, line=1.3, cex = 0.9)
par(mgp=c(2, 0.6, 0))
axis(2, at=0:3, cex.axis=0.925)

par(mar=c(2.4, 1.8, 2, 0.7),
    mgp=c(2, 0.3, 0))
histPlot(change[d[,1] == 'ctrl'],
         xlim=c(-10, 15),
         axes=FALSE,
         xlab='',
         main='',
         breaks=seq(-10, 15, 2.5),
         col=COL[1])
axis(1, x.axis.at, x.axis.labels, cex.axis=cex.axis)
par(mgp=c(2, 0.6, 0))
axis(2, at=0:3, cex.axis=0.925)
mtext('Control (no treatment)', line=0.5, cex=1.1)
mtext('Change in heart pumping function', 1, line=1.3, cex = 0.9)
dev.off()


================================================
FILE: ch_inference_for_means/figures/stemCellTherapyForHeartsPValue/stemCellTherapyForHeartsPValue.R
================================================
library(openintro)
data(COL)

myPDF('stemCellTherapyForHeartsPValue.pdf', 3.9, 2.3,
      mar = c(1.75, 1, 1, 1),
      mgp = c(2, 0.6, 0))

normTail(U = 4.03,
         xlim = c(-3, 5.2),
         df = 3,
         lwd = 1.5,
         border = COL[4],
         col = COL[4],
         axes = FALSE)
text(7.5 - 4, 0.23, "Area representing\np-value", col = COL[4])
arrows(7.5 - 4, 0.17, 4.3, 0.02, length = 0.1, col = COL[4])
axis(1, at = seq(-8,12,2)) #, rep("", 11), tcl = -0.2)
dev.off()


================================================
FILE: ch_inference_for_means/figures/tDistAppendixTwoEx/tDistAppendixTwoEx.R
================================================
library(openintro)
data(COL)

myPDF('tDistAppendixTwoEx.pdf', 6.8, 1.9,
      mar = c(1.6, 1, 0.05, 1),
      mgp = c(5, 0.45, 0),
      mfrow = c(1, 2))
normTail(U = 1.65,
         df = 12,
         xlim = c(-4, 4),
         col = COL[1],
         axes = FALSE)
axis(1)
normTail(L = -2,
         U = 2,
         df = 475,
         xlim = c(-4.5, 4.5),
         col = COL[1],
         axes = FALSE)
axis(1)
dev.off()


================================================
FILE: ch_inference_for_means/figures/tDistCompareToNormalDist/tDistCompareToNormalDist.R
================================================
library(openintro)
data(COL)

myPDF('tDistCompareToNormalDist.pdf', 5, 2.3,
      mar = c(2, 1, 1, 1),
      mgp = c(5, 0.6, 0))
plot(c(-5, 5),
     c(0, dnorm(0)),
     type = 'n',
     axes = FALSE)
axis(1, seq(-6, 6, 2))
abline(h = 0)

xleg <- 2
yleg <- 0.35
yleg.line.offset <- -0.07
line.leg.width <- 0.55
lines(
    c(xleg, xleg + line.leg.width),
    rep(yleg, 2),
    col = COL[4], lty = 3, lwd = 2.5)
lines(
    c(xleg, xleg + line.leg.width),
    rep(yleg + yleg.line.offset, 2),
    col = COL[1], lty = 1, lwd = 1.8)
text(xleg + line.leg.width, yleg,
    "Normal",
    col = COL[4], pos = 4)
text(xleg + line.leg.width, yleg + yleg.line.offset,
    "t-distribution",
    col = COL[1], pos = 4)

X <- seq(-6, 6, 0.01)
Y <- dnorm(X)
lines(X, Y, lty = 3, lwd = 2.5, col = COL[4])

Y <- dt(X, 2)
lines(X, Y, lwd = 1.8, col = COL[1])

dev.off()


================================================
FILE: ch_inference_for_means/figures/tDistConvergeToNormalDist/tDistConvergeToNormalDist.R
================================================
library(openintro)
data(COL)

myPDF('tDistConvergeToNormalDist.pdf', 5.94, 2.53,
      mar = c(2, 1, 1, 1),
      mgp = c(5, 0.6, 0))
plot(c(-5, 5),
     c(0, dnorm(0)),
     type = 'n',
     axes = FALSE)
at <- seq(-10, 10, 2)
axis(1, at)
axis(1, at - 1, rep("", length(at)), tcl = -0.1)
abline(h = 0)

COL. <- fadeColor(COL[1], c("FF", "89", "68", "4C", "33"))
COLt <- fadeColor(COL[1], c("FF", "AA", "85", "60", "45"))
DF   <- c('normal', 8, 4, 2, 1)

X <- seq(-10, 10, 0.02)
Y <- dnorm(X)
lines(X, Y, col = COL.[1])

for (i in 2:5) {
  Y <- dt(X, as.numeric(DF[i]))
  lines(X, Y, col = COL.[i], lwd = 1.5)
}

legend(2.5, 0.4,
       legend = c(DF[1],
       paste('t, df = ', DF[2:5], sep = '')),
       col = COL.,
       text.col = COLt,
       lty = rep(1, 5),
       lwd = 1.5)

dev.off()


================================================
FILE: ch_inference_for_means/figures/tDistDF18LeftTail2Point10/tDistDF18LeftTail2Point10.R
================================================
library(openintro)
data(COL)

myPDF('tDistDF18LeftTail2Point10.pdf', 4, 1.8,
      mar = c(1.6, 1, 0.1, 1),
      mgp = c(5, 0.45, 0))
normTail(L = -2.10,
         df = 10,
         xlim = c(-4, 4),
         col = COL[1],
         axes = FALSE)
axis(1)
dev.off()


================================================
FILE: ch_inference_for_means/figures/tDistDF20RightTail1Point65/tDistDF20RightTail1Point65.R
================================================
library(openintro)
data(COL)

myPDF('tDistDF20RightTail1Point65.pdf', 6.8, 1.9,
      mar = c(1.6, 1, 0.05, 1),
      mgp = c(5, 0.45, 0),
      mfrow = c(1, 2))
normTail(U = 1.65,
         df = 12,
         xlim = c(-4, 4),
         col = COL[1],
         axes = FALSE)
axis(1)
normTail(L = -3,
         U = 3,
         df = 2.3,
         xlim = c(-4.5, 4.5),
         col = COL[1],
         axes = FALSE)
axis(1)
dev.off()


================================================
FILE: ch_inference_for_means/figures/textbooksF18/diffInTextbookPricesF18.R
================================================
library(openintro)
data(textbooks)
data(COL)

d <- as.numeric(na.omit(ucla_textbooks_f18$bookstore_new -
    ucla_textbooks_f18$amazon_new))

myPDF('diffInTextbookPricesF18.pdf', 5, 2.5,
      mar = c(3, 3.5, 0.5, 0.5),
      mgp = c(1.8, 0.5, 0))
histPlot(d, axes = FALSE, # breaks = 20,
         xlab = "UCLA Bookstore Price - Amazon Price (USD)",
         ylab = "",
         col = COL[1])
AxisInDollars(1, at = pretty(d), tck = -0.03)
axis(2, at = seq(0, 30, 10), tck = -0.02)
# axis(2, at = seq(0, 15, 5), tck = -0.02)
par(las = 0)
mtext("Frequency", 2, 2.4)
dev.off()


================================================
FILE: ch_inference_for_means/figures/textbooksF18/textbooksF18HTTails.R
================================================
library(openintro)
data(textbooks)
data(COL)
d <- as.numeric(na.omit(ucla_textbooks_f18$bookstore_new -
    ucla_textbooks_f18$amazon_new))
(m <- mean(d))
(s <- sd(d))
(se <- s / sqrt(length(d)))
(z <- m / se)

myPDF('textbooksF18HTTails.pdf', 4, 1.3,
      mar = c(1.7, 0, 0, 0),
      mgp = c(3, 0.5, 0))
normTail(L = -abs(m),
         U = abs(m),
         s = se,
         df = 20,
         # xlim = 5 * c(-1, 1),
         col = COL[1],
         # border = COL[4],
         axes = FALSE)
at <- c(-100, 0, m, 100)
labels <- expression(0, mu[0]*' = 0', bar(x)[diff]*" = 3.58", 0)
axis(1, at, labels, cex.axis = 0.9)
dev.off()


================================================
FILE: ch_inference_for_means/figures/textbooksS10/diffInTextbookPricesS10.R
================================================
library(openintro)
data(textbooks)
data(COL)

d <- textbooks

myPDF('diffInTextbookPricesS10.pdf', 6, 3,
      mar = c(3, 3.2, 0.5, 0.5),
      mgp = c(1.8, 0.5, 0))
histPlot(d$diff, axes = FALSE, # breaks = 20,
         xlim = c(-20, 80),
         xlab = "UCLA price - Amazon price (USD)",
         ylab = "",
         col = COL[1])
mtext("Frequency", 2, 2.1, las = 0)
axis(1, tck = -0.03)
axis(2, at = seq(0, 30, 10), tck = -0.02)
# axis(2, at = seq(0, 15, 5), tck = -0.02)
dev.off()


================================================
FILE: ch_inference_for_means/figures/textbooksS10/textbooksS10HTTails.R
================================================
library(openintro)
data(textbooks)
data(COL)
d <- textbooks

myPDF('textbooksS10HTTails.pdf', 5, 1.6,
      mar = c(1.7, 0, 0, 0),
      mgp = c(3, 0.5, 0))
normTail(L = -6.5,
         U = 6.5,
         df = 20,
         xlim = c(-8, 8),
         col = COL[4],
         border = COL[4],
         axes = FALSE)
at <- c(-10, 0, 6.5, 10)
labels <- expression(0, mu[0]*' = 0', bar(x)[diff]*" = 12.76", 0)
axis(1, at, labels, cex.axis = 0.9)
segments(c(-9, 9), rep(0, 2),
         c(-6.5, 6.5), rep(0, 2),
         col = COL[4, 2], lwd = 4)
arrows(c(-7, 7), rep(0.1, 2),
       c(-7, 7), rep(0.015, 2),
       length = 0.08,
       col = COL[4])
text(c(-7, 7), rep(0.1, 2),
     c("left tail", "right tail"),
     pos = 3,
     col = COL[4])
dev.off()


================================================
FILE: ch_inference_for_means/figures/textbooks_scatter/textbooks_scatter.R
================================================
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

myPDF('textbooks_scatter.pdf', 6, 4,
      mar = c(3.7, 4.1, 0.5, 0.5),
      mgp = c(2.6, 0.55, 0))
plot(d$bookstore_new, d$amazon_new,
     pch = 19,
     col = COL[1, 2],
     cex = 1.2,
     xlab = 'UCLA Bookstore Price',
     ylab = '',
     axes = FALSE)
m <- lm(amazon_new ~ bookstore_new, d)
abline(m)
AxisInDollars(1, seq(0, 300, 50))
AxisInDollars(2, seq(0, 300, 50))
par(las = 0)
mtext("Amazon Bookstore Price", 2, line = 3)
dev.off()


m <- lm(amazon_new ~ bookstore_new, d)
myPDF('textbooks_scatter_residuals.pdf', 6, 4,
      mar = c(3.7, 4.1, 0.5, 0.5),
      mgp = c(2.6, 0.55, 0))
plot(d$bookstore_new, m$residuals,
     pch = 19,
     col = COL[1, 2],
     cex = 1.2,
     xlab = 'UCLA Bookstore Price',
     ylab = '',
     axes = FALSE,
     ylim = range(m$residuals) + c(-10, 20))
AxisInDollars(1, seq(0, 300, 50))
AxisInDollars(2, seq(-100, 100, 20))
par(las = 0)
mtext("Residuals", 2, line = 3)
dev.off()


xtable(m)



================================================
FILE: ch_inference_for_means/figures/toyANOVA/toyANOVA.R
================================================
library(xtable)
library(openintro)

by(toy_anova$outcome, toy_anova$group, mean)


myPDF("toyANOVA.pdf",
      mar = c(1.7, 3.1, 0.5, 0.5),
      mgp = c(2, 0.5, 0))
plot(toy_anova$outcome,
     xlim = c(0.5, 6.5),
     type = "n",
     axes = FALSE,
     xlab = "",
     ylab = "Outcome")
rect(-100, -100,
     100, 100,
     col = COL[7,3])
abline(h = seq(-10, 10, 2), col = "#FFFFFF", lwd = 3)
abline(h = seq(-10, 10, 1), col = "#FFFFFF", lwd = 0.8)
these <- toy_anova$group %in% c("I", "II", "III")
dotPlot(toy_anova$outcome[these], toy_anova$group[these],
        vertical = TRUE,
        at = 1:3,
        add = TRUE,
        col = COL[1, 3],
        cex = 0.9, pch = 19)
dotPlot(toy_anova$outcome[!these], toy_anova$group[!these],
        vertical = TRUE,
        at = 1:3 + 3,
        add = TRUE,
        col = COL[4, 3],
        cex = 0.9,
        pch = 19)
abline(v = 3.5, col = COL[7], lwd = 5.5)
abline(v = 3.5, col = "#AAAAAA", lwd = 3)
abline(v = 3.5, col = "#333333", lwd = 0.8)
axis(2)
par(mgp = c(2, 0.45, 0.1))
axis(1, at = 1:3, c("I", "II", "III"))
axis(1, at = 4:6, c("IV", "V", "VI"))
box()
dev.off()

xtable(anova(lm(outcome ~ group, toy_anova[these, ])))
xtable(anova(lm(outcome ~ group, toy_anova[!these, ])))


================================================
FILE: ch_inference_for_props/TeX/ch_inference_for_props.tex
================================================
\begin{chapterpage}{Inference for categorical data}
  \chaptertitle{Inference for categorical data}
  \label{inferenceForCategoricalData}
  \label{ch_inference_for_props}
  \chaptersection{singleProportion}
  \chaptersection{differenceOfTwoProportions}
  \chaptersection{oneWayChiSquare}
  \chaptersection{twoWayTablesAndChiSquare}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_inference_for_props}

\chapterintro{In this chapter,
  we apply the methods and ideas from
  Chapter~\ref{ch_foundations_for_inf}
  in several contexts for categorical data.
  We'll start by revisiting what we learned for a single
  proportion, where the normal distribution can be used
  to model the uncertainty in the sample proportion.
  Next, we apply these same ideas to analyze the difference
  of two proportions using the normal model.
  Later in the chapter, we apply inference techniques
  to contingency tables;
  while we will use a different
  distribution in this context, the core
  ideas of hypothesis testing remain the same.}


%__________________
\section{Inference for a single proportion}
\label{singleProportion}

We encountered inference methods for a single proportion
in Chapter~\ref{ch_foundations_for_inf},
exploring point estimates, confidence intervals,
and hypothesis tests.
In this section, we'll do a review of these topics
and also how to choose an appropriate sample size
when collecting data for single proportion contexts.


\subsection{Identifying when the sample proportion is nearly normal}

A sample proportion $\hat{p}$ can be modeled using
a normal distribution when the sample observations
are independent and the sample size is sufficiently
large.


%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:
%\begin{eqnarray*}
%\hat{p} = \frac{\ 0 + 1 + 1 + \cdots + 0\ }{1042} = 0.82
%\end{eqnarray*}
%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.

\begin{onebox}{Sampling distribution of
    $\pmb{\hat{\MakeLowercase{p}}}$}
  The sampling distribution for $\hat{p}$ based on
  a sample of size $n$ from a population with a true
  proportion $p$ is nearly normal when:
  \begin{enumerate}
  \setlength{\itemsep}{0mm}
  \item The sample's observations are independent,
      e.g. are from a simple random sample.
  \item We expected to see at least 10 successes and
      10 failures in the sample, i.e. $np\geq10$ and
      $n(1-p)\geq10$.
      This is called the \term{success-failure condition}.
  \end{enumerate}
  When these conditions are met, then the sampling
  distribution of $\hat{p}$ is nearly normal with mean
  $p$ and standard error
  \index{standard error (SE)!single proportion}%
  $SE = \sqrt{\frac{\ p(1-p)\ }{n}}$.
\end{onebox}

Typically we don't know the true proportion $p$,
so we substitute some value to check conditions
and estimate the standard error.
For confidence intervals, the sample proportion
$\hat{p}$ is used to check the success-failure condition
and compute the standard error.
For hypothesis tests, typically the null value --
that is, the proportion claimed in the null hypothesis --
is used in place of $p$.


\subsection{Confidence intervals for a proportion}
\label{confIntForPropSection}

\index{point estimate!single proportion}

A confidence interval provides a range of
plausible values for the parameter $p$,
and when $\hat{p}$ can be modeled using a
normal distribution, the confidence interval
for $p$ takes the form
\begin{align*}
\hat{p} \pm z^{\star} \times SE
\end{align*}

\index{data!Payday regulation poll|(}

\newcommand{\paydayN}{826}
\newcommand{\paydayNHalf}{413}
\newcommand{\paydayRegPerc}{70\%}
\newcommand{\paydayRegProp}{0.70}
\newcommand{\paydayRegSE}{0.016}
\newcommand{\paydayRegSEPerc}{1.6\%}
\newcommand{\paydayRegLower}{0.669}
\newcommand{\paydayRegUpper}{0.731}
\newcommand{\paydayRegLowerPerc}{66.9\%}
\newcommand{\paydayRegUpperPerc}{73.1\%}
% https://www.pewtrusts.org/-/media/assets/2017/04/payday-loan-customers-want-more-protections-methodology.pdf

\begin{examplewrap}
\begin{nexample}{A simple random sample of \paydayN{}
    payday loan borrowers was surveyed to better
    understand their interests around regulation and costs.
    \paydayRegPerc{} of the responses supported new
    regulations on payday lenders.
    Is it reasonable to model $\hat{p} = \paydayRegProp{}$
    using a normal distribution?}
  The data are a random sample, so the observations are
  independent and representative of the population of
  interest.

  We also must check the success-failure condition,
  which we do using $\hat{p}$ in place
  of $p$ when computing a confidence interval:
  \begin{align*}
  \text{Support: }
      n p &
          \approx \paydayN{} \times \paydayRegProp{}
      = 578
  &\text{Not: }
      n (1 - p) &
        \approx \paydayN{} \times (1 - \paydayRegProp{})
      = 248
  \end{align*}
  Since both values are at least 10, we can use the normal
  distribution to model $\hat{p}$.
\end{nexample}
\end{examplewrap}




\begin{exercisewrap}
\begin{nexercise} \label{seOfPropOfPDBorrowersSupportReg}
Estimate the standard error of $\hat{p} = \paydayRegProp{}$.
Because $p$ is unknown and the standard error is for
a confidence interval, use $\hat{p}$ in place of $p$
in the formula.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$SE = \sqrt{\frac{p(1-p)}{n}} \approx
    \sqrt{\frac{\paydayRegProp{} (1 - \paydayRegProp{})}
        {\paydayN{}}} = \paydayRegSE{}$.}

\begin{examplewrap}
\begin{nexample}{Construct a 95\% confidence interval for $p$,
    the proportion of payday borrowers who support increased
    regulation for payday lenders.}
  Using
  the point estimate \paydayRegProp{},
  $z^{\star} = 1.96$ for a 95\% confidence interval,
  and
  the standard error $SE = \paydayRegSE{}$ from
  Guided Practice~\ref{seOfPropOfPDBorrowersSupportReg},
  the confidence interval is
  \begin{eqnarray*}
  \text{point estimate} \ \pm\ z^{\star} \times SE
      \quad\to\quad
      \paydayRegProp{} \ \pm\ 1.96 \times \paydayRegSE{}
      \quad\to\quad
      (\paydayRegLower{}, \paydayRegUpper{})
  \end{eqnarray*}
  We are 95\% confident that the true proportion of
  payday borrowers who supported regulation at the time
  of the poll was between \paydayRegLower{} and
  \paydayRegUpper{}.
\end{nexample}
\end{examplewrap}

\onepropconfintsummary{}
%\begin{onebox}{Constructing a confidence interval for a proportion}
%  There are three steps to constructing a confidence
%  interval for $p$.
%  \begin{itemize}
%  \setlength{\itemsep}{0mm}
%  \item Check independence and the success-failure condition
%      using $\hat{p}$.
%      If the conditions are met, the sampling distribution
%      of $\hat{p}$ may be well-approximated by the normal model.
%  \item Construct the standard error using $\hat{p}$
%      in place of $p$ in the standard error formula.
%  \item Apply the general confidence interval formula.
%  \end{itemize}
%\end{onebox}

\noindent%
For additional one-proportion confidence interval examples,
see Section~\ref{confidenceIntervals}.


\subsection{Hypothesis testing for a proportion}
\label{htForPropSection}

\newcommand{\paydayCCPerc}{51\%}
\newcommand{\paydayCCProp}{0.51}
\newcommand{\paydayCCSE}{0.017}
\newcommand{\paydayCCSEPerc}{1.7\%}
\newcommand{\paydayCCZ}{0.59}
\newcommand{\paydayCCOneTail}{0.2776}
\newcommand{\paydayCCPvalue}{0.5552}

One possible regulation for payday lenders is that they
would be required to do a credit check and evaluate debt
payments against the borrower's finances.
We would like to know: would borrowers support this form
of regulation?


\begin{exercisewrap}
\begin{nexercise}
\label{paydayCC_hypotheses_gp}%
Set up hypotheses to evaluate whether borrowers
have a majority support or majority opposition for this
type of regulation.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$H_0$: $p = 0.50$. $H_A$: $p \neq 0.50$.}

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.

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
\label{paydayCC_conditions_gp}%
Do payday loan borrowers support a regulation
that would
require lenders to pull their credit report
and evaluate their debt payments?
From a random sample of \paydayN{} borrowers,
\paydayCCPerc{} said they would support such
a regulation.
Is it reasonable to model $\hat{p} = \paydayCCProp{}$
using a normal distribution
for a hypothesis test here?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Independence holds since the poll
    is based on a random sample.
    The success-failure condition also holds,
    which is checked
    using the null value ($p_0 = 0.5$) from $H_0$:
    $np_0 = \paydayN{} \times 0.5 = \paydayNHalf$,
    $n(1 - p_0) = \paydayN{} \times 0.5 = \paydayNHalf$.}
    
\begin{examplewrap}
\begin{nexample}{Using the hypotheses and data from
    Guided Practice~\ref{paydayCC_hypotheses_gp}
    and~\ref{paydayCC_conditions_gp},
    evaluate whether the poll provides convincing evidence
    that a majority of payday loan borrowers support
    a new regulation that would
    require lenders to pull credit reports
    and evaluate debt payments.}
  With hypotheses already set up and conditions checked,
  we can move onto calculations.
  The standard error in the context of a one-proportion
  hypothesis test is computed using the null value, $p_0$:
  \begin{align*}
  SE = \sqrt{\frac{p_0 (1 - p_0)}{n}}
      = \sqrt{\frac{0.5 (1 - 0.5)}{\paydayN{}}}
      = \paydayCCSE{}
  \end{align*}
  A picture of the normal model is shown below
  with the p-value represented by the shaded region.
  \begin{center}
  \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}
  \end{center}
  Based on the normal model, the test statistic can be
  computed as the Z-score of the point estimate:
  \begin{align*}
  Z = \frac{\text{point estimate} - \text{null value}}{SE}
      = \frac{\paydayCCProp{} - 0.50}{\paydayCCSE{}}
      = \paydayCCZ{}
  \end{align*}
  The single tail area is \paydayCCOneTail{}, and the p-value,
  represented by both tail areas together, is \paydayCCPvalue{}.
  Because the p-value is larger than 0.05,
  we do not reject $H_0$.
  The poll does not provide convincing evidence that
  a majority of payday loan borrowers support or oppose
  regulations around credit checks and evaluation of
  debt payments.
\end{nexample}
\end{examplewrap}

\oneprophtsummary{}

%\begin{onebox}{Hypothesis test for a proportion}
%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.
%\end{onebox}

\noindent%
For additional one-proportion hypothesis test examples,
see Section~\ref{hypothesisTesting}.

\index{data!Payday regulation poll|)}

\CalculatorVideos{confidence intervals and hypothesis tests for a single proportion}


\D{\newpage}

\subsection{When one or more conditions aren't met}

We've spent a lot of time discussing conditions for when
$\hat{p}$ can be reasonably modeled by a normal distribution.
What happens when the success-failure condition fails?
What about when the independence condition fails?
In either case, the general ideas of confidence intervals
and hypothesis tests remain the same, but the strategy
or technique used to generate the interval or p-value
change.

When the success-failure condition isn't met
for a hypothesis test, we can simulate the null distribution
of $\hat{p}$ using the null value, $p_0$.
The simulation concept is similar to the ideas used
in the malaria case study presented in
Section~\ref{caseStudyMalariaVaccine},
and an online section outlines this strategy:
\begin{center}
\oiRedirect{stat_sim_prop_ht}
    {www.openintro.org/r?go=stat\_sim\_prop\_ht}
\end{center}
For a confidence interval when the success-failure condition
isn't met, we can use what's called
the \term{Clopper-Pearson interval}.
The details are beyond the scope of this book.
However, there are many internet resources covering
this topic.

The independence condition is a more nuanced requirement.
When it isn't met, it is important to understand how and why
it isn't met.
For example, if we took a cluster sample
(see Section~\ref{section_obs_data_sampling}),
suitable statistical methods are available but would
be beyond the scope of even most second or third courses
in statistics.
On the other hand, we'd be stretched to find any method
that we could confidently apply to correct the inherent biases
of data from a convenience sample.

While this book is scoped to well-constrained statistical
problems, do remember that this is just the first
book in what is a large library of statistical methods that
are suitable for a very wide range of data and contexts.


\D{\newpage}

\subsection{Choosing a sample size when estimating a proportion}

\index{margin of error|(}

When collecting data, we choose a sample size suitable
for the purpose of the study.
Often times this means choosing a sample size large
enough that the \term{margin of error} --
which is the part we add and subtract from the point
estimate in a confidence interval --
is sufficiently small that the sample is useful.
For example, our task might be to find a sample size
$n$ so that the sample proportion is within $\pm 0.04$
of the actual proportion in a 95\% confidence interval.

% 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:
%\begin{align*}
%ME = z^{\star} \times SE \leq m
%\end{align*}
%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.

\index{data!Student football stadium|(}

\begin{examplewrap}
\begin{nexample}{A university newspaper is conducting
    a survey to determine what fraction of students
    support a \$200 per year increase in fees to pay
    for a new football stadium.
    How big of a sample is required to ensure the
    margin of error is smaller than 0.04 using a
    95\% confidence level?}
  The margin of error for a sample proportion is
  \begin{align*}
  z^{\star} \sqrt{\frac{p (1 - p)}{n}}
  \end{align*}
  Our goal is to find the smallest sample size $n$
  so that this margin of error is smaller than $0.04$.
  For a 95\% confidence level, the value $z^{\star}$
  corresponds to 1.96:
  \begin{align*}
  1.96\times \sqrt{\frac{p(1-p)}{n}} \ < \ 0.04
  \end{align*}
  There are two unknowns in the equation: $p$ and $n$.
  If we have an estimate of $p$, perhaps from a prior
  survey, we could enter in that value and solve for~$n$.
  If we have no such estimate, we must use some other
  value for~$p$.
  It turns out that the margin of error is largest
  when $p$ is 0.5, so we typically use this
  \emph{worst case value} if no estimate of the
  proportion is available:
  \begin{align*}
	1.96\times \sqrt{\frac{0.5(1-0.5)}{n}} &\ < \ 0.04 \\
	1.96^2\times \frac{0.5(1-0.5)}{n} &\ < \ 0.04^2 \\
	1.96^2\times \frac{0.5(1-0.5)}{0.04^2} &\ < \ n \\
	600.25 &\ < \  n
  \end{align*}
  We would need over 600.25 participants, which means
  we need 601 participants or more, to ensure the
  sample proportion is within 0.04 of the true proportion
  with 95\% confidence.
\end{nexample}
\end{examplewrap}

\index{data!Student football stadium|)}

When an estimate of the proportion is available, we use it in place of the worst case proportion value,~0.5.

\D{\newpage}

\index{data!Tire failure rate|(}

\begin{exercisewrap}
\begin{nexercise}
\label{tire_failure_rate_3_samp_size_calc}%
A manager is about to oversee the mass
production of a new tire model in her factory,
and she would like to estimate what proportion of
these tires will be rejected through quality control.
The quality control team has monitored the last three
tire models produced by the factory,
failing 1.7\% of tires in the first model,
6.2\% of the second model,
and 1.3\% of the third model.
The manager would like to examine enough tires
to estimate the failure rate of the new tire model
to within about 1\% with a 90\% confidence level.
There are three different failure rates to choose from.
Perform the sample size computation for each separately,
and identify three sample sizes to consider.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{For a 90\% confidence interval, $z^{\star} = 1.6449$,
  and since an estimate of the proportion 0.017 is available,
  we'll use it in the margin of error formula:
  \begin{align*}
  1.6449\times \sqrt{\frac{0.017(1-0.017)}{n}} &\ < \ 0.01
    \qquad\to\qquad
      \frac{0.017(1-0.017)}{n} \ < \ 
          \left(\frac{0.01}{1.6449}\right)^2
    \qquad\to\qquad
      452.15 \ < \ n
  \end{align*}
  For sample size calculations, we always round up,
  so the first tire model suggests 453 tires would
  be sufficient.

  A similar computation can be accomplished using 0.062
  and 0.013 for $p$, and you should verify that using these
  proportions results in minimum sample sizes of 1574
  and~348 tires, respectively.}

\begin{examplewrap}
\begin{nexample}{The sample sizes vary widely in
    Guided Practice~\ref{tire_failure_rate_3_samp_size_calc}.
    Which of the three would you suggest using?
    What would influence your choice?}
  We could examine which of the old models is most
  like the new model, then choose the corresponding sample
  size.
  Or if two of the previous estimates are based on small
  samples while the other is based on a larger sample,
  we might consider the value corresponding to the larger
  sample.
  There are also other reasonable approaches.

  Also observe that the success-failure
  condition would need to be checked in the final sample.
  For instance, if we sampled $n = 1584$ tires and found
  a failure rate of 0.5\%, the normal approximation would
  not be reasonable, and we would require more advanced
  statistical methods for creating the confidence interval.
\end{nexample}
\end{examplewrap}

\index{data!Tire failure rate|)}
\index{data!Payday regulation poll|(}

\begin{exercisewrap}
\begin{nexercise}
Suppose we want to continually track the support
of payday borrowers for regulation on lenders,
where we would conduct a new poll every month.
Running such frequent polls is expensive, so we decide
a wider margin of error of 5\% for each individual survey
would be acceptable.
Based on the original sample of borrowers where
\paydayRegPerc{} supported some form of regulation,
how big should our monthly sample be for a margin
of error of 0.05 with 95\% confidence?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{We complete the same computations as before,
   except now we use $\paydayRegProp{}$ instead of $0.5$
   for $p$:
   \begin{align*}
   1.96\times \sqrt{\frac{p(1-p)}{n}}
       \approx 1.96\times
           \sqrt{\frac{\paydayRegProp{}(1-\paydayRegProp{})}
               {n}}
       &\leq 0.05
     \qquad\to\qquad
       n \geq 322.7
  \end{align*}
  A sample size of 323 or more would be reasonable.
  (Reminder: always round up for sample size calculations!)
  Given that we plan to track this poll over time,
  we also may want to periodically repeat these calculations
  to ensure that we're being thoughtful in our sample
  size recommendations in case the baseline rate fluctuates.}

\index{data!Payday regulation poll|)}
\index{margin of error|)}

{\input{ch_inference_for_props/TeX/inference_for_a_single_proportion.tex}}





%__________________
\section{Difference of two proportions}
\label{differenceOfTwoProportions}

We would like to extend the methods from
Section~\ref{singleProportion}
to apply confidence intervals and hypothesis tests
to differences in population proportions:
\mbox{$p_1 - p_2$}.
%We~consider three examples.
%In the first, we compare the utility of a blood thinner
%for heart attack patients.
%In the second application, we examine the efficacy of
%mammograms in reducing deaths from breast cancer.
%In the last example, a quadcopter company weighs whether
%to switch to a higher quality manufacturer of rotor blades.
In our investigations, we'll identify a reasonable
point estimate of $p_1 - p_2$ based on the sample,
and you may have already guessed its form:
$\hat{p}_1 - \hat{p}_2$.
\index{point estimate!difference of proportions}%
Next, we'll apply the same processes we used in
the single-proportion context:
we verify that the point estimate
can be modeled using a normal distribution,
we compute the estimate's standard error, and
we apply our inferential framework.


\subsection{Sampling distribution of the difference
    of two proportions}

Like with $\hat{p}$, the difference of two sample
proportions $\hat{p}_1 - \hat{p}_2$ can be modeled
using a normal distribution when certain conditions
are met.
First, we require a broader independence condition,
and secondly,
the success-failure condition must be met by both groups.

\begin{onebox}{Conditions for the
    sampling distribution of
    $\pmb{\hat{\MakeLowercase{p}}_1 -
        \hat{\MakeLowercase{p}}_2}$
    to be normal}
  The difference $\hat{p}_1 - \hat{p}_2$ can be modeled
  using a normal distribution when
  \begin{itemize}
  \setlength{\itemsep}{0mm}
  \item \emph{Independence, extended.}
    The data are independent within and between
    the two groups.
    Generally this is satisfied if the data come
    from two independent random samples
    or if the data come from a randomized experiment.
  \item \emph{Success-failure condition.}
    The success-failure condition holds for both
    groups, where we check successes and failures
    in each group separately.
  \end{itemize}
  When these conditions are satisfied,
  the standard error of $\hat{p}_1 - \hat{p}_2$ is
  \index{standard error (SE)!difference in proportions}
  \begin{eqnarray*}
  SE %_{\hat{p}_1 - \hat{p}_2}
    %= \sqrt{SE_{\hat{p}_1}^2 + SE_{\hat{p}_2}^2}
    = \sqrt{\frac{p_1(1-p_1)}{n_1} + \frac{p_2(1-p_2)}{n_2}}
  \label{seForDiffOfProp}
  \end{eqnarray*}
  where $p_1$ and $p_2$ represent the population proportions,
  and $n_1$ and $n_2$ represent the sample~sizes.
\end{onebox}

%\noindent%
%Ultimately, we can check the two conditions by
%thinking of it as a broader independence check
%along with a check on the success-failure condition
%for each group:
%\begin{description}
%\item[Independence, extended.]
%    The data are independent within and between
%    the two groups.
%    Generally this is satisfied if the data come
%    from two independent random samples
%    or if the data come from a randomized experiment.
%\item[Success-failure condition.]
%    The success-failure condition holds for both
%    groups, where we check successes and failures
%    in each group separately.
%\end{description}

%For the difference in two means, the standard error formula took the following form:
%\begin{eqnarray*}
%SE_{\bar{x}_{1} - \bar{x}_{2}} = \sqrt{SE_{\bar{x}_1}^2 + SE_{\bar{x}_2}^2}
%\end{eqnarray*}
%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}.


%\D{\newpage}
\subsection[Confidence intervals for $p_1 - p_2$]
    {Confidence intervals for $\pmb{p_1 - p_2}$}

\index{data!CPR and blood thinner|(}

%In the setting of confidence intervals for a difference
%of two proportions, the two sample proportions are used
%to verify the success-failure condition and also compute
%the standard error, just as was the case with a single
%proportion.
\noindent%
We can apply the generic confidence interval formula
for a difference of two proportions,
where we use $\hat{p}_1 - \hat{p}_2$ as the point
estimate and substitute the $SE$ formula:
\begin{align*}
&\text{point estimate} \ \pm\  z^{\star} \times SE
&&\to
&&\hat{p}_1 - \hat{p}_2 \ \pm\ 
    z^{\star} \times
   \sqrt{\frac{p_1(1-p_1)}{n_1} + \frac{p_2(1-p_2)}{n_2}}
\end{align*}
We can also follow the same
Prepare, Check, Calculate, Conclude steps for
computing a confidence interval
or completing a hypothesis test.
The details change a little,
but the general approach remain the same.
Think about these steps when you apply statistical methods.

\begin{examplewrap}
\begin{nexample}{We consider an experiment for patients
    who underwent cardiopulmonary resuscitation (CPR)
    for a heart attack and were
    subsequently admitted to a
    hospital.
    These patients were randomly divided into a treatment
    group where they received a blood thinner or the control
    group where they did not receive a blood thinner.
    The outcome variable of interest was whether the
    patients survived for at least 24 hours.
    The results are shown in
    Figure~\ref{resultsForCPRStudyInSmallSampleSection}.
    Check whether we can model the difference in
    sample proportions using the normal distribution.}

  We first check for independence:
  since this is a randomized experiment,
  this condition is satisfied.
  
  Next, we check the success-failure condition for
  each group.
  We have at least 10 successes and 10 failures in
  each experiment arm (11, 14, 39, 26),
  so this condition is also satisfied.

  With both conditions satisfied,
  the difference in sample proportions can be
  reasonably modeled using a normal distribution
  for these data.
\end{nexample}
\end{examplewrap}

\begin{figure}[ht]
\centering
\begin{tabular}{lccccc}
\hline
			&& Survived 	& Died 	&& Total \\
\hline
Control		&& 11		& 39		&& 50 \\
Treatment		&& 14		& 26		&& 40 \\
\hline
Total			&& 25		& 65		&& 90 \\
\hline
\end{tabular}
\caption{Results for the CPR study.
    Patients in the treatment group were given
    a blood thinner, and patients in the control
    group were not.}
\label{resultsForCPRStudyInSmallSampleSection}
\end{figure}

\begin{examplewrap}
\begin{nexample}{
    Create and interpret a 90\% confidence interval of the
    difference for the survival rates in the CPR study.}

  We'll use $p_t$ for the survival
  rate in the treatment group and $p_c$ for the control
  group:
  \begin{align*}
  \hat{p}_{t} - \hat{p}_{c}
    = \frac{14}{40} - \frac{11}{50}
    = 0.35 - 0.22
    = 0.13
  \end{align*}
  We use the standard error formula provided on
  page~\pageref{seForDiffOfProp}.
  As with the one-sample proportion case,
  we use the sample estimates of each proportion
  in the formula in the confidence interval context:
  \begin{align*}
  SE \approx \sqrt{\frac{0.35 (1 - 0.35)}{40} +
      \frac{0.22 (1 - 0.22)}{50}}
    = 0.095
  \end{align*}
  For a 90\% confidence interval, we use $z^{\star} = 1.6449$:
  \begin{align*}
  \text{point estimate} \ \pm\ z^{\star} \times SE
    \quad \to \quad 0.13 \ \pm\ 1.6449 \times  0.095
    \quad \to \quad (-0.026, 0.286)
  \end{align*}
  We are 90\% confident that blood thinners have
  a difference of -2.6\% to +28.6\% percentage point
  impact on survival rate for patients who are like
  those in the study.
  Because 0\% is contained in the interval,
  we do not have enough information to say
  whether blood thinners help or harm
  heart attack patients who have been admitted after
  they have undergone CPR.
\end{nexample}
\end{examplewrap}

\index{data!CPR and blood thinner|)}

%\begin{onebox}{Confidence interval for a difference
%    of two proportions}
%  Once you've determined a confidence interval for the
%  difference of two proportions would be helpful for an
%  application, there are four steps to constructing the interval:
%  \begin{description}
%  \item[Prepare.]
%      Identify the sample proportions and sample sizes
%      for each of the two groups,
%      determine what confidence level you wish to use.
%  \item[Check.]
%      Verify the conditions to ensure each sample
%      proportion is nearly normal.
%      The success-failure condition should be checked
%      for each group.
%  \item[Calculate.]
%      If the conditions hold, compute $SE$,
%      find $z^{\star}$, and construct the interval.
%  \item[Conclude.]
%      Interpret the confidence interval in the context
%      of the problem.
%  \end{description}
%\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
A 5-year experiment
was conducted to evaluate the effectiveness
of fish oils on reducing cardiovascular events,
where each subject was randomized into one of two
treatment groups.
We'll consider heart attack outcomes in these patients:
\begin{center}
\begin{tabular}{l ccc}
  \hline
  & heart attack &
      no event & Total \\
  \hline
  fish oil & 145 & 12788 & 12933 \\
  placebo & 200 & 12738 & 12938 \\
  \hline
\end{tabular}
\end{center}
% library(openintro); library(xtable); xtable(fish_oil_18[[3]], digits = 0)
Create a 95\% confidence interval for the effect of fish oils
on heart attacks for patients who are well-represented by
those in the study.
Also interpret the interval in the context of the
study.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{
  Because the patients were randomized,
  the subjects are independent, both within and between
  the two groups.
  The success-failure condition is also met for both
  groups as all counts are at least~10.
  This satisfies the conditions necessary to model
  the difference in proportions using a normal distribution.

  Compute the sample proportions
  ($\hat{p}_{\text{fish oil}} = 0.0112$,
    $\hat{p}_{\text{placebo}} = 0.0155$),
  point estimate of the difference ($0.0112 - 0.0155 = -0.0043$),
  and standard error
  ($SE = \sqrt{\frac{0.0112 \times 0.9888}{12933} +
      \frac{0.0155 \times 0.9845}{12938}}
    = 0.00145$).
  Next, plug the values into the general formula for
  a confidence interval, where we'll use a 95\%
  confidence level with $z^{\star} = 1.96$:
  \begin{align*}
  -0.0043 \pm 1.96 \times 0.00145
      \quad \to \quad
      (-0.0071, -0.0015)
  \end{align*}
  We are 95\% confident that fish oils decreases
  heart attacks by
  0.15 to 0.71 percentage points
  (off of a baseline of about 1.55\%)
  over a 5-year period for subjects who are similar
  to those in the study.
  Because the interval is entirely below~0,
  the data provide strong evidence
  that fish oil supplements reduce heart attacks
  in patients like those in the~study.}


\subsection%[Hypothesis tests for $p_1 - p_2$]
    {Hypothesis tests for the difference of two proportions}

\index{data!mammography|(}
\index{data!breast cancer|(}

%We'll explore an experiment evaluating the benefits
%of mammograms using a hypothesis test.
A mammogram is an X-ray procedure used to check for
breast cancer.
Whether mammograms should be used is part of a
controversial discussion, and it's the topic of our
next example where we learn about 2-proportion
hypothesis tests when $H_0$~is~$p_1 - p_2 = 0$
(or equivalently, $p_1 = p_2$).

A 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}.

If 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.

\begin{figure}[h]
\centering
\begin{tabular}{rrcc}
	& \multicolumn{3}{c}{Death from breast cancer?} \\
  \cline{2-4}
 & \ \hspace{3mm}\ & Yes & No \\
  \hline
Mammogram && 500 & 44,425 \\
Control && 505 & 44,405 \\
   \hline
\end{tabular}
\caption{Summary results for breast cancer study.}
\label{mammogramStudySummaryTable}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
Is this study an experiment or an observational study?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{This is an experiment. Patients were randomized
    to receive mammograms or a standard breast cancer exam.
    We will be able to make causal conclusions based on this study.}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise} \label{htFormammogramStudySummaryTable}
Set up hypotheses to test whether there was a difference
in breast cancer deaths in the mammogram and control groups.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$H_0$: the breast cancer death rate for patients
    screened using mammograms is the same as the breast cancer
    death rate for patients in the control,
    $p_{mgm} - p_{ctrl} = 0$. \\
    $H_A$: the breast cancer death rate for patients screened
    using mammograms is different than the breast cancer death
    rate for patients in the control,
    $p_{mgm} - p_{ctrl} \neq 0$.}

In Example~\ref{condFormammogramStudySummaryTableNormalInference},
we will check the conditions for using a normal distribution to
analyze the results of the study.
The details are very similar to that of confidence intervals.
However, when the null hypothesis is that $p_1 - p_2 = 0$,
we use a special proportion called the
\term{pooled proportion} to check the success-failure condition:
\begin{align*}
\hat{p}_{\textit{pooled}}
    &= \frac
        {\text{\# of patients who died from breast cancer in the
            entire study}}
        {\text{\# of patients in the entire study}} \\
	&= \frac{500 + 505}{500 + \text{44,425} + 505 + \text{44,405}} \\
	&= 0.0112
\end{align*}
This proportion is an estimate of the breast cancer death rate
across the entire study, and it's our best estimate of the
proportions $p_{mgm}$ and $p_{ctrl}$
\emph{if the null hypothesis is true that $p_{mgm} = p_{ctrl}$}.
We~will also use this pooled proportion when computing
the standard error.

\begin{examplewrap}
\begin{nexample}{Is it reasonable to model the difference
    in proportions using a normal distribution in this
    study?}
  \label{condFormammogramStudySummaryTableNormalInference}%
  Because the patients are randomized, they can be treated
  as independent, both within and between groups.
  We also must check the success-failure condition for each group.
  Under the null hypothesis, the proportions $p_{mgm}$
  and $p_{ctrl}$ are equal, so we check the success-failure
  condition with our best estimate of these values under $H_0$,
  the \hiddenterm{pooled proportion} from the two samples,
  $\hat{p}_{\textit{pooled}} = 0.0112$:
  \begin{align*}
  \hat{p}_{\textit{pooled}} \times n_{mgm}
      &= 0.0112 \times \text{44,925} = 503
    & (1 - \hat{p}_{\textit{pooled}}) \times n_{mgm}
      &= 0.9888 \times \text{44,925} = \text{44,422} \\
  \hat{p}_{\textit{pooled}} \times n_{ctrl}
      &= 0.0112 \times \text{44,910} = 503
    & (1 - \hat{p}_{\textit{pooled}}) \times n_{ctrl}
      &= 0.9888 \times \text{44,910} = \text{44,407}
  \end{align*}
  The success-failure condition is satisfied since
  all values are at least 10.
  With both conditions satisfied, we can safely model
  the difference in proportions using a normal
  distribution.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Use the pooled proportion when
    $\pmb{H_0}$ is $\pmb{\MakeLowercase{p_1 - p_2 = 0}}$}
  When the null hypothesis is that the proportions are equal,
  use the pooled proportion ($\hat{p}_{\textit{pooled}}$)
  to verify the
  success-failure condition and estimate the standard error:
  \begin{eqnarray*}
  \hat{p}_{\textit{pooled}}
    = \frac{\text{number of ``successes''}}
      {\text{number of cases}}
    = \frac{\hat{p}_1 n_1 + \hat{p}_2 n_2}{n_1 + n_2}
  \end{eqnarray*}
  Here $\hat{p}_1 n_1$ represents the number of successes in
  sample 1 since
  \begin{eqnarray*}
  \hat{p}_1
    = \frac{\text{number of successes in sample 1}}{n_1}
  \end{eqnarray*}
  Similarly, $\hat{p}_2 n_2$ represents the number
  of successes in sample~2.
\end{onebox}

In Example~\ref{condFormammogramStudySummaryTableNormalInference},
the pooled proportion was used to check the success-failure
condition.\footnote{For an example of a two-proportion
  hypothesis test that does not require the
  success-failure condition to be met, see
  Section~\ref{caseStudyMalariaVaccine}.}
In the next example, we see the second place where the pooled
proportion comes into play: the standard error calculation.

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{Compute the point estimate of the difference
    in breast cancer death rates in the two groups,
    and use the pooled proportion
    $\hat{p}_{\textit{pooled}} = 0.0112$ to calculate
    the standard error.}
  The point estimate of the difference in breast cancer death
  rates is
  \begin{align*}
  \hat{p}_{mgm} - \hat{p}_{ctrl}
    &= \frac{500}{500 + 44,425} - \frac{505}{505 + 44,405} \\
    &= 0.01113 - 0.01125 \\
    &= -0.00012
  \end{align*}
  The breast cancer death rate in the mammogram group
  was 0.012\% less than in the control group.
  Next, the standard error is calculated
  \emph{using the pooled proportion},~$\hat{p}_{\textit{pooled}}$:
\begin{align*}
SE = \sqrt{
      \frac{\hat{p}_{\textit{pooled}}(1-\hat{p}_{\textit{pooled}})}
          {n_{mgm}}
      + \frac{\hat{p}_{\textit{pooled}}(1-\hat{p}_{\textit{pooled}})}
          {n_{ctrl}}
    }
	= 0.00070
\end{align*}
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\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.}
Just like in past tests, we first compute a test statistic and draw a picture:
\begin{align*}
Z = \frac{\text{point estimate} - \text{null value}}{SE}
	= \frac{-0.00012 - 0}{0.00070}
	= -0.17
\end{align*}
\begin{center}
\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}
\end{center}
The 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.
\end{nexample}
\end{examplewrap}

Can we conclude that mammograms have no benefits or harm?
Here are a few considerations to keep in mind when reviewing
the mammogram study as well as any other medical study:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item
    We do not reject the null hypothesis, which means
    we don't have sufficient evidence to conclude that
    mammograms reduce or increase breast cancer deaths.
\item
    If mammograms are helpful or harmful, the data
    suggest the effect isn't very large.
\item
    Are mammograms more or less expensive than
    a non-mammogram breast exam?
    If~one option is much more expensive than the
    other and doesn't offer clear benefits,
    then we should lean towards the less expensive
    option.
\item
    The study's authors also found that mammograms
    led to overdiagnosis of breast cancer,
    which means some breast cancers were found
    (or thought to be found) but that these cancers
    would not cause symptoms during patients' lifetimes.
    That is, something else would kill the patient
    before breast cancer symptoms appeared.
    This means some patients may have been treated
    for breast cancer unnecessarily, and this
    treatment is another cost to consider.
    It is also important to recognize that
    overdiagnosis can cause unnecessary physical
    or emotional harm to patients.
\end{itemize}
These 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.

\index{data!breast cancer|)}
\index{data!mammography|)}

%\begin{onebox}{Hypothesis testing when $\mathbf{H_0}$ is
%    $\mathbf{p_1 - p_2 = 0}$}
%  Once you've determined a hypothesis test for the difference
%  of two proportions is the correct procedure, there are four
%  steps to completing the test:
%  \begin{description}
%  \item[Prepare.]
%      Identify the parameter of interest,
%      list out hypotheses,
%      identify the significance level,
%      and compute summary statistics for each group.
%  \item[Check.]
%      Verify the conditions to ensure
%      $\hat{p}_1 - \hat{p}_2$ is nearly normal under $H_0$.
%      When the null hypothesis is that the difference is~0,
%      use a pooled proportion to check the success-failure
%      condition for each group.
%  \item[Calculate.]
%      If the conditions hold, compute the standard
%      error, again using the pooled proportion,
%      compute the Z-score, and identify the p-value.
%  \item[Conclude.]
%      Evaluate the hypothesis test by comparing the p-value
%      to $\alpha$, and provide a conclusion in the context
%      of the problem.
%  \end{description}
%\end{onebox}



\D{\newpage}

\subsection{More on 2-proportion hypothesis tests (special topic)}

When we conduct a 2-proportion hypothesis test,
usually $H_0$ is $p_1 - p_2 = 0$. However, there are rare
situations where we want to check for some difference in
$p_1$ and $p_2$ that is some value other than 0.
For example, maybe we care about checking a null hypothesis
where $p_1 - p_2 = 0.1$. %\footnote{We can
%  also encounter a similar situation with a difference of
%  two means, though no such example is given in
%  Chapter~\ref{inferenceForNumericalData} since the methods
%  remain exactly the same in the context of sample means.
%  On the other hand, the success-failure condition and the
%  calculation of the standard error vary slightly in different
%  proportion contexts.}
In contexts like these, we generally use $\hat{p}_1$ and
$\hat{p}_2$ to check the success-failure condition and
construct the standard error.

\begin{exercisewrap}
\begin{nexercise}
\label{carWheelBladeManufacturer}%
A quadcopter company is considering a new manufacturer
for rotor blades.
The new manufacturer would be more expensive,
but they claim
their higher-quality blades are more reliable,
with 3\% more blades passing inspection than their
competitor.
Set up appropriate hypotheses for the test.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$H_0$: The higher-quality blades will pass
  inspection 3\% more frequently than the standard-quality blades.
  $p_{highQ} - p_{standard} = 0.03$.
  $H_A$: The higher-quality blades will pass inspection
  some amount different than 3\% more often than the
  standard-quality blades.
  $p_{highQ} - p_{standard} \neq 0.03$.}

\captionsetup{width=85mm}

\begin{figure}[h]
\centering
\Figures[A photo of a Phantom quadcopter drone.]{0.6}{quadcopter}{quadcopter_david_j}
\caption{A Phantom quadcopter.\vspace{-1mm} \\
   -----------------------------\vspace{-2mm}\\
   {\footnotesize Photo by David J
   (\oiRedirect{textbook-quadcopter_david_j}
       {http://flic.kr/p/oiWLNu}).
   \oiRedirect{textbook-CC_BY_2}{CC-BY 2.0 license.}
   This photo has been cropped and a border has been added.}}
\label{quadcopter_david_j}
\end{figure}

\captionsetup{width=\mycaptionwidth}

\D{\newpage}

%\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.}

\begin{examplewrap}
\begin{nexample}{The quality control engineer from
    Guided Practice~\ref{carWheelBladeManufacturer}
    collects a sample of blades, examining 1000 blades
    from each company, and she finds that 899 blades pass
    inspection from the current supplier and 958 pass
    inspection from the prospective supplier.
    Using these data, evaluate the hypotheses from
    Guided Practice~\ref{carWheelBladeManufacturer}
    with a significance level of 5\%.}
  \label{qualityCtrlEngHypothesisEval}%
  First, we check the conditions.
  The sample is not necessarily random, so to proceed
  we must assume the blades are all independent;
  for this sample we will suppose this assumption
  is reasonable, but the engineer would be more knowledgeable
  as to whether this assumption is appropriate.
  The success-failure condition also holds for each sample.
  Thus, the difference in sample proportions,
  $0.958 - 0.899 = 0.059$, can be said to come from a nearly
  normal distribution.

  The standard error is computed using the two sample
  proportions since we do not use a pooled proportion
  for this context:
  \begin{align*}
  SE
    = \sqrt{\frac{0.958(1-0.958)}{1000} +
        \frac{0.899(1-0.899)}{1000}}
    = 0.0114
  \end{align*}
  In this hypothesis test, because the null is that
  $p_1 - p_2 = 0.03$, the sample proportions were used
  for the standard error calculation rather than a pooled
  proportion.

  Next, we compute the test statistic and use it to find the
  p-value, which is depicted in
  Figure~\ref{bladesTwoSampleHTPValueQC}.
  \begin{align*}
  Z = \frac{\text{point estimate} - \text{null value}}{SE}
    = \frac{0.059 - 0.03}{0.0114} = 2.54
  \end{align*}
  Using a standard normal distribution for this test statistic,
  we identify the right tail area as 0.006,
  and we double it to get the p-value: 0.012.
  We reject the null hypothesis because 0.012 is less than 0.05.
  Since we observed a larger-than-3\% increase in blades
  that pass inspection, we have statistically significant
  evidence that the higher-quality blades pass inspection
  \emph{more than} 3\% as often as the currently used blades,
  exceeding the company's claims.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
  \caption{Distribution of the test statistic if the null
      hypothesis was true.
      The p-value is represented by the shaded areas.}
  \label{bladesTwoSampleHTPValueQC}
\end{figure}


\D{\newpage}

\subsection{Examining the standard error formula
    (special topic)}

This subsection covers more theoretical topics
that offer deeper insights into the origins of the
standard error formula for the difference of two
proportions.
Ultimately, all of the standard error formulas
we encounter in this chapter and in
Chapter~\ref{ch_inference_for_means}
can be derived from the probability principles of
Section~\ref{randomVariablesSection}.

The formula for the standard error of the difference
in two proportions can be deconstructed into the formulas
for the standard errors of the individual sample proportions.
Recall that the standard error of the individual
sample proportions $\hat{p}_1$ and $\hat{p}_2$ are
\begin{align*}
&SE_{\hat{p}_1} = \sqrt{\frac{{p}_1 (1 - {p}_1)}{n_1}}
&&SE_{\hat{p}_2} = \sqrt{\frac{{p}_2 (1 - {p}_2)}{n_2}}
\end{align*}
The standard error of the difference of two sample proportions
can be deconstructed from the standard errors of the separate
sample proportions:
\begin{align*}
SE_{\hat{p}_{1} - \hat{p}_{2}}
	= \sqrt{SE_{\hat{p}_1}^2 + SE_{\hat{p}_2}^2}
	= \sqrt{\frac{{p}_1 (1 - {p}_1)}{n_1}
	    + \frac{{p}_2 (1 - {p}_2)}{n_2}}
\end{align*}
This special relationship follows from probability theory.

\begin{exercisewrap}
\begin{nexercise}
\label{derivingSEForDiffOfTwoMeansExercise}%
Prerequisite: Section~\ref{randomVariablesSection}.
We can rewrite the equation above in a different way:
\begin{align*}
SE_{\hat{p}_{1} - \hat{p}_{2}}^2
  = SE_{\hat{p}_1}^2 + SE_{\hat{p}_2}^2
\end{align*}
Explain where this formula comes from using
the formula for the variability of the sum of
two random variables.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\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 $\hat{p}_1 - \hat{p}_2$ 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 $SE_{\hat{p}_1}^2 + SE_{\hat{p}_2}^2$.}



%%__________________
%\section{Determining a sample size for an experiment}
%\label{SampleSizeFor2Proportions}
%
%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:
%\begin{itemize}
%\setlength{\itemsep}{0mm}
%\item We want to collect enough data that we can detect important effects.
%\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.
%\end{itemize}
%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.
%
%\begin{examplewrap}
%\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 }
%\end{nexample}
%\end{examplewrap}
%
%
%, 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.


{\input{ch_inference_for_props/TeX/difference_of_two_proportions.tex}}






%__________________
\section{Testing for goodness of fit using chi-square}
\label{oneWayChiSquare}

In this section, we develop a method for assessing a null model when the data are binned.
This technique is commonly used in two circumstances:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item Given a sample of cases that can be classified into several groups, determine if the sample is representative of the general population.
\item Evaluate whether data resemble a particular distribution, such as a normal distribution or a geometric distribution.
\end{itemize}
Each of these scenarios can be addressed using the same statistical test: a chi-square test.

\index{data!racial make-up of jury|(}

In 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.

\begin{figure}[h]
\centering
\begin{tabular}{ll ccc c ll}
\hline
Race	 & \hspace{2mm} & White & Black & Hispanic & Other & \hspace{2mm} & Total \\
\hline
Representation in juries &	& 205 & 26 & 25 & 19 & & 275 \\
Registered voters	 & 		& 0.72 & 0.07 & 0.12 & 0.09 & & 1.00 \\
\hline
\end{tabular}
\caption{Representation by race in a city's juries and population.}
\label{juryRepresentationAndCityRepresentationForRace}
\end{figure}

While 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.

A 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.

In 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.


\subsection{Creating a test statistic for one-way tables}

\begin{examplewrap}
\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?}
About 72\% of the population is White, so we would expect about 72\% of the jurors to be White: $0.72\times 275 = 198$.

Similarly, we would expect about 7\% of the jurors to be Black, which would correspond to about $0.07\times 275 = 19.25$ Black jurors.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Twelve 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}.
\end{nexercise}
\end{exercisewrap}

\begin{figure}[h]
\centering
\begin{tabular}{ll ccc c ll}
\hline
Race	 & \hspace{2mm} & White & Black & Hispanic & Other & \hspace{2mm} & Total \\
\hline
Observed data			&	& 205 & 26	& 25 & 19	&	& 275 \\
Expected counts	 &	& 198 & 19.25 & 33 & 24.75 & & 275 \\
\hline
\end{tabular}
\caption{Actual and expected make-up of the jurors.}
\label{expectedJuryRepresentationIfNoBias}
\end{figure}

The 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:
\begin{itemize}
\setlength{\itemsep}{0mm}
\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.
\item[$H_A$:] The jurors are not randomly sampled, i.e. there is racial bias in juror selection.
\end{itemize}
To 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.


\subsection{The chi-square test statistic}
\label{chiSquareTestStatistic}

In previous hypothesis tests, we constructed a test statistic of the following form:
\begin{align*}
\frac{\text{point estimate} - \text{null value}}
    {\text{SE of point estimate}}
\end{align*}
This 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.

Our 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:
\begin{align*}
Z_{1} = \frac{\text{observed White count} - \text{null White count}}
				{\text{SE of observed White count}}
\end{align*}
The 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:
\begin{align*}
Z_1 = \frac{205 - 198}{\sqrt{198}} = 0.50
\end{align*}
The 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:
\begin{align*}
&Black && Hispanic	&&Other \\
& Z_2 = \frac{26-19.25}{\sqrt{19.25}}=1.54\ \ \ \ 
	&& Z_3 = \frac{25-33}{\sqrt{33}}=-1.39\ \ \ \ 
	&& Z_4 = \frac{19-24.75}{\sqrt{24.75}}=-1.16 \\
\end{align*}
We 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:
\begin{align*}
|Z_1| + |Z_2| + |Z_3| + |Z_4| = 4.58
\end{align*}
Indeed, 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:
\begin{align*}
Z_1^2 + Z_2^2 + Z_3^2 + Z_4^2 = 5.89
\end{align*}
Squaring each standardized difference before adding them together does two things:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item Any standardized difference that is squared will now be positive.
\item Differences that already look unusual -- e.g. a standardized difference of 2.5 -- will become much larger after being squared.
\end{itemize}
The 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:
\index{data!racial make-up of jury|)}
\begin{align*}
X^2 &=
	\frac
	{\text{\footnotesize$(\text{observed count}_1 - \text{null count}_1)^2$}}
	{\text{\footnotesize$\text{null count}_1$}}
	+ \dots + \frac
	{\text{\footnotesize$(\text{observed count}_4 - \text{null count}_4)^2$}}
	{\text{\footnotesize$\text{null count}_4$}}
\end{align*}
The 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.


\subsection{The chi-square distribution and finding areas}

The \term{chi-square distribution} is sometimes used to
characterize data sets and statistics that are always positive
and typically right skewed. Recall a normal distribution had
two parameters -- mean and standard deviation -- that could be
used to describe its exact characteristics.
The chi-square distribution has just one parameter called
\termsub{degrees of freedom (df)}{degrees of freedom (df)!chi-square},
which influences the shape, center, and spread of the distribution.

\begin{exercisewrap}
\begin{nexercise}\label{exerChiSquareDistributionDescriptionWithMoreDOF}%
Figure~\ref{chiSquareDistributionWithInceasingDF} shows three chi-square distributions. \\
(a) How does the center of the distribution change when the degrees of freedom is larger? \\
(b) What about the variability (spread)? \\
(c) How does the shape change?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{figure}[h]
\centering
\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}
%\includegraphics[width=0.8\textwidth]{ch_inference_for_props/figures/chiSquareDistributionWithInceasingDF/chiSquareDistributionWithInceasingDF}
\caption{Three chi-square distributions with varying degrees of freedom.}
\label{chiSquareDistributionWithInceasingDF}
\end{figure}

\D{\newpage}

Figure~\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.

Our principal interest in the chi-square distribution
is the calculation of p-values, which (as we have seen before)
is related to finding the relevant area in the tail of
a distribution.
The most common ways to do this are using computer software,
using a graphing calculator, or using a table.
For folks wanting to use the table option,
we provide an outline of how to read the chi-square table in
Appendix~\ref{chiSquareProbabilityTable},
which is also where you may find the table.
%\Comment{If giving some \R{} in the text, then put \R{} code
%  in the examples / exercises below.}
For the examples below, use your preferred approach
to confirm you get the same answers.

\begin{examplewrap}
\begin{nexample}{Figure~\ref{chiSquareAreaAbove6Point25WithDF3}
    shows a chi-square distribution with 3 degrees of freedom
    and an upper shaded tail starting at 6.25.
    Find the shaded area.}
  Using statistical software or a graphing calculator,
  we can find that the upper tail area for a chi-square
  distribution with 3 degrees of freedom ($df$)
  and a cutoff of 6.25 is 0.1001.
  That is, the shaded upper tail of
  Figure~\ref{chiSquareAreaAbove6Point25WithDF3}
  has area 0.1.
\end{nexample}
\end{examplewrap}

\begin{figure}
\centering
\subfigure[]{
\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}
\label{chiSquareAreaAbove6Point25WithDF3}
}
\subfigure[]{
\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}
\label{chiSquareAreaAbove4Point3WithDF2}
}
\subfigure[]{
\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}
\label{chiSquareAreaAbove5Point1WithDF5}
}
\subfigure[]{
\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}
\label{chiSquareAreaAbove11Point7WithDF7}
}
\subfigure[]{
\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}
\label{chiSquareAreaAbove10WithDF4}
}
\subfigure[]{
\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}
\label{chiSquareAreaAbove9Point21WithDF3}
}
\caption{
\textbf{\subref{chiSquareAreaAbove6Point25WithDF3}}~Chi-square distribution with 3~degrees of freedom, area above 6.25 shaded.
\textbf{\subref{chiSquareAreaAbove4Point3WithDF2}}~2~degrees of freedom, area above 4.3 shaded.
\textbf{\subref{chiSquareAreaAbove5Point1WithDF5}}~5~degrees of freedom, area above 5.1 shaded.
\textbf{\subref{chiSquareAreaAbove11Point7WithDF7}}~7~degrees of freedom, area above 11.7 shaded.
\textbf{\subref{chiSquareAreaAbove10WithDF4}}~4~degrees of freedom, area above 10 shaded.
\textbf{\subref{chiSquareAreaAbove9Point21WithDF3}}~3~degrees of freedom, area above 9.21 shaded.
}
\label{arrayOfFigureAreasForChiSquareDistribution}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Figure~\ref{chiSquareAreaAbove4Point3WithDF2}
    shows the upper tail of a chi-square distribution with
    2~degrees of freedom.
    The bound for this upper tail is at 4.3.
    Find the tail area.}
  Using software, we can find that the tail area shaded in
  Figure~\ref{chiSquareAreaAbove4Point3WithDF2}
  to be 0.1165.
  If using a table, we would only be able to find
  a range of values for the tail area:
  between 0.1 and 0.2.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Figure~\ref{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.}
  Using software, we would obtain a tail area of 0.4038.
  If using the table in Appendix~\ref{chiSquareProbabilityTable},
  we would have identified that the tail area is larger than 0.3
  but not be able to give the precise value.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Figure~\ref{chiSquareAreaAbove11Point7WithDF7} shows a cutoff
of 11.7 on a chi-square distribution with 7 degrees of freedom.
Find the area of the upper tail.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{
  The area is 0.1109.
  If using a table, we would identify that it falls
  between 0.1 and 0.2.}

\begin{exercisewrap}
\begin{nexercise}
Figure~\ref{chiSquareAreaAbove10WithDF4} shows a cutoff
of 10 on a chi-square distribution with 4 degrees of freedom.
Find the area of the upper tail.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Precise value: 0.0404.
  If using the table: between 0.02 and 0.05.}

\begin{exercisewrap}
\begin{nexercise}
Figure~\ref{chiSquareAreaAbove9Point21WithDF3} shows a cutoff
of 9.21 with a chi-square distribution with 3 df.
Find the area of the upper tail.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Precise value: 0.0266.
  If using the table: between 0.02 and 0.05.}


\D{\newpage}

\subsection{Finding a p-value for a chi-square distribution}
\label{pValueForAChiSquareTest}

\index{data!racial make-up of jury|(}
In 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.

We 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.

\begin{examplewrap}
\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 $X^2$?}
In 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.
\end{nexample}
\end{examplewrap}

Just like we checked sample size conditions to use a normal
distribution in earlier sections, we must also check a sample
size condition to safely apply the chi-square distribution
for~$X^2$.
Each expected count must be at least 5. In the juror example,
the expected counts were 198, 19.25, 33, and 24.75, all easily
above~5, so we can apply the chi-square model to the test
statistic, $X^2=5.89$.

\begin{examplewrap}
\begin{nexample}{If the null hypothesis is true,
    the test statistic $X^2=5.89$ would be closely
    associated with a chi-square distribution with
    three degrees of freedom.
    Using this distribution and test statistic,
    identify the p-value.}
  The chi-square distribution and p-value are shown in
  Figure~\ref{jurorHTPValueShown}.
  Because larger chi-square values correspond to stronger
  evidence against the null hypothesis, we shade the upper
  tail to represent the p-value.
  Using statistical software (or the table in
  Appendix~\ref{chiSquareProbabilityTable}),
  we can determine that the area is 0.1171.
  Generally we do not reject the null hypothesis
  with such a large p-value.
  In other words, the data do not provide convincing evidence
  of racial bias in the juror selection.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
\centering
\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}
\caption{The p-value for the juror hypothesis test is shaded in the chi-square distribution with $df=3$.}
\label{jurorHTPValueShown}
\end{figure}

\index{data!racial make-up of jury|)}

\begin{onebox}{Chi-square test for one-way table}
  Suppose we are to evaluate whether there is convincing
  evidence that a set of observed counts $O_1$, $O_2$, ...,
  $O_k$ in $k$ categories are unusually different from what
  might be expected under a null hypothesis.
  Call the \emph{expected counts} that are based on the null
  hypothesis $E_1$, $E_2$, ..., $E_k$.
  If each expected count is at least 5 and the null hypothesis
  is true, then the test statistic below follows a chi-square
  distribution with $k-1$ degrees of freedom:
  \begin{align*}
  X^2
    = \frac{(O_1 - E_1)^2}{E_1} +
        \frac{(O_2 - E_2)^2}{E_2} +
        \cdots +
        \frac{(O_k - E_k)^2}{E_k}
  \end{align*}
  The p-value for this test statistic is found by looking
  at the upper tail of this chi-square distribution.
  We consider the upper tail because larger values of $X^2$
  would provide greater evidence against the null hypothesis.
\end{onebox}

\begin{onebox}{Conditions for the chi-square test}
  There are two conditions that must be checked before
  performing a chi-square test:\vspace{-1mm}
  \begin{description}
  \setlength{\itemsep}{0mm}
  \item[Independence.] Each case that contributes a count to
      the table must be independent of all the other cases in
      the table.
  \item[Sample size / distribution.] Each particular scenario
      (i.e. cell count) must have at least 5~expected cases.
  \end{description}
  Failing to check conditions may affect the test's error rates.
\end{onebox}

%\begin{onebox}{Chi-square test for one-way table}
%  Suppose we are to evaluate whether there is convincing
%  evidence that a set of observed counts $O_1$, $O_2$, ...,
%  $O_k$ in $k$ categories are unusually different from what
%  might be expected under a null hypothesis.
%  \begin{description}
%  \item[Prepare.]
%      List out hypotheses and identify the significance level.
%  \item[Check.]
%      Verify the conditions are met,
%      which will include finding the expected value
%      for each of the $k$ cells based on the null hypothesis,
%      which we'll label as $E_1$, $E_2$, ..., $E_k$.
%  \item[Calculate.]
%      Compute the degrees of freedom $df = k - 1$ and
%      the test statistic using the expected values
%      against the observed values $O_1, ..., O_k$:
%      \begin{align*}
%      X^2
%        = \frac{(O_1 - E_1)^2}{E_1} +
%            \frac{(O_2 - E_2)^2}{E_2} +
%            \cdots +
%            \frac{(O_k - E_k)^2}{E_k}
%      \end{align*}
%      Identify the p-value as the upper tail in the chi-square
%      distribution using the test statistic as a cutoff.
%  \item[Conclude.]
%      Evaluate the hypothesis test by comparing the p-value
%      to $\alpha$, and provide a conclusion in the context
%      of the problem.
%  \end{description}
%\end{onebox}

When examining a table with just two bins,
pick a single bin and use the one-proportion methods
introduced in Section~\ref{singleProportion}.


\D{\newpage}

\subsection{Evaluating goodness of fit for a distribution}

Section~\ref{geomDist} would be useful background reading
for this example, but it is not a prerequisite.

\index{data!S\&P500 stock data|(}

\newcommand{\spyears}{10}
\newcommand{\spdays}{1362}
\newcommand{\spdaysA}{717}
\newcommand{\spdaysB}{369}
\newcommand{\spdaysC}{155}
\newcommand{\spdaysD}{69}
\newcommand{\spdaysE}{28}
\newcommand{\spdaysF}{14}
\newcommand{\spdaysG}{10}
\newcommand{\spdaysEA}{743}
\newcommand{\spdaysEB}{338}
\newcommand{\spdaysEC}{154}
\newcommand{\spdaysED}{70}
\newcommand{\spdaysEE}{32}
\newcommand{\spdaysEF}{14}
\newcommand{\spdaysEG}{12}
\newcommand{\spdaysEProp}{0.1128}
\newcommand{\spdaysEPerc}{11.28\%}
\newcommand{\spUpProp}{0.545}
\newcommand{\spUpPerc}{54.5\%}
\newcommand{\spDownProp}{0.455}
\newcommand{\spDownPerc}{45.5\%}
\newcommand{\spdaysXSq}{4.61}
\newcommand{\spdaysN}{7}
\newcommand{\spdaysDF}{6}
\newcommand{\spdaysPvalue}{0.5951}

We can apply the chi-square testing framework to the
second problem in this section:
evaluating whether a certain statistical model fits
a data set.
Daily 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:
\begin{center}\footnotesize
\begin{tabular}{lc ccc ccc ccc cc}
Change in price		&\hspace{-1mm}	& \footnotesize2.52 &
	\footnotesize-1.46 & \footnotesize 0.51 &
	\footnotesize-4.07 & \footnotesize3.36 &
	\footnotesize1.10 &
	\footnotesize-5.46 & \footnotesize-1.03 & \footnotesize-2.99 & \footnotesize1.71 \\
Outcome	 & \hspace{-1mm} &
	Up &
	D & Up &
	D & Up &
	Up &
	D & D & D & Up \\
\footnotesize Days to Up & \hspace{-1mm} & 1 & - & 2 & - & 2 & 1 & - & - & - & 4 \\
\end{tabular}
\end{center}
If 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.

\begin{figure}[h]
\centering
\begin{tabular}{ll ccc ccc c ll}
  \hline
  Days	 & \hspace{2mm} & 1 & 2 & 3 & 4 & 5 & 6 & 7+ &
      \hspace{2mm} & Total \\
  Observed &		& \spdaysA{} & \spdaysB{} & \spdaysC{} &
      \spdaysD{} & \spdaysE{} & \spdaysF{} & \spdaysG{} & &
      \spdays{} \\
  \hline
\end{tabular}
\caption{Observed distribution of the waiting time until
    a positive trading day for the S\&P500.}
\label{sAndP500TimeToPosTrade}
\end{figure}

We 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:
\begin{itemize}
\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.
\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.
\end{itemize}
There 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.

We 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.

\begin{figure}
\centering
\begin{tabular}{ll ccc ccc c ll}
  \hline
  Days & \hspace{1mm} &
      1 & 2 & 3 & 4 & 5 & 6 & 7+ & \hspace{1mm} & Total \\
  \hline
  Observed & & \spdaysA{} & \spdaysB{} & \spdaysC{} &
      \spdaysD{} & \spdaysE{} & \spdaysF{} & \spdaysG{} & &
      \spdays{} \\
  Geometric Model & & \spdaysEA{} & \spdaysEB{} & \spdaysEC{} &
      \spdaysED{} & \spdaysEE{} & \spdaysEF{} & \spdaysEG{} & &
      \spdays{} \\
  \hline
\end{tabular}
\caption{Distribution of the waiting time until a positive
    trading day.
    The expected counts based on the geometric model are
    shown in the last row.
    To find each expected count, we identify the probability
    of waiting $D$ days based on the geometric model
    ($P(D) = (1-\spUpProp{})^{D-1}(\spUpProp{})$)
    and multiply by the total number of streaks, \spdays{}.
    For example, waiting for three days occurs under the
    geometric model about
    $\spDownProp{}^2\times \spUpProp{} = \spdaysEPerc{}$
    of the time, which corresponds to
    $\spdaysEProp{} \times \spdays{} = \spdaysEC$ streaks.}
\label{sAndP500TimeToPosTrade2}
\end{figure}

\begin{figure}
  \centering
  \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}
  \caption{Side-by-side bar plot of the observed
      and expected counts for each waiting time.}
  \label{geomFitEvaluationForSP500}
\end{figure}

Because applying the chi-square framework requires expected counts
to be at least~5, we have \emph{binned} together all the cases
where the waiting time was at least \spdaysN{} days to ensure each
expected count is well above this minimum.
The actual data, shown in the \emph{Observed} row in
Figure~\ref{sAndP500TimeToPosTrade2}, can be compared to the
expected counts from the \emph{Geometric Model} row.
The method for computing expected counts is discussed in
Figure~\ref{sAndP500TimeToPosTrade2}.
In general, the expected counts are determined by
(1)~identifying the null proportion associated with each bin,
then (2)~multiplying each null proportion by the total count
to obtain the expected counts.
That is, this strategy identifies what proportion of the total
count we would expect to be in each bin.

\begin{examplewrap}
\begin{nexample}{Do you notice any unusually large deviations
    in the graph?
    Can you tell if these deviations are due to chance just
    by looking?}
  It is not obvious whether differences in the observed counts
  and the expected counts from the geometric distribution are
  significantly different.
  That is, it is not clear whether these deviations might be
  due to chance or whether they are so strong that the data
  provide convincing evidence against the null hypothesis.
  However, we can perform a chi-square test using the counts
  in Figure~\ref{sAndP500TimeToPosTrade2}.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Figure~\ref{sAndP500TimeToPosTrade2}
provides a set of count data for waiting times
($O_1=\spdaysA{}$, $O_2=\spdaysB{}$, ...)
and expected counts under the geometric distribution
($E_1=\spdaysEA{}$, $E_2=\spdaysEB{}$, ...).
Compute the chi-square test statistic, $X^2$.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$X^2 =
      \frac{(\spdaysA{}-\spdaysEA{})^2}{\spdaysEA{}} +
      \frac{(\spdaysB{}-\spdaysEB{})^2}{\spdaysEB{}} +
      \cdots +
      \frac{(\spdaysG{}-\spdaysEG{})^2}{\spdaysEG{}}
    = \spdaysXSq{}$}

\begin{exercisewrap}
\begin{nexercise}
Because the expected counts are all at least~5,
we can safely apply the chi-square distribution to $X^2$.
However, how many degrees of freedom should
we~use?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{There are $k = \spdaysN{}$ groups, so we use
    $df = k - 1 = \spdaysDF{}$.}

\begin{examplewrap}
\begin{nexample}{If the observed counts follow the
    geometric model, then the chi-square test statistic
    $X^2 = \spdaysXSq{}$ would closely follow a chi-square
    distribution with $df = \spdaysDF{}$.
    Using this information, compute a p-value.} 
  \label{DNRejectGeomModelForSP500}%
  Figure~\ref{geomFitPValueForSP500} shows the
  chi-square distribution, cutoff, and the shaded p-value.
  % We could look up $X^2 = \spdaysXSq{}$ in
  % Appendix~\ref{chiSquareProbabilityTable} to determine
  % that the p-value is greater than 0.3.
  Using software, we can find the p-value: \spdaysPvalue{}.
  Ultimately, we do not have sufficient evidence to reject
  the notion that the wait times follow a geometric
  distribution for the last \spyears{} years of data
  for the S\&P500,
  i.e. we cannot reject the notion that trading days
  are independent.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
  \caption{Chi-square distribution with \spdaysDF{}
      degrees of freedom.
      The p-value for the stock analysis is shaded.}
  \label{geomFitPValueForSP500}
\end{figure}

\begin{examplewrap}
\begin{nexample}{In
    Example~\ref{DNRejectGeomModelForSP500},
    we did not reject the null hypothesis that the trading days
    are independent during the last \spyears{} of data.
    Why is this so important?}
  It may be tempting to think the market is ``due'' for
  an \resp{Up} day if there have been several consecutive
  days where it has been down.
  However, we haven't found strong evidence that there's
  any such property where the market is ``due'' for
  a correction.
  At the very least, the analysis suggests any dependence
  between days is very weak.
\end{nexample}
\end{examplewrap}

\index{data!S\&P500 stock data|)}

\CalculatorVideos{the chi-square goodness of fit test}


{\input{ch_inference_for_props/TeX/testing_for_goodness_of_fit_using_chi-square.tex}}





%__________________
\section{Testing for independence in two-way tables}
\label{twoWayTablesAndChiSquare}

\index{data!iPod|(}

\newcommand{\iPodAA}{2}
\newcommand{\iPodAB}{23}
\newcommand{\iPodAC}{36}
\newcommand{\iPodAD}{61}
\newcommand{\iPodAFraction}{0.2785}
\newcommand{\iPodAExpected}{20.33}
\newcommand{\iPodBA}{71}
\newcommand{\iPodBB}{50}
\newcommand{\iPodBC}{37}
\newcommand{\iPodBD}{158}
\newcommand{\iPodBFraction}{0.7215}
\newcommand{\iPodBExpected}{52.67}
\newcommand{\iPodDA}{73}
\newcommand{\iPodDB}{73}
\newcommand{\iPodDC}{73}
\newcommand{\iPodDD}{219}
\newcommand{\iPodN}{\iPodDD}

We all buy used products --
cars, computers, textbooks, and so on --
and we sometimes assume the sellers of those products
will be forthright about any underlying problems with
what they're selling.
This is not something we should take for granted.
Researchers recruited \iPodN{} participants in a study where they
would sell a used iPod\footnote{For readers not as old as
  the authors, an iPod is basically an iPhone without
  any cellular service, assuming it was one of the later
  generations. Earlier generations were more basic.}
that was known to have frozen twice in the past.
The participants were incentivized to get as much money
as they could for the iPod since they would receive a 5\%
cut of the sale on top of \$10 for participating.
The researchers wanted to understand what types of questions
would elicit the seller to disclose the freezing issue.

Unbeknownst to the participants who were the sellers
in the study,
the buyers were collaborating with the researchers
to evaluate the influence of different questions
on the likelihood of getting the sellers to disclose
the past issues with the iPod.
The scripted buyers started with
``Okay, I guess I'm supposed to go first.
  So you've had the iPod for 2 years ...''
and ended with one of three questions:
\begin{itemize}
\item General: What can you tell me about it?
\item Positive Assumption: It doesn't have any problems, does it?
\item Negative Assumption: What problems does it have?
\end{itemize}
The question is the treatment given to the sellers,
and the response is whether the question prompted them
to disclose the freezing issue with the iPod.
The results are shown in Figure~\ref{ipod_ask_data_summary},
and the data suggest that asking the,
\emph{What problems does it have?},
was the most effective at getting the seller to disclose
the past freezing issues.
However, you should also be asking yourself:
could we see these results due to chance alone,
or is this in fact evidence that some questions
are more effective for getting at the truth?

\begin{figure}[ht]
\centering
\begin{tabular}{l ccc l}
  \hline
  & General & Positive Assumption &
      Negative Assumption & Total \\ 
  \hline
  Disclose Problem & \iPodAA{} &  \iPodAB{} &
      \iPodAC{} & \iPodAD{} \\ 
  Hide Problem &  \iPodBA{} &  \iPodBB{} &
      \iPodBC{} & \iPodBD{} \\ 
  \hline
  Total & \iPodDA{} & \iPodDB{} &
      \iPodDC{} & \iPodDD{} \\
  \hline
\end{tabular}
\caption{Summary of the iPod study, where a question was
  posed to the study participant who acted}
\label{ipod_ask_data_summary}
\end{figure}

\begin{onebox}{Differences of one-way tables vs two-way tables}
  A one-way table describes counts for each outcome in a single
  variable.
  A two-way table describes counts for \emph{combinations}
  of outcomes for two variables.
  When we consider a two-way table, we often would like to know,
  are these variables related in any way?
  That is, are they dependent (versus independent)?
\end{onebox}

The hypothesis test for the iPod experiment is really about
assessing whether there is statistically significant evidence
that the success each question had on getting the participant
to disclose the problem with the iPod.
In other words, the goal is to check whether the buyer's
question was independent of whether the seller disclosed
a problem.


\D{\newpage}

\subsection{Expected counts in two-way tables}

\noindent%
Like with one-way tables, we will need to compute
estimated counts for each cell in a two-way table.

\begin{examplewrap}
\begin{nexample}{From the experiment,
    we can compute the proportion of all sellers who disclosed
    the freezing problem as $\iPodAD{}/\iPodDD = \iPodAFraction{}$.
    If there really is no difference among the questions
    and 27.85\% of sellers were going to disclose the freezing
    problem no matter the question that was put to them,
    how many of the \iPodDA{} people in the \resp{General}
    group would we have expected to disclose the freezing
    problem?} \label{iPodExComputeExpAA}
  We would predict that $\iPodAFraction{} \times \iPodDA{} = \iPodAExpected{}$
  sellers would disclose the problem.
  Obviously we observed fewer than this, though it is not
  yet clear if that is due to chance variation or whether
  that is because the questions vary in how effective they
  are at getting to the truth.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}\label{iPodExComputeExpBB}
If the questions were actually equally effective,
meaning about 27.85\% of respondents would disclose the
freezing issue regardless of what question they were asked,
about how many sellers would we expect to \emph{hide} the
freezing problem from the Positive Assumption
group?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{We would expect
    $(1 - \iPodAFraction{}) \times \iPodDA{} = \iPodBExpected{}$.
    It is okay that this result,
    like the result from Example~\ref{iPodExComputeExpAA},
    is a fraction.}

We can compute the expected number of sellers who we would
expect to disclose or hide the freezing issue for all groups,
if the questions had no impact on what they disclosed,
using the same strategy employed in
Example~\ref{iPodExComputeExpAA} and
Guided Practice~\ref{iPodExComputeExpBB}.
These expected counts were used to construct Figure~\ref{ipod_ask_data_summary_expected},
which is the same as Figure~\ref{ipod_ask_data_summary},
except now the expected counts have been added in parentheses.

\begin{figure}[h]
\centering
\begin{tabular}{l lll l}
  \hline
  & General & Positive Assumption &
      Negative Assumption & Total \\ 
  \hline
  Disclose Problem &
      \iPodAA{} \ \highlightO{\footnotesize(\iPodAExpected{})} &
      \iPodAB{} \highlightO{\footnotesize(\iPodAExpected{})} &
      \iPodAC{} \highlightO{\footnotesize(\iPodAExpected{})} &
      \iPodAD{} \\ 
  Hide Problem &
      \iPodBA{} \highlightO{\footnotesize(\iPodBExpected{})} &
      \iPodBB{} \highlightO{\footnotesize(\iPodBExpected{})} &
      \iPodBC{} \highlightO{\footnotesize(\iPodBExpected{})} &
      \iPodBD{} \\ 
  \hline
  Total & \iPodDA{} & \iPodDB{} &
      \iPodDC{} & \iPodDD{} \\
  \hline
\end{tabular}
\caption{The observed counts and the
    \highlightO{(expected counts)}.}
\label{ipod_ask_data_summary_expected}
\end{figure}

The examples and exercises above provided some help
in computing expected counts.
In general, expected counts for a two-way table may
be computed using the row totals, column totals,
and the table total.
For instance, if there was no difference between the groups,
then about 27.85\% of each column should be in the first row:
\begin{align*}
\iPodAFraction{}\times (\text{column 1 total}) &= \iPodAExpected{} \\
\iPodAFraction{}\times (\text{column 2 total}) &= \iPodAExpected{} \\
\iPodAFraction{}\times (\text{column 3 total}) &= \iPodAExpected{}
\end{align*}
Looking back to how \iPodAFraction{} was computed --
as the fraction of sellers who disclosed the freezing issue
($\iPodBD{}/\iPodDD{}$) --
these three expected counts could have been computed as
\begin{align*}
\left(\frac{\text{row 1 total}}{\text{table total}}\right)
    \text{(column 1 total)} &= \iPodAExpected{} \\
\left(\frac{\text{row 1 total}}{\text{table total}}\right)
    \text{(column 2 total)} &= \iPodAExpected{} \\
\left(\frac{\text{row 1 total}}{\text{table total}}\right)
    \text{(column 3 total)} &= \iPodAExpected{}
\end{align*}
This leads us to a general formula for computing expected
counts in a two-way table when we would like to test whether
there is strong evidence of an association between the column
variable and row variable.

\D{\newpage}

\begin{onebox}{Computing expected counts in a two-way table}
  To identify the expected count for the $i^{th}$ row
  and $j^{th}$ column, compute
  \begin{align*}
  \text{Expected Count}_{\text{row }i,\text{ col }j}
    = \frac{(\text{row $i$ total}) \times
        (\text{column $j$ total})}{\text{table total}}\vspace{2mm}
  \end{align*}
\end{onebox}


\subsection{The chi-square test for two-way tables}

The chi-square test statistic for a two-way table is found
the same way it is found for a one-way table.
For each table count, compute
\begin{align*}
&\text{General formula} &&
    \frac{(\text{observed count } - \text{expected count})^2}
        {\text{expected count}} \\
&\text{Row 1, Col 1} &&
    \frac{(\iPodAA - \iPodAExpected)^2}{\iPodAExpected} = 16.53 \\
&\text{Row 1, Col 2} &&
    \frac{(\iPodAB - \iPodAExpected)^2}{\iPodAExpected} = 0.35 \\
& \hspace{9mm}\vdots &&
    \hspace{13mm}\vdots \\
&\text{Row 2, Col 3} &&
    \frac{(\iPodBC - \iPodBExpected)^2}{\iPodBExpected} = 4.66
\end{align*}
Adding the computed value for each cell gives the chi-square test statistic $X^2$:
\begin{align*}
X^2 = 16.53 + 0.35 + \dots + 4.66 = 40.13
\end{align*}
Just 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
\begin{align*}
df = \text{(number of rows minus 1)}\times \text{(number of columns minus 1)}
\end{align*}
In our example, the degrees of freedom parameter is
\begin{align*}
df = (2-1)\times (3-1) = 2
\end{align*}
If the null hypothesis is true
(i.e. the questions had no impact on the sellers in
    the experiment),
then the test statistic $X^2 = 40.13$ closely follows
a chi-square distribution with 2 degrees of freedom.
Using this information, we can compute the p-value for
the test, which is depicted in
Figure~\ref{iPodChiSqTail}.

\begin{onebox}{Computing degrees of freedom for a two-way table}
  When applying the chi-square test to a two-way table,
  we use
  \begin{align*}
  df = (R-1)\times (C-1)
  \end{align*}
  where $R$ is the number of rows in the table
  and $C$ is the number of columns.
\end{onebox}

When analyzing 2-by-2 contingency tables, one guideline
is to use the two-proportion methods introduced in
Section~\ref{differenceOfTwoProportions}.

\D{\newpage}

\begin{figure}[h]
\centering
\includegraphics[width=0.65\textwidth]{ch_inference_for_props/figures/iPodChiSqTail/iPodChiSqTail}
\caption{Visualization of the p-value for $X^2 = 40.13$
    when $df = 2$.}
\label{iPodChiSqTail}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Find the p-value and draw a conclusion
    about whether the question affects the sellers likelihood
    of reporting the freezing problem.}
  % Looking in Appendix~\ref{chiSquareProbabilityTable}
  % on page~\pageref{chiSquareProbabilityTable},
  % we examine the row corresponding to 2 degrees of freedom.
  % The test statistic, $X^2 = 40.13$,
  % is larger than the value in the last column,
  % meaning the tail area and p-value are smaller than 0.001.
  Using a computer, we can compute a very precise value
  for the tail area above $X^2 = 40.13$ for a chi-square
  distribution with 2 degrees of freedom:
  0.000000002.
  (If using the table in
    Appendix~\ref{chiSquareProbabilityTable},
    we would identify the p-value is smaller
    than 0.001.)
  Using a significance level of $\alpha=0.05$,
  the null hypothesis is rejected since the p-value is smaller.
  That is, the data provide convincing evidence that the
  question asked did affect a seller's likelihood to tell
  the truth about problems with the iPod.
\end{nexample}
\end{examplewrap}

\index{data!iPod|)}

\index{data!diabetes|(}

\begin{examplewrap}
\begin{nexample}{Figure~\ref{diabetes2ExpMetRosiLifestyleSummary}
    summarizes the results of an experiment evaluating
    three treatments for Type~2 Diabetes in patients
    aged 10-17 who were being treated with metformin.
    The three treatments considered were
    continued treatment with metformin (\resp{met}),
    treatment with metformin combined with rosiglitazone
    (\resp{rosi}),
    or a lifestyle intervention program.
    Each patient had a primary outcome, which was either lacked
    glycemic control (failure)
    or did not lack that control (success).
    What are appropriate hypotheses for this test?}
  \label{diabetes2ExpMetRosiLifestyleIntroExample}
  \begin{itemize}
  \item[$H_0$:] There is no difference in the effectiveness
      of the three treatments.
  \item[$H_A$:] There is some difference in effectiveness
      between the three treatments, e.g. perhaps the
      \resp{rosi} treatment performed better than
      \resp{lifestyle}.
  \end{itemize}
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
\centering
\begin{tabular}{l ccc l}
\hline
 & Failure & Success & Total \\ 
\hline
\resp{lifestyle} & 109 & 125 & 234 \\ 
\resp{met} & 120 & 112 & 232 \\ 
\resp{rosi} &  90 & 143 & 233 \\ 
\hline
Total & 319 & 380 & 699 \\
\hline
\end{tabular}
\caption{Results for the Type~2 Diabetes study.}
\label{diabetes2ExpMetRosiLifestyleSummary}
\end{figure}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
A chi-square test for a two-way table may be used to test
the hypotheses in
Example~\ref{diabetes2ExpMetRosiLifestyleIntroExample}.
As a first step, compute the expected values for each of the
six table cells.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The expected count for
    row one / column one is found by multiplying the
    row one total (234) and column one total (319),
    then dividing by the table total (699):
    $\frac{234\times 319}{699} = 106.8$.
    Similarly for the second column and the first row:
    $\frac{234\times 380}{699} = 127.2$.
    Row 2: 105.9 and 126.1.
    Row 3: 106.3 and 126.7.}

\begin{exercisewrap}
\begin{nexercise}
Compute the chi-square test statistic for the data in
Figure~\ref{diabetes2ExpMetRosiLifestyleSummary}.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{For each cell,
    compute $\frac{(\text{obs} - \text{exp})^2}{exp}$.
    For instance, the first row and first column:
    $\frac{(109-106.8)^2}{106.8} = 0.05$.
    Adding the results of each cell gives the
    chi-square test statistic:
    {\scriptsize$X^2 = 0.05 + \cdots + 2.11 = 8.16$}.}

\begin{exercisewrap}
\begin{nexercise}
Because there are 3 rows and 2 columns,
the degrees of freedom for the test is
$df = (3 - 1) \times (2 - 1) = 2$.
Use $X^2 = 8.16$, $df = 2$, evaluate whether
to reject the null hypothesis using a significance level
of~0.05.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{
    If using a computer, we can identify the p-value
    as 0.017.
    That is, we reject the null hypothesis because
    the p-value is less than 0.05, and we conclude
    that at least one of the treatments is more or
    less effective than the others at treating
    Type~2 Diabetes for glycemic control.}

\index{data!diabetes|)}

\CalculatorVideos{the chi-square test for independence}


{\input{ch_inference_for_props/TeX/testing_for_independence_in_two-way_tables.tex}}


================================================
FILE: ch_inference_for_props/TeX/difference_of_two_proportions.tex
================================================
\exercisesheader{}

% 17

\eoce{\qt{Social experiment, Part I\label{social_experiment_conditions}} A ``social 
experiment" conducted by a TV program questioned what people do when they see 
a very obviously bruised woman getting picked on by her boyfriend. On two 
different occasions at the same restaurant, the same couple was depicted. In 
one scenario the woman was dressed ``provocatively'' and in the other 
scenario the woman was dressed ``conservatively''. The table below shows how 
many restaurant diners were present under each scenario, and whether or not 
they intervened.
\begin{center}
\begin{tabular}{ll cc c} 
            &               & \multicolumn{2}{c}{\textit{Scenario}} \\
\cline{3-4}
                            &       & Provocative   & Conservative  & Total \\
\cline{2-5}
\multirow{2}{*}{\textit{Intervene}} &Yes & 5   & 15  & 20    \\
                            &No     & 15      & 10  & 25 \\
\cline{2-5}
                            &Total  & 20      & 25  & 45 \\
\end{tabular}
\end{center}
Explain why the sampling distribution of the difference between the 
proportions of interventions under provocative and conservative scenarios 
does not follow an approximately normal distribution.
}{}

% 18

\eoce{\qt{Heart transplant success\label{heart_transplant_conditions}} The Stanford 
University Heart Transplant Study was conducted to determine whether an 
experimental heart transplant program increased lifespan. Each patient 
entering the program was officially designated a heart transplant candidate, 
meaning that he was gravely ill and might benefit from a new heart. Patients 
were randomly assigned into treatment and control groups. Patients in the 
treatment group received a transplant, and those in the control group did 
not. The table below displays how many patients survived and died in each 
group. \footfullcite{Turnbull+Brown+Hu:1974}\vspace{-2mm}
\begin{center}
\begin{tabular}{rcc}
\hline
            & control   & treatment \\ 
\hline
alive       & 4         & 24 \\ 
dead        & 30        & 45 \\ 
\hline
\end{tabular}
\end{center}
Suppose we are interested in estimating the difference in survival rate between 
the control and treatment groups using a confidence interval.
Explain why we cannot construct such an interval using the normal 
approximation. What might go wrong if we constructed the confidence interval 
despite this problem?
}{}

% 19

\eoce{\qt{Gender and color preference\label{gender_color_preference_CI_concept}} 
A study asked 1,924 male and 3,666 female undergraduate college students 
their favorite color.
A 95\% confidence interval for the difference between 
the proportions of males and females whose favorite color is black 
$(p_{male} - p_{female})$ was calculated to be (0.02, 0.06).
Based on this 
information, determine if the following statements about
undergraduate college students are true or false, and 
explain your reasoning for each statement you identify as false.
\footfullcite{Ellis:2001}
\begin{parts}
\item We are 95\% confident that the true proportion of males whose favorite 
color is black is 2\% lower to 6\% higher than the true proportion of females 
whose favorite color is black.
\item We are 95\% confident that the true proportion of males whose favorite 
color is black is 2\% to 6\% higher than the true proportion of females whose 
favorite color is black.
\item 95\% of random samples will produce 95\% confidence intervals that 
include the true difference between the population proportions of males and 
females whose favorite color is black.
\item We can conclude that there is a significant difference between the 
proportions of males and females whose favorite color is black and that the 
difference between the two sample proportions is too large to plausibly be 
due to chance.
\item The 95\% confidence interval for $(p_{female} - p_{male})$ cannot be 
calculated with only the information given in this exercise.
\end{parts}
}{}

\D{\newpage}

% 20

\eoce{\qt{Government shutdown\label{government_shutdown_CI_concept}}
The United States federal government shutdown of 2018–2019 occurred
from December 22, 2018 until January 25, 2019, a span of 35 days.
A~Survey USA poll of 614 randomly sampled Americans during this time
period reported that 48\% of those who make less than \$40,000 per
year and 55\% of those who make \$40,000 or more per year said the
government shutdown has not at all affected them personally.
A~95\% confidence interval for $(p_\text{$<$40K} - p_\text{$\ge$40K})$,
where $p$ is the proportion of those who said the government shutdown
has not at all affected them personally, is (-0.16, 0.02).
Based on this information, determine if the following statements are 
true or false, and explain your reasoning if you identify the statement
as false.\footfullcite{data:govt_shuthown}
\begin{parts}
\item
    At the 5\% significance level, the data provide convincing
    evidence of a real difference in the proportion who are
    not affected personally between Americans who make less than
    \$40,000 annually and Americans who make \$40,000 annually.
\item
    We are 95\% confident that 16\% more to 2\% fewer Americans
    who make less than \$40,000 per year are not at all personally
    affected by the government shutdown compared to those who make
    \$40,000 or more per year.
\item
    A 90\% confidence interval for
    $(p_\text{$<$40K} - p_\text{$\ge$40K})$
    would be wider than the $(-0.16, 0.02)$ interval.
\item
    A 95\% confidence interval for
    $(p_\text{$\ge$40K} - p_\text{$<$40K})$
    is (-0.02, 0.16).
\end{parts}
% p1 = 0.48
% p2 = 0.55
% n1 = 162
% n2 = 452
% ((p1 - p2) + c(-1,1) * 1.96 * sqrt( (p1*(1-p1)/n1) + (p2*(1-p2)/n2)) ) %>% round(2)
% (-0.16, 0.02)
}{}

% 21

\eoce{\qt{National Health Plan,
    Part III\label{national_health_plan_CI_replaced}}
Exercise~\ref{national_health_plan_HT}
presents the results of a poll evaluating support for
a generically branded ``National Health Plan''
in the United States.
79\% of 347 Democrats and 55\% of 617 Independents
support a National Health Plan.
\begin{parts}
\item
    Calculate a 95\% confidence interval for the
    difference between the proportion of Democrats
    and Independents who support a National
    Health Plan $(p_{D} - p_{I})$, and interpret
    it in this context.
    We have already checked conditions for you.
\item
    True or false:
    If we had picked a random Democrat and a random
    Independent at the time of this poll, it is more
    likely that the Democrat would support the National
    Health Plan than the Independent.
\end{parts}
}{}

% 22

\eoce{\qt{Sleep deprivation, CA vs. OR, Part I\label{sleep_OR_CA_CI}} According to 
a report on sleep deprivation by the Centers for Disease Control and Prevention, 
the proportion of California residents who reported insufficient rest or sleep 
during each of the preceding 30 days is 8.0\%, while this proportion is 8.8\% 
for Oregon residents. These data are based on simple random samples of 11,545 
California and 4,691 Oregon residents. Calculate a 95\% confidence interval 
for the difference between the proportions of Californians and Oregonians who 
are sleep deprived and interpret it in context of the data.\footfullcite{data:sleepCAandOR}
}{}

% 23

\eoce{\qt{Offshore drilling, Part I\label{offshore_drill_edu_dontknow_HT}}
A survey asked 827 randomly sampled registered voters in California
``Do you support? Or do you oppose? Drilling for oil and natural gas
off the Coast of California? Or do you not know enough to say?''
Below is the distribution of 
responses, separated based on whether or not the respondent graduated from 
college. \footfullcite{data:prop19_and_offshoreDrill} \\[1.3mm]

\noindent\begin{minipage}[c]{0.6\textwidth}
\begin{parts}
\item What percent of college graduates and what percent of the non-college 
graduates in this sample do not know enough to have an opinion on drilling 
for oil and natural gas off the Coast of California?
\item Conduct a hypothesis test to determine if the data provide strong 
evidence that the proportion of college graduates who do not have an opinion 
on this issue is different than that of non-college graduates.
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.4\textwidth}
\begin{center}
\begin{tabular}{l c c}
			& \multicolumn{2}{c}{\textit{College Grad}} \\
\cline{2-3}
			& Yes		& No	\\
\cline{1-3}
Support		& 154		& 132	\\
Oppose		& 180		& 126	\\
Do not know	& 104		& 131	\\
\cline{1-3}
 Total		& 438		& 389		
\end{tabular}
\end{center}
\end{minipage}
}{}

% 24

\eoce{\qt{Sleep deprivation, CA vs. OR, Part II\label{sleep_OR_CA_HT}} 
Exercise~\ref{sleep_OR_CA_CI} provides data on sleep deprivation rates of 
Californians and Oregonians. The proportion of California residents who 
reported insufficient rest or sleep during each of the preceding 30 days is 
8.0\%, while this proportion is 8.8\% for Oregon residents. These data are 
based on simple random samples of 11,545 California and 4,691 Oregon 
residents. 
\begin{parts}
\item Conduct a hypothesis test to determine if these data provide strong 
evidence the rate of sleep deprivation is different for the two states. 
(Reminder: Check conditions)
\item It is possible the conclusion of the test in part (a) is incorrect. If 
this is the case, what type of error was made?
\end{parts}
}{}

\D{\newpage}

% 25

\eoce{\qt{Offshore drilling, Part II\label{offshore_drill_edu_support_HT}}
Results of a poll evaluating support for drilling for oil
and natural gas off the coast of California were introduced
in Exercise~\ref{offshore_drill_edu_dontknow_HT}.
\begin{center}
\begin{tabular}{l c c}
				& \multicolumn{2}{c}{\textit{College Grad}} \\
\cline{2-3}
						& Yes		& No				\\
\cline{1-3}
Support		& 154		& 132			\\
Oppose		& 180		& 126			\\
Do not know	& 104		& 131			\\
\cline{1-3}
 Total		& 438		& 389		
\end{tabular}
\end{center}
\begin{parts}
\item What percent of college graduates and what percent of the non-college 
graduates in this sample support drilling for oil and natural gas off the Coast 
of California?
\item Conduct a hypothesis test to determine if the data provide strong evidence 
that the proportion of college graduates who support off-shore drilling in California 
is different than that of non-college graduates.
\end{parts}
}{}

% 26

\eoce{\qt{Full body scan, Part I\label{full_body_scan_HT_Error}} A news article 
reports that ``Americans have differing views on two potentially inconvenient 
and invasive practices that airports could implement to uncover potential 
terrorist attacks." This news piece was based on a survey conducted among a 
random sample of 1,137 adults nationwide, where one of the questions on the 
survey was ``Some airports are 
now using `full-body' digital x-ray machines to electronically screen 
passengers in airport security lines. Do you think these new x-ray machines 
should or should not be used at airports?" Below is a summary of responses 
based on party affiliation. \footfullcite{news:fullBodyScan}
\begin{center}
\begin{tabular}{ll  cc c} 
            &   & \multicolumn{3}{c}{\textit{Party Affiliation}} \\
\cline{3-5}
                                &           & Republican & Democrat & Independent   \\
\cline{2-5}
\multirow{3}{*}{\textit{Answer}}& Should    & 264        & 299      & 351 \\
                                & Should not& 38         & 55       & 77 \\
                                & Don't know/No answer & 16 & 15    & 22 \\
\cline{2-5}
                                & Total      & 318       & 369      & 450
\end{tabular}
\end{center}
\begin{parts}
\item Conduct an appropriate hypothesis test evaluating whether there is a 
difference in the proportion of Republicans and Democrats who think the full-
body scans should be applied in airports. Assume that all relevant conditions 
are met.
\item The conclusion of the test in part (a) may be incorrect, meaning a 
testing error was made. If an error was made, was it a Type~1 or a Type~2 
Error? Explain.
\end{parts}
}{}

% 27

\eoce{\qt{Sleep deprived transportation workers\label{sleep_deprived_driver_HT}} 
The National Sleep Foundation conducted a survey on the sleep habits of 
randomly sampled transportation workers and a control sample of non-transportation 
workers. The results of the survey are shown below.
\footfullcite{data:sleepTransport}\vspace{-1.8mm}
\begin{center}
\begin{tabular}{l c c c c c }
						& 			& \multicolumn{4}{c}{\textit{Transportation Professionals}} \\
\cline{3-6}
			& 			& 		& Truck	& Train		& Bus/Taxi/Limo		\\
			& \textit{Control}& Pilots	& Drivers	& Operators	& Drivers	\\
\cline{1-6}
Less than 6 hours of sleep	& 35		& 19		& 35	& 29	& 21	\\
6 to 8 hours of sleep		& 193		& 132	    & 117	& 119	& 131	\\
More than 8 hours			& 64		& 51		& 51	& 32	& 58	\\
\cline{1-6}
Total						& 292		& 202	    & 203	& 180	& 210		
\end{tabular}
\end{center}\vspace{-1.2mm}
Conduct a hypothesis test to evaluate if these data provide evidence of a 
difference between the proportions of truck drivers and non-transportation 
workers (the control group) who get less than 6 hours of sleep per day, i.e. 
are considered sleep deprived.
}{}

\D{\newpage}

% 28

\eoce{\qt{Prenatal vitamins and Autism\label{prenatal_vitamin_autism_HT}} 
Researchers studying the link between prenatal vitamin use and autism 
surveyed the mothers of a random sample of children aged 24 - 60 months with 
autism and conducted another separate random sample for children with typical 
development. The table below shows the number of mothers in each group who 
did and did not use prenatal vitamins during the three months before 
pregnancy (periconceptional period).\footfullcite{Schmidt:2011}\vspace{-1.8mm}
\begin{center}
\begin{tabular}{l l c c c}
		&			& \multicolumn{2}{c}{\textit{Autism}}	&		\\
\cline{3-4}
		&			& Autism		& Typical development		& Total	\\
\cline{2-5}
\textit{Periconceptional}	& No vitamin	& 111	& 70		& 181	\\
\textit{prenatal vitamin}	& Vitamin	& 143		& 159		& 302	\\
\cline{2-5}
							& Total		& 254		& 229		& 483
\end{tabular}
\end{center}\vspace{-4.2mm}
\begin{parts}
\item State appropriate hypotheses to test for independence of use of 
prenatal vitamins during the three months before pregnancy and autism.
\item Complete the hypothesis test and state an appropriate conclusion. 
(Reminder: Verify any necessary conditions for the test.)
\item A New York Times article reporting on this study was titled ``Prenatal 
Vitamins May Ward Off Autism". Do you find the title of this article to be 
appropriate? Explain your answer. Additionally, propose an alternative title.
\footfullcite{news:prenatalVitAutism}
\end{parts}
}{}

% 29

\eoce{\qt{HIV in sub-Saharan Africa\label{hiv_africa_HT}}
In July 2008 the US National Institutes of Health announced
that it was stopping a clinical study early because of unexpected
results.
The study population consisted of HIV-infected women in sub-Saharan
Africa who had been given single dose Nevaripine (a treatment for HIV)
while giving birth, to prevent transmission of HIV to the infant.
The study was a randomized comparison of continued
treatment of a woman (after successful childbirth)
with Nevaripine vs Lopinavir, a second drug used to treat HIV.
240 women participated in the study;
120 were randomized to each of the two treatments.
Twenty-four weeks after starting the study treatment,
each woman was tested to determine if the HIV infection
was becoming worse (an outcome called \textit{virologic failure}).
Twenty-six of the 120 women treated with Nevaripine experienced
virologic failure, while 10 of the 120 women treated with the
other drug experienced virologic failure.\footfullcite{Lockman:2007}
\begin{parts}
\item Create a two-way table presenting the results of this study.
\item State appropriate hypotheses to test for difference in virologic failure
rates between treatment groups.
\item Complete the hypothesis test and state an appropriate conclusion. 
(Reminder: Verify any necessary conditions for the test.)
\end{parts}
}{}

% 30

\eoce{\qt{An apple a day keeps the doctor
    away\label{apple_doctor_HT_concept}}
A physical education teacher at a high school wanting
to increase awareness on issues of nutrition and health
asked her students at the beginning of the semester
whether they believed the expression
``an apple a day keeps the doctor away'',
and 40\% of the students responded yes.
Throughout the semester she started each class with
a brief discussion of a study highlighting positive
effects of eating more fruits and vegetables.
She conducted the same apple-a-day survey at the end
of the semester, and this time 60\% of the students
responded yes.
Can she used a two-proportion method from this section
for this analysis?
Explain your reasoning.
}{}


================================================
FILE: ch_inference_for_props/TeX/inference_for_a_single_proportion.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Vegetarian college students\label{veg_coll_students_CLT}} Suppose that 8\% 
of college students are vegetarians. Determine if the following statements are 
true or false, and explain your reasoning.
\begin{parts}
\item The distribution of the sample proportions of vegetarians in random 
samples of size 60 is approximately normal since $n \ge 30$. 
\item The distribution of the sample proportions of vegetarian college 
students in random samples of size 50 is right skewed.
\item A random sample of 125 college students where 12\% are vegetarians 
would be considered unusual. 
\item A random sample of 250 college students where 12\% are vegetarians 
would be considered unusual.
\item The standard error would be reduced by one-half if we increased the 
sample size from 125 to~250.
\end{parts}
 
}{}

% 2

\eoce{\qt{Young Americans, Part I\label{young_americans_CLT_1}} About 77\% of 
young adults think they can achieve the American dream. Determine if the 
following statements are true or false, and explain your reasoning.
\footfullcite{news:youngAmericans1}
\begin{parts}
\item The distribution of sample proportions of young Americans who think 
they can achieve the American dream in samples of size 20 is left skewed.
\item The distribution of sample proportions of young Americans who think 
they can achieve the American dream in random samples of size 40 is 
approximately normal since $n \ge 30$. 
\item A random sample of 60 young Americans where 85\% think they can achieve 
the American dream would be considered unusual.
\item A random sample of 120 young Americans where 85\% think they can 
achieve the American dream would be considered unusual.
\end{parts}
}{}

% 3

\eoce{\qt{Orange tabbies\label{orange_tabbies_CLT}} Suppose that 90\% of orange
tabby cats are male. Determine if the following statements are true or false, 
and explain your reasoning.
\begin{parts}
\item The distribution of sample proportions of random samples of size 30 is 
left skewed.
\item Using a sample size that is 4 times as large will reduce the standard 
error of the sample proportion by one-half.
\item The distribution of sample proportions of random samples of size 140 is 
approximately normal.
\item The distribution of sample proportions of random samples of size 280 is 
approximately normal.
\end{parts}
}{}

% 4

\eoce{\qt{Young Americans, Part II\label{young_americans_CLT_2}} About 25\% of 
young Americans have delayed starting a family due to the continued economic 
slump. Determine if the following statements are true or false, and explain 
your reasoning.\footfullcite{news:youngAmericans2}
\begin{parts}
\item The distribution of sample proportions of young Americans who have 
delayed starting a family due to the continued economic slump in random 
samples of size 12 is right skewed.
\item In order for the distribution of sample proportions of young Americans 
who have delayed starting a family due to the continued economic slump to be 
approximately normal, we need random samples where the sample size is at 
least 40.
\item A random sample of 50 young Americans where 20\% have delayed starting 
a family due to the continued economic slump would be considered unusual.
\item A random sample of 150 young Americans where 20\% have delayed 
starting a family due to the continued economic slump would be considered 
unusual.
\item Tripling the sample size will reduce the standard error of the sample 
proportion by one-third.
\end{parts}
}{}

\D{\newpage}

% 5

\eoce{\qt{Gender equality\label{gender_equality}}
The General Social Survey asked a random sample of
1,390 Americans the following question:
``On the whole, do you think it should or should not be
the government's responsibility to  promote equality
between men and women?''
82\% of the respondents said it ``should be''.
At a 95\% confidence level, this sample has 2\% margin of error.
Based on this information, determine if the following statements
are true or false, and explain your reasoning.\footfullcite{data:gss}
\begin{parts}
\item We are 95\% confident that between 80\% and 84\% of Americans in this 
sample think it's the government's responsibility to promote equality between 
men and women.
\item We are 95\% confident that between 80\% and 84\% of all Americans 
think it's the government's responsibility to promote equality between 
men and women.
\item If we considered many random samples of 1,390 Americans, and we calculated 
95\% confidence intervals for each, 95\% of these intervals would include the 
true population proportion of Americans who think it's the government's 
responsibility to promote equality between men and women.
\item In order to decrease the margin of error to 1\%, we would need to 
quadruple (multiply by 4) the sample size.
\item Based on this confidence interval, there is sufficient evidence to 
conclude that a majority of Americans think it's the government's responsibility 
to promote equality between men and women.
\end{parts}

% n = 1390
% should be: 1142
% p = 1142/1390 = 0.82
% me = sqrt(.82*.08/1390)*1.96 = 0.02
}{}

% 6

\eoce{\qt{Elderly drivers\label{elderly_drivers_CI_concept}} 
The Marist Poll published a report stating that 66\% of adults nationally 
think licensed drivers should be required to retake their road test once 
they reach 65 years of age. It was also reported that interviews were 
conducted on 1,018 American adults, and that the margin of error was 3\% 
using a 95\% confidence level. \footfullcite{data:elderlyDriving}
\begin{parts}
\item Verify the margin of error reported by The Marist Poll. 
\item Based on a 95\% confidence interval, does the poll provide convincing 
evidence that \textit{more than} 70\% of the population think that licensed 
drivers should be required to retake their road test once they turn 65?
\end{parts}
}{}

% 7

\eoce{\qt{Fireworks on July 4$^{\text{th}}$\label{fireworks_CI_concept}} A local 
news outlet reported that 56\% of 600 randomly sampled Kansas residents planned 
to set off fireworks on July~$4^{th}$. Determine the margin of error for the 
56\% point estimate using a 95\% confidence level.\footfullcite{data:july4}
}{}

% 8

\eoce{\qt{Life rating in Greece\label{greece_life_rating_CI}} Greece has faced a 
severe economic crisis since the end of 2009. A Gallup poll surveyed 1,000 
randomly sampled Greeks in 2011 and found that 25\% of them said they would 
rate their lives poorly enough to be considered ``suffering''.\footfullcite{data:suffering}
\begin{parts}
\item Describe the population parameter of interest. What is the value of the 
point estimate of this parameter?
\item Check if the conditions required for constructing a confidence interval 
based on these data are met.
\item Construct a 95\% confidence interval for the proportion of Greeks who 
are ``suffering".
\item Without doing any calculations, describe what would happen to the 
confidence interval if we decided to use a higher confidence level.
\item Without doing any calculations, describe what would happen to the 
confidence interval if we used a larger sample.
\end{parts}
}{}

% 9

\eoce{\qt{Study abroad\label{study_abroad_CI_decision}}
A survey on 1,509 high school seniors who took the SAT
and who completed an optional web survey shows that
55\% of high school seniors are fairly certain that
they will participate in a study abroad program in 
college.\footfullcite{data:studyAbroad}
\begin{parts}
\item
    Is this sample a representative sample from the population
    of all high school seniors in the US?
    Explain your reasoning.
\item
    Let's suppose the conditions for inference are met.
    Even if your answer to part (a) indicated that this approach
    would not be reliable, this analysis may still be interesting
    to carry out (though not report).
    Construct a 90\% confidence interval for the proportion of high
    school seniors (of those who took the SAT) who are fairly certain
    they will participate in a study abroad program in college,
    and interpret this interval in context.
\item
    What does ``90\% confidence" mean?
\item
    Based on this interval, would it be appropriate to claim that
    the majority of high school seniors are fairly certain that they
    will participate in a study abroad program in college?
\end{parts}
}{}

% 10

\eoce{\qt{Legalization of marijuana, Part I\label{legalize_marijuana_CI_decision}} 
The General Social Survey asked 1,578 US residents:
``Do you think the use of marijuana should be made legal, or not?''
61\% of the respondents said 
it should be made legal.\footfullcite{data:gss}
\begin{parts}
\item Is 61\% a sample statistic or a population parameter? Explain.
\item Construct a 95\% confidence interval for the proportion of US 
residents who think marijuana should be made legal, and interpret it in the 
context of the data.
\item A critic points out that this 95\% confidence interval is only 
accurate if the statistic follows a normal distribution, or if the normal 
model is a good approximation. Is this true for these data? Explain.
\item A news piece on this survey's findings states, ``Majority of Americans 
think marijuana should be legalized.'' Based on your confidence 
interval, is this news piece's statement justified? 
\end{parts}

% 2348 surveyed
% 770 not asked question
% 2348 - 770 = 1578 asked question
% 968 said legalize
% 968 / 1578 = 0.61
}{}

% 11

\eoce{\qt{National Health Plan, Part I\label{national_health_plan_HT}}
A \textit{Kaiser Family Foundation} poll for US adults
in 2019 found that 79\% of Democrats, 55\% of Independents,
and 24\% of Republicans supported a generic ``National Health Plan''.
There were 347 Democrats, 298 Republicans, and 617 Independents
surveyed.\footfullcite{data:KFF2019_nat_health_plan}
\begin{parts}
\item
    A political pundit on TV claims that a majority of Independents
    support a National Health Plan.
    Do these data provide strong evidence to support this type
    of statement?
\item
    Would you expect a confidence interval for the proportion
    of Independents who oppose the public option plan to
    include 0.5?
    Explain.
\end{parts}
}{}

% 12

\eoce{\qt{Is college worth it? Part I\label{college_worth_it_HT_CI}} Among a simple 
random sample of 331 American adults who do not have a four-year college degree 
and are not currently enrolled in school, 48\% said they decided not to go to 
college because they could not afford school. \footfullcite{data:collegeWorthIt}
\begin{parts}
\item A newspaper article states that only a minority of the Americans who 
decide not to go to college do so because they cannot afford it and uses the 
point estimate from this survey as evidence. Conduct a hypothesis test to 
determine if these data provide strong evidence supporting this statement.
\item Would you expect a confidence interval for the proportion of American 
adults who decide not to go to college because they cannot afford it to 
include 0.5? Explain.
\end{parts}
}{}

% 13

\eoce{\qt{Taste test\label{taste_test_HT_2_sided}}
Some people claim that they can tell the 
difference between a diet soda and a regular soda
in the first sip.
A researcher wanting to test this claim randomly sampled 80 such people.
He then filled 80 
plain white cups with soda, half diet and half regular
through random assignment, 
and asked each person to take one sip from their cup
and identify the soda as 
diet or regular.
53 participants correctly identified the soda.
\begin{parts}
\item Do these data provide strong evidence that these
  people are any better or worse than random guessing at
  telling the difference between diet and regular soda?
\item Interpret the p-value in this context.
\end{parts}
}{}

% 14

\eoce{\qt{Is college worth it? Part II\label{college_worth_it_CI_sample_size}} 
Exercise~\ref{college_worth_it_HT_CI} presents the results of a poll where 
48\% of 331 Americans who decide to not go to college do so because they 
cannot afford it.
\begin{parts}
\item Calculate a 90\% confidence interval for the proportion of Americans 
who decide to not go to college because they cannot afford it, and interpret 
the interval in context.
\item Suppose we wanted the margin of error for the 90\% confidence level to 
be about 1.5\%. How large of a survey would you recommend?
\end{parts}
}{}

% 15

\eoce{\qt{National Health Plan,
    Part II\label{national_health_plan_CI_sample_size_replaced}} 
Exercise~\ref{national_health_plan_HT} presents the results
of a poll evaluating support for a generic
``National Health Plan'' in the US in 2019,
reporting that 55\% of Independents are supportive.
If we wanted to estimate this number to within 1\% with
90\% confidence, what would be an appropriate sample size?
}{}

% 16

\eoce{\qt{Legalize Marijuana, Part II\label{legalize_marijuana_CI_sample_size}} As 
discussed in Exercise~\ref{legalize_marijuana_CI_decision},
the General Social Survey reported a sample where about
61\% of US residents thought marijuana should be made legal.
If we wanted to limit the margin of error of 
a 95\% confidence interval to 2\%, about how many
Americans would we need to survey?
}{}


================================================
FILE: ch_inference_for_props/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 39

\eoce{\qt{Active learning\label{active_learning_HT_concept}} A teacher wanting to 
increase the active learning component of her course is concerned about 
student reactions to changes she is planning to make. She conducts a survey 
in her class, asking students whether they believe more active learning in 
the classroom (hands on exercises) instead of traditional lecture will helps 
improve their learning. She does this at the beginning and end of the 
semester and wants to evaluate whether students' opinions have changed over 
the semester. Can she used the methods we learned in this chapter for this 
analysis? Explain your reasoning.
}{}

% 40

\eoce{\qt{Website experiment\label{web_ctr_exp_chisq}}
The OpenIntro website occasionally experiments with design
and link placement.
We conducted one experiment testing three different placements
of a download link for this textbook on the book's main page
to see which location, if any, led to the most downloads.
The number of site visitors included in the experiment
was~701 and is captured in one of the response combinations
in the following table:
\begin{center}
\begin{tabular}{r cc}
  \hline
            & Download & No Download \\ 
  \hline
Position 1  & 13.8\%   & 18.3\% \\ 
Position 2  & 14.6\%   & 18.5\% \\ 
Position 3  & 12.1\%   & 22.7\% \\ 
  \hline
\end{tabular}
\end{center}
% x <- matrix(c(97, 102, 85, 128, 130, 159), 3, 2)
\begin{parts}
\item
    Calculate the actual number of site visitors in each
    of the six response categories.
\item
    Each individual in the experiment had an equal chance of
    being in any of the three experiment groups.
    However, we see that there are slightly different
    totals for the groups.
    Is there any evidence that the groups were actually imbalanced?
    Make sure to clearly state hypotheses, check conditions,
    calculate the appropriate test statistic and the p-value,
    and make your conclusion in context of the data.
\item
   Complete an appropriate hypothesis test to check whether
   there is evidence that there is a higher rate of site visitors
   clicking on the textbook link in any of the three groups.
\end{parts}
}{}

% 41

\eoce{\qt{Shipping holiday gifts\label{ship_gifts_chisq_indep_conditions}} A 
local news survey asked 500 randomly sampled Los Angeles residents 
which shipping carrier they prefer to use for shipping holiday gifts. 
The table below shows the distribution of responses by age group as 
well as the expected counts for each cell (shown in parentheses).
\begin{center}
\begin{tabular}{l l | c c | c c | c c | c }
		&		& \multicolumn{6}{c|}{\textit{Age}}	&		\\
\cline{3-8}
		&		& \multicolumn{2}{c|}{18-34}		& \multicolumn{2}{c|}{35-54}	& \multicolumn{2}{c|}{55+}	& Total	\\
\cline{2-9}
\multirow{5}{*}{\textit{Shipping Method}}	& USPS		& 72	& \ec{81}	& 97	& \ec{102}	& 76 	& \ec{62}		& 245 \\
		& UPS		& 52	& \ec{53}	& 76	& \ec{68}	& 34	& \ec{41}		& 162 \\
		& FedEx		& 31	& \ec{21}	& 24	& \ec{27}	& 9	& \ec{16}		& 64 \\
		& Something else	& 7 & \ec{5}	& 6	& \ec{7}	& 3	& \ec{4}		& 16 \\
		& Not sure	& 3	& \ec{5}	& 6	& \ec{5}	& 4	& \ec{3}		& 13 \\
\cline{2-9}
		& Total		& \multicolumn{2}{c|}{165}		& \multicolumn{2}{c|}{209}		& \multicolumn{2}{c|}{126}		& 500
\end{tabular}
\end{center} 
\begin{parts}
\item State the null and alternative hypotheses for testing for independence 
of age and preferred shipping method for holiday gifts among Los Angeles residents.
\item Are the conditions for inference using a chi-square test satisfied?
\end{parts}
}{}

% 42

\eoce{\qt{The Civil War\label{civil_war_HT_CI_2_sided}}
A national survey conducted 
among a simple random sample of 1,507 adults shows that 56\% of
Americans think the Civil War is still relevant to American politics and 
political life.%
\footfullcite{data:civilWar} 
\begin{parts}
\item Conduct a hypothesis test to determine if these data provide strong 
evidence that the majority of the Americans think the Civil War is still 
relevant.
\item Interpret the p-value in this context.
\item Calculate a 90\% confidence interval for the proportion of Americans 
who think the Civil War is still relevant. Interpret the interval in this 
context, and comment on whether or not the confidence interval agrees with 
the conclusion of the hypothesis test.
\end{parts}
}{}

\D{\newpage}

% 43

\eoce{\qt{College smokers\label{college_smokers_CI_sample_size}} We are interested 
in estimating the proportion of students at a university who smoke. Out of a 
random sample of 200 students from this university, 40 students smoke.
\begin{parts}
\item Calculate a 95\% confidence interval for the proportion of students at 
this university who smoke, and interpret this interval in context. 
(Reminder: Check conditions.)
\item If we wanted the margin of error to be no larger than 2\% at a 95\% 
confidence level for the proportion of students who smoke, how big of a 
sample would we need? 
\end{parts}
}{}

% 44

\eoce{\qt{Acetaminophen and liver damage\label{acetaminophen_CI_sample_size}} It 
is believed that large doses of acetaminophen (the active ingredient in over 
the counter pain relievers like Tylenol) may cause damage to the liver. A 
researcher wants to conduct a study to estimate the proportion of 
acetaminophen users who have liver damage. For participating in this study, 
he will pay each subject \$20 and provide a free medical consultation if the 
patient has liver damage.
\begin{parts}
\item If he wants to limit the margin of error of his 98\% confidence 
interval to 2\%, what is the minimum amount of money he needs to set aside 
to pay his subjects?
\item The amount you calculated in part (a) is substantially over his budget 
so he decides to use fewer subjects. How will this affect the width of his 
confidence interval?
\end{parts}
}{}

% 45

\eoce{\qt{Life after college\label{life_after_college_CI}} We are interested in 
estimating the proportion of graduates at a mid-sized university who found 
a job within one year of completing their undergraduate degree. Suppose we 
conduct a survey and find out that 348 of the 400 randomly sampled graduates 
found jobs. The graduating class under consideration included over 4500 students.
\begin{parts}
\item Describe the population parameter of interest. What is the value of 
the point estimate of this parameter?
\item Check if the conditions for constructing a confidence interval based 
on these data are met.
\item Calculate a 95\% confidence interval for the proportion of graduates 
who found a job within one year of completing their undergraduate degree at 
this university, and interpret it in the context of the data.
\item What does ``95\% confidence" mean?
\item Now calculate a 99\% confidence interval for the same parameter and 
interpret it in the context of the data.
\item Compare the widths of the 95\% and 99\% confidence intervals. Which 
one is wider? Explain.
\end{parts}
}{}

% 46

\eoce{\qt{Diabetes and unemployment\label{diabetes_unemp_effect_size}} A 
Gallup poll surveyed Americans about their employment status and whether or 
not they have diabetes. The survey results indicate that 1.5\% of the 47,774 
employed (full or part time) and 2.5\% of the 5,855 unemployed 18-29 year 
olds have diabetes.\footfullcite{data:employmentDiabetes}
\begin{parts}
\item Create a two-way table presenting the results of this study.
\item State appropriate hypotheses to test for difference in proportions of 
diabetes between employed and unemployed Americans.
\item The sample difference is about 1\%. If we completed the hypothesis 
test, we would find that the p-value is very small (about 0), meaning the 
difference is statistically significant. Use this result to explain the 
difference between statistically significant and practically significant 
findings.
\end{parts}
}{}

% 47

\eoce{\qt{Rock-paper-scissors\label{rps_chisq_GOF}} Rock-paper-scissors is a hand 
game played by two or more people where players choose to sign either rock, 
paper, or scissors with their hands. For your statistics class project, 
you want to evaluate whether players choose between these three options 
randomly, or if certain options are favored above others. You ask two friends 
to play rock-paper-scissors and count the times each option is played. The 
following table summarizes the data:
\begin{center}
\begin{tabular}{c c c}
Rock	& Paper	& Scissors 	 \\
\hline
43		& 21	& 35	
\end{tabular}
\end{center}
Use these data to evaluate whether players choose between these three options 
randomly, or if certain options are favored above others. Make sure to clearly 
outline each step of your analysis, and interpret your results in context of 
the data and the research question.
}{}

\D{\newpage}

% 48

\eoce{\qt{2010 Healthcare Law\label{healthcare_CI_concept}} On June 28, 2012 the 
U.S. Supreme Court upheld the much debated 2010 healthcare law, declaring it 
constitutional. A Gallup poll released the day after this decision indicates 
that 46\% of 1,012 Americans agree with this decision. At a 95\% confidence 
level, this sample has a 3\% margin of error. Based on this information, 
determine if the following statements are true or false, and explain your 
reasoning.\footfullcite{data:healthcare2010}
\begin{parts}
\item We are 95\% confident that between  43\% and 49\% of Americans in this 
sample support the decision of the U.S. Supreme Court on the 2010 healthcare 
law.
\item We are 95\% confident that between 43\% and 49\% of Americans support 
the decision of the U.S. Supreme Court on the 2010 healthcare law.
\item If we considered many random samples of 1,012 Americans, and we 
calculated the sample proportions of those who support the decision of the 
U.S. Supreme Court, 95\% of those sample proportions will be between 43\% and 
49\%.
\item The margin of error at a 90\% confidence level would be higher than 3\%.
\end{parts}
}{}

% 49

\eoce{\qt{Browsing on the mobile device\label{mobile_browsing_HT_CI}} A 
survey of 2,254 American adults indicates that 17\% of cell phone owners
browse the internet exclusively on their phone rather than a computer
or other device.
\footfullcite{data:mobileBrowse}
\begin{parts}
\item  According to an online article, a report from a mobile research 
company indicates that 38 percent of Chinese mobile web users only access 
the internet through their cell phones.
\footfullcite{news:mobileBrowseChinese} Conduct a hypothesis test to 
determine if these data provide strong evidence that the proportion of 
Americans who only use their cell phones to access the internet is different 
than the Chinese proportion of 38\%.
\item Interpret the p-value in this context.
\item Calculate a 95\% confidence interval for the proportion of Americans 
who access the internet on their cell phones, and interpret the interval in 
this context.
\end{parts}
}{}

% 50

\eoce{\qt{Coffee and Depression\label{coffee_depression_chisq_indep}} 
Researchers conducted a study investigating the relationship between 
caffeinated coffee consumption and risk of depression in women. They 
collected data on 50,739 women free of depression symptoms at the start 
of the study in the year 1996, and these women were followed through 
2006. The researchers used questionnaires to collect data on 
caffeinated coffee consumption, asked each individual about physician-
diagnosed depression, and also asked about the use of antidepressants. 
The table below shows the distribution of incidences of depression by 
amount of caffeinated coffee consumption.\footfullcite{Lucas:2011}
\begin{adjustwidth}{-4em}{-4em}
{\small
\begin{center}
\begin{tabular}{l  l rrrrrr}
	&  \multicolumn{1}{c}{}		& \multicolumn{5}{c}{\textit{Caffeinated coffee consumption}} \\
\cline{3-7}
	&		& $\le$ 1	& 2-6	& 1	& 2-3	& $\ge$ 4	&   \\
	&		& cup/week	& cups/week	& cup/day	& cups/day	& cups/day	& Total  \\
\cline{2-8}
\textit{Clinical} & Yes	& 670 & \fbox{\textcolor{oiB}{373}}	& 905	& 564	& 95 	& 2,607 \\
\textit{depression}	& No& 11,545	& 6,244	& 16,329	& 11,726	& 2,288 	& 48,132 \\
\cline{2-8}
				& Total	& 12,215	& 6,617 & 17,234	& 12,290	& 2,383 	& 50,739 \\
\cline{2-8}
\end{tabular}
\end{center}
}
\end{adjustwidth}
\begin{parts}
\item What type of test is appropriate for evaluating if there is an 
association between coffee intake and depression?
\item Write the hypotheses for the test you identified in part (a).
\item Calculate the overall proportion of women who do and do not 
suffer from depression.
\item Identify the expected count for the highlighted cell, and 
calculate the contribution of this cell to the test statistic, i.e. 
$(Observed-Expected)^2/Expected$.
\item The test statistic is $\chi^2=20.93$. What is the p-value?
\item What is the conclusion of the hypothesis test?
\item One of the authors of this study was quoted on the NYTimes as 
saying it was ``too early to recommend that women load up on extra 
coffee" based on just this study.\footfullcite{news:coffeeDepression} 
Do you agree with this statement? Explain your reasoning.
\end{parts}
}{}


================================================
FILE: ch_inference_for_props/TeX/testing_for_goodness_of_fit_using_chi-square.tex
================================================
\exercisesheader{}

% 31

\eoce{\qt{True or false, Part I\label{tf_chisq_1}} Determine if the statements below 
are true or false. For each false statement, suggest an alternative wording to 
make it a true statement.
\begin{parts}
\item The chi-square distribution, just like the normal distribution, has two 
parameters, mean and standard deviation.
\item The chi-square distribution is always right skewed, regardless of the 
value of the degrees of freedom parameter.
\item The chi-square statistic is always positive.
\item As the degrees of freedom increases, the shape of the chi-square 
distribution becomes more skewed.
\end{parts}
}{}

% 32

\eoce{\qt{True or false, Part II\label{tf_chisq_2}} Determine if the statements below 
are true or false. For each false statement, suggest an alternative wording to 
make it a true statement.
\begin{parts}
\item As the degrees of freedom increases, the mean of the chi-square 
distribution increases.
\item If you found $\chi^2 = 10$ with $df = 5$ you would fail to reject $H_0$ 
at the 5\% significance level.
\item When finding the p-value of a chi-square test, we always shade the tail 
areas in both tails.
\item As the degrees of freedom increases, the variability of the chi-square 
distribution decreases.
\end{parts}
}{}

% 33

\eoce{\qt{Open source textbook\label{opensource_text_chisq_GOF}} A professor using 
an open source introductory statistics book predicts that 60\% of the 
students will purchase a hard copy of the book, 25\% will print it out from 
the web, and 15\% will read it online. At the end of the semester he asks his 
students to complete a survey where they indicate what format of the book 
they used. Of the 126 students, 71 said they bought a hard copy of the book, 
30 said they printed it out from the web, and 25 said they read it online.
\begin{parts}
\item State the hypotheses for testing if the professor's predictions were 
inaccurate.
\item How many students did the professor expect to buy the book, print the 
book, and read the book exclusively online?
\item This is an appropriate setting for a chi-square test. List the 
conditions required for a test and verify they are satisfied.
\item Calculate the chi-squared statistic, the degrees of freedom associated 
with it, and the p-value.
\item Based on the p-value calculated in part (d), what is the conclusion of 
the hypothesis test? Interpret your conclusion in this context.
\end{parts}
}{}

% 34

\eoce{\qt{Barking deer\label{barking_deer_chisq_GOF}}
Microhabitat factors associated with forage and bed sites
of barking deer in Hainan Island, China were examined.
In this region woods make up 4.8\% of the land,
cultivated grass plot makes up 14.7\%, and deciduous forests
make up 39.6\%.
Of the 426 sites where the deer forage, 4 were categorized
as woods, 16 as cultivated grassplot, and 61 as deciduous forests.
The table below summarizes these data.\footfullcite{Teng:2004}
\begin{center}
\begin{tabular}{c c c c c}
Woods	& Cultivated grassplot	& Deciduous forests	 & Other & Total \\
\hline 
4		& 16					& 61			     & 345	 & 426 \\
\end{tabular}
\end{center}

\noindent \begin{minipage}[c]{0.7\textwidth}
\begin{parts}
\item Write the hypotheses for testing if barking deer prefer to forage in 
certain habitats over others.
\item What type of test can we use to answer this research question?
\item Check if the assumptions and conditions required for this test are 
satisfied.
\item Do these data provide convincing evidence that barking deer prefer to 
forage in certain habitats over others? Conduct an appropriate hypothesis 
test to answer this research question.
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.03\textwidth}
$\:$ \\
\end{minipage}
\begin{minipage}[c]{0.28\textwidth}
\begin{center}
\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} \\
{\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}}
\end{center}
\end{minipage}
}{}


================================================
FILE: ch_inference_for_props/TeX/testing_for_independence_in_two-way_tables.tex
================================================
\exercisesheader{}

% 35

\eoce{\qt{Quitters\label{quitters_chisq_independence}} Does being part of a 
support group affect the ability of people to quit smoking? A county 
health department enrolled 300 smokers in a randomized experiment. 150 
participants were assigned to a group that used a nicotine patch and 
met weekly with a support group; the other 150 received the patch and 
did not meet with a support group. At the end of the study, 40 of the 
participants in the patch plus support group had quit smoking while 
only 30 smokers had  quit in the other group.
\begin{parts}
\item Create a two-way table presenting the results of this study.
\item Answer each of the following questions under the null hypothesis 
that being part of a support group does not affect the ability of 
people to quit smoking, and indicate whether the expected values are 
higher or lower than the observed values.
\begin{subparts}
\item How many subjects in the ``patch + support" group would you 
expect to quit?
\item How many subjects in the ``patch only" group would you expect to 
not quit?
\end{subparts}
\end{parts}
}{}

% 36

\eoce{\qt{Full body scan, Part II\label{full_body_scan_chisq_indep}} The 
table below summarizes a data set we first encountered in 
Exercise~\ref{full_body_scan_HT_Error} regarding views on full-body 
scans and political affiliation. The differences in each political 
group may be due to chance. Complete the following computations under 
the null hypothesis of independence between an individual's party 
affiliation and his support of full-body scans. It may be useful to 
first add on an extra column for row totals before proceeding with the 
computations.
\begin{center}
\begin{tabular}{ll  cc c} 
            &   & \multicolumn{3}{c}{\textit{Party Affiliation}} \\
\cline{3-5}
                                &           & Republican & Democrat & Independent   \\
\cline{2-5}
\multirow{3}{*}{\textit{Answer}}& Should    & 264        & 299      & 351 \\
                                & Should not& 38         & 55       & 77 \\
                                & Don't know/No answer & 16 & 15    & 22 \\
\cline{2-5}
                                & Total      & 318       & 369      & 450
\end{tabular}
\end{center}
\begin{parts}
\item How many Republicans would you expect to not support the use of 
full-body scans?
\item How many Democrats would you expect to support the use of full-
body scans?
\item How many Independents would you expect to not know or not answer?
\end{parts}
}{}

% 37

\eoce{\qt{Offshore drilling, Part III\label{offshore_drilling_chisq_indep}} 
The table below summarizes a data set we first encountered in 
Exercise~\ref{offshore_drill_edu_dontknow_HT} that examines the 
responses of a random sample of college graduates and non-graduates on 
the topic of oil drilling. Complete a chi-square test for these data to 
check whether there is a statistically significant difference in 
responses from college graduates and non-graduates.
\begin{center}
\begin{tabular}{l c c}
			& \multicolumn{2}{c}{\textit{College Grad}} \\
\cline{2-3}
			& Yes		& No				\\
\cline{1-3}
Support		& 154		& 132			\\
Oppose		& 180		& 126			\\
Do not know	& 104		& 131			\\
\cline{1-3}
 Total		& 438		& 389		
\end{tabular}
\end{center}
}{}

% 38

\eoce{\qt{Parasitic worm\label{parasitic_worm_chisq}}
Lymphatic filariasis is a disease caused by a parasitic worm.
Complications of the disease can lead to extreme swelling
and other complications.
Here we consider results from a randomized experiment
that compared three
different drug treatment options to clear people of the
this parasite, which people are working to eliminate entirely.
The results for the second year of the study are
given below:\footfullcite{King_Suamani_2018}
\begin{center}
\begin{tabular}{l cc}
  \hline
  & Clear at Year 2 & Not Clear at Year 2 \\ 
  \hline
  Three drugs & 52 & 2 \\ 
  Two drugs & 31 & 24 \\ 
  Two drugs annually & 42 & 14 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item\label{parasitic_worm_chisq_hyp}
    Set up hypotheses for evaluating
    whether there is any difference in the
    performance of the treatments,
    and also check conditions.
\item
    Statistical software was used to run
    a chi-square test, which output:
    \begin{align*}
    &X^2 = 23.7
    &&df = 2
    &&\text{p-value} = \text{7.2e-6}
    \end{align*}
    Use these results to evaluate the hypotheses
    from part~(\ref{parasitic_worm_chisq_hyp}),
    and provide a conclusion
    in the context of the problem.
\end{parts}
}{}


================================================
FILE: ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove10WithDF4/chiSquareAreaAbove10WithDF4.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove10WithDF4.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(10,
              4,
              c(0, 18),
              col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove11Point7WithDF7/chiSquareAreaAbove11Point7WithDF7.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove11Point7WithDF7.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(11.7,
              7,
              c(0, 25),
              col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove4Point3WithDF2/chiSquareAreaAbove4WithDF2.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove4Point3WithDF2.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(4.3,
              2,
              c(0, 15),
              col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove5Point1WithDF5/chiSquareAreaAbove5Point1WithDF5.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove5Point1WithDF5.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(5.1,
              5,
              c(0, 25),
              col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove6Point25WithDF3/chiSquareAreaAbove6Point25WithDF3.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove6Point25WithDF3.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(6.25,
              3,
              c(0, 15),
              col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove9Point21WithDF3/chiSquareAreaAbove9Point21WithDF3.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareAreaAbove9Point21WithDF3.pdf', 5, 3,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.6, 0))
ChiSquareTail(9.21,
              3,
              c(0, 15),
              col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/bladesTwoSampleHTPValueQC/bladesTwoSampleHTPValueQC.R
================================================
library(openintro)
data(COL)

myPDF('bladesTwoSampleHTPValueQC.pdf', 3.04, 1.56,
      mar = c(2.4, 0, 0.5, 0),
      mgp = c(3, 0.45, 0))
normTail(U = 2.3, L = -2.3, col = COL[1], axes = FALSE)
at <- c(-5, 0, 2.3, 5)
labels <- c(0, 0.03, 0.059, 0)
axis(1, at, labels, cex.axis = 0.9)
par(mgp = c(5, 1.3, 0))
axis(1, at = 0, '(null value)', cex.axis = 0.7)
arrows(2.5, 0.19,
       2.5, 0.05,
       length = 0.1,
       col = COL[1])
text(2.5, 0.18, "0.006",
     pos = 3,
     cex = 0.8,
     col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/chiSquareDistributionWithInceasingDF/chiSquareDistributionWithInceasingDF.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareDistributionWithInceasingDF.pdf', 6.5, 3,
      mar = c(2, 0.5, 0.25, 0.5),
      mgp = c(2.1, 0.7, 0))
x <- c(0, seq(0.0000001, 40, 0.05))
DF <- c(2.0000001, 4, 9)
y <- list()
for (i in 1:length(DF)) {
  y[[i]] <- dchisq(x, DF[i])
}
plot(0, 0,
     type = 'n',
     xlim = c(0, 25),
     ylim = range(c(y, recursive = TRUE)),
     axes = FALSE)
for (i in 1:length(DF)) {
  lines(x, y[[i]],
        lty = i,
        col = COL[ifelse(i == 3, 4, i)],
        lwd = 1.5 + i / 2)
}
abline(h = 0)
axis(1)
legend('topright',
       lwd = 0.3 + 1:4 / 1.25,
       col = COL[c(1, 2, 4)],
       lty = 1:4,
       legend = paste(round(DF)),
       title = 'Degrees of Freedom',
       cex = 1)
dev.off()


================================================
FILE: ch_inference_for_props/figures/eoce/assisted_reproduction_one_sample_randomization/assisted_reproduction_one_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set sample size and number of simulations -------------------------
n = 25
N = 10^4

# randomize ---------------------------------------------------------

set.seed(15)

p <- 0.31

pHat <- rbinom(N, n, p)/n
M    <- max(pHat)*n

pHatObs <- 0.4

sum(pHat >= pHatObs)/N

# plot randomization dist for question ------------------------------

pdf("assisted_reproduction_one_sample_randomization.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at = (0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

abline(h = seq(250, 1500, 250), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("assisted_reproduction_one_sample_randomization_soln.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at = (0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

histPlot(pHat[pHat >= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, 
         col = COL[1], add = TRUE)

lines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)
text(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_inference_for_props/figures/eoce/egypt_revolution_one_sample_randomization/egypt_revolution_one_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set sample size and number of simulations -------------------------
n = 20
N = 10^4

# randomize ---------------------------------------------------------

set.seed(5)

pHat <- rbinom(N, n, 0.69)/n
M    <- max(pHat)*n

pHatObs <- 0.57

sum(pHat <= pHatObs)/N

# plot randomization dist for question ------------------------------

pdf("egypt_revolution_one_sample_randomization.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at=(0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

abline(h = seq(250,1500,250), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("egypt_revolution_one_sample_randomization_soln.pdf", height = 3, width = 6)

par(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))

histPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, 
         xlab = expression(hat(p)[sim]*"    "), 
         col = COL[7,3], ylab = "", axes = FALSE)
axis(1)
axis(2, at=(0:3)*N/20, labels=c("0","0.05","0.10","0.15"))
abline(h = 0)

histPlot(pHat[pHat <= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, 
         col = COL[1], add = TRUE)

lines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)
text(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_inference_for_props/figures/eoce/social_experiment_two_sample_randomization/social_experiment_two_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set number of simulations -----------------------------------------
N = 10^4

# randomize ---------------------------------------------------------

pHatObs = -0.35

set.seed(3)

sc <- c(rep("p", 20), rep("c",25))
int <- c(rep(c("y", "n"), c(5, 15)), rep(c("y", "n"), c(15, 10)))

d <- rep(NA, N)
for(i in 1:N){
	scf  <- sample(sc)
	p1   <- sum(int[scf == "p"] == "y") / 20
	p2   <- sum(int[scf == "c"] == "y") / 25
	d[i] <- p1 - p2
}
sum((d) <= pHatObs) / N

# plot randomization dist for question ------------------------------

pdf("social_experiment_two_sample_randomization.pdf", height = 3, width = 6)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

temp1 <- sort(unique(d))
temp2 <- diff(temp1[1:2])/2
br    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)

histPlot(d, breaks = br, col=COL[7,4], 
         main="", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*"    "), 
         ylab="", axes=FALSE)
axis(1, seq(-0.4, 0.4, 0.2))
axis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)
abline(h = 0)

abline(h = c((1:4)*N/20), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("social_experiment_two_sample_randomization_soln.pdf", height = 3, width = 6)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

temp1 <- sort(unique(d))
temp2 <- diff(temp1[1:2])/2
br    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)

histPlot(d, breaks = br, col=COL[7,4], 
         main="", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*"    "), 
         ylab="", axes=FALSE)
axis(1, seq(-0.4, 0.4, 0.2))
axis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)
abline(h = 0)

histPlot(d[d <= pHatObs], breaks=br, col=COL[1], add=TRUE)
abline(h=0)
lines(rep(pHatObs, 2), c(0, 3)*N/25, lty=3, lwd=1.7)
text(pHatObs, 3*N/25, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_inference_for_props/figures/eoce/yawning_two_sample_randomization/yawning_two_sample_randomization.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# set number of simulations -----------------------------------------
N = 10^4

# randomize ---------------------------------------------------------

pHatObs = 0.04

set.seed(29)

gr <- c(rep("trtmt", 34), rep("ctrl",16))
yawn <- c(rep(c("y", "n"), c(10, 24)), rep(c("y", "n"), c(4, 12)))

d <- rep(NA, N)
for(i in 1:N){
  grf  <- sample(gr)
  p1   <- sum(yawn[grf == "trtmt"] == "y") / 34
  p2   <- sum(yawn[grf == "ctrl"] == "y") / 16
  d[i] <- p2 - p1
}
sum((d) >= pHatObs) / N

# plot randomization dist for question ------------------------------

pdf("yawning_two_sample_randomization.pdf", height = 3.5, width = 6.7)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

histPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], 
         main="", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*"    "), 
         ylab="", axes=FALSE)
axis(1)
axis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)
abline(h = 0)

abline(h = c((1:5)*N/20), lty = 3, lwd = 2, col = COL[7])

dev.off()

# plot randomization dist for solution ------------------------------

pdf("yawning_two_sample_randomization_soln.pdf", height = 3.5, width = 6.7)

par(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))

histPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], 
         main="", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*"    "), 
         ylab="", axes=FALSE)
axis(1)
axis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)
abline(h = 0)

histPlot(d[d >= pHatObs], breaks=seq(-0.6, 0.7, 0.02), col=COL[1], add=TRUE)
abline(h=0)
lines(rep(pHatObs, 2), c(0, 6.1)*N/25, lty=3, lwd=1.7)
text(pHatObs, 6*N/25, as.character(pHatObs), pos=3, cex=1.25)

dev.off()

================================================
FILE: ch_inference_for_props/figures/geomFitEvaluationForSP500/geomFitEvaluationForSP500.R
================================================
library(openintro)
d <- sp500_1950_2018  # read.csv("sp500_1950_2018.csv")
d <- subset(d, "2009-01-01" <= as.Date(Date) & as.Date(Date) <= "2018-12-31")
d. <- diff(d$Adj.Close)
mean(d. > 0)

# Not worrying about case where d. == 0.
R  <- ifelse(d. > 0, 1, 0)
CC <- table(diff(which(R == 1)))
CC[names(CC) == 7] <- sum(CC[names(CC) %in% 7:100])
CC <- CC[- which(names(CC) %in% 8:100)]
p  <- mean(R)
pr <- p * (1 - p)^(0:5)
pr <- append(pr, 1 - sum(pr))

p
(CC <- c(CC))
sum(CC)
C  <- rep(1:7, CC)
(EE <- round(pr * sum(CC)))
E  <- rep(1:7, EE)
(X2 <- sum((CC - EE)^2 / EE))
pchisq(X2, length(CC) - 1, lower.tail = FALSE)

myPDF('geomFitEvaluationForSP500.pdf', 7, 3.5,
      mar = c(3.2, 4.2, 0.2, 1),
      mgp = c(2.1, 0.7, 0))
ylim <- c(0, round(max(CC, EE) + 50, -2))
histPlot(C - 0.13,
         breaks = seq(0, 8, 0.25),
         xlim = c(0.5, 7.5),
         ylim = ylim,
         xlab = 'Wait Until Positive Day',
         ylab = '',
         axes = FALSE,
         col = COL[1])
histPlot(E + 0.13,
         breaks = seq(0, 8, 0.25),
         add = TRUE,
         col = COL[3])
axis(1, 1:7, c(1:6, "7+"))
axis(2, at = seq(0, ylim[2], 200))
par(las = 0)
mtext('Frequency', 2, line = 3)
legend('topright',
       fill = COL[c(1, 3)],
       legend = c('Observed', 'Expected'))
dev.off()



================================================
FILE: ch_inference_for_props/figures/geomFitEvaluationForSP500/sp500_1950_2018.csv
================================================
Date,Open,High,Low,Close,Adj Close,Volume
1950-01-03,16.660000,16.660000,16.660000,16.660000,16.660000,1260000
1950-01-04,16.850000,16.850000,16.850000,16.850000,16.850000,1890000
1950-01-05,16.930000,16.930000,16.930000,16.930000,16.930000,2550000
1950-01-06,16.980000,16.980000,16.980000,16.980000,16.980000,2010000
1950-01-09,17.080000,17.080000,17.080000,17.080000,17.080000,2520000
1950-01-10,17.030001,17.030001,17.030001,17.030001,17.030001,2160000
1950-01-11,17.090000,17.090000,17.090000,17.090000,17.090000,2630000
1950-01-12,16.760000,16.760000,16.760000,16.760000,16.760000,2970000
1950-01-13,16.670000,16.670000,16.670000,16.670000,16.670000,3330000
1950-01-16,16.719999,16.719999,16.719999,16.719999,16.719999,1460000
1950-01-17,16.860001,16.860001,16.860001,16.860001,16.860001,1790000
1950-01-18,16.850000,16.850000,16.850000,16.850000,16.850000,1570000
1950-01-19,16.870001,16.870001,16.870001,16.870001,16.870001,1170000
1950-01-20,16.900000,16.900000,16.900000,16.900000,16.900000,1440000
1950-01-23,16.920000,16.920000,16.920000,16.920000,16.920000,1340000
1950-01-24,16.860001,16.860001,16.860001,16.860001,16.860001,1250000
1950-01-25,16.740000,16.740000,16.740000,16.740000,16.740000,1700000
1950-01-26,16.730000,16.730000,16.730000,16.730000,16.730000,1150000
1950-01-27,16.820000,16.820000,16.820000,16.820000,16.820000,1250000
1950-01-30,17.020000,17.020000,17.020000,17.020000,17.020000,1640000
1950-01-31,17.049999,17.049999,17.049999,17.049999,17.049999,1690000
1950-02-01,17.049999,17.049999,17.049999,17.049999,17.049999,1810000
1950-02-02,17.230000,17.230000,17.230000,17.230000,17.230000,2040000
1950-02-03,17.290001,17.290001,17.290001,17.290001,17.290001,2210000
1950-02-06,17.320000,17.320000,17.320000,17.320000,17.320000,1490000
1950-02-07,17.230000,17.230000,17.230000,17.230000,17.230000,1360000
1950-02-08,17.209999,17.209999,17.209999,17.209999,17.209999,1470000
1950-02-09,17.280001,17.280001,17.280001,17.280001,17.280001,1810000
1950-02-10,17.240000,17.240000,17.240000,17.240000,17.240000,1790000
1950-02-14,17.059999,17.059999,17.059999,17.059999,17.059999,2210000
1950-02-15,17.059999,17.059999,17.059999,17.059999,17.059999,1730000
1950-02-16,16.990000,16.990000,16.990000,16.990000,16.990000,1920000
1950-02-17,17.150000,17.150000,17.150000,17.150000,17.150000,1940000
1950-02-20,17.200001,17.200001,17.200001,17.200001,17.200001,1420000
1950-02-21,17.170000,17.170000,17.170000,17.170000,17.170000,1260000
1950-02-23,17.209999,17.209999,17.209999,17.209999,17.209999,1310000
1950-02-24,17.280001,17.280001,17.280001,17.280001,17.280001,1710000
1950-02-27,17.280001,17.280001,17.280001,17.280001,17.280001,1410000
1950-02-28,17.219999,17.219999,17.219999,17.219999,17.219999,1310000
1950-03-01,17.240000,17.240000,17.240000,17.240000,17.240000,1410000
1950-03-02,17.230000,17.230000,17.230000,17.230000,17.230000,1340000
1950-03-03,17.290001,17.290001,17.290001,17.290001,17.290001,1520000
1950-03-06,17.320000,17.320000,17.320000,17.320000,17.320000,1470000
1950-03-07,17.200001,17.200001,17.200001,17.200001,17.200001,1590000
1950-03-08,17.190001,17.190001,17.190001,17.190001,17.190001,1360000
1950-03-09,17.070000,17.070000,17.070000,17.070000,17.070000,1330000
1950-03-10,17.090000,17.090000,17.090000,17.090000,17.090000,1260000
1950-03-13,17.120001,17.120001,17.120001,17.120001,17.120001,1060000
1950-03-14,17.250000,17.250000,17.250000,17.250000,17.250000,1140000
1950-03-15,17.450001,17.450001,17.450001,17.450001,17.450001,1830000
1950-03-16,17.490000,17.490000,17.490000,17.490000,17.490000,2060000
1950-03-17,17.450001,17.450001,17.450001,17.450001,17.450001,1600000
1950-03-20,17.440001,17.440001,17.440001,17.440001,17.440001,1430000
1950-03-21,17.450001,17.450001,17.450001,17.450001,17.450001,1400000
1950-03-22,17.549999,17.549999,17.549999,17.549999,17.549999,2010000
1950-03-23,17.559999,17.559999,17.559999,17.559999,17.559999,2020000
1950-03-24,17.559999,17.559999,17.559999,17.559999,17.559999,1570000
1950-03-27,17.459999,17.459999,17.459999,17.459999,17.459999,1930000
1950-03-28,17.530001,17.530001,17.530001,17.530001,17.530001,1780000
1950-03-29,17.440001,17.440001,17.440001,17.440001,17.440001,2090000
1950-03-30,17.299999,17.299999,17.299999,17.299999,17.299999,2370000
1950-03-31,17.290001,17.290001,17.290001,17.290001,17.290001,1880000
1950-04-03,17.530001,17.530001,17.530001,17.530001,17.530001,1570000
1950-04-04,17.549999,17.549999,17.549999,17.549999,17.549999,2010000
1950-04-05,17.629999,17.629999,17.629999,17.629999,17.629999,1430000
1950-04-06,17.780001,17.780001,17.780001,17.780001,17.780001,2000000
1950-04-10,17.850000,17.850000,17.850000,17.850000,17.850000,2070000
1950-04-11,17.750000,17.750000,17.750000,17.750000,17.750000,2010000
1950-04-12,17.940001,17.940001,17.940001,17.940001,17.940001,2010000
1950-04-13,17.980000,17.980000,17.980000,17.980000,17.980000,2410000
1950-04-14,17.959999,17.959999,17.959999,17.959999,17.959999,2750000
1950-04-17,17.879999,17.879999,17.879999,17.879999,17.879999,2520000
1950-04-18,18.030001,18.030001,18.030001,18.030001,18.030001,3320000
1950-04-19,18.049999,18.049999,18.049999,18.049999,18.049999,2950000
1950-04-20,17.930000,17.930000,17.930000,17.930000,17.930000,2590000
1950-04-21,17.959999,17.959999,17.959999,17.959999,17.959999,2710000
1950-04-24,17.830000,17.830000,17.830000,17.830000,17.830000,2310000
1950-04-25,17.830000,17.830000,17.830000,17.830000,17.830000,1830000
1950-04-26,17.760000,17.760000,17.760000,17.760000,17.760000,1880000
1950-04-27,17.860001,17.860001,17.860001,17.860001,17.860001,2070000
1950-04-28,17.959999,17.959999,17.959999,17.959999,17.959999,2190000
1950-05-01,18.219999,18.219999,18.219999,18.219999,18.219999,2390000
1950-05-02,18.110001,18.110001,18.110001,18.110001,18.110001,2250000
1950-05-03,18.270000,18.270000,18.270000,18.270000,18.270000,2120000
1950-05-04,18.120001,18.120001,18.120001,18.120001,18.120001,2150000
1950-05-05,18.219999,18.219999,18.219999,18.219999,18.219999,1800000
1950-05-08,18.270000,18.270000,18.270000,18.270000,18.270000,1680000
1950-05-09,18.270000,18.270000,18.270000,18.270000,18.270000,1720000
1950-05-10,18.290001,18.290001,18.290001,18.290001,18.290001,1880000
1950-05-11,18.290001,18.290001,18.290001,18.290001,18.290001,1750000
1950-05-12,18.180000,18.180000,18.180000,18.180000,18.180000,1790000
1950-05-15,18.260000,18.260000,18.260000,18.260000,18.260000,1220000
1950-05-16,18.440001,18.440001,18.440001,18.440001,18.440001,1730000
1950-05-17,18.520000,18.520000,18.520000,18.520000,18.520000,2020000
1950-05-18,18.559999,18.559999,18.559999,18.559999,18.559999,5240000
1950-05-19,18.680000,18.680000,18.680000,18.680000,18.680000,2110000
1950-05-22,18.600000,18.600000,18.600000,18.600000,18.600000,1620000
1950-05-23,18.709999,18.709999,18.709999,18.709999,18.709999,1460000
1950-05-24,18.690001,18.690001,18.690001,18.690001,18.690001,1850000
1950-05-25,18.690001,18.690001,18.690001,18.690001,18.690001,1480000
1950-05-26,18.670000,18.670000,18.670000,18.670000,18.670000,1330000
1950-05-29,18.719999,18.719999,18.719999,18.719999,18.719999,1110000
1950-05-31,18.780001,18.780001,18.780001,18.780001,18.780001,1530000
1950-06-01,18.770000,18.770000,18.770000,18.770000,18.770000,1580000
1950-06-02,18.790001,18.790001,18.790001,18.790001,18.790001,1450000
1950-06-05,18.600000,18.600000,18.600000,18.600000,18.600000,1630000
1950-06-06,18.879999,18.879999,18.879999,18.879999,18.879999,2250000
1950-06-07,18.930000,18.930000,18.930000,18.930000,18.930000,1750000
1950-06-08,19.139999,19.139999,19.139999,19.139999,19.139999,1780000
1950-06-09,19.260000,19.260000,19.260000,19.260000,19.260000,2130000
1950-06-12,19.400000,19.400000,19.400000,19.400000,19.400000,1790000
1950-06-13,19.250000,19.250000,19.250000,19.250000,19.250000,1790000
1950-06-14,18.980000,18.980000,18.980000,18.980000,18.980000,1650000
1950-06-15,18.930000,18.930000,18.930000,18.930000,18.930000,1530000
1950-06-16,18.969999,18.969999,18.969999,18.969999,18.969999,1180000
1950-06-19,18.920000,18.920000,18.920000,18.920000,18.920000,1290000
1950-06-20,18.830000,18.830000,18.830000,18.830000,18.830000,1470000
1950-06-21,19.000000,19.000000,19.000000,19.000000,19.000000,1750000
1950-06-22,19.160000,19.160000,19.160000,19.160000,19.160000,1830000
1950-06-23,19.139999,19.139999,19.139999,19.139999,19.139999,1700000
1950-06-26,18.110001,18.110001,18.110001,18.110001,18.110001,3950000
1950-06-27,17.910000,17.910000,17.910000,17.910000,17.910000,4860000
1950-06-28,18.110001,18.110001,18.110001,18.110001,18.110001,2600000
1950-06-29,17.440001,17.440001,17.440001,17.440001,17.440001,3040000
1950-06-30,17.690001,17.690001,17.690001,17.690001,17.690001,2660000
1950-07-03,17.639999,17.639999,17.639999,17.639999,17.639999,1550000
1950-07-05,17.809999,17.809999,17.809999,17.809999,17.809999,1400000
1950-07-06,17.910000,17.910000,17.910000,17.910000,17.910000,1570000
1950-07-07,17.670000,17.670000,17.670000,17.670000,17.670000,1870000
1950-07-10,17.590000,17.590000,17.590000,17.590000,17.590000,1960000
1950-07-11,17.320000,17.320000,17.320000,17.320000,17.320000,3250000
1950-07-12,16.870001,16.870001,16.870001,16.870001,16.870001,3200000
1950-07-13,16.690001,16.690001,16.690001,16.690001,16.690001,2660000
1950-07-14,16.870001,16.870001,16.870001,16.870001,16.870001,1900000
1950-07-17,16.680000,16.680000,16.680000,16.680000,16.680000,1520000
1950-07-18,17.059999,17.059999,17.059999,17.059999,17.059999,1820000
1950-07-19,17.360001,17.360001,17.360001,17.360001,17.360001,2430000
1950-07-20,17.610001,17.610001,17.610001,17.610001,17.610001,3160000
1950-07-21,17.590000,17.590000,17.590000,17.590000,17.590000,2810000
1950-07-24,17.480000,17.480000,17.480000,17.480000,17.480000,2300000
1950-07-25,17.230000,17.230000,17.230000,17.230000,17.230000,2770000
1950-07-26,17.270000,17.270000,17.270000,17.270000,17.270000,2460000
1950-07-27,17.500000,17.500000,17.500000,17.500000,17.500000,2300000
1950-07-28,17.690001,17.690001,17.690001,17.690001,17.690001,2050000
1950-07-31,17.840000,17.840000,17.840000,17.840000,17.840000,1590000
1950-08-01,18.020000,18.020000,18.020000,18.020000,18.020000,1970000
1950-08-02,17.950001,17.950001,17.950001,17.950001,17.950001,1980000
1950-08-03,17.990000,17.990000,17.990000,17.990000,17.990000,1660000
1950-08-04,18.139999,18.139999,18.139999,18.139999,18.139999,1600000
1950-08-07,18.410000,18.410000,18.410000,18.410000,18.410000,1850000
1950-08-08,18.459999,18.459999,18.459999,18.459999,18.459999,2180000
1950-08-09,18.610001,18.610001,18.610001,18.610001,18.610001,1760000
1950-08-10,18.480000,18.480000,18.480000,18.480000,18.480000,1870000
1950-08-11,18.280001,18.280001,18.280001,18.280001,18.280001,1680000
1950-08-14,18.290001,18.290001,18.290001,18.290001,18.290001,1280000
1950-08-15,18.320000,18.320000,18.320000,18.320000,18.320000,1330000
1950-08-16,18.340000,18.340000,18.340000,18.340000,18.340000,1770000
1950-08-17,18.540001,18.540001,18.540001,18.540001,18.540001,2170000
1950-08-18,18.680000,18.680000,18.680000,18.680000,18.680000,1780000
1950-08-21,18.700001,18.700001,18.700001,18.700001,18.700001,1840000
1950-08-22,18.680000,18.680000,18.680000,18.680000,18.680000,1550000
1950-08-23,18.820000,18.820000,18.820000,18.820000,18.820000,1580000
1950-08-24,18.790001,18.790001,18.790001,18.790001,18.790001,1620000
1950-08-25,18.540001,18.540001,18.540001,18.540001,18.540001,1610000
1950-08-28,18.530001,18.530001,18.530001,18.530001,18.530001,1300000
1950-08-29,18.540001,18.540001,18.540001,18.540001,18.540001,1890000
1950-08-30,18.430000,18.430000,18.430000,18.430000,18.430000,1490000
1950-08-31,18.420000,18.420000,18.420000,18.420000,18.420000,1140000
1950-09-01,18.549999,18.549999,18.549999,18.549999,18.549999,1290000
1950-09-05,18.680000,18.680000,18.680000,18.680000,18.680000,1250000
1950-09-06,18.540001,18.540001,18.540001,18.540001,18.540001,1300000
1950-09-07,18.590000,18.590000,18.590000,18.590000,18.590000,1340000
1950-09-08,18.750000,18.750000,18.750000,18.750000,18.750000,1960000
1950-09-11,18.610001,18.610001,18.610001,18.610001,18.610001,1860000
1950-09-12,18.870001,18.870001,18.870001,18.870001,18.870001,1680000
1950-09-13,19.090000,19.090000,19.090000,19.090000,19.090000,2600000
1950-09-14,19.180000,19.180000,19.180000,19.180000,19.180000,2350000
1950-09-15,19.290001,19.290001,19.290001,19.290001,19.290001,2410000
1950-09-18,19.370001,19.370001,19.370001,19.370001,19.370001,2040000
1950-09-19,19.309999,19.309999,19.309999,19.309999,19.309999,1590000
1950-09-20,19.209999,19.209999,19.209999,19.209999,19.209999,2100000
1950-09-21,19.370001,19.370001,19.370001,19.370001,19.370001,1650000
1950-09-22,19.440001,19.440001,19.440001,19.440001,19.440001,2510000
1950-09-25,19.420000,19.420000,19.420000,19.420000,19.420000,2020000
1950-09-26,19.139999,19.139999,19.139999,19.139999,19.139999,2280000
1950-09-27,19.410000,19.410000,19.410000,19.410000,19.410000,2360000
1950-09-28,19.420000,19.420000,19.420000,19.420000,19.420000,2200000
1950-09-29,19.450001,19.450001,19.450001,19.450001,19.450001,1800000
1950-10-02,19.690001,19.690001,19.690001,19.690001,19.690001,2200000
1950-10-03,19.660000,19.660000,19.660000,19.660000,19.660000,2480000
1950-10-04,20.000000,20.000000,20.000000,20.000000,20.000000,2920000
1950-10-05,19.889999,19.889999,19.889999,19.889999,19.889999,2490000
1950-10-06,20.120001,20.120001,20.120001,20.120001,20.120001,2360000
1950-10-09,20.000000,20.000000,20.000000,20.000000,20.000000,2330000
1950-10-10,19.780001,19.780001,19.780001,19.780001,19.780001,1870000
1950-10-11,19.860001,19.860001,19.860001,19.860001,19.860001,2200000
1950-10-13,19.850000,19.850000,19.850000,19.850000,19.850000,2030000
1950-10-16,19.709999,19.709999,19.709999,19.709999,19.709999,1630000
1950-10-17,19.889999,19.889999,19.889999,19.889999,19.889999,2010000
1950-10-18,20.010000,20.010000,20.010000,20.010000,20.010000,2410000
1950-10-19,20.020000,20.020000,20.020000,20.020000,20.020000,2250000
1950-10-20,19.959999,19.959999,19.959999,19.959999,19.959999,1840000
1950-10-23,19.959999,19.959999,19.959999,19.959999,19.959999,1850000
1950-10-24,20.080000,20.080000,20.080000,20.080000,20.080000,1790000
1950-10-25,20.049999,20.049999,20.049999,20.049999,20.049999,1930000
1950-10-26,19.610001,19.610001,19.610001,19.610001,19.610001,3000000
1950-10-27,19.770000,19.770000,19.770000,19.770000,19.770000,1800000
1950-10-30,19.610001,19.610001,19.610001,19.610001,19.610001,1790000
1950-10-31,19.530001,19.530001,19.530001,19.530001,19.530001,2010000
1950-11-01,19.559999,19.559999,19.559999,19.559999,19.559999,1780000
1950-11-02,19.730000,19.730000,19.730000,19.730000,19.730000,1580000
1950-11-03,19.850000,19.850000,19.850000,19.850000,19.850000,1560000
1950-11-06,19.360001,19.360001,19.360001,19.360001,19.360001,2580000
1950-11-08,19.559999,19.559999,19.559999,19.559999,19.559999,1850000
1950-11-09,19.790001,19.790001,19.790001,19.790001,19.790001,1760000
1950-11-10,19.940001,19.940001,19.940001,19.940001,19.940001,1640000
1950-11-13,20.010000,20.010000,20.010000,20.010000,20.010000,1630000
1950-11-14,19.860001,19.860001,19.860001,19.860001,19.860001,1780000
1950-11-15,19.820000,19.820000,19.820000,19.820000,19.820000,1620000
1950-11-16,19.719999,19.719999,19.719999,19.719999,19.719999,1760000
1950-11-17,19.860001,19.860001,19.860001,19.860001,19.860001,2130000
1950-11-20,19.930000,19.930000,19.930000,19.930000,19.930000,2250000
1950-11-21,19.879999,19.879999,19.879999,19.879999,19.879999,2010000
1950-11-22,20.160000,20.160000,20.160000,20.160000,20.160000,2730000
1950-11-24,20.320000,20.320000,20.320000,20.320000,20.320000,2620000
1950-11-27,20.180000,20.180000,20.180000,20.180000,20.180000,1740000
1950-11-28,19.559999,19.559999,19.559999,19.559999,19.559999,2970000
1950-11-29,19.370001,19.370001,19.370001,19.370001,19.370001,2770000
1950-11-30,19.510000,19.510000,19.510000,19.510000,19.510000,2080000
1950-12-01,19.660000,19.660000,19.660000,19.660000,19.660000,1870000
1950-12-04,19.000000,19.000000,19.000000,19.000000,19.000000,2510000
1950-12-05,19.309999,19.309999,19.309999,19.309999,19.309999,1940000
1950-12-06,19.450001,19.450001,19.450001,19.450001,19.450001,2010000
1950-12-07,19.400000,19.400000,19.400000,19.400000,19.400000,1810000
1950-12-08,19.400000,19.400000,19.400000,19.400000,19.400000,2310000
1950-12-11,19.719999,19.719999,19.719999,19.719999,19.719999,2600000
1950-12-12,19.680000,19.680000,19.680000,19.680000,19.680000,2140000
1950-12-13,19.670000,19.670000,19.670000,19.670000,19.670000,2030000
1950-12-14,19.430000,19.430000,19.430000,19.430000,19.430000,2660000
1950-12-15,19.330000,19.330000,19.330000,19.330000,19.330000,2420000
1950-12-18,19.850000,19.850000,19.850000,19.850000,19.850000,4500000
1950-12-19,19.959999,19.959999,19.959999,19.959999,19.959999,3650000
1950-12-20,19.969999,19.969999,19.969999,19.969999,19.969999,3510000
1950-12-21,19.980000,19.980000,19.980000,19.980000,19.980000,3990000
1950-12-22,20.070000,20.070000,20.070000,20.070000,20.070000,2720000
1950-12-26,19.920000,19.920000,19.920000,19.920000,19.920000,2660000
1950-12-27,20.299999,20.299999,20.299999,20.299999,20.299999,2940000
1950-12-28,20.379999,20.379999,20.379999,20.379999,20.379999,3560000
1950-12-29,20.430000,20.430000,20.430000,20.430000,20.430000,3440000
1951-01-02,20.770000,20.770000,20.770000,20.770000,20.770000,3030000
1951-01-03,20.690001,20.690001,20.690001,20.690001,20.690001,3370000
1951-01-04,20.870001,20.870001,20.870001,20.870001,20.870001,3390000
1951-01-05,20.870001,20.870001,20.870001,20.870001,20.870001,3390000
1951-01-08,21.000000,21.000000,21.000000,21.000000,21.000000,2780000
1951-01-09,21.120001,21.120001,21.120001,21.120001,21.120001,3800000
1951-01-10,20.850000,20.850000,20.850000,20.850000,20.850000,3270000
1951-01-11,21.190001,21.190001,21.190001,21.190001,21.190001,3490000
1951-01-12,21.110001,21.110001,21.110001,21.110001,21.110001,2950000
1951-01-15,21.299999,21.299999,21.299999,21.299999,21.299999,2830000
1951-01-16,21.459999,21.459999,21.459999,21.459999,21.459999,3740000
1951-01-17,21.549999,21.549999,21.549999,21.549999,21.549999,3880000
1951-01-18,21.400000,21.400000,21.400000,21.400000,21.400000,3490000
1951-01-19,21.360001,21.360001,21.360001,21.360001,21.360001,3170000
1951-01-22,21.180000,21.180000,21.180000,21.180000,21.180000,2570000
1951-01-23,21.260000,21.260000,21.260000,21.260000,21.260000,2080000
1951-01-24,21.160000,21.160000,21.160000,21.160000,21.160000,1990000
1951-01-25,21.030001,21.030001,21.030001,21.030001,21.030001,2520000
1951-01-26,21.260000,21.260000,21.260000,21.260000,21.260000,2230000
1951-01-29,21.670000,21.670000,21.670000,21.670000,21.670000,2630000
1951-01-30,21.740000,21.740000,21.740000,21.740000,21.740000,2480000
1951-01-31,21.660000,21.660000,21.660000,21.660000,21.660000,2340000
1951-02-01,21.770000,21.770000,21.770000,21.770000,21.770000,2380000
1951-02-02,21.959999,21.959999,21.959999,21.959999,21.959999,3030000
1951-02-05,22.200001,22.200001,22.200001,22.200001,22.200001,2680000
1951-02-06,22.120001,22.120001,22.120001,22.120001,22.120001,2370000
1951-02-07,21.990000,21.990000,21.990000,21.990000,21.990000,2020000
1951-02-08,22.090000,22.090000,22.090000,22.090000,22.090000,2120000
1951-02-09,22.170000,22.170000,22.170000,22.170000,22.170000,2550000
1951-02-13,22.180000,22.180000,22.180000,22.180000,22.180000,2400000
1951-02-14,22.120001,22.120001,22.120001,22.120001,22.120001,2050000
1951-02-15,22.000000,22.000000,22.000000,22.000000,22.000000,1700000
1951-02-16,22.129999,22.129999,22.129999,22.129999,22.129999,1860000
1951-02-19,21.830000,21.830000,21.830000,21.830000,21.830000,1910000
1951-02-20,21.790001,21.790001,21.790001,21.790001,21.790001,2010000
1951-02-21,21.860001,21.860001,21.860001,21.860001,21.860001,1670000
1951-02-23,21.920000,21.920000,21.920000,21.920000,21.920000,1540000
1951-02-26,21.930000,21.930000,21.930000,21.930000,21.930000,1650000
1951-02-27,21.760000,21.760000,21.760000,21.760000,21.760000,1680000
1951-02-28,21.799999,21.799999,21.799999,21.799999,21.799999,1640000
1951-03-01,21.850000,21.850000,21.850000,21.850000,21.850000,1610000
1951-03-02,21.930000,21.930000,21.930000,21.930000,21.930000,1570000
1951-03-05,21.790001,21.790001,21.790001,21.790001,21.790001,1690000
1951-03-06,21.790001,21.790001,21.790001,21.790001,21.790001,1490000
1951-03-07,21.860001,21.860001,21.860001,21.860001,21.860001,1770000
1951-03-08,21.950001,21.950001,21.950001,21.950001,21.950001,1440000
1951-03-09,21.950001,21.950001,21.950001,21.950001,21.950001,1610000
1951-03-12,21.700001,21.700001,21.700001,21.700001,21.700001,1640000
1951-03-13,21.410000,21.410000,21.410000,21.410000,21.410000,2330000
1951-03-14,21.250000,21.250000,21.250000,21.250000,21.250000,2110000
1951-03-15,21.290001,21.290001,21.290001,21.290001,21.290001,2070000
1951-03-16,21.639999,21.639999,21.639999,21.639999,21.639999,1660000
1951-03-19,21.559999,21.559999,21.559999,21.559999,21.559999,1120000
1951-03-20,21.520000,21.520000,21.520000,21.520000,21.520000,1020000
1951-03-21,21.639999,21.639999,21.639999,21.639999,21.639999,1310000
1951-03-22,21.730000,21.730000,21.730000,21.730000,21.730000,1290000
1951-03-26,21.530001,21.530001,21.530001,21.530001,21.530001,1230000
1951-03-27,21.510000,21.510000,21.510000,21.510000,21.510000,1250000
1951-03-28,21.260000,21.260000,21.260000,21.260000,21.260000,1770000
1951-03-29,21.330000,21.330000,21.330000,21.330000,21.330000,1300000
1951-03-30,21.480000,21.480000,21.480000,21.480000,21.480000,1150000
1951-04-02,21.320000,21.320000,21.320000,21.320000,21.320000,1280000
1951-04-03,21.260000,21.260000,21.260000,21.260000,21.260000,1220000
1951-04-04,21.400000,21.400000,21.400000,21.400000,21.400000,1300000
1951-04-05,21.690001,21.690001,21.690001,21.690001,21.690001,1790000
1951-04-06,21.719999,21.719999,21.719999,21.719999,21.719999,1450000
1951-04-09,21.680000,21.680000,21.680000,21.680000,21.680000,1110000
1951-04-10,21.650000,21.650000,21.650000,21.650000,21.650000,1280000
1951-04-11,21.639999,21.639999,21.639999,21.639999,21.639999,1420000
1951-04-12,21.830000,21.830000,21.830000,21.830000,21.830000,1530000
1951-04-13,22.090000,22.090000,22.090000,22.090000,22.090000,2120000
1951-04-16,22.040001,22.040001,22.040001,22.040001,22.040001,1730000
1951-04-17,22.090000,22.090000,22.090000,22.090000,22.090000,1470000
1951-04-18,22.129999,22.129999,22.129999,22.129999,22.129999,1780000
1951-04-19,22.040001,22.040001,22.040001,22.040001,22.040001,1520000
1951-04-20,22.040001,22.040001,22.040001,22.040001,22.040001,940000
1951-04-23,22.049999,22.049999,22.049999,22.049999,22.049999,1160000
1951-04-24,21.959999,21.959999,21.959999,21.959999,21.959999,1420000
1951-04-25,21.969999,21.969999,21.969999,21.969999,21.969999,1520000
1951-04-26,22.160000,22.160000,22.160000,22.160000,22.160000,1800000
1951-04-27,22.389999,22.389999,22.389999,22.389999,22.389999,2120000
1951-04-30,22.430000,22.430000,22.430000,22.430000,22.430000,1790000
1951-05-01,22.530001,22.530001,22.530001,22.530001,22.530001,1760000
1951-05-02,22.620001,22.620001,22.620001,22.620001,22.620001,1900000
1951-05-03,22.809999,22.809999,22.809999,22.809999,22.809999,2060000
1951-05-04,22.770000,22.770000,22.770000,22.770000,22.770000,2050000
1951-05-07,22.629999,22.629999,22.629999,22.629999,22.629999,1580000
1951-05-08,22.610001,22.610001,22.610001,22.610001,22.610001,1600000
1951-05-09,22.639999,22.639999,22.639999,22.639999,22.639999,1960000
1951-05-10,22.510000,22.510000,22.510000,22.510000,22.510000,1660000
1951-05-11,22.330000,22.330000,22.330000,22.330000,22.330000,1640000
1951-05-14,22.180000,22.180000,22.180000,22.180000,22.180000,1250000
1951-05-15,21.760000,21.760000,21.760000,21.760000,21.760000,2020000
1951-05-16,21.690001,21.690001,21.690001,21.690001,21.690001,1660000
1951-05-17,21.910000,21.910000,21.910000,21.910000,21.910000,1370000
1951-05-18,21.510000,21.510000,21.510000,21.510000,21.510000,1660000
1951-05-21,21.459999,21.459999,21.459999,21.459999,21.459999,1580000
1951-05-22,21.360001,21.360001,21.360001,21.360001,21.360001,1440000
1951-05-23,21.160000,21.160000,21.160000,21.160000,21.160000,1540000
1951-05-24,21.049999,21.049999,21.049999,21.049999,21.049999,2580000
1951-05-25,21.030001,21.030001,21.030001,21.030001,21.030001,1210000
1951-05-28,21.209999,21.209999,21.209999,21.209999,21.209999,1240000
1951-05-29,21.350000,21.350000,21.350000,21.350000,21.350000,1190000
1951-05-31,21.520000,21.520000,21.520000,21.520000,21.520000,1220000
1951-06-01,21.480000,21.480000,21.480000,21.480000,21.480000,9810000
1951-06-04,21.240000,21.240000,21.240000,21.240000,21.240000,1100000
1951-06-05,21.330000,21.330000,21.330000,21.330000,21.330000,1180000
1951-06-06,21.480000,21.480000,21.480000,21.480000,21.480000,1200000
1951-06-07,21.559999,21.559999,21.559999,21.559999,21.559999,1340000
1951-06-08,21.490000,21.490000,21.490000,21.490000,21.490000,1000000
1951-06-11,21.610001,21.610001,21.610001,21.610001,21.610001,1220000
1951-06-12,21.520000,21.520000,21.520000,21.520000,21.520000,1200000
1951-06-13,21.549999,21.549999,21.549999,21.549999,21.549999,1060000
1951-06-14,21.840000,21.840000,21.840000,21.840000,21.840000,1300000
1951-06-15,22.040001,22.040001,22.040001,22.040001,22.040001,1370000
1951-06-18,22.049999,22.049999,22.049999,22.049999,22.049999,1050000
1951-06-19,22.020000,22.020000,22.020000,22.020000,22.020000,1100000
1951-06-20,21.910000,21.910000,21.910000,21.910000,21.910000,1120000
1951-06-21,21.780001,21.780001,21.780001,21.780001,21.780001,1100000
1951-06-22,21.549999,21.549999,21.549999,21.549999,21.549999,1340000
1951-06-25,21.290001,21.290001,21.290001,21.290001,21.290001,2440000
1951-06-26,21.299999,21.299999,21.299999,21.299999,21.299999,1260000
1951-06-27,21.370001,21.370001,21.370001,21.370001,21.370001,1360000
1951-06-28,21.100000,21.100000,21.100000,21.100000,21.100000,1940000
1951-06-29,20.959999,20.959999,20.959999,20.959999,20.959999,1730000
1951-07-02,21.100000,21.100000,21.100000,21.100000,21.100000,1350000
1951-07-03,21.230000,21.230000,21.230000,21.230000,21.230000,1250000
1951-07-05,21.639999,21.639999,21.639999,21.639999,21.639999,1410000
1951-07-06,21.639999,21.639999,21.639999,21.639999,21.639999,1170000
1951-07-09,21.730000,21.730000,21.730000,21.730000,21.730000,1110000
1951-07-10,21.629999,21.629999,21.629999,21.629999,21.629999,990000
1951-07-11,21.680000,21.680000,21.680000,21.680000,21.680000,970000
1951-07-12,21.799999,21.799999,21.799999,21.799999,21.799999,1050000
1951-07-13,21.980000,21.980000,21.980000,21.980000,21.980000,1320000
1951-07-16,21.730000,21.730000,21.730000,21.730000,21.730000,1200000
1951-07-17,21.920000,21.920000,21.920000,21.920000,21.920000,1280000
1951-07-18,21.879999,21.879999,21.879999,21.879999,21.879999,1370000
1951-07-19,21.840000,21.840000,21.840000,21.840000,21.840000,1120000
1951-07-20,21.879999,21.879999,21.879999,21.879999,21.879999,1390000
1951-07-23,22.100000,22.100000,22.100000,22.100000,22.100000,1320000
1951-07-24,22.440001,22.440001,22.440001,22.440001,22.440001,1740000
1951-07-25,22.320000,22.320000,22.320000,22.320000,22.320000,1870000
1951-07-26,22.469999,22.469999,22.469999,22.469999,22.469999,1480000
1951-07-27,22.530001,22.530001,22.530001,22.530001,22.530001,1450000
1951-07-30,22.629999,22.629999,22.629999,22.629999,22.629999,1600000
1951-07-31,22.400000,22.400000,22.400000,22.400000,22.400000,1550000
1951-08-01,22.510000,22.510000,22.510000,22.510000,22.510000,1680000
1951-08-02,22.820000,22.820000,22.820000,22.820000,22.820000,2130000
1951-08-03,22.850000,22.850000,22.850000,22.850000,22.850000,1570000
1951-08-06,23.010000,23.010000,23.010000,23.010000,23.010000,1600000
1951-08-07,23.030001,23.030001,23.030001,23.030001,23.030001,1810000
1951-08-08,22.930000,22.930000,22.930000,22.930000,22.930000,1410000
1951-08-09,22.840000,22.840000,22.840000,22.840000,22.840000,1500000
1951-08-10,22.790001,22.790001,22.790001,22.790001,22.790001,1260000
1951-08-13,22.799999,22.799999,22.799999,22.799999,22.799999,1320000
1951-08-14,22.700001,22.700001,22.700001,22.700001,22.700001,1180000
1951-08-15,22.790001,22.790001,22.790001,22.790001,22.790001,1340000
1951-08-16,22.870001,22.870001,22.870001,22.870001,22.870001,1750000
1951-08-17,22.940001,22.940001,22.940001,22.940001,22.940001,1620000
1951-08-20,22.930000,22.930000,22.930000,22.930000,22.930000,1130000
1951-08-21,22.830000,22.830000,22.830000,22.830000,22.830000,1400000
1951-08-22,22.750000,22.750000,22.750000,22.750000,22.750000,1130000
1951-08-23,22.900000,22.900000,22.900000,22.900000,22.900000,1230000
1951-08-24,22.879999,22.879999,22.879999,22.879999,22.879999,1210000
1951-08-27,22.850000,22.850000,22.850000,22.850000,22.850000,1080000
1951-08-28,22.900000,22.900000,22.900000,22.900000,22.900000,1280000
1951-08-29,23.080000,23.080000,23.080000,23.080000,23.080000,1520000
1951-08-30,23.240000,23.240000,23.240000,23.240000,23.240000,1950000
1951-08-31,23.280001,23.280001,23.280001,23.280001,23.280001,1530000
1951-09-04,23.280001,23.280001,23.280001,23.280001,23.280001,1520000
1951-09-05,23.420000,23.420000,23.420000,23.420000,23.420000,1850000
1951-09-06,23.469999,23.469999,23.469999,23.469999,23.469999,2150000
1951-09-07,23.530001,23.530001,23.530001,23.530001,23.530001,1970000
1951-09-10,23.620001,23.620001,23.620001,23.620001,23.620001,2190000
1951-09-11,23.500000,23.500000,23.500000,23.500000,23.500000,2040000
1951-09-12,23.600000,23.600000,23.600000,23.600000,23.600000,2180000
1951-09-13,23.709999,23.709999,23.709999,23.709999,23.709999,2350000
1951-09-14,23.690001,23.690001,23.690001,23.690001,23.690001,2170000
1951-09-17,23.620001,23.620001,23.620001,23.620001,23.620001,1800000
1951-09-18,23.590000,23.590000,23.590000,23.590000,23.590000,2030000
1951-09-19,23.590000,23.590000,23.590000,23.590000,23.590000,2070000
1951-09-20,23.570000,23.570000,23.570000,23.570000,23.570000,2100000
1951-09-21,23.400000,23.400000,23.400000,23.400000,23.400000,2180000
1951-09-24,23.299999,23.299999,23.299999,23.299999,23.299999,1630000
1951-09-25,23.379999,23.379999,23.379999,23.379999,23.379999,1740000
1951-09-26,23.400000,23.400000,23.400000,23.400000,23.400000,1520000
1951-09-27,23.270000,23.270000,23.270000,23.270000,23.270000,1540000
1951-09-28,23.260000,23.260000,23.260000,23.260000,23.260000,1390000
1951-10-01,23.469999,23.469999,23.469999,23.469999,23.469999,1330000
1951-10-02,23.639999,23.639999,23.639999,23.639999,23.639999,1870000
1951-10-03,23.790001,23.790001,23.790001,23.790001,23.790001,2780000
1951-10-04,23.719999,23.719999,23.719999,23.719999,23.719999,1810000
1951-10-05,23.780001,23.780001,23.780001,23.780001,23.780001,2080000
1951-10-08,23.750000,23.750000,23.750000,23.750000,23.750000,1860000
1951-10-09,23.650000,23.650000,23.650000,23.650000,23.650000,1750000
1951-10-10,23.610001,23.610001,23.610001,23.610001,23.610001,1320000
1951-10-11,23.700001,23.700001,23.700001,23.700001,23.700001,1760000
1951-10-15,23.850000,23.850000,23.850000,23.850000,23.850000,1720000
1951-10-16,23.770000,23.770000,23.770000,23.770000,23.770000,1730000
1951-10-17,23.690001,23.690001,23.690001,23.690001,23.690001,1460000
1951-10-18,23.670000,23.670000,23.670000,23.670000,23.670000,1450000
1951-10-19,23.320000,23.320000,23.320000,23.320000,23.320000,1990000
1951-10-22,22.750000,22.750000,22.750000,22.750000,22.750000,2690000
1951-10-23,22.840000,22.840000,22.840000,22.840000,22.840000,2110000
1951-10-24,23.030001,23.030001,23.030001,23.030001,23.030001,1670000
1951-10-25,22.959999,22.959999,22.959999,22.959999,22.959999,1360000
1951-10-26,22.809999,22.809999,22.809999,22.809999,22.809999,1710000
1951-10-29,22.690001,22.690001,22.690001,22.690001,22.690001,1780000
1951-10-30,22.660000,22.660000,22.660000,22.660000,22.660000,1530000
1951-10-31,22.940001,22.940001,22.940001,22.940001,22.940001,1490000
1951-11-01,23.100000,23.100000,23.100000,23.100000,23.100000,1430000
1951-11-02,22.930000,22.930000,22.930000,22.930000,22.930000,1230000
1951-11-05,22.820000,22.820000,22.820000,22.820000,22.820000,1130000
1951-11-07,22.490000,22.490000,22.490000,22.490000,22.490000,1490000
1951-11-08,22.469999,22.469999,22.469999,22.469999,22.469999,1410000
1951-11-09,22.750000,22.750000,22.750000,22.750000,22.750000,1470000
1951-11-13,22.790001,22.790001,22.790001,22.790001,22.790001,1160000
1951-11-14,22.850000,22.850000,22.850000,22.850000,22.850000,1220000
1951-11-15,22.840000,22.840000,22.840000,22.840000,22.840000,1200000
1951-11-16,22.820000,22.820000,22.820000,22.820000,22.820000,1140000
1951-11-19,22.730000,22.730000,22.730000,22.730000,22.730000,1030000
1951-11-20,22.680000,22.680000,22.680000,22.680000,22.680000,1130000
1951-11-21,22.639999,22.639999,22.639999,22.639999,22.639999,1090000
1951-11-23,22.400000,22.400000,22.400000,22.400000,22.400000,1210000
1951-11-26,22.430000,22.430000,22.430000,22.430000,22.430000,1180000
1951-11-27,22.660000,22.660000,22.660000,22.660000,22.660000,1310000
1951-11-28,22.610001,22.610001,22.610001,22.610001,22.610001,1150000
1951-11-29,22.670000,22.670000,22.670000,22.670000,22.670000,1070000
1951-11-30,22.879999,22.879999,22.879999,22.879999,22.879999,1530000
1951-12-03,23.010000,23.010000,23.010000,23.010000,23.010000,1220000
1951-12-04,23.139999,23.139999,23.139999,23.139999,23.139999,1280000
1951-12-05,23.070000,23.070000,23.070000,23.070000,23.070000,1330000
1951-12-06,23.340000,23.340000,23.340000,23.340000,23.340000,1840000
1951-12-07,23.379999,23.379999,23.379999,23.379999,23.379999,1990000
1951-12-10,23.420000,23.420000,23.420000,23.420000,23.420000,1340000
1951-12-11,23.299999,23.299999,23.299999,23.299999,23.299999,1360000
1951-12-12,23.370001,23.370001,23.370001,23.370001,23.370001,1280000
1951-12-13,23.389999,23.389999,23.389999,23.389999,23.389999,1380000
1951-12-14,23.370001,23.370001,23.370001,23.370001,23.370001,1360000
1951-12-17,23.410000,23.410000,23.410000,23.410000,23.410000,1220000
1951-12-18,23.490000,23.490000,23.490000,23.490000,23.490000,1290000
1951-12-19,23.570000,23.570000,23.570000,23.570000,23.570000,1510000
1951-12-20,23.570000,23.570000,23.570000,23.570000,23.570000,1340000
1951-12-21,23.510000,23.510000,23.510000,23.510000,23.510000,1250000
1951-12-24,23.540001,23.540001,23.540001,23.540001,23.540001,680000
1951-12-26,23.440001,23.440001,23.440001,23.440001,23.440001,1520000
1951-12-27,23.650000,23.650000,23.650000,23.650000,23.650000,1460000
1951-12-28,23.690001,23.690001,23.690001,23.690001,23.690001,1470000
1951-12-31,23.770000,23.770000,23.770000,23.770000,23.770000,1440000
1952-01-02,23.799999,23.799999,23.799999,23.799999,23.799999,1070000
1952-01-03,23.879999,23.879999,23.879999,23.879999,23.879999,1220000
1952-01-04,23.920000,23.920000,23.920000,23.920000,23.920000,1480000
1952-01-07,23.910000,23.910000,23.910000,23.910000,23.910000,1540000
1952-01-08,23.820000,23.820000,23.820000,23.820000,23.820000,1390000
1952-01-09,23.740000,23.740000,23.740000,23.740000,23.740000,1370000
1952-01-10,23.860001,23.860001,23.860001,23.860001,23.860001,1520000
1952-01-11,23.980000,23.980000,23.980000,23.980000,23.980000,1760000
1952-01-14,24.160000,24.160000,24.160000,24.160000,24.160000,1510000
1952-01-15,24.059999,24.059999,24.059999,24.059999,24.059999,1340000
1952-01-16,24.090000,24.090000,24.090000,24.090000,24.090000,1430000
1952-01-17,24.200001,24.200001,24.200001,24.200001,24.200001,1590000
1952-01-18,24.250000,24.250000,24.250000,24.250000,24.250000,1740000
1952-01-21,24.459999,24.459999,24.459999,24.459999,24.459999,1730000
1952-01-22,24.660000,24.660000,24.660000,24.660000,24.660000,1920000
1952-01-23,24.540001,24.540001,24.540001,24.540001,24.540001,1680000
1952-01-24,24.559999,24.559999,24.559999,24.559999,24.559999,1570000
1952-01-25,24.549999,24.549999,24.549999,24.549999,24.549999,1650000
1952-01-28,24.610001,24.610001,24.610001,24.610001,24.610001,1590000
1952-01-29,24.570000,24.570000,24.570000,24.570000,24.570000,1730000
1952-01-30,24.230000,24.230000,24.230000,24.230000,24.230000,1880000
1952-01-31,24.139999,24.139999,24.139999,24.139999,24.139999,1810000
1952-02-01,24.299999,24.299999,24.299999,24.299999,24.299999,1350000
1952-02-04,24.120001,24.120001,24.120001,24.120001,24.120001,1640000
1952-02-05,24.110001,24.110001,24.110001,24.110001,24.110001,1590000
1952-02-06,24.180000,24.180000,24.180000,24.180000,24.180000,1310000
1952-02-07,24.110001,24.110001,24.110001,24.110001,24.110001,1170000
1952-02-08,24.240000,24.240000,24.240000,24.240000,24.240000,1350000
1952-02-11,24.110001,24.110001,24.110001,24.110001,24.110001,1140000
1952-02-13,23.920000,23.920000,23.920000,23.920000,23.920000,1300000
1952-02-14,23.870001,23.870001,23.870001,23.870001,23.870001,1340000
1952-02-15,23.860001,23.860001,23.860001,23.860001,23.860001,1200000
1952-02-18,23.740000,23.740000,23.740000,23.740000,23.740000,1140000
1952-02-19,23.360001,23.360001,23.360001,23.360001,23.360001,1630000
1952-02-20,23.090000,23.090000,23.090000,23.090000,23.090000,1970000
1952-02-21,23.160000,23.160000,23.160000,23.160000,23.160000,1360000
1952-02-25,23.230000,23.230000,23.230000,23.230000,23.230000,1200000
1952-02-26,23.150000,23.150000,23.150000,23.150000,23.150000,1080000
1952-02-27,23.180000,23.180000,23.180000,23.180000,23.180000,1260000
1952-02-28,23.290001,23.290001,23.290001,23.290001,23.290001,1150000
1952-02-29,23.260000,23.260000,23.260000,23.260000,23.260000,1000000
1952-03-03,23.290001,23.290001,23.290001,23.290001,23.290001,1020000
1952-03-04,23.680000,23.680000,23.680000,23.680000,23.680000,1570000
1952-03-05,23.709999,23.709999,23.709999,23.709999,23.709999,1380000
1952-03-06,23.690001,23.690001,23.690001,23.690001,23.690001,1210000
1952-03-07,23.719999,23.719999,23.719999,23.719999,23.719999,1410000
1952-03-10,23.600000,23.600000,23.600000,23.600000,23.600000,1170000
1952-03-11,23.620001,23.620001,23.620001,23.620001,23.620001,1210000
1952-03-12,23.730000,23.730000,23.730000,23.730000,23.730000,1310000
1952-03-13,23.750000,23.750000,23.750000,23.750000,23.750000,1270000
1952-03-14,23.750000,23.750000,23.750000,23.750000,23.750000,1350000
1952-03-17,23.920000,23.920000,23.920000,23.920000,23.920000,1150000
1952-03-18,23.870001,23.870001,23.870001,23.870001,23.870001,1170000
1952-03-19,23.820000,23.820000,23.820000,23.820000,23.820000,1090000
1952-03-20,23.889999,23.889999,23.889999,23.889999,23.889999,1240000
1952-03-21,23.930000,23.930000,23.930000,23.930000,23.930000,1290000
1952-03-24,23.930000,23.930000,23.930000,23.930000,23.930000,1040000
1952-03-25,23.790001,23.790001,23.790001,23.790001,23.790001,1060000
1952-03-26,23.780001,23.780001,23.780001,23.780001,23.780001,1030000
1952-03-27,23.990000,23.990000,23.990000,23.990000,23.990000,1370000
1952-03-28,24.180000,24.180000,24.180000,24.180000,24.180000,1560000
1952-03-31,24.370001,24.370001,24.370001,24.370001,24.370001,1680000
1952-04-01,24.180000,24.180000,24.180000,24.180000,24.180000,1720000
1952-04-02,24.120001,24.120001,24.120001,24.120001,24.120001,1260000
1952-04-03,24.120001,24.120001,24.120001,24.120001,24.120001,1280000
1952-04-04,24.020000,24.020000,24.020000,24.020000,24.020000,1190000
1952-04-07,23.799999,23.799999,23.799999,23.799999,23.799999,1230000
1952-04-08,23.910000,23.910000,23.910000,23.910000,23.910000,1090000
1952-04-09,23.940001,23.940001,23.940001,23.940001,23.940001,980000
1952-04-10,24.110001,24.110001,24.110001,24.110001,24.110001,1130000
1952-04-14,23.950001,23.950001,23.950001,23.950001,23.950001,1790000
1952-04-15,23.650000,23.650000,23.650000,23.650000,23.650000,1720000
1952-04-16,23.580000,23.580000,23.580000,23.580000,23.580000,1400000
1952-04-17,23.410000,23.410000,23.410000,23.410000,23.410000,1620000
1952-04-18,23.500000,23.500000,23.500000,23.500000,23.500000,1240000
1952-04-21,23.690001,23.690001,23.690001,23.690001,23.690001,1110000
1952-04-22,23.580000,23.580000,23.580000,23.580000,23.580000,1240000
1952-04-23,23.480000,23.480000,23.480000,23.480000,23.480000,1090000
1952-04-24,23.430000,23.430000,23.430000,23.430000,23.430000,1580000
1952-04-25,23.540001,23.540001,23.540001,23.540001,23.540001,1240000
1952-04-28,23.549999,23.549999,23.549999,23.549999,23.549999,980000
1952-04-29,23.490000,23.490000,23.490000,23.490000,23.490000,1170000
1952-04-30,23.320000,23.320000,23.320000,23.320000,23.320000,1000000
1952-05-01,23.170000,23.170000,23.170000,23.170000,23.170000,1400000
1952-05-02,23.559999,23.559999,23.559999,23.559999,23.559999,1300000
1952-05-05,23.660000,23.660000,23.660000,23.660000,23.660000,860000
1952-05-06,23.670000,23.670000,23.670000,23.670000,23.670000,1120000
1952-05-07,23.809999,23.809999,23.809999,23.809999,23.809999,1120000
1952-05-08,23.860001,23.860001,23.860001,23.860001,23.860001,1230000
1952-05-09,23.840000,23.840000,23.840000,23.840000,23.840000,960000
1952-05-12,23.750000,23.750000,23.750000,23.750000,23.750000,800000
1952-05-13,23.780001,23.780001,23.780001,23.780001,23.780001,890000
1952-05-14,23.680000,23.680000,23.680000,23.680000,23.680000,950000
1952-05-15,23.600000,23.600000,23.600000,23.600000,23.600000,1050000
1952-05-16,23.559999,23.559999,23.559999,23.559999,23.559999,910000
1952-05-19,23.610001,23.610001,23.610001,23.610001,23.610001,780000
1952-05-20,23.740000,23.740000,23.740000,23.740000,23.740000,1150000
1952-05-21,23.780001,23.780001,23.780001,23.780001,23.780001,1210000
1952-05-22,23.910000,23.910000,23.910000,23.910000,23.910000,1360000
1952-05-23,23.889999,23.889999,23.889999,23.889999,23.889999,1150000
1952-05-26,23.940001,23.940001,23.940001,23.940001,23.940001,940000
1952-05-27,23.879999,23.879999,23.879999,23.879999,23.879999,1040000
1952-05-28,23.840000,23.840000,23.840000,23.840000,23.840000,1130000
1952-05-29,23.860001,23.860001,23.860001,23.860001,23.860001,1100000
1952-06-02,23.799999,23.799999,23.799999,23.799999,23.799999,1190000
1952-06-03,23.780001,23.780001,23.780001,23.780001,23.780001,940000
1952-06-04,23.950001,23.950001,23.950001,23.950001,23.950001,1200000
1952-06-05,24.100000,24.100000,24.100000,24.100000,24.100000,1410000
1952-06-06,24.260000,24.260000,24.260000,24.260000,24.260000,1520000
1952-06-09,24.370001,24.370001,24.370001,24.370001,24.370001,1270000
1952-06-10,24.230000,24.230000,24.230000,24.230000,24.230000,1220000
1952-06-11,24.309999,24.309999,24.309999,24.309999,24.309999,1190000
1952-06-12,24.309999,24.309999,24.309999,24.309999,24.309999,1370000
1952-06-13,24.370001,24.370001,24.370001,24.370001,24.370001,1130000
1952-06-16,24.299999,24.299999,24.299999,24.299999,24.299999,980000
1952-06-17,24.330000,24.330000,24.330000,24.330000,24.330000,920000
1952-06-18,24.430000,24.430000,24.430000,24.430000,24.430000,1270000
1952-06-19,24.510000,24.510000,24.510000,24.510000,24.510000,1320000
1952-06-20,24.590000,24.590000,24.590000,24.590000,24.590000,1190000
1952-06-23,24.559999,24.559999,24.559999,24.559999,24.559999,1200000
1952-06-24,24.600000,24.600000,24.600000,24.600000,24.600000,1200000
1952-06-25,24.660000,24.660000,24.660000,24.660000,24.660000,1230000
1952-06-26,24.750000,24.750000,24.750000,24.750000,24.750000,1190000
1952-06-27,24.830000,24.830000,24.830000,24.830000,24.830000,1210000
1952-06-30,24.959999,24.959999,24.959999,24.959999,24.959999,1380000
1952-07-01,25.120001,25.120001,25.120001,25.120001,25.120001,1450000
1952-07-02,25.059999,25.059999,25.059999,25.059999,25.059999,1320000
1952-07-03,25.049999,25.049999,25.049999,25.049999,25.049999,1150000
1952-07-07,24.969999,24.969999,24.969999,24.969999,24.969999,1080000
1952-07-08,24.959999,24.959999,24.959999,24.959999,24.959999,850000
1952-07-09,24.860001,24.860001,24.860001,24.860001,24.860001,1120000
1952-07-10,24.809999,24.809999,24.809999,24.809999,24.809999,1010000
1952-07-11,24.980000,24.980000,24.980000,24.980000,24.980000,1040000
1952-07-14,25.030001,25.030001,25.030001,25.030001,25.030001,1090000
1952-07-15,25.160000,25.160000,25.160000,25.160000,25.160000,1220000
1952-07-16,25.160000,25.160000,25.160000,25.160000,25.160000,1120000
1952-07-17,25.049999,25.049999,25.049999,25.049999,25.049999,1010000
1952-07-18,24.850000,24.850000,24.850000,24.850000,24.850000,1020000
1952-07-21,24.950001,24.950001,24.950001,24.950001,24.950001,780000
1952-07-22,25.000000,25.000000,25.000000,25.000000,25.000000,910000
1952-07-23,25.110001,25.110001,25.110001,25.110001,25.110001,1020000
1952-07-24,25.240000,25.240000,25.240000,25.240000,25.240000,1270000
1952-07-25,25.160000,25.160000,25.160000,25.160000,25.160000,1130000
1952-07-28,25.200001,25.200001,25.200001,25.200001,25.200001,1030000
1952-07-29,25.260000,25.260000,25.260000,25.260000,25.260000,1010000
1952-07-30,25.370001,25.370001,25.370001,25.370001,25.370001,1240000
1952-07-31,25.400000,25.400000,25.400000,25.400000,25.400000,1230000
1952-08-01,25.450001,25.450001,25.450001,25.450001,25.450001,1050000
1952-08-04,25.430000,25.430000,25.430000,25.430000,25.430000,950000
1952-08-05,25.459999,25.459999,25.459999,25.459999,25.459999,1050000
1952-08-06,25.440001,25.440001,25.440001,25.440001,25.440001,1140000
1952-08-07,25.520000,25.520000,25.520000,25.520000,25.520000,1180000
1952-08-08,25.549999,25.549999,25.549999,25.549999,25.549999,1170000
1952-08-11,25.520000,25.520000,25.520000,25.520000,25.520000,1160000
1952-08-12,25.309999,25.309999,25.309999,25.309999,25.309999,1110000
1952-08-13,25.280001,25.280001,25.280001,25.280001,25.280001,990000
1952-08-14,25.280001,25.280001,25.280001,25.280001,25.280001,930000
1952-08-15,25.200001,25.200001,25.200001,25.200001,25.200001,890000
1952-08-18,24.940001,24.940001,24.940001,24.940001,24.940001,1090000
1952-08-19,24.889999,24.889999,24.889999,24.889999,24.889999,980000
1952-08-20,24.950001,24.950001,24.950001,24.950001,24.950001,960000
1952-08-21,24.980000,24.980000,24.980000,24.980000,24.980000,800000
1952-08-22,24.990000,24.990000,24.990000,24.990000,24.990000,910000
1952-08-25,24.870001,24.870001,24.870001,24.870001,24.870001,840000
1952-08-26,24.830000,24.830000,24.830000,24.830000,24.830000,890000
1952-08-27,24.940001,24.940001,24.940001,24.940001,24.940001,930000
1952-08-28,24.969999,24.969999,24.969999,24.969999,24.969999,980000
1952-08-29,25.030001,25.030001,25.030001,25.030001,25.030001,890000
1952-09-02,25.150000,25.150000,25.150000,25.150000,25.150000,970000
1952-09-03,25.250000,25.250000,25.250000,25.250000,25.250000,1200000
1952-09-04,25.240000,25.240000,25.240000,25.240000,25.240000,1120000
1952-09-05,25.209999,25.209999,25.209999,25.209999,25.209999,1040000
1952-09-08,25.110001,25.110001,25.110001,25.110001,25.110001,1170000
1952-09-09,24.860001,24.860001,24.860001,24.860001,24.860001,1310000
1952-09-10,24.690001,24.690001,24.690001,24.690001,24.690001,1590000
1952-09-11,24.719999,24.719999,24.719999,24.719999,24.719999,970000
1952-09-12,24.709999,24.709999,24.709999,24.709999,24.709999,1040000
1952-09-15,24.450001,24.450001,24.450001,24.450001,24.450001,1100000
1952-09-16,24.530001,24.530001,24.530001,24.530001,24.530001,1140000
1952-09-17,24.580000,24.580000,24.580000,24.580000,24.580000,1000000
1952-09-18,24.510000,24.510000,24.510000,24.510000,24.510000,1030000
1952-09-19,24.570000,24.570000,24.570000,24.570000,24.570000,1150000
1952-09-22,24.590000,24.590000,24.590000,24.590000,24.590000,1160000
1952-09-23,24.700001,24.700001,24.700001,24.700001,24.700001,1240000
1952-09-24,24.790001,24.790001,24.790001,24.790001,24.790001,1390000
1952-09-25,24.809999,24.809999,24.809999,24.809999,24.809999,1210000
1952-09-26,24.730000,24.730000,24.730000,24.730000,24.730000,1180000
1952-09-29,24.680000,24.680000,24.680000,24.680000,24.680000,970000
1952-09-30,24.540001,24.540001,24.540001,24.540001,24.540001,1120000
1952-10-01,24.480000,24.480000,24.480000,24.480000,24.480000,1060000
1952-10-02,24.520000,24.520000,24.520000,24.520000,24.520000,1040000
1952-10-03,24.500000,24.500000,24.500000,24.500000,24.500000,980000
1952-10-06,24.440001,24.440001,24.440001,24.440001,24.440001,1070000
1952-10-07,24.400000,24.400000,24.400000,24.400000,24.400000,950000
1952-10-08,24.580000,24.580000,24.580000,24.580000,24.580000,1260000
1952-10-09,24.570000,24.570000,24.570000,24.570000,24.570000,1090000
1952-10-10,24.549999,24.549999,24.549999,24.549999,24.549999,1070000
1952-10-14,24.480000,24.480000,24.480000,24.480000,24.480000,1130000
1952-10-15,24.059999,24.059999,24.059999,24.059999,24.059999,1730000
1952-10-16,23.910000,23.910000,23.910000,23.910000,23.910000,1730000
1952-10-17,24.200001,24.200001,24.200001,24.200001,24.200001,1360000
1952-10-20,24.129999,24.129999,24.129999,24.129999,24.129999,1050000
1952-10-21,24.070000,24.070000,24.070000,24.070000,24.070000,990000
1952-10-22,23.799999,23.799999,23.799999,23.799999,23.799999,1160000
1952-10-23,23.870001,23.870001,23.870001,23.870001,23.870001,1260000
1952-10-24,24.030001,24.030001,24.030001,24.030001,24.030001,1060000
1952-10-27,24.090000,24.090000,24.090000,24.090000,24.090000,1000000
1952-10-28,24.129999,24.129999,24.129999,24.129999,24.129999,1080000
1952-10-29,24.150000,24.150000,24.150000,24.150000,24.150000,1020000
1952-10-30,24.150000,24.150000,24.150000,24.150000,24.150000,1090000
1952-10-31,24.520000,24.520000,24.520000,24.520000,24.520000,1760000
1952-11-03,24.600000,24.600000,24.600000,24.600000,24.600000,1670000
1952-11-05,24.670000,24.670000,24.670000,24.670000,24.670000,2030000
1952-11-06,24.770000,24.770000,24.770000,24.770000,24.770000,1390000
1952-11-07,24.780001,24.780001,24.780001,24.780001,24.780001,1540000
1952-11-10,24.770000,24.770000,24.770000,24.770000,24.770000,1360000
1952-11-12,24.650000,24.650000,24.650000,24.650000,24.650000,1490000
1952-11-13,24.709999,24.709999,24.709999,24.709999,24.709999,1330000
1952-11-14,24.750000,24.750000,24.750000,24.750000,24.750000,1700000
1952-11-17,24.799999,24.799999,24.799999,24.799999,24.799999,1490000
1952-11-18,25.160000,25.160000,25.160000,25.160000,25.160000,2250000
1952-11-19,25.330000,25.330000,25.330000,25.330000,25.330000,2350000
1952-11-20,25.280001,25.280001,25.280001,25.280001,25.280001,1740000
1952-11-21,25.270000,25.270000,25.270000,25.270000,25.270000,1760000
1952-11-24,25.420000,25.420000,25.420000,25.420000,25.420000,2100000
1952-11-25,25.360001,25.360001,25.360001,25.360001,25.360001,1930000
1952-11-26,25.520000,25.520000,25.520000,25.520000,25.520000,1920000
1952-11-28,25.660000,25.660000,25.660000,25.660000,25.660000,2160000
1952-12-01,25.680000,25.680000,25.680000,25.680000,25.680000,2100000
1952-12-02,25.740000,25.740000,25.740000,25.740000,25.740000,1610000
1952-12-03,25.709999,25.709999,25.709999,25.709999,25.709999,1610000
1952-12-04,25.610001,25.610001,25.610001,25.610001,25.610001,1570000
1952-12-05,25.620001,25.620001,25.620001,25.620001,25.620001,1510000
1952-12-08,25.760000,25.760000,25.760000,25.760000,25.760000,1790000
1952-12-09,25.930000,25.930000,25.930000,25.930000,25.930000,2120000
1952-12-10,25.980000,25.980000,25.980000,25.980000,25.980000,1880000
1952-12-11,25.959999,25.959999,25.959999,25.959999,25.959999,1790000
1952-12-12,26.040001,26.040001,26.040001,26.040001,26.040001,2030000
1952-12-15,26.040001,26.040001,26.040001,26.040001,26.040001,1940000
1952-12-16,26.070000,26.070000,26.070000,26.070000,26.070000,1980000
1952-12-17,26.040001,26.040001,26.040001,26.040001,26.040001,1700000
1952-12-18,26.030001,26.030001,26.030001,26.030001,26.030001,1860000
1952-12-19,26.150000,26.150000,26.150000,26.150000,26.150000,2050000
1952-12-22,26.299999,26.299999,26.299999,26.299999,26.299999,2100000
1952-12-23,26.190001,26.190001,26.190001,26.190001,26.190001,2100000
1952-12-24,26.209999,26.209999,26.209999,26.209999,26.209999,1510000
1952-12-26,26.250000,26.250000,26.250000,26.250000,26.250000,1290000
1952-12-29,26.400000,26.400000,26.400000,26.400000,26.400000,1820000
1952-12-30,26.590000,26.590000,26.590000,26.590000,26.590000,2070000
1952-12-31,26.570000,26.570000,26.570000,26.570000,26.570000,2050000
1953-01-02,26.540001,26.540001,26.540001,26.540001,26.540001,1450000
1953-01-05,26.660000,26.660000,26.660000,26.660000,26.660000,2130000
1953-01-06,26.480000,26.480000,26.480000,26.480000,26.480000,2080000
1953-01-07,26.370001,26.370001,26.370001,26.370001,26.370001,1760000
1953-01-08,26.330000,26.330000,26.330000,26.330000,26.330000,1780000
1953-01-09,26.080000,26.080000,26.080000,26.080000,26.080000,2080000
1953-01-12,25.860001,25.860001,25.860001,25.860001,25.860001,1500000
1953-01-13,26.020000,26.020000,26.020000,26.020000,26.020000,1680000
1953-01-14,26.080000,26.080000,26.080000,26.080000,26.080000,1370000
1953-01-15,26.129999,26.129999,26.129999,26.129999,26.129999,1450000
1953-01-16,26.020000,26.020000,26.020000,26.020000,26.020000,1710000
1953-01-19,26.010000,26.010000,26.010000,26.010000,26.010000,1360000
1953-01-20,26.139999,26.139999,26.139999,26.139999,26.139999,1490000
1953-01-21,26.090000,26.090000,26.090000,26.090000,26.090000,1300000
1953-01-22,26.120001,26.120001,26.120001,26.120001,26.120001,1380000
1953-01-23,26.070000,26.070000,26.070000,26.070000,26.070000,1340000
1953-01-26,26.020000,26.020000,26.020000,26.020000,26.020000,1420000
1953-01-27,26.049999,26.049999,26.049999,26.049999,26.049999,1550000
1953-01-28,26.129999,26.129999,26.129999,26.129999,26.129999,1640000
1953-01-29,26.200001,26.200001,26.200001,26.200001,26.200001,1830000
1953-01-30,26.379999,26.379999,26.379999,26.379999,26.379999,1760000
1953-02-02,26.510000,26.510000,26.510000,26.510000,26.510000,1890000
1953-02-03,26.540001,26.540001,26.540001,26.540001,26.540001,1560000
1953-02-04,26.420000,26.420000,26.420000,26.420000,26.420000,1660000
1953-02-05,26.150000,26.150000,26.150000,26.150000,26.150000,1900000
1953-02-06,26.510000,26.510000,26.510000,26.510000,26.510000,1870000
1953-02-09,25.690001,25.690001,25.690001,25.690001,25.690001,1780000
1953-02-10,25.620001,25.620001,25.620001,25.620001,25.620001,1350000
1953-02-11,25.639999,25.639999,25.639999,25.639999,25.639999,1240000
1953-02-13,25.740000,25.740000,25.740000,25.740000,25.740000,1350000
1953-02-16,25.650000,25.650000,25.650000,25.650000,25.650000,1330000
1953-02-17,25.500000,25.500000,25.500000,25.500000,25.500000,1290000
1953-02-18,25.480000,25.480000,25.480000,25.480000,25.480000,1220000
1953-02-19,25.570000,25.570000,25.570000,25.570000,25.570000,1390000
1953-02-20,25.629999,25.629999,25.629999,25.629999,25.629999,1400000
1953-02-24,25.750000,25.750000,25.750000,25.750000,25.750000,2300000
1953-02-25,25.910000,25.910000,25.910000,25.910000,25.910000,2360000
1953-02-26,25.950001,25.950001,25.950001,25.950001,25.950001,2290000
1953-02-27,25.900000,25.900000,25.900000,25.900000,25.900000,1990000
1953-03-02,25.930000,25.930000,25.930000,25.930000,25.930000,1760000
1953-03-03,26.000000,26.000000,26.000000,26.000000,26.000000,1850000
1953-03-04,25.780001,25.780001,25.780001,25.780001,25.780001,2010000
1953-03-05,25.790001,25.790001,25.790001,25.790001,25.790001,1540000
1953-03-06,25.840000,25.840000,25.840000,25.840000,25.840000,1690000
1953-03-09,25.830000,25.830000,25.830000,25.830000,25.830000,1600000
1953-03-10,25.910000,25.910000,25.910000,25.910000,25.910000,1530000
1953-03-11,26.120001,26.120001,26.120001,26.120001,26.120001,1890000
1953-03-12,26.129999,26.129999,26.129999,26.129999,26.129999,1780000
1953-03-13,26.180000,26.180000,26.180000,26.180000,26.180000,1760000
1953-03-16,26.219999,26.219999,26.219999,26.219999,26.219999,1770000
1953-03-17,26.330000,26.330000,26.330000,26.330000,26.330000,2110000
1953-03-18,26.240000,26.240000,26.240000,26.240000,26.240000,2110000
1953-03-19,26.219999,26.219999,26.219999,26.219999,26.219999,1840000
1953-03-20,26.180000,26.180000,26.180000,26.180000,26.180000,1730000
1953-03-23,26.020000,26.020000,26.020000,26.020000,26.020000,1750000
1953-03-24,26.170000,26.170000,26.170000,26.170000,26.170000,1970000
1953-03-25,26.100000,26.100000,26.100000,26.100000,26.100000,2320000
1953-03-26,25.950001,25.950001,25.950001,25.950001,25.950001,2000000
1953-03-27,25.990000,25.990000,25.990000,25.990000,25.990000,1640000
1953-03-30,25.610001,25.610001,25.610001,25.610001,25.610001,2740000
1953-03-31,25.290001,25.290001,25.290001,25.290001,25.290001,3120000
1953-04-01,25.250000,25.250000,25.250000,25.250000,25.250000,2240000
1953-04-02,25.230000,25.230000,25.230000,25.230000,25.230000,1720000
1953-04-06,24.610001,24.610001,24.610001,24.610001,24.610001,3050000
1953-04-07,24.709999,24.709999,24.709999,24.709999,24.709999,2500000
1953-04-08,24.930000,24.930000,24.930000,24.930000,24.930000,1860000
1953-04-09,24.879999,24.879999,24.879999,24.879999,24.879999,1520000
1953-04-10,24.820000,24.820000,24.820000,24.820000,24.820000,1360000
1953-04-13,24.770000,24.770000,24.770000,24.770000,24.770000,1280000
1953-04-14,24.860001,24.860001,24.860001,24.860001,24.860001,1480000
1953-04-15,24.959999,24.959999,24.959999,24.959999,24.959999,1580000
1953-04-16,24.910000,24.910000,24.910000,24.910000,24.910000,1310000
1953-04-17,24.620001,24.620001,24.620001,24.620001,24.620001,1430000
1953-04-20,24.730000,24.730000,24.730000,24.730000,24.730000,1520000
1953-04-21,24.670000,24.670000,24.670000,24.670000,24.670000,1250000
1953-04-22,24.459999,24.459999,24.459999,24.459999,24.459999,1390000
1953-04-23,24.190001,24.190001,24.190001,24.190001,24.190001,1920000
1953-04-24,24.200001,24.200001,24.200001,24.200001,24.200001,1780000
1953-04-27,24.340000,24.340000,24.340000,24.340000,24.340000,1400000
1953-04-28,24.520000,24.520000,24.520000,24.520000,24.520000,1330000
1953-04-29,24.680000,24.680000,24.680000,24.680000,24.680000,1310000
1953-04-30,24.620001,24.620001,24.620001,24.620001,24.620001,1140000
1953-05-01,24.730000,24.730000,24.730000,24.730000,24.730000,1200000
1953-05-04,25.000000,25.000000,25.000000,25.000000,25.000000,1520000
1953-05-05,25.030001,25.030001,25.030001,25.030001,25.030001,1290000
1953-05-06,25.000000,25.000000,25.000000,25.000000,25.000000,1110000
1953-05-07,24.900000,24.900000,24.900000,24.900000,24.900000,1110000
1953-05-08,24.900000,24.900000,24.900000,24.900000,24.900000,1220000
1953-05-11,24.910000,24.910000,24.910000,24.910000,24.910000,1010000
1953-05-12,24.740000,24.740000,24.740000,24.740000,24.740000,1080000
1953-05-13,24.709999,24.709999,24.709999,24.709999,24.709999,1120000
1953-05-14,24.850000,24.850000,24.850000,24.850000,24.850000,1210000
1953-05-15,24.840000,24.840000,24.840000,24.840000,24.840000,1200000
1953-05-18,24.750000,24.750000,24.750000,24.750000,24.750000,1080000
1953-05-19,24.700001,24.700001,24.700001,24.700001,24.700001,1120000
1953-05-20,24.930000,24.930000,24.930000,24.930000,24.930000,1690000
1953-05-21,25.059999,25.059999,25.059999,25.059999,25.059999,1590000
1953-05-22,25.030001,25.030001,25.030001,25.030001,25.030001,1350000
1953-05-25,24.990000,24.990000,24.990000,24.990000,24.990000,1180000
1953-05-26,24.870001,24.870001,24.870001,24.870001,24.870001,1160000
1953-05-27,24.639999,24.639999,24.639999,24.639999,24.639999,1330000
1953-05-28,24.459999,24.459999,24.459999,24.459999,24.459999,1240000
1953-05-29,24.540001,24.540001,24.540001,24.540001,24.540001,920000
1953-06-01,24.150000,24.150000,24.150000,24.150000,24.150000,1490000
1953-06-02,24.219999,24.219999,24.219999,24.219999,24.219999,1450000
1953-06-03,24.180000,24.180000,24.180000,24.180000,24.180000,1050000
1953-06-04,24.030001,24.030001,24.030001,24.030001,24.030001,1400000
1953-06-05,24.090000,24.090000,24.090000,24.090000,24.090000,1160000
1953-06-08,24.010000,24.010000,24.010000,24.010000,24.010000,1000000
1953-06-09,23.600000,23.600000,23.600000,23.600000,23.600000,2200000
1953-06-10,23.540001,23.540001,23.540001,23.540001,23.540001,1960000
1953-06-11,23.750000,23.750000,23.750000,23.750000,23.750000,1220000
1953-06-12,23.820000,23.820000,23.820000,23.820000,23.820000,920000
1953-06-15,23.620001,23.620001,23.620001,23.620001,23.620001,1090000
1953-06-16,23.549999,23.549999,23.549999,23.549999,23.549999,1370000
1953-06-17,23.850000,23.850000,23.850000,23.850000,23.850000,1150000
1953-06-18,23.840000,23.840000,23.840000,23.840000,23.840000,1010000
1953-06-19,23.840000,23.840000,23.840000,23.840000,23.840000,890000
1953-06-22,23.959999,23.959999,23.959999,23.959999,23.959999,1030000
1953-06-23,24.120001,24.120001,24.120001,24.120001,24.120001,1050000
1953-06-24,24.090000,24.090000,24.090000,24.090000,24.090000,1030000
1953-06-25,24.190001,24.190001,24.190001,24.190001,24.190001,1160000
1953-06-26,24.209999,24.209999,24.209999,24.209999,24.209999,830000
1953-06-29,24.139999,24.139999,24.139999,24.139999,24.139999,800000
1953-06-30,24.139999,24.139999,24.139999,24.139999,24.139999,820000
1953-07-01,24.240000,24.240000,24.240000,24.240000,24.240000,910000
1953-07-02,24.309999,24.309999,24.309999,24.309999,24.309999,1030000
1953-07-03,24.360001,24.360001,24.360001,24.360001,24.360001,830000
1953-07-06,24.379999,24.379999,24.379999,24.379999,24.379999,820000
1953-07-07,24.510000,24.510000,24.510000,24.510000,24.510000,1030000
1953-07-08,24.500000,24.500000,24.500000,24.500000,24.500000,950000
1953-07-09,24.430000,24.430000,24.430000,24.430000,24.430000,910000
1953-07-10,24.410000,24.410000,24.410000,24.410000,24.410000,860000
1953-07-13,24.170000,24.170000,24.170000,24.170000,24.170000,1120000
1953-07-14,24.080000,24.080000,24.080000,24.080000,24.080000,1030000
1953-07-15,24.150000,24.150000,24.150000,24.150000,24.150000,840000
1953-07-16,24.180000,24.180000,24.180000,24.180000,24.180000,790000
1953-07-17,24.350000,24.350000,24.350000,24.350000,24.350000,840000
1953-07-20,24.219999,24.219999,24.219999,24.219999,24.219999,830000
1953-07-21,24.160000,24.160000,24.160000,24.160000,24.160000,850000
1953-07-22,24.190001,24.190001,24.190001,24.190001,24.190001,900000
1953-07-23,24.230000,24.230000,24.230000,24.230000,24.230000,1000000
1953-07-24,24.230000,24.230000,24.230000,24.230000,24.230000,890000
1953-07-27,24.070000,24.070000,24.070000,24.070000,24.070000,1210000
1953-07-28,24.110001,24.110001,24.110001,24.110001,24.110001,1080000
1953-07-29,24.260000,24.260000,24.260000,24.260000,24.260000,1000000
1953-07-30,24.490000,24.490000,24.490000,24.490000,24.490000,1200000
1953-07-31,24.750000,24.750000,24.750000,24.750000,24.750000,1320000
1953-08-03,24.840000,24.840000,24.840000,24.840000,24.840000,1160000
1953-08-04,24.780001,24.780001,24.780001,24.780001,24.780001,1000000
1953-08-05,24.680000,24.680000,24.680000,24.680000,24.680000,1080000
1953-08-06,24.799999,24.799999,24.799999,24.799999,24.799999,1200000
1953-08-07,24.780001,24.780001,24.780001,24.780001,24.780001,950000
1953-08-10,24.750000,24.750000,24.750000,24.750000,24.750000,1090000
1953-08-11,24.719999,24.719999,24.719999,24.719999,24.719999,940000
1953-08-12,24.780001,24.780001,24.780001,24.780001,24.780001,990000
1953-08-13,24.730000,24.730000,24.730000,24.730000,24.730000,1040000
1953-08-14,24.620001,24.620001,24.620001,24.620001,24.620001,1000000
1953-08-17,24.559999,24.559999,24.559999,24.559999,24.559999,910000
1953-08-18,24.459999,24.459999,24.459999,24.459999,24.459999,1030000
1953-08-19,24.309999,24.309999,24.309999,24.309999,24.309999,1400000
1953-08-20,24.290001,24.290001,24.290001,24.290001,24.290001,860000
1953-08-21,24.350000,24.350000,24.350000,24.350000,24.350000,850000
1953-08-24,24.090000,24.090000,24.090000,24.090000,24.090000,1320000
1953-08-25,23.930000,23.930000,23.930000,23.930000,23.930000,1470000
1953-08-26,23.860001,23.860001,23.860001,23.860001,23.860001,1060000
1953-08-27,23.790001,23.790001,23.790001,23.790001,23.790001,1290000
1953-08-28,23.740000,23.740000,23.740000,23.740000,23.740000,1060000
1953-08-31,23.320000,23.320000,23.320000,23.320000,23.320000,2190000
1953-09-01,23.420000,23.420000,23.420000,23.420000,23.420000,1580000
1953-09-02,23.559999,23.559999,23.559999,23.559999,23.559999,1110000
1953-09-03,23.510000,23.510000,23.510000,23.510000,23.510000,900000
1953-09-04,23.570000,23.570000,23.570000,23.570000,23.570000,770000
1953-09-08,23.610001,23.610001,23.610001,23.610001,23.610001,740000
1953-09-09,23.650000,23.650000,23.650000,23.650000,23.650000,860000
1953-09-10,23.410000,23.410000,23.410000,23.410000,23.410000,1010000
1953-09-11,23.139999,23.139999,23.139999,23.139999,23.139999,1930000
1953-09-14,22.709999,22.709999,22.709999,22.709999,22.709999,2550000
1953-09-15,22.900000,22.900000,22.900000,22.900000,22.900000,2850000
1953-09-16,23.010000,23.010000,23.010000,23.010000,23.010000,1570000
1953-09-17,23.070000,23.070000,23.070000,23.070000,23.070000,1290000
1953-09-18,22.950001,22.950001,22.950001,22.950001,22.950001,1190000
1953-09-21,22.879999,22.879999,22.879999,22.879999,22.879999,1070000
1953-09-22,23.200001,23.200001,23.200001,23.200001,23.200001,1300000
1953-09-23,23.230000,23.230000,23.230000,23.230000,23.230000,1240000
1953-09-24,23.240000,23.240000,23.240000,23.240000,23.240000,1020000
1953-09-25,23.299999,23.299999,23.299999,23.299999,23.299999,910000
1953-09-28,23.450001,23.450001,23.450001,23.450001,23.450001,1150000
1953-09-29,23.490000,23.490000,23.490000,23.490000,23.490000,1170000
1953-09-30,23.350000,23.350000,23.350000,23.350000,23.350000,940000
1953-10-01,23.490000,23.490000,23.490000,23.490000,23.490000,940000
1953-10-02,23.590000,23.590000,23.590000,23.590000,23.590000,890000
1953-10-05,23.480000,23.480000,23.480000,23.480000,23.480000,930000
1953-10-06,23.389999,23.389999,23.389999,23.389999,23.389999,1100000
1953-10-07,23.580000,23.580000,23.580000,23.580000,23.580000,1010000
1953-10-08,23.620001,23.620001,23.620001,23.620001,23.620001,960000
1953-10-09,23.660000,23.660000,23.660000,23.660000,23.660000,900000
1953-10-13,23.570000,23.570000,23.570000,23.570000,23.570000,1130000
1953-10-14,23.680000,23.680000,23.680000,23.680000,23.680000,1290000
1953-10-15,23.950001,23.950001,23.950001,23.950001,23.950001,1710000
1953-10-16,24.139999,24.139999,24.139999,24.139999,24.139999,1620000
1953-10-19,24.160000,24.160000,24.160000,24.160000,24.160000,1190000
1953-10-20,24.170000,24.170000,24.170000,24.170000,24.170000,1280000
1953-10-21,24.190001,24.190001,24.190001,24.190001,24.190001,1320000
1953-10-22,24.299999,24.299999,24.299999,24.299999,24.299999,1330000
1953-10-23,24.350000,24.350000,24.350000,24.350000,24.350000,1330000
1953-10-26,24.309999,24.309999,24.309999,24.309999,24.309999,1340000
1953-10-27,24.260000,24.260000,24.260000,24.260000,24.260000,1170000
1953-10-28,24.290001,24.290001,24.290001,24.290001,24.290001,1260000
1953-10-29,24.580000,24.580000,24.580000,24.580000,24.580000,1610000
1953-10-30,24.540001,24.540001,24.540001,24.540001,24.540001,1400000
1953-11-02,24.660000,24.660000,24.660000,24.660000,24.660000,1340000
1953-11-04,24.510000,24.510000,24.510000,24.510000,24.510000,1480000
1953-11-05,24.639999,24.639999,24.639999,24.639999,24.639999,1720000
1953-11-06,24.610001,24.610001,24.610001,24.610001,24.610001,1700000
1953-11-09,24.660000,24.660000,24.660000,24.660000,24.660000,1440000
1953-11-10,24.370001,24.370001,24.370001,24.370001,24.370001,1340000
1953-11-12,24.459999,24.459999,24.459999,24.459999,24.459999,1390000
1953-11-13,24.540001,24.540001,24.540001,24.540001,24.540001,1540000
1953-11-16,24.379999,24.379999,24.379999,24.379999,24.379999,1490000
1953-11-17,24.250000,24.250000,24.250000,24.250000,24.250000,1250000
1953-11-18,24.290001,24.290001,24.290001,24.290001,24.290001,1250000
1953-11-19,24.400000,24.400000,24.400000,24.400000,24.400000,1420000
1953-11-20,24.440001,24.440001,24.440001,24.440001,24.440001,1300000
1953-11-23,24.360001,24.360001,24.360001,24.360001,24.360001,1410000
1953-11-24,24.500000,24.500000,24.500000,24.500000,24.500000,1470000
1953-11-25,24.520000,24.520000,24.520000,24.520000,24.520000,1540000
1953-11-27,24.660000,24.660000,24.660000,24.660000,24.660000,1600000
1953-11-30,24.760000,24.760000,24.760000,24.760000,24.760000,1960000
1953-12-01,24.780001,24.780001,24.780001,24.780001,24.780001,1580000
1953-12-02,24.950001,24.950001,24.950001,24.950001,24.950001,1850000
1953-12-03,24.969999,24.969999,24.969999,24.969999,24.969999,1740000
1953-12-04,24.980000,24.980000,24.980000,24.980000,24.980000,1390000
1953-12-07,24.950001,24.950001,24.950001,24.950001,24.950001,1410000
1953-12-08,24.870001,24.870001,24.870001,24.870001,24.870001,1390000
1953-12-09,24.840000,24.840000,24.840000,24.840000,24.840000,1410000
1953-12-10,24.780001,24.780001,24.780001,24.780001,24.780001,1420000
1953-12-11,24.760000,24.760000,24.760000,24.760000,24.760000,1440000
1953-12-14,24.690001,24.690001,24.690001,24.690001,24.690001,1540000
1953-12-15,24.709999,24.709999,24.709999,24.709999,24.709999,1450000
1953-12-16,24.959999,24.959999,24.959999,24.959999,24.959999,1880000
1953-12-17,24.940001,24.940001,24.940001,24.940001,24.940001,1600000
1953-12-18,24.990000,24.990000,24.990000,24.990000,24.990000,1550000
1953-12-21,24.950001,24.950001,24.950001,24.950001,24.950001,1690000
1953-12-22,24.760000,24.760000,24.760000,24.760000,24.760000,1720000
1953-12-23,24.690001,24.690001,24.690001,24.690001,24.690001,1570000
1953-12-24,24.799999,24.799999,24.799999,24.799999,24.799999,1270000
1953-12-28,24.709999,24.709999,24.709999,24.709999,24.709999,1570000
1953-12-29,24.549999,24.549999,24.549999,24.549999,24.549999,2140000
1953-12-30,24.760000,24.760000,24.760000,24.760000,24.760000,2050000
1953-12-31,24.809999,24.809999,24.809999,24.809999,24.809999,2490000
1954-01-04,24.950001,24.950001,24.950001,24.950001,24.950001,1310000
1954-01-05,25.100000,25.100000,25.100000,25.100000,25.100000,1520000
1954-01-06,25.139999,25.139999,25.139999,25.139999,25.139999,1460000
1954-01-07,25.059999,25.059999,25.059999,25.059999,25.059999,1540000
1954-01-08,24.930000,24.930000,24.930000,24.930000,24.930000,1260000
1954-01-11,24.799999,24.799999,24.799999,24.799999,24.799999,1220000
1954-01-12,24.930000,24.930000,24.930000,24.930000,24.930000,1250000
1954-01-13,25.070000,25.070000,25.070000,25.070000,25.070000,1420000
1954-01-14,25.190001,25.190001,25.190001,25.190001,25.190001,1530000
1954-01-15,25.430000,25.430000,25.430000,25.430000,25.430000,2180000
1954-01-18,25.430000,25.430000,25.430000,25.430000,25.430000,1580000
1954-01-19,25.680000,25.680000,25.680000,25.680000,25.680000,1840000
1954-01-20,25.750000,25.750000,25.750000,25.750000,25.750000,1960000
1954-01-21,25.790001,25.790001,25.790001,25.790001,25.790001,1780000
1954-01-22,25.850000,25.850000,25.850000,25.850000,25.850000,1890000
1954-01-25,25.930000,25.930000,25.930000,25.930000,25.930000,1860000
1954-01-26,26.090000,26.090000,26.090000,26.090000,26.090000,2120000
1954-01-27,26.010000,26.010000,26.010000,26.010000,26.010000,2020000
1954-01-28,26.020000,26.020000,26.020000,26.020000,26.020000,1730000
1954-01-29,26.080000,26.080000,26.080000,26.080000,26.080000,1950000
1954-02-01,25.990000,25.990000,25.990000,25.990000,25.990000,1740000
1954-02-02,25.920000,25.920000,25.920000,25.920000,25.920000,1420000
1954-02-03,26.010000,26.010000,26.010000,26.010000,26.010000,1690000
1954-02-04,26.200001,26.200001,26.200001,26.200001,26.200001,2040000
1954-02-05,26.299999,26.299999,26.299999,26.299999,26.299999,2030000
1954-02-08,26.230000,26.230000,26.230000,26.230000,26.230000,2180000
1954-02-09,26.170000,26.170000,26.170000,26.170000,26.170000,1880000
1954-02-10,26.139999,26.139999,26.139999,26.139999,26.139999,1790000
1954-02-11,26.059999,26.059999,26.059999,26.059999,26.059999,1860000
1954-02-12,26.120001,26.120001,26.120001,26.120001,26.120001,1730000
1954-02-15,26.040001,26.040001,26.040001,26.040001,26.040001,2080000
1954-02-16,25.809999,25.809999,25.809999,25.809999,25.809999,1870000
1954-02-17,25.860001,25.860001,25.860001,25.860001,25.860001,1740000
1954-02-18,25.860001,25.860001,25.860001,25.860001,25.860001,1500000
1954-02-19,25.920000,25.920000,25.920000,25.920000,25.920000,1510000
1954-02-23,25.830000,25.830000,25.830000,25.830000,25.830000,1470000
1954-02-24,25.830000,25.830000,25.830000,25.830000,25.830000,1350000
1954-02-25,25.910000,25.910000,25.910000,25.910000,25.910000,1470000
1954-02-26,26.150000,26.150000,26.150000,26.150000,26.150000,1910000
1954-03-01,26.250000,26.250000,26.250000,26.250000,26.250000,2040000
1954-03-02,26.320000,26.320000,26.320000,26.320000,26.320000,1980000
1954-03-03,26.320000,26.320000,26.320000,26.320000,26.320000,2240000
1954-03-04,26.410000,26.410000,26.410000,26.410000,26.410000,1830000
1954-03-05,26.520000,26.520000,26.520000,26.520000,26.520000,2030000
1954-03-08,26.450001,26.450001,26.450001,26.450001,26.450001,1650000
1954-03-09,26.510000,26.510000,26.510000,26.510000,26.510000,1630000
1954-03-10,26.570000,26.570000,26.570000,26.570000,26.570000,1870000
1954-03-11,26.690001,26.690001,26.690001,26.690001,26.690001,2050000
1954-03-12,26.690001,26.690001,26.690001,26.690001,26.690001,1980000
1954-03-15,26.570000,26.570000,26.570000,26.570000,26.570000,1680000
1954-03-16,26.559999,26.559999,26.559999,26.559999,26.559999,1540000
1954-03-17,26.620001,26.620001,26.620001,26.620001,26.620001,1740000
1954-03-18,26.730000,26.730000,26.730000,26.730000,26.730000,2020000
1954-03-19,26.809999,26.809999,26.809999,26.809999,26.809999,1930000
1954-03-22,26.790001,26.790001,26.790001,26.790001,26.790001,1800000
1954-03-23,26.600000,26.600000,26.600000,26.600000,26.600000,2180000
1954-03-24,26.469999,26.469999,26.469999,26.469999,26.469999,1900000
1954-03-25,26.420000,26.420000,26.420000,26.420000,26.420000,1720000
1954-03-26,26.559999,26.559999,26.559999,26.559999,26.559999,1550000
1954-03-29,26.660000,26.660000,26.660000,26.660000,26.660000,1870000
1954-03-30,26.690001,26.690001,26.690001,26.690001,26.690001,2130000
1954-03-31,26.940001,26.940001,26.940001,26.940001,26.940001,2690000
1954-04-01,27.170000,27.170000,27.170000,27.170000,27.170000,2270000
1954-04-02,27.209999,27.209999,27.209999,27.209999,27.209999,1830000
1954-04-05,27.260000,27.260000,27.260000,27.260000,27.260000,1710000
1954-04-06,27.010000,27.010000,27.010000,27.010000,27.010000,2120000
1954-04-07,27.110001,27.110001,27.110001,27.110001,27.110001,1830000
1954-04-08,27.379999,27.379999,27.379999,27.379999,27.379999,2300000
1954-04-09,27.379999,27.379999,27.379999,27.379999,27.379999,2360000
1954-04-12,27.570000,27.570000,27.570000,27.570000,27.570000,1790000
1954-04-13,27.639999,27.639999,27.639999,27.639999,27.639999,2020000
1954-04-14,27.850000,27.850000,27.850000,27.850000,27.850000,2330000
1954-04-15,27.940001,27.940001,27.940001,27.940001,27.940001,2200000
1954-04-19,27.760000,27.760000,27.760000,27.760000,27.760000,2430000
1954-04-20,27.750000,27.750000,27.750000,27.750000,27.750000,1860000
1954-04-21,27.639999,27.639999,27.639999,27.639999,27.639999,1870000
1954-04-22,27.680000,27.680000,27.680000,27.680000,27.680000,1750000
1954-04-23,27.780001,27.780001,27.780001,27.780001,27.780001,1990000
1954-04-26,27.879999,27.879999,27.879999,27.879999,27.879999,2150000
1954-04-27,27.709999,27.709999,27.709999,27.709999,27.709999,1970000
1954-04-28,27.760000,27.760000,27.760000,27.760000,27.760000,2120000
1954-04-29,28.180000,28.180000,28.180000,28.180000,28.180000,2150000
1954-04-30,28.260000,28.260000,28.260000,28.260000,28.260000,2450000
1954-05-03,28.209999,28.209999,28.209999,28.209999,28.209999,1870000
1954-05-04,28.280001,28.280001,28.280001,28.280001,28.280001,1990000
1954-05-05,28.290001,28.290001,28.290001,28.290001,28.290001,2020000
1954-05-06,28.510000,28.510000,28.510000,28.510000,28.510000,1980000
1954-05-07,28.650000,28.650000,28.650000,28.650000,28.650000,2070000
1954-05-10,28.620001,28.620001,28.620001,28.620001,28.620001,1800000
1954-05-11,28.490000,28.490000,28.490000,28.490000,28.490000,1770000
1954-05-12,28.719999,28.719999,28.719999,28.719999,28.719999,2210000
1954-05-13,28.559999,28.559999,28.559999,28.559999,28.559999,2340000
1954-05-14,28.799999,28.799999,28.799999,28.799999,28.799999,1970000
1954-05-17,28.840000,28.840000,28.840000,28.840000,28.840000,2040000
1954-05-18,28.850000,28.850000,28.850000,28.850000,28.850000,2250000
1954-05-19,28.719999,28.719999,28.719999,28.719999,28.719999,2170000
1954-05-20,28.820000,28.820000,28.820000,28.820000,28.820000,2070000
1954-05-21,28.990000,28.990000,28.990000,28.990000,28.990000,2620000
1954-05-24,29.000000,29.000000,29.000000,29.000000,29.000000,2330000
1954-05-25,28.930000,28.930000,28.930000,28.930000,28.930000,2050000
1954-05-26,29.170000,29.170000,29.170000,29.170000,29.170000,2180000
1954-05-27,29.049999,29.049999,29.049999,29.049999,29.049999,2230000
1954-05-28,29.190001,29.190001,29.190001,29.190001,29.190001,1940000
1954-06-01,29.190001,29.190001,29.190001,29.190001,29.190001,1850000
1954-06-02,29.160000,29.160000,29.160000,29.160000,29.160000,1930000
1954-06-03,29.150000,29.150000,29.150000,29.150000,29.150000,1810000
1954-06-04,29.100000,29.100000,29.100000,29.100000,29.100000,1720000
1954-06-07,28.990000,28.990000,28.990000,28.990000,28.990000,1520000
1954-06-08,28.340000,28.340000,28.340000,28.340000,28.340000,2540000
1954-06-09,28.150000,28.150000,28.150000,28.150000,28.150000,2360000
1954-06-10,28.340000,28.340000,28.340000,28.340000,28.340000,1610000
1954-06-11,28.580000,28.580000,28.580000,28.580000,28.580000,1630000
1954-06-14,28.620001,28.620001,28.620001,28.620001,28.620001,1420000
1954-06-15,28.830000,28.830000,28.830000,28.830000,28.830000,1630000
1954-06-16,29.040001,29.040001,29.040001,29.040001,29.040001,2070000
1954-06-17,28.959999,28.959999,28.959999,28.959999,28.959999,1810000
1954-06-18,29.040001,29.040001,29.040001,29.040001,29.040001,1580000
1954-06-21,29.059999,29.059999,29.059999,29.059999,29.059999,1820000
1954-06-22,29.080000,29.080000,29.080000,29.080000,29.080000,2100000
1954-06-23,29.129999,29.129999,29.129999,29.129999,29.129999,2090000
1954-06-24,29.260000,29.260000,29.260000,29.260000,29.260000,2260000
1954-06-25,29.200001,29.200001,29.200001,29.200001,29.200001,2060000
1954-06-28,29.280001,29.280001,29.280001,29.280001,29.280001,1890000
1954-06-29,29.430000,29.430000,29.430000,29.430000,29.430000,2580000
1954-06-30,29.209999,29.209999,29.209999,29.209999,29.209999,1950000
1954-07-01,29.209999,29.209999,29.209999,29.209999,29.209999,1860000
1954-07-02,29.590000,29.590000,29.590000,29.590000,29.590000,1980000
1954-07-06,29.920000,29.920000,29.920000,29.920000,29.920000,2560000
1954-07-07,29.940001,29.940001,29.940001,29.940001,29.940001,2380000
1954-07-08,29.940001,29.940001,29.940001,29.940001,29.940001,2080000
1954-07-09,30.139999,30.139999,30.139999,30.139999,30.139999,2240000
1954-07-12,30.120001,30.120001,30.120001,30.120001,30.120001,2330000
1954-07-13,30.020000,30.020000,30.020000,30.020000,30.020000,2430000
1954-07-14,30.090000,30.090000,30.090000,30.090000,30.090000,2520000
1954-07-15,30.190001,30.190001,30.190001,30.190001,30.190001,3000000
1954-07-16,30.059999,30.059999,30.059999,30.059999,30.059999,2540000
1954-07-19,29.980000,29.980000,29.980000,29.980000,29.980000,2370000
1954-07-20,29.840000,29.840000,29.840000,29.840000,29.840000,2580000
1954-07-21,30.030001,30.030001,30.030001,30.030001,30.030001,2510000
1954-07-22,30.270000,30.270000,30.270000,30.270000,30.270000,2890000
1954-07-23,30.309999,30.309999,30.309999,30.309999,30.309999,2520000
1954-07-26,30.340000,30.340000,30.340000,30.340000,30.340000,2110000
1954-07-27,30.520000,30.520000,30.520000,30.520000,30.520000,2690000
1954-07-28,30.580000,30.580000,30.580000,30.580000,30.580000,2740000
1954-07-29,30.690001,30.690001,30.690001,30.690001,30.690001,2710000
1954-07-30,30.879999,30.879999,30.879999,30.879999,30.879999,2800000
1954-08-02,30.990000,30.990000,30.990000,30.990000,30.990000,2850000
1954-08-03,30.930000,30.930000,30.930000,30.930000,30.930000,2970000
1954-08-04,30.900000,30.900000,30.900000,30.900000,30.900000,3620000
1954-08-05,30.770000,30.770000,30.770000,30.770000,30.770000,3150000
1954-08-06,30.379999,30.379999,30.379999,30.379999,30.379999,3350000
1954-08-09,30.120001,30.120001,30.120001,30.120001,30.120001,2280000
1954-08-10,30.370001,30.370001,30.370001,30.370001,30.370001,2890000
1954-08-11,30.719999,30.719999,30.719999,30.719999,30.719999,3440000
1954-08-12,30.590000,30.590000,30.590000,30.590000,30.590000,2680000
1954-08-13,30.719999,30.719999,30.719999,30.719999,30.719999,2500000
1954-08-16,31.049999,31.049999,31.049999,31.049999,31.049999,2760000
1954-08-17,31.120001,31.120001,31.120001,31.120001,31.120001,2900000
1954-08-18,31.090000,31.090000,31.090000,31.090000,31.090000,2390000
1954-08-19,31.160000,31.160000,31.160000,31.160000,31.160000,2320000
1954-08-20,31.209999,31.209999,31.209999,31.209999,31.209999,2110000
1954-08-23,31.000000,31.000000,31.000000,31.000000,31.000000,2020000
1954-08-24,30.870001,30.870001,30.870001,30.870001,30.870001,2000000
1954-08-25,30.650000,30.650000,30.650000,30.650000,30.650000,2280000
1954-08-26,30.570000,30.570000,30.570000,30.570000,30.570000,2060000
1954-08-27,30.660000,30.660000,30.660000,30.660000,30.660000,1740000
1954-08-30,30.350000,30.350000,30.350000,30.350000,30.350000,1950000
1954-08-31,29.830000,29.830000,29.830000,29.830000,29.830000,2640000
1954-09-01,30.040001,30.040001,30.040001,30.040001,30.040001,1790000
1954-09-02,30.270000,30.270000,30.270000,30.270000,30.270000,1600000
1954-09-03,30.500000,30.500000,30.500000,30.500000,30.500000,1630000
1954-09-07,30.660000,30.660000,30.660000,30.660000,30.660000,1860000
1954-09-08,30.680000,30.680000,30.680000,30.680000,30.680000,1970000
1954-09-09,30.730000,30.730000,30.730000,30.730000,30.730000,1700000
1954-09-10,30.840000,30.840000,30.840000,30.840000,30.840000,1870000
1954-09-13,31.120001,31.120001,31.120001,31.120001,31.120001,2030000
1954-09-14,31.280001,31.280001,31.280001,31.280001,31.280001,2120000
1954-09-15,31.290001,31.290001,31.290001,31.290001,31.290001,2110000
1954-09-16,31.459999,31.459999,31.459999,31.459999,31.459999,1880000
1954-09-17,31.709999,31.709999,31.709999,31.709999,31.709999,2250000
1954-09-20,31.570000,31.570000,31.570000,31.570000,31.570000,2060000
1954-09-21,31.790001,31.790001,31.790001,31.790001,31.790001,1770000
1954-09-22,32.000000,32.000000,32.000000,32.000000,32.000000,2260000
1954-09-23,32.180000,32.180000,32.180000,32.180000,32.180000,2340000
1954-09-24,32.400002,32.400002,32.400002,32.400002,32.400002,2340000
1954-09-27,32.529999,32.529999,32.529999,32.529999,32.529999,2190000
1954-09-28,32.689999,32.689999,32.689999,32.689999,32.689999,1800000
1954-09-29,32.500000,32.500000,32.500000,32.500000,32.500000,1810000
1954-09-30,32.310001,32.310001,32.310001,32.310001,32.310001,1840000
1954-10-01,32.290001,32.290001,32.290001,32.290001,32.290001,1850000
1954-10-04,32.470001,32.470001,32.470001,32.470001,32.470001,2000000
1954-10-05,32.630001,32.630001,32.630001,32.630001,32.630001,2300000
1954-10-06,32.759998,32.759998,32.759998,32.759998,32.759998,2570000
1954-10-07,32.689999,32.689999,32.689999,32.689999,32.689999,1810000
1954-10-08,32.669998,32.669998,32.669998,32.669998,32.669998,2120000
1954-10-11,32.410000,32.410000,32.410000,32.410000,32.410000,2100000
1954-10-12,32.279999,32.279999,32.279999,32.279999,32.279999,1620000
1954-10-13,32.270000,32.270000,32.270000,32.270000,32.270000,2070000
1954-10-14,31.879999,31.879999,31.879999,31.879999,31.879999,2540000
1954-10-15,31.709999,31.709999,31.709999,31.709999,31.709999,2250000
1954-10-18,31.830000,31.830000,31.830000,31.830000,31.830000,1790000
1954-10-19,31.910000,31.910000,31.910000,31.910000,31.910000,1900000
1954-10-20,32.169998,32.169998,32.169998,32.169998,32.169998,2380000
1954-10-21,32.130001,32.130001,32.130001,32.130001,32.130001,2320000
1954-10-22,32.130001,32.130001,32.130001,32.130001,32.130001,2080000
1954-10-25,31.959999,31.959999,31.959999,31.959999,31.959999,2340000
1954-10-26,31.940001,31.940001,31.940001,31.940001,31.940001,2010000
1954-10-27,32.020000,32.020000,32.020000,32.020000,32.020000,2030000
1954-10-28,31.879999,31.879999,31.879999,31.879999,31.879999,2190000
1954-10-29,31.680000,31.680000,31.680000,31.680000,31.680000,1900000
1954-11-01,31.790001,31.790001,31.790001,31.790001,31.790001,1790000
1954-11-03,32.439999,32.439999,32.439999,32.439999,32.439999,2700000
1954-11-04,32.820000,32.820000,32.820000,32.820000,32.820000,3140000
1954-11-05,32.709999,32.709999,32.709999,32.709999,32.709999,2950000
1954-11-08,33.020000,33.020000,33.020000,33.020000,33.020000,3180000
1954-11-09,33.150002,33.150002,33.150002,33.150002,33.150002,3240000
1954-11-10,33.180000,33.180000,33.180000,33.180000,33.180000,2070000
1954-11-11,33.470001,33.470001,33.470001,33.470001,33.470001,2960000
1954-11-12,33.540001,33.540001,33.540001,33.540001,33.540001,3720000
1954-11-15,33.470001,33.470001,33.470001,33.470001,33.470001,3080000
1954-11-16,33.570000,33.570000,33.570000,33.570000,33.570000,3260000
1954-11-17,33.630001,33.630001,33.630001,33.630001,33.630001,3830000
1954-11-18,33.439999,33.439999,33.439999,33.439999,33.439999,3530000
1954-11-19,33.450001,33.450001,33.450001,33.450001,33.450001,3130000
1954-11-22,33.580002,33.580002,33.580002,33.580002,33.580002,3000000
1954-11-23,34.029999,34.029999,34.029999,34.029999,34.029999,3690000
1954-11-24,34.220001,34.220001,34.220001,34.220001,34.220001,3990000
1954-11-26,34.549999,34.549999,34.549999,34.549999,34.549999,3010000
1954-11-29,34.540001,34.540001,34.540001,34.540001,34.540001,3300000
1954-11-30,34.240002,34.240002,34.240002,34.240002,34.240002,3440000
1954-12-01,33.990002,33.990002,33.990002,33.990002,33.990002,3100000
1954-12-02,34.180000,34.180000,34.180000,34.180000,34.180000,3190000
1954-12-03,34.490002,34.490002,34.490002,34.490002,34.490002,3790000
1954-12-06,34.759998,34.759998,34.759998,34.759998,34.759998,3960000
1954-12-07,34.919998,34.919998,34.919998,34.919998,34.919998,3820000
1954-12-08,34.860001,34.860001,34.860001,34.860001,34.860001,4150000
1954-12-09,34.689999,34.689999,34.689999,34.689999,34.689999,3300000
1954-12-10,34.560001,34.560001,34.560001,34.560001,34.560001,3250000
1954-12-13,34.590000,34.590000,34.590000,34.590000,34.590000,2750000
1954-12-14,34.349998,34.349998,34.349998,34.349998,34.349998,2650000
1954-12-15,34.560001,34.560001,34.560001,34.560001,34.560001,2740000
1954-12-16,34.930000,34.930000,34.930000,34.930000,34.930000,3390000
1954-12-17,35.919998,35.919998,35.919998,35.919998,35.919998,3730000
1954-12-20,35.330002,35.330002,35.330002,35.330002,35.330002,3770000
1954-12-21,35.380001,35.380001,35.380001,35.380001,35.380001,3630000
1954-12-22,35.340000,35.340000,35.340000,35.340000,35.340000,3460000
1954-12-23,35.369999,35.369999,35.369999,35.369999,35.369999,3310000
1954-12-27,35.070000,35.070000,35.070000,35.070000,35.070000,2970000
1954-12-28,35.430000,35.430000,35.430000,35.430000,35.430000,3660000
1954-12-29,35.740002,35.740002,35.740002,35.740002,35.740002,4430000
1954-12-30,35.740002,35.740002,35.740002,35.740002,35.740002,3590000
1954-12-31,35.980000,35.980000,35.980000,35.980000,35.980000,3840000
1955-01-03,36.750000,36.750000,36.750000,36.750000,36.750000,4570000
1955-01-04,36.419998,36.419998,36.419998,36.419998,36.419998,4420000
1955-01-05,35.520000,35.520000,35.520000,35.520000,35.520000,4640000
1955-01-06,35.040001,35.040001,35.040001,35.040001,35.040001,5300000
1955-01-07,35.330002,35.330002,35.330002,35.330002,35.330002,4030000
1955-01-10,35.790001,35.790001,35.790001,35.790001,35.790001,4300000
1955-01-11,35.680000,35.680000,35.680000,35.680000,35.680000,3680000
1955-01-12,35.580002,35.580002,35.580002,35.580002,35.580002,3400000
1955-01-13,35.430000,35.430000,35.430000,35.430000,35.430000,3350000
1955-01-14,35.279999,35.279999,35.279999,35.279999,35.279999,2630000
1955-01-17,34.580002,34.580002,34.580002,34.580002,34.580002,3360000
1955-01-18,34.799999,34.799999,34.799999,34.799999,34.799999,3020000
1955-01-19,34.959999,34.959999,34.959999,34.959999,34.959999,2760000
1955-01-20,35.130001,35.130001,35.130001,35.130001,35.130001,2210000
1955-01-21,35.439999,35.439999,35.439999,35.439999,35.439999,2690000
1955-01-24,35.520000,35.520000,35.520000,35.520000,35.520000,2910000
1955-01-25,35.509998,35.509998,35.509998,35.509998,35.509998,3230000
1955-01-26,35.950001,35.950001,35.950001,35.950001,35.950001,3860000
1955-01-27,35.990002,35.990002,35.990002,35.990002,35.990002,3500000
1955-01-28,36.189999,36.189999,36.189999,36.189999,36.189999,3290000
1955-01-31,36.630001,36.630001,36.630001,36.630001,36.630001,3500000
1955-02-01,36.720001,36.720001,36.720001,36.720001,36.720001,3320000
1955-02-02,36.610001,36.610001,36.610001,36.610001,36.610001,3210000
1955-02-03,36.439999,36.439999,36.439999,36.439999,36.439999,2890000
1955-02-04,36.959999,36.959999,36.959999,36.959999,36.959999,3370000
1955-02-07,36.959999,36.959999,36.959999,36.959999,36.959999,3610000
1955-02-08,36.459999,36.459999,36.459999,36.459999,36.459999,3400000
1955-02-09,36.750000,36.750000,36.750000,36.750000,36.750000,3360000
1955-02-10,37.080002,37.080002,37.080002,37.080002,37.080002,3460000
1955-02-11,37.150002,37.150002,37.150002,37.150002,37.150002,3260000
1955-02-14,36.889999,36.889999,36.889999,36.889999,36.889999,2950000
1955-02-15,36.889999,36.889999,36.889999,36.889999,36.889999,3510000
1955-02-16,36.770000,36.770000,36.770000,36.770000,36.770000,3660000
1955-02-17,36.840000,36.840000,36.840000,36.840000,36.840000,3030000
1955-02-18,36.889999,36.889999,36.889999,36.889999,36.889999,3660000
1955-02-21,36.849998,36.849998,36.849998,36.849998,36.849998,3010000
1955-02-23,36.820000,36.820000,36.820000,36.820000,36.820000,3030000
1955-02-24,36.619999,36.619999,36.619999,36.619999,36.619999,2920000
1955-02-25,36.570000,36.570000,36.570000,36.570000,36.570000,2540000
1955-02-28,36.759998,36.759998,36.759998,36.759998,36.759998,2620000
1955-03-01,36.830002,36.830002,36.830002,36.830002,36.830002,2830000
1955-03-02,37.150002,37.150002,37.150002,37.150002,37.150002,3370000
1955-03-03,37.290001,37.290001,37.290001,37.290001,37.290001,3330000
1955-03-04,37.520000,37.520000,37.520000,37.520000,37.520000,2770000
1955-03-07,37.279999,37.279999,37.279999,37.279999,37.279999,2630000
1955-03-08,36.580002,36.580002,36.580002,36.580002,36.580002,3160000
1955-03-09,36.220001,36.220001,36.220001,36.220001,36.220001,3590000
1955-03-10,36.450001,36.450001,36.450001,36.450001,36.450001,2760000
1955-03-11,35.820000,35.820000,35.820000,35.820000,35.820000,3040000
1955-03-14,34.959999,34.959999,34.959999,34.959999,34.959999,4220000
1955-03-15,35.709999,35.709999,35.709999,35.709999,35.709999,3160000
1955-03-16,35.980000,35.980000,35.980000,35.980000,35.980000,2900000
1955-03-17,36.119999,36.119999,36.119999,36.119999,36.119999,2200000
1955-03-18,36.180000,36.180000,36.180000,36.180000,36.180000,2050000
1955-03-21,35.950001,35.950001,35.950001,35.950001,35.950001,2020000
1955-03-22,36.169998,36.169998,36.169998,36.169998,36.169998,1910000
1955-03-23,36.639999,36.639999,36.639999,36.639999,36.639999,2730000
1955-03-24,36.930000,36.930000,36.930000,36.930000,36.930000,3170000
1955-03-25,36.959999,36.959999,36.959999,36.959999,36.959999,2540000
1955-03-28,36.830002,36.830002,36.830002,36.830002,36.830002,2540000
1955-03-29,36.849998,36.849998,36.849998,36.849998,36.849998,2770000
1955-03-30,36.520000,36.520000,36.520000,36.520000,36.520000,3410000
1955-03-31,36.580002,36.580002,36.580002,36.580002,36.580002,2680000
1955-04-01,36.950001,36.950001,36.950001,36.950001,36.950001,2660000
1955-04-04,36.830002,36.830002,36.830002,36.830002,36.830002,2500000
1955-04-05,36.980000,36.980000,36.980000,36.980000,36.980000,2100000
1955-04-06,37.169998,37.169998,37.169998,37.169998,37.169998,2500000
1955-04-07,37.340000,37.340000,37.340000,37.340000,37.340000,2330000
1955-04-11,37.439999,37.439999,37.439999,37.439999,37.439999,2680000
1955-04-12,37.660000,37.660000,37.660000,37.660000,37.660000,2770000
1955-04-13,37.709999,37.709999,37.709999,37.709999,37.709999,2820000
1955-04-14,37.790001,37.790001,37.790001,37.790001,37.790001,2890000
1955-04-15,37.959999,37.959999,37.959999,37.959999,37.959999,3180000
1955-04-18,38.270000,38.270000,38.270000,38.270000,38.270000,3080000
1955-04-19,38.220001,38.220001,38.220001,38.220001,38.220001,2700000
1955-04-20,38.279999,38.279999,38.279999,38.279999,38.279999,3090000
1955-04-21,38.320000,38.320000,38.320000,38.320000,38.320000,2810000
1955-04-22,38.009998,38.009998,38.009998,38.009998,38.009998,2800000
1955-04-25,38.110001,38.110001,38.110001,38.110001,38.110001,2720000
1955-04-26,38.310001,38.310001,38.310001,38.310001,38.310001,2720000
1955-04-27,38.110001,38.110001,38.110001,38.110001,38.110001,2660000
1955-04-28,37.680000,37.680000,37.680000,37.680000,37.680000,2550000
1955-04-29,37.959999,37.959999,37.959999,37.959999,37.959999,2230000
1955-05-02,38.040001,38.040001,38.040001,38.040001,38.040001,2220000
1955-05-03,37.700001,37.700001,37.700001,37.700001,37.700001,2630000
1955-05-04,37.639999,37.639999,37.639999,37.639999,37.639999,2220000
1955-05-05,37.820000,37.820000,37.820000,37.820000,37.820000,2270000
1955-05-06,37.889999,37.889999,37.889999,37.889999,37.889999,2250000
1955-05-09,37.930000,37.930000,37.930000,37.930000,37.930000,2090000
1955-05-10,37.849998,37.849998,37.849998,37.849998,37.849998,2150000
1955-05-11,37.419998,37.419998,37.419998,37.419998,37.419998,2120000
1955-05-12,37.200001,37.200001,37.200001,37.200001,37.200001,2830000
1955-05-13,37.439999,37.439999,37.439999,37.439999,37.439999,1860000
1955-05-16,37.020000,37.020000,37.020000,37.020000,37.020000,2160000
1955-05-17,36.970001,36.970001,36.970001,36.970001,36.970001,1900000
1955-05-18,37.279999,37.279999,37.279999,37.279999,37.279999,2010000
1955-05-19,37.490002,37.490002,37.490002,37.490002,37.490002,2380000
1955-05-20,37.740002,37.740002,37.740002,37.740002,37.740002,2240000
1955-05-23,37.480000,37.480000,37.480000,37.480000,37.480000,1900000
1955-05-24,37.459999,37.459999,37.459999,37.459999,37.459999,1650000
1955-05-25,37.599998,37.599998,37.599998,37.599998,37.599998,2100000
1955-05-26,37.849998,37.849998,37.849998,37.849998,37.849998,2260000
1955-05-27,37.930000,37.930000,37.930000,37.930000,37.930000,2220000
1955-05-31,37.910000,37.910000,37.910000,37.910000,37.910000,1990000
1955-06-01,37.959999,37.959999,37.959999,37.959999,37.959999,2510000
1955-06-02,38.009998,38.009998,38.009998,38.009998,38.009998,2610000
1955-06-03,38.369999,38.369999,38.369999,38.369999,38.369999,2590000
1955-06-06,39.689999,39.689999,39.689999,39.689999,39.689999,2560000
1955-06-07,39.959999,39.959999,39.959999,39.959999,39.959999,3230000
1955-06-08,39.220001,39.220001,39.220001,39.220001,39.220001,3300000
1955-06-09,39.009998,39.009998,39.009998,39.009998,39.009998,2960000
1955-06-10,39.250000,39.250000,39.250000,39.250000,39.250000,2470000
1955-06-13,39.619999,39.619999,39.619999,39.619999,39.619999,2770000
1955-06-14,39.669998,39.669998,39.669998,39.669998,39.669998,2860000
1955-06-15,39.889999,39.889999,39.889999,39.889999,39.889999,2650000
1955-06-16,39.959999,39.959999,39.959999,39.959999,39.959999,2760000
1955-06-17,40.099998,40.099998,40.099998,40.099998,40.099998,2340000
1955-06-20,40.139999,40.139999,40.139999,40.139999,40.139999,2490000
1955-06-21,40.509998,40.509998,40.509998,40.509998,40.509998,2720000
1955-06-22,40.599998,40.599998,40.599998,40.599998,40.599998,3010000
1955-06-23,40.750000,40.750000,40.750000,40.750000,40.750000,2900000
1955-06-24,40.959999,40.959999,40.959999,40.959999,40.959999,2410000
1955-06-27,40.990002,40.990002,40.990002,40.990002,40.990002,2250000
1955-06-28,40.770000,40.770000,40.770000,40.770000,40.770000,2180000
1955-06-29,40.790001,40.790001,40.790001,40.790001,40.790001,2180000
1955-06-30,41.029999,41.029999,41.029999,41.029999,41.029999,2370000
1955-07-01,41.189999,41.189999,41.189999,41.189999,41.189999,2540000
1955-07-05,41.689999,41.689999,41.689999,41.689999,41.689999,2680000
1955-07-06,43.180000,43.180000,43.180000,43.180000,43.180000,3140000
1955-07-07,42.580002,42.580002,42.580002,42.580002,42.580002,3300000
1955-07-08,42.639999,42.639999,42.639999,42.639999,42.639999,2450000
1955-07-11,42.750000,42.750000,42.750000,42.750000,42.750000,2420000
1955-07-12,42.750000,42.750000,42.750000,42.750000,42.750000,2630000
1955-07-13,42.240002,42.240002,42.240002,42.240002,42.240002,2360000
1955-07-14,42.250000,42.250000,42.250000,42.250000,42.250000,1980000
1955-07-15,42.400002,42.400002,42.400002,42.400002,42.400002,2230000
1955-07-18,42.360001,42.360001,42.360001,42.360001,42.360001,2160000
1955-07-19,42.099998,42.099998,42.099998,42.099998,42.099998,2300000
1955-07-20,42.230000,42.230000,42.230000,42.230000,42.230000,2080000
1955-07-21,42.639999,42.639999,42.639999,42.639999,42.639999,2530000
1955-07-22,43.000000,43.000000,43.000000,43.000000,43.000000,2500000
1955-07-25,43.480000,43.480000,43.480000,43.480000,43.480000,2500000
1955-07-26,43.580002,43.580002,43.580002,43.580002,43.580002,2340000
1955-07-27,43.759998,43.759998,43.759998,43.759998,43.759998,2170000
1955-07-28,43.500000,43.500000,43.500000,43.500000,43.500000,2090000
1955-07-29,43.520000,43.520000,43.520000,43.520000,43.520000,2070000
1955-08-01,42.930000,42.930000,42.930000,42.930000,42.930000,2190000
1955-08-02,43.029999,43.029999,43.029999,43.029999,43.029999,2260000
1955-08-03,43.090000,43.090000,43.090000,43.090000,43.090000,2190000
1955-08-04,42.360001,42.360001,42.360001,42.360001,42.360001,2210000
1955-08-05,42.560001,42.560001,42.560001,42.560001,42.560001,1690000
1955-08-08,42.310001,42.310001,42.310001,42.310001,42.310001,1730000
1955-08-09,41.750000,41.750000,41.750000,41.750000,41.750000,2240000
1955-08-10,41.740002,41.740002,41.740002,41.740002,41.740002,1580000
1955-08-11,42.130001,42.130001,42.130001,42.130001,42.130001,1620000
1955-08-12,42.209999,42.209999,42.209999,42.209999,42.209999,1530000
1955-08-15,42.169998,42.169998,42.169998,42.169998,42.169998,1230000
1955-08-16,41.860001,41.860001,41.860001,41.860001,41.860001,1520000
1955-08-17,41.900002,41.900002,41.900002,41.900002,41.900002,1570000
1955-08-18,41.840000,41.840000,41.840000,41.840000,41.840000,1560000
1955-08-19,42.020000,42.020000,42.020000,42.020000,42.020000,1400000
1955-08-22,41.980000,41.980000,41.980000,41.980000,41.980000,1430000
1955-08-23,42.549999,42.549999,42.549999,42.549999,42.549999,1890000
1955-08-24,42.610001,42.610001,42.610001,42.610001,42.610001,2140000
1955-08-25,42.799999,42.799999,42.799999,42.799999,42.799999,2120000
1955-08-26,42.990002,42.990002,42.990002,42.990002,42.990002,2200000
1955-08-29,42.959999,42.959999,42.959999,42.959999,42.959999,1910000
1955-08-30,42.919998,42.919998,42.919998,42.919998,42.919998,1740000
1955-08-31,43.180000,43.180000,43.180000,43.180000,43.180000,1850000
1955-09-01,43.369999,43.369999,43.369999,43.369999,43.369999,1860000
1955-09-02,43.599998,43.599998,43.599998,43.599998,43.599998,1700000
1955-09-06,43.860001,43.860001,43.860001,43.860001,43.860001,2360000
1955-09-07,43.849998,43.849998,43.849998,43.849998,43.849998,2380000
1955-09-08,43.880001,43.880001,43.880001,43.880001,43.880001,2470000
1955-09-09,43.889999,43.889999,43.889999,43.889999,43.889999,2480000
1955-09-12,44.189999,44.189999,44.189999,44.189999,44.189999,2520000
1955-09-13,44.799999,44.799999,44.799999,44.799999,44.799999,2580000
1955-09-14,44.990002,44.990002,44.990002,44.990002,44.990002,2570000
1955-09-15,44.750000,44.750000,44.750000,44.750000,44.750000,2890000
1955-09-16,45.090000,45.090000,45.090000,45.090000,45.090000,2540000
1955-09-19,45.160000,45.160000,45.160000,45.160000,45.160000,2390000
1955-09-20,45.130001,45.130001,45.130001,45.130001,45.130001,2090000
1955-09-21,45.389999,45.389999,45.389999,45.389999,45.389999,2460000
1955-09-22,45.389999,45.389999,45.389999,45.389999,45.389999,2550000
1955-09-23,45.630001,45.630001,45.630001,45.630001,45.630001,2540000
1955-09-26,42.610001,42.610001,42.610001,42.610001,42.610001,7720000
1955-09-27,43.580002,43.580002,43.580002,43.580002,43.580002,5500000
1955-09-28,44.310001,44.310001,44.310001,44.310001,44.310001,3780000
1955-09-29,44.029999,44.029999,44.029999,44.029999,44.029999,2560000
1955-09-30,43.669998,43.669998,43.669998,43.669998,43.669998,2140000
1955-10-03,42.490002,42.490002,42.490002,42.490002,42.490002,2720000
1955-10-04,42.820000,42.820000,42.820000,42.820000,42.820000,2020000
1955-10-05,42.990002,42.990002,42.990002,42.990002,42.990002,1920000
1955-10-06,42.700001,42.700001,42.700001,42.700001,42.700001,1690000
1955-10-07,42.380001,42.380001,42.380001,42.380001,42.380001,2150000
1955-10-10,41.150002,41.150002,41.150002,41.150002,41.150002,3100000
1955-10-11,40.799999,40.799999,40.799999,40.799999,40.799999,3590000
1955-10-12,41.520000,41.520000,41.520000,41.520000,41.520000,1900000
1955-10-13,41.389999,41.389999,41.389999,41.389999,41.389999,1980000
1955-10-14,41.220001,41.220001,41.220001,41.220001,41.220001,1640000
1955-10-17,41.349998,41.349998,41.349998,41.349998,41.349998,1480000
1955-10-18,41.650002,41.650002,41.650002,41.650002,41.650002,1550000
1955-10-19,42.070000,42.070000,42.070000,42.070000,42.070000,1760000
1955-10-20,42.590000,42.590000,42.590000,42.590000,42.590000,2160000
1955-10-21,42.590000,42.590000,42.590000,42.590000,42.590000,1710000
1955-10-24,42.910000,42.910000,42.910000,42.910000,42.910000,1820000
1955-10-25,42.630001,42.630001,42.630001,42.630001,42.630001,1950000
1955-10-26,42.290001,42.290001,42.290001,42.290001,42.290001,1660000
1955-10-27,42.340000,42.340000,42.340000,42.340000,42.340000,1830000
1955-10-28,42.369999,42.369999,42.369999,42.369999,42.369999,1720000
1955-10-31,42.340000,42.340000,42.340000,42.340000,42.340000,1800000
1955-11-01,42.279999,42.279999,42.279999,42.279999,42.279999,1590000
1955-11-02,42.349998,42.349998,42.349998,42.349998,42.349998,1610000
1955-11-03,43.240002,43.240002,43.240002,43.240002,43.240002,2260000
1955-11-04,43.959999,43.959999,43.959999,43.959999,43.959999,2430000
1955-11-07,44.150002,44.150002,44.150002,44.150002,44.150002,2230000
1955-11-09,44.610001,44.610001,44.610001,44.610001,44.610001,2580000
1955-11-10,44.720001,44.720001,44.720001,44.720001,44.720001,2550000
1955-11-11,45.240002,45.240002,45.240002,45.240002,45.240002,2000000
1955-11-14,46.410000,46.410000,46.410000,46.410000,46.410000,2760000
1955-11-15,46.209999,46.209999,46.209999,46.209999,46.209999,2560000
1955-11-16,45.910000,45.910000,45.910000,45.910000,45.910000,2460000
1955-11-17,45.590000,45.590000,45.590000,45.590000,45.590000,2310000
1955-11-18,45.540001,45.540001,45.540001,45.540001,45.540001,2320000
1955-11-21,45.220001,45.220001,45.220001,45.220001,45.220001,1960000
1955-11-22,45.660000,45.660000,45.660000,45.660000,45.660000,2270000
1955-11-23,45.720001,45.720001,45.720001,45.720001,45.720001,2550000
1955-11-25,45.680000,45.680000,45.680000,45.680000,45.680000,2190000
1955-11-28,45.380001,45.380001,45.380001,45.380001,45.380001,2460000
1955-11-29,45.560001,45.560001,45.560001,45.560001,45.560001,2370000
1955-11-30,45.509998,45.509998,45.509998,45.509998,45.509998,2900000
1955-12-01,45.349998,45.349998,45.349998,45.349998,45.349998,2370000
1955-12-02,45.439999,45.439999,45.439999,45.439999,45.439999,2400000
1955-12-05,45.700001,45.700001,45.700001,45.700001,45.700001,2440000
1955-12-06,45.700001,45.700001,45.700001,45.700001,45.700001,2540000
1955-12-07,45.549999,45.549999,45.549999,45.549999,45.549999,2480000
1955-12-08,45.820000,45.820000,45.820000,45.820000,45.820000,2970000
1955-12-09,45.889999,45.889999,45.889999,45.889999,45.889999,2660000
1955-12-12,45.419998,45.419998,45.419998,45.419998,45.419998,2510000
1955-12-13,45.450001,45.450001,45.450001,45.450001,45.450001,2430000
1955-12-14,45.070000,45.070000,45.070000,45.070000,45.070000,2670000
1955-12-15,45.060001,45.060001,45.060001,45.060001,45.060001,2260000
1955-12-16,45.130001,45.130001,45.130001,45.130001,45.130001,2310000
1955-12-19,45.020000,45.020000,45.020000,45.020000,45.020000,2380000
1955-12-20,44.950001,44.950001,44.950001,44.950001,44.950001,2280000
1955-12-21,45.840000,45.840000,45.840000,45.840000,45.840000,2540000
1955-12-22,45.410000,45.410000,45.410000,45.410000,45.410000,2650000
1955-12-23,45.500000,45.500000,45.500000,45.500000,45.500000,2090000
1955-12-27,45.220001,45.220001,45.220001,45.220001,45.220001,2010000
1955-12-28,45.049999,45.049999,45.049999,45.049999,45.049999,1990000
1955-12-29,45.150002,45.150002,45.150002,45.150002,45.150002,2190000
1955-12-30,45.480000,45.480000,45.480000,45.480000,45.480000,2820000
1956-01-03,45.160000,45.160000,45.160000,45.160000,45.160000,2390000
1956-01-04,45.000000,45.000000,45.000000,45.000000,45.000000,2290000
1956-01-05,44.950001,44.950001,44.950001,44.950001,44.950001,2110000
1956-01-06,45.139999,45.139999,45.139999,45.139999,45.139999,2570000
1956-01-09,44.509998,44.509998,44.509998,44.509998,44.509998,2700000
1956-01-10,44.160000,44.160000,44.160000,44.160000,44.160000,2640000
1956-01-11,44.380001,44.380001,44.380001,44.380001,44.380001,2310000
1956-01-12,44.750000,44.750000,44.750000,44.750000,44.750000,2330000
1956-01-13,44.669998,44.669998,44.669998,44.669998,44.669998,2120000
1956-01-16,44.139999,44.139999,44.139999,44.139999,44.139999,2260000
1956-01-17,44.470001,44.470001,44.470001,44.470001,44.470001,2050000
1956-01-18,44.169998,44.169998,44.169998,44.169998,44.169998,2110000
1956-01-19,43.720001,43.720001,43.720001,43.720001,43.720001,2500000
1956-01-20,43.220001,43.220001,43.220001,43.220001,43.220001,2430000
1956-01-23,43.110001,43.110001,43.110001,43.110001,43.110001,2720000
1956-01-24,43.650002,43.650002,43.650002,43.650002,43.650002,2160000
1956-01-25,43.720001,43.720001,43.720001,43.720001,43.720001,1950000
1956-01-26,43.459999,43.459999,43.459999,43.459999,43.459999,1840000
1956-01-27,43.349998,43.349998,43.349998,43.349998,43.349998,1950000
1956-01-30,43.500000,43.500000,43.500000,43.500000,43.500000,1830000
1956-01-31,43.820000,43.820000,43.820000,43.820000,43.820000,1900000
1956-02-01,44.029999,44.029999,44.029999,44.029999,44.029999,2010000
1956-02-02,44.220001,44.220001,44.220001,44.220001,44.220001,1900000
1956-02-03,44.779999,44.779999,44.779999,44.779999,44.779999,2110000
1956-02-06,44.810001,44.810001,44.810001,44.810001,44.810001,2230000
1956-02-07,44.599998,44.599998,44.599998,44.599998,44.599998,2060000
1956-02-08,44.160000,44.160000,44.160000,44.160000,44.160000,2170000
1956-02-09,43.660000,43.660000,43.660000,43.660000,43.660000,2080000
1956-02-10,43.639999,43.639999,43.639999,43.639999,43.639999,1770000
1956-02-13,43.580002,43.580002,43.580002,43.580002,43.580002,1420000
1956-02-14,43.419998,43.419998,43.419998,43.419998,43.419998,1590000
1956-02-15,44.040001,44.040001,44.040001,44.040001,44.040001,3000000
1956-02-16,43.820000,43.820000,43.820000,43.820000,43.820000,1750000
1956-02-17,44.520000,44.520000,44.520000,44.520000,44.520000,2840000
1956-02-20,44.450001,44.450001,44.450001,44.450001,44.450001,2530000
1956-02-21,44.560001,44.560001,44.560001,44.560001,44.560001,2240000
1956-02-23,44.950001,44.950001,44.950001,44.950001,44.950001,2900000
1956-02-24,45.320000,45.320000,45.320000,45.320000,45.320000,2890000
1956-02-27,45.270000,45.270000,45.270000,45.270000,45.270000,2440000
1956-02-28,45.430000,45.430000,45.430000,45.430000,45.430000,2540000
1956-02-29,45.340000,45.340000,45.340000,45.340000,45.340000,3900000
1956-03-01,45.540001,45.540001,45.540001,45.540001,45.540001,2410000
1956-03-02,45.810001,45.810001,45.810001,45.810001,45.810001,2860000
1956-03-05,46.060001,46.060001,46.060001,46.060001,46.060001,3090000
1956-03-06,46.040001,46.040001,46.040001,46.040001,46.040001,2770000
1956-03-07,46.009998,46.009998,46.009998,46.009998,46.009998,2380000
1956-03-08,46.119999,46.119999,46.119999,46.119999,46.119999,2500000
1956-03-09,46.700001,46.700001,46.700001,46.700001,46.700001,3430000
1956-03-12,47.130001,47.130001,47.130001,47.130001,47.130001,3110000
1956-03-13,47.060001,47.060001,47.060001,47.060001,47.060001,2790000
1956-03-14,47.529999,47.529999,47.529999,47.529999,47.529999,3140000
1956-03-15,47.990002,47.990002,47.990002,47.990002,47.990002,3270000
1956-03-16,48.139999,48.139999,48.139999,48.139999,48.139999,3120000
1956-03-19,48.590000,48.590000,48.590000,48.590000,48.590000,2570000
1956-03-20,48.869999,48.869999,48.869999,48.869999,48.869999,2960000
1956-03-21,48.230000,48.230000,48.230000,48.230000,48.230000,2930000
1956-03-22,48.720001,48.720001,48.720001,48.720001,48.720001,2650000
1956-03-23,48.830002,48.830002,48.830002,48.830002,48.830002,2980000
1956-03-26,48.619999,48.619999,48.619999,48.619999,48.619999,2720000
1956-03-27,48.250000,48.250000,48.250000,48.250000,48.250000,2540000
1956-03-28,48.509998,48.509998,48.509998,48.509998,48.509998,2610000
1956-03-29,48.480000,48.480000,48.480000,48.480000,48.480000,3480000
1956-04-02,48.700001,48.700001,48.700001,48.700001,48.700001,3120000
1956-04-03,48.529999,48.529999,48.529999,48.529999,48.529999,2760000
1956-04-04,48.799999,48.799999,48.799999,48.799999,48.799999,2760000
1956-04-05,48.570000,48.570000,48.570000,48.570000,48.570000,2950000
1956-04-06,48.849998,48.849998,48.849998,48.849998,48.849998,2600000
1956-04-09,48.610001,48.610001,48.610001,48.610001,48.610001,2760000
1956-04-10,47.930000,47.930000,47.930000,47.930000,47.930000,2590000
1956-04-11,48.310001,48.310001,48.310001,48.310001,48.310001,2440000
1956-04-12,48.020000,48.020000,48.020000,48.020000,48.020000,2700000
1956-04-13,47.950001,47.950001,47.950001,47.950001,47.950001,2450000
1956-04-16,47.959999,47.959999,47.959999,47.959999,47.959999,2310000
1956-04-17,47.930000,47.930000,47.930000,47.930000,47.930000,2330000
1956-04-18,47.740002,47.740002,47.740002,47.740002,47.740002,2470000
1956-04-19,47.570000,47.570000,47.570000,47.570000,47.570000,2210000
1956-04-20,47.759998,47.759998,47.759998,47.759998,47.759998,2320000
1956-04-23,47.650002,47.650002,47.650002,47.650002,47.650002,2440000
1956-04-24,47.259998,47.259998,47.259998,47.259998,47.259998,2500000
1956-04-25,47.090000,47.090000,47.090000,47.090000,47.090000,2270000
1956-04-26,47.490002,47.490002,47.490002,47.490002,47.490002,2630000
1956-04-27,47.990002,47.990002,47.990002,47.990002,47.990002,2760000
1956-04-30,48.380001,48.380001,48.380001,48.380001,48.380001,2730000
1956-05-01,48.160000,48.160000,48.160000,48.160000,48.160000,2500000
1956-05-02,48.169998,48.169998,48.169998,48.169998,48.169998,2440000
1956-05-03,48.340000,48.340000,48.340000,48.340000,48.340000,2640000
1956-05-04,48.509998,48.509998,48.509998,48.509998,48.509998,2860000
1956-05-07,48.220001,48.220001,48.220001,48.220001,48.220001,2550000
1956-05-08,48.020000,48.020000,48.020000,48.020000,48.020000,2440000
1956-05-09,47.939999,47.939999,47.939999,47.939999,47.939999,2550000
1956-05-10,47.160000,47.160000,47.160000,47.160000,47.160000,2850000
1956-05-11,47.119999,47.119999,47.119999,47.119999,47.119999,2450000
1956-05-14,46.860001,46.860001,46.860001,46.860001,46.860001,2440000
1956-05-15,46.369999,46.369999,46.369999,46.369999,46.369999,2650000
1956-05-16,46.049999,46.049999,46.049999,46.049999,46.049999,2080000
1956-05-17,46.610001,46.610001,46.610001,46.610001,46.610001,1970000
1956-05-18,46.389999,46.389999,46.389999,46.389999,46.389999,2020000
1956-05-21,45.990002,45.990002,45.990002,45.990002,45.990002,1940000
1956-05-22,45.259998,45.259998,45.259998,45.259998,45.259998,2290000
1956-05-23,45.020000,45.020000,45.020000,45.020000,45.020000,2140000
1956-05-24,44.599998,44.599998,44.599998,44.599998,44.599998,2600000
1956-05-25,44.619999,44.619999,44.619999,44.619999,44.619999,2570000
1956-05-28,44.099998,44.099998,44.099998,44.099998,44.099998,2780000
1956-05-29,45.110001,45.110001,45.110001,45.110001,45.110001,2430000
1956-05-31,45.200001,45.200001,45.200001,45.200001,45.200001,2020000
1956-06-01,45.580002,45.580002,45.580002,45.580002,45.580002,1440000
1956-06-04,45.849998,45.849998,45.849998,45.849998,45.849998,1500000
1956-06-05,45.860001,45.860001,45.860001,45.860001,45.860001,1650000
1956-06-06,45.630001,45.630001,45.630001,45.630001,45.630001,1460000
1956-06-07,45.990002,45.990002,45.990002,45.990002,45.990002,1630000
1956-06-08,45.139999,45.139999,45.139999,45.139999,45.139999,3630000
1956-06-11,45.709999,45.709999,45.709999,45.709999,45.709999,2000000
1956-06-12,46.360001,46.360001,46.360001,46.360001,46.360001,1900000
1956-06-13,46.419998,46.419998,46.419998,46.419998,46.419998,1760000
1956-06-14,46.310001,46.310001,46.310001,46.310001,46.310001,1670000
1956-06-15,46.369999,46.369999,46.369999,46.369999,46.369999,1550000
1956-06-18,46.169998,46.169998,46.169998,46.169998,46.169998,1440000
1956-06-19,46.220001,46.220001,46.220001,46.220001,46.220001,1430000
1956-06-20,46.410000,46.410000,46.410000,46.410000,46.410000,1670000
1956-06-21,46.730000,46.730000,46.730000,46.730000,46.730000,1820000
1956-06-22,46.590000,46.590000,46.590000,46.590000,46.590000,1630000
1956-06-25,46.410000,46.410000,46.410000,46.410000,46.410000,1500000
1956-06-26,46.720001,46.720001,46.720001,46.720001,46.720001,1730000
1956-06-27,47.070000,47.070000,47.070000,47.070000,47.070000,2090000
1956-06-28,47.130001,47.130001,47.130001,47.130001,47.130001,1900000
1956-06-29,46.970001,46.970001,46.970001,46.970001,46.970001,1780000
1956-07-02,46.930000,46.930000,46.930000,46.930000,46.930000,1610000
1956-07-03,47.320000,47.320000,47.320000,47.320000,47.320000,1840000
1956-07-05,47.799999,47.799999,47.799999,47.799999,47.799999,2240000
1956-07-06,48.040001,48.040001,48.040001,48.040001,48.040001,2180000
1956-07-09,48.250000,48.250000,48.250000,48.250000,48.250000,2180000
1956-07-10,48.540001,48.540001,48.540001,48.540001,48.540001,2450000
1956-07-11,48.689999,48.689999,48.689999,48.689999,48.689999,2520000
1956-07-12,48.580002,48.580002,48.580002,48.580002,48.580002,2180000
1956-07-13,48.720001,48.720001,48.720001,48.720001,48.720001,2020000
1956-07-16,49.139999,49.139999,49.139999,49.139999,49.139999,2260000
1956-07-17,49.310001,49.310001,49.310001,49.310001,49.310001,2520000
1956-07-18,49.299999,49.299999,49.299999,49.299999,49.299999,2530000
1956-07-19,49.320000,49.320000,49.320000,49.320000,49.320000,1950000
1956-07-20,49.349998,49.349998,49.349998,49.349998,49.349998,2020000
1956-07-23,49.330002,49.330002,49.330002,49.330002,49.330002,1970000
1956-07-24,49.330002,49.330002,49.330002,49.330002,49.330002,2040000
1956-07-25,49.439999,49.439999,49.439999,49.439999,49.439999,2220000
1956-07-26,49.480000,49.480000,49.480000,49.480000,49.480000,2060000
1956-07-27,49.080002,49.080002,49.080002,49.080002,49.080002,2240000
1956-07-30,49.000000,49.000000,49.000000,49.000000,49.000000,2100000
1956-07-31,49.389999,49.389999,49.389999,49.389999,49.389999,2520000
1956-08-01,49.619999,49.619999,49.619999,49.619999,49.619999,2230000
1956-08-02,49.639999,49.639999,49.639999,49.639999,49.639999,2530000
1956-08-03,49.639999,49.639999,49.639999,49.639999,49.639999,2210000
1956-08-06,48.959999,48.959999,48.959999,48.959999,48.959999,2280000
1956-08-07,49.160000,49.160000,49.160000,49.160000,49.160000,2180000
1956-08-08,49.360001,49.360001,49.360001,49.360001,49.360001,2480000
1956-08-09,49.320000,49.320000,49.320000,49.320000,49.320000,2550000
1956-08-10,49.090000,49.090000,49.090000,49.090000,49.090000,2040000
1956-08-13,48.580002,48.580002,48.580002,48.580002,48.580002,1730000
1956-08-14,48.000000,48.000000,48.000000,48.000000,48.000000,1790000
1956-08-15,48.990002,48.990002,48.990002,48.990002,48.990002,2000000
1956-08-16,48.880001,48.880001,48.880001,48.880001,48.880001,1790000
1956-08-17,48.820000,48.820000,48.820000,48.820000,48.820000,1720000
1956-08-20,48.250000,48.250000,48.250000,48.250000,48.250000,1770000
1956-08-21,47.889999,47.889999,47.889999,47.889999,47.889999,2440000
1956-08-22,47.419998,47.419998,47.419998,47.419998,47.419998,1570000
1956-08-23,48.000000,48.000000,48.000000,48.000000,48.000000,1590000
1956-08-24,47.950001,47.950001,47.950001,47.950001,47.950001,1530000
1956-08-27,47.660000,47.660000,47.660000,47.660000,47.660000,1420000
1956-08-28,47.570000,47.570000,47.570000,47.570000,47.570000,1400000
1956-08-29,47.360001,47.360001,47.360001,47.360001,47.360001,1530000
1956-08-30,46.939999,46.939999,46.939999,46.939999,46.939999,2050000
1956-08-31,47.509998,47.509998,47.509998,47.509998,47.509998,1620000
1956-09-04,47.889999,47.889999,47.889999,47.889999,47.889999,1790000
1956-09-05,48.020000,48.020000,48.020000,48.020000,48.020000,2130000
1956-09-06,48.099998,48.099998,48.099998,48.099998,48.099998,1550000
1956-09-07,47.810001,47.810001,47.810001,47.810001,47.810001,1690000
1956-09-10,47.560001,47.560001,47.560001,47.560001,47.560001,1860000
1956-09-11,47.380001,47.380001,47.380001,47.380001,47.380001,1920000
1956-09-12,47.049999,47.049999,47.049999,47.049999,47.049999,1930000
1956-09-13,46.090000,46.090000,46.090000,46.090000,46.090000,2000000
1956-09-14,47.209999,47.209999,47.209999,47.209999,47.209999,2110000
1956-09-17,47.099998,47.099998,47.099998,47.099998,47.099998,1940000
1956-09-18,46.790001,46.790001,46.790001,46.790001,46.790001,2200000
1956-09-19,46.240002,46.240002,46.240002,46.240002,46.240002,2040000
1956-09-20,46.209999,46.209999,46.209999,46.209999,46.209999,2150000
1956-09-21,46.580002,46.580002,46.580002,46.580002,46.580002,2110000
1956-09-24,46.400002,46.400002,46.400002,46.400002,46.400002,1840000
1956-09-25,45.750000,45.750000,45.750000,45.750000,45.750000,2100000
1956-09-26,45.820000,45.820000,45.820000,45.820000,45.820000,2370000
1956-09-27,45.599998,45.599998,45.599998,45.599998,45.599998,1770000
1956-09-28,45.349998,45.349998,45.349998,45.349998,45.349998,1720000
1956-10-01,44.700001,44.700001,44.700001,44.700001,44.700001,2600000
1956-10-02,45.520000,45.520000,45.520000,45.520000,45.520000,2400000
1956-10-03,46.279999,46.279999,46.279999,46.279999,46.279999,2180000
1956-10-04,46.290001,46.290001,46.290001,46.290001,46.290001,1600000
1956-10-05,46.450001,46.450001,46.450001,46.450001,46.450001,1580000
1956-10-08,46.430000,46.430000,46.430000,46.430000,46.430000,1450000
1956-10-09,46.200001,46.200001,46.200001,46.200001,46.200001,1220000
1956-10-10,46.840000,46.840000,46.840000,46.840000,46.840000,1620000
1956-10-11,46.810001,46.810001,46.810001,46.810001,46.810001,1760000
1956-10-12,47.000000,47.000000,47.000000,47.000000,47.000000,1330000
1956-10-15,46.860001,46.860001,46.860001,46.860001,46.860001,1610000
1956-10-16,46.619999,46.619999,46.619999,46.619999,46.619999,1580000
1956-10-17,46.259998,46.259998,46.259998,46.259998,46.259998,1640000
1956-10-18,46.340000,46.340000,46.340000,46.340000,46.340000,1640000
1956-10-19,46.240002,46.240002,46.240002,46.240002,46.240002,1720000
1956-10-22,46.230000,46.230000,46.230000,46.230000,46.230000,1430000
1956-10-23,46.119999,46.119999,46.119999,46.119999,46.119999,1390000
1956-10-24,45.930000,45.930000,45.930000,45.930000,45.930000,1640000
1956-10-25,45.849998,45.849998,45.849998,45.849998,45.849998,1580000
1956-10-26,46.270000,46.270000,46.270000,46.270000,46.270000,1800000
1956-10-29,46.400002,46.400002,46.400002,46.400002,46.400002,2420000
1956-10-30,46.369999,46.369999,46.369999,46.369999,46.369999,1830000
1956-10-31,45.580002,45.580002,45.580002,45.580002,45.580002,2280000
1956-11-01,46.520000,46.520000,46.520000,46.520000,46.520000,1890000
1956-11-02,46.980000,46.980000,46.980000,46.980000,46.980000,2180000
1956-11-05,47.599998,47.599998,47.599998,47.599998,47.599998,2830000
1956-11-07,47.110001,47.110001,47.110001,47.110001,47.110001,2650000
1956-11-08,46.730000,46.730000,46.730000,46.730000,46.730000,1970000
1956-11-09,46.340000,46.340000,46.340000,46.340000,46.340000,1690000
1956-11-12,46.490002,46.490002,46.490002,46.490002,46.490002,1600000
1956-11-13,46.270000,46.270000,46.270000,46.270000,46.270000,2140000
1956-11-14,46.009998,46.009998,46.009998,46.009998,46.009998,2290000
1956-11-15,45.720001,45.720001,45.720001,45.720001,45.720001,2210000
1956-11-16,45.740002,45.740002,45.740002,45.740002,45.740002,1820000
1956-11-19,45.290001,45.290001,45.290001,45.290001,45.290001,2560000
1956-11-20,44.889999,44.889999,44.889999,44.889999,44.889999,2240000
1956-11-21,44.669998,44.669998,44.669998,44.669998,44.669998,2310000
1956-11-23,45.139999,45.139999,45.139999,45.139999,45.139999,1880000
1956-11-26,44.869999,44.869999,44.869999,44.869999,44.869999,2230000
1956-11-27,44.910000,44.910000,44.910000,44.910000,44.910000,2130000
1956-11-28,44.430000,44.430000,44.430000,44.430000,44.430000,2190000
1956-11-29,44.380001,44.380001,44.380001,44.380001,44.380001,2440000
1956-11-30,45.080002,45.080002,45.080002,45.080002,45.080002,2300000
1956-12-03,45.980000,45.980000,45.980000,45.980000,45.980000,2570000
1956-12-04,45.840000,45.840000,45.840000,45.840000,45.840000,2180000
1956-12-05,46.389999,46.389999,46.389999,46.389999,46.389999,2360000
1956-12-06,46.810001,46.810001,46.810001,46.810001,46.810001,2470000
1956-12-07,47.040001,47.040001,47.040001,47.040001,47.040001,2400000
1956-12-10,46.799999,46.799999,46.799999,46.799999,46.799999,2600000
1956-12-11,46.480000,46.480000,46.480000,46.480000,46.480000,2210000
1956-12-12,46.130001,46.130001,46.130001,46.130001,46.130001,2180000
1956-12-13,46.500000,46.500000,46.500000,46.500000,46.500000,2370000
1956-12-14,46.540001,46.540001,46.540001,46.540001,46.540001,2450000
1956-12-17,46.540001,46.540001,46.540001,46.540001,46.540001,2500000
1956-12-18,46.540001,46.540001,46.540001,46.540001,46.540001,2370000
1956-12-19,46.430000,46.430000,46.430000,46.430000,46.430000,1900000
1956-12-20,46.070000,46.070000,46.070000,46.070000,46.070000,2060000
1956-12-21,46.369999,46.369999,46.369999,46.369999,46.369999,2380000
1956-12-26,46.389999,46.389999,46.389999,46.389999,46.389999,2440000
1956-12-27,46.349998,46.349998,46.349998,46.349998,46.349998,2420000
1956-12-28,46.560001,46.560001,46.560001,46.560001,46.560001,2790000
1956-12-31,46.669998,46.669998,46.669998,46.669998,46.669998,3680000
1957-01-02,46.200001,46.200001,46.200001,46.200001,46.200001,1960000
1957-01-03,46.599998,46.599998,46.599998,46.599998,46.599998,2260000
1957-01-04,46.660000,46.660000,46.660000,46.660000,46.660000,2710000
1957-01-07,46.419998,46.419998,46.419998,46.419998,46.419998,2500000
1957-01-08,46.250000,46.250000,46.250000,46.250000,46.250000,2230000
1957-01-09,46.160000,46.160000,46.160000,46.160000,46.160000,2330000
1957-01-10,46.270000,46.270000,46.270000,46.270000,46.270000,2470000
1957-01-11,46.180000,46.180000,46.180000,46.180000,46.180000,2340000
1957-01-14,45.860001,45.860001,45.860001,45.860001,45.860001,2350000
1957-01-15,45.180000,45.180000,45.180000,45.180000,45.180000,2370000
1957-01-16,45.230000,45.230000,45.230000,45.230000,45.230000,2210000
1957-01-17,45.220001,45.220001,45.220001,45.220001,45.220001,2140000
1957-01-18,44.639999,44.639999,44.639999,44.639999,44.639999,2400000
1957-01-21,44.400002,44.400002,44.400002,44.400002,44.400002,2740000
1957-01-22,44.529999,44.529999,44.529999,44.529999,44.529999,1920000
1957-01-23,44.869999,44.869999,44.869999,44.869999,44.869999,1920000
1957-01-24,45.029999,45.029999,45.029999,45.029999,45.029999,1910000
1957-01-25,44.820000,44.820000,44.820000,44.820000,44.820000,2010000
1957-01-28,44.490002,44.490002,44.490002,44.490002,44.490002,1700000
1957-01-29,44.709999,44.709999,44.709999,44.709999,44.709999,1800000
1957-01-30,44.910000,44.910000,44.910000,44.910000,44.910000,1950000
1957-01-31,44.720001,44.720001,44.720001,44.720001,44.720001,1920000
1957-02-01,44.619999,44.619999,44.619999,44.619999,44.619999,1680000
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1957-02-20,43.630001,43.630001,43.630001,43.630001,43.630001,1790000
1957-02-21,43.480000,43.480000,43.480000,43.480000,43.480000,1680000
1957-02-25,43.380001,43.380001,43.380001,43.380001,43.380001,1710000
1957-02-26,43.450001,43.450001,43.450001,43.450001,43.450001,1580000
1957-02-27,43.410000,43.410000,43.410000,43.410000,43.410000,1620000
1957-02-28,43.259998,43.259998,43.259998,43.259998,43.259998,1620000
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1957-03-19,44.040001,44.040001,44.040001,44.040001,44.040001,1540000
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1957-03-22,44.060001,44.060001,44.060001,44.060001,44.060001,1610000
1957-03-25,43.880001,43.880001,43.880001,43.880001,43.880001,1590000
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1957-03-27,44.090000,44.090000,44.090000,44.090000,44.090000,1710000
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1957-04-09,44.790001,44.790001,44.790001,44.790001,44.790001,2400000
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1957-04-17,45.080002,45.080002,45.080002,45.080002,45.080002,2290000
1957-04-18,45.410000,45.410000,45.410000,45.410000,45.410000,2480000
1957-04-22,45.480000,45.480000,45.480000,45.480000,45.480000,2560000
1957-04-23,45.650002,45.650002,45.650002,45.650002,45.650002,2840000
1957-04-24,45.720001,45.720001,45.720001,45.720001,45.720001,2990000
1957-04-25,45.560001,45.560001,45.560001,45.560001,45.560001,2640000
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1957-04-29,45.730000,45.730000,45.730000,45.730000,45.730000,2290000
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1957-05-03,46.340000,46.340000,46.340000,46.340000,46.340000,2390000
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1957-05-07,46.130001,46.130001,46.130001,46.130001,46.130001,2300000
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1957-05-09,46.360001,46.360001,46.360001,46.360001,46.360001,2520000
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1957-05-13,46.880001,46.880001,46.880001,46.880001,46.880001,2720000
1957-05-14,46.669998,46.669998,46.669998,46.669998,46.669998,2580000
1957-05-15,46.830002,46.830002,46.830002,46.830002,46.830002,2590000
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1957-05-17,47.150002,47.150002,47.150002,47.150002,47.150002,2510000
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1957-05-21,47.330002,47.330002,47.330002,47.330002,47.330002,2370000
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1957-05-24,47.209999,47.209999,47.209999,47.209999,47.209999,2340000
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1957-05-28,46.689999,46.689999,46.689999,46.689999,46.689999,2070000
1957-05-29,47.110001,47.110001,47.110001,47.110001,47.110001,2270000
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1957-06-17,48.240002,48.240002,48.240002,48.240002,48.240002,2220000
1957-06-18,48.040001,48.040001,48.040001,48.040001,48.040001,2440000
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1957-07-02,47.900002,47.900002,47.900002,47.900002,47.900002,2450000
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1957-07-11,48.860001,48.860001,48.860001,48.860001,48.860001,2830000
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1957-07-15,49.130001,49.130001,49.130001,49.130001,49.130001,2480000
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1957-09-25,42.980000,42.980000,42.980000,42.980000,42.980000,2770000
1957-09-26,42.570000,42.570000,42.570000,42.570000,42.570000,2130000
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1957-10-02,43.099998,43.099998,43.099998,43.099998,43.099998,1760000
1957-10-03,43.139999,43.139999,43.139999,43.139999,43.139999,1590000
1957-10-04,42.790001,42.790001,42.790001,42.790001,42.790001,1520000
1957-10-07,42.220001,42.220001,42.220001,42.220001,42.220001,2490000
1957-10-08,41.950001,41.950001,41.950001,41.950001,41.950001,3190000
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1957-10-11,40.939999,40.939999,40.939999,40.939999,40.939999,4460000
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1957-10-15,41.669998,41.669998,41.669998,41.669998,41.669998,2620000
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1957-10-18,40.330002,40.330002,40.330002,40.330002,40.330002,2670000
1957-10-21,39.150002,39.150002,39.150002,39.150002,39.150002,4670000
1957-10-22,38.980000,38.980000,38.980000,38.980000,38.980000,5090000
1957-10-23,40.730000,40.730000,40.730000,40.730000,40.730000,4600000
1957-10-24,40.709999,40.709999,40.709999,40.709999,40.709999,4030000
1957-10-25,40.590000,40.590000,40.590000,40.590000,40.590000,2400000
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1957-10-29,40.689999,40.689999,40.689999,40.689999,40.689999,1860000
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1957-11-18,40.040001,40.040001,40.040001,40.040001,40.040001,2110000
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1957-11-20,39.919998,39.919998,39.919998,39.919998,39.919998,2400000
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1957-11-22,40.869999,40.869999,40.869999,40.869999,40.869999,2850000
1957-11-25,41.180000,41.180000,41.180000,41.180000,41.180000,2600000
1957-11-26,40.090000,40.090000,40.090000,40.090000,40.090000,3650000
1957-11-27,41.250000,41.250000,41.250000,41.250000,41.250000,3330000
1957-11-29,41.720001,41.720001,41.720001,41.720001,41.720001,2740000
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1957-12-06,41.310001,41.310001,41.310001,41.310001,41.310001,2350000
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1957-12-12,40.549999,40.549999,40.549999,40.549999,40.549999,2330000
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1957-12-23,39.480000,39.480000,39.480000,39.480000,39.480000,2790000
1957-12-24,39.520000,39.520000,39.520000,39.520000,39.520000,2220000
1957-12-26,39.919998,39.919998,39.919998,39.919998,39.919998,2280000
1957-12-27,39.779999,39.779999,39.779999,39.779999,39.779999,2620000
1957-12-30,39.580002,39.580002,39.580002,39.580002,39.580002,3750000
1957-12-31,39.990002,39.990002,39.990002,39.990002,39.990002,5070000
1958-01-02,40.330002,40.330002,40.330002,40.330002,40.330002,1800000
1958-01-03,40.869999,40.869999,40.869999,40.869999,40.869999,2440000
1958-01-06,40.680000,40.680000,40.680000,40.680000,40.680000,2500000
1958-01-07,41.000000,41.000000,41.000000,41.000000,41.000000,2220000
1958-01-08,40.990002,40.990002,40.990002,40.990002,40.990002,2230000
1958-01-09,40.750000,40.750000,40.750000,40.750000,40.750000,2180000
1958-01-10,40.369999,40.369999,40.369999,40.369999,40.369999,2010000
1958-01-13,40.490002,40.490002,40.490002,40.490002,40.490002,1860000
1958-01-14,40.669998,40.669998,40.669998,40.669998,40.669998,2010000
1958-01-15,40.990002,40.990002,40.990002,40.990002,40.990002,2080000
1958-01-16,41.060001,41.060001,41.060001,41.060001,41.060001,3950000
1958-01-17,41.099998,41.099998,41.099998,41.099998,41.099998,2200000
1958-01-20,41.349998,41.349998,41.349998,41.349998,41.349998,2310000
1958-01-21,41.299999,41.299999,41.299999,41.299999,41.299999,2160000
1958-01-22,41.200001,41.200001,41.200001,41.200001,41.200001,2390000
1958-01-23,41.360001,41.360001,41.360001,41.360001,41.360001,1910000
1958-01-24,41.709999,41.709999,41.709999,41.709999,41.709999,2830000
1958-01-27,41.590000,41.590000,41.590000,41.590000,41.590000,2320000
1958-01-28,41.630001,41.630001,41.630001,41.630001,41.630001,2030000
1958-01-29,41.880001,41.880001,41.880001,41.880001,41.880001,2220000
1958-01-30,41.680000,41.680000,41.680000,41.680000,41.680000,2150000
1958-01-31,41.700001,41.700001,41.700001,41.700001,41.700001,2030000
1958-02-03,42.040001,42.040001,42.040001,42.040001,42.040001,2490000
1958-02-04,42.459999,42.459999,42.459999,42.459999,42.459999,2970000
1958-02-05,42.189999,42.189999,42.189999,42.189999,42.189999,2480000
1958-02-06,42.099998,42.099998,42.099998,42.099998,42.099998,2210000
1958-02-07,41.730000,41.730000,41.730000,41.730000,41.730000,2220000
1958-02-10,41.480000,41.480000,41.480000,41.480000,41.480000,1900000
1958-02-11,41.110001,41.110001,41.110001,41.110001,41.110001,2110000
1958-02-12,40.930000,40.930000,40.930000,40.930000,40.930000,2030000
1958-02-13,40.939999,40.939999,40.939999,40.939999,40.939999,1880000
1958-02-14,41.330002,41.330002,41.330002,41.330002,41.330002,2070000
1958-02-17,41.110001,41.110001,41.110001,41.110001,41.110001,1700000
1958-02-18,41.169998,41.169998,41.169998,41.169998,41.169998,1680000
1958-02-19,41.150002,41.150002,41.150002,41.150002,41.150002,2070000
1958-02-20,40.910000,40.910000,40.910000,40.910000,40.910000,2060000
1958-02-21,40.880001,40.880001,40.880001,40.880001,40.880001,1700000
1958-02-24,40.650002,40.650002,40.650002,40.650002,40.650002,1570000
1958-02-25,40.610001,40.610001,40.610001,40.610001,40.610001,1920000
1958-02-26,40.919998,40.919998,40.919998,40.919998,40.919998,1880000
1958-02-27,40.680000,40.680000,40.680000,40.680000,40.680000,1670000
1958-02-28,40.840000,40.840000,40.840000,40.840000,40.840000,1580000
1958-03-03,41.130001,41.130001,41.130001,41.130001,41.130001,1810000
1958-03-04,41.349998,41.349998,41.349998,41.349998,41.349998,2010000
1958-03-05,41.470001,41.470001,41.470001,41.470001,41.470001,2020000
1958-03-06,42.000000,42.000000,42.000000,42.000000,42.000000,2470000
1958-03-07,42.070000,42.070000,42.070000,42.070000,42.070000,2130000
1958-03-10,42.209999,42.209999,42.209999,42.209999,42.209999,1980000
1958-03-11,42.509998,42.509998,42.509998,42.509998,42.509998,2640000
1958-03-12,42.410000,42.410000,42.410000,42.410000,42.410000,2420000
1958-03-13,42.459999,42.459999,42.459999,42.459999,42.459999,2830000
1958-03-14,42.330002,42.330002,42.330002,42.330002,42.330002,2150000
1958-03-17,42.040001,42.040001,42.040001,42.040001,42.040001,2130000
1958-03-18,41.889999,41.889999,41.889999,41.889999,41.889999,2070000
1958-03-19,42.090000,42.090000,42.090000,42.090000,42.090000,2410000
1958-03-20,42.110001,42.110001,42.110001,42.110001,42.110001,2280000
1958-03-21,42.419998,42.419998,42.419998,42.419998,42.419998,2430000
1958-03-24,42.580002,42.580002,42.580002,42.580002,42.580002,2580000
1958-03-25,42.439999,42.439999,42.439999,42.439999,42.439999,2210000
1958-03-26,42.299999,42.299999,42.299999,42.299999,42.299999,1990000
1958-03-27,42.169998,42.169998,42.169998,42.169998,42.169998,2140000
1958-03-28,42.200001,42.200001,42.200001,42.200001,42.200001,1930000
1958-03-31,42.099998,42.099998,42.099998,42.099998,42.099998,2050000
1958-04-01,41.930000,41.930000,41.930000,41.930000,41.930000,2070000
1958-04-02,41.599998,41.599998,41.599998,41.599998,41.599998,2390000
1958-04-03,41.480000,41.480000,41.480000,41.480000,41.480000,2130000
1958-04-07,41.330002,41.330002,41.330002,41.330002,41.330002,2090000
1958-04-08,41.430000,41.430000,41.430000,41.430000,41.430000,2190000
1958-04-09,41.650002,41.650002,41.650002,41.650002,41.650002,2040000
1958-04-10,41.700001,41.700001,41.700001,41.700001,41.700001,2000000
1958-04-11,41.740002,41.740002,41.740002,41.740002,41.740002,2060000
1958-04-14,42.000000,42.000000,42.000000,42.000000,42.000000,2180000
1958-04-15,42.430000,42.430000,42.430000,42.430000,42.430000,2590000
1958-04-16,42.099998,42.099998,42.099998,42.099998,42.099998,2240000
1958-04-17,42.250000,42.250000,42.250000,42.250000,42.250000,2500000
1958-04-18,42.709999,42.709999,42.709999,42.709999,42.709999,2700000
1958-04-21,42.930000,42.930000,42.930000,42.930000,42.930000,2550000
1958-04-22,42.799999,42.799999,42.799999,42.799999,42.799999,2440000
1958-04-23,42.799999,42.799999,42.799999,42.799999,42.799999,2720000
1958-04-24,43.139999,43.139999,43.139999,43.139999,43.139999,2870000
1958-04-25,43.360001,43.360001,43.360001,43.360001,43.360001,3020000
1958-04-28,43.220001,43.220001,43.220001,43.220001,43.220001,2400000
1958-04-29,43.000000,43.000000,43.000000,43.000000,43.000000,2190000
1958-04-30,43.439999,43.439999,43.439999,43.439999,43.439999,2900000
1958-05-01,43.540001,43.540001,43.540001,43.540001,43.540001,2630000
1958-05-02,43.689999,43.689999,43.689999,43.689999,43.689999,2290000
1958-05-05,43.790001,43.790001,43.790001,43.790001,43.790001,2670000
1958-05-06,44.009998,44.009998,44.009998,44.009998,44.009998,3110000
1958-05-07,43.930000,43.930000,43.930000,43.930000,43.930000,2770000
1958-05-08,43.990002,43.990002,43.990002,43.990002,43.990002,2790000
1958-05-09,44.090000,44.090000,44.090000,44.090000,44.090000,2760000
1958-05-12,43.750000,43.750000,43.750000,43.750000,43.750000,2780000
1958-05-13,43.619999,43.619999,43.619999,43.619999,43.619999,2940000
1958-05-14,43.119999,43.119999,43.119999,43.119999,43.119999,3060000
1958-05-15,43.340000,43.340000,43.340000,43.340000,43.340000,2470000
1958-05-16,43.360001,43.360001,43.360001,43.360001,43.360001,2030000
1958-05-19,43.240002,43.240002,43.240002,43.240002,43.240002,1910000
1958-05-20,43.610001,43.610001,43.610001,43.610001,43.610001,2500000
1958-05-21,43.549999,43.549999,43.549999,43.549999,43.549999,2580000
1958-05-22,43.779999,43.779999,43.779999,43.779999,43.779999,2950000
1958-05-23,43.869999,43.869999,43.869999,43.869999,43.869999,2570000
1958-05-26,43.849998,43.849998,43.849998,43.849998,43.849998,2500000
1958-05-27,43.790001,43.790001,43.790001,43.790001,43.790001,2180000
1958-05-28,43.849998,43.849998,43.849998,43.849998,43.849998,2260000
1958-05-29,44.090000,44.090000,44.090000,44.090000,44.090000,2350000
1958-06-02,44.310001,44.310001,44.310001,44.310001,44.310001,2770000
1958-06-03,44.459999,44.459999,44.459999,44.459999,44.459999,2780000
1958-06-04,44.500000,44.500000,44.500000,44.500000,44.500000,2690000
1958-06-05,44.549999,44.549999,44.549999,44.549999,44.549999,2600000
1958-06-06,44.639999,44.639999,44.639999,44.639999,44.639999,2680000
1958-06-09,44.570000,44.570000,44.570000,44.570000,44.570000,2380000
1958-06-10,44.480000,44.480000,44.480000,44.480000,44.480000,2390000
1958-06-11,44.490002,44.490002,44.490002,44.490002,44.490002,2570000
1958-06-12,44.750000,44.750000,44.750000,44.750000,44.750000,2760000
1958-06-13,45.020000,45.020000,45.020000,45.020000,45.020000,3100000
1958-06-16,45.180000,45.180000,45.180000,45.180000,45.180000,2870000
1958-06-17,44.939999,44.939999,44.939999,44.939999,44.939999,2950000
1958-06-18,45.340000,45.340000,45.340000,45.340000,45.340000,2640000
1958-06-19,44.610001,44.610001,44.610001,44.610001,44.610001,2690000
1958-06-20,44.849998,44.849998,44.849998,44.849998,44.849998,2590000
1958-06-23,44.689999,44.689999,44.689999,44.689999,44.689999,2340000
1958-06-24,44.520000,44.520000,44.520000,44.520000,44.520000,2560000
1958-06-25,44.630001,44.630001,44.630001,44.630001,44.630001,2720000
1958-06-26,44.840000,44.840000,44.840000,44.840000,44.840000,2910000
1958-06-27,44.900002,44.900002,44.900002,44.900002,44.900002,2800000
1958-06-30,45.240002,45.240002,45.240002,45.240002,45.240002,2820000
1958-07-01,45.279999,45.279999,45.279999,45.279999,45.279999,2600000
1958-07-02,45.320000,45.320000,45.320000,45.320000,45.320000,2370000
1958-07-03,45.470001,45.470001,45.470001,45.470001,45.470001,2630000
1958-07-07,45.619999,45.619999,45.619999,45.619999,45.619999,2510000
1958-07-08,45.400002,45.400002,45.400002,45.400002,45.400002,2430000
1958-07-09,45.250000,45.250000,45.250000,45.250000,45.250000,2630000
1958-07-10,45.419998,45.419998,45.419998,45.419998,45.419998,2510000
1958-07-11,45.720001,45.720001,45.720001,45.720001,45.720001,2400000
1958-07-14,45.139999,45.139999,45.139999,45.139999,45.139999,2540000
1958-07-15,45.110001,45.110001,45.110001,45.110001,45.110001,3090000
1958-07-16,45.250000,45.250000,45.250000,45.250000,45.250000,3240000
1958-07-17,45.549999,45.549999,45.549999,45.549999,45.549999,3180000
1958-07-18,45.770000,45.770000,45.770000,45.770000,45.770000,3350000
1958-07-21,46.330002,46.330002,46.330002,46.330002,46.330002,3440000
1958-07-22,46.410000,46.410000,46.410000,46.410000,46.410000,3420000
1958-07-23,46.400002,46.400002,46.400002,46.400002,46.400002,3550000
1958-07-24,46.650002,46.650002,46.650002,46.650002,46.650002,3740000
1958-07-25,46.970001,46.970001,46.970001,46.970001,46.970001,4430000
1958-07-28,47.150002,47.150002,47.150002,47.150002,47.150002,3940000
1958-07-29,46.959999,46.959999,46.959999,46.959999,46.959999,3310000
1958-07-30,47.090000,47.090000,47.090000,47.090000,47.090000,3680000
1958-07-31,47.189999,47.189999,47.189999,47.189999,47.189999,4440000
1958-08-01,47.490002,47.490002,47.490002,47.490002,47.490002,3380000
1958-08-04,47.939999,47.939999,47.939999,47.939999,47.939999,4000000
1958-08-05,47.750000,47.750000,47.750000,47.750000,47.750000,4210000
1958-08-06,47.459999,47.459999,47.459999,47.459999,47.459999,3440000
1958-08-07,47.770000,47.770000,47.770000,47.770000,47.770000,3200000
1958-08-08,48.049999,48.049999,48.049999,48.049999,48.049999,3650000
1958-08-11,48.160000,48.160000,48.160000,48.160000,48.160000,2870000
1958-08-12,47.730000,47.730000,47.730000,47.730000,47.730000,2600000
1958-08-13,47.810001,47.810001,47.810001,47.810001,47.810001,2790000
1958-08-14,47.910000,47.910000,47.910000,47.910000,47.910000,3370000
1958-08-15,47.500000,47.500000,47.500000,47.500000,47.500000,2960000
1958-08-18,47.220001,47.220001,47.220001,47.220001,47.220001,2390000
1958-08-19,47.299999,47.299999,47.299999,47.299999,47.299999,2250000
1958-08-20,47.320000,47.320000,47.320000,47.320000,47.320000,2460000
1958-08-21,47.630001,47.630001,47.630001,47.630001,47.630001,2500000
1958-08-22,47.730000,47.730000,47.730000,47.730000,47.730000,2660000
1958-08-25,47.740002,47.740002,47.740002,47.740002,47.740002,2610000
1958-08-26,47.900002,47.900002,47.900002,47.900002,47.900002,2910000
1958-08-27,47.910000,47.910000,47.910000,47.910000,47.910000,3250000
1958-08-28,47.660000,47.660000,47.660000,47.660000,47.660000,2540000
1958-08-29,47.750000,47.750000,47.750000,47.750000,47.750000,2260000
1958-09-02,48.000000,48.000000,48.000000,48.000000,48.000000,2930000
1958-09-03,48.180000,48.180000,48.180000,48.180000,48.180000,3240000
1958-09-04,48.099998,48.099998,48.099998,48.099998,48.099998,3100000
1958-09-05,47.970001,47.970001,47.970001,47.970001,47.970001,2520000
1958-09-08,48.130001,48.130001,48.130001,48.130001,48.130001,3030000
1958-09-09,48.459999,48.459999,48.459999,48.459999,48.459999,3480000
1958-09-10,48.310001,48.310001,48.310001,48.310001,48.310001,2820000
1958-09-11,48.639999,48.639999,48.639999,48.639999,48.639999,3300000
1958-09-12,48.529999,48.529999,48.529999,48.529999,48.529999,3100000
1958-09-15,48.959999,48.959999,48.959999,48.959999,48.959999,3040000
1958-09-16,49.349998,49.349998,49.349998,49.349998,49.349998,3940000
1958-09-17,49.349998,49.349998,49.349998,49.349998,49.349998,3790000
1958-09-18,49.380001,49.380001,49.380001,49.380001,49.380001,3460000
1958-09-19,49.400002,49.400002,49.400002,49.400002,49.400002,3880000
1958-09-22,49.200001,49.200001,49.200001,49.200001,49.200001,3490000
1958-09-23,49.560001,49.560001,49.560001,49.560001,49.560001,3950000
1958-09-24,49.779999,49.779999,49.779999,49.779999,49.779999,3120000
1958-09-25,49.570000,49.570000,49.570000,49.570000,49.570000,4490000
1958-09-26,49.660000,49.660000,49.660000,49.660000,49.660000,3420000
1958-09-29,49.869999,49.869999,49.869999,49.869999,49.869999,3680000
1958-09-30,50.060001,50.060001,50.060001,50.060001,50.060001,4160000
1958-10-01,49.980000,49.980000,49.980000,49.980000,49.980000,3780000
1958-10-02,50.169998,50.169998,50.169998,50.169998,50.169998,3750000
1958-10-03,50.369999,50.369999,50.369999,50.369999,50.369999,3830000
1958-10-06,51.070000,51.070000,51.070000,51.070000,51.070000,3570000
1958-10-07,51.070000,51.070000,51.070000,51.070000,51.070000,3570000
1958-10-08,51.060001,51.060001,51.060001,51.060001,51.060001,3680000
1958-10-09,51.049999,51.049999,51.049999,51.049999,51.049999,3670000
1958-10-10,51.389999,51.389999,51.389999,51.389999,51.389999,4610000
1958-10-13,51.619999,51.619999,51.619999,51.619999,51.619999,4550000
1958-10-14,51.259998,51.259998,51.259998,51.259998,51.259998,5110000
1958-10-15,50.580002,50.580002,50.580002,50.580002,50.580002,4810000
1958-10-16,50.939999,50.939999,50.939999,50.939999,50.939999,4560000
1958-10-17,51.459999,51.459999,51.459999,51.459999,51.459999,5360000
1958-10-20,51.270000,51.270000,51.270000,51.270000,51.270000,4560000
1958-10-21,51.270000,51.270000,51.270000,51.270000,51.270000,4010000
1958-10-22,51.070000,51.070000,51.070000,51.070000,51.070000,3500000
1958-10-23,50.970001,50.970001,50.970001,50.970001,50.970001,3610000
1958-10-24,50.810001,50.810001,50.810001,50.810001,50.810001,3770000
1958-10-27,50.419998,50.419998,50.419998,50.419998,50.419998,3980000
1958-10-28,50.580002,50.580002,50.580002,50.580002,50.580002,3670000
1958-10-29,51.070000,51.070000,51.070000,51.070000,51.070000,4790000
1958-10-30,51.270000,51.270000,51.270000,51.270000,51.270000,4360000
1958-10-31,51.330002,51.330002,51.330002,51.330002,51.330002,3920000
1958-11-03,51.560001,51.560001,51.560001,51.560001,51.560001,3240000
1958-11-05,52.029999,52.029999,52.029999,52.029999,52.029999,4080000
1958-11-06,52.450001,52.450001,52.450001,52.450001,52.450001,4890000
1958-11-07,52.259998,52.259998,52.259998,52.259998,52.259998,3700000
1958-11-10,52.570000,52.570000,52.570000,52.570000,52.570000,3730000
1958-11-11,52.980000,52.980000,52.980000,52.980000,52.980000,4040000
1958-11-12,53.049999,53.049999,53.049999,53.049999,53.049999,4440000
1958-11-13,52.830002,52.830002,52.830002,52.830002,52.830002,4200000
1958-11-14,53.090000,53.090000,53.090000,53.090000,53.090000,4390000
1958-11-17,53.240002,53.240002,53.240002,53.240002,53.240002,4540000
1958-11-18,53.130001,53.130001,53.130001,53.130001,53.130001,3820000
1958-11-19,53.200001,53.200001,53.200001,53.200001,53.200001,4090000
1958-11-20,53.209999,53.209999,53.209999,53.209999,53.209999,4320000
1958-11-21,52.700001,52.700001,52.700001,52.700001,52.700001,3950000
1958-11-24,52.029999,52.029999,52.029999,52.029999,52.029999,4770000
1958-11-25,51.020000,51.020000,51.020000,51.020000,51.020000,3940000
1958-11-26,51.900002,51.900002,51.900002,51.900002,51.900002,4090000
1958-11-28,52.480000,52.480000,52.480000,52.480000,52.480000,4120000
1958-12-01,52.689999,52.689999,52.689999,52.689999,52.689999,3800000
1958-12-02,52.459999,52.459999,52.459999,52.459999,52.459999,3320000
1958-12-03,52.529999,52.529999,52.529999,52.529999,52.529999,3460000
1958-12-04,52.549999,52.549999,52.549999,52.549999,52.549999,3630000
1958-12-05,52.459999,52.459999,52.459999,52.459999,52.459999,3360000
1958-12-08,52.459999,52.459999,52.459999,52.459999,52.459999,3590000
1958-12-09,52.820000,52.820000,52.820000,52.820000,52.820000,3790000
1958-12-10,53.459999,53.459999,53.459999,53.459999,53.459999,4340000
1958-12-11,53.349998,53.349998,53.349998,53.349998,53.349998,4250000
1958-12-12,53.220001,53.220001,53.220001,53.220001,53.220001,3140000
1958-12-15,53.369999,53.369999,53.369999,53.369999,53.369999,3340000
1958-12-16,53.570000,53.570000,53.570000,53.570000,53.570000,3970000
1958-12-17,53.919998,53.919998,53.919998,53.919998,53.919998,3900000
1958-12-18,54.150002,54.150002,54.150002,54.150002,54.150002,3900000
1958-12-19,54.070000,54.070000,54.070000,54.070000,54.070000,3540000
1958-12-22,53.709999,53.709999,53.709999,53.709999,53.709999,3030000
1958-12-23,53.419998,53.419998,53.419998,53.419998,53.419998,2870000
1958-12-24,54.110001,54.110001,54.110001,54.110001,54.110001,3050000
1958-12-29,54.740002,54.740002,54.740002,54.740002,54.740002,3790000
1958-12-30,54.930000,54.930000,54.930000,54.930000,54.930000,3900000
1958-12-31,55.209999,55.209999,55.209999,55.209999,55.209999,3970000
1959-01-02,55.439999,55.439999,55.439999,55.439999,55.439999,3380000
1959-01-05,55.660000,55.660000,55.660000,55.660000,55.660000,4210000
1959-01-06,55.590000,55.590000,55.590000,55.590000,55.590000,3690000
1959-01-07,54.889999,54.889999,54.889999,54.889999,54.889999,4140000
1959-01-08,55.400002,55.400002,55.400002,55.400002,55.400002,4030000
1959-01-09,55.770000,55.770000,55.770000,55.770000,55.770000,4760000
1959-01-12,55.779999,55.779999,55.779999,55.779999,55.779999,4320000
1959-01-13,55.470001,55.470001,55.470001,55.470001,55.470001,3790000
1959-01-14,55.619999,55.619999,55.619999,55.619999,55.619999,4090000
1959-01-15,55.830002,55.830002,55.830002,55.830002,55.830002,4500000
1959-01-16,55.810001,55.810001,55.810001,55.810001,55.810001,4300000
1959-01-19,55.680000,55.680000,55.680000,55.680000,55.680000,3840000
1959-01-20,55.720001,55.720001,55.720001,55.720001,55.720001,3680000
1959-01-21,56.040001,56.040001,56.040001,56.040001,56.040001,3940000
1959-01-22,55.970001,55.970001,55.970001,55.970001,55.970001,4250000
1959-01-23,56.000000,56.000000,56.000000,56.000000,56.000000,3600000
1959-01-26,55.770000,55.770000,55.770000,55.770000,55.770000,3980000
1959-01-27,55.779999,55.779999,55.779999,55.779999,55.779999,3480000
1959-01-28,55.160000,55.160000,55.160000,55.160000,55.160000,4190000
1959-01-29,55.200001,55.200001,55.200001,55.200001,55.200001,3470000
1959-01-30,55.450001,55.450001,55.450001,55.450001,55.450001,3600000
1959-02-02,55.209999,55.209999,55.209999,55.209999,55.209999,3610000
1959-02-03,55.279999,55.279999,55.279999,55.279999,55.279999,3220000
1959-02-04,55.060001,55.060001,55.060001,55.060001,55.060001,3170000
1959-02-05,54.810001,54.810001,54.810001,54.810001,54.810001,3140000
1959-02-06,54.369999,54.369999,54.369999,54.369999,54.369999,3010000
1959-02-09,53.580002,53.580002,53.580002,53.580002,53.580002,3130000
1959-02-10,54.320000,54.320000,54.320000,54.320000,54.320000,2960000
1959-02-11,54.349998,54.349998,54.349998,54.349998,54.349998,3000000
1959-02-12,54.000000,54.000000,54.000000,54.000000,54.000000,2630000
1959-02-13,54.419998,54.419998,54.419998,54.419998,54.419998,3070000
1959-02-16,54.500000,54.500000,54.500000,54.500000,54.500000,3480000
1959-02-17,54.290001,54.290001,54.290001,54.290001,54.290001,3190000
1959-02-18,54.299999,54.299999,54.299999,54.299999,54.299999,3480000
1959-02-19,55.500000,55.500000,55.500000,55.500000,55.500000,4160000
1959-02-20,55.520000,55.520000,55.520000,55.520000,55.520000,4190000
1959-02-24,55.480000,55.480000,55.480000,55.480000,55.480000,4340000
1959-02-25,55.240002,55.240002,55.240002,55.240002,55.240002,3780000
1959-02-26,55.340000,55.340000,55.340000,55.340000,55.340000,3930000
1959-02-27,55.410000,55.410000,55.410000,55.410000,55.410000,4300000
1959-03-02,55.730000,55.730000,55.730000,55.730000,55.730000,4210000
1959-03-03,56.250000,56.250000,56.250000,56.250000,56.250000,4790000
1959-03-04,56.349998,56.349998,56.349998,56.349998,56.349998,4150000
1959-03-05,56.430000,56.430000,56.430000,56.430000,56.430000,3930000
1959-03-06,56.209999,56.209999,56.209999,56.209999,56.209999,3930000
1959-03-09,56.150002,56.150002,56.150002,56.150002,56.150002,3530000
1959-03-10,56.310001,56.310001,56.310001,56.310001,56.310001,3920000
1959-03-11,56.349998,56.349998,56.349998,56.349998,56.349998,4160000
1959-03-12,56.599998,56.599998,56.599998,56.599998,56.599998,4690000
1959-03-13,56.669998,56.669998,56.669998,56.669998,56.669998,4880000
1959-03-16,56.060001,56.060001,56.060001,56.060001,56.060001,4420000
1959-03-17,56.520000,56.520000,56.520000,56.520000,56.520000,4730000
1959-03-18,56.389999,56.389999,56.389999,56.389999,56.389999,4530000
1959-03-19,56.340000,56.340000,56.340000,56.340000,56.340000,4150000
1959-03-20,56.389999,56.389999,56.389999,56.389999,56.389999,3770000
1959-03-23,55.869999,55.869999,55.869999,55.869999,55.869999,3700000
1959-03-24,55.959999,55.959999,55.959999,55.959999,55.959999,3000000
1959-03-25,55.880001,55.880001,55.880001,55.880001,55.880001,3280000
1959-03-26,55.759998,55.759998,55.759998,55.759998,55.759998,2900000
1959-03-30,55.450001,55.450001,55.450001,55.450001,55.450001,2940000
1959-03-31,55.439999,55.439999,55.439999,55.439999,55.439999,2820000
1959-04-01,55.689999,55.689999,55.689999,55.689999,55.689999,2980000
1959-04-02,56.000000,56.000000,56.000000,56.000000,56.000000,3220000
1959-04-03,56.439999,56.439999,56.439999,56.439999,56.439999,3680000
1959-04-06,56.599998,56.599998,56.599998,56.599998,56.599998,3510000
1959-04-07,56.480000,56.480000,56.480000,56.480000,56.480000,3020000
1959-04-08,56.209999,56.209999,56.209999,56.209999,56.209999,3260000
1959-04-09,56.169998,56.169998,56.169998,56.169998,56.169998,2830000
1959-04-10,56.220001,56.220001,56.220001,56.220001,56.220001,3000000
1959-04-13,56.430000,56.430000,56.430000,56.430000,56.430000,3140000
1959-04-14,56.709999,56.709999,56.709999,56.709999,56.709999,3320000
1959-04-15,56.959999,56.959999,56.959999,56.959999,56.959999,3680000
1959-04-16,57.430000,57.430000,57.430000,57.430000,57.430000,3790000
1959-04-17,57.919998,57.919998,57.919998,57.919998,57.919998,3870000
1959-04-20,58.169998,58.169998,58.169998,58.169998,58.169998,3610000
1959-04-21,58.110001,58.110001,58.110001,58.110001,58.110001,3650000
1959-04-22,57.730000,57.730000,57.730000,57.730000,57.730000,3430000
1959-04-23,57.599998,57.599998,57.599998,57.599998,57.599998,3310000
1959-04-24,57.959999,57.959999,57.959999,57.959999,57.959999,3790000
1959-04-27,58.139999,58.139999,58.139999,58.139999,58.139999,3850000
1959-04-28,57.919998,57.919998,57.919998,57.919998,57.919998,3920000
1959-04-29,57.689999,57.689999,57.689999,57.689999,57.689999,3470000
1959-04-30,57.590000,57.590000,57.590000,57.590000,57.590000,3510000
1959-05-01,57.650002,57.650002,57.650002,57.650002,57.650002,3020000
1959-05-04,57.650002,57.650002,57.650002,57.650002,57.650002,3060000
1959-05-05,57.750000,57.750000,57.750000,57.750000,57.750000,3360000
1959-05-06,57.610001,57.610001,57.610001,57.610001,57.610001,4110000
1959-05-07,56.880001,56.880001,56.880001,56.880001,56.880001,4530000
1959-05-08,57.320000,57.320000,57.320000,57.320000,57.320000,3930000
1959-05-11,57.959999,57.959999,57.959999,57.959999,57.959999,3860000
1959-05-12,57.959999,57.959999,57.959999,57.959999,57.959999,3550000
1959-05-13,57.970001,57.970001,57.970001,57.970001,57.970001,3540000
1959-05-14,58.369999,58.369999,58.369999,58.369999,58.369999,3660000
1959-05-15,58.160000,58.160000,58.160000,58.160000,58.160000,3510000
1959-05-18,58.150002,58.150002,58.150002,58.150002,58.150002,2970000
1959-05-19,58.320000,58.320000,58.320000,58.320000,58.320000,3170000
1959-05-20,58.090000,58.090000,58.090000,58.090000,58.090000,3550000
1959-05-21,58.139999,58.139999,58.139999,58.139999,58.139999,3230000
1959-05-22,58.330002,58.330002,58.330002,58.330002,58.330002,3030000
1959-05-25,58.180000,58.180000,58.180000,58.180000,58.180000,3260000
1959-05-26,58.090000,58.090000,58.090000,58.090000,58.090000,2910000
1959-05-27,58.189999,58.189999,58.189999,58.189999,58.189999,2940000
1959-05-28,58.389999,58.389999,58.389999,58.389999,58.389999,2970000
1959-05-29,58.680000,58.680000,58.680000,58.680000,58.680000,2790000
1959-06-01,58.630001,58.630001,58.630001,58.630001,58.630001,2730000
1959-06-02,58.230000,58.230000,58.230000,58.230000,58.230000,3120000
1959-06-03,58.250000,58.250000,58.250000,58.250000,58.250000,2910000
1959-06-04,57.630001,57.630001,57.630001,57.630001,57.630001,3210000
1959-06-05,57.509998,57.509998,57.509998,57.509998,57.509998,2800000
1959-06-08,56.759998,56.759998,56.759998,56.759998,56.759998,2970000
1959-06-09,56.360001,56.360001,56.360001,56.360001,56.360001,3490000
1959-06-10,57.189999,57.189999,57.189999,57.189999,57.189999,3310000
1959-06-11,57.250000,57.250000,57.250000,57.250000,57.250000,3120000
1959-06-12,57.290001,57.290001,57.290001,57.290001,57.290001,2580000
1959-06-15,56.990002,56.990002,56.990002,56.990002,56.990002,2410000
1959-06-16,56.560001,56.560001,56.560001,56.560001,56.560001,2440000
1959-06-17,57.090000,57.090000,57.090000,57.090000,57.090000,2850000
1959-06-18,57.049999,57.049999,57.049999,57.049999,57.049999,3150000
1959-06-19,57.130001,57.130001,57.130001,57.130001,57.130001,2260000
1959-06-22,57.130001,57.130001,57.130001,57.130001,57.130001,2630000
1959-06-23,57.119999,57.119999,57.119999,57.119999,57.119999,2600000
1959-06-24,57.410000,57.410000,57.410000,57.410000,57.410000,3180000
1959-06-25,57.730000,57.730000,57.730000,57.730000,57.730000,3250000
1959-06-26,57.980000,57.980000,57.980000,57.980000,57.980000,3100000
1959-06-29,58.369999,58.369999,58.369999,58.369999,58.369999,3000000
1959-06-30,58.470001,58.470001,58.470001,58.470001,58.470001,3200000
1959-07-01,58.970001,58.970001,58.970001,58.970001,58.970001,3150000
1959-07-02,59.279999,59.279999,59.279999,59.279999,59.279999,3610000
1959-07-06,59.650002,59.650002,59.650002,59.650002,59.650002,3720000
1959-07-07,60.009998,60.009998,60.009998,60.009998,60.009998,3840000
1959-07-08,60.029999,60.029999,60.029999,60.029999,60.029999,4010000
1959-07-09,59.970001,59.970001,59.970001,59.970001,59.970001,3560000
1959-07-10,59.910000,59.910000,59.910000,59.910000,59.910000,3600000
1959-07-13,59.410000,59.410000,59.410000,59.410000,59.410000,3360000
1959-07-14,59.549999,59.549999,59.549999,59.549999,59.549999,3230000
1959-07-15,59.590000,59.590000,59.590000,59.590000,59.590000,3280000
1959-07-16,59.410000,59.410000,59.410000,59.410000,59.410000,3170000
1959-07-17,59.189999,59.189999,59.189999,59.189999,59.189999,2510000
1959-07-20,58.910000,58.910000,58.910000,58.910000,58.910000,2500000
1959-07-21,59.410000,59.410000,59.410000,59.410000,59.410000,2950000
1959-07-22,59.610001,59.610001,59.610001,59.610001,59.610001,3310000
1959-07-23,59.669998,59.669998,59.669998,59.669998,59.669998,3310000
1959-07-24,59.650002,59.650002,59.650002,59.650002,59.650002,2720000
1959-07-27,60.020000,60.020000,60.020000,60.020000,60.020000,2910000
1959-07-28,60.320000,60.320000,60.320000,60.320000,60.320000,3190000
1959-07-29,60.619999,60.619999,60.619999,60.619999,60.619999,3460000
1959-07-30,60.500000,60.500000,60.500000,60.500000,60.500000,3240000
1959-07-31,60.509998,60.509998,60.509998,60.509998,60.509998,2270000
1959-08-03,60.709999,60.709999,60.709999,60.709999,60.709999,2410000
1959-08-04,60.610001,60.610001,60.610001,60.610001,60.610001,2530000
1959-08-05,60.299999,60.299999,60.299999,60.299999,60.299999,2630000
1959-08-06,60.240002,60.240002,60.240002,60.240002,60.240002,2610000
1959-08-07,59.869999,59.869999,59.869999,59.869999,59.869999,2580000
1959-08-10,58.619999,58.619999,58.619999,58.619999,58.619999,4190000
1959-08-11,59.389999,59.389999,59.389999,59.389999,59.389999,2980000
1959-08-12,59.250000,59.250000,59.250000,59.250000,59.250000,2700000
1959-08-13,59.150002,59.150002,59.150002,59.150002,59.150002,2020000
1959-08-14,59.290001,59.290001,59.290001,59.290001,59.290001,1990000
1959-08-17,59.169998,59.169998,59.169998,59.169998,59.169998,1980000
1959-08-18,58.619999,58.619999,58.619999,58.619999,58.619999,2280000
1959-08-19,58.270000,58.270000,58.270000,58.270000,58.270000,3050000
1959-08-20,59.139999,59.139999,59.139999,59.139999,59.139999,2450000
1959-08-21,59.080002,59.080002,59.080002,59.080002,59.080002,2000000
1959-08-24,58.869999,58.869999,58.869999,58.869999,58.869999,1860000
1959-08-25,58.990002,58.990002,58.990002,58.990002,58.990002,1960000
1959-08-26,59.070000,59.070000,59.070000,59.070000,59.070000,2210000
1959-08-27,59.580002,59.580002,59.580002,59.580002,59.580002,2550000
1959-08-28,59.599998,59.599998,59.599998,59.599998,59.599998,1930000
1959-08-31,59.599998,59.599998,59.599998,59.599998,59.599998,2140000
1959-09-01,58.869999,58.869999,58.869999,58.869999,58.869999,2430000
1959-09-02,58.919998,58.919998,58.919998,58.919998,58.919998,2370000
1959-09-03,58.259998,58.259998,58.259998,58.259998,58.259998,2330000
1959-09-04,58.540001,58.540001,58.540001,58.540001,58.540001,2300000
1959-09-08,57.700001,57.700001,57.700001,57.700001,57.700001,2940000
1959-09-09,57.290001,57.290001,57.290001,57.290001,57.290001,3030000
1959-09-10,56.990002,56.990002,56.990002,56.990002,56.990002,2520000
1959-09-11,57.410000,57.410000,57.410000,57.410000,57.410000,2640000
1959-09-14,56.990002,56.990002,56.990002,56.990002,56.990002,2590000
1959-09-15,56.680000,56.680000,56.680000,56.680000,56.680000,2830000
1959-09-16,56.720001,56.720001,56.720001,56.720001,56.720001,2180000
1959-09-17,56.410000,56.410000,56.410000,56.410000,56.410000,2090000
1959-09-18,56.189999,56.189999,56.189999,56.189999,56.189999,2530000
1959-09-21,55.270000,55.270000,55.270000,55.270000,55.270000,3240000
1959-09-22,55.139999,55.139999,55.139999,55.139999,55.139999,3000000
1959-09-23,55.820000,55.820000,55.820000,55.820000,55.820000,3010000
1959-09-24,56.779999,56.779999,56.779999,56.779999,56.779999,3480000
1959-09-25,56.730000,56.730000,56.730000,56.730000,56.730000,3280000
1959-09-28,57.150002,57.150002,57.150002,57.150002,57.150002,2640000
1959-09-29,57.509998,57.509998,57.509998,57.509998,57.509998,3220000
1959-09-30,56.880001,56.880001,56.880001,56.880001,56.880001,2850000
1959-10-01,56.939999,56.939999,56.939999,56.939999,56.939999,2660000
1959-10-02,57.200001,57.200001,57.200001,57.200001,57.200001,2270000
1959-10-05,57.139999,57.139999,57.139999,57.139999,57.139999,2100000
1959-10-06,57.090000,57.090000,57.090000,57.090000,57.090000,2330000
1959-10-07,56.939999,56.939999,56.939999,56.939999,56.939999,2380000
1959-10-08,56.810001,56.810001,56.810001,56.810001,56.810001,2510000
1959-10-09,57.000000,57.000000,57.000000,57.000000,57.000000,2540000
1959-10-12,57.320000,57.320000,57.320000,57.320000,57.320000,1750000
1959-10-13,57.160000,57.160000,57.160000,57.160000,57.160000,2530000
1959-10-14,56.709999,56.709999,56.709999,56.709999,56.709999,2320000
1959-10-15,56.869999,56.869999,56.869999,56.869999,56.869999,2190000
1959-10-16,57.330002,57.330002,57.330002,57.330002,57.330002,2760000
1959-10-19,57.009998,57.009998,57.009998,57.009998,57.009998,2470000
1959-10-20,56.660000,56.660000,56.660000,56.660000,56.660000,2740000
1959-10-21,56.549999,56.549999,56.549999,56.549999,56.549999,2730000
1959-10-22,56.000000,56.000000,56.000000,56.000000,56.000000,3060000
1959-10-23,56.560001,56.560001,56.560001,56.560001,56.560001,2880000
1959-10-26,56.939999,56.939999,56.939999,56.939999,56.939999,3580000
1959-10-27,57.419998,57.419998,57.419998,57.419998,57.419998,4160000
1959-10-28,57.459999,57.459999,57.459999,57.459999,57.459999,3920000
1959-10-29,57.410000,57.410000,57.410000,57.410000,57.410000,3890000
1959-10-30,57.520000,57.520000,57.520000,57.520000,57.520000,3560000
1959-11-02,57.410000,57.410000,57.410000,57.410000,57.410000,3320000
1959-11-04,57.259998,57.259998,57.259998,57.259998,57.259998,3940000
1959-11-05,57.320000,57.320000,57.320000,57.320000,57.320000,3170000
1959-11-06,57.599998,57.599998,57.599998,57.599998,57.599998,3450000
1959-11-09,57.500000,57.500000,57.500000,57.500000,57.500000,3700000
1959-11-10,57.480000,57.480000,57.480000,57.480000,57.480000,3020000
1959-11-11,57.490002,57.490002,57.490002,57.490002,57.490002,2820000
1959-11-12,57.169998,57.169998,57.169998,57.169998,57.169998,3600000
1959-11-13,56.849998,56.849998,56.849998,56.849998,56.849998,3050000
1959-11-16,56.220001,56.220001,56.220001,56.220001,56.220001,3710000
1959-11-17,56.380001,56.380001,56.380001,56.380001,56.380001,3570000
1959-11-18,56.990002,56.990002,56.990002,56.990002,56.990002,3660000
1959-11-19,56.939999,56.939999,56.939999,56.939999,56.939999,3230000
1959-11-20,56.970001,56.970001,56.970001,56.970001,56.970001,2960000
1959-11-23,57.080002,57.080002,57.080002,57.080002,57.080002,3400000
1959-11-24,57.349998,57.349998,57.349998,57.349998,57.349998,3650000
1959-11-25,57.439999,57.439999,57.439999,57.439999,57.439999,3550000
1959-11-27,57.700001,57.700001,57.700001,57.700001,57.700001,3030000
1959-11-30,58.279999,58.279999,58.279999,58.279999,58.279999,3670000
1959-12-01,58.700001,58.700001,58.700001,58.700001,58.700001,3990000
1959-12-02,58.599998,58.599998,58.599998,58.599998,58.599998,3490000
1959-12-03,58.730000,58.730000,58.730000,58.730000,58.730000,3280000
1959-12-04,58.849998,58.849998,58.849998,58.849998,58.849998,3590000
1959-12-07,58.959999,58.959999,58.959999,58.959999,58.959999,3620000
1959-12-08,59.340000,59.340000,59.340000,59.340000,59.340000,3870000
1959-12-09,58.970001,58.970001,58.970001,58.970001,58.970001,3430000
1959-12-10,59.020000,59.020000,59.020000,59.020000,59.020000,3170000
1959-12-11,58.880001,58.880001,58.880001,58.880001,58.880001,2910000
1959-12-14,59.040001,59.040001,59.040001,59.040001,59.040001,3100000
1959-12-15,58.900002,58.900002,58.900002,58.900002,58.900002,3450000
1959-12-16,58.970001,58.970001,58.970001,58.970001,58.970001,3270000
1959-12-17,58.860001,58.860001,58.860001,58.860001,58.860001,3040000
1959-12-18,59.139999,59.139999,59.139999,59.139999,59.139999,3230000
1959-12-21,59.209999,59.209999,59.209999,59.209999,59.209999,3290000
1959-12-22,59.139999,59.139999,59.139999,59.139999,59.139999,2930000
1959-12-23,58.959999,58.959999,58.959999,58.959999,58.959999,2890000
1959-12-24,59.000000,59.000000,59.000000,59.000000,59.000000,2320000
1959-12-28,58.980000,58.980000,58.980000,58.980000,58.980000,2830000
1959-12-29,59.299999,59.299999,59.299999,59.299999,59.299999,3020000
1959-12-30,59.770000,59.770000,59.770000,59.770000,59.770000,3680000
1959-12-31,59.889999,59.889999,59.889999,59.889999,59.889999,3810000
1960-01-04,59.910000,59.910000,59.910000,59.910000,59.910000,3990000
1960-01-05,60.389999,60.389999,60.389999,60.389999,60.389999,3710000
1960-01-06,60.130001,60.130001,60.130001,60.130001,60.130001,3730000
1960-01-07,59.689999,59.689999,59.689999,59.689999,59.689999,3310000
1960-01-08,59.500000,59.500000,59.500000,59.500000,59.500000,3290000
1960-01-11,58.770000,58.770000,58.770000,58.770000,58.770000,3470000
1960-01-12,58.410000,58.410000,58.410000,58.410000,58.410000,3760000
1960-01-13,58.080002,58.080002,58.080002,58.080002,58.080002,3470000
1960-01-14,58.400002,58.400002,58.400002,58.400002,58.400002,3560000
1960-01-15,58.380001,58.380001,58.380001,58.380001,58.380001,3400000
1960-01-18,57.889999,57.889999,57.889999,57.889999,57.889999,3020000
1960-01-19,57.270000,57.270000,57.270000,57.270000,57.270000,3100000
1960-01-20,57.070000,57.070000,57.070000,57.070000,57.070000,2720000
1960-01-21,57.209999,57.209999,57.209999,57.209999,57.209999,2700000
1960-01-22,57.380001,57.380001,57.380001,57.380001,57.380001,2690000
1960-01-25,56.779999,56.779999,56.779999,56.779999,56.779999,2790000
1960-01-26,56.860001,56.860001,56.860001,56.860001,56.860001,3060000
1960-01-27,56.720001,56.720001,56.720001,56.720001,56.720001,2460000
1960-01-28,56.130001,56.130001,56.130001,56.130001,56.130001,2630000
1960-01-29,55.610001,55.610001,55.610001,55.610001,55.610001,3060000
1960-02-01,55.959999,55.959999,55.959999,55.959999,55.959999,2820000
1960-02-02,56.820000,56.820000,56.820000,56.820000,56.820000,3080000
1960-02-03,56.320000,56.320000,56.320000,56.320000,56.320000,3020000
1960-02-04,56.270000,56.270000,56.270000,56.270000,56.270000,2600000
1960-02-05,55.980000,55.980000,55.980000,55.980000,55.980000,2530000
1960-02-08,55.320000,55.320000,55.320000,55.320000,55.320000,3350000
1960-02-09,55.840000,55.840000,55.840000,55.840000,55.840000,2860000
1960-02-10,55.490002,55.490002,55.490002,55.490002,55.490002,2440000
1960-02-11,55.180000,55.180000,55.180000,55.180000,55.180000,2610000
1960-02-12,55.459999,55.459999,55.459999,55.459999,55.459999,2230000
1960-02-15,55.169998,55.169998,55.169998,55.169998,55.169998,2780000
1960-02-16,54.730000,54.730000,54.730000,54.730000,54.730000,3270000
1960-02-17,55.029999,55.029999,55.029999,55.029999,55.029999,4210000
1960-02-18,55.799999,55.799999,55.799999,55.799999,55.799999,3800000
1960-02-19,56.240002,56.240002,56.240002,56.240002,56.240002,3230000
1960-02-23,55.939999,55.939999,55.939999,55.939999,55.939999,2960000
1960-02-24,55.740002,55.740002,55.740002,55.740002,55.740002,2740000
1960-02-25,55.930000,55.930000,55.930000,55.930000,55.930000,3600000
1960-02-26,56.160000,56.160000,56.160000,56.160000,56.160000,3380000
1960-02-29,56.119999,56.119999,56.119999,56.119999,56.119999,2990000
1960-03-01,56.009998,56.009998,56.009998,56.009998,56.009998,2920000
1960-03-02,55.619999,55.619999,55.619999,55.619999,55.619999,3110000
1960-03-03,54.779999,54.779999,54.779999,54.779999,54.779999,3160000
1960-03-04,54.570000,54.570000,54.570000,54.570000,54.570000,4060000
1960-03-07,54.020000,54.020000,54.020000,54.020000,54.020000,2900000
1960-03-08,53.470001,53.470001,53.470001,53.470001,53.470001,3370000
1960-03-09,54.040001,54.040001,54.040001,54.040001,54.040001,3580000
1960-03-10,53.830002,53.830002,53.830002,53.830002,53.830002,3350000
1960-03-11,54.240002,54.240002,54.240002,54.240002,54.240002,2770000
1960-03-14,54.320000,54.320000,54.320000,54.320000,54.320000,2530000
1960-03-15,54.740002,54.740002,54.740002,54.740002,54.740002,2690000
1960-03-16,55.040001,55.040001,55.040001,55.040001,55.040001,2960000
1960-03-17,54.959999,54.959999,54.959999,54.959999,54.959999,2140000
1960-03-18,55.009998,55.009998,55.009998,55.009998,55.009998,2620000
1960-03-21,55.070000,55.070000,55.070000,55.070000,55.070000,2500000
1960-03-22,55.290001,55.290001,55.290001,55.290001,55.290001,2490000
1960-03-23,55.740002,55.740002,55.740002,55.740002,55.740002,3020000
1960-03-24,55.980000,55.980000,55.980000,55.980000,55.980000,2940000
1960-03-25,55.980000,55.980000,55.980000,55.980000,55.980000,2640000
1960-03-28,55.860001,55.860001,55.860001,55.860001,55.860001,2500000
1960-03-29,55.779999,55.779999,55.779999,55.779999,55.779999,2320000
1960-03-30,55.660000,55.660000,55.660000,55.660000,55.660000,2450000
1960-03-31,55.340000,55.340000,55.340000,55.340000,55.340000,2690000
1960-04-01,55.430000,55.430000,55.430000,55.430000,55.430000,2260000
1960-04-04,55.540001,55.540001,55.540001,55.540001,55.540001,2450000
1960-04-05,55.369999,55.369999,55.369999,55.369999,55.369999,2840000
1960-04-06,56.509998,56.509998,56.509998,56.509998,56.509998,3450000
1960-04-07,56.520000,56.520000,56.520000,56.520000,56.520000,3070000
1960-04-08,56.389999,56.389999,56.389999,56.389999,56.389999,2820000
1960-04-11,56.169998,56.169998,56.169998,56.169998,56.169998,2520000
1960-04-12,56.299999,56.299999,56.299999,56.299999,56.299999,2470000
1960-04-13,56.299999,56.299999,56.299999,56.299999,56.299999,2730000
1960-04-14,56.430000,56.430000,56.430000,56.430000,56.430000,2730000
1960-04-18,56.590000,56.590000,56.590000,56.590000,56.590000,3200000
1960-04-19,56.130001,56.130001,56.130001,56.130001,56.130001,3080000
1960-04-20,55.439999,55.439999,55.439999,55.439999,55.439999,3150000
1960-04-21,55.590000,55.590000,55.590000,55.590000,55.590000,2700000
1960-04-22,55.419998,55.419998,55.419998,55.419998,55.419998,2850000
1960-04-25,54.860001,54.860001,54.860001,54.860001,54.860001,2980000
1960-04-26,55.040001,55.040001,55.040001,55.040001,55.040001,2940000
1960-04-27,55.040001,55.040001,55.040001,55.040001,55.040001,3020000
1960-04-28,54.560001,54.560001,54.560001,54.560001,54.560001,3190000
1960-04-29,54.369999,54.369999,54.369999,54.369999,54.369999,2850000
1960-05-02,54.130001,54.130001,54.130001,54.130001,54.130001,2930000
1960-05-03,54.830002,54.830002,54.830002,54.830002,54.830002,2910000
1960-05-04,55.040001,55.040001,55.040001,55.040001,55.040001,2870000
1960-05-05,54.860001,54.860001,54.860001,54.860001,54.860001,2670000
1960-05-06,54.750000,54.750000,54.750000,54.750000,54.750000,2560000
1960-05-09,54.799999,54.799999,54.799999,54.799999,54.799999,2670000
1960-05-10,54.419998,54.419998,54.419998,54.419998,54.419998,2870000
1960-05-11,54.570000,54.570000,54.570000,54.570000,54.570000,2900000
1960-05-12,54.849998,54.849998,54.849998,54.849998,54.849998,3220000
1960-05-13,55.299999,55.299999,55.299999,55.299999,55.299999,3750000
1960-05-16,55.250000,55.250000,55.250000,55.250000,55.250000,3530000
1960-05-17,55.459999,55.459999,55.459999,55.459999,55.459999,4080000
1960-05-18,55.439999,55.439999,55.439999,55.439999,55.439999,5240000
1960-05-19,55.680000,55.680000,55.680000,55.680000,55.680000,3700000
1960-05-20,55.730000,55.730000,55.730000,55.730000,55.730000,3170000
1960-05-23,55.759998,55.759998,55.759998,55.759998,55.759998,2530000
1960-05-24,55.700001,55.700001,55.700001,55.700001,55.700001,3240000
1960-05-25,55.669998,55.669998,55.669998,55.669998,55.669998,3440000
1960-05-26,55.709999,55.709999,55.709999,55.709999,55.709999,3720000
1960-05-27,55.740002,55.740002,55.740002,55.740002,55.740002,3040000
1960-05-31,55.830002,55.830002,55.830002,55.830002,55.830002,3750000
1960-06-01,55.889999,55.889999,55.889999,55.889999,55.889999,3770000
1960-06-02,56.130001,56.130001,56.130001,56.130001,56.130001,3730000
1960-06-03,56.230000,56.230000,56.230000,56.230000,56.230000,3340000
1960-06-06,56.889999,56.889999,56.889999,56.889999,56.889999,3220000
1960-06-07,57.430000,57.430000,57.430000,57.430000,57.430000,3710000
1960-06-08,57.889999,57.889999,57.889999,57.889999,57.889999,3800000
1960-06-09,58.000000,58.000000,58.000000,58.000000,58.000000,3820000
1960-06-10,57.970001,57.970001,57.970001,57.970001,57.970001,2940000
1960-06-13,57.990002,57.990002,57.990002,57.990002,57.990002,3180000
1960-06-14,57.910000,57.910000,57.910000,57.910000,57.910000,3430000
1960-06-15,57.570000,57.570000,57.570000,57.570000,57.570000,3630000
1960-06-16,57.500000,57.500000,57.500000,57.500000,57.500000,3540000
1960-06-17,57.439999,57.439999,57.439999,57.439999,57.439999,3920000
1960-06-20,57.160000,57.160000,57.160000,57.160000,57.160000,3970000
1960-06-21,57.110001,57.110001,57.110001,57.110001,57.110001,3860000
1960-06-22,57.279999,57.279999,57.279999,57.279999,57.279999,3600000
1960-06-23,57.590000,57.590000,57.590000,57.590000,57.590000,3620000
1960-06-24,57.680000,57.680000,57.680000,57.680000,57.680000,3220000
1960-06-27,57.330002,57.330002,57.330002,57.330002,57.330002,2960000
1960-06-28,56.939999,56.939999,56.939999,56.939999,56.939999,3120000
1960-06-29,56.939999,56.939999,56.939999,56.939999,56.939999,3160000
1960-06-30,56.919998,56.919998,56.919998,56.919998,56.919998,2940000
1960-07-01,57.060001,57.060001,57.060001,57.060001,57.060001,2620000
1960-07-05,57.020000,57.020000,57.020000,57.020000,57.020000,2780000
1960-07-06,56.939999,56.939999,56.939999,56.939999,56.939999,2970000
1960-07-07,57.240002,57.240002,57.240002,57.240002,57.240002,3050000
1960-07-08,57.380001,57.380001,57.380001,57.380001,57.380001,3010000
1960-07-11,56.869999,56.869999,56.869999,56.869999,56.869999,2920000
1960-07-12,56.250000,56.250000,56.250000,56.250000,56.250000,2860000
1960-07-13,56.099998,56.099998,56.099998,56.099998,56.099998,2590000
1960-07-14,56.119999,56.119999,56.119999,56.119999,56.119999,2480000
1960-07-15,56.049999,56.049999,56.049999,56.049999,56.049999,2140000
1960-07-18,55.700001,55.700001,55.700001,55.700001,55.700001,2350000
1960-07-19,55.700001,55.700001,55.700001,55.700001,55.700001,2490000
1960-07-20,55.610001,55.610001,55.610001,55.610001,55.610001,2370000
1960-07-21,55.099998,55.099998,55.099998,55.099998,55.099998,2510000
1960-07-22,54.720001,54.720001,54.720001,54.720001,54.720001,2850000
1960-07-25,54.180000,54.180000,54.180000,54.180000,54.180000,2840000
1960-07-26,54.509998,54.509998,54.509998,54.509998,54.509998,2720000
1960-07-27,54.169998,54.169998,54.169998,54.169998,54.169998,2560000
1960-07-28,54.570000,54.570000,54.570000,54.570000,54.570000,3020000
1960-07-29,55.509998,55.509998,55.509998,55.509998,55.509998,2730000
1960-08-01,55.529999,55.529999,55.529999,55.529999,55.529999,2440000
1960-08-02,55.040001,55.040001,55.040001,55.040001,55.040001,2090000
1960-08-03,54.720001,54.720001,54.720001,54.720001,54.720001,2470000
1960-08-04,54.889999,54.889999,54.889999,54.889999,54.889999,2840000
1960-08-05,55.439999,55.439999,55.439999,55.439999,55.439999,3000000
1960-08-08,55.520000,55.520000,55.520000,55.520000,55.520000,2960000
1960-08-09,55.840000,55.840000,55.840000,55.840000,55.840000,2700000
1960-08-10,56.070000,56.070000,56.070000,56.070000,56.070000,2810000
1960-08-11,56.279999,56.279999,56.279999,56.279999,56.279999,3070000
1960-08-12,56.660000,56.660000,56.660000,56.660000,56.660000,3160000
1960-08-15,56.610001,56.610001,56.610001,56.610001,56.610001,2450000
1960-08-16,56.720001,56.720001,56.720001,56.720001,56.720001,2710000
1960-08-17,56.840000,56.840000,56.840000,56.840000,56.840000,3090000
1960-08-18,56.810001,56.810001,56.810001,56.810001,56.810001,2890000
1960-08-19,57.009998,57.009998,57.009998,57.009998,57.009998,2570000
1960-08-22,57.189999,57.189999,57.189999,57.189999,57.189999,2760000
1960-08-23,57.750000,57.750000,57.750000,57.750000,57.750000,3560000
1960-08-24,58.070000,58.070000,58.070000,58.070000,58.070000,3500000
1960-08-25,57.790001,57.790001,57.790001,57.790001,57.790001,2680000
1960-08-26,57.599998,57.599998,57.599998,57.599998,57.599998,2780000
1960-08-29,57.439999,57.439999,57.439999,57.439999,57.439999,2780000
1960-08-30,56.840000,56.840000,56.840000,56.840000,56.840000,2890000
1960-08-31,56.959999,56.959999,56.959999,56.959999,56.959999,3130000
1960-09-01,57.090000,57.090000,57.090000,57.090000,57.090000,3460000
1960-09-02,57.000000,57.000000,57.000000,57.000000,57.000000,2680000
1960-09-06,56.490002,56.490002,56.490002,56.490002,56.490002,2580000
1960-09-07,55.790001,55.790001,55.790001,55.790001,55.790001,2850000
1960-09-08,55.740002,55.740002,55.740002,55.740002,55.740002,2670000
1960-09-09,56.110001,56.110001,56.110001,56.110001,56.110001,2750000
1960-09-12,55.720001,55.720001,55.720001,55.720001,55.720001,2160000
1960-09-13,55.830002,55.830002,55.830002,55.830002,55.830002,2180000
1960-09-14,55.439999,55.439999,55.439999,55.439999,55.439999,2530000
1960-09-15,55.220001,55.220001,55.220001,55.220001,55.220001,2870000
1960-09-16,55.110001,55.110001,55.110001,55.110001,55.110001,2340000
1960-09-19,53.860001,53.860001,53.860001,53.860001,53.860001,3790000
1960-09-20,54.009998,54.009998,54.009998,54.009998,54.009998,3660000
1960-09-21,54.570000,54.570000,54.570000,54.570000,54.570000,2930000
1960-09-22,54.360001,54.360001,54.360001,54.360001,54.360001,1970000
1960-09-23,53.900002,53.900002,53.900002,53.900002,53.900002,2580000
1960-09-26,53.060001,53.060001,53.060001,53.060001,53.060001,3930000
1960-09-27,52.939999,52.939999,52.939999,52.939999,52.939999,3170000
1960-09-28,52.480000,52.480000,52.480000,52.480000,52.480000,3520000
1960-09-29,52.619999,52.619999,52.619999,52.619999,52.619999,2850000
1960-09-30,53.520000,53.520000,53.520000,53.520000,53.520000,3370000
1960-10-03,53.360001,53.360001,53.360001,53.360001,53.360001,2220000
1960-10-04,52.990002,52.990002,52.990002,52.990002,52.990002,2270000
1960-10-05,53.389999,53.389999,53.389999,53.389999,53.389999,2650000
1960-10-06,53.720001,53.720001,53.720001,53.720001,53.720001,2510000
1960-10-07,54.029999,54.029999,54.029999,54.029999,54.029999,2530000
1960-10-10,54.139999,54.139999,54.139999,54.139999,54.139999,2030000
1960-10-11,54.220001,54.220001,54.220001,54.220001,54.220001,2350000
1960-10-12,54.150002,54.150002,54.150002,54.150002,54.150002,1890000
1960-10-13,54.570000,54.570000,54.570000,54.570000,54.570000,2220000
1960-10-14,54.860001,54.860001,54.860001,54.860001,54.860001,2470000
1960-10-17,54.630001,54.630001,54.630001,54.630001,54.630001,2280000
1960-10-18,54.349998,54.349998,54.349998,54.349998,54.349998,2220000
1960-10-19,54.250000,54.250000,54.250000,54.250000,54.250000,2410000
1960-10-20,53.860001,53.860001,53.860001,53.860001,53.860001,2910000
1960-10-21,53.720001,53.720001,53.720001,53.720001,53.720001,3090000
1960-10-24,52.700001,52.700001,52.700001,52.700001,52.700001,4420000
1960-10-25,52.200001,52.200001,52.200001,52.200001,52.200001,3030000
1960-10-26,53.049999,53.049999,53.049999,53.049999,53.049999,3020000
1960-10-27,53.619999,53.619999,53.619999,53.619999,53.619999,2900000
1960-10-28,53.410000,53.410000,53.410000,53.410000,53.410000,2490000
1960-10-31,53.389999,53.389999,53.389999,53.389999,53.389999,2460000
1960-11-01,53.939999,53.939999,53.939999,53.939999,53.939999,2600000
1960-11-02,54.220001,54.220001,54.220001,54.220001,54.220001,2780000
1960-11-03,54.430000,54.430000,54.430000,54.430000,54.430000,2580000
1960-11-04,54.900002,54.900002,54.900002,54.900002,54.900002,3050000
1960-11-07,55.110001,55.110001,55.110001,55.110001,55.110001,3540000
1960-11-09,55.349998,55.349998,55.349998,55.349998,55.349998,3450000
1960-11-10,56.430000,56.430000,56.430000,56.430000,56.430000,4030000
1960-11-11,55.869999,55.869999,55.869999,55.869999,55.869999,2730000
1960-11-14,55.590000,55.590000,55.590000,55.590000,55.590000,2660000
1960-11-15,55.810001,55.810001,55.810001,55.810001,55.810001,2990000
1960-11-16,55.700001,55.700001,55.700001,55.700001,55.700001,3110000
1960-11-17,55.549999,55.549999,55.549999,55.549999,55.549999,2450000
1960-11-18,55.820000,55.820000,55.820000,55.820000,55.820000,2760000
1960-11-21,55.930000,55.930000,55.930000,55.930000,55.930000,3090000
1960-11-22,55.720001,55.720001,55.720001,55.720001,55.720001,3430000
1960-11-23,55.799999,55.799999,55.799999,55.799999,55.799999,3000000
1960-11-25,56.130001,56.130001,56.130001,56.130001,56.130001,3190000
1960-11-28,56.029999,56.029999,56.029999,56.029999,56.029999,3860000
1960-11-29,55.830002,55.830002,55.830002,55.830002,55.830002,3630000
1960-11-30,55.540001,55.540001,55.540001,55.540001,55.540001,3080000
1960-12-01,55.299999,55.299999,55.299999,55.299999,55.299999,3090000
1960-12-02,55.389999,55.389999,55.389999,55.389999,55.389999,3140000
1960-12-05,55.310001,55.310001,55.310001,55.310001,55.310001,3290000
1960-12-06,55.470001,55.470001,55.470001,55.470001,55.470001,3360000
1960-12-07,56.020000,56.020000,56.020000,56.020000,56.020000,3660000
1960-12-08,56.150002,56.150002,56.150002,56.150002,56.150002,3540000
1960-12-09,56.650002,56.650002,56.650002,56.650002,56.650002,4460000
1960-12-12,56.849998,56.849998,56.849998,56.849998,56.849998,3020000
1960-12-13,56.880001,56.880001,56.880001,56.880001,56.880001,3500000
1960-12-14,56.840000,56.840000,56.840000,56.840000,56.840000,3880000
1960-12-15,56.680000,56.680000,56.680000,56.680000,56.680000,3660000
1960-12-16,57.200001,57.200001,57.200001,57.200001,57.200001,3770000
1960-12-19,57.130001,57.130001,57.130001,57.130001,57.130001,3630000
1960-12-20,57.090000,57.090000,57.090000,57.090000,57.090000,3340000
1960-12-21,57.549999,57.549999,57.549999,57.549999,57.549999,4060000
1960-12-22,57.389999,57.389999,57.389999,57.389999,57.389999,3820000
1960-12-23,57.439999,57.439999,57.439999,57.439999,57.439999,3580000
1960-12-27,57.520000,57.520000,57.520000,57.520000,57.520000,3270000
1960-12-28,57.779999,57.779999,57.779999,57.779999,57.779999,3620000
1960-12-29,58.049999,58.049999,58.049999,58.049999,58.049999,4340000
1960-12-30,58.110001,58.110001,58.110001,58.110001,58.110001,5300000
1961-01-03,57.570000,57.570000,57.570000,57.570000,57.570000,2770000
1961-01-04,58.360001,58.360001,58.360001,58.360001,58.360001,3840000
1961-01-05,58.570000,58.570000,58.570000,58.570000,58.570000,4130000
1961-01-06,58.400002,58.400002,58.400002,58.400002,58.400002,3620000
1961-01-09,58.810001,58.810001,58.810001,58.810001,58.810001,4210000
1961-01-10,58.970001,58.970001,58.970001,58.970001,58.970001,4840000
1961-01-11,59.139999,59.139999,59.139999,59.139999,59.139999,4370000
1961-01-12,59.320000,59.320000,59.320000,59.320000,59.320000,4270000
1961-01-13,59.599998,59.599998,59.599998,59.599998,59.599998,4520000
1961-01-16,59.580002,59.580002,59.580002,59.580002,59.580002,4510000
1961-01-17,59.639999,59.639999,59.639999,59.639999,59.639999,3830000
1961-01-18,59.680000,59.680000,59.680000,59.680000,59.680000,4390000
1961-01-19,59.770000,59.770000,59.770000,59.770000,59.770000,4740000
1961-01-20,59.959999,59.959999,59.959999,59.959999,59.959999,3270000
1961-01-23,60.290001,60.290001,60.290001,60.290001,60.290001,4450000
1961-01-24,60.450001,60.450001,60.450001,60.450001,60.450001,4280000
1961-01-25,60.529999,60.529999,60.529999,60.529999,60.529999,4470000
1961-01-26,60.619999,60.619999,60.619999,60.619999,60.619999,4110000
1961-01-27,61.240002,61.240002,61.240002,61.240002,61.240002,4510000
1961-01-30,61.970001,61.970001,61.970001,61.970001,61.970001,5190000
1961-01-31,61.779999,61.779999,61.779999,61.779999,61.779999,4690000
1961-02-01,61.900002,61.900002,61.900002,61.900002,61.900002,4380000
1961-02-02,62.299999,62.299999,62.299999,62.299999,62.299999,4900000
1961-02-03,62.220001,62.220001,62.220001,62.220001,62.220001,5210000
1961-02-06,61.759998,61.759998,61.759998,61.759998,61.759998,3890000
1961-02-07,61.650002,61.650002,61.650002,61.650002,61.650002,4020000
1961-02-08,62.209999,62.209999,62.209999,62.209999,62.209999,4940000
1961-02-09,62.020000,62.020000,62.020000,62.020000,62.020000,5590000
1961-02-10,61.500000,61.500000,61.500000,61.500000,61.500000,4840000
1961-02-13,61.139999,61.139999,61.139999,61.139999,61.139999,3560000
1961-02-14,61.410000,61.410000,61.410000,61.410000,61.410000,4490000
1961-02-15,61.919998,61.919998,61.919998,61.919998,61.919998,5200000
1961-02-16,62.299999,62.299999,62.299999,62.299999,62.299999,5070000
1961-02-17,62.099998,62.099998,62.099998,62.099998,62.099998,4640000
1961-02-20,62.320000,62.320000,62.320000,62.320000,62.320000,4680000
1961-02-21,62.360001,62.360001,62.360001,62.360001,62.360001,5070000
1961-02-23,62.590000,62.590000,62.590000,62.590000,62.590000,5620000
1961-02-24,62.840000,62.840000,62.840000,62.840000,62.840000,5330000
1961-02-27,63.299999,63.299999,63.299999,63.299999,63.299999,5470000
1961-02-28,63.439999,63.439999,63.439999,63.439999,63.439999,5830000
1961-03-01,63.430000,63.430000,63.430000,63.430000,63.430000,4970000
1961-03-02,63.849998,63.849998,63.849998,63.849998,63.849998,5300000
1961-03-03,63.950001,63.950001,63.950001,63.950001,63.950001,5530000
1961-03-06,64.050003,64.050003,64.050003,64.050003,64.050003,5650000
1961-03-07,63.470001,63.470001,63.470001,63.470001,63.470001,5540000
1961-03-08,63.439999,63.439999,63.439999,63.439999,63.439999,5910000
1961-03-09,63.500000,63.500000,63.500000,63.500000,63.500000,6010000
1961-03-10,63.480000,63.480000,63.480000,63.480000,63.480000,5950000
1961-03-13,63.660000,63.660000,63.660000,63.660000,63.660000,5080000
1961-03-14,63.380001,63.380001,63.380001,63.380001,63.380001,4900000
1961-03-15,63.570000,63.570000,63.570000,63.570000,63.570000,4900000
1961-03-16,64.209999,64.209999,64.209999,64.209999,64.209999,5610000
1961-03-17,64.000000,64.000000,64.000000,64.000000,64.000000,5960000
1961-03-20,64.860001,64.860001,64.860001,64.860001,64.860001,5780000
1961-03-21,64.739998,64.739998,64.739998,64.739998,64.739998,5800000
1961-03-22,64.699997,64.699997,64.699997,64.699997,64.699997,5840000
1961-03-23,64.529999,64.529999,64.529999,64.529999,64.529999,2170000
1961-03-24,64.419998,64.419998,64.419998,64.419998,64.419998,4390000
1961-03-27,64.349998,64.349998,64.349998,64.349998,64.349998,4190000
1961-03-28,64.379997,64.379997,64.379997,64.379997,64.379997,4630000
1961-03-29,64.930000,64.930000,64.930000,64.930000,64.930000,5330000
1961-03-30,65.059998,65.059998,65.059998,65.059998,65.059998,5610000
1961-04-03,65.599998,65.599998,65.599998,65.599998,65.599998,6470000
1961-04-04,65.660004,65.660004,65.660004,65.660004,65.660004,7080000
1961-04-05,65.459999,65.459999,65.459999,65.459999,65.459999,5430000
1961-04-06,65.610001,65.610001,65.610001,65.610001,65.610001,4910000
1961-04-07,65.959999,65.959999,65.959999,65.959999,65.959999,5100000
1961-04-10,66.529999,66.529999,66.529999,66.529999,66.529999,5550000
1961-04-11,66.620003,66.620003,66.620003,66.620003,66.620003,5230000
1961-04-12,66.309998,66.309998,66.309998,66.309998,66.309998,4870000
1961-04-13,66.260002,66.260002,66.260002,66.260002,66.260002,4770000
1961-04-14,66.370003,66.370003,66.370003,66.370003,66.370003,5240000
1961-04-17,68.680000,68.680000,68.680000,68.680000,68.680000,5860000
1961-04-18,66.199997,66.199997,66.199997,66.199997,66.199997,4830000
1961-04-19,65.809998,65.809998,65.809998,65.809998,65.809998,4870000
1961-04-20,65.820000,65.820000,65.820000,65.820000,65.820000,4810000
1961-04-21,65.769997,65.769997,65.769997,65.769997,65.769997,4340000
1961-04-24,64.400002,64.400002,64.400002,64.400002,64.400002,4590000
1961-04-25,65.300003,65.300003,65.300003,65.300003,65.300003,4670000
1961-04-26,65.550003,65.550003,65.550003,65.550003,65.550003,4980000
1961-04-27,65.459999,65.459999,65.459999,65.459999,65.459999,4450000
1961-04-28,65.309998,65.309998,65.309998,65.309998,65.309998,3710000
1961-05-01,65.169998,65.169998,65.169998,65.169998,65.169998,3710000
1961-05-02,65.639999,65.639999,65.639999,65.639999,65.639999,4110000
1961-05-03,66.180000,66.180000,66.180000,66.180000,66.180000,4940000
1961-05-04,66.440002,66.440002,66.440002,66.440002,66.440002,5350000
1961-05-05,66.519997,66.519997,66.519997,66.519997,66.519997,4980000
1961-05-08,66.410004,66.410004,66.410004,66.410004,66.410004,5170000
1961-05-09,66.470001,66.470001,66.470001,66.470001,66.470001,5380000
1961-05-10,66.410004,66.410004,66.410004,66.410004,66.410004,5450000
1961-05-11,66.389999,66.389999,66.389999,66.389999,66.389999,5170000
1961-05-12,66.500000,66.500000,66.500000,66.500000,66.500000,4840000
1961-05-15,66.830002,66.830002,66.830002,66.830002,66.830002,4840000
1961-05-16,67.080002,67.080002,67.080002,67.080002,67.080002,5110000
1961-05-17,67.389999,67.389999,67.389999,67.389999,67.389999,5520000
1961-05-18,66.989998,66.989998,66.989998,66.989998,66.989998,4610000
1961-05-19,67.269997,67.269997,67.269997,67.269997,67.269997,4200000
1961-05-22,66.849998,66.849998,66.849998,66.849998,66.849998,4070000
1961-05-23,66.680000,66.680000,66.680000,66.680000,66.680000,3660000
1961-05-24,66.260002,66.260002,66.260002,66.260002,66.260002,3970000
1961-05-25,66.010002,66.010002,66.010002,66.010002,66.010002,3760000
1961-05-26,66.430000,66.430000,66.430000,66.430000,66.430000,3780000
1961-05-31,66.559998,66.559998,66.559998,66.559998,66.559998,4320000
1961-06-01,66.559998,66.559998,66.559998,66.559998,66.559998,3770000
1961-06-02,66.730003,66.730003,66.730003,66.730003,66.730003,3670000
1961-06-05,67.080002,67.080002,67.080002,67.080002,67.080002,4150000
1961-06-06,66.889999,66.889999,66.889999,66.889999,66.889999,4250000
1961-06-07,65.639999,65.639999,65.639999,65.639999,65.639999,3980000
1961-06-08,66.669998,66.669998,66.669998,66.669998,66.669998,3810000
1961-06-09,66.660004,66.660004,66.660004,66.660004,66.660004,3520000
1961-06-12,66.150002,66.150002,66.150002,66.150002,66.150002,3260000
1961-06-13,65.800003,65.800003,65.800003,65.800003,65.800003,3030000
1961-06-14,65.980003,65.980003,65.980003,65.980003,65.980003,3430000
1961-06-15,65.690002,65.690002,65.690002,65.690002,65.690002,3220000
1961-06-16,65.180000,65.180000,65.180000,65.180000,65.180000,3380000
1961-06-19,64.580002,64.580002,64.580002,64.580002,64.580002,3980000
1961-06-20,65.150002,65.150002,65.150002,65.150002,65.150002,3280000
1961-06-21,65.139999,65.139999,65.139999,65.139999,65.139999,3210000
1961-06-22,64.900002,64.900002,64.900002,64.900002,64.900002,2880000
1961-06-23,65.160004,65.160004,65.160004,65.160004,65.160004,2720000
1961-06-26,64.470001,64.470001,64.470001,64.470001,64.470001,2690000
1961-06-27,64.470001,64.470001,64.470001,64.470001,64.470001,3090000
1961-06-28,64.589996,64.589996,64.589996,64.589996,64.589996,2830000
1961-06-29,64.519997,64.519997,64.519997,64.519997,64.519997,2560000
1961-06-30,64.639999,64.639999,64.639999,64.639999,64.639999,2380000
1961-07-03,65.209999,65.209999,65.209999,65.209999,65.209999,2180000
1961-07-05,65.629997,65.629997,65.629997,65.629997,65.629997,3270000
1961-07-06,65.809998,65.809998,65.809998,65.809998,65.809998,3470000
1961-07-07,65.769997,65.769997,65.769997,65.769997,65.769997,3030000
1961-07-10,65.709999,65.709999,65.709999,65.709999,65.709999,3180000
1961-07-11,65.690002,65.690002,65.690002,65.690002,65.690002,3160000
1961-07-12,65.320000,65.320000,65.320000,65.320000,65.320000,3070000
1961-07-13,64.860001,64.860001,64.860001,64.860001,64.860001,2670000
1961-07-14,65.279999,65.279999,65.279999,65.279999,65.279999,2760000
1961-07-17,64.790001,64.790001,64.790001,64.790001,64.790001,2690000
1961-07-18,64.410004,64.410004,64.410004,64.410004,64.410004,3010000
1961-07-19,64.699997,64.699997,64.699997,64.699997,64.699997,2940000
1961-07-20,64.709999,64.709999,64.709999,64.709999,64.709999,2530000
1961-07-21,64.860001,64.860001,64.860001,64.860001,64.860001,2360000
1961-07-24,64.870003,64.870003,64.870003,64.870003,64.870003,2490000
1961-07-25,65.230003,65.230003,65.230003,65.230003,65.230003,3010000
1961-07-26,65.839996,65.839996,65.839996,65.839996,65.839996,4070000
1961-07-27,66.610001,66.610001,66.610001,66.610001,66.610001,4170000
1961-07-28,66.709999,66.709999,66.709999,66.709999,66.709999,3610000
1961-07-31,66.760002,66.760002,66.760002,66.760002,66.760002,3170000
1961-08-01,67.370003,67.370003,67.370003,67.370003,67.370003,3990000
1961-08-02,66.940002,66.940002,66.940002,66.940002,66.940002,4300000
1961-08-03,67.290001,67.290001,67.290001,67.290001,67.290001,3650000
1961-08-04,67.680000,67.680000,67.680000,67.680000,67.680000,3710000
1961-08-07,67.669998,67.669998,67.669998,67.669998,67.669998,3560000
1961-08-08,67.820000,67.820000,67.820000,67.820000,67.820000,4050000
1961-08-09,67.739998,67.739998,67.739998,67.739998,67.739998,3710000
1961-08-10,67.949997,67.949997,67.949997,67.949997,67.949997,3570000
1961-08-11,68.059998,68.059998,68.059998,68.059998,68.059998,3260000
1961-08-14,67.720001,67.720001,67.720001,67.720001,67.720001,3120000
1961-08-15,67.550003,67.550003,67.550003,67.550003,67.550003,3320000
1961-08-16,67.730003,67.730003,67.730003,67.730003,67.730003,3430000
1961-08-17,68.110001,68.110001,68.110001,68.110001,68.110001,4130000
1961-08-18,68.290001,68.290001,68.290001,68.290001,68.290001,4030000
1961-08-21,68.430000,68.430000,68.430000,68.430000,68.430000,3880000
1961-08-22,68.440002,68.440002,68.440002,68.440002,68.440002,3640000
1961-08-23,67.980003,67.980003,67.980003,67.980003,67.980003,3550000
1961-08-24,67.589996,67.589996,67.589996,67.589996,67.589996,3090000
1961-08-25,67.669998,67.669998,67.669998,67.669998,67.669998,3050000
1961-08-28,67.699997,67.699997,67.699997,67.699997,67.699997,3150000
1961-08-29,67.550003,67.550003,67.550003,67.550003,67.550003,3160000
1961-08-30,67.809998,67.809998,67.809998,67.809998,67.809998,3220000
1961-08-31,68.070000,68.070000,68.070000,68.070000,68.070000,2920000
1961-09-01,68.190002,68.190002,68.190002,68.190002,68.190002,2710000
1961-09-05,67.959999,67.959999,67.959999,67.959999,67.959999,3000000
1961-09-06,68.459999,68.459999,68.459999,68.459999,68.459999,3440000
1961-09-07,68.349998,68.349998,68.349998,68.349998,68.349998,3900000
1961-09-08,67.879997,67.879997,67.879997,67.879997,67.879997,3430000
1961-09-11,67.279999,67.279999,67.279999,67.279999,67.279999,2790000
1961-09-12,67.959999,67.959999,67.959999,67.959999,67.959999,2950000
1961-09-13,68.010002,68.010002,68.010002,68.010002,68.010002,3110000
1961-09-14,67.529999,67.529999,67.529999,67.529999,67.529999,2920000
1961-09-15,67.650002,67.650002,67.650002,67.650002,67.650002,3130000
1961-09-18,67.209999,67.209999,67.209999,67.209999,67.209999,3550000
1961-09-19,66.080002,66.080002,66.080002,66.080002,66.080002,3260000
1961-09-20,66.959999,66.959999,66.959999,66.959999,66.959999,2700000
1961-09-21,66.989998,66.989998,66.989998,66.989998,66.989998,3340000
1961-09-22,66.720001,66.720001,66.720001,66.720001,66.720001,3070000
1961-09-25,65.769997,65.769997,65.769997,65.769997,65.769997,3700000
1961-09-26,65.779999,65.779999,65.779999,65.779999,65.779999,3320000
1961-09-27,66.470001,66.470001,66.470001,66.470001,66.470001,3440000
1961-09-28,66.580002,66.580002,66.580002,66.580002,66.580002,3000000
1961-09-29,66.730003,66.730003,66.730003,66.730003,66.730003,3060000
1961-10-02,66.769997,66.769997,66.769997,66.769997,66.769997,2800000
1961-10-03,66.730003,66.730003,66.730003,66.730003,66.730003,2680000
1961-10-04,67.180000,67.180000,67.180000,67.180000,67.180000,3380000
1961-10-05,67.769997,67.769997,67.769997,67.769997,67.769997,3920000
1961-10-06,66.970001,66.970001,66.970001,66.970001,66.970001,3470000
1961-10-09,67.940002,67.940002,67.940002,67.940002,67.940002,2920000
1961-10-10,68.110001,68.110001,68.110001,68.110001,68.110001,3430000
1961-10-11,68.169998,68.169998,68.169998,68.169998,68.169998,3670000
1961-10-12,68.160004,68.160004,68.160004,68.160004,68.160004,3060000
1961-10-13,68.040001,68.040001,68.040001,68.040001,68.040001,3090000
1961-10-16,67.849998,67.849998,67.849998,67.849998,67.849998,2840000
1961-10-17,67.870003,67.870003,67.870003,67.870003,67.870003,3110000
1961-10-18,68.209999,68.209999,68.209999,68.209999,68.209999,3520000
1961-10-19,68.449997,68.449997,68.449997,68.449997,68.449997,3850000
1961-10-20,68.000000,68.000000,68.000000,68.000000,68.000000,3470000
1961-10-23,68.059998,68.059998,68.059998,68.059998,68.059998,3440000
1961-10-24,67.980003,67.980003,67.980003,67.980003,67.980003,3430000
1961-10-25,68.339996,68.339996,68.339996,68.339996,68.339996,3590000
1961-10-26,68.459999,68.459999,68.459999,68.459999,68.459999,3330000
1961-10-27,68.339996,68.339996,68.339996,68.339996,68.339996,3200000
1961-10-30,68.419998,68.419998,68.419998,68.419998,68.419998,3430000
1961-10-31,68.620003,68.620003,68.620003,68.620003,68.620003,3350000
1961-11-01,68.730003,68.730003,68.730003,68.730003,68.730003,3210000
1961-11-02,69.110001,69.110001,69.110001,69.110001,69.110001,3890000
1961-11-03,69.470001,69.470001,69.470001,69.470001,69.470001,4070000
1961-11-06,70.010002,70.010002,70.010002,70.010002,70.010002,4340000
1961-11-08,70.870003,70.870003,70.870003,70.870003,70.870003,6090000
1961-11-09,70.769997,70.769997,70.769997,70.769997,70.769997,4680000
1961-11-10,71.070000,71.070000,71.070000,71.070000,71.070000,4180000
1961-11-13,71.269997,71.269997,71.269997,71.269997,71.269997,4540000
1961-11-14,71.660004,71.660004,71.660004,71.660004,71.660004,4750000
1961-11-15,71.669998,71.669998,71.669998,71.669998,71.669998,4660000
1961-11-16,71.620003,71.620003,71.620003,71.620003,71.620003,3980000
1961-11-17,71.620003,71.620003,71.620003,71.620003,71.620003,3960000
1961-11-20,71.720001,71.720001,71.720001,71.720001,71.720001,4190000
1961-11-21,71.779999,71.779999,71.779999,71.779999,71.779999,4890000
1961-11-22,71.699997,71.699997,71.699997,71.699997,71.699997,4500000
1961-11-24,71.839996,71.839996,71.839996,71.839996,71.839996,4020000
1961-11-27,71.849998,71.849998,71.849998,71.849998,71.849998,4700000
1961-11-28,71.750000,71.750000,71.750000,71.750000,71.750000,4360000
1961-11-29,71.699997,71.699997,71.699997,71.699997,71.699997,4550000
1961-11-30,71.320000,71.320000,71.320000,71.320000,71.320000,4210000
1961-12-01,71.779999,71.779999,71.779999,71.779999,71.779999,4420000
1961-12-04,72.010002,72.010002,72.010002,72.010002,72.010002,4560000
1961-12-05,71.930000,71.930000,71.930000,71.930000,71.930000,4330000
1961-12-06,71.989998,71.989998,71.989998,71.989998,71.989998,4200000
1961-12-07,71.699997,71.699997,71.699997,71.699997,71.699997,3900000
1961-12-08,72.040001,72.040001,72.040001,72.040001,72.040001,4010000
1961-12-11,72.389999,72.389999,72.389999,72.389999,72.389999,4360000
1961-12-12,72.639999,72.639999,72.639999,72.639999,72.639999,4680000
1961-12-13,72.529999,72.529999,72.529999,72.529999,72.529999,4890000
1961-12-14,71.980003,71.980003,71.980003,71.980003,71.980003,4350000
1961-12-15,72.010002,72.010002,72.010002,72.010002,72.010002,3710000
1961-12-18,71.760002,71.760002,71.760002,71.760002,71.760002,3810000
1961-12-19,71.260002,71.260002,71.260002,71.260002,71.260002,3440000
1961-12-20,71.120003,71.120003,71.120003,71.120003,71.120003,3640000
1961-12-21,70.860001,70.860001,70.860001,70.860001,70.860001,3440000
1961-12-22,70.910004,70.910004,70.910004,70.910004,70.910004,3390000
1961-12-26,71.019997,71.019997,71.019997,71.019997,71.019997,3180000
1961-12-27,71.650002,71.650002,71.650002,71.650002,71.650002,4170000
1961-12-28,71.690002,71.690002,71.690002,71.690002,71.690002,4530000
1961-12-29,71.550003,71.550003,71.550003,71.550003,71.550003,5370000
1962-01-02,71.550003,71.959999,70.709999,70.959999,70.959999,3120000
1962-01-03,70.959999,71.480003,70.379997,71.129997,71.129997,3590000
1962-01-04,71.129997,71.620003,70.449997,70.639999,70.639999,4450000
1962-01-05,70.639999,70.839996,69.349998,69.660004,69.660004,4630000
1962-01-08,69.660004,69.839996,68.169998,69.120003,69.120003,4620000
1962-01-09,69.120003,69.930000,68.830002,69.150002,69.150002,3600000
1962-01-10,69.150002,69.580002,68.620003,68.959999,68.959999,3300000
1962-01-11,68.959999,69.540001,68.570000,69.370003,69.370003,3390000
1962-01-12,69.370003,70.169998,69.230003,69.610001,69.610001,3730000
1962-01-15,69.610001,69.959999,69.059998,69.470001,69.470001,3450000
1962-01-16,69.470001,69.610001,68.680000,69.070000,69.070000,3650000
1962-01-17,69.070000,69.309998,68.129997,68.320000,68.320000,3780000
1962-01-18,68.320000,68.730003,67.750000,68.389999,68.389999,3460000
1962-01-19,68.389999,70.080002,68.139999,68.750000,68.750000,3800000
1962-01-22,68.750000,69.370003,68.449997,68.809998,68.809998,3810000
1962-01-23,68.809998,68.959999,68.000000,68.290001,68.290001,3350000
1962-01-24,68.290001,68.680000,67.550003,68.400002,68.400002,3760000
1962-01-25,68.400002,69.050003,68.099998,68.349998,68.349998,3560000
1962-01-26,68.349998,68.669998,67.830002,68.129997,68.129997,3330000
1962-01-29,68.129997,68.500000,67.550003,67.900002,67.900002,3050000
1962-01-30,67.900002,68.650002,67.620003,68.169998,68.169998,3520000
1962-01-31,68.169998,69.089996,68.120003,68.839996,68.839996,3840000
1962-02-01,68.839996,69.650002,68.559998,69.260002,69.260002,4260000
1962-02-02,69.260002,70.019997,69.019997,69.809998,69.809998,3950000
1962-02-05,69.809998,70.300003,69.419998,69.879997,69.879997,3890000
1962-02-06,69.879997,70.320000,69.410004,69.959999,69.959999,3650000
1962-02-07,69.959999,70.669998,69.779999,70.419998,70.419998,4140000
1962-02-08,70.419998,70.949997,70.160004,70.580002,70.580002,3810000
1962-02-09,70.580002,70.830002,69.930000,70.480003,70.480003,3370000
1962-02-12,70.480003,70.809998,70.139999,70.459999,70.459999,2620000
1962-02-13,70.459999,70.889999,70.070000,70.449997,70.449997,3400000
1962-02-14,70.449997,70.790001,70.029999,70.419998,70.419998,3630000
1962-02-15,70.419998,71.059998,70.230003,70.739998,70.739998,3470000
1962-02-16,70.739998,71.129997,70.269997,70.589996,70.589996,3700000
1962-02-19,70.589996,70.959999,70.120003,70.410004,70.410004,3350000
1962-02-20,70.410004,70.910004,70.129997,70.660004,70.660004,3300000
1962-02-21,70.660004,70.970001,70.120003,70.320000,70.320000,3310000
1962-02-23,70.320000,70.570000,69.730003,70.160004,70.160004,3230000
1962-02-26,70.160004,70.330002,69.440002,69.760002,69.760002,2910000
1962-02-27,69.760002,70.320000,69.480003,69.889999,69.889999,3110000
1962-02-28,69.889999,70.419998,69.570000,69.959999,69.959999,3030000
1962-03-01,69.959999,70.599998,69.760002,70.199997,70.199997,2960000
1962-03-02,70.160004,70.160004,69.750000,70.160004,70.160004,2980000
1962-03-05,70.160004,70.480003,69.650002,70.010002,70.010002,3020000
1962-03-06,70.010002,70.239998,69.459999,69.779999,69.779999,2870000
1962-03-07,69.779999,70.070000,69.370003,69.690002,69.690002,2890000
1962-03-08,69.690002,70.370003,69.400002,70.190002,70.190002,3210000
1962-03-09,70.190002,70.709999,70.000000,70.419998,70.419998,3340000
1962-03-12,70.419998,70.760002,70.019997,70.400002,70.400002,3280000
1962-03-13,70.400002,70.860001,70.059998,70.599998,70.599998,3200000
1962-03-14,70.599998,71.250000,70.480003,70.910004,70.910004,3670000
1962-03-15,70.910004,71.440002,70.589996,71.059998,71.059998,3250000
1962-03-16,71.059998,71.339996,70.669998,70.940002,70.940002,3060000
1962-03-19,70.940002,71.309998,70.529999,70.849998,70.849998,3220000
1962-03-20,70.849998,71.080002,70.400002,70.660004,70.660004,3060000
1962-03-21,70.660004,70.930000,70.160004,70.510002,70.510002,3360000
1962-03-22,70.510002,70.839996,70.139999,70.400002,70.400002,3130000
1962-03-23,70.400002,70.779999,70.120003,70.449997,70.449997,3050000
1962-03-26,70.449997,70.629997,69.730003,69.889999,69.889999,3040000
1962-03-27,69.889999,70.199997,69.410004,69.699997,69.699997,3090000
1962-03-28,69.699997,70.330002,69.540001,70.040001,70.040001,2940000
1962-03-29,70.040001,70.500000,69.809998,70.010002,70.010002,2870000
1962-03-30,70.010002,70.089996,69.160004,69.550003,69.550003,2950000
1962-04-02,69.550003,69.820000,69.129997,69.370003,69.370003,2790000
1962-04-03,69.370003,69.529999,68.529999,68.809998,68.809998,3350000
1962-04-04,68.809998,69.220001,68.330002,68.489998,68.489998,3290000
1962-04-05,68.489998,69.089996,68.120003,68.910004,68.910004,3130000
1962-04-06,68.910004,69.419998,68.580002,68.839996,68.839996,2730000
1962-04-09,68.839996,69.019997,68.089996,68.309998,68.309998,3020000
1962-04-10,68.309998,68.800003,67.940002,68.559998,68.559998,2880000
1962-04-11,68.559998,69.260002,68.239998,68.410004,68.410004,3240000
1962-04-12,68.410004,68.430000,67.470001,67.900002,67.900002,3320000
1962-04-13,67.900002,68.110001,67.029999,67.900002,67.900002,3470000
1962-04-16,67.900002,68.190002,67.209999,67.599998,67.599998,3070000
1962-04-17,67.599998,68.199997,67.239998,67.900002,67.900002,2940000
1962-04-18,67.900002,68.720001,67.830002,68.269997,68.269997,3350000
1962-04-19,68.269997,68.900002,68.070000,68.589996,68.589996,3100000
1962-04-23,68.589996,69.010002,68.169998,68.529999,68.529999,3240000
1962-04-24,68.529999,68.910004,68.160004,68.459999,68.459999,3040000
1962-04-25,68.459999,68.580002,67.529999,67.709999,67.709999,3340000
1962-04-26,67.709999,67.970001,66.919998,67.050003,67.050003,3650000
1962-04-27,67.050003,67.610001,65.989998,66.300003,66.300003,4140000
1962-04-30,66.300003,66.900002,64.949997,65.239998,65.239998,4150000
1962-05-01,65.239998,65.940002,63.759998,65.699997,65.699997,5100000
1962-05-02,65.699997,66.669998,65.559998,65.989998,65.989998,3780000
1962-05-03,65.989998,66.930000,65.809998,66.529999,66.529999,3320000
1962-05-04,66.529999,66.800003,65.800003,66.239998,66.239998,3010000
1962-05-07,66.239998,66.559998,65.660004,66.019997,66.019997,2530000
1962-05-08,66.019997,66.129997,64.879997,65.169998,65.169998,3020000
1962-05-09,65.169998,65.169998,64.019997,64.260002,64.260002,3670000
1962-05-10,64.260002,64.389999,62.990002,63.570000,63.570000,4730000
1962-05-11,63.570000,64.099998,62.439999,62.650002,62.650002,4510000
1962-05-14,62.650002,63.310001,61.110001,63.099998,63.099998,5990000
1962-05-15,63.410000,64.870003,63.410000,64.290001,64.290001,4780000
1962-05-16,64.290001,64.879997,63.820000,64.269997,64.269997,3360000
1962-05-17,64.269997,64.410004,63.380001,63.930000,63.930000,2950000
1962-05-18,63.930000,64.139999,63.290001,63.820000,63.820000,2490000
1962-05-21,63.820000,64.000000,63.209999,63.590000,63.590000,2260000
1962-05-22,63.590000,63.689999,62.259998,62.340000,62.340000,3640000
1962-05-23,62.340000,62.419998,60.900002,61.110001,61.110001,5450000
1962-05-24,61.110001,61.790001,60.360001,60.619999,60.619999,5250000
1962-05-25,60.619999,60.980000,59.000000,59.470001,59.470001,6380000
1962-05-28,59.150002,59.150002,55.419998,55.500000,55.500000,9350000
1962-05-29,55.500000,58.290001,53.130001,58.080002,58.080002,14750000
1962-05-31,58.799999,60.820000,58.799999,59.630001,59.630001,10710000
1962-06-01,59.630001,59.959999,58.520000,59.380001,59.380001,5760000
1962-06-04,59.119999,59.119999,57.139999,57.270000,57.270000,5380000
1962-06-05,57.270000,58.419998,56.330002,57.570000,57.570000,6140000
1962-06-06,57.639999,59.169998,57.639999,58.389999,58.389999,4190000
1962-06-07,58.389999,58.900002,58.000000,58.400002,58.400002,2760000
1962-06-08,58.400002,58.970001,58.139999,58.450001,58.450001,2560000
1962-06-11,58.450001,58.580002,57.509998,57.820000,57.820000,2870000
1962-06-12,57.660000,57.660000,56.230000,56.340000,56.340000,4690000
1962-06-13,56.340000,56.799999,55.240002,55.500000,55.500000,5850000
1962-06-14,55.500000,56.000000,54.119999,54.330002,54.330002,6240000
1962-06-15,54.330002,55.959999,53.660000,55.889999,55.889999,7130000
1962-06-18,55.889999,56.529999,54.970001,55.740002,55.740002,4580000
1962-06-19,55.740002,55.880001,54.980000,55.540001,55.540001,2680000
1962-06-20,55.540001,55.919998,54.660000,54.779999,54.779999,3360000
1962-06-21,54.779999,54.779999,53.500000,53.590000,53.590000,4560000
1962-06-22,53.590000,53.779999,52.480000,52.680000,52.680000,5640000
1962-06-25,52.680000,52.959999,51.349998,52.450001,52.450001,7090000
1962-06-26,52.450001,53.580002,52.099998,52.320000,52.320000,4630000
1962-06-27,52.320000,52.830002,51.770000,52.599998,52.599998,3890000
1962-06-28,52.980000,54.639999,52.980000,54.410000,54.410000,5440000
1962-06-29,54.410000,55.470001,54.200001,54.750000,54.750000,4720000
1962-07-02,54.750000,56.020000,54.470001,55.860001,55.860001,3450000
1962-07-03,55.860001,56.740002,55.570000,56.490002,56.490002,3920000
1962-07-05,56.490002,57.099998,56.150002,56.810001,56.810001,3350000
1962-07-06,56.730000,56.730000,55.639999,56.169998,56.169998,3110000
1962-07-09,56.169998,56.730000,55.540001,56.549999,56.549999,2950000
1962-07-10,56.990002,58.360001,56.990002,57.200001,57.200001,7120000
1962-07-11,57.200001,57.950001,56.770000,57.730000,57.730000,4250000
1962-07-12,57.730000,58.669998,57.590000,58.029999,58.029999,5370000
1962-07-13,58.029999,58.180000,57.230000,57.830002,57.830002,3380000
1962-07-16,57.830002,58.099998,57.180000,57.830002,57.830002,3130000
1962-07-17,57.830002,57.959999,56.680000,56.779999,56.779999,3500000
1962-07-18,56.779999,56.810001,55.860001,56.200001,56.200001,3620000
1962-07-19,56.200001,56.950001,55.959999,56.419998,56.419998,3090000
1962-07-20,56.419998,57.090000,56.270000,56.810001,56.810001,2610000
1962-07-23,56.810001,57.320000,56.529999,56.799999,56.799999,2770000
1962-07-24,56.799999,56.930000,56.139999,56.360001,56.360001,2560000
1962-07-25,56.360001,56.669998,55.779999,56.459999,56.459999,2910000
1962-07-26,56.459999,57.180000,56.160000,56.770000,56.770000,2790000
1962-07-27,56.770000,57.360001,56.560001,57.200001,57.200001,2890000
1962-07-30,57.200001,57.980000,57.080002,57.830002,57.830002,3200000
1962-07-31,57.830002,58.580002,57.740002,58.230000,58.230000,4190000
1962-08-01,58.230000,58.299999,57.509998,57.750000,57.750000,3100000
1962-08-02,57.750000,58.200001,57.380001,57.980000,57.980000,3410000
1962-08-03,57.980000,58.320000,57.630001,58.119999,58.119999,5990000
1962-08-06,58.119999,58.349998,57.540001,57.750000,57.750000,3110000
1962-08-07,57.750000,57.810001,57.070000,57.360001,57.360001,2970000
1962-08-08,57.360001,57.639999,56.759998,57.509998,57.509998,3080000
1962-08-09,57.509998,57.880001,57.189999,57.570000,57.570000,2670000
1962-08-10,57.570000,57.849998,57.160000,57.549999,57.549999,2470000
1962-08-13,57.549999,57.900002,57.220001,57.630001,57.630001,2670000
1962-08-14,57.630001,58.430000,57.410000,58.250000,58.250000,3640000
1962-08-15,58.250000,59.110001,58.220001,58.660000,58.660000,4880000
1962-08-16,58.660000,59.110001,58.240002,58.639999,58.639999,4180000
1962-08-17,58.639999,59.240002,58.430000,59.009998,59.009998,3430000
1962-08-20,59.009998,59.720001,58.900002,59.369999,59.369999,4580000
1962-08-21,59.369999,59.660000,58.900002,59.119999,59.119999,3730000
1962-08-22,59.119999,59.930000,58.910000,59.779999,59.779999,4520000
1962-08-23,59.779999,60.330002,59.470001,59.700001,59.700001,4770000
1962-08-24,59.700001,59.919998,59.180000,59.580002,59.580002,2890000
1962-08-27,59.580002,59.939999,59.240002,59.549999,59.549999,3140000
1962-08-28,59.549999,59.610001,58.660000,58.790001,58.790001,3180000
1962-08-29,58.790001,58.959999,58.169998,58.660000,58.660000,2900000
1962-08-30,58.660000,59.060001,58.389999,58.680000,58.680000,2260000
1962-08-31,58.680000,59.250000,58.450001,59.119999,59.119999,2830000
1962-09-04,59.119999,59.490002,58.439999,58.560001,58.560001,2970000
1962-09-05,58.560001,58.770000,57.950001,58.119999,58.119999,3050000
1962-09-06,58.119999,58.599998,57.720001,58.360001,58.360001,3180000
1962-09-07,58.360001,58.900002,58.090000,58.380001,58.380001,2890000
1962-09-10,58.380001,58.639999,57.880001,58.450001,58.450001,2520000
1962-09-11,58.450001,58.930000,58.169998,58.590000,58.590000,3040000
1962-09-12,58.590000,59.060001,58.400002,58.840000,58.840000,3100000
1962-09-13,58.840000,59.180000,58.459999,58.700001,58.700001,3100000
1962-09-14,58.700001,59.139999,58.400002,58.889999,58.889999,2880000
1962-09-17,58.889999,59.419998,58.650002,59.080002,59.080002,3330000
1962-09-18,59.080002,59.540001,58.770000,59.029999,59.029999,3690000
1962-09-19,59.029999,59.259998,58.590000,58.950001,58.950001,2950000
1962-09-20,58.950001,59.290001,58.330002,58.540001,58.540001,3350000
1962-09-21,58.540001,58.639999,57.430000,57.689999,57.689999,4280000
1962-09-24,57.450001,57.450001,56.299999,56.630001,56.630001,5000000
1962-09-25,56.630001,57.220001,56.119999,56.959999,56.959999,3620000
1962-09-26,56.959999,57.290001,55.919998,56.150002,56.150002,3550000
1962-09-27,56.150002,56.549999,55.529999,55.770000,55.770000,3540000
1962-09-28,55.770000,56.580002,55.590000,56.270000,56.270000,2850000
1962-10-01,56.270000,56.310001,55.259998,55.490002,55.490002,3090000
1962-10-02,55.490002,56.459999,55.310001,56.099998,56.099998,3000000
1962-10-03,56.099998,56.709999,55.840000,56.160000,56.160000,2610000
1962-10-04,56.160000,56.840000,55.900002,56.700001,56.700001,2530000
1962-10-05,56.700001,57.299999,56.549999,57.070000,57.070000,2730000
1962-10-08,57.070000,57.410000,56.680000,57.070000,57.070000,1950000
1962-10-09,57.070000,57.400002,56.709999,57.200001,57.200001,2340000
1962-10-10,57.200001,57.830002,56.959999,57.240002,57.240002,3040000
1962-10-11,57.240002,57.459999,56.779999,57.049999,57.049999,2460000
1962-10-12,57.049999,57.209999,56.660000,56.950001,56.950001,2020000
1962-10-15,56.950001,57.500000,56.660000,57.270000,57.270000,2640000
1962-10-16,57.270000,57.630001,56.869999,57.080002,57.080002,2860000
1962-10-17,57.080002,57.230000,56.369999,56.889999,56.889999,3240000
1962-10-18,56.889999,57.020000,56.180000,56.340000,56.340000,3280000
1962-10-19,56.340000,56.540001,55.340000,55.590000,55.590000,4650000
1962-10-22,55.480000,55.480000,54.380001,54.959999,54.959999,5690000
1962-10-23,54.959999,55.189999,53.240002,53.490002,53.490002,6110000
1962-10-24,53.490002,55.439999,52.549999,55.209999,55.209999,6720000
1962-10-25,55.169998,55.169998,53.820000,54.689999,54.689999,3950000
1962-10-26,54.689999,54.959999,54.080002,54.540001,54.540001,2580000
1962-10-29,55.340000,56.380001,55.340000,55.720001,55.720001,4280000
1962-10-30,55.720001,56.840000,55.520000,56.540001,56.540001,3830000
1962-10-31,56.540001,57.000000,56.189999,56.520000,56.520000,3090000
1962-11-01,56.520000,57.310001,55.900002,57.119999,57.119999,3400000
1962-11-02,57.119999,58.189999,56.779999,57.750000,57.750000,5470000
1962-11-05,57.750000,58.700001,57.689999,58.349998,58.349998,4320000
1962-11-07,58.349998,59.110001,57.759998,58.709999,58.709999,4580000
1962-11-08,58.709999,59.119999,58.090000,58.320000,58.320000,4160000
1962-11-09,58.320000,58.990002,57.900002,58.779999,58.779999,4340000
1962-11-12,58.779999,60.000000,58.590000,59.590000,59.590000,5090000
1962-11-13,59.590000,60.060001,59.060001,59.459999,59.459999,4550000
1962-11-14,59.459999,60.410000,59.180000,60.160000,60.160000,5090000
1962-11-15,60.160000,60.669998,59.740002,59.970001,59.970001,5050000
1962-11-16,59.970001,60.459999,59.459999,60.160000,60.160000,4000000
1962-11-19,60.160000,60.419998,59.459999,59.820000,59.820000,3410000
1962-11-20,59.820000,60.630001,59.570000,60.450001,60.450001,4290000
1962-11-21,60.450001,61.180000,60.189999,60.810001,60.810001,5100000
1962-11-23,60.810001,62.029999,60.660000,61.540001,61.540001,5660000
1962-11-26,61.540001,62.130001,60.950001,61.360001,61.360001,5650000
1962-11-27,61.360001,62.040001,60.980000,61.730000,61.730000,5500000
1962-11-28,61.730000,62.480000,61.509998,62.119999,62.119999,5980000
1962-11-29,62.119999,62.720001,61.689999,62.410000,62.410000,5810000
1962-11-30,62.410000,62.779999,61.779999,62.259998,62.259998,4570000
1962-12-03,62.259998,62.450001,61.279999,61.939999,61.939999,3810000
1962-12-04,61.939999,62.930000,61.770000,62.639999,62.639999,5210000
1962-12-05,62.639999,63.500000,62.369999,62.389999,62.389999,6280000
1962-12-06,62.389999,63.360001,62.279999,62.930000,62.930000,4600000
1962-12-07,62.930000,63.430000,62.450001,63.060001,63.060001,3900000
1962-12-10,63.060001,63.349998,61.959999,62.270000,62.270000,4270000
1962-12-11,62.270000,62.580002,61.720001,62.320000,62.320000,3700000
1962-12-12,62.320000,63.160000,62.130001,62.630001,62.630001,3760000
1962-12-13,62.630001,63.070000,62.090000,62.419998,62.419998,3380000
1962-12-14,62.419998,62.830002,61.959999,62.570000,62.570000,3280000
1962-12-17,62.570000,62.950001,62.139999,62.369999,62.369999,3590000
1962-12-18,62.369999,62.660000,61.779999,62.070000,62.070000,3620000
1962-12-19,62.070000,62.810001,61.720001,62.580002,62.580002,4000000
1962-12-20,62.580002,63.279999,62.439999,62.820000,62.820000,4220000
1962-12-21,62.820000,63.130001,62.259998,62.639999,62.639999,3470000
1962-12-24,62.639999,63.029999,62.189999,62.630001,62.630001,3180000
1962-12-26,62.630001,63.320000,62.560001,63.020000,63.020000,3370000
1962-12-27,63.020000,63.410000,62.669998,62.930000,62.930000,3670000
1962-12-28,62.930000,63.250000,62.529999,62.959999,62.959999,4140000
1962-12-31,62.959999,63.430000,62.680000,63.099998,63.099998,5420000
1963-01-02,63.099998,63.389999,62.320000,62.689999,62.689999,2540000
1963-01-03,62.689999,63.889999,62.669998,63.720001,63.720001,4570000
1963-01-04,63.720001,64.449997,63.570000,64.129997,64.129997,5400000
1963-01-07,64.129997,64.589996,63.669998,64.120003,64.120003,4440000
1963-01-08,64.120003,64.980003,64.000000,64.739998,64.739998,5410000
1963-01-09,64.739998,65.220001,64.320000,64.589996,64.589996,5110000
1963-01-10,64.589996,65.160004,64.330002,64.709999,64.709999,4520000
1963-01-11,64.709999,65.099998,64.309998,64.849998,64.849998,4410000
1963-01-14,64.849998,65.500000,64.610001,65.199997,65.199997,5000000
1963-01-15,65.199997,65.620003,64.820000,65.110001,65.110001,5930000
1963-01-16,65.110001,65.250000,64.419998,64.669998,64.669998,4260000
1963-01-17,64.669998,65.400002,64.349998,65.129997,65.129997,5230000
1963-01-18,65.129997,65.699997,64.860001,65.180000,65.180000,4760000
1963-01-21,65.180000,65.519997,64.639999,65.279999,65.279999,4090000
1963-01-22,65.279999,65.800003,65.029999,65.440002,65.440002,4810000
1963-01-23,65.440002,65.910004,65.230003,65.620003,65.620003,4820000
1963-01-24,65.620003,66.089996,65.330002,65.750000,65.750000,4810000
1963-01-25,65.750000,66.230003,65.379997,65.919998,65.919998,4770000
1963-01-28,65.919998,66.589996,65.769997,66.239998,66.239998,4720000
1963-01-29,66.239998,66.580002,65.830002,66.230003,66.230003,4360000
1963-01-30,66.230003,66.330002,65.550003,65.849998,65.849998,3740000
1963-01-31,65.849998,66.449997,65.510002,66.199997,66.199997,4270000
1963-02-01,66.309998,66.309998,66.309998,66.309998,66.309998,4280000
1963-02-04,66.309998,66.660004,65.889999,66.169998,66.169998,3670000
1963-02-05,66.169998,66.349998,65.379997,66.110001,66.110001,4050000
1963-02-06,66.110001,66.760002,65.879997,66.400002,66.400002,4340000
1963-02-07,66.400002,66.809998,65.910004,66.169998,66.169998,4240000
1963-02-08,66.169998,66.449997,65.650002,66.169998,66.169998,3890000
1963-02-11,66.169998,66.410004,65.500000,65.760002,65.760002,3880000
1963-02-12,65.760002,66.010002,65.160004,65.830002,65.830002,3710000
1963-02-13,65.830002,66.529999,65.559998,66.150002,66.150002,4960000
1963-02-14,66.150002,66.750000,65.930000,66.349998,66.349998,5640000
1963-02-15,66.349998,66.739998,65.959999,66.410004,66.410004,4410000
1963-02-18,66.410004,66.959999,66.099998,66.519997,66.519997,4700000
1963-02-19,66.519997,66.669998,65.919998,66.199997,66.199997,4130000
1963-02-20,66.199997,66.279999,65.440002,65.830002,65.830002,4120000
1963-02-21,65.830002,66.230003,65.360001,65.919998,65.919998,3980000
1963-02-25,65.919998,66.089996,65.239998,65.459999,65.459999,3680000
1963-02-26,65.459999,65.860001,65.059998,65.470001,65.470001,3670000
1963-02-27,65.470001,65.739998,64.860001,65.010002,65.010002,3680000
1963-02-28,65.010002,65.139999,64.080002,64.290001,64.290001,4090000
1963-03-01,64.290001,64.750000,63.799999,64.099998,64.099998,3920000
1963-03-04,64.099998,65.080002,63.880001,64.720001,64.720001,3650000
1963-03-05,64.720001,65.269997,64.410004,64.739998,64.739998,3280000
1963-03-06,64.739998,65.059998,64.309998,64.849998,64.849998,3100000
1963-03-07,64.849998,65.599998,64.809998,65.260002,65.260002,3350000
1963-03-08,65.260002,65.739998,65.029999,65.330002,65.330002,3360000
1963-03-11,65.330002,65.860001,65.110001,65.510002,65.510002,3180000
1963-03-12,65.510002,65.970001,65.260002,65.669998,65.669998,3350000
1963-03-13,65.669998,66.269997,65.540001,65.910004,65.910004,4120000
1963-03-14,65.910004,66.209999,65.389999,65.599998,65.599998,3540000
1963-03-15,65.599998,66.220001,65.389999,65.930000,65.930000,3400000
1963-03-18,65.930000,66.169998,65.360001,65.610001,65.610001,3250000
1963-03-19,65.610001,65.849998,65.190002,65.470001,65.470001,3180000
1963-03-20,65.470001,66.150002,65.300003,65.949997,65.949997,3690000
1963-03-21,65.949997,66.250000,65.599998,65.849998,65.849998,3220000
1963-03-22,65.849998,66.440002,65.680000,66.190002,66.190002,3820000
1963-03-25,66.190002,66.599998,65.919998,66.209999,66.209999,3700000
1963-03-26,66.209999,66.730003,66.010002,66.400002,66.400002,4100000
1963-03-27,66.400002,66.930000,66.209999,66.680000,66.680000,4270000
1963-03-28,66.680000,67.010002,66.320000,66.580002,66.580002,3890000
1963-03-29,66.580002,66.900002,66.230003,66.570000,66.570000,3390000
1963-04-01,66.570000,67.180000,66.230003,66.849998,66.849998,3890000
1963-04-02,66.849998,67.360001,66.510002,66.839996,66.839996,4360000
1963-04-03,66.839996,67.550003,66.629997,67.360001,67.360001,4660000
1963-04-04,67.360001,68.120003,67.279999,67.849998,67.849998,5300000
1963-04-05,67.849998,68.459999,67.459999,68.279999,68.279999,5240000
1963-04-08,68.279999,68.910004,68.050003,68.519997,68.519997,5940000
1963-04-09,68.519997,68.839996,68.029999,68.449997,68.449997,5090000
1963-04-10,68.449997,68.889999,67.660004,68.290001,68.290001,5880000
1963-04-11,68.290001,69.070000,67.970001,68.769997,68.769997,5250000
1963-04-15,68.769997,69.559998,68.580002,69.089996,69.089996,5930000
1963-04-16,69.089996,69.610001,68.660004,69.139999,69.139999,5570000
1963-04-17,69.139999,69.370003,68.470001,68.919998,68.919998,5220000
1963-04-18,68.919998,69.339996,68.559998,68.889999,68.889999,4770000
1963-04-19,68.889999,69.459999,68.599998,69.230003,69.230003,4660000
1963-04-22,69.230003,69.820000,69.010002,69.300003,69.300003,5180000
1963-04-23,69.300003,69.830002,68.949997,69.529999,69.529999,5220000
1963-04-24,69.529999,70.120003,69.339996,69.720001,69.720001,5910000
1963-04-25,69.720001,70.080002,69.250000,69.760002,69.760002,5070000
1963-04-26,69.760002,70.110001,69.230003,69.699997,69.699997,4490000
1963-04-29,69.699997,70.040001,69.260002,69.650002,69.650002,3980000
1963-04-30,69.650002,70.180000,69.260002,69.800003,69.800003,4680000
1963-05-01,69.800003,70.430000,69.610001,69.970001,69.970001,5060000
1963-05-02,69.970001,70.500000,69.750000,70.169998,70.169998,4480000
1963-05-03,70.169998,70.510002,69.779999,70.029999,70.029999,4760000
1963-05-06,70.029999,70.309998,69.320000,69.529999,69.529999,4090000
1963-05-07,69.529999,69.919998,69.029999,69.440002,69.440002,4140000
1963-05-08,69.440002,70.239998,69.230003,70.010002,70.010002,5140000
1963-05-09,70.010002,70.739998,69.860001,70.349998,70.349998,5600000
1963-05-10,70.349998,70.809998,69.989998,70.519997,70.519997,5260000
1963-05-13,70.519997,70.889999,70.110001,70.480003,70.480003,4920000
1963-05-14,70.480003,70.730003,69.919998,70.209999,70.209999,4740000
1963-05-15,70.209999,70.769997,69.870003,70.430000,70.430000,5650000
1963-05-16,70.430000,70.809998,69.910004,70.250000,70.250000,5640000
1963-05-17,70.250000,70.629997,69.830002,70.290001,70.290001,4410000
1963-05-20,70.290001,70.480003,69.589996,69.959999,69.959999,4710000
1963-05-21,69.959999,70.510002,69.620003,70.139999,70.139999,5570000
1963-05-22,70.139999,70.680000,69.820000,70.139999,70.139999,5560000
1963-05-23,70.139999,70.529999,69.790001,70.099998,70.099998,4400000
1963-05-24,70.099998,70.440002,69.660004,70.019997,70.019997,4320000
1963-05-27,70.019997,70.269997,69.480003,69.870003,69.870003,3760000
1963-05-28,69.870003,70.410004,69.550003,70.010002,70.010002,3860000
1963-05-29,70.010002,70.650002,69.860001,70.330002,70.330002,4320000
1963-05-31,70.330002,71.139999,70.269997,70.800003,70.800003,4680000
1963-06-03,70.800003,71.239998,70.389999,70.690002,70.690002,5400000
1963-06-04,70.690002,71.080002,70.199997,70.699997,70.699997,5970000
1963-06-05,70.699997,71.169998,70.169998,70.529999,70.529999,5860000
1963-06-06,70.529999,70.949997,70.110001,70.580002,70.580002,4990000
1963-06-07,70.580002,70.980003,70.099998,70.410004,70.410004,5110000
1963-06-10,70.410004,70.510002,69.570000,69.940002,69.940002,4690000
1963-06-11,69.940002,70.410004,69.580002,70.029999,70.029999,4390000
1963-06-12,70.029999,70.809998,69.910004,70.410004,70.410004,5210000
1963-06-13,70.410004,70.849998,69.980003,70.230003,70.230003,4690000
1963-06-14,70.230003,70.599998,69.870003,70.250000,70.250000,3840000
1963-06-17,69.949997,69.949997,69.949997,69.949997,69.949997,3510000
1963-06-18,69.949997,70.430000,69.629997,70.019997,70.019997,3910000
1963-06-19,70.019997,70.470001,69.750000,70.089996,70.089996,3970000
1963-06-20,70.089996,70.360001,69.309998,70.010002,70.010002,4970000
1963-06-21,70.010002,70.570000,69.790001,70.250000,70.250000,4190000
1963-06-24,70.250000,70.669998,69.839996,70.199997,70.199997,3700000
1963-06-25,70.199997,70.510002,69.750000,70.040001,70.040001,4120000
1963-06-26,70.040001,70.099998,69.169998,69.410004,69.410004,4500000
1963-06-27,69.410004,69.809998,68.779999,69.070000,69.070000,4540000
1963-06-28,69.070000,69.680000,68.930000,69.370003,69.370003,3020000
1963-07-01,69.370003,69.529999,68.580002,68.860001,68.860001,3360000
1963-07-02,68.860001,69.720001,68.739998,69.459999,69.459999,3540000
1963-07-03,69.459999,70.279999,69.419998,69.940002,69.940002,4030000
1963-07-05,69.940002,70.480003,69.779999,70.220001,70.220001,2910000
1963-07-08,70.220001,70.349998,69.470001,69.739998,69.739998,3290000
1963-07-09,69.739998,70.389999,69.550003,70.040001,70.040001,3830000
1963-07-10,70.040001,70.309998,69.559998,69.889999,69.889999,3730000
1963-07-11,69.889999,70.300003,69.519997,69.760002,69.760002,4100000
1963-07-12,69.760002,70.129997,69.360001,69.639999,69.639999,3660000
1963-07-15,69.639999,69.730003,68.970001,69.199997,69.199997,3290000
1963-07-16,69.199997,69.510002,68.849998,69.139999,69.139999,3000000
1963-07-17,69.139999,69.529999,68.680000,68.930000,68.930000,3940000
1963-07-18,68.930000,69.269997,68.339996,68.489998,68.489998,3710000
1963-07-19,68.489998,68.699997,67.900002,68.349998,68.349998,3340000
1963-07-22,68.349998,68.599998,67.540001,67.900002,67.900002,3700000
1963-07-23,67.900002,68.570000,67.650002,67.910004,67.910004,3500000
1963-07-24,67.910004,68.540001,67.760002,68.279999,68.279999,2810000
1963-07-25,68.279999,68.919998,68.019997,68.260002,68.260002,3710000
1963-07-26,68.260002,68.760002,68.029999,68.540001,68.540001,2510000
1963-07-29,68.540001,68.959999,68.320000,68.669998,68.669998,2840000
1963-07-30,68.669998,69.449997,68.580002,69.239998,69.239998,3550000
1963-07-31,69.239998,69.830002,68.910004,69.129997,69.129997,3960000
1963-08-01,69.129997,69.470001,68.639999,69.070000,69.070000,3410000
1963-08-02,69.070000,69.559998,68.860001,69.300003,69.300003,2940000
1963-08-05,69.300003,69.970001,69.199997,69.709999,69.709999,3370000
1963-08-06,69.709999,70.400002,69.570000,70.169998,70.169998,3760000
1963-08-07,70.169998,70.529999,69.690002,69.959999,69.959999,3790000
1963-08-08,69.959999,70.309998,69.580002,70.019997,70.019997,3460000
1963-08-09,70.019997,70.650002,69.830002,70.480003,70.480003,4050000
1963-08-12,70.480003,71.000000,70.190002,70.589996,70.589996,4770000
1963-08-13,70.589996,71.089996,70.320000,70.790001,70.790001,4450000
1963-08-14,70.790001,71.320000,70.389999,71.070000,71.070000,4420000
1963-08-15,71.070000,71.709999,70.809998,71.379997,71.379997,4980000
1963-08-16,71.379997,71.949997,71.050003,71.489998,71.489998,4130000
1963-08-19,71.489998,71.919998,71.150002,71.440002,71.440002,3650000
1963-08-20,71.440002,71.910004,71.029999,71.379997,71.379997,3660000
1963-08-21,71.379997,71.730003,71.000000,71.290001,71.290001,3820000
1963-08-22,71.290001,71.809998,70.949997,71.540001,71.540001,4540000
1963-08-23,71.540001,72.139999,71.330002,71.760002,71.760002,4880000
1963-08-26,71.760002,72.300003,71.570000,71.910004,71.910004,4700000
1963-08-27,71.910004,72.040001,71.269997,71.519997,71.519997,4080000
1963-08-28,71.519997,72.389999,71.489998,72.040001,72.040001,5120000
1963-08-29,72.040001,72.559998,71.830002,72.160004,72.160004,5110000
1963-08-30,72.160004,72.709999,71.879997,72.500000,72.500000,4560000
1963-09-03,72.500000,73.089996,72.300003,72.660004,72.660004,5570000
1963-09-04,72.660004,73.180000,72.320000,72.639999,72.639999,6070000
1963-09-05,72.639999,73.190002,72.150002,73.000000,73.000000,5700000
1963-09-06,73.000000,73.510002,72.510002,72.839996,72.839996,7160000
1963-09-09,72.839996,73.230003,72.260002,72.580002,72.580002,5020000
1963-09-10,72.580002,73.269997,72.250000,72.989998,72.989998,5310000
1963-09-11,72.989998,73.790001,72.830002,73.199997,73.199997,6670000
1963-09-12,73.199997,73.599998,72.720001,73.150002,73.150002,5560000
1963-09-13,73.150002,73.589996,72.820000,73.169998,73.169998,5230000
1963-09-16,73.169998,73.629997,72.800003,73.070000,73.070000,4740000
1963-09-17,73.070000,73.639999,72.790001,73.120003,73.120003,4950000
1963-09-18,73.120003,73.440002,72.510002,72.800003,72.800003,5070000
1963-09-19,72.800003,73.470001,72.610001,73.220001,73.220001,4080000
1963-09-20,73.220001,73.709999,72.919998,73.300003,73.300003,5310000
1963-09-23,73.300003,73.529999,72.620003,72.959999,72.959999,5140000
1963-09-24,72.959999,73.669998,72.589996,73.300003,73.300003,5520000
1963-09-25,73.300003,73.870003,72.580002,72.889999,72.889999,6340000
1963-09-26,72.889999,73.070000,72.010002,72.269997,72.269997,5100000
1963-09-27,72.269997,72.599998,71.599998,72.129997,72.129997,4350000
1963-09-30,72.129997,72.370003,71.279999,71.699997,71.699997,3730000
1963-10-01,71.699997,72.650002,71.570000,72.220001,72.220001,4420000
1963-10-02,72.220001,72.669998,71.919998,72.300003,72.300003,3780000
1963-10-03,72.300003,73.099998,72.099998,72.830002,72.830002,4510000
1963-10-04,72.830002,73.190002,72.459999,72.849998,72.849998,5120000
1963-10-07,72.849998,73.269997,72.389999,72.699997,72.699997,4050000
1963-10-08,72.699997,73.139999,72.239998,72.599998,72.599998,4920000
1963-10-09,71.980003,71.980003,71.599998,71.870003,71.870003,5520000
1963-10-10,71.870003,72.519997,71.599998,72.199997,72.199997,4470000
1963-10-11,72.199997,72.709999,71.870003,72.269997,72.269997,4740000
1963-10-14,72.269997,72.430000,71.849998,72.300003,72.300003,4270000
1963-10-15,72.300003,72.790001,71.989998,72.400002,72.400002,4550000
1963-10-16,72.400002,73.199997,72.080002,72.970001,72.970001,5570000
1963-10-17,72.970001,73.769997,72.839996,73.260002,73.260002,6790000
1963-10-18,73.260002,73.739998,72.849998,73.320000,73.320000,5830000
1963-10-21,73.320000,73.870003,73.029999,73.379997,73.379997,5450000
1963-10-22,73.379997,73.550003,72.480003,72.959999,72.959999,6420000
1963-10-23,72.959999,73.550003,72.589996,73.000000,73.000000,5830000
1963-10-24,73.000000,73.730003,72.739998,73.279999,73.279999,6280000
1963-10-25,73.279999,74.410004,73.059998,74.010002,74.010002,6390000
1963-10-28,74.010002,75.150002,73.750000,74.480003,74.480003,7150000
1963-10-29,74.480003,75.180000,73.970001,74.459999,74.459999,6100000
1963-10-30,74.459999,74.589996,73.430000,73.800003,73.800003,5170000
1963-10-31,73.800003,74.349998,73.250000,74.010002,74.010002,5030000
1963-11-01,74.010002,74.440002,73.470001,73.830002,73.830002,5240000
1963-11-04,73.830002,74.269997,73.089996,73.449997,73.449997,5440000
1963-11-06,73.449997,73.470001,72.330002,72.809998,72.809998,5600000
1963-11-07,72.809998,73.480003,72.580002,73.059998,73.059998,4320000
1963-11-08,73.059998,73.660004,72.800003,73.360001,73.360001,4570000
1963-11-11,73.519997,73.519997,73.519997,73.519997,73.519997,3970000
1963-11-12,73.230003,73.230003,73.230003,73.230003,73.230003,4610000
1963-11-13,73.230003,73.669998,72.889999,73.290001,73.290001,4710000
1963-11-14,73.290001,73.529999,72.629997,72.949997,72.949997,4610000
1963-11-15,72.949997,73.199997,72.089996,72.349998,72.349998,4790000
1963-11-18,72.349998,72.519997,71.419998,71.830002,71.830002,4730000
1963-11-19,71.830002,72.610001,71.419998,71.900002,71.900002,4430000
1963-11-20,71.900002,73.139999,71.489998,72.559998,72.559998,5330000
1963-11-21,72.559998,72.860001,71.400002,71.620003,71.620003,5670000
1963-11-22,71.620003,72.169998,69.480003,69.610001,69.610001,6630000
1963-11-26,71.400002,72.739998,71.400002,72.379997,72.379997,9320000
1963-11-27,72.379997,72.779999,71.760002,72.250000,72.250000,5210000
1963-11-29,72.250000,73.470001,72.050003,73.230003,73.230003,4810000
1963-12-02,73.230003,74.080002,73.019997,73.660004,73.660004,4770000
1963-12-03,73.660004,74.010002,73.139999,73.620003,73.620003,4520000
1963-12-04,73.620003,74.180000,73.209999,73.800003,73.800003,4790000
1963-12-05,73.800003,74.570000,73.449997,74.279999,74.279999,5190000
1963-12-06,74.279999,74.629997,73.620003,74.000000,74.000000,4830000
1963-12-09,74.000000,74.410004,73.559998,73.959999,73.959999,4430000
1963-12-10,73.959999,74.480003,73.400002,73.989998,73.989998,4560000
1963-12-11,73.989998,74.370003,73.580002,73.900002,73.900002,4400000
1963-12-12,73.900002,74.309998,73.580002,73.910004,73.910004,4220000
1963-12-13,73.910004,74.389999,73.680000,74.059998,74.059998,4290000
1963-12-16,74.059998,74.660004,73.779999,74.300003,74.300003,4280000
1963-12-17,74.300003,75.080002,74.070000,74.739998,74.739998,5140000
1963-12-18,74.739998,75.209999,74.250000,74.629997,74.629997,6000000
1963-12-19,74.629997,74.919998,74.080002,74.400002,74.400002,4410000
1963-12-20,74.400002,74.750000,73.849998,74.279999,74.279999,4600000
1963-12-23,74.279999,74.449997,73.489998,73.809998,73.809998,4540000
1963-12-24,73.809998,74.480003,73.440002,73.970001,73.970001,3970000
1963-12-26,73.970001,74.629997,73.739998,74.320000,74.320000,3700000
1963-12-27,74.320000,74.910004,74.089996,74.440002,74.440002,4360000
1963-12-30,74.440002,74.940002,74.129997,74.559998,74.559998,4930000
1963-12-31,74.559998,75.360001,74.400002,75.019997,75.019997,6730000
1964-01-02,75.019997,75.790001,74.820000,75.430000,75.430000,4680000
1964-01-03,75.430000,76.040001,75.089996,75.500000,75.500000,5550000
1964-01-06,75.500000,76.120003,75.180000,75.669998,75.669998,5480000
1964-01-07,75.669998,76.239998,75.250000,75.690002,75.690002,5700000
1964-01-08,75.690002,76.349998,75.389999,76.000000,76.000000,5380000
1964-01-09,76.000000,76.639999,75.599998,76.279999,76.279999,5180000
1964-01-10,76.279999,76.669998,75.739998,76.239998,76.239998,5260000
1964-01-13,76.239998,76.709999,75.779999,76.220001,76.220001,5440000
1964-01-14,76.220001,76.849998,75.879997,76.360001,76.360001,6500000
1964-01-15,76.360001,77.059998,75.959999,76.639999,76.639999,6750000
1964-01-16,76.639999,77.209999,76.050003,76.550003,76.550003,6200000
1964-01-17,76.550003,77.089996,76.019997,76.559998,76.559998,5600000
1964-01-20,76.559998,77.190002,76.019997,76.410004,76.410004,5570000
1964-01-21,76.410004,76.989998,75.870003,76.620003,76.620003,4800000
1964-01-22,76.620003,77.620003,76.449997,77.029999,77.029999,5430000
1964-01-23,77.029999,77.620003,76.669998,77.089996,77.089996,5380000
1964-01-24,77.089996,77.559998,76.580002,77.110001,77.110001,5080000
1964-01-27,77.110001,77.779999,76.639999,77.080002,77.080002,5240000
1964-01-28,77.080002,77.559998,76.629997,77.099998,77.099998,4720000
1964-01-29,77.099998,77.360001,76.330002,76.629997,76.629997,4450000
1964-01-30,76.629997,77.199997,76.260002,76.699997,76.699997,4230000
1964-01-31,76.699997,77.370003,76.389999,77.040001,77.040001,4000000
1964-02-03,77.040001,77.550003,76.529999,76.970001,76.970001,4140000
1964-02-04,76.970001,77.309998,76.459999,76.879997,76.879997,4320000
1964-02-05,76.879997,77.279999,76.360001,76.750000,76.750000,4010000
1964-02-06,76.750000,77.260002,76.470001,76.930000,76.930000,4110000
1964-02-07,76.930000,77.510002,76.660004,77.180000,77.180000,4710000
1964-02-10,77.180000,77.769997,76.830002,77.050003,77.050003,4150000
1964-02-11,77.050003,77.650002,76.809998,77.330002,77.330002,4040000
1964-02-12,77.330002,77.879997,77.139999,77.570000,77.570000,4650000
1964-02-13,77.570000,77.930000,77.099998,77.519997,77.519997,4820000
1964-02-14,77.519997,77.820000,77.019997,77.480003,77.480003,4360000
1964-02-17,77.480003,77.930000,77.040001,77.459999,77.459999,4780000
1964-02-18,77.459999,77.900002,77.000000,77.470001,77.470001,4660000
1964-02-19,77.470001,77.980003,77.129997,77.550003,77.550003,4280000
1964-02-20,77.550003,77.989998,77.160004,77.620003,77.620003,4690000
1964-02-24,77.620003,78.160004,77.269997,77.680000,77.680000,5630000
1964-02-25,77.680000,78.309998,77.190002,77.680000,77.680000,5010000
1964-02-26,77.680000,78.129997,77.330002,77.870003,77.870003,5350000
1964-02-27,77.870003,78.290001,77.379997,77.620003,77.620003,5420000
1964-02-28,77.620003,78.059998,77.199997,77.800003,77.800003,4980000
1964-03-02,77.800003,78.379997,77.500000,77.970001,77.970001,5690000
1964-03-03,77.970001,78.660004,77.690002,78.220001,78.220001,5350000
1964-03-04,78.220001,78.699997,77.699997,78.070000,78.070000,5250000
1964-03-05,78.070000,78.440002,77.580002,78.059998,78.059998,4680000
1964-03-06,78.059998,78.599998,77.849998,78.309998,78.309998,4790000
1964-03-09,78.309998,78.879997,77.949997,78.330002,78.330002,5510000
1964-03-10,78.330002,78.900002,77.949997,78.589996,78.589996,5500000
1964-03-11,78.589996,79.419998,78.449997,78.949997,78.949997,6180000
1964-03-12,78.949997,79.410004,78.550003,79.080002,79.080002,5290000
1964-03-13,79.080002,79.589996,78.739998,79.139999,79.139999,5660000
1964-03-16,79.139999,79.599998,78.720001,79.139999,79.139999,5140000
1964-03-17,79.139999,79.650002,78.769997,79.320000,79.320000,5480000
1964-03-18,79.320000,79.889999,78.900002,79.379997,79.379997,5890000
1964-03-19,79.379997,79.849998,78.940002,79.300003,79.300003,5670000
1964-03-20,79.300003,79.349998,78.919998,78.919998,78.919998,5020000
1964-03-23,78.919998,79.330002,78.449997,78.930000,78.930000,4940000
1964-03-24,78.930000,79.339996,78.510002,78.790001,78.790001,5210000
1964-03-25,78.790001,79.330002,78.169998,78.980003,78.980003,5420000
1964-03-26,78.980003,79.580002,78.669998,79.190002,79.190002,5760000
1964-03-30,79.190002,79.669998,78.750000,79.139999,79.139999,6060000
1964-03-31,79.139999,79.510002,78.570000,78.980003,78.980003,5270000
1964-04-01,78.980003,79.580002,78.669998,79.239998,79.239998,5510000
1964-04-02,79.239998,80.089996,79.129997,79.699997,79.699997,6840000
1964-04-03,79.699997,80.370003,79.449997,79.940002,79.940002,5990000
1964-04-06,79.940002,80.449997,79.550003,80.019997,80.019997,5840000
1964-04-07,80.019997,80.440002,79.410004,79.739998,79.739998,5900000
1964-04-08,79.739998,80.169998,79.260002,79.750000,79.750000,5380000
1964-04-09,79.750000,80.230003,79.360001,79.699997,79.699997,5300000
1964-04-10,79.699997,80.260002,79.430000,79.849998,79.849998,4990000
1964-04-13,79.849998,80.300003,79.419998,79.769997,79.769997,5330000
1964-04-14,79.769997,80.370003,79.459999,79.989998,79.989998,5120000
1964-04-15,79.989998,80.500000,79.629997,80.089996,80.089996,5270000
1964-04-16,80.089996,80.620003,79.730003,80.199997,80.199997,5240000
1964-04-17,80.199997,80.980003,79.989998,80.550003,80.550003,6030000
1964-04-20,80.550003,81.040001,80.110001,80.500000,80.500000,5560000
1964-04-21,80.500000,80.980003,80.050003,80.540001,80.540001,5750000
1964-04-22,80.540001,80.919998,80.059998,80.489998,80.489998,5390000
1964-04-23,80.489998,81.199997,80.089996,80.379997,80.379997,6690000
1964-04-24,80.379997,80.620003,79.449997,79.750000,79.750000,5610000
1964-04-27,79.750000,80.010002,78.900002,79.349998,79.349998,5070000
1964-04-28,79.349998,80.260002,79.139999,79.900002,79.900002,4790000
1964-04-29,79.900002,80.599998,79.290001,79.699997,79.699997,6200000
1964-04-30,79.699997,80.080002,79.080002,79.459999,79.459999,5690000
1964-05-01,79.459999,80.470001,79.459999,80.169998,80.169998,5990000
1964-05-04,80.169998,81.010002,79.870003,80.470001,80.470001,5360000
1964-05-05,80.470001,81.199997,79.989998,80.879997,80.879997,5340000
1964-05-06,80.879997,81.570000,80.529999,81.059998,81.059998,5560000
1964-05-07,81.059998,81.720001,80.669998,81.150002,81.150002,5600000
1964-05-08,81.000000,81.000000,81.000000,81.000000,81.000000,4910000
1964-05-11,81.000000,81.510002,80.580002,80.900002,80.900002,4490000
1964-05-12,80.900002,81.809998,80.660004,81.160004,81.160004,5200000
1964-05-13,81.160004,81.650002,80.660004,80.970001,80.970001,5890000
1964-05-14,80.970001,81.279999,80.370003,80.860001,80.860001,4720000
1964-05-15,80.860001,81.449997,80.489998,81.099998,81.099998,5070000
1964-05-18,81.099998,81.470001,80.419998,80.720001,80.720001,4590000
1964-05-19,80.720001,81.040001,79.959999,80.300003,80.300003,4360000
1964-05-20,80.300003,81.019997,80.089996,80.660004,80.660004,4790000
1964-05-21,80.660004,81.489998,80.360001,80.940002,80.940002,5350000
1964-05-22,80.940002,81.150002,80.360001,80.730003,80.730003,4640000
1964-05-25,80.730003,81.160004,80.209999,80.559998,80.559998,3990000
1964-05-26,80.559998,80.940002,80.120003,80.389999,80.389999,4290000
1964-05-27,80.389999,80.720001,79.779999,80.260002,80.260002,4450000
1964-05-28,80.260002,80.750000,79.879997,80.370003,80.370003,4560000
1964-06-01,80.370003,80.830002,79.830002,80.110001,80.110001,4300000
1964-06-02,80.110001,80.599998,79.500000,79.699997,79.699997,4180000
1964-06-03,79.699997,80.120003,79.269997,79.489998,79.489998,3990000
1964-06-04,79.489998,79.750000,78.440002,78.669998,78.669998,4880000
1964-06-05,78.669998,79.449997,78.500000,79.019997,79.019997,4240000
1964-06-08,79.019997,79.440002,78.440002,78.639999,78.639999,4010000
1964-06-09,78.639999,79.389999,78.150002,79.139999,79.139999,4470000
1964-06-10,79.139999,79.839996,79.019997,79.440002,79.440002,4170000
1964-06-11,79.440002,80.129997,79.239998,79.730003,79.730003,3620000
1964-06-12,79.730003,80.050003,79.190002,79.599998,79.599998,3840000
1964-06-15,79.599998,80.330002,79.389999,79.970001,79.970001,4110000
1964-06-16,79.970001,80.720001,79.849998,80.400002,80.400002,4590000
1964-06-17,80.400002,81.129997,80.220001,80.809998,80.809998,5340000
1964-06-18,80.809998,81.339996,80.430000,80.790001,80.790001,4730000
1964-06-19,80.790001,81.230003,80.389999,80.889999,80.889999,4050000
1964-06-22,80.889999,81.540001,80.660004,81.110001,81.110001,4540000
1964-06-23,81.110001,81.430000,80.500000,80.769997,80.769997,4060000
1964-06-24,80.769997,81.449997,80.410004,81.059998,81.059998,4840000
1964-06-25,81.059998,81.730003,80.750000,81.209999,81.209999,5010000
1964-06-26,81.209999,81.779999,80.860001,81.459999,81.459999,4440000
1964-06-29,81.459999,82.099998,81.099998,81.639999,81.639999,4380000
1964-06-30,81.639999,82.070000,81.190002,81.690002,81.690002,4360000
1964-07-01,81.690002,82.510002,81.459999,82.269997,82.269997,5320000
1964-07-02,82.269997,82.980003,82.089996,82.599998,82.599998,5230000
1964-07-06,82.599998,83.379997,82.370003,82.980003,82.980003,5080000
1964-07-07,82.980003,83.529999,82.599998,83.120003,83.120003,5240000
1964-07-08,83.120003,83.559998,82.580002,83.120003,83.120003,4760000
1964-07-09,83.120003,83.639999,82.739998,83.220001,83.220001,5040000
1964-07-10,83.220001,83.989998,82.870003,83.360001,83.360001,5420000
1964-07-13,83.360001,83.860001,82.919998,83.309998,83.309998,4800000
1964-07-14,83.309998,83.709999,82.720001,83.059998,83.059998,4760000
1964-07-15,83.059998,83.669998,82.720001,83.339996,83.339996,4610000
1964-07-16,83.339996,83.980003,83.059998,83.639999,83.639999,4640000
1964-07-17,83.639999,84.330002,83.370003,84.010002,84.010002,4640000
1964-07-20,84.010002,84.330002,83.440002,83.739998,83.739998,4390000
1964-07-21,83.739998,83.989998,83.059998,83.540001,83.540001,4570000
1964-07-22,83.540001,83.949997,82.959999,83.519997,83.519997,4570000
1964-07-23,83.519997,83.910004,83.059998,83.480003,83.480003,4560000
1964-07-24,83.480003,83.919998,83.070000,83.459999,83.459999,4210000
1964-07-27,83.459999,83.820000,82.820000,83.080002,83.080002,4090000
1964-07-28,83.080002,83.300003,82.400002,82.849998,82.849998,3860000
1964-07-29,82.849998,83.300003,82.470001,82.919998,82.919998,4050000
1964-07-30,82.919998,83.500000,82.629997,83.089996,83.089996,4530000
1964-07-31,83.089996,83.570000,82.720001,83.180000,83.180000,4220000
1964-08-03,83.180000,83.489998,82.650002,83.000000,83.000000,3780000
1964-08-04,83.000000,83.019997,81.680000,81.959999,81.959999,4780000
1964-08-05,81.959999,82.410004,80.800003,82.089996,82.089996,6160000
1964-08-06,82.089996,82.449997,81.199997,81.339996,81.339996,3940000
1964-08-07,81.339996,82.199997,81.190002,81.860001,81.860001,3190000
1964-08-10,81.860001,82.230003,81.430000,81.779999,81.779999,3050000
1964-08-11,81.779999,82.250000,81.449997,81.760002,81.760002,3450000
1964-08-12,81.760002,82.529999,81.599998,82.169998,82.169998,4140000
1964-08-13,82.169998,82.870003,81.980003,82.410004,82.410004,4600000
1964-08-14,82.410004,82.830002,82.029999,82.349998,82.349998,4080000
1964-08-17,82.349998,82.849998,82.019997,82.360001,82.360001,3780000
1964-08-18,82.360001,82.790001,82.010002,82.400002,82.400002,4180000
1964-08-19,82.400002,82.800003,81.989998,82.320000,82.320000,4160000
1964-08-20,82.320000,82.570000,81.599998,81.940002,81.940002,3840000
1964-08-21,81.940002,82.430000,81.639999,82.070000,82.070000,3620000
1964-08-24,82.070000,82.480003,81.639999,81.910004,81.910004,3790000
1964-08-25,81.910004,82.129997,81.199997,81.440002,81.440002,3780000
1964-08-26,81.440002,81.739998,80.989998,81.320000,81.320000,3300000
1964-08-27,81.320000,81.940002,81.070000,81.699997,81.699997,3560000
1964-08-28,81.699997,82.290001,81.540001,81.989998,81.989998,3760000
1964-08-31,81.989998,82.480003,81.459999,81.830002,81.830002,3340000
1964-09-01,81.830002,82.500000,81.570000,82.180000,82.180000,4650000
1964-09-02,82.180000,82.760002,81.949997,82.309998,82.309998,4800000
1964-09-03,82.309998,82.830002,82.040001,82.559998,82.559998,4310000
1964-09-04,82.559998,83.029999,82.309998,82.760002,82.760002,4210000
1964-09-08,82.760002,83.239998,82.459999,82.870003,82.870003,4090000
1964-09-09,82.870003,83.510002,82.540001,83.050003,83.050003,5690000
1964-09-10,83.050003,83.500000,82.599998,83.099998,83.099998,5470000
1964-09-11,83.099998,83.839996,82.790001,83.449997,83.449997,5630000
1964-09-14,83.449997,83.889999,82.879997,83.220001,83.220001,5370000
1964-09-15,83.220001,83.680000,82.690002,83.000000,83.000000,5690000
1964-09-16,83.000000,83.519997,82.570000,83.239998,83.239998,4230000
1964-09-17,83.239998,84.180000,83.169998,83.790001,83.790001,6380000
1964-09-18,83.790001,84.290001,83.029999,83.480003,83.480003,6160000
1964-09-21,83.480003,84.320000,83.410004,83.860001,83.860001,5310000
1964-09-22,83.860001,84.440002,83.529999,83.889999,83.889999,5250000
1964-09-23,83.889999,84.370003,83.449997,83.910004,83.910004,5920000
1964-09-24,83.910004,84.430000,83.449997,84.000000,84.000000,5840000
1964-09-25,84.000000,84.620003,83.559998,84.209999,84.209999,6170000
1964-09-28,84.209999,84.730003,83.790001,84.279999,84.279999,4810000
1964-09-29,84.279999,84.800003,83.839996,84.239998,84.239998,5070000
1964-09-30,84.239998,84.660004,83.860001,84.180000,84.180000,4720000
1964-10-01,84.180000,84.529999,83.739998,84.080002,84.080002,4470000
1964-10-02,84.080002,84.639999,83.709999,84.360001,84.360001,4370000
1964-10-05,84.360001,85.250000,84.199997,84.739998,84.739998,4850000
1964-10-06,84.739998,85.239998,84.370003,84.790001,84.790001,4820000
1964-10-07,84.790001,85.250000,84.419998,84.800003,84.800003,5090000
1964-10-08,84.800003,85.400002,84.470001,85.040001,85.040001,5060000
1964-10-09,85.040001,85.599998,84.720001,85.220001,85.220001,5290000
1964-10-12,85.220001,85.580002,84.879997,85.239998,85.239998,4110000
1964-10-13,85.239998,85.570000,84.629997,84.959999,84.959999,5400000
1964-10-14,84.959999,85.290001,84.500000,84.790001,84.790001,4530000
1964-10-15,84.790001,84.989998,83.650002,84.250000,84.250000,6500000
1964-10-16,84.250000,85.099998,84.099998,84.830002,84.830002,5140000
1964-10-19,84.830002,85.360001,84.470001,84.930000,84.930000,5010000
1964-10-20,84.930000,85.570000,84.559998,85.180000,85.180000,5140000
1964-10-21,85.180000,85.639999,84.769997,85.099998,85.099998,5170000
1964-10-22,85.099998,85.440002,84.510002,84.940002,84.940002,4670000
1964-10-23,84.940002,85.419998,84.570000,85.139999,85.139999,3830000
1964-10-26,85.139999,85.699997,84.650002,85.000000,85.000000,5230000
1964-10-27,85.000000,85.400002,84.610001,85.000000,85.000000,4470000
1964-10-28,85.000000,85.370003,84.430000,84.690002,84.690002,4890000
1964-10-29,84.690002,85.150002,84.360001,84.730003,84.730003,4390000
1964-10-30,84.730003,85.220001,84.410004,84.860001,84.860001,4120000
1964-11-02,84.860001,85.540001,84.510002,85.180000,85.180000,4430000
1964-11-04,85.180000,85.900002,84.800003,85.139999,85.139999,4720000
1964-11-05,85.139999,85.620003,84.720001,85.160004,85.160004,4380000
1964-11-06,85.160004,85.550003,84.650002,85.230003,85.230003,4810000
1964-11-09,85.230003,85.720001,84.930000,85.190002,85.190002,4560000
1964-11-10,85.190002,85.550003,84.489998,84.839996,84.839996,5020000
1964-11-11,84.839996,85.300003,84.489998,85.080002,85.080002,3790000
1964-11-12,85.080002,85.629997,84.750000,85.190002,85.190002,5250000
1964-11-13,85.190002,85.680000,84.760002,85.209999,85.209999,4860000
1964-11-16,85.209999,85.940002,84.879997,85.650002,85.650002,4870000
1964-11-17,85.650002,86.550003,85.480003,86.029999,86.029999,5920000
1964-11-18,86.029999,86.800003,85.730003,86.220001,86.220001,6560000
1964-11-19,86.220001,86.570000,85.599998,86.180000,86.180000,5570000
1964-11-20,86.180000,86.800003,85.730003,86.279999,86.279999,5210000
1964-11-23,86.279999,86.589996,85.480003,86.000000,86.000000,4860000
1964-11-24,86.000000,86.120003,85.150002,85.730003,85.730003,5070000
1964-11-25,85.730003,86.180000,85.099998,85.440002,85.440002,4800000
1964-11-27,85.440002,85.680000,84.550003,85.160004,85.160004,4070000
1964-11-30,85.160004,85.410004,84.099998,84.419998,84.419998,4890000
1964-12-01,84.419998,84.559998,83.360001,83.550003,83.550003,4940000
1964-12-02,83.550003,84.230003,83.120003,83.790001,83.790001,4930000
1964-12-03,83.790001,84.739998,83.709999,84.180000,84.180000,4250000
1964-12-04,84.349998,84.349998,84.349998,84.349998,84.349998,4340000
1964-12-07,84.349998,85.029999,84.040001,84.330002,84.330002,4770000
1964-12-08,84.330002,84.709999,83.690002,84.000000,84.000000,4990000
1964-12-09,84.000000,84.239998,83.239998,83.459999,83.459999,5120000
1964-12-10,83.459999,83.959999,82.980003,83.449997,83.449997,4790000
1964-12-11,83.449997,84.050003,83.089996,83.660004,83.660004,4530000
1964-12-14,83.660004,84.169998,83.099998,83.449997,83.449997,4340000
1964-12-15,83.449997,83.790001,82.650002,83.220001,83.220001,5340000
1964-12-16,83.220001,83.940002,83.000000,83.550003,83.550003,4610000
1964-12-17,83.550003,84.239998,83.339996,83.900002,83.900002,4850000
1964-12-18,83.900002,84.650002,83.730003,84.290001,84.290001,4630000
1964-12-21,84.290001,84.910004,84.110001,84.379997,84.379997,4470000
1964-12-22,84.379997,84.879997,83.940002,84.330002,84.330002,4520000
1964-12-23,84.330002,84.760002,83.790001,84.150002,84.150002,4470000
1964-12-24,84.150002,84.589996,83.739998,84.150002,84.150002,3600000
1964-12-28,84.150002,84.580002,83.699997,84.070000,84.070000,3990000
1964-12-29,84.070000,84.349998,83.379997,83.809998,83.809998,4450000
1964-12-30,83.809998,84.629997,83.629997,84.300003,84.300003,5610000
1964-12-31,84.300003,85.180000,84.180000,84.750000,84.750000,6470000
1965-01-04,84.750000,85.150002,83.769997,84.230003,84.230003,3930000
1965-01-05,84.230003,85.019997,84.019997,84.629997,84.629997,4110000
1965-01-06,84.629997,85.379997,84.449997,84.889999,84.889999,4850000
1965-01-07,84.889999,85.620003,84.660004,85.260002,85.260002,5080000
1965-01-08,85.260002,85.839996,84.910004,85.370003,85.370003,5340000
1965-01-11,85.370003,85.809998,84.900002,85.400002,85.400002,5440000
1965-01-12,85.400002,85.980003,85.129997,85.610001,85.610001,5400000
1965-01-13,85.610001,86.269997,85.349998,85.839996,85.839996,6160000
1965-01-14,85.839996,86.379997,85.410004,85.839996,85.839996,5810000
1965-01-15,85.839996,86.519997,85.599998,86.209999,86.209999,5340000
1965-01-18,86.209999,87.150002,85.989998,86.489998,86.489998,5550000
1965-01-19,86.489998,87.089996,86.150002,86.629997,86.629997,5550000
1965-01-20,86.629997,87.099998,86.260002,86.599998,86.599998,5550000
1965-01-21,86.599998,86.900002,86.019997,86.519997,86.519997,4780000
1965-01-22,86.519997,87.150002,86.199997,86.739998,86.739998,5430000
1965-01-25,86.739998,87.269997,86.389999,86.860001,86.860001,5370000
1965-01-26,86.860001,87.449997,86.510002,86.940002,86.940002,5760000
1965-01-27,86.940002,87.669998,86.699997,87.230003,87.230003,6010000
1965-01-28,87.230003,87.879997,86.889999,87.480003,87.480003,6730000
1965-01-29,87.480003,88.190002,87.180000,87.559998,87.559998,6940000
1965-02-01,87.559998,88.010002,87.050003,87.580002,87.580002,5690000
1965-02-02,87.580002,87.940002,87.029999,87.550003,87.550003,5460000
1965-02-03,87.550003,88.010002,87.070000,87.629997,87.629997,6130000
1965-02-04,87.629997,88.059998,87.059998,87.570000,87.570000,6230000
1965-02-05,87.570000,87.980003,86.900002,87.290001,87.290001,5690000
1965-02-08,87.000000,87.000000,85.949997,86.949997,86.949997,6010000
1965-02-09,86.949997,87.639999,86.699997,87.239998,87.239998,5690000
1965-02-10,87.239998,87.699997,86.199997,86.459999,86.459999,7210000
1965-02-11,86.459999,86.889999,85.400002,85.540001,85.540001,5800000
1965-02-12,85.540001,86.480003,85.540001,86.169998,86.169998,4960000
1965-02-15,86.169998,86.860001,85.750000,86.070000,86.070000,5760000
1965-02-16,86.070000,86.309998,85.330002,85.669998,85.669998,5000000
1965-02-17,85.669998,86.250000,85.250000,85.769997,85.769997,5510000
1965-02-18,85.769997,86.480003,85.470001,86.050003,86.050003,6060000
1965-02-19,86.050003,86.669998,85.709999,86.209999,86.209999,5560000
1965-02-23,86.209999,87.010002,86.029999,86.639999,86.639999,5880000
1965-02-24,86.639999,87.720001,86.430000,87.169998,87.169998,7160000
1965-02-25,87.169998,87.699997,86.699997,87.199997,87.199997,6680000
1965-02-26,87.199997,87.839996,86.809998,87.430000,87.430000,5800000
1965-03-01,87.430000,87.930000,86.919998,87.250000,87.250000,5780000
1965-03-02,87.250000,87.790001,86.839996,87.400002,87.400002,5730000
1965-03-03,87.400002,87.830002,86.879997,87.260002,87.260002,6600000
1965-03-04,87.260002,87.720001,86.629997,86.980003,86.980003,7300000
1965-03-05,86.980003,87.260002,86.000000,86.800003,86.800003,6120000
1965-03-08,86.800003,87.279999,86.309998,86.830002,86.830002,5250000
1965-03-09,86.830002,87.269997,86.330002,86.690002,86.690002,5210000
1965-03-10,86.690002,87.070000,86.199997,86.540001,86.540001,5100000
1965-03-11,86.540001,87.290001,86.169998,86.900002,86.900002,5770000
1965-03-12,86.900002,87.650002,86.599998,87.209999,87.209999,6370000
1965-03-15,87.209999,87.919998,86.820000,87.239998,87.239998,6000000
1965-03-16,87.239998,87.610001,86.669998,87.129997,87.129997,5480000
1965-03-17,87.129997,87.510002,86.629997,87.019997,87.019997,5120000
1965-03-18,87.019997,87.480003,86.500000,86.809998,86.809998,4990000
1965-03-19,86.809998,87.370003,86.430000,86.839996,86.839996,5040000
1965-03-22,86.839996,87.339996,86.410004,86.830002,86.830002,4920000
1965-03-23,86.830002,87.339996,86.449997,86.930000,86.930000,4820000
1965-03-24,86.930000,87.550003,86.680000,87.089996,87.089996,5420000
1965-03-25,87.089996,87.500000,86.550003,86.839996,86.839996,5460000
1965-03-26,86.839996,87.059998,85.959999,86.199997,86.199997,5020000
1965-03-29,86.199997,86.660004,85.650002,86.029999,86.029999,4590000
1965-03-30,86.029999,86.529999,85.690002,86.199997,86.199997,4270000
1965-03-31,86.199997,86.639999,85.830002,86.160004,86.160004,4470000
1965-04-01,86.160004,86.730003,85.870003,86.320000,86.320000,4890000
1965-04-02,86.320000,86.889999,86.080002,86.529999,86.529999,5060000
1965-04-05,86.529999,87.080002,86.139999,86.529999,86.529999,4920000
1965-04-06,86.529999,86.910004,86.080002,86.500000,86.500000,4610000
1965-04-07,86.500000,86.879997,86.139999,86.550003,86.550003,4430000
1965-04-08,86.550003,87.349998,86.339996,87.040001,87.040001,5770000
1965-04-09,87.040001,87.870003,86.860001,87.559998,87.559998,6580000
1965-04-12,87.559998,88.360001,87.309998,87.940002,87.940002,6040000
1965-04-13,87.940002,88.480003,87.540001,88.040001,88.040001,6690000
1965-04-14,88.040001,88.650002,87.709999,88.239998,88.239998,6580000
1965-04-15,88.239998,88.629997,87.550003,88.150002,88.150002,5830000
1965-04-19,88.150002,88.900002,87.900002,88.510002,88.510002,5700000
1965-04-20,88.510002,89.070000,88.019997,88.459999,88.459999,6480000
1965-04-21,88.459999,88.820000,87.699997,88.300003,88.300003,5590000
1965-04-22,88.300003,89.129997,88.120003,88.779999,88.779999,5990000
1965-04-23,88.779999,89.410004,88.480003,88.879997,88.879997,5860000
1965-04-26,88.879997,89.290001,88.300003,88.889999,88.889999,5410000
1965-04-27,88.889999,89.639999,88.709999,89.040001,89.040001,6310000
1965-04-28,89.040001,89.480003,88.510002,89.000000,89.000000,5680000
1965-04-29,89.000000,89.430000,88.470001,88.930000,88.930000,5510000
1965-04-30,88.930000,89.440002,88.500000,89.110001,89.110001,5190000
1965-05-03,89.110001,89.680000,88.620003,89.230003,89.230003,5340000
1965-05-04,89.230003,89.889999,88.820000,89.510002,89.510002,5720000
1965-05-05,89.510002,90.400002,89.139999,89.709999,89.709999,6350000
1965-05-06,89.709999,90.570000,89.389999,89.919998,89.919998,6340000
1965-05-07,89.919998,90.300003,89.330002,89.849998,89.849998,5820000
1965-05-10,89.849998,90.220001,89.220001,89.660004,89.660004,5600000
1965-05-11,89.660004,89.980003,89.050003,89.550003,89.550003,5150000
1965-05-12,89.550003,90.309998,89.300003,89.940002,89.940002,6310000
1965-05-13,89.940002,90.680000,89.680000,90.269997,90.269997,6460000
1965-05-14,90.269997,90.660004,89.629997,90.099998,90.099998,5860000
1965-05-17,90.099998,90.440002,89.239998,89.540001,89.540001,4980000
1965-05-18,89.540001,89.839996,88.870003,89.459999,89.459999,5130000
1965-05-19,89.459999,90.150002,89.169998,89.669998,89.669998,5860000
1965-05-20,89.669998,89.860001,88.739998,89.180000,89.180000,5750000
1965-05-21,89.180000,89.410004,88.400002,88.750000,88.750000,4660000
1965-05-24,88.750000,88.889999,87.750000,88.089996,88.089996,4790000
1965-05-25,88.089996,88.959999,87.820000,88.599998,88.599998,4950000
1965-05-26,88.599998,89.220001,88.040001,88.300003,88.300003,5330000
1965-05-27,88.300003,88.360001,87.239998,87.839996,87.839996,5520000
1965-05-28,87.839996,88.680000,87.580002,88.419998,88.419998,4270000
1965-06-01,88.419998,88.800003,87.879997,88.720001,88.720001,4830000
1965-06-02,87.870003,87.870003,86.250000,87.089996,87.089996,6790000
1965-06-03,87.089996,88.050003,86.580002,86.900002,86.900002,5720000
1965-06-04,86.900002,87.459999,86.360001,87.110001,87.110001,4530000
1965-06-07,87.110001,87.449997,86.040001,86.879997,86.879997,4680000
1965-06-08,86.879997,87.099998,85.739998,85.930000,85.930000,4660000
1965-06-09,85.930000,86.370003,84.750000,85.040001,85.040001,7070000
1965-06-10,85.040001,85.820000,84.099998,84.730003,84.730003,7470000
1965-06-11,84.730003,85.680000,84.500000,85.120003,85.120003,5350000
1965-06-14,85.120003,85.680000,83.639999,84.010002,84.010002,5920000
1965-06-15,84.010002,84.860001,83.010002,84.489998,84.489998,8450000
1965-06-16,84.580002,85.790001,84.580002,85.199997,85.199997,6290000
1965-06-17,85.199997,86.220001,84.980003,85.739998,85.739998,5220000
1965-06-18,85.739998,86.099998,84.900002,85.339996,85.339996,4330000
1965-06-21,85.339996,85.639999,84.529999,85.050003,85.050003,3280000
1965-06-22,85.050003,85.699997,84.760002,85.209999,85.209999,3330000
1965-06-23,85.209999,85.589996,84.519997,84.669998,84.669998,3580000
1965-06-24,84.669998,84.730003,83.300003,83.559998,83.559998,5840000
1965-06-25,83.559998,83.830002,82.599998,83.059998,83.059998,5790000
1965-06-28,83.059998,83.339996,81.360001,81.599998,81.599998,7650000
1965-06-29,81.599998,83.040001,80.730003,82.410004,82.410004,10450000
1965-06-30,82.970001,84.629997,82.970001,84.120003,84.120003,6930000
1965-07-01,84.120003,84.639999,83.570000,84.480003,84.480003,4520000
1965-07-02,84.480003,85.400002,84.129997,85.160004,85.160004,4260000
1965-07-06,85.160004,85.629997,84.570000,84.989998,84.989998,3400000
1965-07-07,84.989998,85.139999,84.279999,84.669998,84.669998,3020000
1965-07-08,84.669998,85.599998,84.290001,85.389999,85.389999,4380000
1965-07-09,85.389999,86.110001,85.110001,85.709999,85.709999,4800000
1965-07-12,85.709999,86.080002,85.239998,85.690002,85.690002,3690000
1965-07-13,85.690002,86.010002,85.120003,85.589996,85.589996,3260000
1965-07-14,85.589996,86.230003,85.180000,85.870003,85.870003,4100000
1965-07-15,85.870003,86.470001,85.440002,85.720001,85.720001,4420000
1965-07-16,85.720001,86.139999,85.260002,85.690002,85.690002,3520000
1965-07-19,85.690002,86.040001,85.209999,85.629997,85.629997,3220000
1965-07-20,85.629997,85.849998,84.389999,84.550003,84.550003,4670000
1965-07-21,84.550003,84.839996,83.760002,84.070000,84.070000,4350000
1965-07-22,84.070000,84.449997,83.529999,83.849998,83.849998,3310000
1965-07-23,83.849998,84.519997,83.570000,84.070000,84.070000,3600000
1965-07-26,84.070000,84.470001,83.489998,84.050003,84.050003,3790000
1965-07-27,84.050003,84.589996,83.580002,83.870003,83.870003,4190000
1965-07-28,83.870003,84.519997,83.300003,84.029999,84.029999,4760000
1965-07-29,84.029999,85.000000,83.790001,84.680000,84.680000,4690000
1965-07-30,84.680000,85.639999,84.639999,85.250000,85.250000,5200000
1965-08-02,85.250000,85.870003,84.870003,85.419998,85.419998,4220000
1965-08-03,85.419998,85.809998,84.800003,85.459999,85.459999,4640000
1965-08-04,85.459999,86.120003,85.220001,85.790001,85.790001,4830000
1965-08-05,85.790001,86.279999,85.430000,85.790001,85.790001,4920000
1965-08-06,85.790001,86.400002,85.419998,86.070000,86.070000,4200000
1965-08-09,86.070000,86.540001,85.519997,85.860001,85.860001,4540000
1965-08-10,85.860001,86.309998,85.449997,85.870003,85.870003,4690000
1965-08-11,85.870003,86.480003,85.639999,86.129997,86.129997,5030000
1965-08-12,86.129997,86.750000,85.849998,86.379997,86.379997,5160000
1965-08-13,86.379997,87.139999,86.089996,86.769997,86.769997,5430000
1965-08-16,86.769997,87.430000,86.459999,86.870003,86.870003,5270000
1965-08-17,86.870003,87.419998,86.480003,87.040001,87.040001,4520000
1965-08-18,87.040001,87.570000,86.629997,86.989998,86.989998,5850000
1965-08-19,86.989998,87.480003,86.489998,86.790001,86.790001,5000000
1965-08-20,86.790001,87.139999,86.209999,86.690002,86.690002,4170000
1965-08-23,86.690002,87.099998,86.220001,86.559998,86.559998,4470000
1965-08-24,86.559998,87.190002,86.220001,86.709999,86.709999,4740000
1965-08-25,86.709999,87.269997,86.330002,86.809998,86.809998,6240000
1965-08-26,86.809998,87.519997,86.400002,87.139999,87.139999,6010000
1965-08-27,87.139999,87.739998,86.809998,87.199997,87.199997,5570000
1965-08-30,87.199997,87.639999,86.760002,87.209999,87.209999,4400000
1965-08-31,87.209999,87.790001,86.779999,87.169998,87.169998,5170000
1965-09-01,87.169998,87.629997,86.690002,87.169998,87.169998,5890000
1965-09-02,87.169998,87.959999,86.980003,87.650002,87.650002,6470000
1965-09-03,87.650002,88.410004,87.519997,88.059998,88.059998,6010000
1965-09-07,88.059998,88.769997,87.760002,88.360001,88.360001,5750000
1965-09-08,88.360001,89.080002,87.930000,88.660004,88.660004,6240000
1965-09-09,88.660004,89.459999,88.349998,88.889999,88.889999,7360000
1965-09-10,88.889999,89.849998,88.410004,89.120003,89.120003,6650000
1965-09-13,89.120003,89.910004,88.769997,89.379997,89.379997,7020000
1965-09-14,89.379997,90.010002,88.690002,89.029999,89.029999,7830000
1965-09-15,89.029999,89.959999,88.709999,89.519997,89.519997,6220000
1965-09-16,90.019997,90.019997,90.019997,90.019997,90.019997,7410000
1965-09-17,90.019997,90.470001,89.320000,90.050003,90.050003,6610000
1965-09-20,90.050003,90.669998,89.510002,90.080002,90.080002,7040000
1965-09-21,90.080002,90.660004,89.430000,89.809998,89.809998,7750000
1965-09-22,89.809998,90.669998,89.449997,90.220001,90.220001,8290000
1965-09-23,90.220001,90.779999,89.430000,89.860001,89.860001,9990000
1965-09-24,89.860001,90.470001,89.129997,90.019997,90.019997,7810000
1965-09-27,90.650002,90.650002,90.650002,90.650002,90.650002,6820000
1965-09-28,90.650002,91.129997,89.830002,90.430000,90.430000,8750000
1965-09-29,90.430000,91.110001,89.559998,90.019997,90.019997,10600000
1965-09-30,90.019997,90.709999,89.510002,89.959999,89.959999,8670000
1965-10-01,89.959999,90.480003,89.300003,89.900002,89.900002,7470000
1965-10-04,89.900002,90.559998,89.470001,90.080002,90.080002,5590000
1965-10-05,90.080002,91.019997,89.919998,90.629997,90.629997,6980000
1965-10-06,90.629997,90.940002,89.739998,90.540001,90.540001,6010000
1965-10-07,90.540001,91.089996,90.089996,90.470001,90.470001,6670000
1965-10-08,90.470001,91.309998,90.300003,90.849998,90.849998,7670000
1965-10-11,90.849998,91.839996,90.730003,91.370003,91.370003,9600000
1965-10-12,91.370003,91.940002,90.830002,91.349998,91.349998,9470000
1965-10-13,91.349998,91.809998,90.730003,91.339996,91.339996,9470000
1965-10-14,91.339996,91.900002,90.709999,91.190002,91.190002,8580000
1965-10-15,91.190002,92.089996,90.760002,91.379997,91.379997,7470000
1965-10-18,91.379997,92.279999,91.059998,91.680000,91.680000,8180000
1965-10-19,91.680000,92.449997,91.349998,91.800003,91.800003,8620000
1965-10-20,91.800003,92.260002,91.120003,91.779999,91.779999,8200000
1965-10-21,91.779999,92.510002,91.419998,91.940002,91.940002,9170000
1965-10-22,91.940002,92.739998,91.540001,91.980003,91.980003,8960000
1965-10-25,91.980003,92.720001,91.339996,91.669998,91.669998,7090000
1965-10-26,91.669998,92.629997,91.360001,92.199997,92.199997,6750000
1965-10-27,92.199997,93.190002,91.949997,92.510002,92.510002,7670000
1965-10-28,92.510002,92.949997,91.599998,92.209999,92.209999,7230000
1965-10-29,92.209999,92.940002,91.830002,92.419998,92.419998,7240000
1965-11-01,92.419998,92.919998,91.730003,92.230003,92.230003,6340000
1965-11-03,92.230003,92.790001,91.620003,92.309998,92.309998,7520000
1965-11-04,92.309998,93.070000,91.900002,92.459999,92.459999,8380000
1965-11-05,92.459999,92.919998,91.779999,92.370003,92.370003,7310000
1965-11-08,92.370003,92.970001,91.629997,92.230003,92.230003,7000000
1965-11-09,92.230003,92.650002,91.470001,91.930000,91.930000,6680000
1965-11-10,91.930000,92.400002,91.349998,91.830002,91.830002,4860000
1965-11-11,91.830002,92.370003,91.309998,92.110001,92.110001,5430000
1965-11-12,92.110001,93.070000,91.830002,92.550003,92.550003,7780000
1965-11-15,92.550003,93.300003,92.040001,92.629997,92.629997,8310000
1965-11-16,92.629997,93.129997,91.900002,92.410004,92.410004,8380000
1965-11-17,92.410004,93.279999,91.849998,92.599998,92.599998,9120000
1965-11-18,92.599998,92.940002,91.720001,92.220001,92.220001,7040000
1965-11-19,92.220001,92.879997,91.730003,92.239998,92.239998,6850000
1965-11-22,92.239998,92.480003,91.160004,91.639999,91.639999,6370000
1965-11-23,91.639999,92.239998,91.150002,91.779999,91.779999,7150000
1965-11-24,91.779999,92.500000,91.139999,91.940002,91.940002,7870000
1965-11-26,91.940002,92.650002,91.389999,92.029999,92.029999,6970000
1965-11-29,92.029999,92.599998,91.370003,91.800003,91.800003,8760000
1965-11-30,91.800003,92.139999,90.809998,91.610001,91.610001,8990000
1965-12-01,91.610001,92.260002,91.019997,91.500000,91.500000,10140000
1965-12-02,91.500000,91.949997,90.690002,91.209999,91.209999,9070000
1965-12-03,91.209999,91.800003,90.529999,91.269997,91.269997,8160000
1965-12-06,91.199997,91.199997,89.199997,90.589996,90.589996,11440000
1965-12-07,90.589996,92.000000,90.449997,91.389999,91.389999,9340000
1965-12-08,91.389999,92.239998,90.839996,91.279999,91.279999,10120000
1965-12-09,91.279999,92.059998,90.870003,91.559998,91.559998,9150000
1965-12-10,91.559998,92.279999,91.139999,91.800003,91.800003,8740000
1965-12-13,91.800003,92.449997,91.269997,91.830002,91.830002,8660000
1965-12-14,91.830002,92.589996,91.349998,91.879997,91.879997,9920000
1965-12-15,91.879997,92.669998,91.300003,92.019997,92.019997,9560000
1965-12-16,92.019997,92.949997,91.529999,92.120003,92.120003,9950000
1965-12-17,92.120003,92.760002,91.510002,92.080002,92.080002,9490000
1965-12-20,92.080002,92.349998,91.089996,91.650002,91.650002,7350000
1965-12-21,91.650002,92.589996,91.239998,92.010002,92.010002,8230000
1965-12-22,92.010002,93.070000,91.529999,92.290001,92.290001,9720000
1965-12-23,92.290001,92.889999,91.580002,92.190002,92.190002,6870000
1965-12-27,92.190002,92.709999,91.279999,91.519997,91.519997,5950000
1965-12-28,91.519997,92.129997,90.629997,91.529999,91.529999,7280000
1965-12-29,91.529999,92.389999,91.139999,91.809998,91.809998,7610000
1965-12-30,91.809998,92.680000,91.519997,92.199997,92.199997,7060000
1965-12-31,92.199997,93.050003,91.820000,92.430000,92.430000,7240000
1966-01-03,92.430000,92.870003,91.629997,92.180000,92.180000,5950000
1966-01-04,92.180000,93.040001,91.680000,92.260002,92.260002,7540000
1966-01-05,92.260002,93.330002,91.989998,92.849998,92.849998,9650000
1966-01-06,92.849998,93.650002,92.510002,93.059998,93.059998,7880000
1966-01-07,93.059998,93.639999,92.470001,93.139999,93.139999,7600000
1966-01-10,93.139999,93.940002,92.750000,93.330002,93.330002,7720000
1966-01-11,93.330002,94.050003,92.849998,93.410004,93.410004,8910000
1966-01-12,93.410004,93.980003,92.800003,93.190002,93.190002,8530000
1966-01-13,93.190002,94.000000,92.680000,93.360001,93.360001,8860000
1966-01-14,93.360001,94.139999,92.980003,93.500000,93.500000,9210000
1966-01-17,93.500000,94.459999,93.099998,93.769997,93.769997,9430000
1966-01-18,93.769997,94.639999,93.230003,93.949997,93.949997,9790000
1966-01-19,93.949997,94.620003,93.160004,93.690002,93.690002,10230000
1966-01-20,93.690002,94.330002,92.870003,93.360001,93.360001,8670000
1966-01-21,93.360001,93.970001,92.599998,93.470001,93.470001,9180000
1966-01-24,93.470001,94.410004,93.070000,93.709999,93.709999,8780000
1966-01-25,93.709999,94.559998,93.239998,93.849998,93.849998,9300000
1966-01-26,93.849998,94.529999,93.180000,93.699997,93.699997,9910000
1966-01-27,93.699997,94.339996,93.089996,93.669998,93.669998,8970000
1966-01-28,93.669998,94.150002,92.839996,93.309998,93.309998,9000000
1966-01-31,93.309998,93.769997,92.459999,92.879997,92.879997,7800000
1966-02-01,92.879997,93.360001,91.610001,92.160004,92.160004,9090000
1966-02-02,92.160004,92.910004,91.320000,92.529999,92.529999,8130000
1966-02-03,92.529999,93.669998,92.110001,92.650002,92.650002,8160000
1966-02-04,92.650002,93.699997,92.330002,93.260002,93.260002,7560000
1966-02-07,93.260002,94.220001,92.849998,93.589996,93.589996,8000000
1966-02-08,93.589996,94.290001,92.580002,93.550003,93.550003,10560000
1966-02-09,93.550003,94.720001,93.290001,94.059998,94.059998,9760000
1966-02-10,94.059998,94.699997,93.320000,93.830002,93.830002,9790000
1966-02-11,93.830002,94.519997,93.250000,93.809998,93.809998,8150000
1966-02-14,93.809998,94.400002,93.150002,93.529999,93.529999,8360000
1966-02-15,93.529999,94.040001,92.669998,93.169998,93.169998,8750000
1966-02-16,93.169998,93.739998,92.629997,93.160004,93.160004,9180000
1966-02-17,93.160004,93.580002,92.110001,92.660004,92.660004,9330000
1966-02-18,92.660004,93.139999,91.800003,92.410004,92.410004,8470000
1966-02-21,92.410004,92.830002,91.349998,91.870003,91.870003,8510000
1966-02-23,91.870003,92.209999,90.989998,91.480003,91.480003,8080000
1966-02-24,91.480003,91.809998,90.449997,90.889999,90.889999,7860000
1966-02-25,90.889999,91.879997,90.430000,91.139999,91.139999,8140000
1966-02-28,91.139999,91.949997,90.650002,91.220001,91.220001,9910000
1966-03-01,91.220001,91.650002,89.760002,90.059998,90.059998,11030000
1966-03-02,90.059998,90.650002,88.699997,89.150002,89.150002,10470000
1966-03-03,89.150002,90.029999,88.260002,89.470001,89.470001,9900000
1966-03-04,89.470001,90.250000,88.720001,89.239998,89.239998,9000000
1966-03-07,89.239998,89.389999,87.669998,88.040001,88.040001,9370000
1966-03-08,88.040001,89.000000,87.169998,88.180000,88.180000,10120000
1966-03-09,88.180000,89.209999,87.959999,88.959999,88.959999,7980000
1966-03-10,88.959999,90.139999,88.360001,88.959999,88.959999,10310000
1966-03-11,88.959999,89.629997,88.300003,88.849998,88.849998,7000000
1966-03-14,88.849998,88.919998,87.559998,87.849998,87.849998,7400000
1966-03-15,87.849998,88.199997,86.690002,87.349998,87.349998,9440000
1966-03-16,87.349998,88.550003,87.089996,87.860001,87.860001,7330000
1966-03-17,87.860001,88.599998,87.449997,88.169998,88.169998,5460000
1966-03-18,88.169998,89.230003,87.820000,88.529999,88.529999,6450000
1966-03-21,88.529999,89.730003,88.400002,89.199997,89.199997,7230000
1966-03-22,89.199997,90.279999,89.010002,89.459999,89.459999,8910000
1966-03-23,89.459999,89.800003,88.690002,89.129997,89.129997,6720000
1966-03-24,89.129997,89.800003,88.680000,89.290001,89.290001,7880000
1966-03-25,89.290001,90.139999,88.959999,89.540001,89.540001,7750000
1966-03-28,89.540001,90.410004,89.150002,89.620003,89.620003,8640000
1966-03-29,89.620003,90.040001,88.629997,89.269997,89.269997,8300000
1966-03-30,89.269997,89.570000,88.309998,88.779999,88.779999,7980000
1966-03-31,88.779999,89.699997,88.470001,89.230003,89.230003,6690000
1966-04-01,89.230003,90.370003,88.959999,89.940002,89.940002,9050000
1966-04-04,89.940002,91.330002,89.919998,90.760002,90.760002,9360000
1966-04-05,90.760002,92.040001,90.470001,91.309998,91.309998,10560000
1966-04-06,91.309998,92.099998,90.769997,91.559998,91.559998,9040000
1966-04-07,91.559998,92.419998,90.989998,91.760002,91.760002,9650000
1966-04-11,91.760002,92.599998,91.080002,91.790001,91.790001,9310000
1966-04-12,91.790001,92.510002,90.919998,91.449997,91.449997,10500000
1966-04-13,91.449997,92.809998,90.730003,91.540001,91.540001,10440000
1966-04-14,91.540001,92.800003,91.120003,91.870003,91.870003,12980000
1966-04-15,91.870003,92.750000,91.279999,91.989998,91.989998,10270000
1966-04-18,91.989998,92.589996,91.089996,91.580002,91.580002,9150000
1966-04-19,91.580002,92.309998,90.889999,91.570000,91.570000,8820000
1966-04-20,91.570000,92.750000,91.339996,92.080002,92.080002,10530000
1966-04-21,92.080002,93.019997,91.779999,92.419998,92.419998,9560000
1966-04-22,92.419998,92.870003,91.599998,92.269997,92.269997,8650000
1966-04-25,92.269997,92.860001,91.410004,92.080002,92.080002,7270000
1966-04-26,92.080002,92.769997,91.470001,91.989998,91.989998,7540000
1966-04-27,91.989998,92.489998,91.099998,91.760002,91.760002,7950000
1966-04-28,91.760002,91.919998,90.239998,91.129997,91.129997,8310000
1966-04-29,91.129997,91.860001,90.570000,91.059998,91.059998,7220000
1966-05-02,91.059998,91.750000,90.430000,90.900002,90.900002,7070000
1966-05-03,90.900002,91.099998,89.459999,89.849998,89.849998,8020000
1966-05-04,89.849998,90.110001,88.540001,89.389999,89.389999,9740000
1966-05-05,89.389999,89.769997,87.599998,87.930000,87.930000,10100000
1966-05-06,87.930000,88.519997,86.239998,87.839996,87.839996,13110000
1966-05-09,87.839996,87.959999,85.919998,86.320000,86.320000,9290000
1966-05-10,86.320000,87.879997,86.120003,87.080002,87.080002,9050000
1966-05-11,87.080002,88.379997,86.839996,87.230003,87.230003,7470000
1966-05-12,87.230003,87.489998,85.720001,86.230003,86.230003,8210000
1966-05-13,86.230003,86.309998,84.769997,85.470001,85.470001,8970000
1966-05-16,85.470001,86.040001,83.900002,84.410004,84.410004,9260000
1966-05-17,84.410004,85.029999,83.180000,83.629997,83.629997,9870000
1966-05-18,83.720001,85.639999,83.720001,85.120003,85.120003,9310000
1966-05-19,85.120003,86.330002,84.540001,85.019997,85.019997,8640000
1966-05-20,85.019997,85.790001,84.209999,85.430000,85.430000,6430000
1966-05-23,85.430000,86.910004,85.290001,86.199997,86.199997,7080000
1966-05-24,86.199997,87.699997,86.190002,86.769997,86.769997,7210000
1966-05-25,86.769997,87.480003,86.050003,87.070000,87.070000,5820000
1966-05-26,87.070000,87.879997,86.540001,87.070000,87.070000,6080000
1966-05-27,87.070000,87.419998,86.430000,87.330002,87.330002,4790000
1966-05-31,87.330002,87.650002,85.800003,86.129997,86.129997,5770000
1966-06-01,86.129997,86.650002,85.279999,86.099998,86.099998,5290000
1966-06-02,86.099998,86.849998,85.550003,85.959999,85.959999,5080000
1966-06-03,85.959999,86.550003,85.430000,86.059998,86.059998,4430000
1966-06-06,86.059998,86.279999,85.029999,85.419998,85.419998,4260000
1966-06-07,85.419998,85.540001,84.250000,84.830002,84.830002,5040000
1966-06-08,84.830002,85.430000,84.309998,84.930000,84.930000,4580000
1966-06-09,84.930000,85.980003,84.559998,85.500000,85.500000,5810000
1966-06-10,85.500000,86.970001,85.320000,86.440002,86.440002,8240000
1966-06-13,86.440002,87.589996,86.199997,86.830002,86.830002,7600000
1966-06-14,86.830002,87.570000,86.019997,87.070000,87.070000,7600000
1966-06-15,87.070000,87.739998,86.330002,86.730003,86.730003,8520000
1966-06-16,86.730003,87.180000,85.879997,86.470001,86.470001,6870000
1966-06-17,86.470001,87.110001,85.889999,86.510002,86.510002,6580000
1966-06-20,86.510002,87.029999,85.839996,86.480003,86.480003,5940000
1966-06-21,86.480003,87.279999,86.070000,86.709999,86.709999,6860000
1966-06-22,86.709999,87.379997,86.150002,86.849998,86.849998,7800000
1966-06-23,86.849998,87.730003,86.110001,86.500000,86.500000,7930000
1966-06-24,86.500000,87.309998,85.680000,86.580002,86.580002,7140000
1966-06-27,86.580002,87.309998,85.769997,86.080002,86.080002,5330000
1966-06-28,86.080002,86.430000,85.000000,85.669998,85.669998,6280000
1966-06-29,85.669998,85.980003,84.519997,84.860001,84.860001,6020000
1966-06-30,84.860001,85.370003,83.750000,84.739998,84.739998,7250000
1966-07-01,84.739998,86.080002,84.739998,85.610001,85.610001,5200000
1966-07-05,85.610001,86.410004,85.089996,85.820000,85.820000,4610000
1966-07-06,85.820000,87.379997,85.570000,87.059998,87.059998,6860000
1966-07-07,87.059998,88.019997,86.669998,87.379997,87.379997,7200000
1966-07-08,87.379997,88.040001,86.849998,87.610001,87.610001,6100000
1966-07-11,87.610001,88.190002,86.970001,87.449997,87.449997,6200000
1966-07-12,87.449997,87.779999,86.449997,86.879997,86.879997,5180000
1966-07-13,86.879997,87.059998,85.830002,86.300003,86.300003,5580000
1966-07-14,86.300003,87.339996,85.849998,86.820000,86.820000,5950000
1966-07-15,86.820000,87.680000,86.440002,87.080002,87.080002,6090000
1966-07-18,87.080002,87.589996,86.419998,86.989998,86.989998,5110000
1966-07-19,86.989998,87.169998,85.750000,86.330002,86.330002,5960000
1966-07-20,86.330002,86.639999,85.260002,85.510002,85.510002,5470000
1966-07-21,85.510002,86.239998,84.769997,85.519997,85.519997,6200000
1966-07-22,85.519997,86.110001,84.930000,85.410004,85.410004,6540000
1966-07-25,85.410004,85.570000,83.559998,83.830002,83.830002,7050000
1966-07-26,83.830002,84.669998,83.050003,83.699997,83.699997,7610000
1966-07-27,83.699997,84.830002,83.500000,84.099998,84.099998,6070000
1966-07-28,84.099998,84.760002,83.440002,83.769997,83.769997,5680000
1966-07-29,83.769997,84.300003,83.099998,83.599998,83.599998,5150000
1966-08-01,83.500000,83.500000,81.980003,82.309998,82.309998,5880000
1966-08-02,82.309998,83.040001,81.769997,82.330002,82.330002,5710000
1966-08-03,82.330002,83.709999,82.300003,83.150002,83.150002,6220000
1966-08-04,83.150002,84.540001,83.070000,83.930000,83.930000,6880000
1966-08-05,83.930000,84.699997,83.430000,84.000000,84.000000,5500000
1966-08-08,84.000000,84.309998,82.970001,83.750000,83.750000,4900000
1966-08-09,83.750000,84.360001,83.040001,83.489998,83.489998,6270000
1966-08-10,83.489998,83.830002,82.690002,83.110001,83.110001,5290000
1966-08-11,83.110001,83.529999,82.339996,83.019997,83.019997,5700000
1966-08-12,83.019997,83.879997,82.570000,83.169998,83.169998,6230000
1966-08-15,83.169998,83.690002,82.389999,82.739998,82.739998,5680000
1966-08-16,82.709999,82.709999,81.260002,81.629997,81.629997,6130000
1966-08-17,81.629997,81.900002,80.529999,81.180000,81.180000,6630000
1966-08-18,81.180000,81.379997,79.599998,80.160004,80.160004,7000000
1966-08-19,80.160004,80.779999,79.239998,79.620003,79.620003,7070000
1966-08-22,79.620003,79.879997,77.580002,78.239998,78.239998,8690000
1966-08-23,78.239998,79.239998,77.050003,78.110001,78.110001,9830000
1966-08-24,78.110001,79.629997,77.919998,79.070000,79.070000,7050000
1966-08-25,79.070000,79.790001,77.800003,78.059998,78.059998,6760000
1966-08-26,77.849998,77.849998,76.099998,76.410004,76.410004,8190000
1966-08-29,76.239998,76.239998,74.180000,74.529999,74.529999,10900000
1966-08-30,74.529999,76.459999,73.910004,75.860001,75.860001,11230000
1966-08-31,75.980003,78.059998,75.980003,77.099998,77.099998,8690000
1966-09-01,77.099998,78.500000,76.660004,77.699997,77.699997,6250000
1966-09-02,77.699997,78.199997,76.269997,77.419998,77.419998,6080000
1966-09-06,77.419998,78.160004,76.550003,76.959999,76.959999,4350000
1966-09-07,76.959999,77.260002,75.769997,76.370003,76.370003,5530000
1966-09-08,76.370003,76.949997,75.029999,76.050003,76.050003,6660000
1966-09-09,76.050003,76.940002,75.430000,76.290001,76.290001,5280000
1966-09-12,76.470001,78.339996,76.470001,77.910004,77.910004,6780000
1966-09-13,77.910004,79.160004,77.660004,78.320000,78.320000,6870000
1966-09-14,78.320000,79.430000,77.730003,79.129997,79.129997,6250000
1966-09-15,79.129997,80.599998,78.870003,80.080002,80.080002,6140000
1966-09-16,80.080002,80.809998,79.330002,79.989998,79.989998,5150000
1966-09-19,79.989998,80.500000,79.019997,79.589996,79.589996,4920000
1966-09-20,79.589996,79.900002,78.570000,79.040001,79.040001,4560000
1966-09-21,79.040001,79.150002,77.519997,77.709999,77.709999,5360000
1966-09-22,77.709999,78.410004,76.809998,77.940002,77.940002,5760000
1966-09-23,77.940002,78.430000,77.150002,77.669998,77.669998,4560000
1966-09-26,77.669998,78.339996,76.879997,77.860001,77.860001,4960000
1966-09-27,77.860001,79.099998,77.559998,78.099998,78.099998,6300000
1966-09-28,78.099998,78.360001,76.699997,77.110001,77.110001,5990000
1966-09-29,77.110001,77.279999,75.849998,76.309998,76.309998,6110000
1966-09-30,76.309998,77.089996,75.449997,76.559998,76.559998,6170000
1966-10-03,76.559998,76.980003,74.709999,74.900002,74.900002,6490000
1966-10-04,74.900002,75.760002,73.910004,75.099998,75.099998,8910000
1966-10-05,75.099998,76.099998,74.309998,74.690002,74.690002,5880000
1966-10-06,74.690002,75.089996,73.470001,74.050003,74.050003,8110000
1966-10-07,74.050003,74.669998,72.769997,73.199997,73.199997,8140000
1966-10-10,73.199997,74.970001,72.279999,74.529999,74.529999,9630000
1966-10-11,74.529999,76.199997,74.220001,74.910004,74.910004,8430000
1966-10-12,74.910004,77.260002,74.370003,77.040001,77.040001,6910000
1966-10-13,77.040001,78.449997,76.220001,76.889999,76.889999,8680000
1966-10-14,76.889999,77.800003,76.010002,76.599998,76.599998,5610000
1966-10-17,76.599998,78.410004,76.480003,77.470001,77.470001,5570000
1966-10-18,77.470001,79.080002,77.349998,78.680000,78.680000,7180000
1966-10-19,78.680000,79.339996,77.540001,78.050003,78.050003,6460000
1966-10-20,78.050003,78.959999,77.260002,77.839996,77.839996,6840000
1966-10-21,77.839996,78.620003,77.160004,78.190002,78.190002,5690000
1966-10-24,78.190002,79.199997,77.730003,78.419998,78.419998,5780000
1966-10-25,78.419998,79.220001,77.559998,78.900002,78.900002,6190000
1966-10-26,78.900002,80.290001,78.699997,79.580002,79.580002,6760000
1966-10-27,79.580002,80.720001,79.279999,80.230003,80.230003,6670000
1966-10-28,80.230003,80.910004,79.489998,80.239998,80.239998,6420000
1966-10-31,80.239998,80.820000,79.339996,80.199997,80.199997,5860000
1966-11-01,80.199997,81.180000,79.790001,80.809998,80.809998,6480000
1966-11-02,80.809998,81.680000,80.300003,80.879997,80.879997,6740000
1966-11-03,80.879997,81.349998,79.980003,80.559998,80.559998,5860000
1966-11-04,80.559998,81.209999,79.639999,80.809998,80.809998,6530000
1966-11-07,80.809998,81.480003,80.160004,80.730003,80.730003,6120000
1966-11-09,80.730003,81.900002,80.459999,81.379997,81.379997,8390000
1966-11-10,81.379997,82.430000,81.000000,81.889999,81.889999,8870000
1966-11-11,81.889999,82.360001,81.269997,81.940002,81.940002,6690000
1966-11-14,81.940002,82.180000,80.809998,81.370003,81.370003,6540000
1966-11-15,81.370003,82.070000,80.820000,81.690002,81.690002,7190000
1966-11-16,81.690002,83.010002,81.550003,82.370003,82.370003,10350000
1966-11-17,82.370003,82.800003,81.239998,81.800003,81.800003,8900000
1966-11-18,81.800003,82.050003,80.790001,81.260002,81.260002,6900000
1966-11-21,81.089996,81.089996,79.510002,80.089996,80.089996,7450000
1966-11-22,80.089996,80.320000,78.889999,79.669998,79.669998,6430000
1966-11-23,79.669998,80.849998,79.389999,80.209999,80.209999,7350000
1966-11-25,80.209999,81.370003,79.830002,80.849998,80.849998,6810000
1966-11-28,80.849998,81.379997,79.959999,80.709999,80.709999,7630000
1966-11-29,80.709999,81.160004,79.940002,80.419998,80.419998,7320000
1966-11-30,80.419998,80.900002,79.620003,80.449997,80.449997,7230000
1966-12-01,80.449997,81.040001,79.660004,80.080002,80.080002,8480000
1966-12-02,80.080002,81.290001,79.489998,80.129997,80.129997,6230000
1966-12-05,80.129997,80.809998,79.599998,80.239998,80.239998,6470000
1966-12-06,80.239998,81.290001,79.949997,80.839996,80.839996,7670000
1966-12-07,80.839996,82.190002,80.589996,81.720001,81.720001,8980000
1966-12-08,81.720001,82.720001,81.339996,82.050003,82.050003,8370000
1966-12-09,82.050003,82.680000,81.330002,82.139999,82.139999,7650000
1966-12-12,82.139999,83.540001,81.940002,83.000000,83.000000,9530000
1966-12-13,83.000000,83.879997,82.279999,82.730003,82.730003,9650000
1966-12-14,82.730003,83.349998,81.970001,82.639999,82.639999,7470000
1966-12-15,82.639999,82.889999,81.199997,81.639999,81.639999,7150000
1966-12-16,81.639999,82.209999,80.940002,81.580002,81.580002,6980000
1966-12-19,81.580002,82.059998,80.559998,81.269997,81.269997,7340000
1966-12-20,81.269997,81.690002,80.309998,80.959999,80.959999,6830000
1966-12-21,80.959999,81.910004,80.419998,81.379997,81.379997,7690000
1966-12-22,81.379997,82.339996,81.000000,81.690002,81.690002,8560000
1966-12-23,81.690002,82.220001,80.970001,81.470001,81.470001,7350000
1966-12-27,81.470001,81.839996,80.550003,81.000000,81.000000,6280000
1966-12-28,81.000000,81.669998,80.290001,80.610001,80.610001,7160000
1966-12-29,80.610001,81.080002,79.839996,80.370003,80.370003,7900000
1966-12-30,80.370003,81.139999,79.660004,80.330002,80.330002,11330000
1967-01-03,80.330002,81.610001,79.589996,80.379997,80.379997,6100000
1967-01-04,80.379997,81.010002,79.430000,80.550003,80.550003,6150000
1967-01-05,80.550003,81.930000,80.500000,81.599998,81.599998,7320000
1967-01-06,81.599998,82.790001,81.320000,82.180000,82.180000,7830000
1967-01-09,82.180000,83.309998,81.779999,82.809998,82.809998,9180000
1967-01-10,82.809998,83.540001,82.220001,82.809998,82.809998,8120000
1967-01-11,82.809998,83.919998,81.370003,83.470001,83.470001,13230000
1967-01-12,83.470001,84.800003,83.110001,83.910004,83.910004,12830000
1967-01-13,83.910004,84.900002,83.099998,84.529999,84.529999,10000000
1967-01-16,84.529999,85.279999,83.730003,84.309998,84.309998,10280000
1967-01-17,84.309998,85.809998,84.029999,85.239998,85.239998,11590000
1967-01-18,85.239998,86.360001,84.900002,85.790001,85.790001,11390000
1967-01-19,85.790001,86.610001,85.169998,85.820000,85.820000,10230000
1967-01-20,85.820000,86.470001,85.070000,86.070000,86.070000,9530000
1967-01-23,86.070000,88.169998,85.639999,86.389999,86.389999,10830000
1967-01-24,86.389999,87.000000,85.290001,86.510002,86.510002,10430000
1967-01-25,86.510002,87.019997,85.470001,85.849998,85.849998,10260000
1967-01-26,85.849998,86.660004,84.870003,85.809998,85.809998,10630000
1967-01-27,85.809998,86.760002,85.339996,86.160004,86.160004,9690000
1967-01-30,86.160004,87.349998,85.839996,86.660004,86.660004,10250000
1967-01-31,86.660004,87.459999,86.059998,86.610001,86.610001,11540000
1967-02-01,86.610001,87.040001,85.680000,86.430000,86.430000,9580000
1967-02-02,86.430000,87.309998,85.870003,86.730003,86.730003,10720000
1967-02-03,86.730003,87.970001,86.510002,87.360001,87.360001,12010000
1967-02-06,87.360001,87.980003,86.610001,87.180000,87.180000,10680000
1967-02-07,87.180000,87.519997,86.480003,86.949997,86.949997,6400000
1967-02-08,86.949997,88.250000,86.639999,87.720001,87.720001,11220000
1967-02-09,87.720001,88.570000,86.989998,87.360001,87.360001,10970000
1967-02-10,87.360001,88.190002,86.790001,87.629997,87.629997,8850000
1967-02-13,87.629997,88.190002,86.949997,87.580002,87.580002,7570000
1967-02-14,87.580002,88.739998,87.150002,88.169998,88.169998,9760000
1967-02-15,88.169998,89.000000,87.620003,88.269997,88.269997,10480000
1967-02-16,88.269997,88.800003,87.430000,87.860001,87.860001,8490000
1967-02-17,87.860001,88.400002,87.250000,87.889999,87.889999,8530000
1967-02-20,87.889999,88.129997,86.650002,87.400002,87.400002,8640000
1967-02-21,87.400002,88.010002,86.800003,87.339996,87.339996,9030000
1967-02-23,87.339996,88.000000,86.639999,87.449997,87.449997,10010000
1967-02-24,87.449997,88.160004,86.760002,87.410004,87.410004,9830000
1967-02-27,87.410004,87.610001,85.680000,86.459999,86.459999,10210000
1967-02-28,86.459999,87.260002,85.610001,86.779999,86.779999,9970000
1967-03-01,86.779999,88.360001,86.669998,87.680000,87.680000,11510000
1967-03-02,87.680000,88.849998,87.389999,88.160004,88.160004,11900000
1967-03-03,88.160004,89.000000,87.510002,88.290001,88.290001,11100000
1967-03-06,88.290001,89.080002,87.459999,88.099998,88.099998,10400000
1967-03-07,88.099998,88.739998,87.339996,88.160004,88.160004,9810000
1967-03-08,88.160004,89.099998,87.690002,88.269997,88.269997,11070000
1967-03-09,88.269997,89.040001,87.699997,88.529999,88.529999,10480000
1967-03-10,88.529999,90.370003,88.459999,88.889999,88.889999,14900000
1967-03-13,88.889999,89.410004,87.930000,88.430000,88.430000,9910000
1967-03-14,88.430000,89.070000,87.580002,88.349998,88.349998,10260000
1967-03-15,88.349998,89.599998,88.000000,89.190002,89.190002,10830000
1967-03-16,89.190002,90.660004,89.089996,90.089996,90.089996,12170000
1967-03-17,90.089996,90.839996,89.389999,90.250000,90.250000,10020000
1967-03-20,90.250000,90.870003,89.349998,90.199997,90.199997,9040000
1967-03-21,90.199997,91.050003,89.519997,90.000000,90.000000,9820000
1967-03-22,90.000000,90.699997,89.169998,90.250000,90.250000,8820000
1967-03-23,90.250000,91.510002,90.040001,90.940002,90.940002,9500000
1967-03-27,90.940002,91.720001,90.190002,90.870003,90.870003,9260000
1967-03-28,90.870003,91.620003,90.230003,90.910004,90.910004,8940000
1967-03-29,90.910004,91.449997,90.169998,90.730003,90.730003,8430000
1967-03-30,90.730003,91.320000,90.059998,90.699997,90.699997,8340000
1967-03-31,90.699997,91.150002,89.750000,90.199997,90.199997,8130000
1967-04-03,90.199997,90.370003,88.760002,89.239998,89.239998,8530000
1967-04-04,89.239998,89.930000,88.449997,89.220001,89.220001,8750000
1967-04-05,89.220001,90.309998,88.919998,89.790001,89.790001,8810000
1967-04-06,89.790001,90.739998,89.440002,89.940002,89.940002,9470000
1967-04-07,89.940002,90.599998,88.959999,89.360001,89.360001,9090000
1967-04-10,89.320000,89.320000,87.860001,88.239998,88.239998,8110000
1967-04-11,88.239998,89.339996,87.919998,88.879997,88.879997,7710000
1967-04-12,88.879997,89.540001,88.360001,88.779999,88.779999,7750000
1967-04-13,88.779999,89.860001,88.489998,89.459999,89.459999,7610000
1967-04-14,89.459999,91.080002,89.260002,90.430000,90.430000,8810000
1967-04-17,90.430000,91.779999,90.180000,91.070000,91.070000,9070000
1967-04-18,91.070000,92.309998,90.699997,91.860001,91.860001,10500000
1967-04-19,91.860001,92.730003,91.250000,91.940002,91.940002,10860000
1967-04-20,91.940002,92.610001,91.209999,92.110001,92.110001,9690000
1967-04-21,92.110001,92.900002,91.480003,92.300003,92.300003,10210000
1967-04-24,92.300003,93.449997,91.779999,92.620003,92.620003,10250000
1967-04-25,92.620003,93.570000,92.010002,93.110001,93.110001,10420000
1967-04-26,93.110001,93.989998,92.440002,93.019997,93.019997,10560000
1967-04-27,93.019997,94.250000,92.410004,93.809998,93.809998,10250000
1967-04-28,93.809998,94.769997,93.330002,94.010002,94.010002,11200000
1967-05-01,94.010002,94.599998,93.080002,93.839996,93.839996,9410000
1967-05-02,93.839996,94.419998,93.059998,93.669998,93.669998,10260000
1967-05-03,93.669998,94.480003,92.940002,93.910004,93.910004,11550000
1967-05-04,93.910004,94.919998,93.410004,94.320000,94.320000,12850000
1967-05-05,94.320000,95.139999,93.639999,94.440002,94.440002,10630000
1967-05-08,94.440002,95.220001,93.709999,94.580002,94.580002,10330000
1967-05-09,94.580002,95.250000,93.279999,93.599998,93.599998,10830000
1967-05-10,93.599998,94.040001,92.510002,93.349998,93.349998,10410000
1967-05-11,93.349998,94.370003,92.900002,93.750000,93.750000,10320000
1967-05-12,93.750000,94.449997,92.940002,93.480003,93.480003,10470000
1967-05-15,93.480003,93.750000,92.269997,92.709999,92.709999,8320000
1967-05-16,92.709999,93.849998,92.190002,93.139999,93.139999,10700000
1967-05-17,93.139999,93.750000,92.339996,92.779999,92.779999,9560000
1967-05-18,92.779999,93.300003,91.980003,92.529999,92.529999,10290000
1967-05-19,92.529999,92.860001,91.400002,92.070000,92.070000,10560000
1967-05-22,92.070000,92.400002,90.830002,91.669998,91.669998,9600000
1967-05-23,91.669998,92.070000,90.580002,91.230003,91.230003,9810000
1967-05-24,91.230003,91.360001,89.680000,90.180000,90.180000,10290000
1967-05-25,90.180000,91.839996,90.040001,91.190002,91.190002,8960000
1967-05-26,91.190002,91.699997,90.339996,90.980003,90.980003,7810000
1967-05-29,90.980003,91.220001,89.919998,90.489998,90.489998,6590000
1967-05-31,90.389999,90.389999,88.709999,89.080002,89.080002,8870000
1967-06-01,89.080002,90.760002,88.809998,90.230003,90.230003,9040000
1967-06-02,90.230003,90.900002,89.269997,89.790001,89.790001,8070000
1967-06-05,89.559998,89.559998,87.190002,88.430000,88.430000,11110000
1967-06-06,88.480003,90.589996,88.480003,90.230003,90.230003,9230000
1967-06-07,90.230003,91.750000,89.919998,90.910004,90.910004,10170000
1967-06-08,90.910004,91.779999,90.239998,91.400002,91.400002,8300000
1967-06-09,91.400002,92.260002,90.769997,91.559998,91.559998,9650000
1967-06-12,91.559998,92.660004,91.120003,92.040001,92.040001,10230000
1967-06-13,92.040001,93.269997,91.650002,92.620003,92.620003,11570000
1967-06-14,92.620003,93.209999,91.809998,92.400002,92.400002,10960000
1967-06-15,92.400002,93.260002,91.760002,92.489998,92.489998,11240000
1967-06-16,92.489998,93.279999,91.980003,92.540001,92.540001,10740000
1967-06-19,92.510002,92.510002,92.510002,92.510002,92.510002,8570000
1967-06-20,92.480003,92.480003,92.480003,92.480003,92.480003,10350000
1967-06-21,92.199997,92.199997,92.199997,92.199997,92.199997,9760000
1967-06-22,91.970001,91.970001,91.970001,91.970001,91.970001,9550000
1967-06-23,92.000000,92.000000,92.000000,92.000000,92.000000,9130000
1967-06-26,91.639999,91.639999,91.639999,91.639999,91.639999,9040000
1967-06-27,91.300003,91.300003,91.300003,91.300003,91.300003,8780000
1967-06-28,91.309998,91.309998,91.309998,91.309998,91.309998,9310000
1967-06-29,90.849998,90.849998,90.849998,90.849998,90.849998,9940000
1967-06-30,90.639999,90.639999,90.639999,90.639999,90.639999,7850000
1967-07-03,90.639999,91.320000,90.120003,90.910004,90.910004,6040000
1967-07-05,90.910004,91.910004,90.559998,91.360001,91.360001,9170000
1967-07-06,91.360001,92.029999,90.639999,91.320000,91.320000,10170000
1967-07-07,91.320000,92.279999,90.760002,91.690002,91.690002,11540000
1967-07-10,91.690002,92.800003,91.110001,92.050003,92.050003,12130000
1967-07-11,92.050003,93.160004,91.580002,92.480003,92.480003,12400000
1967-07-12,92.480003,93.099998,91.620003,92.400002,92.400002,11240000
1967-07-13,92.400002,93.169998,91.820000,92.419998,92.419998,10730000
1967-07-14,92.419998,93.349998,91.870003,92.739998,92.739998,10880000
1967-07-17,92.739998,93.529999,92.099998,92.750000,92.750000,10390000
1967-07-18,92.750000,94.050003,92.300003,93.500000,93.500000,12060000
1967-07-19,93.500000,94.400002,92.830002,93.650002,93.650002,12850000
1967-07-20,93.650002,94.489998,93.010002,93.849998,93.849998,11160000
1967-07-21,93.849998,94.919998,93.239998,94.040001,94.040001,11710000
1967-07-24,94.040001,94.680000,92.910004,93.730003,93.730003,9580000
1967-07-25,93.730003,94.559998,93.029999,93.239998,93.239998,9890000
1967-07-26,93.239998,94.709999,93.120003,94.059998,94.059998,11160000
1967-07-27,94.059998,95.190002,93.510002,94.349998,94.349998,12400000
1967-07-28,94.349998,95.230003,93.769997,94.489998,94.489998,10900000
1967-07-31,94.489998,95.510002,94.010002,94.750000,94.750000,10330000
1967-08-01,94.750000,95.839996,94.199997,95.370003,95.370003,12290000
1967-08-02,95.370003,96.639999,95.029999,95.779999,95.779999,13510000
1967-08-03,95.779999,96.360001,94.419998,95.660004,95.660004,13440000
1967-08-04,95.660004,96.540001,95.150002,95.830002,95.830002,11130000
1967-08-07,95.830002,96.430000,95.019997,95.580002,95.580002,10160000
1967-08-08,95.580002,96.279999,95.040001,95.690002,95.690002,8970000
1967-08-09,95.690002,96.470001,95.110001,95.779999,95.779999,10100000
1967-08-10,95.779999,96.669998,95.050003,95.529999,95.529999,9040000
1967-08-11,95.529999,95.980003,94.620003,95.150002,95.150002,8250000
1967-08-14,95.150002,95.400002,94.019997,94.639999,94.639999,7990000
1967-08-15,94.639999,95.540001,94.180000,94.769997,94.769997,8710000
1967-08-16,94.769997,95.150002,93.930000,94.550003,94.550003,8220000
1967-08-17,94.550003,95.330002,94.110001,94.629997,94.629997,8790000
1967-08-18,94.629997,95.400002,94.160004,94.779999,94.779999,8250000
1967-08-21,94.779999,95.220001,93.790001,94.250000,94.250000,8600000
1967-08-22,94.250000,94.720001,93.349998,93.739998,93.739998,7940000
1967-08-23,93.739998,94.150002,92.769997,93.610001,93.610001,8760000
1967-08-24,93.610001,94.279999,92.769997,93.089996,93.089996,7740000
1967-08-25,93.089996,93.379997,92.040001,92.699997,92.699997,7250000
1967-08-28,92.699997,93.309998,92.010002,92.639999,92.639999,6270000
1967-08-29,92.639999,93.580002,92.169998,92.879997,92.879997,6350000
1967-08-30,92.879997,93.669998,92.430000,93.070000,93.070000,7200000
1967-08-31,93.070000,94.190002,92.839996,93.639999,93.639999,8840000
1967-09-01,93.639999,94.209999,93.000000,93.680000,93.680000,7460000
1967-09-05,93.680000,94.699997,93.360001,94.209999,94.209999,8320000
1967-09-06,94.209999,95.059998,93.720001,94.389999,94.389999,9550000
1967-09-07,94.389999,94.949997,93.699997,94.330002,94.330002,8910000
1967-09-08,94.330002,95.040001,93.699997,94.360001,94.360001,9300000
1967-09-11,94.360001,95.260002,93.879997,94.540001,94.540001,9170000
1967-09-12,94.540001,95.480003,94.010002,94.989998,94.989998,9930000
1967-09-13,94.989998,96.620003,94.800003,95.989998,95.989998,12400000
1967-09-14,95.989998,97.400002,95.589996,96.199997,96.199997,12220000
1967-09-15,96.199997,96.940002,95.470001,96.269997,96.269997,10270000
1967-09-18,96.269997,97.309998,95.730003,96.529999,96.529999,11620000
1967-09-19,96.529999,97.349998,95.839996,96.169998,96.169998,11540000
1967-09-20,96.169998,96.839996,95.389999,96.129997,96.129997,10980000
1967-09-21,96.129997,97.500000,95.669998,96.750000,96.750000,11290000
1967-09-22,96.750000,97.610001,96.110001,97.000000,97.000000,11160000
1967-09-25,97.000000,98.309998,96.739998,97.589996,97.589996,10910000
1967-09-26,97.589996,98.199997,96.400002,96.760002,96.760002,10940000
1967-09-27,96.760002,97.540001,96.000000,96.790001,96.790001,8810000
1967-09-28,96.790001,97.589996,96.190002,96.790001,96.790001,10470000
1967-09-29,96.790001,97.370003,96.059998,96.709999,96.709999,9710000
1967-10-02,96.709999,97.250000,95.820000,96.320000,96.320000,9240000
1967-10-03,96.320000,97.230003,95.750000,96.650002,96.650002,10320000
1967-10-04,96.650002,97.470001,95.940002,96.430000,96.430000,11520000
1967-10-05,96.430000,97.250000,95.889999,96.669998,96.669998,8490000
1967-10-06,96.669998,97.830002,96.339996,97.260002,97.260002,9830000
1967-10-09,97.260002,98.250000,96.699997,97.510002,97.510002,11180000
1967-10-10,97.510002,98.150002,96.379997,96.839996,96.839996,12000000
1967-10-11,96.839996,97.339996,95.699997,96.370003,96.370003,11230000
1967-10-12,96.370003,96.699997,95.320000,95.750000,95.750000,7770000
1967-10-13,95.750000,96.690002,95.160004,96.000000,96.000000,9040000
1967-10-16,96.000000,96.550003,94.849998,95.250000,95.250000,9080000
1967-10-17,95.250000,95.919998,94.190002,95.000000,95.000000,10290000
1967-10-18,95.000000,95.820000,94.339996,95.250000,95.250000,10500000
1967-10-19,95.250000,96.459999,94.860001,95.430000,95.430000,11620000
1967-10-20,95.430000,96.120003,94.620003,95.379997,95.379997,9510000
1967-10-23,95.379997,95.690002,93.919998,94.959999,94.959999,9680000
1967-10-24,94.959999,95.980003,94.050003,94.419998,94.419998,11110000
1967-10-25,94.419998,95.180000,93.470001,94.519997,94.519997,10300000
1967-10-26,94.519997,95.559998,93.989998,94.940002,94.940002,9920000
1967-10-27,94.940002,95.790001,94.309998,94.959999,94.959999,9880000
1967-10-30,94.959999,95.669998,94.139999,94.790001,94.790001,10250000
1967-10-31,94.790001,95.250000,93.290001,93.300003,93.300003,12020000
1967-11-01,93.300003,94.209999,92.449997,92.709999,92.709999,10930000
1967-11-02,92.709999,93.690002,91.849998,92.339996,92.339996,10760000
1967-11-03,92.339996,92.900002,91.330002,91.779999,91.779999,8800000
1967-11-06,91.779999,92.230003,90.389999,91.480003,91.480003,10320000
1967-11-08,91.480003,93.070000,90.800003,91.139999,91.139999,12630000
1967-11-09,91.139999,92.250000,90.610001,91.589996,91.589996,8890000
1967-11-10,91.589996,92.839996,91.290001,92.209999,92.209999,9960000
1967-11-13,92.209999,93.230003,91.459999,91.970001,91.970001,10130000
1967-11-14,91.970001,92.489998,90.809998,91.389999,91.389999,10350000
1967-11-15,91.389999,92.250000,90.440002,91.760002,91.760002,10000000
1967-11-16,91.760002,93.279999,91.500000,92.599998,92.599998,10570000
1967-11-17,92.599998,93.620003,92.019997,92.820000,92.820000,10050000
1967-11-20,92.379997,92.379997,90.089996,91.650002,91.650002,12750000
1967-11-21,91.650002,93.709999,91.639999,93.099998,93.099998,12300000
1967-11-22,93.099998,94.410004,92.699997,93.650002,93.650002,12180000
1967-11-24,93.650002,94.459999,92.739998,93.900002,93.900002,9470000
1967-11-27,93.900002,94.800003,93.320000,94.169998,94.169998,10040000
1967-11-28,94.169998,95.080002,93.570000,94.489998,94.489998,11040000
1967-11-29,94.489998,95.510002,93.849998,94.470001,94.470001,11400000
1967-11-30,94.470001,94.940002,93.489998,94.000000,94.000000,8860000
1967-12-01,94.000000,94.949997,93.410004,94.500000,94.500000,9740000
1967-12-04,94.500000,95.680000,94.089996,95.099998,95.099998,11740000
1967-12-05,95.099998,96.269997,94.519997,95.230003,95.230003,12940000
1967-12-06,95.230003,96.160004,94.099998,95.639999,95.639999,11940000
1967-12-07,95.639999,96.669998,95.040001,95.529999,95.529999,12490000
1967-12-08,95.529999,96.250000,94.779999,95.419998,95.419998,10710000
1967-12-11,95.419998,95.989998,94.500000,95.120003,95.120003,10500000
1967-12-12,95.120003,95.779999,94.339996,95.010002,95.010002,10860000
1967-12-13,95.010002,96.000000,94.580002,95.339996,95.339996,12480000
1967-12-14,95.339996,96.349998,94.849998,95.470001,95.470001,12310000
1967-12-15,95.470001,96.199997,94.510002,95.029999,95.029999,11530000
1967-12-18,95.029999,95.879997,94.169998,94.769997,94.769997,10320000
1967-12-19,94.769997,95.410004,94.000000,94.629997,94.629997,10610000
1967-12-20,94.629997,95.750000,94.169998,95.150002,95.150002,11390000
1967-12-21,95.150002,96.250000,94.690002,95.379997,95.379997,11010000
1967-12-22,95.379997,96.110001,94.610001,95.199997,95.199997,9570000
1967-12-26,95.199997,96.019997,94.610001,95.260002,95.260002,9150000
1967-12-27,95.260002,96.419998,94.820000,95.910004,95.910004,12690000
1967-12-28,95.910004,96.650002,94.910004,95.889999,95.889999,12530000
1967-12-29,95.889999,96.900002,95.849998,96.470001,96.470001,14950000
1968-01-02,96.470001,97.330002,95.309998,96.110001,96.110001,11080000
1968-01-03,96.110001,96.949997,95.040001,95.669998,95.669998,12650000
1968-01-04,95.669998,96.230003,94.309998,95.360001,95.360001,13440000
1968-01-05,95.360001,96.660004,94.970001,95.940002,95.940002,11880000
1968-01-08,95.940002,97.400002,95.540001,96.620003,96.620003,14260000
1968-01-09,96.620003,97.839996,95.889999,96.500000,96.500000,13720000
1968-01-10,96.500000,97.260002,95.660004,96.519997,96.519997,11670000
1968-01-11,96.519997,97.820000,95.879997,96.620003,96.620003,13220000
1968-01-12,96.620003,97.440002,95.870003,96.720001,96.720001,13080000
1968-01-15,96.720001,97.459999,95.849998,96.419998,96.419998,12640000
1968-01-16,96.419998,96.910004,95.320000,95.820000,95.820000,12340000
1968-01-17,95.820000,96.410004,94.779999,95.639999,95.639999,12910000
1968-01-18,95.639999,96.660004,95.010002,95.559998,95.559998,13840000
1968-01-19,95.559998,96.220001,94.599998,95.239998,95.239998,11950000
1968-01-22,95.239998,95.400002,93.550003,94.029999,94.029999,10630000
1968-01-23,94.029999,94.660004,92.879997,93.660004,93.660004,11030000
1968-01-24,93.660004,94.120003,92.449997,93.169998,93.169998,10570000
1968-01-25,93.169998,94.110001,91.959999,93.300003,93.300003,12410000
1968-01-26,93.300003,94.339996,92.769997,93.449997,93.449997,9980000
1968-01-29,93.449997,94.379997,92.709999,93.349998,93.349998,9950000
1968-01-30,93.349998,93.709999,92.180000,92.889999,92.889999,10110000
1968-01-31,92.889999,93.260002,91.269997,92.239998,92.239998,9410000
1968-02-01,92.239998,93.139999,91.570000,92.559998,92.559998,10590000
1968-02-02,92.559998,93.440002,91.690002,92.269997,92.269997,10120000
1968-02-05,92.269997,92.720001,91.239998,91.870003,91.870003,8980000
1968-02-06,91.870003,92.519997,91.150002,91.900002,91.900002,8560000
1968-02-07,91.900002,92.739998,91.480003,92.059998,92.059998,8380000
1968-02-08,92.059998,92.400002,90.599998,90.900002,90.900002,9660000
1968-02-09,90.900002,91.000000,89.230003,89.860001,89.860001,11850000
1968-02-13,89.860001,90.459999,86.730003,89.070000,89.070000,10830000
1968-02-14,89.070000,90.599998,88.660004,90.139999,90.139999,11390000
1968-02-15,90.300003,90.300003,90.300003,90.300003,90.300003,9770000
1968-02-16,90.300003,90.620003,89.279999,89.959999,89.959999,9070000
1968-02-19,89.959999,90.870003,89.419998,90.309998,90.309998,7270000
1968-02-20,90.309998,91.339996,89.949997,91.239998,91.239998,8800000
1968-02-21,91.239998,91.870003,90.540001,91.239998,91.239998,9170000
1968-02-23,91.239998,91.800003,90.279999,90.889999,90.889999,8810000
1968-02-26,90.889999,91.080002,89.669998,90.180000,90.180000,7810000
1968-02-27,90.180000,90.910004,89.559998,90.529999,90.529999,7600000
1968-02-28,90.529999,91.190002,89.709999,90.080002,90.080002,8020000
1968-02-29,90.080002,90.239998,88.930000,89.360001,89.360001,7700000
1968-03-01,89.360001,89.820000,88.580002,89.110001,89.110001,8610000
1968-03-04,89.110001,89.330002,87.519997,87.919998,87.919998,10590000
1968-03-05,87.919998,88.720001,86.989998,87.720001,87.720001,11440000
1968-03-06,87.720001,89.760002,87.639999,89.260002,89.260002,9900000
1968-03-07,89.260002,89.980003,88.440002,89.099998,89.099998,8630000
1968-03-08,89.099998,89.570000,88.230003,89.029999,89.029999,7410000
1968-03-11,89.029999,90.559998,88.809998,90.129997,90.129997,9520000
1968-03-12,90.129997,90.779999,89.389999,90.230003,90.230003,9250000
1968-03-13,90.230003,90.709999,89.400002,90.029999,90.029999,8990000
1968-03-14,89.750000,89.750000,87.809998,88.320000,88.320000,11640000
1968-03-15,88.320000,89.750000,87.610001,89.099998,89.099998,11210000
1968-03-18,89.110001,91.089996,89.110001,89.589996,89.589996,10800000
1968-03-19,89.589996,90.050003,88.610001,88.989998,88.989998,7410000
1968-03-20,88.989998,89.650002,88.480003,88.980003,88.980003,7390000
1968-03-21,88.980003,89.480003,88.050003,88.330002,88.330002,8580000
1968-03-22,88.330002,89.139999,87.500000,88.419998,88.419998,9900000
1968-03-25,88.419998,88.879997,87.650002,88.330002,88.330002,6700000
1968-03-26,88.330002,89.500000,88.099998,88.930000,88.930000,8670000
1968-03-27,88.930000,90.199997,88.879997,89.660004,89.660004,8970000
1968-03-28,89.660004,90.400002,89.050003,89.570000,89.570000,8000000
1968-03-29,89.570000,90.919998,89.209999,90.199997,90.199997,9000000
1968-04-01,91.110001,93.550003,91.110001,92.480003,92.480003,17730000
1968-04-02,92.480003,93.440002,91.389999,92.639999,92.639999,14520000
1968-04-03,92.639999,95.129997,92.239998,93.470001,93.470001,19290000
1968-04-04,93.470001,94.589996,92.629997,93.839996,93.839996,14340000
1968-04-05,93.839996,94.510002,92.669998,93.290001,93.290001,12570000
1968-04-08,93.290001,95.449997,93.110001,94.949997,94.949997,13010000
1968-04-10,94.949997,97.110001,94.739998,95.669998,95.669998,20410000
1968-04-11,95.669998,96.930000,94.809998,96.529999,96.529999,14230000
1968-04-15,96.529999,97.360001,95.330002,96.589996,96.589996,14220000
1968-04-16,96.589996,97.540001,95.720001,96.620003,96.620003,15680000
1968-04-17,96.620003,97.400002,95.760002,96.809998,96.809998,14090000
1968-04-18,96.809998,97.889999,96.120003,97.080002,97.080002,15890000
1968-04-19,97.080002,97.080002,95.150002,95.849998,95.849998,14560000
1968-04-22,95.849998,96.070000,94.220001,95.320000,95.320000,11720000
1968-04-23,95.680000,97.480003,95.680000,96.620003,96.620003,14010000
1968-04-24,96.620003,97.809998,95.980003,96.919998,96.919998,14810000
1968-04-25,96.919998,97.480003,95.680000,96.620003,96.620003,14430000
1968-04-26,96.620003,97.830002,96.220001,97.209999,97.209999,13500000
1968-04-29,97.209999,98.610001,96.809998,97.970001,97.970001,12030000
1968-04-30,97.970001,98.169998,96.580002,97.459999,97.459999,14380000
1968-05-01,97.459999,98.610001,96.839996,97.970001,97.970001,14440000
1968-05-02,97.970001,99.180000,97.529999,98.589996,98.589996,14260000
1968-05-03,98.589996,100.190002,97.980003,98.660004,98.660004,17990000
1968-05-06,98.660004,99.110001,97.269997,98.349998,98.349998,12160000
1968-05-07,98.349998,99.589996,97.860001,98.900002,98.900002,13920000
1968-05-08,98.900002,99.739998,98.250000,98.910004,98.910004,13120000
1968-05-09,98.910004,99.470001,97.680000,98.389999,98.389999,12890000
1968-05-10,98.389999,99.300003,97.760002,98.500000,98.500000,11700000
1968-05-13,98.500000,99.099998,97.519997,98.190002,98.190002,11860000
1968-05-14,98.190002,98.849998,97.330002,98.120003,98.120003,13160000
1968-05-15,98.120003,98.790001,97.320000,98.070000,98.070000,13180000
1968-05-16,98.070000,98.690002,97.050003,97.599998,97.599998,13030000
1968-05-17,97.599998,97.809998,96.110001,96.900002,96.900002,11830000
1968-05-20,96.900002,97.410004,95.800003,96.449997,96.449997,11180000
1968-05-21,96.449997,97.519997,95.919998,96.930000,96.930000,13160000
1968-05-22,96.930000,98.169998,96.470001,97.180000,97.180000,14200000
1968-05-23,97.180000,97.790001,96.379997,96.970001,96.970001,12840000
1968-05-24,96.970001,97.730003,96.209999,97.150002,97.150002,13300000
1968-05-27,97.150002,97.809998,96.290001,96.989998,96.989998,12720000
1968-05-28,96.989998,98.199997,96.410004,97.620003,97.620003,13850000
1968-05-29,97.620003,98.739998,97.010002,97.919998,97.919998,14100000
1968-05-31,97.919998,99.400002,97.660004,98.680000,98.680000,13090000
1968-06-03,98.720001,100.620003,98.720001,99.989998,99.989998,14970000
1968-06-04,99.989998,101.260002,99.320000,100.379997,100.379997,18030000
1968-06-05,100.379997,101.129997,99.260002,99.889999,99.889999,15590000
1968-06-06,99.889999,101.589996,99.500000,100.650002,100.650002,16130000
1968-06-07,100.650002,101.889999,100.239998,101.269997,101.269997,17320000
1968-06-10,101.269997,102.250000,100.419998,101.410004,101.410004,14640000
1968-06-11,101.410004,102.400002,100.739998,101.660004,101.660004,15700000
1968-06-13,101.660004,102.839996,100.550003,101.250000,101.250000,21350000
1968-06-14,101.250000,101.820000,99.980003,101.129997,101.129997,14690000
1968-06-17,101.129997,101.709999,99.430000,100.129997,100.129997,12570000
1968-06-18,100.129997,101.089996,99.430000,99.989998,99.989998,13630000
1968-06-20,99.989998,101.599998,99.519997,101.510002,101.510002,16290000
1968-06-21,101.510002,101.589996,99.800003,100.660004,100.660004,13450000
1968-06-24,100.660004,101.480003,99.660004,100.389999,100.389999,12320000
1968-06-25,100.389999,101.099998,99.279999,100.080002,100.080002,13200000
1968-06-27,100.080002,101.010002,99.110001,99.980003,99.980003,15370000
1968-06-28,99.980003,100.629997,98.910004,99.580002,99.580002,12040000
1968-07-01,99.580002,100.330002,98.769997,99.400002,99.400002,11280000
1968-07-02,99.400002,100.599998,98.599998,99.739998,99.739998,13350000
1968-07-03,99.739998,101.360001,99.599998,100.910004,100.910004,14390000
1968-07-08,100.910004,102.760002,100.720001,101.940002,101.940002,16860000
1968-07-09,101.940002,102.930000,101.190002,102.230003,102.230003,16540000
1968-07-11,102.230003,103.669998,101.410004,102.389999,102.389999,20290000
1968-07-12,102.389999,103.239998,101.389999,102.339996,102.339996,14810000
1968-07-15,102.339996,103.150002,101.440002,102.260002,102.260002,13390000
1968-07-16,102.260002,102.720001,100.970001,101.699997,101.699997,13380000
1968-07-18,101.699997,102.650002,100.489998,101.440002,101.440002,17420000
1968-07-19,101.440002,101.820000,99.800003,100.459999,100.459999,14620000
1968-07-22,100.459999,100.879997,98.510002,99.330002,99.330002,13530000
1968-07-23,99.330002,99.930000,97.889999,99.209999,99.209999,13570000
1968-07-25,99.209999,100.070000,97.430000,97.940002,97.940002,16140000
1968-07-26,97.940002,99.139999,97.220001,98.339996,98.339996,11690000
1968-07-29,98.339996,98.779999,96.889999,97.650002,97.650002,10940000
1968-07-30,97.650002,98.620003,96.839996,97.739998,97.739998,10250000
1968-08-01,97.739998,98.820000,96.779999,97.279999,97.279999,14380000
1968-08-02,97.279999,97.470001,95.790001,96.629997,96.629997,9860000
1968-08-05,96.629997,97.510002,95.949997,96.849998,96.849998,8850000
1968-08-06,96.849998,97.820000,96.419998,97.250000,97.250000,9620000
1968-08-08,97.250000,98.320000,96.580002,97.040001,97.040001,12920000
1968-08-09,97.040001,97.559998,96.110001,97.010002,97.010002,8390000
1968-08-12,97.010002,98.489998,96.720001,98.010002,98.010002,10420000
1968-08-13,98.010002,99.199997,97.680000,98.529999,98.529999,12730000
1968-08-15,98.529999,99.360001,97.480003,98.070000,98.070000,12710000
1968-08-16,98.070000,99.209999,97.620003,98.680000,98.680000,9940000
1968-08-19,98.680000,99.639999,98.160004,99.000000,99.000000,9900000
1968-08-20,99.000000,99.650002,98.080002,98.959999,98.959999,10640000
1968-08-22,98.959999,99.580002,97.709999,98.699997,98.699997,15140000
1968-08-23,98.699997,99.570000,97.709999,98.690002,98.690002,9890000
1968-08-26,98.690002,99.669998,98.290001,98.940002,98.940002,9740000
1968-08-27,98.940002,99.610001,98.160004,98.809998,98.809998,9710000
1968-08-29,98.809998,99.489998,97.900002,98.739998,98.739998,10940000
1968-08-30,98.739998,99.519997,98.199997,98.860001,98.860001,8190000
1968-09-03,98.860001,99.889999,98.309998,99.320000,99.320000,8620000
1968-09-04,99.320000,100.489998,98.949997,100.019997,100.019997,10040000
1968-09-05,100.019997,101.339996,99.629997,100.739998,100.739998,12980000
1968-09-06,100.739998,101.879997,100.230003,101.199997,101.199997,13180000
1968-09-09,101.199997,102.089996,100.470001,101.230003,101.230003,11890000
1968-09-10,101.230003,101.809998,100.120003,100.730003,100.730003,11430000
1968-09-12,100.730003,101.400002,99.699997,100.519997,100.519997,14630000
1968-09-13,100.519997,101.529999,99.889999,100.860001,100.860001,13070000
1968-09-16,100.860001,102.010002,100.330002,101.239998,101.239998,13260000
1968-09-17,101.239998,102.180000,100.639999,101.500000,101.500000,13920000
1968-09-19,101.500000,102.529999,100.839996,101.589996,101.589996,17910000
1968-09-20,101.589996,102.370003,100.809998,101.660004,101.660004,14190000
1968-09-23,101.660004,102.820000,101.199997,102.239998,102.239998,11550000
1968-09-24,102.239998,103.209999,101.589996,102.589996,102.589996,15210000
1968-09-26,102.589996,103.629997,101.589996,102.360001,102.360001,18950000
1968-09-27,102.360001,103.070000,101.360001,102.309998,102.309998,13860000
1968-09-30,102.309998,103.290001,101.709999,102.669998,102.669998,13610000
1968-10-01,102.669998,103.580002,101.800003,102.860001,102.860001,15560000
1968-10-03,102.860001,104.129997,102.339996,103.220001,103.220001,21110000
1968-10-04,103.220001,104.349998,102.650002,103.709999,103.709999,15350000
1968-10-07,103.709999,104.400002,102.930000,103.699997,103.699997,12420000
1968-10-08,103.699997,104.449997,102.839996,103.739998,103.739998,14000000
1968-10-10,103.739998,104.300003,102.610001,103.290001,103.290001,17000000
1968-10-11,103.290001,103.900002,102.389999,103.180000,103.180000,12650000
1968-10-14,103.180000,104.029999,102.480003,103.320000,103.320000,11980000
1968-10-15,103.320000,104.250000,102.660004,103.529999,103.529999,13410000
1968-10-17,103.809998,105.010002,103.809998,104.010002,104.010002,21060000
1968-10-18,104.010002,105.339996,103.540001,104.820000,104.820000,15130000
1968-10-21,104.820000,105.779999,104.089996,104.989998,104.989998,14380000
1968-10-22,104.989998,105.480003,103.839996,104.570000,104.570000,13970000
1968-10-24,104.570000,105.150002,103.150002,103.839996,103.839996,18300000
1968-10-25,103.839996,104.809998,103.139999,104.199997,104.199997,14150000
1968-10-28,104.199997,104.889999,103.160004,103.900002,103.900002,11740000
1968-10-29,103.900002,104.500000,102.650002,103.300003,103.300003,12340000
1968-10-31,103.300003,104.570000,102.430000,103.410004,103.410004,17650000
1968-11-01,103.410004,104.300003,102.360001,103.059998,103.059998,14480000
1968-11-04,103.059998,103.690002,101.849998,103.099998,103.099998,10930000
1968-11-06,103.099998,104.410004,102.449997,103.269997,103.269997,12640000
1968-11-07,103.269997,104.470001,102.309998,103.500000,103.500000,11660000
1968-11-08,103.500000,104.589996,102.959999,103.949997,103.949997,14250000
1968-11-12,103.949997,105.279999,103.510002,104.620003,104.620003,17250000
1968-11-13,104.620003,105.760002,104.080002,105.129997,105.129997,15660000
1968-11-14,105.129997,106.010002,104.339996,105.199997,105.199997,14900000
1968-11-15,105.199997,106.440002,104.610001,105.779999,105.779999,15040000
1968-11-18,105.779999,106.739998,105.050003,105.919998,105.919998,14390000
1968-11-19,105.919998,106.839996,105.059998,106.139999,106.139999,15120000
1968-11-21,106.139999,106.769997,104.849998,105.970001,105.970001,18320000
1968-11-22,105.970001,106.889999,105.209999,106.300003,106.300003,15420000
1968-11-25,106.300003,107.290001,105.470001,106.480003,106.480003,14490000
1968-11-26,106.480003,107.930000,106.110001,107.260002,107.260002,16360000
1968-11-27,107.260002,108.550003,106.589996,107.760002,107.760002,16550000
1968-11-29,107.760002,109.089996,107.320000,108.370003,108.370003,14390000
1968-12-02,108.370003,109.370003,107.150002,108.120003,108.120003,15390000
1968-12-03,108.120003,108.739998,107.019997,108.019997,108.019997,15460000
1968-12-05,108.019997,108.900002,106.709999,107.669998,107.669998,19330000
1968-12-06,107.669998,108.910004,106.849998,107.930000,107.930000,15320000
1968-12-09,107.930000,108.769997,106.889999,107.660004,107.660004,15800000
1968-12-10,107.660004,108.330002,106.680000,107.389999,107.389999,14500000
1968-12-12,107.389999,108.430000,106.330002,107.320000,107.320000,18160000
1968-12-13,107.320000,108.500000,106.559998,107.580002,107.580002,16740000
1968-12-16,107.580002,108.400002,106.400002,107.099998,107.099998,15950000
1968-12-17,107.099998,107.650002,105.860001,106.660004,106.660004,14700000
1968-12-19,106.660004,107.669998,105.099998,106.970001,106.970001,19630000
1968-12-20,106.970001,107.980003,105.730003,106.339996,106.339996,15910000
1968-12-23,106.339996,106.680000,104.610001,105.209999,105.209999,12970000
1968-12-24,105.209999,105.949997,104.370003,105.040001,105.040001,11540000
1968-12-26,105.040001,106.029999,104.290001,105.150002,105.150002,9670000
1968-12-27,105.150002,105.870003,104.199997,104.739998,104.739998,11200000
1968-12-30,104.739998,104.989998,103.089996,103.800003,103.800003,12080000
1968-12-31,103.800003,104.610001,102.980003,103.860001,103.860001,13130000
1969-01-02,103.860001,104.849998,103.209999,103.930000,103.930000,9800000
1969-01-03,103.930000,104.870003,103.169998,103.989998,103.989998,12750000
1969-01-06,103.989998,104.360001,101.940002,102.470001,102.470001,12720000
1969-01-07,102.470001,102.680000,100.150002,101.220001,101.220001,15740000
1969-01-08,101.220001,102.120003,100.139999,100.800003,100.800003,13840000
1969-01-09,100.800003,102.089996,100.349998,101.220001,101.220001,12100000
1969-01-10,101.220001,102.139999,100.320000,100.930000,100.930000,12680000
1969-01-13,100.930000,101.349998,96.629997,100.440002,100.440002,11160000
1969-01-14,100.440002,101.629997,99.040001,101.129997,101.129997,10700000
1969-01-15,101.129997,102.480003,100.779999,101.620003,101.620003,11810000
1969-01-16,101.620003,103.250000,101.269997,102.180000,102.180000,13120000
1969-01-17,102.180000,103.059998,101.320000,102.029999,102.029999,11590000
1969-01-20,102.029999,102.599998,101.000000,101.690002,101.690002,10950000
1969-01-21,101.690002,102.400002,100.879997,101.629997,101.629997,10910000
1969-01-22,101.629997,102.550003,101.059998,101.980003,101.980003,11480000
1969-01-23,101.980003,103.209999,101.570000,102.430000,102.430000,13140000
1969-01-24,102.430000,103.230003,101.709999,102.379997,102.379997,12520000
1969-01-27,102.379997,103.150002,101.639999,102.400002,102.400002,11020000
1969-01-28,102.400002,103.300003,101.559998,102.410004,102.410004,12070000
1969-01-29,102.410004,103.309998,101.690002,102.510002,102.510002,11470000
1969-01-30,102.510002,103.330002,101.730003,102.550003,102.550003,13010000
1969-01-31,102.550003,103.639999,102.080002,103.010002,103.010002,12020000
1969-02-03,103.010002,103.750000,102.040001,102.889999,102.889999,12510000
1969-02-04,102.889999,103.589996,102.150002,102.919998,102.919998,12550000
1969-02-05,102.919998,103.839996,102.260002,103.199997,103.199997,13750000
1969-02-06,103.199997,104.300003,102.550003,103.540001,103.540001,12570000
1969-02-07,103.540001,104.220001,102.500000,103.529999,103.529999,12780000
1969-02-11,103.529999,104.610001,102.959999,103.650002,103.650002,12320000
1969-02-12,103.650002,104.339996,102.980003,103.629997,103.629997,11530000
1969-02-13,103.629997,104.360001,102.860001,103.709999,103.709999,12010000
1969-02-14,103.709999,104.370003,102.879997,103.610001,103.610001,11460000
1969-02-17,103.610001,104.029999,102.040001,102.440002,102.440002,11670000
1969-02-18,102.269997,102.269997,100.580002,101.400002,101.400002,12490000
1969-02-19,101.400002,102.070000,100.300003,100.650002,100.650002,10390000
1969-02-20,100.650002,101.029999,99.290001,99.790001,99.790001,10990000
1969-02-24,99.790001,100.070000,98.089996,98.599998,98.599998,12730000
1969-02-25,98.599998,99.650002,97.500000,97.980003,97.980003,9540000
1969-02-26,97.980003,99.099998,97.360001,98.449997,98.449997,9540000
1969-02-27,98.449997,99.000000,97.500000,98.139999,98.139999,9670000
1969-02-28,98.139999,99.019997,97.529999,98.129997,98.129997,8990000
1969-03-03,98.129997,99.080002,97.610001,98.379997,98.379997,8260000
1969-03-04,98.379997,99.760002,98.169998,99.320000,99.320000,9320000
1969-03-05,99.320000,100.480003,98.949997,99.709999,99.709999,11370000
1969-03-06,99.709999,99.930000,98.110001,98.699997,98.699997,9670000
1969-03-07,98.699997,99.129997,97.320000,98.650002,98.650002,10830000
1969-03-10,98.650002,99.470001,97.870003,98.989998,98.989998,8920000
1969-03-11,98.989998,100.139999,98.580002,99.320000,99.320000,9870000
1969-03-12,99.320000,99.870003,98.349998,99.050003,99.050003,8720000
1969-03-13,99.050003,99.349998,97.820000,98.389999,98.389999,10030000
1969-03-14,98.389999,98.699997,97.400002,98.000000,98.000000,8640000
1969-03-17,98.000000,98.709999,97.059998,98.250000,98.250000,9150000
1969-03-18,98.250000,99.410004,97.830002,98.489998,98.489998,11210000
1969-03-19,98.489998,99.699997,98.029999,99.209999,99.209999,9740000
1969-03-20,99.209999,100.389999,98.900002,99.839996,99.839996,10260000
1969-03-21,99.839996,100.370003,98.879997,99.629997,99.629997,9830000
1969-03-24,99.629997,100.160004,98.849998,99.500000,99.500000,8110000
1969-03-25,99.500000,100.300003,98.879997,99.660004,99.660004,9820000
1969-03-26,99.660004,100.860001,99.239998,100.389999,100.389999,11030000
1969-03-27,100.389999,101.809998,100.029999,101.099998,101.099998,11900000
1969-03-28,101.099998,102.349998,100.730003,101.510002,101.510002,12430000
1969-04-01,101.510002,102.449997,100.839996,101.419998,101.419998,12360000
1969-04-02,101.419998,101.650002,100.610001,100.779999,100.779999,10110000
1969-04-03,100.779999,101.300003,99.870003,100.680000,100.680000,10300000
1969-04-07,100.629997,100.629997,99.080002,99.889999,99.889999,9430000
1969-04-08,99.889999,101.269997,99.349998,100.139999,100.139999,9360000
1969-04-09,100.139999,101.440002,99.879997,101.019997,101.019997,12530000
1969-04-10,101.019997,102.220001,100.730003,101.550003,101.550003,12200000
1969-04-11,101.550003,102.279999,100.970001,101.650002,101.650002,10650000
1969-04-14,101.650002,102.400002,101.019997,101.570000,101.570000,8990000
1969-04-15,101.570000,102.150002,100.760002,101.529999,101.529999,9610000
1969-04-16,101.529999,101.779999,100.160004,100.629997,100.629997,9680000
1969-04-17,100.629997,101.410004,99.989998,100.779999,100.779999,9360000
1969-04-18,100.779999,102.089996,100.300003,101.239998,101.239998,10850000
1969-04-21,101.239998,101.680000,100.110001,100.559998,100.559998,10010000
1969-04-22,100.559998,101.290001,99.519997,100.779999,100.779999,10250000
1969-04-23,100.779999,101.769997,100.150002,100.800003,100.800003,12220000
1969-04-24,100.800003,101.800003,100.209999,101.269997,101.269997,11340000
1969-04-25,101.269997,102.290001,100.809998,101.720001,101.720001,12480000
1969-04-28,101.720001,102.650002,100.970001,102.029999,102.029999,11120000
1969-04-29,102.029999,103.309998,101.510002,102.790001,102.790001,14730000
1969-04-30,102.790001,104.559998,102.500000,103.690002,103.690002,19350000
1969-05-01,103.690002,104.589996,102.739998,103.510002,103.510002,14380000
1969-05-02,103.510002,104.629997,102.980003,104.000000,104.000000,13070000
1969-05-05,104.000000,105.080002,103.480003,104.370003,104.370003,13300000
1969-05-06,104.370003,105.500000,103.839996,104.860001,104.860001,14700000
1969-05-07,104.860001,105.589996,103.830002,104.669998,104.669998,14030000
1969-05-08,104.669998,105.739998,104.099998,105.099998,105.099998,13050000
1969-05-09,105.099998,106.010002,104.349998,105.050003,105.050003,12530000
1969-05-12,105.050003,105.650002,104.120003,104.889999,104.889999,10550000
1969-05-13,104.889999,105.910004,104.309998,105.339996,105.339996,12910000
1969-05-14,105.339996,106.739998,105.070000,106.160004,106.160004,14360000
1969-05-15,106.160004,106.690002,105.080002,105.849998,105.849998,11930000
1969-05-16,105.849998,106.589996,105.180000,105.940002,105.940002,12280000
1969-05-19,105.940002,106.150002,104.519997,104.970001,104.970001,9790000
1969-05-20,104.970001,105.160004,103.559998,104.040001,104.040001,10280000
1969-05-21,104.040001,105.029999,103.370003,104.470001,104.470001,12100000
1969-05-22,104.470001,105.660004,103.919998,104.599998,104.599998,13710000
1969-05-23,104.599998,105.320000,103.779999,104.589996,104.589996,10900000
1969-05-26,104.589996,105.139999,103.800003,104.360001,104.360001,9030000
1969-05-27,104.360001,104.680000,103.120003,103.570000,103.570000,10580000
1969-05-28,103.570000,103.910004,102.290001,103.260002,103.260002,11330000
1969-05-29,103.260002,104.269997,102.760002,103.459999,103.459999,11770000
1969-06-02,103.459999,103.750000,102.400002,102.940002,102.940002,9180000
1969-06-03,102.940002,103.599998,102.089996,102.629997,102.629997,11190000
1969-06-04,102.629997,103.449997,102.070000,102.589996,102.589996,10840000
1969-06-05,102.589996,103.449997,102.050003,102.760002,102.760002,12350000
1969-06-06,102.760002,103.410004,101.680000,102.120003,102.120003,12520000
1969-06-09,102.120003,102.160004,100.540001,101.199997,101.199997,10650000
1969-06-10,101.199997,101.760002,100.019997,100.419998,100.419998,10660000
1969-06-11,100.419998,100.709999,99.019997,99.050003,99.050003,13640000
1969-06-12,99.050003,99.779999,97.959999,98.260002,98.260002,11790000
1969-06-13,98.260002,99.510002,97.589996,98.650002,98.650002,13070000
1969-06-16,98.650002,99.639999,97.910004,98.320000,98.320000,10400000
1969-06-17,98.320000,98.709999,96.879997,97.949997,97.949997,12210000
1969-06-18,97.949997,99.199997,97.449997,97.809998,97.809998,11290000
1969-06-19,97.809998,98.379997,96.610001,97.239998,97.239998,11160000
1969-06-20,97.239998,98.220001,96.290001,96.669998,96.669998,11360000
1969-06-23,96.669998,97.169998,95.209999,96.230003,96.230003,12900000
1969-06-24,96.290001,98.040001,96.290001,97.320000,97.320000,11460000
1969-06-25,97.320000,98.300003,96.559998,97.010002,97.010002,10490000
1969-06-26,97.010002,97.910004,95.970001,97.250000,97.250000,10310000
1969-06-27,97.250000,98.150002,96.650002,97.330002,97.330002,9020000
1969-06-30,97.330002,98.639999,96.820000,97.709999,97.709999,8640000
1969-07-01,97.709999,98.660004,97.129997,98.080002,98.080002,9890000
1969-07-02,98.080002,99.500000,97.809998,98.940002,98.940002,11350000
1969-07-03,98.940002,100.250000,98.620003,99.610001,99.610001,10110000
1969-07-07,99.610001,100.330002,98.449997,99.029999,99.029999,9970000
1969-07-08,98.980003,98.980003,97.150002,97.629997,97.629997,9320000
1969-07-09,97.629997,97.849998,96.330002,96.879997,96.879997,9320000
1969-07-10,96.879997,97.040001,95.029999,95.379997,95.379997,11450000
1969-07-11,95.379997,96.650002,94.809998,95.769997,95.769997,11730000
1969-07-14,95.769997,96.169998,94.199997,94.550003,94.550003,8310000
1969-07-15,94.550003,95.000000,93.110001,94.239998,94.239998,11110000
1969-07-16,94.239998,95.830002,94.220001,95.180000,95.180000,10470000
1969-07-17,95.180000,96.709999,95.070000,95.760002,95.760002,10450000
1969-07-18,95.760002,95.839996,94.180000,94.949997,94.949997,8590000
1969-07-22,94.949997,95.449997,93.150002,93.519997,93.519997,9780000
1969-07-23,93.519997,93.989998,92.070000,93.120003,93.120003,11680000
1969-07-24,93.120003,93.870003,92.290001,92.800003,92.800003,9750000
1969-07-25,92.800003,93.279999,91.540001,92.059998,92.059998,9800000
1969-07-28,91.910004,91.910004,89.830002,90.209999,90.209999,11800000
1969-07-29,90.209999,91.559998,89.059998,89.480003,89.480003,13630000
1969-07-30,89.480003,90.820000,88.040001,89.930000,89.930000,15580000
1969-07-31,89.959999,92.400002,89.959999,91.830002,91.830002,14160000
1969-08-01,91.919998,94.190002,91.919998,93.470001,93.470001,15070000
1969-08-04,93.470001,94.419998,92.290001,92.989998,92.989998,10700000
1969-08-05,92.989998,94.019997,92.129997,93.410004,93.410004,8940000
1969-08-06,93.410004,94.760002,93.019997,93.919998,93.919998,11100000
1969-08-07,93.919998,94.769997,93.169998,93.989998,93.989998,9450000
1969-08-08,93.989998,94.629997,93.290001,93.940002,93.940002,8760000
1969-08-11,93.940002,94.239998,92.769997,93.360001,93.360001,6680000
1969-08-12,93.360001,93.660004,92.190002,92.629997,92.629997,7870000
1969-08-13,92.629997,93.260002,91.480003,92.699997,92.699997,9910000
1969-08-14,92.699997,93.870003,92.320000,93.339996,93.339996,9690000
1969-08-15,93.339996,94.500000,92.919998,94.000000,94.000000,10210000
1969-08-18,94.000000,95.000000,93.510002,94.570000,94.570000,9420000
1969-08-19,94.570000,95.180000,93.949997,95.070000,95.070000,12640000
1969-08-20,95.070000,95.639999,94.250000,95.070000,95.070000,9680000
1969-08-21,95.070000,95.870003,94.559998,95.349998,95.349998,8420000
1969-08-22,95.349998,96.430000,94.910004,95.919998,95.919998,10140000
1969-08-25,95.919998,96.129997,94.519997,94.930000,94.930000,8410000
1969-08-26,94.930000,95.040001,93.650002,94.300003,94.300003,8910000
1969-08-27,94.300003,95.160004,93.760002,94.489998,94.489998,9100000
1969-08-28,94.489998,95.379997,94.040001,94.889999,94.889999,7730000
1969-08-29,94.889999,95.510002,94.459999,95.510002,95.510002,8850000
1969-09-02,95.510002,96.309998,94.849998,95.540001,95.540001,8560000
1969-09-03,95.540001,96.110001,94.379997,94.980003,94.980003,8760000
1969-09-04,94.980003,95.199997,93.660004,94.199997,94.199997,9380000
1969-09-05,94.199997,94.510002,93.089996,93.639999,93.639999,8890000
1969-09-08,93.639999,93.760002,92.349998,92.699997,92.699997,8310000
1969-09-09,92.699997,93.940002,91.769997,93.379997,93.379997,10980000
1969-09-10,93.379997,95.349998,93.230003,94.949997,94.949997,11490000
1969-09-11,94.949997,95.769997,93.720001,94.220001,94.220001,12370000
1969-09-12,94.220001,95.040001,93.260002,94.129997,94.129997,10800000
1969-09-15,94.129997,95.610001,93.730003,94.870003,94.870003,10680000
1969-09-16,94.870003,95.730003,94.059998,94.949997,94.949997,11160000
1969-09-17,94.949997,95.699997,94.040001,94.760002,94.760002,10980000
1969-09-18,94.760002,95.529999,94.050003,94.900002,94.900002,11170000
1969-09-19,94.900002,95.919998,94.349998,95.190002,95.190002,12270000
1969-09-22,95.190002,96.129997,94.580002,95.629997,95.629997,9280000
1969-09-23,95.629997,96.620003,94.860001,95.629997,95.629997,13030000
1969-09-24,95.629997,96.199997,94.750000,95.500000,95.500000,11320000
1969-09-25,95.500000,95.919998,94.279999,94.769997,94.769997,10690000
1969-09-26,94.769997,95.230003,93.529999,94.160004,94.160004,9680000
1969-09-29,94.160004,94.449997,92.620003,93.410004,93.410004,10170000
1969-09-30,93.410004,94.050003,92.550003,93.120003,93.120003,9180000
1969-10-01,93.120003,93.510002,92.120003,92.519997,92.519997,9090000
1969-10-02,92.519997,93.629997,91.660004,93.239998,93.239998,11430000
1969-10-03,93.239998,94.389999,92.650002,93.190002,93.190002,12410000
1969-10-06,93.190002,93.989998,92.500000,93.379997,93.379997,9180000
1969-10-07,93.379997,94.029999,92.589996,93.089996,93.089996,10050000
1969-10-08,93.089996,93.559998,92.040001,92.669998,92.669998,10370000
1969-10-09,92.669998,93.550003,91.750000,93.029999,93.029999,10420000
1969-10-10,93.029999,94.190002,92.599998,93.559998,93.559998,12210000
1969-10-13,93.559998,94.860001,93.199997,94.550003,94.550003,13620000
1969-10-14,94.550003,96.529999,94.320000,95.699997,95.699997,19950000
1969-10-15,95.699997,96.559998,94.650002,95.720001,95.720001,15740000
1969-10-16,95.720001,97.540001,95.050003,96.370003,96.370003,19500000
1969-10-17,96.370003,97.239998,95.379997,96.260002,96.260002,13740000
1969-10-20,96.260002,97.169998,95.290001,96.459999,96.459999,13540000
1969-10-21,96.459999,97.839996,95.860001,97.199997,97.199997,16460000
1969-10-22,97.199997,98.610001,96.559998,97.830002,97.830002,19320000
1969-10-23,97.830002,98.389999,96.459999,97.459999,97.459999,14780000
1969-10-24,97.459999,98.830002,96.970001,98.120003,98.120003,15430000
1969-10-27,98.120003,98.779999,97.489998,97.970001,97.970001,12160000
1969-10-28,97.970001,98.550003,97.019997,97.660004,97.660004,12410000
1969-10-29,97.660004,97.919998,96.260002,96.809998,96.809998,12380000
1969-10-30,96.809998,97.470001,95.610001,96.930000,96.930000,12820000
1969-10-31,96.930000,98.029999,96.330002,97.120003,97.120003,13100000
1969-11-03,97.120003,97.820000,96.190002,97.150002,97.150002,11140000
1969-11-04,97.150002,97.820000,95.839996,97.209999,97.209999,12340000
1969-11-05,97.209999,98.389999,96.750000,97.639999,97.639999,12110000
1969-11-06,97.639999,98.309998,96.800003,97.669998,97.669998,11110000
1969-11-07,97.669998,99.010002,97.180000,98.260002,98.260002,13280000
1969-11-10,98.260002,99.230003,97.650002,98.330002,98.330002,12490000
1969-11-11,98.330002,98.790001,97.449997,98.070000,98.070000,10080000
1969-11-12,98.070000,98.720001,97.279999,97.889999,97.889999,12480000
1969-11-13,97.889999,98.339996,96.540001,97.419998,97.419998,12090000
1969-11-14,97.419998,97.440002,96.360001,97.070000,97.070000,10580000
1969-11-17,97.070000,97.360001,95.820000,96.410004,96.410004,10120000
1969-11-18,96.410004,97.000000,95.570000,96.389999,96.389999,11010000
1969-11-19,96.389999,96.949997,95.360001,95.900002,95.900002,11240000
1969-11-20,95.900002,95.940002,94.120003,94.910004,94.910004,12010000
1969-11-21,94.910004,95.339996,93.870003,94.320000,94.320000,9840000
1969-11-24,94.320000,94.430000,92.629997,93.239998,93.239998,10940000
1969-11-25,93.239998,94.169998,92.379997,92.940002,92.940002,11560000
1969-11-26,92.940002,93.849998,92.239998,93.269997,93.269997,10630000
1969-11-28,93.269997,94.410004,92.879997,93.809998,93.809998,8550000
1969-12-01,93.809998,94.470001,92.779999,93.220001,93.220001,9950000
1969-12-02,93.220001,93.540001,91.949997,92.650002,92.650002,9940000
1969-12-03,92.650002,93.050003,91.250000,91.650002,91.650002,11300000
1969-12-04,91.650002,92.449997,90.360001,91.949997,91.949997,13230000
1969-12-05,91.949997,92.910004,91.139999,91.730003,91.730003,11150000
1969-12-08,91.730003,92.050003,90.290001,90.839996,90.839996,9990000
1969-12-09,90.839996,91.790001,89.930000,90.550003,90.550003,12290000
1969-12-10,90.550003,91.220001,89.330002,90.480003,90.480003,12590000
1969-12-11,90.480003,91.370003,89.739998,90.519997,90.519997,10430000
1969-12-12,90.519997,91.669998,90.050003,90.809998,90.809998,11630000
1969-12-15,90.809998,91.419998,89.959999,90.540001,90.540001,11100000
1969-12-16,90.540001,91.050003,89.230003,89.720001,89.720001,11880000
1969-12-17,89.720001,90.320000,88.940002,89.199997,89.199997,12840000
1969-12-18,89.199997,91.150002,88.620003,90.610001,90.610001,15950000
1969-12-19,90.610001,92.339996,90.330002,91.379997,91.379997,15420000
1969-12-22,91.379997,92.029999,90.099998,90.580002,90.580002,12680000
1969-12-23,90.580002,91.129997,89.400002,90.230003,90.230003,13890000
1969-12-24,90.230003,91.889999,89.930000,91.180000,91.180000,11670000
1969-12-26,91.180000,92.300003,90.940002,91.889999,91.889999,6750000
1969-12-29,91.889999,92.489998,90.660004,91.250000,91.250000,12500000
1969-12-30,91.250000,92.199997,90.470001,91.599998,91.599998,15790000
1969-12-31,91.599998,92.940002,91.150002,92.059998,92.059998,19380000
1970-01-02,92.059998,93.540001,91.790001,93.000000,93.000000,8050000
1970-01-05,93.000000,94.250000,92.529999,93.459999,93.459999,11490000
1970-01-06,93.459999,93.809998,92.129997,92.820000,92.820000,11460000
1970-01-07,92.820000,93.379997,91.930000,92.629997,92.629997,10010000
1970-01-08,92.629997,93.470001,91.989998,92.680000,92.680000,10670000
1970-01-09,92.680000,93.250000,91.820000,92.400002,92.400002,9380000
1970-01-12,92.400002,92.669998,91.199997,91.699997,91.699997,8900000
1970-01-13,91.699997,92.610001,90.989998,91.919998,91.919998,9870000
1970-01-14,91.919998,92.400002,90.879997,91.650002,91.650002,10380000
1970-01-15,91.650002,92.349998,90.730003,91.680000,91.680000,11120000
1970-01-16,91.680000,92.489998,90.360001,90.919998,90.919998,11940000
1970-01-19,90.720001,90.720001,89.139999,89.650002,89.650002,9500000
1970-01-20,89.650002,90.449997,88.639999,89.830002,89.830002,11050000
1970-01-21,89.830002,90.610001,89.199997,89.949997,89.949997,9880000
1970-01-22,89.949997,90.800003,89.199997,90.040001,90.040001,11050000
1970-01-23,90.040001,90.449997,88.739998,89.370003,89.370003,11000000
1970-01-26,89.230003,89.230003,87.489998,88.169998,88.169998,10670000
1970-01-27,88.169998,88.540001,86.919998,87.620003,87.620003,9630000
1970-01-28,87.620003,88.239998,86.440002,86.790001,86.790001,10510000
1970-01-29,86.790001,87.089996,85.019997,85.690002,85.690002,12210000
1970-01-30,85.690002,86.330002,84.419998,85.019997,85.019997,12320000
1970-02-02,85.019997,86.760002,84.760002,85.750000,85.750000,13440000
1970-02-03,85.750000,87.540001,84.639999,86.769997,86.769997,16050000
1970-02-04,86.769997,87.660004,85.589996,86.239998,86.239998,11040000
1970-02-05,86.239998,86.620003,84.949997,85.900002,85.900002,9430000
1970-02-06,85.900002,86.879997,85.230003,86.330002,86.330002,10150000
1970-02-09,86.330002,87.849998,86.160004,87.010002,87.010002,10830000
1970-02-10,87.010002,87.400002,85.580002,86.099998,86.099998,10110000
1970-02-11,86.099998,87.379997,85.300003,86.940002,86.940002,12260000
1970-02-12,86.940002,87.540001,85.930000,86.730003,86.730003,10010000
1970-02-13,86.730003,87.300003,85.709999,86.540001,86.540001,11060000
1970-02-16,86.540001,87.300003,85.800003,86.470001,86.470001,19780000
1970-02-17,86.470001,87.080002,85.570000,86.370003,86.370003,10140000
1970-02-18,86.370003,88.070000,86.190002,87.440002,87.440002,11950000
1970-02-19,87.440002,88.699997,86.940002,87.760002,87.760002,12890000
1970-02-20,87.760002,88.739998,86.870003,88.029999,88.029999,10790000
1970-02-24,88.029999,88.910004,87.279999,87.989998,87.989998,10810000
1970-02-25,87.989998,89.800003,87.110001,89.349998,89.349998,13210000
1970-02-26,89.349998,89.629997,87.629997,88.900002,88.900002,11540000
1970-02-27,88.900002,90.330002,88.419998,89.500000,89.500000,12890000
1970-03-02,89.500000,90.800003,88.919998,89.709999,89.709999,12270000
1970-03-03,89.709999,90.669998,88.959999,90.230003,90.230003,11700000
1970-03-04,90.230003,91.050003,89.320000,90.040001,90.040001,11850000
1970-03-05,90.040001,90.989998,89.379997,90.000000,90.000000,11370000
1970-03-06,90.000000,90.360001,88.839996,89.440002,89.440002,10980000
1970-03-09,89.430000,89.430000,87.940002,88.510002,88.510002,9760000
1970-03-10,88.510002,89.410004,87.889999,88.750000,88.750000,9450000
1970-03-11,88.750000,89.580002,88.110001,88.690002,88.690002,9180000
1970-03-12,88.690002,89.089996,87.680000,88.330002,88.330002,9140000
1970-03-13,88.330002,89.430000,87.290001,87.860001,87.860001,9560000
1970-03-16,87.860001,87.970001,86.389999,86.910004,86.910004,8910000
1970-03-17,86.910004,87.860001,86.360001,87.290001,87.290001,9090000
1970-03-18,87.290001,88.279999,86.930000,87.540001,87.540001,9790000
1970-03-19,87.540001,88.199997,86.879997,87.419998,87.419998,8930000
1970-03-20,87.419998,87.769997,86.430000,87.059998,87.059998,7910000
1970-03-23,87.059998,87.639999,86.190002,86.989998,86.989998,7330000
1970-03-24,86.989998,88.430000,86.900002,87.980003,87.980003,8840000
1970-03-25,88.110001,91.070000,88.110001,89.769997,89.769997,17500000
1970-03-26,89.769997,90.650002,89.180000,89.919998,89.919998,11350000
1970-03-30,89.919998,90.410004,88.910004,89.629997,89.629997,9600000
1970-03-31,89.629997,90.169998,88.849998,89.629997,89.629997,8370000
1970-04-01,89.629997,90.620003,89.300003,90.070000,90.070000,9810000
1970-04-02,90.070000,90.699997,89.279999,89.790001,89.790001,10520000
1970-04-03,89.790001,90.160004,88.809998,89.389999,89.389999,9920000
1970-04-06,89.389999,89.610001,88.150002,88.760002,88.760002,8380000
1970-04-07,88.760002,89.309998,87.940002,88.519997,88.519997,8490000
1970-04-08,88.519997,89.089996,87.830002,88.489998,88.489998,9070000
1970-04-09,88.489998,89.320000,87.959999,88.529999,88.529999,9060000
1970-04-10,88.529999,89.139999,87.820000,88.239998,88.239998,10020000
1970-04-13,88.239998,88.669998,87.150002,87.639999,87.639999,8810000
1970-04-14,87.639999,87.730003,86.010002,86.889999,86.889999,10840000
1970-04-15,86.889999,87.709999,86.529999,86.730003,86.730003,9410000
1970-04-16,86.730003,87.129997,85.510002,85.879997,85.879997,10250000
1970-04-17,85.879997,86.360001,84.750000,85.669998,85.669998,10990000
1970-04-20,85.669998,86.360001,84.989998,85.830002,85.830002,8280000
1970-04-21,85.830002,86.540001,84.989998,85.379997,85.379997,8490000
1970-04-22,85.379997,85.510002,83.839996,84.269997,84.269997,10780000
1970-04-23,84.269997,84.300003,82.610001,83.040001,83.040001,11050000
1970-04-24,83.040001,83.620003,81.959999,82.769997,82.769997,10410000
1970-04-27,82.769997,83.080002,81.080002,81.459999,81.459999,10240000
1970-04-28,81.459999,82.160004,79.860001,80.269997,80.269997,12620000
1970-04-29,80.269997,83.230003,79.309998,81.809998,81.809998,15800000
1970-04-30,81.809998,82.570000,80.760002,81.519997,81.519997,9880000
1970-05-01,81.519997,82.320000,80.269997,81.440002,81.440002,8290000
1970-05-04,81.279999,81.279999,78.849998,79.370003,79.370003,11450000
1970-05-05,79.370003,79.830002,78.019997,78.599998,78.599998,10580000
1970-05-06,78.599998,80.910004,78.230003,79.470001,79.470001,14380000
1970-05-07,79.470001,80.599998,78.889999,79.830002,79.830002,9530000
1970-05-08,79.830002,80.150002,78.709999,79.440002,79.440002,6930000
1970-05-11,79.440002,79.720001,78.290001,78.599998,78.599998,6650000
1970-05-12,78.599998,79.150002,77.059998,77.849998,77.849998,10850000
1970-05-13,77.750000,77.750000,75.919998,76.529999,76.529999,10720000
1970-05-14,76.529999,76.639999,74.029999,75.440002,75.440002,13920000
1970-05-15,75.440002,77.419998,74.589996,76.900002,76.900002,14570000
1970-05-18,76.900002,77.680000,76.070000,76.959999,76.959999,8280000
1970-05-19,76.959999,77.199997,75.209999,75.459999,75.459999,9480000
1970-05-20,75.349998,75.349998,73.250000,73.519997,73.519997,13020000
1970-05-21,73.510002,73.510002,70.940002,72.160004,72.160004,16710000
1970-05-22,72.160004,73.419998,71.419998,72.250000,72.250000,12170000
1970-05-25,72.160004,72.160004,69.919998,70.250000,70.250000,12660000
1970-05-26,70.250000,71.169998,68.610001,69.290001,69.290001,17030000
1970-05-27,69.370003,73.220001,69.370003,72.769997,72.769997,17460000
1970-05-28,72.769997,75.440002,72.589996,74.610001,74.610001,18910000
1970-05-29,74.610001,76.919998,73.529999,76.550003,76.550003,14630000
1970-06-01,76.550003,78.400002,75.839996,77.839996,77.839996,15020000
1970-06-02,77.839996,78.730003,76.510002,77.839996,77.839996,13480000
1970-06-03,77.839996,79.220001,76.970001,78.519997,78.519997,16600000
1970-06-04,78.519997,79.419998,76.989998,77.360001,77.360001,14380000
1970-06-05,77.360001,77.480003,75.250000,76.169998,76.169998,12450000
1970-06-08,76.169998,77.370003,75.300003,76.290001,76.290001,8040000
1970-06-09,76.290001,79.959999,75.580002,76.250000,76.250000,7050000
1970-06-10,76.250000,76.620003,74.919998,75.480003,75.480003,7240000
1970-06-11,75.480003,75.519997,73.959999,74.449997,74.449997,7770000
1970-06-12,74.449997,74.839996,73.250000,73.879997,73.879997,8890000
1970-06-15,73.879997,75.269997,73.669998,74.580002,74.580002,6920000
1970-06-16,74.580002,76.760002,74.209999,76.150002,76.150002,11330000
1970-06-17,76.150002,78.040001,75.629997,76.000000,76.000000,9870000
1970-06-18,76.000000,77.169998,74.989998,76.510002,76.510002,8870000
1970-06-19,76.510002,78.050003,76.309998,77.050003,77.050003,10980000
1970-06-22,77.050003,77.430000,75.610001,76.639999,76.639999,8700000
1970-06-23,76.639999,76.830002,74.519997,74.760002,74.760002,10790000
1970-06-24,74.760002,75.419998,73.400002,73.970001,73.970001,12630000
1970-06-25,73.970001,74.930000,73.300003,74.019997,74.019997,8200000
1970-06-26,74.019997,74.680000,73.089996,73.470001,73.470001,9160000
1970-06-29,73.470001,73.860001,72.339996,72.889999,72.889999,8770000
1970-06-30,72.889999,73.889999,72.250000,72.720001,72.720001,9280000
1970-07-01,72.720001,73.660004,72.110001,72.940002,72.940002,8610000
1970-07-02,72.940002,73.919998,72.430000,72.919998,72.919998,8440000
1970-07-06,72.919998,73.120003,71.379997,71.779999,71.779999,9340000
1970-07-07,71.779999,72.320000,70.690002,71.230003,71.230003,10470000
1970-07-08,71.230003,73.300003,70.989998,73.000000,73.000000,10970000
1970-07-09,73.000000,74.769997,72.879997,74.059998,74.059998,12820000
1970-07-10,74.059998,75.209999,73.489998,74.449997,74.449997,10160000
1970-07-13,74.449997,75.370003,73.830002,74.550003,74.550003,7450000
1970-07-14,74.550003,75.040001,73.779999,74.419998,74.419998,7360000
1970-07-15,74.419998,75.680000,74.059998,75.230003,75.230003,8860000
1970-07-16,75.230003,77.089996,75.120003,76.339996,76.339996,12200000
1970-07-17,76.370003,78.230003,76.370003,77.690002,77.690002,13870000
1970-07-20,77.690002,78.720001,77.040001,77.790001,77.790001,11660000
1970-07-21,77.790001,77.940002,76.389999,76.980003,76.980003,9940000
1970-07-22,76.980003,78.199997,76.220001,77.029999,77.029999,12460000
1970-07-23,77.029999,78.510002,76.459999,78.000000,78.000000,12460000
1970-07-24,78.000000,78.480003,76.959999,77.820000,77.820000,9520000
1970-07-27,77.820000,78.269997,77.070000,77.650002,77.650002,7460000
1970-07-28,77.650002,78.349998,76.959999,77.769997,77.769997,9040000
1970-07-29,77.769997,78.809998,77.279999,78.040001,78.040001,12580000
1970-07-30,78.040001,78.660004,77.360001,78.070000,78.070000,10430000
1970-07-31,78.070000,79.029999,77.440002,78.050003,78.050003,11640000
1970-08-03,78.050003,78.239998,76.559998,77.019997,77.019997,7650000
1970-08-04,77.019997,77.559998,76.120003,77.190002,77.190002,8310000
1970-08-05,77.190002,77.860001,76.589996,77.180000,77.180000,7660000
1970-08-06,77.180000,77.680000,76.389999,77.080002,77.080002,7560000
1970-08-07,77.080002,78.089996,76.459999,77.279999,77.279999,9370000
1970-08-10,77.279999,77.400002,75.720001,76.199997,76.199997,7580000
1970-08-11,76.199997,76.330002,75.160004,75.820000,75.820000,7330000
1970-08-12,75.820000,76.239998,75.040001,75.419998,75.419998,7440000
1970-08-13,75.419998,75.690002,74.129997,74.760002,74.760002,8640000
1970-08-14,74.760002,75.739998,74.389999,75.180000,75.180000,7850000
1970-08-17,75.180000,75.790001,74.519997,75.330002,75.330002,6940000
1970-08-18,75.330002,76.790001,75.300003,76.199997,76.199997,9500000
1970-08-19,76.199997,77.580002,76.010002,76.959999,76.959999,9870000
1970-08-20,76.959999,77.989998,76.300003,77.839996,77.839996,10170000
1970-08-21,77.839996,79.599998,77.459999,79.239998,79.239998,13420000
1970-08-24,79.410004,81.620003,79.410004,80.989998,80.989998,18910000
1970-08-25,80.989998,81.809998,79.690002,81.120003,81.120003,17520000
1970-08-26,81.120003,82.260002,80.599998,81.209999,81.209999,15970000
1970-08-27,81.209999,81.910004,80.129997,81.080002,81.080002,12440000
1970-08-28,81.080002,82.470001,80.690002,81.860001,81.860001,13820000
1970-08-31,81.860001,82.330002,80.949997,81.519997,81.519997,10740000
1970-09-01,81.519997,81.800003,80.430000,80.949997,80.949997,10960000
1970-09-02,80.949997,81.349998,79.949997,80.959999,80.959999,9710000
1970-09-03,80.959999,82.629997,80.879997,82.089996,82.089996,14110000
1970-09-04,82.089996,83.419998,81.790001,82.830002,82.830002,15360000
1970-09-08,82.830002,83.690002,81.480003,83.040001,83.040001,17110000
1970-09-09,83.040001,83.779999,81.900002,82.790001,82.790001,16250000
1970-09-10,82.790001,82.980003,81.620003,82.300003,82.300003,11900000
1970-09-11,82.300003,83.190002,81.809998,82.519997,82.519997,12140000
1970-09-14,82.519997,83.129997,81.430000,82.070000,82.070000,11900000
1970-09-15,82.070000,82.110001,80.750000,81.360001,81.360001,9830000
1970-09-16,81.360001,82.570000,80.610001,81.790001,81.790001,12090000
1970-09-17,81.790001,83.089996,81.510002,82.290001,82.290001,15530000
1970-09-18,82.290001,83.500000,81.769997,82.620003,82.620003,15900000
1970-09-21,82.620003,83.150002,81.519997,81.910004,81.910004,12540000
1970-09-22,81.910004,82.239998,80.820000,81.860001,81.860001,12110000
1970-09-23,81.860001,83.150002,81.519997,81.910004,81.910004,16940000
1970-09-24,81.910004,82.239998,80.820000,81.660004,81.660004,21340000
1970-09-25,81.660004,83.599998,81.410004,82.830002,82.830002,20470000
1970-09-28,82.830002,84.559998,82.610001,83.910004,83.910004,14390000
1970-09-29,83.910004,84.570000,83.110001,83.860001,83.860001,17880000
1970-09-30,83.860001,84.989998,82.779999,84.300003,84.300003,14830000
1970-10-01,84.300003,84.699997,83.459999,84.320000,84.320000,9700000
1970-10-02,84.320000,85.559998,84.059998,85.160004,85.160004,15420000
1970-10-05,85.160004,86.989998,85.010002,86.470001,86.470001,19760000
1970-10-06,86.470001,87.750000,86.040001,86.849998,86.849998,20240000
1970-10-07,86.849998,87.470001,85.550003,86.889999,86.889999,15610000
1970-10-08,86.889999,87.370003,85.550003,85.949997,85.949997,14500000
1970-10-09,85.949997,86.250000,84.540001,85.080002,85.080002,13980000
1970-10-12,85.050003,85.050003,83.580002,84.169998,84.169998,8570000
1970-10-13,84.169998,84.699997,83.239998,84.059998,84.059998,9500000
1970-10-14,84.059998,84.830002,83.419998,84.190002,84.190002,9920000
1970-10-15,84.190002,85.279999,83.820000,84.650002,84.650002,11250000
1970-10-16,84.650002,85.209999,83.830002,84.279999,84.279999,11300000
1970-10-19,84.279999,84.290001,82.809998,83.150002,83.150002,9890000
1970-10-20,83.150002,84.190002,82.620003,83.639999,83.639999,10630000
1970-10-21,83.639999,84.720001,83.209999,83.660004,83.660004,11330000
1970-10-22,83.660004,84.040001,82.769997,83.379997,83.379997,9000000
1970-10-23,83.379997,84.300003,82.910004,83.769997,83.769997,10270000
1970-10-26,83.769997,84.260002,82.889999,83.309998,83.309998,9200000
1970-10-27,83.309998,83.730003,82.519997,83.120003,83.120003,9680000
1970-10-28,83.120003,83.809998,82.290001,83.430000,83.430000,10660000
1970-10-29,83.430000,84.099998,82.820000,83.360001,83.360001,10440000
1970-10-30,83.360001,83.800003,82.519997,83.250000,83.250000,10520000
1970-11-02,83.250000,83.989998,82.660004,83.510002,83.510002,9470000
1970-11-03,83.510002,84.769997,83.209999,84.220001,84.220001,11760000
1970-11-04,84.220001,85.260002,83.820000,84.389999,84.389999,12180000
1970-11-05,84.389999,84.790001,83.529999,84.099998,84.099998,10800000
1970-11-06,84.099998,84.730003,83.550003,84.220001,84.220001,9970000
1970-11-09,84.220001,85.269997,83.820000,84.669998,84.669998,10890000
1970-11-10,84.669998,85.690002,84.180000,84.790001,84.790001,12030000
1970-11-11,84.790001,86.239998,84.690002,85.029999,85.029999,13520000
1970-11-12,85.029999,85.540001,83.809998,84.150002,84.150002,12520000
1970-11-13,84.150002,84.330002,82.919998,83.370003,83.370003,11890000
1970-11-16,83.370003,83.750000,82.339996,83.239998,83.239998,9160000
1970-11-17,83.239998,84.169998,82.809998,83.470001,83.470001,9450000
1970-11-18,83.470001,83.529999,82.410004,82.790001,82.790001,9850000
1970-11-19,82.790001,83.480003,82.230003,82.910004,82.910004,9280000
1970-11-20,82.910004,84.059998,82.489998,83.720001,83.720001,10920000
1970-11-23,83.720001,84.919998,83.470001,84.239998,84.239998,12720000
1970-11-24,84.239998,85.180000,83.589996,84.779999,84.779999,12560000
1970-11-25,84.779999,85.699997,84.349998,85.089996,85.089996,13490000
1970-11-27,85.089996,86.209999,84.669998,85.930000,85.930000,10130000
1970-11-30,85.930000,87.599998,85.790001,87.199997,87.199997,17700000
1970-12-01,87.199997,88.610001,86.110001,87.470001,87.470001,20170000
1970-12-02,87.470001,88.830002,86.720001,88.480003,88.480003,17960000
1970-12-03,88.480003,89.870003,88.110001,88.900002,88.900002,20480000
1970-12-04,88.900002,89.889999,88.120003,89.459999,89.459999,15980000
1970-12-07,89.459999,90.389999,88.760002,89.940002,89.940002,15530000
1970-12-08,89.940002,90.470001,88.870003,89.470001,89.470001,14370000
1970-12-09,89.470001,90.029999,88.480003,89.540001,89.540001,13550000
1970-12-10,89.540001,90.870003,89.010002,89.919998,89.919998,14610000
1970-12-11,89.919998,90.930000,89.440002,90.260002,90.260002,15790000
1970-12-14,90.260002,90.809998,89.279999,89.800003,89.800003,13810000
1970-12-15,89.800003,90.320000,88.930000,89.660004,89.660004,13420000
1970-12-16,89.660004,90.220001,88.769997,89.720001,89.720001,14240000
1970-12-17,89.720001,90.610001,89.309998,90.040001,90.040001,13660000
1970-12-18,90.040001,90.769997,89.419998,90.220001,90.220001,14360000
1970-12-21,90.220001,90.769997,89.360001,89.940002,89.940002,12690000
1970-12-22,89.940002,90.839996,89.349998,90.040001,90.040001,14510000
1970-12-23,90.040001,90.860001,89.349998,90.099998,90.099998,15400000
1970-12-24,90.099998,91.080002,89.809998,90.610001,90.610001,12140000
1970-12-28,90.610001,91.489998,90.279999,91.089996,91.089996,12290000
1970-12-29,91.089996,92.379997,90.730003,92.080002,92.080002,17750000
1970-12-30,92.080002,92.989998,91.599998,92.269997,92.269997,19140000
1970-12-31,92.269997,92.790001,91.360001,92.150002,92.150002,13390000
1971-01-04,92.150002,92.190002,90.639999,91.150002,91.150002,10010000
1971-01-05,91.150002,92.279999,90.690002,91.800003,91.800003,12600000
1971-01-06,91.800003,93.000000,91.500000,92.349998,92.349998,16960000
1971-01-07,92.349998,93.260002,91.750000,92.379997,92.379997,16460000
1971-01-08,92.379997,93.019997,91.599998,92.190002,92.190002,14100000
1971-01-11,92.190002,92.669998,90.989998,91.980003,91.980003,14720000
1971-01-12,91.980003,93.279999,91.629997,92.720001,92.720001,17820000
1971-01-13,92.720001,93.660004,91.879997,92.559998,92.559998,19070000
1971-01-14,92.559998,93.360001,91.669998,92.800003,92.800003,17600000
1971-01-15,92.800003,93.940002,92.250000,93.029999,93.029999,18010000
1971-01-18,93.029999,94.110001,92.629997,93.410004,93.410004,15400000
1971-01-19,93.410004,94.279999,92.849998,93.760002,93.760002,15800000
1971-01-20,93.760002,94.529999,93.070000,93.779999,93.779999,18330000
1971-01-21,93.779999,94.690002,93.150002,94.190002,94.190002,19060000
1971-01-22,94.190002,95.529999,93.959999,94.879997,94.879997,21680000
1971-01-25,94.879997,95.930000,94.160004,95.279999,95.279999,19050000
1971-01-26,95.279999,96.360001,94.690002,95.589996,95.589996,21380000
1971-01-27,95.589996,95.779999,93.959999,94.889999,94.889999,20640000
1971-01-28,94.889999,95.779999,94.120003,95.209999,95.209999,18840000
1971-01-29,95.209999,96.489998,94.790001,95.879997,95.879997,20960000
1971-02-01,95.879997,97.050003,95.379997,96.419998,96.419998,20650000
1971-02-02,96.419998,97.190002,95.599998,96.430000,96.430000,22030000
1971-02-03,96.430000,97.190002,95.580002,96.629997,96.629997,21680000
1971-02-04,96.629997,97.260002,95.690002,96.620003,96.620003,20860000
1971-02-05,96.620003,97.580002,95.839996,96.930000,96.930000,20480000
1971-02-08,96.930000,98.040001,96.129997,97.449997,97.449997,25590000
1971-02-09,97.449997,98.500000,96.900002,97.510002,97.510002,28250000
1971-02-10,97.510002,97.970001,96.230003,97.389999,97.389999,19040000
1971-02-11,97.389999,98.489998,96.989998,97.910004,97.910004,19260000
1971-02-12,97.910004,98.959999,97.559998,98.430000,98.430000,18470000
1971-02-16,98.430000,99.589996,97.849998,98.660004,98.660004,21350000
1971-02-17,98.660004,99.320000,97.320000,98.199997,98.199997,18720000
1971-02-18,98.199997,98.599998,96.959999,97.559998,97.559998,16650000
1971-02-19,97.559998,97.790001,96.250000,96.739998,96.739998,17860000
1971-02-22,96.650002,96.650002,94.970001,95.720001,95.720001,15840000
1971-02-23,95.720001,96.669998,94.919998,96.089996,96.089996,15080000
1971-02-24,96.089996,97.339996,95.860001,96.730003,96.730003,15930000
1971-02-25,96.730003,97.709999,96.080002,96.959999,96.959999,16200000
1971-02-26,96.959999,97.540001,95.839996,96.750000,96.750000,17250000
1971-03-01,96.750000,97.480003,96.110001,97.000000,97.000000,13020000
1971-03-02,97.000000,97.599998,96.320000,96.980003,96.980003,14870000
1971-03-03,96.980003,97.540001,96.300003,96.949997,96.949997,14680000
1971-03-04,96.949997,98.379997,96.900002,97.919998,97.919998,17350000
1971-03-05,97.919998,99.489998,97.820000,98.959999,98.959999,22430000
1971-03-08,98.959999,99.440002,98.419998,99.379997,99.379997,19340000
1971-03-09,99.379997,100.309998,98.720001,99.459999,99.459999,20490000
1971-03-10,99.459999,100.099998,98.629997,99.300003,99.300003,17220000
1971-03-11,99.300003,100.290001,98.570000,99.389999,99.389999,19830000
1971-03-12,99.389999,100.089996,98.639999,99.570000,99.570000,14680000
1971-03-15,99.570000,101.150002,99.120003,100.709999,100.709999,18920000
1971-03-16,100.709999,101.940002,100.360001,101.209999,101.209999,22270000
1971-03-17,101.209999,101.660004,99.980003,101.120003,101.120003,17070000
1971-03-18,101.120003,102.029999,100.430000,101.190002,101.190002,17910000
1971-03-19,101.190002,101.739998,100.349998,101.010002,101.010002,15150000
1971-03-22,101.010002,101.459999,100.080002,100.620003,100.620003,14290000
1971-03-23,100.620003,101.059998,99.620003,100.279999,100.279999,16470000
1971-03-24,100.279999,100.629997,99.150002,99.620003,99.620003,15770000
1971-03-25,99.620003,100.029999,98.360001,99.610001,99.610001,15870000
1971-03-26,99.610001,100.650002,99.180000,99.949997,99.949997,15560000
1971-03-29,99.949997,100.739998,99.360001,100.029999,100.029999,13650000
1971-03-30,100.029999,100.860001,99.410004,100.260002,100.260002,15430000
1971-03-31,100.260002,101.050003,99.690002,100.309998,100.309998,17610000
1971-04-01,100.309998,100.989998,99.629997,100.389999,100.389999,13470000
1971-04-02,100.389999,101.230003,99.860001,100.559998,100.559998,14520000
1971-04-05,100.559998,101.410004,99.879997,100.790001,100.790001,16040000
1971-04-06,100.790001,102.110001,100.300003,101.510002,101.510002,19990000
1971-04-07,101.510002,102.870003,101.129997,101.980003,101.980003,22270000
1971-04-08,101.980003,102.860001,101.300003,102.099998,102.099998,17590000
1971-04-12,102.099998,103.540001,101.750000,102.879997,102.879997,19410000
1971-04-13,102.879997,103.959999,102.250000,102.980003,102.980003,23200000
1971-04-14,102.980003,104.010002,102.279999,103.370003,103.370003,19440000
1971-04-15,103.370003,104.400002,102.760002,103.519997,103.519997,22540000
1971-04-16,103.519997,104.180000,102.680000,103.489998,103.489998,18280000
1971-04-19,103.489998,104.629997,103.089996,104.010002,104.010002,17730000
1971-04-20,104.010002,104.580002,103.059998,103.610001,103.610001,17880000
1971-04-21,103.610001,104.160004,102.550003,103.360001,103.360001,17040000
1971-04-22,103.360001,104.269997,102.580002,103.559998,103.559998,19270000
1971-04-23,103.559998,104.629997,102.790001,104.050003,104.050003,20150000
1971-04-26,104.050003,104.830002,103.190002,103.940002,103.940002,18860000
1971-04-27,103.940002,105.070000,103.230003,104.589996,104.589996,21250000
1971-04-28,104.589996,105.599998,103.849998,104.769997,104.769997,24820000
1971-04-29,104.769997,105.580002,103.900002,104.629997,104.629997,20340000
1971-04-30,104.629997,104.959999,103.250000,103.949997,103.949997,17490000
1971-05-03,103.949997,104.110001,102.370003,103.290001,103.290001,16120000
1971-05-04,103.290001,104.360001,102.709999,103.790001,103.790001,17310000
1971-05-05,103.790001,104.279999,102.680000,103.779999,103.779999,17270000
1971-05-06,103.779999,104.419998,102.800003,103.230003,103.230003,19300000
1971-05-07,103.230003,103.500000,101.860001,102.870003,102.870003,16490000
1971-05-10,102.870003,103.150002,101.709999,102.360001,102.360001,12810000
1971-05-11,102.360001,103.370003,101.500000,102.620003,102.620003,17730000
1971-05-12,102.620003,103.570000,102.120003,102.900002,102.900002,15140000
1971-05-13,102.900002,103.570000,101.980003,102.690002,102.690002,17640000
1971-05-14,102.690002,103.169998,101.650002,102.209999,102.209999,16430000
1971-05-17,102.080002,102.080002,100.250000,100.690002,100.690002,15980000
1971-05-18,100.690002,101.620003,99.680000,100.830002,100.830002,17640000
1971-05-19,100.830002,101.750000,100.300003,101.070000,101.070000,17640000
1971-05-20,101.070000,102.169998,100.610001,101.309998,101.309998,11740000
1971-05-21,101.309998,101.839996,100.410004,100.989998,100.989998,12090000
1971-05-24,100.989998,101.239998,99.720001,100.129997,100.129997,12060000
1971-05-25,100.129997,100.389999,98.730003,99.470001,99.470001,16050000
1971-05-26,99.470001,100.489998,98.930000,99.589996,99.589996,13550000
1971-05-27,99.589996,100.139999,98.779999,99.400002,99.400002,12610000
1971-05-28,99.400002,100.169998,98.680000,99.629997,99.629997,11760000
1971-06-01,99.629997,100.760002,99.220001,100.199997,100.199997,11930000
1971-06-02,100.199997,101.529999,99.889999,100.959999,100.959999,17740000
1971-06-03,100.959999,102.070000,100.300003,101.010002,101.010002,18790000
1971-06-04,101.010002,101.879997,100.430000,101.300003,101.300003,14400000
1971-06-07,101.300003,102.019997,100.550003,101.089996,101.089996,13800000
1971-06-08,101.089996,101.500000,99.910004,100.320000,100.320000,13610000
1971-06-09,100.320000,100.970001,99.279999,100.290001,100.290001,14250000
1971-06-10,100.290001,101.230003,99.779999,100.639999,100.639999,12450000
1971-06-11,100.639999,101.709999,100.180000,101.070000,101.070000,12270000
1971-06-14,101.070000,101.279999,99.779999,100.220001,100.220001,11530000
1971-06-15,100.220001,101.099998,99.449997,100.320000,100.320000,13550000
1971-06-16,100.320000,101.290001,99.680000,100.519997,100.519997,14300000
1971-06-17,100.519997,101.370003,99.870003,100.500000,100.500000,13980000
1971-06-18,100.500000,100.629997,98.650002,98.970001,98.970001,15040000
1971-06-21,98.970001,99.180000,97.220001,97.870003,97.870003,16490000
1971-06-22,97.870003,98.660004,96.919998,97.589996,97.589996,15200000
1971-06-23,97.589996,98.949997,97.360001,98.410004,98.410004,12640000
1971-06-24,98.410004,99.000000,97.589996,98.129997,98.129997,11360000
1971-06-25,98.129997,98.660004,97.330002,97.989998,97.989998,10580000
1971-06-28,97.989998,98.480003,97.019997,97.739998,97.739998,9810000
1971-06-29,97.739998,99.389999,97.610001,98.820000,98.820000,14460000
1971-06-30,98.820000,100.290001,98.680000,98.699997,98.699997,15410000
1971-07-01,99.160004,100.650002,99.160004,99.779999,99.779999,13090000
1971-07-02,99.779999,100.309998,99.089996,99.779999,99.779999,9960000
1971-07-06,99.779999,100.349998,99.099998,99.760002,99.760002,10440000
1971-07-07,99.760002,100.830002,99.250000,100.040001,100.040001,14520000
1971-07-08,100.040001,101.029999,99.589996,100.339996,100.339996,13920000
1971-07-09,100.339996,101.330002,99.860001,100.690002,100.690002,12640000
1971-07-12,100.690002,101.519997,100.190002,100.820000,100.820000,12020000
1971-07-13,100.820000,101.059998,99.070000,99.500000,99.500000,13540000
1971-07-14,99.500000,99.830002,98.230003,99.220001,99.220001,14360000
1971-07-15,99.220001,100.480003,98.760002,99.279999,99.279999,13080000
1971-07-16,99.279999,100.349998,98.639999,99.110001,99.110001,13870000
1971-07-19,99.110001,99.570000,98.110001,98.930000,98.930000,11430000
1971-07-20,98.930000,100.010002,98.599998,99.320000,99.320000,12540000
1971-07-21,99.320000,100.000000,98.739998,99.279999,99.279999,11920000
1971-07-22,99.279999,99.820000,98.500000,99.110001,99.110001,12570000
1971-07-23,99.110001,99.599998,98.260002,98.940002,98.940002,12370000
1971-07-26,98.940002,99.470001,96.669998,98.139999,98.139999,9930000
1971-07-27,98.139999,98.989998,97.419998,97.779999,97.779999,11560000
1971-07-28,97.779999,98.150002,96.510002,97.070000,97.070000,13940000
1971-07-29,97.070000,97.220001,95.370003,96.019997,96.019997,14570000
1971-07-30,96.019997,96.779999,95.080002,95.580002,95.580002,12970000
1971-08-02,95.580002,96.760002,95.220001,95.959999,95.959999,11870000
1971-08-03,95.959999,96.110001,94.059998,94.510002,94.510002,13490000
1971-08-04,94.510002,95.339996,93.349998,93.889999,93.889999,15410000
1971-08-05,93.889999,94.889999,93.330002,94.089996,94.089996,12100000
1971-08-06,94.089996,94.910004,93.629997,94.250000,94.250000,9490000
1971-08-09,94.250000,94.550003,93.169998,93.529999,93.529999,8110000
1971-08-10,93.529999,94.129997,92.809998,93.540001,93.540001,9460000
1971-08-11,93.540001,95.059998,93.349998,94.660004,94.660004,11370000
1971-08-12,94.809998,96.500000,94.809998,96.000000,96.000000,15910000
1971-08-13,96.000000,96.529999,95.190002,95.690002,95.690002,9960000
1971-08-16,97.900002,100.959999,97.900002,98.760002,98.760002,31730000
1971-08-17,98.760002,101.000000,98.489998,99.989998,99.989998,26790000
1971-08-18,99.989998,100.190002,98.059998,98.599998,98.599998,20680000
1971-08-19,98.599998,99.070000,97.349998,98.160004,98.160004,14190000
1971-08-20,98.160004,98.940002,97.519997,98.330002,98.330002,11890000
1971-08-23,98.330002,99.959999,98.089996,99.250000,99.250000,13040000
1971-08-24,99.250000,101.019997,99.150002,100.400002,100.400002,18700000
1971-08-25,100.400002,101.510002,99.769997,100.410004,100.410004,18280000
1971-08-26,100.410004,101.120003,99.400002,100.239998,100.239998,13990000
1971-08-27,100.239998,101.220001,99.760002,100.480003,100.480003,12490000
1971-08-30,100.480003,100.889999,99.169998,99.519997,99.519997,11140000
1971-08-31,99.519997,99.760002,98.320000,99.029999,99.029999,10430000
1971-09-01,99.029999,99.839996,98.500000,99.070000,99.070000,10770000
1971-09-02,99.070000,99.800003,98.519997,99.290001,99.290001,10690000
1971-09-03,99.290001,100.930000,99.099998,100.690002,100.690002,14040000
1971-09-07,100.690002,102.250000,100.430000,101.150002,101.150002,17080000
1971-09-08,101.150002,101.940002,100.519997,101.339996,101.339996,14230000
1971-09-09,101.339996,101.879997,100.379997,100.800003,100.800003,15790000
1971-09-10,100.800003,101.010002,99.690002,100.419998,100.419998,11380000
1971-09-13,100.419998,100.839996,99.489998,100.070000,100.070000,10000000
1971-09-14,100.070000,100.349998,98.989998,99.339996,99.339996,11410000
1971-09-15,99.339996,100.239998,98.790001,99.769997,99.769997,11080000
1971-09-16,99.769997,100.349998,99.070000,99.660004,99.660004,10550000
1971-09-17,99.660004,100.519997,99.260002,99.959999,99.959999,11020000
1971-09-20,99.959999,100.400002,99.139999,99.680000,99.680000,9540000
1971-09-21,99.680000,100.080002,98.709999,99.339996,99.339996,10640000
1971-09-22,99.339996,99.720001,98.150002,98.470001,98.470001,14250000
1971-09-23,98.470001,99.120003,97.610001,98.379997,98.379997,13250000
1971-09-24,98.379997,99.349998,97.779999,98.150002,98.150002,13460000
1971-09-27,98.150002,98.410004,96.970001,97.620003,97.620003,10220000
1971-09-28,97.620003,98.550003,97.120003,97.879997,97.879997,11250000
1971-09-29,97.879997,98.510002,97.290001,97.900002,97.900002,8580000
1971-09-30,97.900002,98.970001,97.480003,98.339996,98.339996,13490000
1971-10-01,98.339996,99.489998,97.959999,98.930000,98.930000,13400000
1971-10-04,98.930000,100.040001,98.620003,99.209999,99.209999,14570000
1971-10-05,99.209999,99.779999,98.339996,99.110001,99.110001,12360000
1971-10-06,99.110001,100.129997,98.489998,99.820000,99.820000,15630000
1971-10-07,99.820000,100.959999,99.419998,100.019997,100.019997,17780000
1971-10-08,100.019997,100.300003,98.870003,99.360001,99.360001,13870000
1971-10-11,99.360001,99.620003,98.580002,99.209999,99.209999,7800000
1971-10-12,99.209999,100.199997,98.620003,99.570000,99.570000,14340000
1971-10-13,99.570000,100.080002,98.610001,99.029999,99.029999,13540000
1971-10-14,99.029999,99.250000,97.739998,98.129997,98.129997,12870000
1971-10-15,98.129997,98.449997,97.029999,97.790001,97.790001,13120000
1971-10-18,97.790001,98.330002,96.980003,97.349998,97.349998,10420000
1971-10-19,97.349998,97.660004,96.050003,97.000000,97.000000,13040000
1971-10-20,97.000000,97.449997,95.230003,95.650002,95.650002,16340000
1971-10-21,95.650002,96.330002,94.589996,95.599998,95.599998,14990000
1971-10-22,95.599998,96.830002,94.970001,95.570000,95.570000,14560000
1971-10-25,95.570000,95.760002,94.570000,95.099998,95.099998,7340000
1971-10-26,95.019997,95.019997,94.379997,94.739998,94.739998,13390000
1971-10-27,94.739998,94.989998,93.389999,93.790001,93.790001,13480000
1971-10-28,93.790001,94.750000,92.959999,93.959999,93.959999,15530000
1971-10-29,93.959999,94.709999,93.279999,94.230003,94.230003,11710000
1971-11-01,94.230003,94.430000,92.480003,92.800003,92.800003,10960000
1971-11-02,92.800003,93.730003,91.839996,93.180000,93.180000,13330000
1971-11-03,93.269997,95.309998,93.269997,94.910004,94.910004,14590000
1971-11-04,94.910004,96.080002,94.370003,94.790001,94.790001,15750000
1971-11-05,94.790001,95.010002,93.639999,94.459999,94.459999,10780000
1971-11-08,94.459999,94.970001,93.779999,94.389999,94.389999,8520000
1971-11-09,94.389999,95.309998,93.940002,94.459999,94.459999,12080000
1971-11-10,94.459999,94.839996,93.099998,93.410004,93.410004,13410000
1971-11-11,93.410004,93.540001,91.639999,92.120003,92.120003,13310000
1971-11-12,92.120003,92.900002,90.930000,92.120003,92.120003,14540000
1971-11-15,92.120003,92.690002,91.379997,91.809998,91.809998,9370000
1971-11-16,91.809998,93.150002,91.209999,92.709999,92.709999,13300000
1971-11-17,92.709999,93.349998,91.800003,92.849998,92.849998,12840000
1971-11-18,92.849998,93.620003,91.879997,92.129997,92.129997,13010000
1971-11-19,92.129997,92.379997,90.949997,91.610001,91.610001,12420000
1971-11-22,91.610001,92.120003,90.510002,90.790001,90.790001,11390000
1971-11-23,90.790001,91.099998,89.339996,90.160004,90.160004,16840000
1971-11-24,90.160004,91.139999,89.730003,90.330002,90.330002,11870000
1971-11-26,90.330002,92.190002,90.269997,91.940002,91.940002,10870000
1971-11-29,92.040001,94.900002,92.040001,93.410004,93.410004,18910000
1971-11-30,93.410004,94.430000,92.510002,93.989998,93.989998,18320000
1971-12-01,93.989998,96.120003,93.949997,95.440002,95.440002,21040000
1971-12-02,95.440002,96.589996,94.730003,95.839996,95.839996,17780000
1971-12-03,95.839996,97.570000,95.360001,97.059998,97.059998,16760000
1971-12-06,97.059998,98.169998,96.070000,96.510002,96.510002,17480000
1971-12-07,96.510002,97.349998,95.400002,96.870003,96.870003,15250000
1971-12-08,96.870003,97.650002,96.080002,96.919998,96.919998,16650000
1971-12-09,96.959999,96.959999,96.959999,96.959999,96.959999,14710000
1971-12-10,97.690002,97.690002,97.690002,97.690002,97.690002,17510000
1971-12-13,97.970001,97.970001,97.970001,97.970001,97.970001,17020000
1971-12-14,97.669998,97.669998,97.669998,97.669998,97.669998,16070000
1971-12-15,98.540001,98.540001,98.540001,98.540001,98.540001,16890000
1971-12-16,99.739998,99.739998,99.739998,99.739998,99.739998,21070000
1971-12-17,100.260002,100.260002,100.260002,100.260002,100.260002,18270000
1971-12-20,101.550003,101.550003,101.550003,101.550003,101.550003,23810000
1971-12-21,101.800003,101.800003,101.800003,101.800003,101.800003,20460000
1971-12-22,101.180000,101.180000,101.180000,101.180000,101.180000,18930000
1971-12-23,100.739998,100.739998,100.739998,100.739998,100.739998,16000000
1971-12-27,100.949997,100.949997,100.949997,100.949997,100.949997,11890000
1971-12-28,101.949997,101.949997,101.949997,101.949997,101.949997,15090000
1971-12-29,102.209999,102.209999,102.209999,102.209999,102.209999,17150000
1971-12-30,101.779999,101.779999,101.779999,101.779999,101.779999,13810000
1971-12-31,102.089996,102.089996,102.089996,102.089996,102.089996,14040000
1972-01-03,102.089996,102.849998,101.190002,101.669998,101.669998,12570000
1972-01-04,101.669998,102.589996,100.870003,102.089996,102.089996,15190000
1972-01-05,102.089996,103.690002,101.900002,103.059998,103.059998,21350000
1972-01-06,103.059998,104.199997,102.660004,103.510002,103.510002,21100000
1972-01-07,103.510002,104.290001,102.379997,103.470001,103.470001,17140000
1972-01-10,103.470001,103.970001,102.440002,103.320000,103.320000,15320000
1972-01-11,103.320000,104.300003,102.849998,103.650002,103.650002,17970000
1972-01-12,103.650002,104.660004,103.050003,103.589996,103.589996,20970000
1972-01-13,103.589996,103.800003,102.290001,102.989998,102.989998,16410000
1972-01-14,102.989998,103.889999,102.410004,103.389999,103.389999,14960000
1972-01-17,103.389999,104.239998,102.800003,103.699997,103.699997,15860000
1972-01-18,103.699997,104.849998,103.349998,104.050003,104.050003,21070000
1972-01-19,104.050003,104.610001,102.830002,103.879997,103.879997,18800000
1972-01-20,103.879997,105.000000,103.320000,103.879997,103.879997,20210000
1972-01-21,103.879997,104.400002,102.750000,103.650002,103.650002,18810000
1972-01-24,103.650002,104.029999,102.199997,102.570000,102.570000,15640000
1972-01-25,102.570000,103.589996,101.629997,102.699997,102.699997,17570000
1972-01-26,102.699997,103.309998,101.809998,102.500000,102.500000,14940000
1972-01-27,102.500000,103.930000,102.199997,103.500000,103.500000,20360000
1972-01-28,103.500000,104.980003,103.220001,104.160004,104.160004,25000000
1972-01-31,104.160004,104.879997,103.300003,103.940002,103.940002,18250000
1972-02-01,103.940002,104.570000,103.099998,104.010002,104.010002,19600000
1972-02-02,104.010002,105.410004,103.500000,104.680000,104.680000,24070000
1972-02-03,104.680000,105.430000,103.849998,104.639999,104.639999,19880000
1972-02-04,104.639999,105.480003,104.050003,104.860001,104.860001,17890000
1972-02-07,104.860001,105.459999,103.970001,104.540001,104.540001,16930000
1972-02-08,104.540001,105.220001,103.900002,104.739998,104.739998,17390000
1972-02-09,104.739998,106.029999,104.360001,105.550003,105.550003,19850000
1972-02-10,105.550003,106.690002,104.970001,105.589996,105.589996,23460000
1972-02-11,105.589996,105.910004,104.449997,105.080002,105.080002,17850000
1972-02-14,105.080002,105.529999,104.029999,104.589996,104.589996,15840000
1972-02-15,104.589996,105.589996,104.099998,105.029999,105.029999,17770000
1972-02-16,105.029999,106.250000,104.650002,105.620003,105.620003,20670000
1972-02-17,105.620003,106.650002,104.959999,105.589996,105.589996,22330000
1972-02-18,105.589996,106.010002,104.470001,105.279999,105.279999,16590000
1972-02-22,105.279999,106.180000,104.650002,105.290001,105.290001,16670000
1972-02-23,105.290001,106.180000,104.720001,105.379997,105.379997,16770000
1972-02-24,105.379997,106.239998,104.760002,105.449997,105.449997,16000000
1972-02-25,105.449997,106.730003,105.040001,106.180000,106.180000,18180000
1972-02-28,106.180000,107.040001,105.370003,106.190002,106.190002,18200000
1972-02-29,106.190002,107.160004,105.449997,106.570000,106.570000,20320000
1972-03-01,106.570000,108.129997,106.209999,107.349998,107.349998,23670000
1972-03-02,107.349998,108.389999,106.629997,107.320000,107.320000,22200000
1972-03-03,107.320000,108.510002,106.779999,107.940002,107.940002,20420000
1972-03-06,107.940002,109.400002,107.639999,108.769997,108.769997,21000000
1972-03-07,108.769997,109.720001,108.019997,108.870003,108.870003,22640000
1972-03-08,108.870003,109.680000,108.040001,108.959999,108.959999,21290000
1972-03-09,108.959999,109.750000,108.190002,108.940002,108.940002,21460000
1972-03-10,108.940002,109.370003,107.769997,108.379997,108.379997,19690000
1972-03-13,108.379997,108.519997,106.709999,107.330002,107.330002,16730000
1972-03-14,107.330002,108.199997,106.709999,107.610001,107.610001,22370000
1972-03-15,107.610001,108.550003,107.089996,107.750000,107.750000,19460000
1972-03-16,107.750000,108.220001,106.550003,107.500000,107.500000,16700000
1972-03-17,107.500000,108.610001,106.889999,107.919998,107.919998,16040000
1972-03-20,107.919998,108.809998,107.180000,107.589996,107.589996,16420000
1972-03-21,107.589996,107.680000,105.860001,106.690002,106.690002,18610000
1972-03-22,106.690002,107.519997,106.000000,106.839996,106.839996,15400000
1972-03-23,106.839996,108.330002,106.669998,107.750000,107.750000,18380000
1972-03-24,107.750000,108.360001,106.949997,107.519997,107.519997,15390000
1972-03-27,107.519997,108.000000,106.529999,107.300003,107.300003,12180000
1972-03-28,107.300003,108.080002,106.220001,107.169998,107.169998,15380000
1972-03-29,107.169998,107.410004,105.980003,106.489998,106.489998,13860000
1972-03-30,106.489998,107.669998,106.070000,107.199997,107.199997,14360000
1972-04-03,107.199997,108.260002,106.750000,107.480003,107.480003,14990000
1972-04-04,107.480003,108.620003,106.769997,108.120003,108.120003,18110000
1972-04-05,108.120003,109.639999,107.959999,109.000000,109.000000,22960000
1972-04-06,109.000000,110.290001,108.529999,109.529999,109.529999,22830000
1972-04-07,109.529999,110.150002,108.529999,109.620003,109.620003,19900000
1972-04-10,109.620003,110.540001,108.889999,109.449997,109.449997,19470000
1972-04-11,109.449997,110.379997,108.760002,109.760002,109.760002,19930000
1972-04-12,109.760002,111.110001,109.360001,110.180000,110.180000,24690000
1972-04-13,110.180000,110.790001,109.370003,109.910004,109.910004,17990000
1972-04-14,109.910004,110.559998,109.070000,109.839996,109.839996,17460000
1972-04-17,109.839996,110.220001,108.769997,109.510002,109.510002,15390000
1972-04-18,109.510002,110.639999,109.019997,109.769997,109.769997,19410000
1972-04-19,109.769997,110.349998,108.709999,109.199997,109.199997,19180000
1972-04-20,109.199997,109.690002,108.080002,109.040001,109.040001,18190000
1972-04-21,109.040001,109.919998,108.300003,108.889999,108.889999,18200000
1972-04-24,108.889999,109.190002,107.620003,108.190002,108.190002,14650000
1972-04-25,108.190002,108.290001,106.699997,107.120003,107.120003,17030000
1972-04-26,107.120003,107.889999,106.180000,106.889999,106.889999,17710000
1972-04-27,106.889999,107.889999,106.419998,107.050003,107.050003,15740000
1972-04-28,107.050003,108.279999,106.699997,107.669998,107.669998,14160000
1972-05-01,107.669998,108.000000,106.300003,106.690002,106.690002,12880000
1972-05-02,106.690002,107.370003,105.550003,106.080002,106.080002,15370000
1972-05-03,106.080002,107.239998,105.440002,105.989998,105.989998,15900000
1972-05-04,105.989998,106.809998,105.139999,106.250000,106.250000,14790000
1972-05-05,106.250000,107.330002,105.699997,106.629997,106.629997,13210000
1972-05-08,106.629997,106.809998,105.360001,106.139999,106.139999,11250000
1972-05-09,106.059998,106.059998,103.830002,104.739998,104.739998,19910000
1972-05-10,104.739998,106.099998,104.430000,105.419998,105.419998,13870000
1972-05-11,105.419998,106.449997,104.900002,105.769997,105.769997,12900000
1972-05-12,105.769997,107.019997,105.489998,106.379997,106.379997,13990000
1972-05-15,106.379997,107.449997,106.059998,106.860001,106.860001,13600000
1972-05-16,106.860001,107.550003,106.129997,106.660004,106.660004,14070000
1972-05-17,106.660004,107.379997,106.019997,106.889999,106.889999,13600000
1972-05-18,106.889999,108.389999,106.720001,107.940002,107.940002,17370000
1972-05-19,107.940002,109.589996,107.739998,108.980003,108.980003,19580000
1972-05-22,108.980003,110.370003,108.790001,109.690002,109.690002,16030000
1972-05-23,109.690002,110.459999,108.910004,109.779999,109.779999,16410000
1972-05-24,109.779999,111.070000,109.389999,110.309998,110.309998,17870000
1972-05-25,110.309998,111.199997,109.669998,110.459999,110.459999,16480000
1972-05-26,110.459999,111.309998,109.839996,110.660004,110.660004,15730000
1972-05-30,110.660004,111.480003,109.779999,110.349998,110.349998,15810000
1972-05-31,110.349998,110.519997,108.919998,109.529999,109.529999,15230000
1972-06-01,109.529999,110.349998,108.970001,109.690002,109.690002,14910000
1972-06-02,109.690002,110.510002,108.930000,109.730003,109.730003,15400000
1972-06-05,109.730003,109.919998,108.279999,108.820000,108.820000,13450000
1972-06-06,108.820000,109.320000,107.709999,108.209999,108.209999,15980000
1972-06-07,108.209999,108.519997,106.910004,107.650002,107.650002,15220000
1972-06-08,107.650002,108.519997,106.900002,107.279999,107.279999,13820000
1972-06-09,107.279999,107.680000,106.300003,106.860001,106.860001,12790000
1972-06-12,106.860001,107.919998,106.290001,107.010002,107.010002,13390000
1972-06-13,107.010002,108.029999,106.379997,107.550003,107.550003,15710000
1972-06-14,107.550003,109.150002,107.379997,108.389999,108.389999,18320000
1972-06-15,108.389999,109.519997,107.779999,108.440002,108.440002,16940000
1972-06-16,108.440002,108.940002,107.540001,108.360001,108.360001,13010000
1972-06-19,108.360001,108.779999,107.370003,108.110001,108.110001,11660000
1972-06-20,108.110001,109.120003,107.639999,108.559998,108.559998,14970000
1972-06-21,108.559998,109.660004,107.980003,108.790001,108.790001,15510000
1972-06-22,108.790001,109.260002,107.620003,108.680000,108.680000,13410000
1972-06-23,108.680000,109.330002,107.690002,108.269997,108.269997,13940000
1972-06-26,108.230003,108.230003,106.680000,107.480003,107.480003,12720000
1972-06-27,107.480003,108.290001,106.699997,107.370003,107.370003,13750000
1972-06-28,107.370003,107.870003,106.489998,107.019997,107.019997,12140000
1972-06-29,107.019997,107.470001,105.940002,106.820000,106.820000,14610000
1972-06-30,106.820000,107.910004,106.400002,107.139999,107.139999,12860000
1972-07-03,107.139999,107.949997,106.720001,107.489998,107.489998,8140000
1972-07-05,107.489998,108.800003,107.139999,108.099998,108.099998,14710000
1972-07-06,108.279999,110.269997,108.279999,109.040001,109.040001,19520000
1972-07-07,109.040001,109.660004,108.160004,108.690002,108.690002,12900000
1972-07-10,108.690002,109.160004,107.620003,108.110001,108.110001,11700000
1972-07-11,108.110001,108.349998,106.870003,107.320000,107.320000,12830000
1972-07-12,107.320000,108.150002,106.419998,106.889999,106.889999,16150000
1972-07-13,106.889999,107.300003,105.620003,106.279999,106.279999,14740000
1972-07-14,106.279999,107.580002,105.769997,106.800003,106.800003,13910000
1972-07-17,106.800003,107.370003,105.550003,105.879997,105.879997,13170000
1972-07-18,105.879997,106.400002,104.430000,105.830002,105.830002,16820000
1972-07-19,105.830002,107.360001,105.470001,106.139999,106.139999,17880000
1972-07-20,106.139999,106.680000,105.120003,105.809998,105.809998,15050000
1972-07-21,105.809998,107.050003,104.989998,106.660004,106.660004,14010000
1972-07-24,106.660004,108.669998,106.629997,107.919998,107.919998,18020000
1972-07-25,107.919998,108.879997,107.059998,107.599998,107.599998,17180000
1972-07-26,107.599998,108.419998,106.790001,107.529999,107.529999,14130000
1972-07-27,107.529999,108.309998,106.610001,107.279999,107.279999,13870000
1972-07-28,107.279999,108.029999,106.519997,107.379997,107.379997,13050000
1972-07-31,107.379997,108.059998,106.599998,107.389999,107.389999,11120000
1972-08-01,107.389999,108.849998,107.059998,108.400002,108.400002,15540000
1972-08-02,108.400002,109.849998,108.120003,109.290001,109.290001,17920000
1972-08-03,109.290001,110.879997,108.900002,110.139999,110.139999,19970000
1972-08-04,110.139999,111.120003,109.370003,110.430000,110.430000,15700000
1972-08-07,110.430000,111.379997,109.690002,110.610001,110.610001,13220000
1972-08-08,110.610001,111.320000,109.669998,110.690002,110.690002,14550000
1972-08-09,110.690002,111.570000,109.980003,110.860001,110.860001,15730000
1972-08-10,110.860001,111.680000,110.089996,111.050003,111.050003,15260000
1972-08-11,111.050003,112.400002,110.519997,111.949997,111.949997,16570000
1972-08-14,111.949997,113.449997,111.660004,112.550003,112.550003,18870000
1972-08-15,112.550003,113.040001,111.269997,112.059998,112.059998,16670000
1972-08-16,112.059998,112.800003,110.870003,111.660004,111.660004,14950000
1972-08-17,111.660004,112.410004,110.720001,111.339996,111.339996,14360000
1972-08-18,111.339996,112.529999,110.809998,111.760002,111.760002,16150000
1972-08-21,111.760002,112.739998,110.750000,111.720001,111.720001,14290000
1972-08-22,111.720001,113.160004,111.279999,112.410004,112.410004,18560000
1972-08-23,112.410004,113.269997,111.300003,112.260002,112.260002,18670000
1972-08-24,112.260002,112.809998,110.620003,111.019997,111.019997,18280000
1972-08-25,111.019997,111.529999,109.779999,110.669998,110.669998,13840000
1972-08-28,110.669998,111.239998,109.709999,110.230003,110.230003,10720000
1972-08-29,110.230003,111.019997,109.260002,110.410004,110.410004,12300000
1972-08-30,110.410004,111.330002,109.900002,110.570000,110.570000,12470000
1972-08-31,110.570000,111.519997,110.080002,111.089996,111.089996,12340000
1972-09-01,111.089996,112.120003,110.699997,111.510002,111.510002,11600000
1972-09-05,111.510002,112.080002,110.750000,111.230003,111.230003,10630000
1972-09-06,111.230003,111.379997,110.040001,110.550003,110.550003,12010000
1972-09-07,110.550003,111.059998,109.709999,110.290001,110.290001,11090000
1972-09-08,110.290001,110.900002,109.669998,110.150002,110.150002,10980000
1972-09-11,110.150002,110.570000,109.010002,109.510002,109.510002,10710000
1972-09-12,109.510002,109.839996,107.809998,108.470001,108.470001,13560000
1972-09-13,108.470001,109.360001,107.839996,108.900002,108.900002,13070000
1972-09-14,108.900002,109.639999,108.209999,108.930000,108.930000,12500000
1972-09-15,108.930000,109.489998,108.099998,108.809998,108.809998,11690000
1972-09-18,108.809998,109.220001,107.860001,108.610001,108.610001,8880000
1972-09-19,108.610001,109.570000,108.080002,108.550003,108.550003,13330000
1972-09-20,108.550003,109.120003,107.839996,108.599998,108.599998,11980000
1972-09-21,108.599998,109.129997,107.750000,108.430000,108.430000,11940000
1972-09-22,108.430000,109.199997,107.720001,108.519997,108.519997,12570000
1972-09-25,108.519997,109.089996,107.669998,108.050003,108.050003,10920000
1972-09-26,108.050003,108.970001,107.349998,108.120003,108.120003,13150000
1972-09-27,108.120003,109.919998,107.790001,109.660004,109.660004,14620000
1972-09-28,109.660004,110.750000,108.750000,110.349998,110.349998,14710000
1972-09-29,110.349998,110.550003,108.050003,110.550003,110.550003,16250000
1972-10-02,110.550003,110.980003,109.489998,110.160004,110.160004,12440000
1972-10-03,110.160004,110.900002,109.470001,110.300003,110.300003,13090000
1972-10-04,110.300003,111.349998,109.580002,110.089996,110.089996,16640000
1972-10-05,110.089996,110.519997,108.489998,108.889999,108.889999,17730000
1972-10-06,108.889999,110.489998,107.779999,109.620003,109.620003,16630000
1972-10-09,109.620003,110.440002,109.279999,109.900002,109.900002,7940000
1972-10-10,109.900002,111.110001,109.320000,109.989998,109.989998,13310000
1972-10-11,109.989998,110.510002,108.769997,109.500000,109.500000,11900000
1972-10-12,109.500000,109.690002,108.029999,108.599998,108.599998,13130000
1972-10-13,108.599998,108.879997,107.169998,107.919998,107.919998,12870000
1972-10-16,107.919998,108.400002,106.379997,106.769997,106.769997,10940000
1972-10-17,106.769997,108.040001,106.269997,107.500000,107.500000,13410000
1972-10-18,107.500000,109.110001,107.360001,108.190002,108.190002,17290000
1972-10-19,108.190002,108.809998,107.400002,108.050003,108.050003,13850000
1972-10-20,108.050003,109.790001,107.589996,109.239998,109.239998,15740000
1972-10-23,109.510002,111.099998,109.510002,110.349998,110.349998,14190000
1972-10-24,110.349998,111.339996,109.379997,110.809998,110.809998,15240000
1972-10-25,110.809998,111.559998,109.959999,110.720001,110.720001,17430000
1972-10-26,110.720001,112.260002,110.260002,110.989998,110.989998,20790000
1972-10-27,110.989998,111.620003,109.989998,110.620003,110.620003,15470000
1972-10-30,110.620003,111.190002,109.660004,110.589996,110.589996,11820000
1972-10-31,110.589996,112.050003,110.400002,111.580002,111.580002,15450000
1972-11-01,111.580002,113.309998,111.320000,112.669998,112.669998,21360000
1972-11-02,112.669998,113.809998,111.959999,113.230003,113.230003,20690000
1972-11-03,113.230003,114.809998,112.709999,114.220001,114.220001,22510000
1972-11-06,114.220001,115.169998,112.910004,113.980003,113.980003,21330000
1972-11-08,113.980003,115.230003,112.769997,113.349998,113.349998,24620000
1972-11-09,113.349998,114.110001,112.080002,113.500000,113.500000,17040000
1972-11-10,113.500000,115.150002,112.849998,113.730003,113.730003,24360000
1972-11-13,113.730003,114.750000,112.910004,113.900002,113.900002,17210000
1972-11-14,113.900002,115.410004,113.360001,114.949997,114.949997,20200000
1972-11-15,114.949997,116.070000,113.870003,114.500000,114.500000,23270000
1972-11-16,114.500000,115.570000,113.730003,115.129997,115.129997,19580000
1972-11-17,115.129997,116.230003,114.440002,115.489998,115.489998,20220000
1972-11-20,115.489998,116.250000,114.570000,115.529999,115.529999,16680000
1972-11-21,115.529999,116.839996,115.040001,116.209999,116.209999,22110000
1972-11-22,116.209999,117.610001,115.669998,116.900002,116.900002,24510000
1972-11-24,116.900002,117.910004,116.190002,117.269997,117.269997,15760000
1972-11-27,117.269997,117.550003,115.660004,116.720001,116.720001,18190000
1972-11-28,116.720001,117.480003,115.779999,116.470001,116.470001,19210000
1972-11-29,116.470001,117.139999,115.559998,116.519997,116.519997,17380000
1972-11-30,116.519997,117.389999,115.739998,116.669998,116.669998,19340000
1972-12-01,116.669998,118.180000,116.290001,117.379997,117.379997,22570000
1972-12-04,117.379997,118.540001,116.989998,117.769997,117.769997,19730000
1972-12-05,117.769997,118.419998,116.889999,117.580002,117.580002,17800000
1972-12-06,117.580002,118.559998,116.900002,118.010002,118.010002,18610000
1972-12-07,118.010002,119.169998,117.570000,118.599998,118.599998,19320000
1972-12-08,118.599998,119.540001,117.919998,118.860001,118.860001,18030000
1972-12-11,118.860001,119.779999,118.239998,119.120003,119.120003,17230000
1972-12-12,119.120003,119.790001,118.089996,118.660004,118.660004,17040000
1972-12-13,118.660004,119.230003,117.769997,118.559998,118.559998,16540000
1972-12-14,118.559998,119.190002,117.629997,118.239998,118.239998,17930000
1972-12-15,118.239998,119.250000,117.370003,118.260002,118.260002,18300000
1972-12-18,117.879997,117.879997,115.889999,116.900002,116.900002,17540000
1972-12-19,116.900002,117.370003,115.690002,116.339996,116.339996,17000000
1972-12-20,116.339996,117.129997,115.379997,115.949997,115.949997,18490000
1972-12-21,115.949997,116.599998,114.629997,115.110001,115.110001,18290000
1972-12-22,115.110001,116.400002,114.779999,115.830002,115.830002,12540000
1972-12-26,115.830002,116.870003,115.540001,116.300003,116.300003,11120000
1972-12-27,116.300003,117.550003,115.889999,116.930000,116.930000,19100000
1972-12-29,116.930000,118.769997,116.699997,118.050003,118.050003,27550000
1973-01-02,118.059998,119.900002,118.059998,119.099998,119.099998,17090000
1973-01-03,119.099998,120.449997,118.690002,119.570000,119.570000,20620000
1973-01-04,119.570000,120.169998,118.120003,119.400002,119.400002,20230000
1973-01-05,119.400002,120.709999,118.879997,119.870003,119.870003,19330000
1973-01-08,119.870003,120.550003,119.040001,119.849998,119.849998,16840000
1973-01-09,119.849998,120.400002,118.889999,119.730003,119.730003,16830000
1973-01-10,119.730003,120.440002,118.779999,119.430000,119.430000,20880000
1973-01-11,119.430000,121.739998,119.010002,120.239998,120.239998,25050000
1973-01-12,120.239998,121.269997,118.690002,119.300003,119.300003,22230000
1973-01-15,119.300003,120.820000,118.040001,118.440002,118.440002,21520000
1973-01-16,118.440002,119.169998,117.040001,118.139999,118.139999,19170000
1973-01-17,118.139999,119.349998,117.610001,118.680000,118.680000,17680000
1973-01-18,118.680000,119.930000,118.150002,118.849998,118.849998,17810000
1973-01-19,118.849998,119.449997,117.459999,118.779999,118.779999,17020000
1973-01-22,118.779999,119.629997,117.720001,118.209999,118.209999,15570000
1973-01-23,118.209999,119.000000,116.839996,118.220001,118.220001,19060000
1973-01-24,118.220001,119.040001,116.089996,116.730003,116.730003,20870000
1973-01-26,116.730003,117.290001,114.970001,116.449997,116.449997,21130000
1973-01-29,116.449997,117.180000,115.129997,116.010002,116.010002,14680000
1973-01-30,116.010002,117.110001,115.260002,115.830002,115.830002,15270000
1973-01-31,115.830002,116.839996,115.050003,116.029999,116.029999,14870000
1973-02-01,116.029999,117.010002,114.260002,114.760002,114.760002,20670000
1973-02-02,114.760002,115.400002,113.449997,114.349998,114.349998,17470000
1973-02-05,114.349998,115.150002,113.620003,114.230003,114.230003,14580000
1973-02-06,114.230003,115.330002,113.449997,114.449997,114.449997,15720000
1973-02-07,114.449997,115.480003,113.239998,113.660004,113.660004,17960000
1973-02-08,113.660004,114.050003,111.849998,113.160004,113.160004,18440000
1973-02-09,113.160004,115.199997,113.080002,114.680000,114.680000,19260000
1973-02-12,114.690002,116.660004,114.690002,116.059998,116.059998,16130000
1973-02-13,116.089996,118.980003,116.089996,116.779999,116.779999,25320000
1973-02-14,116.779999,116.919998,114.519997,115.099998,115.099998,16520000
1973-02-15,115.099998,115.680000,113.699997,114.449997,114.449997,13940000
1973-02-16,114.449997,115.470001,113.730003,114.980003,114.980003,13320000
1973-02-20,114.980003,116.260002,114.570000,115.400002,115.400002,14020000
1973-02-21,115.400002,116.010002,114.129997,114.690002,114.690002,14880000
1973-02-22,114.690002,115.199997,113.440002,114.440002,114.440002,14570000
1973-02-23,114.440002,114.669998,112.769997,113.160004,113.160004,15450000
1973-02-26,113.160004,113.260002,111.150002,112.190002,112.190002,15860000
1973-02-27,112.190002,112.900002,110.500000,110.900002,110.900002,16130000
1973-02-28,110.900002,112.209999,109.800003,111.680000,111.680000,17950000
1973-03-01,111.680000,112.980003,110.680000,111.050003,111.050003,18210000
1973-03-02,111.050003,112.620003,109.449997,112.279999,112.279999,17710000
1973-03-05,112.279999,113.430000,111.330002,112.680000,112.680000,13720000
1973-03-06,112.680000,114.709999,112.570000,114.099998,114.099998,17710000
1973-03-07,114.099998,115.120003,112.830002,114.449997,114.449997,19310000
1973-03-08,114.449997,115.230003,113.570000,114.230003,114.230003,15100000
1973-03-09,114.230003,114.550003,112.930000,113.790001,113.790001,14070000
1973-03-12,113.790001,114.800003,113.250000,113.860001,113.860001,13810000
1973-03-13,113.860001,115.050003,113.320000,114.480003,114.480003,14210000
1973-03-14,114.480003,115.610001,113.970001,114.980003,114.980003,14460000
1973-03-15,114.980003,115.470001,113.769997,114.120003,114.120003,14450000
1973-03-16,114.120003,114.620003,112.839996,113.540001,113.540001,15130000
1973-03-19,113.500000,113.500000,111.650002,112.169998,112.169998,12460000
1973-03-20,112.169998,112.680000,111.019997,111.949997,111.949997,13250000
1973-03-21,111.949997,112.809998,110.169998,110.489998,110.489998,16080000
1973-03-22,110.389999,110.389999,108.190002,108.839996,108.839996,17130000
1973-03-23,108.839996,109.970001,107.410004,108.879997,108.879997,18470000
1973-03-26,108.879997,110.400002,108.290001,109.839996,109.839996,14980000
1973-03-27,109.949997,112.070000,109.949997,111.559998,111.559998,17500000
1973-03-28,111.559998,112.470001,110.540001,111.620003,111.620003,15850000
1973-03-29,111.620003,113.220001,111.070000,112.709999,112.709999,16050000
1973-03-30,112.709999,112.870003,110.889999,111.519997,111.519997,13740000
1973-04-02,111.519997,111.699997,109.680000,110.180000,110.180000,10640000
1973-04-03,110.180000,110.349998,108.470001,109.239998,109.239998,12910000
1973-04-04,109.239998,109.959999,108.099998,108.769997,108.769997,11890000
1973-04-05,108.769997,109.150002,107.440002,108.519997,108.519997,12750000
1973-04-06,108.519997,110.040001,108.220001,109.279999,109.279999,13890000
1973-04-09,109.279999,111.239998,108.739998,110.860001,110.860001,13740000
1973-04-10,110.919998,112.849998,110.919998,112.209999,112.209999,16770000
1973-04-11,112.209999,113.269997,111.209999,112.680000,112.680000,14890000
1973-04-12,112.680000,113.650002,111.830002,112.580002,112.580002,16360000
1973-04-13,112.580002,112.910004,111.230003,112.080002,112.080002,14390000
1973-04-16,112.080002,112.610001,110.910004,111.440002,111.440002,11350000
1973-04-17,111.440002,111.809998,110.190002,110.940002,110.940002,12830000
1973-04-18,110.940002,112.029999,109.989998,111.540001,111.540001,13890000
1973-04-19,111.540001,112.930000,111.059998,112.169998,112.169998,14560000
1973-04-23,112.169998,112.660004,110.910004,111.570000,111.570000,12580000
1973-04-24,111.570000,111.889999,109.639999,109.989998,109.989998,13830000
1973-04-25,109.820000,109.820000,107.790001,108.339996,108.339996,15960000
1973-04-26,108.339996,109.660004,107.139999,108.889999,108.889999,16210000
1973-04-27,108.889999,109.279999,106.760002,107.230003,107.230003,13730000
1973-04-30,107.230003,107.900002,105.440002,106.970001,106.970001,14820000
1973-05-01,106.970001,108.000000,105.339996,107.099998,107.099998,15380000
1973-05-02,107.099998,109.059998,106.949997,108.430000,108.430000,14380000
1973-05-03,108.430000,110.639999,106.809998,110.220001,110.220001,17760000
1973-05-04,110.220001,111.989998,109.889999,111.000000,111.000000,19510000
1973-05-07,111.000000,111.379997,109.680000,110.529999,110.529999,12500000
1973-05-08,110.529999,111.720001,109.459999,111.250000,111.250000,13730000
1973-05-09,111.250000,112.250000,109.970001,110.440002,110.440002,16050000
1973-05-10,110.440002,110.860001,108.860001,109.540001,109.540001,13520000
1973-05-11,109.489998,109.489998,107.699997,108.169998,108.169998,12980000
1973-05-14,107.739998,107.739998,105.519997,105.900002,105.900002,13520000
1973-05-15,105.900002,107.160004,104.120003,106.570000,106.570000,18530000
1973-05-16,106.570000,107.610001,105.489998,106.430000,106.430000,13800000
1973-05-17,106.430000,106.820000,105.150002,105.559998,105.559998,13060000
1973-05-18,105.410004,105.410004,103.180000,103.860001,103.860001,17080000
1973-05-21,103.769997,103.769997,101.360001,102.730003,102.730003,20690000
1973-05-22,102.730003,105.040001,102.580002,103.580002,103.580002,18020000
1973-05-23,103.580002,105.099998,102.820000,104.070000,104.070000,14950000
1973-05-24,104.070000,107.440002,103.589996,107.139999,107.139999,17310000
1973-05-25,107.139999,108.860001,106.080002,107.940002,107.940002,19270000
1973-05-29,107.940002,108.580002,106.769997,107.510002,107.510002,11300000
1973-05-30,107.510002,107.639999,105.480003,105.910004,105.910004,11730000
1973-05-31,105.910004,106.300003,104.349998,104.949997,104.949997,12190000
1973-06-01,104.949997,105.040001,103.309998,103.930000,103.930000,10410000
1973-06-04,103.930000,103.980003,102.330002,102.970001,102.970001,11230000
1973-06-05,102.970001,105.269997,102.610001,104.620003,104.620003,14080000
1973-06-06,104.620003,105.779999,103.599998,104.309998,104.309998,13080000
1973-06-07,104.309998,106.389999,104.190002,105.839996,105.839996,14160000
1973-06-08,105.839996,107.750000,105.599998,107.029999,107.029999,14050000
1973-06-11,107.029999,107.790001,106.110001,106.699997,106.699997,9940000
1973-06-12,106.699997,108.779999,106.400002,108.290001,108.290001,13840000
1973-06-13,108.290001,109.519997,107.080002,107.599998,107.599998,15700000
1973-06-14,107.599998,108.269997,105.830002,106.400002,106.400002,13210000
1973-06-15,106.209999,106.209999,104.370003,105.099998,105.099998,11970000
1973-06-18,104.959999,104.959999,103.080002,103.599998,103.599998,11460000
1973-06-19,103.599998,104.959999,102.459999,103.989998,103.989998,12970000
1973-06-20,103.989998,105.129997,103.510002,104.440002,104.440002,10600000
1973-06-21,104.440002,104.769997,102.839996,103.209999,103.209999,11630000
1973-06-22,103.209999,105.660004,103.070000,103.699997,103.699997,18470000
1973-06-25,103.639999,103.639999,101.709999,102.250000,102.250000,11670000
1973-06-26,102.250000,103.779999,101.449997,103.300003,103.300003,14040000
1973-06-27,103.300003,104.230003,102.290001,103.620003,103.620003,12660000
1973-06-28,103.620003,105.169998,103.180000,104.690002,104.690002,12760000
1973-06-29,104.690002,105.300003,103.680000,104.260002,104.260002,10770000
1973-07-02,104.099998,104.099998,102.440002,102.900002,102.900002,9830000
1973-07-03,102.900002,103.019997,101.139999,101.870003,101.870003,10560000
1973-07-05,101.870003,102.480003,100.800003,101.779999,101.779999,10500000
1973-07-06,101.779999,102.220001,100.669998,101.279999,101.279999,9980000
1973-07-09,101.279999,102.449997,100.440002,102.139999,102.139999,11560000
1973-07-10,102.260002,104.199997,102.260002,103.519997,103.519997,15090000
1973-07-11,103.639999,106.209999,103.639999,105.800003,105.800003,18730000
1973-07-12,105.800003,106.620003,104.379997,105.500000,105.500000,16400000
1973-07-13,105.500000,105.800003,103.660004,104.089996,104.089996,11390000
1973-07-16,104.089996,106.010002,103.419998,105.669998,105.669998,12920000
1973-07-17,105.669998,107.279999,104.989998,105.720001,105.720001,18750000
1973-07-18,105.720001,107.050003,104.730003,106.349998,106.349998,17020000
1973-07-19,106.349998,107.580002,105.059998,106.550003,106.550003,18650000
1973-07-20,106.550003,108.019997,105.949997,107.139999,107.139999,16300000
1973-07-23,107.139999,108.419998,106.540001,107.519997,107.519997,15580000
1973-07-24,107.519997,108.629997,106.309998,108.139999,108.139999,16280000
1973-07-25,108.139999,110.760002,107.919998,109.639999,109.639999,22220000
1973-07-26,109.639999,111.040001,108.510002,109.849998,109.849998,18410000
1973-07-27,109.849998,110.489998,108.699997,109.589996,109.589996,12910000
1973-07-30,109.589996,110.120003,108.239998,109.250000,109.250000,11170000
1973-07-31,109.250000,110.089996,107.889999,108.220001,108.220001,13530000
1973-08-01,108.169998,108.169998,106.290001,106.830002,106.830002,13530000
1973-08-02,106.830002,107.379997,105.510002,106.669998,106.669998,16080000
1973-08-03,106.669998,107.169998,105.680000,106.489998,106.489998,9940000
1973-08-06,106.489998,107.540001,105.449997,106.730003,106.730003,12320000
1973-08-07,106.730003,107.570000,105.870003,106.550003,106.550003,13510000
1973-08-08,106.550003,106.730003,105.040001,105.550003,105.550003,12440000
1973-08-09,105.550003,106.650002,104.889999,105.610001,105.610001,12880000
1973-08-10,105.610001,106.029999,104.209999,104.769997,104.769997,10870000
1973-08-13,104.769997,104.830002,103.129997,103.709999,103.709999,11330000
1973-08-14,103.709999,104.290001,102.339996,102.709999,102.709999,11740000
1973-08-15,102.709999,103.790001,101.919998,103.010002,103.010002,12040000
1973-08-16,103.010002,103.970001,101.849998,102.290001,102.290001,12990000
1973-08-17,102.290001,102.980003,101.379997,102.309998,102.309998,11110000
1973-08-20,102.309998,102.540001,101.110001,101.610001,101.610001,8970000
1973-08-21,101.610001,102.099998,100.510002,100.889999,100.889999,11480000
1973-08-22,100.889999,101.389999,99.739998,100.529999,100.529999,10770000
1973-08-23,100.620003,102.500000,100.620003,101.910004,101.910004,11390000
1973-08-24,101.910004,102.650002,100.879997,101.620003,101.620003,11200000
1973-08-27,101.620003,102.820000,101.089996,102.419998,102.419998,9740000
1973-08-28,102.419998,103.660004,102.059998,103.019997,103.019997,11810000
1973-08-29,103.019997,104.919998,102.690002,104.029999,104.029999,15690000
1973-08-30,104.029999,104.839996,103.290001,103.879997,103.879997,12100000
1973-08-31,103.879997,104.720001,103.150002,104.250000,104.250000,10530000
1973-09-04,104.250000,105.349998,103.599998,104.510002,104.510002,14210000
1973-09-05,104.510002,105.330002,103.599998,104.639999,104.639999,14580000
1973-09-06,104.639999,105.949997,104.050003,105.150002,105.150002,15670000
1973-09-07,105.150002,105.870003,104.040001,104.760002,104.760002,14930000
1973-09-10,104.760002,105.120003,103.330002,103.849998,103.849998,11620000
1973-09-11,103.849998,104.089996,102.129997,103.220001,103.220001,12690000
1973-09-12,103.220001,103.980003,102.150002,103.059998,103.059998,12040000
1973-09-13,103.059998,104.089996,102.370003,103.360001,103.360001,11670000
1973-09-14,103.360001,104.750000,102.660004,104.440002,104.440002,13760000
1973-09-17,104.440002,105.410004,103.209999,104.150002,104.150002,15100000
1973-09-18,104.150002,104.620003,102.410004,103.769997,103.769997,16400000
1973-09-19,103.800003,106.430000,103.800003,105.879997,105.879997,24570000
1973-09-20,105.879997,107.550003,105.320000,106.760002,106.760002,25960000
1973-09-21,106.760002,108.019997,105.430000,107.199997,107.199997,23760000
1973-09-24,107.199997,108.360001,106.209999,107.360001,107.360001,19490000
1973-09-25,107.360001,108.790001,106.500000,108.050003,108.050003,21530000
1973-09-26,108.050003,109.610001,107.430000,108.830002,108.830002,21130000
1973-09-27,108.830002,110.449997,108.019997,109.080002,109.080002,23660000
1973-09-28,109.080002,109.419998,107.480003,108.430000,108.430000,16300000
1973-10-01,108.430000,108.980003,107.080002,108.209999,108.209999,15830000
1973-10-02,108.209999,109.459999,107.480003,108.790001,108.790001,20770000
1973-10-03,108.790001,109.949997,107.739998,108.779999,108.779999,22040000
1973-10-04,108.779999,109.529999,107.300003,108.410004,108.410004,19730000
1973-10-05,108.410004,110.459999,107.760002,109.849998,109.849998,18820000
1973-10-08,109.849998,110.930000,108.019997,110.230003,110.230003,18990000
1973-10-09,110.230003,111.190002,109.050003,110.129997,110.129997,19440000
1973-10-10,110.129997,111.309998,108.510002,109.220001,109.220001,19010000
1973-10-11,109.220001,111.769997,108.959999,111.089996,111.089996,20740000
1973-10-12,111.089996,112.820000,110.519997,111.440002,111.440002,22730000
1973-10-15,111.320000,111.320000,109.290001,110.050003,110.050003,16160000
1973-10-16,110.050003,110.800003,108.500000,110.190002,110.190002,18780000
1973-10-17,110.190002,111.410004,109.190002,109.970001,109.970001,18600000
1973-10-18,109.970001,111.430000,108.970001,110.010002,110.010002,19210000
1973-10-19,110.010002,111.559998,109.300003,110.220001,110.220001,17880000
1973-10-22,110.220001,110.559998,108.180000,109.160004,109.160004,14290000
1973-10-23,109.160004,110.910004,107.400002,109.750000,109.750000,17230000
1973-10-24,109.750000,110.980003,109.029999,110.269997,110.269997,15840000
1973-10-25,110.269997,111.330002,108.849998,110.500000,110.500000,15580000
1973-10-26,110.500000,112.309998,110.080002,111.379997,111.379997,17800000
1973-10-29,111.379997,112.559998,110.519997,111.150002,111.150002,17960000
1973-10-30,111.150002,111.300003,108.949997,109.330002,109.330002,17580000
1973-10-31,109.330002,109.820000,107.639999,108.290001,108.290001,17890000
1973-11-01,108.290001,109.199997,106.879997,107.690002,107.690002,16920000
1973-11-02,107.690002,108.349998,106.330002,107.070000,107.070000,16340000
1973-11-05,106.970001,106.970001,104.870003,105.519997,105.519997,17150000
1973-11-06,105.519997,107.000000,104.519997,104.959999,104.959999,16430000
1973-11-07,104.959999,106.720001,104.529999,105.800003,105.800003,16570000
1973-11-08,106.099998,108.449997,106.099998,107.019997,107.019997,19650000
1973-11-09,107.019997,107.269997,104.769997,105.300003,105.300003,17320000
1973-11-12,105.300003,105.750000,103.120003,104.440002,104.440002,19250000
1973-11-13,104.440002,105.419998,102.910004,104.360001,104.360001,20310000
1973-11-14,104.360001,105.250000,101.870003,102.449997,102.449997,22710000
1973-11-15,102.449997,103.849998,100.690002,102.430000,102.430000,24530000
1973-11-16,102.430000,105.410004,101.769997,103.879997,103.879997,22510000
1973-11-19,103.650002,103.650002,100.370003,100.709999,100.709999,16700000
1973-11-20,100.650002,100.650002,97.639999,98.660004,98.660004,23960000
1973-11-21,98.660004,101.330002,97.870003,99.760002,99.760002,24260000
1973-11-23,99.760002,100.489998,98.589996,99.440002,99.440002,11470000
1973-11-26,98.639999,98.639999,95.790001,96.580002,96.580002,19830000
1973-11-27,96.580002,97.699997,94.879997,95.699997,95.699997,19750000
1973-11-28,95.699997,98.400002,95.220001,97.650002,97.650002,19990000
1973-11-29,97.650002,98.720001,96.010002,97.309998,97.309998,18870000
1973-11-30,97.309998,97.550003,95.400002,95.959999,95.959999,15380000
1973-12-03,95.830002,95.830002,92.919998,93.900002,93.900002,17900000
1973-12-04,93.900002,95.230003,92.599998,93.589996,93.589996,19030000
1973-12-05,93.589996,93.930000,91.550003,92.160004,92.160004,19180000
1973-12-06,92.160004,94.889999,91.680000,94.419998,94.419998,23260000
1973-12-07,94.489998,97.580002,94.489998,96.510002,96.510002,23230000
1973-12-10,96.510002,98.580002,95.440002,97.949997,97.949997,18590000
1973-12-11,97.949997,99.089996,95.620003,96.040001,96.040001,20100000
1973-12-12,95.519997,95.519997,92.900002,93.570000,93.570000,18190000
1973-12-13,93.570000,94.680000,91.639999,92.379997,92.379997,18130000
1973-12-14,92.379997,94.529999,91.050003,93.290001,93.290001,20000000
1973-12-17,93.290001,94.000000,91.870003,92.750000,92.750000,12930000
1973-12-18,92.750000,95.410004,92.180000,94.739998,94.739998,19490000
1973-12-19,94.739998,96.830002,93.809998,94.820000,94.820000,20670000
1973-12-20,94.820000,96.260002,93.510002,94.550003,94.550003,17340000
1973-12-21,94.550003,95.110001,92.699997,93.540001,93.540001,18680000
1973-12-24,93.540001,93.769997,91.680000,92.900002,92.900002,11540000
1973-12-26,93.870003,96.519997,93.870003,95.739998,95.739998,18620000
1973-12-27,96.000000,98.529999,96.000000,97.739998,97.739998,22720000
1973-12-28,97.739998,98.760002,96.410004,97.540001,97.540001,21310000
1973-12-31,97.540001,98.300003,95.949997,97.550003,97.550003,23470000
1974-01-02,97.550003,98.379997,96.250000,97.680000,97.680000,12060000
1974-01-03,98.019997,100.940002,98.019997,99.800003,99.800003,24850000
1974-01-04,99.800003,100.699997,97.699997,98.900002,98.900002,21700000
1974-01-07,98.900002,99.309998,96.860001,98.070000,98.070000,19070000
1974-01-08,98.070000,98.260002,95.580002,96.120003,96.120003,18080000
1974-01-09,95.400002,95.400002,92.629997,93.419998,93.419998,18070000
1974-01-10,93.419998,94.629997,91.620003,92.389999,92.389999,16120000
1974-01-11,92.389999,94.570000,91.750000,93.660004,93.660004,15140000
1974-01-14,93.660004,95.239998,92.349998,93.419998,93.419998,14610000
1974-01-15,93.419998,95.260002,92.839996,94.230003,94.230003,13250000
1974-01-16,94.230003,96.199997,93.779999,95.669998,95.669998,14930000
1974-01-17,95.669998,98.349998,95.669998,97.300003,97.300003,21040000
1974-01-18,97.300003,97.629997,95.000000,95.559998,95.559998,16470000
1974-01-21,95.559998,95.959999,93.230003,95.400002,95.400002,15630000
1974-01-22,95.400002,97.410004,94.919998,96.550003,96.550003,17330000
1974-01-23,96.550003,98.110001,95.879997,97.070000,97.070000,16890000
1974-01-24,97.070000,97.750000,95.489998,96.820000,96.820000,15980000
1974-01-25,96.820000,97.639999,95.680000,96.629997,96.629997,14860000
1974-01-28,96.629997,97.320000,95.370003,96.089996,96.089996,13410000
1974-01-29,96.089996,96.809998,94.970001,96.010002,96.010002,12850000
1974-01-30,96.019997,97.900002,96.019997,97.059998,97.059998,16790000
1974-01-31,97.059998,98.059998,96.110001,96.570000,96.570000,14020000
1974-02-01,96.570000,96.629997,94.660004,95.320000,95.320000,12480000
1974-02-04,94.889999,94.889999,92.739998,93.290001,93.290001,14380000
1974-02-05,93.290001,94.169998,92.260002,93.000000,93.000000,12820000
1974-02-06,93.000000,94.089996,92.370003,93.260002,93.260002,11610000
1974-02-07,93.260002,94.089996,92.430000,93.300003,93.300003,11750000
1974-02-08,93.300003,93.790001,91.870003,92.330002,92.330002,12990000
1974-02-11,92.330002,92.540001,90.260002,90.660004,90.660004,12930000
1974-02-12,90.660004,91.599998,89.529999,90.940002,90.940002,12920000
1974-02-13,90.940002,92.129997,90.370003,90.980003,90.980003,10990000
1974-02-14,90.980003,91.889999,90.169998,90.949997,90.949997,12230000
1974-02-15,90.949997,92.980003,90.620003,92.269997,92.269997,12640000
1974-02-19,92.269997,94.440002,91.680000,92.120003,92.120003,15940000
1974-02-20,92.120003,93.919998,91.339996,93.440002,93.440002,11670000
1974-02-21,93.440002,95.190002,93.199997,94.709999,94.709999,13930000
1974-02-22,94.709999,96.190002,94.080002,95.389999,95.389999,16360000
1974-02-25,95.389999,95.959999,94.239998,95.029999,95.029999,12900000
1974-02-26,95.029999,96.379997,94.199997,96.000000,96.000000,15860000
1974-02-27,96.000000,97.430000,95.489998,96.400002,96.400002,18730000
1974-02-28,96.400002,96.980003,95.199997,96.220001,96.220001,13680000
1974-03-01,96.220001,96.400002,94.809998,95.529999,95.529999,12880000
1974-03-04,95.529999,95.949997,94.190002,95.529999,95.529999,12270000
1974-03-05,95.980003,98.169998,95.980003,97.320000,97.320000,21980000
1974-03-06,97.320000,98.570000,96.540001,97.980003,97.980003,19140000
1974-03-07,97.980003,98.199997,96.370003,96.940002,96.940002,14500000
1974-03-08,96.940002,98.279999,95.769997,97.779999,97.779999,16210000
1974-03-11,97.779999,99.400002,96.379997,98.879997,98.879997,18470000
1974-03-12,98.879997,100.019997,97.970001,99.150002,99.150002,17250000
1974-03-13,99.150002,100.730003,98.720001,99.739998,99.739998,16820000
1974-03-14,99.739998,101.050003,98.800003,99.650002,99.650002,19770000
1974-03-15,99.650002,99.989998,98.220001,99.279999,99.279999,14500000
1974-03-18,99.279999,99.709999,97.620003,98.050003,98.050003,14010000
1974-03-19,98.050003,98.199997,96.629997,97.230003,97.230003,12800000
1974-03-20,97.230003,98.220001,96.669998,97.570000,97.570000,12960000
1974-03-21,97.570000,98.589996,96.820000,97.339996,97.339996,12950000
1974-03-22,97.339996,98.040001,96.349998,97.269997,97.269997,11930000
1974-03-25,97.269997,98.019997,95.690002,97.639999,97.639999,10540000
1974-03-26,97.639999,98.660004,97.110001,97.949997,97.949997,11840000
1974-03-27,97.949997,98.260002,96.320000,96.589996,96.589996,11690000
1974-03-28,96.199997,96.199997,94.360001,94.820000,94.820000,14940000
1974-03-29,94.820000,95.120003,93.440002,93.980003,93.980003,12150000
1974-04-01,93.980003,94.680000,92.820000,93.250000,93.250000,11470000
1974-04-02,93.250000,94.150002,92.589996,93.349998,93.349998,12010000
1974-04-03,93.349998,94.699997,92.940002,94.330002,94.330002,11500000
1974-04-04,94.330002,95.139999,93.550003,94.330002,94.330002,11650000
1974-04-05,94.239998,94.239998,92.550003,93.010002,93.010002,11670000
1974-04-08,93.000000,93.000000,91.500000,92.029999,92.029999,10740000
1974-04-09,92.029999,93.279999,91.610001,92.610001,92.610001,11330000
1974-04-10,92.610001,93.519997,91.889999,92.400002,92.400002,11160000
1974-04-11,92.400002,92.919998,91.550003,92.120003,92.120003,9970000
1974-04-15,92.120003,92.940002,91.489998,92.050003,92.050003,10130000
1974-04-16,92.050003,94.059998,92.050003,93.660004,93.660004,14530000
1974-04-17,93.660004,95.040001,93.120003,94.360001,94.360001,14020000
1974-04-18,94.360001,95.419998,93.750000,94.779999,94.779999,12470000
1974-04-19,94.769997,94.769997,93.199997,93.750000,93.750000,10710000
1974-04-22,93.750000,94.120003,92.709999,93.379997,93.379997,10520000
1974-04-23,93.379997,93.510002,91.529999,91.809998,91.809998,14110000
1974-04-24,91.809998,91.820000,89.910004,90.300003,90.300003,16010000
1974-04-25,90.300003,90.529999,88.620003,89.570000,89.570000,15870000
1974-04-26,89.570000,91.099998,89.059998,90.180000,90.180000,13250000
1974-04-29,90.180000,90.779999,89.019997,90.000000,90.000000,10170000
1974-04-30,90.000000,91.089996,89.379997,90.309998,90.309998,10980000
1974-05-01,90.309998,93.029999,89.820000,92.220001,92.220001,15120000
1974-05-02,92.220001,93.589996,91.459999,92.089996,92.089996,13620000
1974-05-03,92.089996,92.269997,90.589996,91.290001,91.290001,11080000
1974-05-06,91.290001,91.599998,90.129997,91.120003,91.120003,9450000
1974-05-07,91.120003,92.360001,90.690002,91.459999,91.459999,10710000
1974-05-08,91.459999,92.339996,90.709999,91.639999,91.639999,11850000
1974-05-09,91.639999,93.489998,91.269997,92.959999,92.959999,14710000
1974-05-10,92.959999,93.570000,91.029999,91.470001,91.470001,15270000
1974-05-13,91.470001,91.720001,89.910004,90.660004,90.660004,11290000
1974-05-14,90.660004,91.680000,90.050003,90.690002,90.690002,10880000
1974-05-15,90.690002,91.220001,89.650002,90.449997,90.449997,11240000
1974-05-16,90.449997,91.309998,89.360001,89.720001,89.720001,12090000
1974-05-17,89.529999,89.529999,87.669998,88.209999,88.209999,13870000
1974-05-20,88.209999,89.089996,87.190002,87.860001,87.860001,10550000
1974-05-21,87.860001,88.980003,87.190002,87.910004,87.910004,12190000
1974-05-22,87.910004,88.790001,86.720001,87.089996,87.089996,15450000
1974-05-23,87.089996,87.980003,86.120003,87.290001,87.290001,14770000
1974-05-24,87.290001,89.269997,87.199997,88.580002,88.580002,13740000
1974-05-28,88.580002,89.370003,87.690002,88.370003,88.370003,10580000
1974-05-29,88.370003,88.839996,86.519997,86.889999,86.889999,12300000
1974-05-30,86.889999,88.089996,85.870003,87.430000,87.430000,13580000
1974-05-31,87.430000,88.019997,86.190002,87.279999,87.279999,10810000
1974-06-03,87.279999,89.400002,86.779999,89.099998,89.099998,12490000
1974-06-04,89.099998,91.129997,89.089996,90.139999,90.139999,16040000
1974-06-05,90.139999,91.419998,89.040001,90.309998,90.309998,13680000
1974-06-06,90.309998,92.309998,89.709999,91.959999,91.959999,13360000
1974-06-07,91.959999,93.760002,91.739998,92.550003,92.550003,19020000
1974-06-10,92.550003,93.639999,91.529999,93.099998,93.099998,13540000
1974-06-11,93.099998,93.570000,91.760002,92.279999,92.279999,12380000
1974-06-12,92.279999,92.610001,90.889999,92.059998,92.059998,11150000
1974-06-13,92.059998,93.330002,91.480003,92.339996,92.339996,11540000
1974-06-14,92.230003,92.230003,90.730003,91.300003,91.300003,10030000
1974-06-17,91.300003,91.339996,89.629997,90.040001,90.040001,9680000
1974-06-18,90.040001,90.529999,88.919998,89.449997,89.449997,10110000
1974-06-19,89.449997,89.800003,88.389999,88.839996,88.839996,10550000
1974-06-20,88.839996,89.349998,87.800003,88.209999,88.209999,11990000
1974-06-21,88.209999,88.309998,86.769997,87.459999,87.459999,11830000
1974-06-24,87.459999,88.379997,86.699997,87.690002,87.690002,9960000
1974-06-25,87.690002,89.480003,87.669998,88.980003,88.980003,11920000
1974-06-26,88.980003,89.120003,87.300003,87.610001,87.610001,11410000
1974-06-27,87.610001,87.610001,85.879997,86.309998,86.309998,12650000
1974-06-28,86.309998,86.779999,85.129997,86.000000,86.000000,12010000
1974-07-01,86.000000,86.889999,85.320000,86.019997,86.019997,10270000
1974-07-02,86.019997,86.260002,83.980003,84.300003,84.300003,13460000
1974-07-03,84.300003,85.150002,83.459999,84.250000,84.250000,13430000
1974-07-05,84.250000,84.449997,83.169998,83.660004,83.660004,7400000
1974-07-08,83.129997,83.129997,80.480003,81.089996,81.089996,15510000
1974-07-09,81.089996,82.500000,80.349998,81.480003,81.480003,15580000
1974-07-10,81.480003,82.220001,79.739998,79.989998,79.989998,13490000
1974-07-11,79.989998,81.080002,79.080002,79.889999,79.889999,14640000
1974-07-12,80.970001,83.650002,80.970001,83.150002,83.150002,17770000
1974-07-15,83.150002,84.889999,82.650002,83.779999,83.779999,13560000
1974-07-16,83.779999,83.849998,82.139999,82.809998,82.809998,9920000
1974-07-17,82.809998,84.129997,81.699997,83.699997,83.699997,11320000
1974-07-18,83.699997,85.389999,83.129997,83.779999,83.779999,13980000
1974-07-19,83.779999,84.669998,82.870003,83.540001,83.540001,11080000
1974-07-22,83.540001,84.440002,82.589996,83.809998,83.809998,9290000
1974-07-23,83.809998,85.629997,83.669998,84.650002,84.650002,12910000
1974-07-24,84.650002,85.639999,83.610001,84.989998,84.989998,12870000
1974-07-25,84.989998,85.669998,83.129997,83.980003,83.980003,13310000
1974-07-26,83.980003,84.169998,82.000000,82.400002,82.400002,10420000
1974-07-29,82.019997,82.019997,80.220001,80.940002,80.940002,11560000
1974-07-30,80.940002,81.519997,79.580002,80.500000,80.500000,11360000
1974-07-31,80.500000,80.820000,78.959999,79.309998,79.309998,10960000
1974-08-01,79.309998,80.019997,77.970001,78.750000,78.750000,11470000
1974-08-02,78.750000,79.389999,77.839996,78.589996,78.589996,10110000
1974-08-05,78.589996,80.309998,78.029999,79.290001,79.290001,11230000
1974-08-06,79.779999,82.650002,79.779999,80.519997,80.519997,15770000
1974-08-07,80.519997,82.930000,80.129997,82.650002,82.650002,13380000
1974-08-08,82.650002,83.529999,80.860001,81.570000,81.570000,16060000
1974-08-09,81.570000,81.879997,80.110001,80.860001,80.860001,10160000
1974-08-12,80.860001,81.260002,79.300003,79.750000,79.750000,7780000
1974-08-13,79.750000,79.949997,77.830002,78.489998,78.489998,10140000
1974-08-14,76.730003,76.730003,76.730003,76.730003,76.730003,11750000
1974-08-15,76.730003,77.519997,75.190002,76.300003,76.300003,11130000
1974-08-16,76.300003,77.019997,75.290001,75.669998,75.669998,10510000
1974-08-19,75.650002,75.650002,73.779999,74.570000,74.570000,11670000
1974-08-20,74.570000,76.110001,73.820000,74.949997,74.949997,13820000
1974-08-21,74.949997,75.500000,73.160004,73.510002,73.510002,11650000
1974-08-22,73.510002,74.050003,71.610001,72.800003,72.800003,15690000
1974-08-23,72.800003,73.709999,70.750000,71.550003,71.550003,13590000
1974-08-26,71.550003,73.169998,70.419998,72.160004,72.160004,14630000
1974-08-27,72.160004,72.500000,70.500000,70.940002,70.940002,12970000
1974-08-28,70.940002,72.169998,70.129997,70.760002,70.760002,16670000
1974-08-29,70.760002,71.220001,69.370003,69.989998,69.989998,13690000
1974-08-30,70.220001,72.680000,70.220001,72.150002,72.150002,16230000
1974-09-03,72.150002,73.010002,70.279999,70.519997,70.519997,12750000
1974-09-04,69.849998,69.849998,67.639999,68.690002,68.690002,16930000
1974-09-05,68.690002,71.300003,68.650002,70.870003,70.870003,14210000
1974-09-06,70.870003,72.419998,70.080002,71.419998,71.419998,15130000
1974-09-09,71.349998,71.349998,69.379997,69.720001,69.720001,11160000
1974-09-10,69.720001,70.470001,68.550003,69.239998,69.239998,11980000
1974-09-11,69.239998,70.000000,68.220001,68.550003,68.550003,11820000
1974-09-12,68.540001,68.540001,66.220001,66.709999,66.709999,16920000
1974-09-13,66.709999,66.910004,64.739998,65.199997,65.199997,16070000
1974-09-16,65.199997,66.919998,64.150002,66.260002,66.260002,18370000
1974-09-17,66.449997,68.839996,66.449997,67.379997,67.379997,13730000
1974-09-18,67.379997,68.139999,65.919998,67.720001,67.720001,11760000
1974-09-19,68.360001,70.760002,68.360001,70.089996,70.089996,17000000
1974-09-20,70.089996,71.120003,68.620003,70.139999,70.139999,16250000
1974-09-23,70.139999,71.019997,68.790001,69.419998,69.419998,12130000
1974-09-24,69.029999,69.029999,67.419998,68.019997,68.019997,9840000
1974-09-25,68.019997,69.769997,66.860001,67.570000,67.570000,17620000
1974-09-26,67.400002,67.400002,65.790001,66.459999,66.459999,9060000
1974-09-27,66.459999,67.089996,64.580002,64.940002,64.940002,12320000
1974-09-30,64.849998,64.849998,62.520000,63.540001,63.540001,15000000
1974-10-01,63.540001,64.370003,61.750000,63.389999,63.389999,16890000
1974-10-02,63.389999,64.620003,62.740002,63.380001,63.380001,12230000
1974-10-03,63.380001,63.480000,61.660000,62.279999,62.279999,13150000
1974-10-04,62.279999,63.230000,60.959999,62.340000,62.340000,15910000
1974-10-07,62.779999,65.400002,62.779999,64.949997,64.949997,15000000
1974-10-08,64.949997,66.070000,63.950001,64.839996,64.839996,15460000
1974-10-09,64.839996,68.150002,63.740002,67.820000,67.820000,18820000
1974-10-10,68.300003,71.480003,68.300003,69.790001,69.790001,26360000
1974-10-11,69.790001,71.989998,68.800003,71.139999,71.139999,20090000
1974-10-14,71.169998,74.430000,71.169998,72.739998,72.739998,19770000
1974-10-15,72.739998,73.349998,70.610001,71.440002,71.440002,17390000
1974-10-16,71.440002,71.980003,69.540001,70.330002,70.330002,14790000
1974-10-17,70.330002,72.000000,69.410004,71.169998,71.169998,14470000
1974-10-18,71.199997,73.339996,71.199997,72.279999,72.279999,16460000
1974-10-21,72.279999,73.919998,71.239998,73.500000,73.500000,14500000
1974-10-22,73.500000,75.089996,72.550003,73.129997,73.129997,18930000
1974-10-23,72.809998,72.809998,70.400002,71.029999,71.029999,14200000
1974-10-24,70.980003,70.980003,68.800003,70.220001,70.220001,14910000
1974-10-25,70.220001,71.589996,69.459999,70.120003,70.120003,12650000
1974-10-28,70.120003,70.669998,68.889999,70.089996,70.089996,10540000
1974-10-29,70.489998,73.190002,70.489998,72.830002,72.830002,15610000
1974-10-30,72.830002,75.449997,72.400002,74.309998,74.309998,20130000
1974-10-31,74.309998,75.900002,73.150002,73.900002,73.900002,18840000
1974-11-01,73.900002,74.849998,72.680000,73.879997,73.879997,13470000
1974-11-04,73.800003,73.800003,71.930000,73.080002,73.080002,12740000
1974-11-05,73.080002,75.360001,72.489998,75.110001,75.110001,15960000
1974-11-06,75.110001,77.410004,74.230003,74.750000,74.750000,23930000
1974-11-07,74.750000,76.300003,73.849998,75.209999,75.209999,17150000
1974-11-08,75.209999,76.000000,74.010002,74.910004,74.910004,15890000
1974-11-11,74.910004,75.699997,74.040001,75.150002,75.150002,13220000
1974-11-12,75.150002,75.589996,73.339996,73.669998,73.669998,15040000
1974-11-13,73.669998,74.250000,72.320000,73.349998,73.349998,16040000
1974-11-14,73.349998,74.540001,72.529999,73.059998,73.059998,13540000
1974-11-15,73.059998,73.269997,71.410004,71.910004,71.910004,12480000
1974-11-18,71.099998,71.099998,68.949997,69.269997,69.269997,15230000
1974-11-19,69.269997,69.709999,67.660004,68.199997,68.199997,15720000
1974-11-20,68.199997,69.250000,67.360001,67.900002,67.900002,12430000
1974-11-21,67.900002,68.940002,66.849998,68.180000,68.180000,13820000
1974-11-22,68.239998,70.000000,68.239998,68.900002,68.900002,13020000
1974-11-25,68.900002,69.680000,67.790001,68.830002,68.830002,11300000
1974-11-26,68.830002,70.360001,68.190002,69.470001,69.470001,13600000
1974-11-27,69.470001,71.309998,69.169998,69.940002,69.940002,14810000
1974-11-29,69.940002,70.489998,69.180000,69.970001,69.970001,7400000
1974-12-02,69.800003,69.800003,67.809998,68.110001,68.110001,11140000
1974-12-03,68.110001,68.129997,66.620003,67.169998,67.169998,13620000
1974-12-04,67.169998,68.320000,66.610001,67.410004,67.410004,12580000
1974-12-05,67.410004,68.000000,65.900002,66.129997,66.129997,12890000
1974-12-06,66.129997,66.199997,64.400002,65.010002,65.010002,15500000
1974-12-09,65.010002,66.290001,64.129997,65.599998,65.599998,14660000
1974-12-10,65.879997,68.169998,65.879997,67.279999,67.279999,15690000
1974-12-11,67.279999,69.029999,66.830002,67.669998,67.669998,15700000
1974-12-12,67.669998,68.610001,66.559998,67.449997,67.449997,15390000
1974-12-13,67.449997,68.150002,66.320000,67.070000,67.070000,14000000
1974-12-16,67.070000,67.739998,66.019997,66.459999,66.459999,15370000
1974-12-17,66.459999,67.919998,65.860001,67.580002,67.580002,16880000
1974-12-18,67.580002,69.010002,67.300003,67.900002,67.900002,18050000
1974-12-19,67.900002,68.620003,66.930000,67.650002,67.650002,15900000
1974-12-20,67.650002,67.930000,66.360001,66.910004,66.910004,15840000
1974-12-23,66.910004,67.180000,65.339996,65.959999,65.959999,18040000
1974-12-24,65.959999,67.250000,65.860001,66.879997,66.879997,9540000
1974-12-26,66.879997,68.190002,66.620003,67.440002,67.440002,11810000
1974-12-27,67.440002,67.989998,66.489998,67.139999,67.139999,13060000
1974-12-30,67.139999,67.650002,66.230003,67.160004,67.160004,18520000
1974-12-31,67.160004,69.040001,67.150002,68.559998,68.559998,20970000
1975-01-02,68.650002,70.919998,68.650002,70.230003,70.230003,14800000
1975-01-03,70.230003,71.639999,69.290001,70.709999,70.709999,15270000
1975-01-06,70.709999,72.239998,70.330002,71.070000,71.070000,17550000
1975-01-07,71.070000,71.750000,69.919998,71.019997,71.019997,14890000
1975-01-08,71.019997,71.529999,69.650002,70.040001,70.040001,15600000
1975-01-09,70.040001,71.419998,69.040001,71.169998,71.169998,16340000
1975-01-10,71.599998,73.750000,71.599998,72.610001,72.610001,25890000
1975-01-13,72.610001,73.809998,71.830002,72.309998,72.309998,19780000
1975-01-14,72.309998,72.699997,71.019997,71.680000,71.680000,16610000
1975-01-15,71.680000,72.769997,70.449997,72.139999,72.139999,16580000
1975-01-16,72.139999,72.930000,71.260002,72.050003,72.050003,17110000
1975-01-17,72.050003,72.360001,70.559998,70.959999,70.959999,14260000
1975-01-20,70.959999,71.459999,69.800003,71.080002,71.080002,13450000
1975-01-21,71.080002,72.040001,70.250000,70.699997,70.699997,14780000
1975-01-22,70.699997,71.970001,69.860001,71.739998,71.739998,15330000
1975-01-23,71.739998,73.110001,71.089996,72.070000,72.070000,17960000
1975-01-24,72.070000,73.570000,71.550003,72.980003,72.980003,20670000
1975-01-27,73.760002,76.029999,73.760002,75.370003,75.370003,32130000
1975-01-28,75.370003,77.589996,75.360001,76.029999,76.029999,31760000
1975-01-29,76.029999,78.029999,75.230003,77.260002,77.260002,27410000
1975-01-30,77.260002,78.690002,75.820000,76.209999,76.209999,29740000
1975-01-31,76.209999,77.720001,75.410004,76.980003,76.980003,24640000
1975-02-03,76.980003,78.550003,76.360001,77.820000,77.820000,25400000
1975-02-04,77.820000,78.370003,76.000000,77.610001,77.610001,25040000
1975-02-05,77.610001,79.400002,76.809998,78.949997,78.949997,25830000
1975-02-06,78.949997,80.720001,78.089996,78.559998,78.559998,32020000
1975-02-07,78.559998,79.120003,77.000000,78.629997,78.629997,19060000
1975-02-10,78.629997,79.400002,77.769997,78.360001,78.360001,16120000
1975-02-11,78.360001,79.070000,77.379997,78.580002,78.580002,16470000
1975-02-12,78.580002,80.209999,77.940002,79.919998,79.919998,19790000
1975-02-13,79.980003,82.529999,79.980003,81.010002,81.010002,35160000
1975-02-14,81.010002,82.330002,80.129997,81.500000,81.500000,23290000
1975-02-18,81.500000,82.449997,80.160004,80.930000,80.930000,23990000
1975-02-19,80.930000,81.940002,79.830002,81.440002,81.440002,21930000
1975-02-20,81.440002,82.779999,80.820000,82.209999,82.209999,22260000
1975-02-21,82.209999,83.559998,81.720001,82.620003,82.620003,24440000
1975-02-24,82.620003,82.709999,80.870003,81.440002,81.440002,19150000
1975-02-25,81.089996,81.089996,79.050003,79.529999,79.529999,20910000
1975-02-26,79.529999,80.889999,78.910004,80.370003,80.370003,18790000
1975-02-27,80.370003,81.639999,80.059998,80.769997,80.769997,16430000
1975-02-28,80.769997,82.019997,80.070000,81.589996,81.589996,17560000
1975-03-03,81.589996,83.459999,81.320000,83.029999,83.029999,24100000
1975-03-04,83.029999,85.430000,82.849998,83.559998,83.559998,34140000
1975-03-05,83.559998,84.709999,82.160004,83.900002,83.900002,24120000
1975-03-06,83.900002,84.169998,81.940002,83.690002,83.690002,21780000
1975-03-07,83.690002,85.139999,83.250000,84.300003,84.300003,25930000
1975-03-10,84.300003,85.470001,83.430000,84.949997,84.949997,25890000
1975-03-11,84.949997,85.889999,83.800003,84.360001,84.360001,31280000
1975-03-12,84.360001,84.730003,82.870003,83.589996,83.589996,21560000
1975-03-13,83.589996,84.260002,82.519997,83.739998,83.739998,18620000
1975-03-14,83.739998,85.430000,83.500000,84.760002,84.760002,24840000
1975-03-17,84.760002,86.519997,84.389999,86.010002,86.010002,26780000
1975-03-18,86.010002,87.080002,84.750000,85.129997,85.129997,29180000
1975-03-19,85.129997,85.169998,83.430000,84.339996,84.339996,19030000
1975-03-20,84.339996,85.300003,83.019997,83.610001,83.610001,20960000
1975-03-21,83.610001,84.110001,82.519997,83.389999,83.389999,15940000
1975-03-24,82.389999,82.389999,80.599998,81.419998,81.419998,17810000
1975-03-25,81.419998,82.669998,80.080002,82.059998,82.059998,18500000
1975-03-26,82.160004,84.239998,82.160004,83.589996,83.589996,18580000
1975-03-27,83.589996,84.879997,83.040001,83.849998,83.849998,18300000
1975-03-31,83.849998,84.620003,82.839996,83.360001,83.360001,16270000
1975-04-01,83.360001,83.589996,81.980003,82.639999,82.639999,14480000
1975-04-02,82.639999,83.570000,81.800003,82.430000,82.430000,15600000
1975-04-03,82.430000,82.839996,80.879997,81.510002,81.510002,13920000
1975-04-04,81.510002,81.900002,80.290001,80.879997,80.879997,14170000
1975-04-07,80.879997,81.110001,79.660004,80.349998,80.349998,13860000
1975-04-08,80.349998,81.650002,80.129997,80.989998,80.989998,14320000
1975-04-09,80.989998,83.220001,80.910004,82.839996,82.839996,18120000
1975-04-10,82.839996,84.699997,82.680000,83.769997,83.769997,24990000
1975-04-11,83.769997,84.680000,82.930000,84.180000,84.180000,20160000
1975-04-14,84.180000,86.120003,83.980003,85.599998,85.599998,26800000
1975-04-15,85.599998,87.239998,85.029999,86.300003,86.300003,29620000
1975-04-16,86.300003,87.099998,84.930000,86.599998,86.599998,22970000
1975-04-17,86.599998,88.790001,86.430000,87.250000,87.250000,32650000
1975-04-18,87.250000,87.589996,85.529999,86.300003,86.300003,26610000
1975-04-21,86.300003,87.989998,85.919998,87.230003,87.230003,23960000
1975-04-22,87.230003,88.639999,86.580002,87.089996,87.089996,26120000
1975-04-23,87.089996,87.419998,85.650002,86.120003,86.120003,20040000
1975-04-24,86.120003,86.919998,85.000000,86.040001,86.040001,19050000
1975-04-25,86.040001,87.500000,85.620003,86.620003,86.620003,20260000
1975-04-28,86.620003,87.330002,85.540001,86.230003,86.230003,17850000
1975-04-29,86.230003,86.790001,85.040001,85.639999,85.639999,17740000
1975-04-30,85.639999,87.610001,85.000000,87.300003,87.300003,18060000
1975-05-01,87.300003,89.099998,86.940002,88.099998,88.099998,20660000
1975-05-02,88.099998,89.980003,87.910004,89.220001,89.220001,25210000
1975-05-05,89.220001,90.820000,88.260002,90.080002,90.080002,22370000
1975-05-06,90.080002,90.860001,88.150002,88.639999,88.639999,25410000
1975-05-07,88.639999,89.750000,87.599998,89.080002,89.080002,22250000
1975-05-08,89.080002,90.129997,88.230003,89.559998,89.559998,22980000
1975-05-09,89.559998,91.239998,89.330002,90.529999,90.529999,28440000
1975-05-12,90.529999,91.669998,89.910004,90.610001,90.610001,22410000
1975-05-13,90.610001,92.260002,89.989998,91.580002,91.580002,24950000
1975-05-14,91.580002,93.230003,91.169998,92.269997,92.269997,29050000
1975-05-15,92.269997,93.510002,90.940002,91.410004,91.410004,27690000
1975-05-16,91.410004,91.589996,89.739998,90.430000,90.430000,16630000
1975-05-19,90.430000,91.070000,88.980003,90.529999,90.529999,17870000
1975-05-20,90.529999,91.449997,89.580002,90.070000,90.070000,18310000
1975-05-21,90.070000,90.250000,88.470001,89.059998,89.059998,17640000
1975-05-22,89.059998,90.300003,88.349998,89.389999,89.389999,17610000
1975-05-23,89.389999,91.019997,89.300003,90.580002,90.580002,17870000
1975-05-27,90.580002,91.290001,89.599998,90.339996,90.339996,17050000
1975-05-28,90.339996,91.139999,89.070000,89.709999,89.709999,21850000
1975-05-29,89.709999,90.589996,88.830002,89.680000,89.680000,18570000
1975-05-30,89.870003,91.620003,89.870003,91.150002,91.150002,22670000
1975-06-02,91.320000,93.410004,91.320000,92.580002,92.580002,28240000
1975-06-03,92.580002,93.760002,91.879997,92.889999,92.889999,26560000
1975-06-04,92.889999,93.610001,91.820000,92.599998,92.599998,24900000
1975-06-05,92.599998,93.160004,91.410004,92.690002,92.690002,21610000
1975-06-06,92.690002,93.599998,91.750000,92.480003,92.480003,22230000
1975-06-09,92.480003,92.870003,90.910004,91.209999,91.209999,20670000
1975-06-10,91.209999,91.209999,89.459999,90.440002,90.440002,21130000
1975-06-11,90.440002,91.669998,90.000000,90.550003,90.550003,18230000
1975-06-12,90.550003,91.360001,89.639999,90.080002,90.080002,15970000
1975-06-13,90.080002,91.059998,89.300003,90.519997,90.519997,16300000
1975-06-16,90.519997,91.849998,90.120003,91.459999,91.459999,16660000
1975-06-17,91.459999,92.220001,90.169998,90.580002,90.580002,19440000
1975-06-18,90.580002,91.070000,89.599998,90.389999,90.389999,15590000
1975-06-19,90.389999,92.370003,90.120003,92.019997,92.019997,21450000
1975-06-20,92.019997,93.750000,91.830002,92.610001,92.610001,26260000
1975-06-23,92.610001,93.980003,91.809998,93.620003,93.620003,20720000
1975-06-24,93.620003,95.230003,93.309998,94.190002,94.190002,26620000
1975-06-25,94.190002,95.290001,93.529999,94.620003,94.620003,21610000
1975-06-26,94.620003,95.720001,93.879997,94.809998,94.809998,24560000
1975-06-27,94.809998,95.660004,94.099998,94.809998,94.809998,18820000
1975-06-30,94.809998,95.849998,94.300003,95.190002,95.190002,19430000
1975-07-01,95.190002,95.730003,94.129997,94.849998,94.849998,20390000
1975-07-02,94.849998,94.910004,93.370003,94.180000,94.180000,18530000
1975-07-03,94.180000,95.040001,93.489998,94.360001,94.360001,19000000
1975-07-07,94.360001,94.820000,93.160004,93.540001,93.540001,15850000
1975-07-08,93.540001,94.029999,92.510002,93.389999,93.389999,18990000
1975-07-09,93.389999,95.220001,93.379997,94.800003,94.800003,26350000
1975-07-10,94.800003,96.190002,94.250000,94.809998,94.809998,28880000
1975-07-11,94.809998,95.690002,93.830002,94.660004,94.660004,22210000
1975-07-14,94.660004,95.760002,94.040001,95.190002,95.190002,21900000
1975-07-15,95.190002,96.580002,94.709999,95.610001,95.610001,28340000
1975-07-16,95.610001,96.370003,94.199997,94.610001,94.610001,25250000
1975-07-17,94.610001,95.029999,92.989998,93.629997,93.629997,21420000
1975-07-18,93.629997,93.959999,92.389999,93.199997,93.199997,16870000
1975-07-21,93.199997,93.930000,92.029999,92.440002,92.440002,16690000
1975-07-22,92.440002,92.489998,90.629997,91.449997,91.449997,20660000
1975-07-23,91.449997,92.150002,89.830002,90.180000,90.180000,20150000
1975-07-24,90.180000,90.949997,88.900002,90.070000,90.070000,20550000
1975-07-25,90.070000,90.720001,88.720001,89.290001,89.290001,15110000
1975-07-28,89.290001,89.680000,88.019997,88.690002,88.690002,14850000
1975-07-29,88.690002,89.910004,87.709999,88.190002,88.190002,19000000
1975-07-30,88.190002,89.489998,87.680000,88.830002,88.830002,16150000
1975-07-31,88.830002,90.070000,88.309998,88.750000,88.750000,14540000
1975-08-01,88.750000,89.040001,87.459999,87.989998,87.989998,13320000
1975-08-04,87.989998,88.169998,86.680000,87.150002,87.150002,12620000
1975-08-05,87.150002,87.809998,85.889999,86.230003,86.230003,15470000
1975-08-06,86.230003,87.040001,85.339996,86.250000,86.250000,16280000
1975-08-07,86.250000,87.239998,85.690002,86.300003,86.300003,12390000
1975-08-08,86.300003,87.000000,85.519997,86.019997,86.019997,11660000
1975-08-11,86.019997,86.889999,85.339996,86.550003,86.550003,12350000
1975-08-12,86.550003,88.169998,86.489998,87.120003,87.120003,14510000
1975-08-13,87.120003,87.410004,85.610001,85.970001,85.970001,12000000
1975-08-14,85.970001,86.339996,85.019997,85.599998,85.599998,12460000
1975-08-15,85.599998,86.760002,85.330002,86.360001,86.360001,10610000
1975-08-18,86.360001,87.209999,85.760002,86.199997,86.199997,10810000
1975-08-19,86.199997,86.470001,84.660004,84.949997,84.949997,14990000
1975-08-20,84.779999,84.779999,82.760002,83.220001,83.220001,18630000
1975-08-21,83.220001,84.150002,82.209999,83.070000,83.070000,16610000
1975-08-22,83.070000,84.610001,82.790001,84.279999,84.279999,13050000
1975-08-25,84.279999,85.580002,84.059998,85.059998,85.059998,11250000
1975-08-26,85.059998,85.400002,83.650002,83.959999,83.959999,11350000
1975-08-27,83.959999,84.790001,83.349998,84.430000,84.430000,11100000
1975-08-28,84.680000,86.639999,84.680000,86.400002,86.400002,14530000
1975-08-29,86.400002,87.730003,86.099998,86.879997,86.879997,15480000
1975-09-02,86.879997,87.419998,85.209999,85.480003,85.480003,11460000
1975-09-03,85.480003,86.379997,84.620003,86.029999,86.029999,12260000
1975-09-04,86.029999,86.910004,85.290001,86.199997,86.199997,12810000
1975-09-05,86.199997,86.489998,85.190002,85.620003,85.620003,11680000
1975-09-08,85.620003,86.309998,84.889999,85.889999,85.889999,11500000
1975-09-09,85.889999,86.730003,84.370003,84.599998,84.599998,15790000
1975-09-10,84.589996,84.589996,83.000000,83.790001,83.790001,14780000
1975-09-11,83.790001,84.300003,82.879997,83.449997,83.449997,11100000
1975-09-12,83.449997,84.470001,82.839996,83.300003,83.300003,12230000
1975-09-15,83.300003,83.489998,82.290001,82.879997,82.879997,8670000
1975-09-16,82.879997,83.430000,81.790001,82.089996,82.089996,13090000
1975-09-17,82.089996,82.930000,81.570000,82.370003,82.370003,12190000
1975-09-18,82.370003,84.339996,82.230003,84.059998,84.059998,14560000
1975-09-19,84.260002,86.389999,84.260002,85.879997,85.879997,20830000
1975-09-22,85.879997,86.699997,84.699997,85.070000,85.070000,14750000
1975-09-23,85.070000,85.510002,83.800003,84.940002,84.940002,12800000
1975-09-24,85.029999,86.699997,85.029999,85.739998,85.739998,16060000
1975-09-25,85.739998,86.410004,84.790001,85.639999,85.639999,12890000
1975-09-26,85.639999,86.860001,85.129997,86.190002,86.190002,12570000
1975-09-29,86.190002,86.379997,84.739998,85.029999,85.029999,10580000
1975-09-30,85.010002,85.010002,83.440002,83.870003,83.870003,12520000
1975-10-01,83.870003,85.449997,82.570000,82.930000,82.930000,14070000
1975-10-02,82.930000,84.330002,82.820000,83.820000,83.820000,14290000
1975-10-03,83.879997,86.209999,83.879997,85.949997,85.949997,16360000
1975-10-06,85.980003,87.639999,85.980003,86.879997,86.879997,15470000
1975-10-07,86.879997,87.320000,85.559998,86.769997,86.769997,13530000
1975-10-08,86.769997,88.459999,86.339996,87.940002,87.940002,17800000
1975-10-09,87.940002,89.419998,87.599998,88.370003,88.370003,17770000
1975-10-10,88.370003,89.169998,87.440002,88.209999,88.209999,14880000
1975-10-13,88.209999,89.669998,87.730003,89.459999,89.459999,12020000
1975-10-14,89.459999,90.800003,88.809998,89.279999,89.279999,19960000
1975-10-15,89.279999,90.070000,88.500000,89.230003,89.230003,14440000
1975-10-16,89.230003,90.730003,88.900002,89.370003,89.370003,18910000
1975-10-17,89.370003,89.870003,88.080002,88.860001,88.860001,15650000
1975-10-20,88.860001,90.139999,88.430000,89.820000,89.820000,13250000
1975-10-21,89.820000,91.430000,89.790001,90.559998,90.559998,20800000
1975-10-22,90.559998,91.379997,89.769997,90.709999,90.709999,16060000
1975-10-23,90.709999,91.750000,90.089996,91.239998,91.239998,17900000
1975-10-24,91.239998,91.519997,89.459999,89.830002,89.830002,18120000
1975-10-27,89.830002,90.400002,88.849998,89.730003,89.730003,13100000
1975-10-28,89.730003,91.010002,89.400002,90.510002,90.510002,17060000
1975-10-29,90.510002,90.610001,88.889999,89.389999,89.389999,16110000
1975-10-30,89.389999,90.199997,88.699997,89.309998,89.309998,15080000
1975-10-31,89.309998,89.800003,88.349998,89.040001,89.040001,12910000
1975-11-03,89.040001,89.209999,87.779999,88.089996,88.089996,11400000
1975-11-04,88.089996,89.029999,87.629997,88.510002,88.510002,11570000
1975-11-05,88.510002,90.080002,88.320000,89.150002,89.150002,17390000
1975-11-06,89.150002,90.150002,88.160004,89.550003,89.550003,18600000
1975-11-07,89.550003,90.180000,88.669998,89.330002,89.330002,15930000
1975-11-10,89.330002,89.980003,88.349998,89.339996,89.339996,14910000
1975-11-11,89.339996,90.470001,89.040001,89.870003,89.870003,14640000
1975-11-12,89.870003,91.629997,89.800003,91.190002,91.190002,23960000
1975-11-13,91.190002,92.330002,90.559998,91.040001,91.040001,25070000
1975-11-14,91.040001,91.589996,90.190002,90.970001,90.970001,16460000
1975-11-17,90.970001,91.989998,90.500000,91.459999,91.459999,17660000
1975-11-18,91.459999,92.300003,90.599998,91.000000,91.000000,20760000
1975-11-19,91.000000,91.279999,89.470001,89.980003,89.980003,16820000
1975-11-20,89.980003,90.680000,89.089996,89.639999,89.639999,16460000
1975-11-21,89.639999,90.230003,88.790001,89.529999,89.529999,14110000
1975-11-24,89.529999,90.169998,88.650002,89.699997,89.699997,13930000
1975-11-25,89.699997,91.099998,89.660004,90.709999,90.709999,17490000
1975-11-26,90.709999,91.580002,90.169998,90.940002,90.940002,18780000
1975-11-28,90.940002,91.739998,90.440002,91.239998,91.239998,12870000
1975-12-01,91.239998,91.900002,90.330002,90.669998,90.669998,16050000
1975-12-02,90.669998,90.809998,89.080002,89.330002,89.330002,17930000
1975-12-03,88.830002,88.830002,87.080002,87.599998,87.599998,21320000
1975-12-04,87.599998,88.389999,86.680000,87.839996,87.839996,16380000
1975-12-05,87.839996,88.379997,86.540001,86.820000,86.820000,14050000
1975-12-08,86.820000,87.750000,86.150002,87.070000,87.070000,14150000
1975-12-09,87.070000,87.800003,86.160004,87.300003,87.300003,16040000
1975-12-10,87.300003,88.389999,86.910004,88.080002,88.080002,15680000
1975-12-11,88.080002,88.790001,87.410004,87.800003,87.800003,15300000
1975-12-12,87.800003,88.220001,87.050003,87.830002,87.830002,13100000
1975-12-15,87.830002,88.639999,87.320000,88.089996,88.089996,13960000
1975-12-16,88.089996,89.489998,87.779999,88.930000,88.930000,18350000
1975-12-17,88.930000,89.800003,88.459999,89.150002,89.150002,16560000
1975-12-18,89.150002,90.089996,88.620003,89.430000,89.430000,18040000
1975-12-19,89.430000,89.809998,88.389999,88.800003,88.800003,17720000
1975-12-22,88.800003,89.129997,87.739998,88.139999,88.139999,15340000
1975-12-23,88.139999,89.230003,87.639999,88.730003,88.730003,17750000
1975-12-24,88.730003,89.839996,88.730003,89.459999,89.459999,11150000
1975-12-26,89.459999,90.449997,89.250000,90.250000,90.250000,10020000
1975-12-29,90.250000,91.089996,89.629997,90.129997,90.129997,17070000
1975-12-30,90.129997,90.550003,89.199997,89.769997,89.769997,16040000
1975-12-31,89.769997,90.750000,89.169998,90.190002,90.190002,16970000
1976-01-02,90.190002,91.180000,89.809998,90.900002,90.900002,10300000
1976-01-05,90.900002,92.839996,90.849998,92.580002,92.580002,21960000
1976-01-06,92.580002,94.180000,92.370003,93.529999,93.529999,31270000
1976-01-07,93.529999,95.150002,92.910004,93.949997,93.949997,33170000
1976-01-08,93.949997,95.470001,93.410004,94.580002,94.580002,29030000
1976-01-09,94.580002,95.709999,94.050003,94.949997,94.949997,26510000
1976-01-12,94.949997,96.760002,94.379997,96.330002,96.330002,30440000
1976-01-13,96.330002,97.389999,95.110001,95.570000,95.570000,34530000
1976-01-14,95.570000,97.470001,94.910004,97.129997,97.129997,30340000
1976-01-15,97.129997,98.339996,96.150002,96.610001,96.610001,38450000
1976-01-16,96.610001,97.730003,95.839996,97.000000,97.000000,25940000
1976-01-19,97.000000,98.839996,96.360001,98.320000,98.320000,29450000
1976-01-20,98.320000,99.440002,97.430000,98.860001,98.860001,36690000
1976-01-21,98.860001,99.239998,97.120003,98.239998,98.239998,34470000
1976-01-22,98.239998,98.790001,97.070000,98.040001,98.040001,27420000
1976-01-23,98.040001,99.879997,97.680000,99.209999,99.209999,33640000
1976-01-26,99.209999,100.750000,98.919998,99.680000,99.680000,34470000
1976-01-27,99.680000,100.519997,98.279999,99.070000,99.070000,32070000
1976-01-28,99.070000,99.639999,97.660004,98.529999,98.529999,27370000
1976-01-29,98.529999,100.540001,98.320000,100.110001,100.110001,29800000
1976-01-30,100.110001,101.989998,99.940002,100.860001,100.860001,38510000
1976-02-02,100.860001,101.389999,99.739998,100.870003,100.870003,24000000
1976-02-03,100.870003,101.970001,99.580002,101.180000,101.180000,34080000
1976-02-04,101.180000,102.570000,100.699997,101.910004,101.910004,38270000
1976-02-05,101.910004,102.300003,100.059998,100.389999,100.389999,33780000
1976-02-06,100.389999,100.529999,98.639999,99.459999,99.459999,27360000
1976-02-09,99.459999,100.660004,98.769997,99.620003,99.620003,25340000
1976-02-10,99.620003,100.959999,99.110001,100.470001,100.470001,27660000
1976-02-11,100.470001,101.800003,100.099998,100.769997,100.769997,32300000
1976-02-12,100.769997,101.550003,99.820000,100.250000,100.250000,28610000
1976-02-13,100.250000,100.660004,99.010002,99.669998,99.669998,23870000
1976-02-17,99.669998,100.250000,98.559998,99.050003,99.050003,25460000
1976-02-18,99.050003,100.430000,98.500000,99.849998,99.849998,29900000
1976-02-19,99.940002,101.919998,99.940002,101.410004,101.410004,39210000
1976-02-20,101.410004,103.070000,101.180000,102.099998,102.099998,44510000
1976-02-23,102.099998,102.540001,100.690002,101.610001,101.610001,31460000
1976-02-24,101.610001,102.919998,101.029999,102.029999,102.029999,34380000
1976-02-25,102.029999,102.709999,100.690002,101.690002,101.690002,34680000
1976-02-26,101.690002,102.360001,99.739998,100.110001,100.110001,34320000
1976-02-27,100.110001,100.529999,98.599998,99.709999,99.709999,26940000
1976-03-01,99.709999,100.639999,98.669998,100.019997,100.019997,22070000
1976-03-02,100.019997,101.260002,99.610001,100.580002,100.580002,25590000
1976-03-03,100.580002,100.970001,99.230003,99.980003,99.980003,25450000
1976-03-04,99.980003,100.400002,98.489998,98.919998,98.919998,24410000
1976-03-05,98.919998,99.879997,98.230003,99.110001,99.110001,23030000
1976-03-08,99.110001,100.709999,98.930000,100.190002,100.190002,25060000
1976-03-09,100.190002,101.900002,99.949997,100.580002,100.580002,31770000
1976-03-10,100.580002,101.800003,99.980003,100.940002,100.940002,24900000
1976-03-11,100.940002,102.410004,100.620003,101.889999,101.889999,27300000
1976-03-12,101.889999,102.459999,100.489998,100.860001,100.860001,26020000
1976-03-15,100.860001,100.900002,99.239998,99.800003,99.800003,19570000
1976-03-16,99.800003,101.250000,99.379997,100.919998,100.919998,22780000
1976-03-17,100.919998,102.010002,100.279999,100.860001,100.860001,26190000
1976-03-18,100.860001,101.370003,99.730003,100.449997,100.449997,20330000
1976-03-19,100.449997,101.230003,99.699997,100.580002,100.580002,18090000
1976-03-22,100.580002,101.529999,100.139999,100.709999,100.709999,19410000
1976-03-23,100.709999,102.540001,100.320000,102.239998,102.239998,22450000
1976-03-24,102.510002,104.389999,102.510002,103.419998,103.419998,32610000
1976-03-25,103.419998,104.000000,102.190002,102.849998,102.849998,22510000
1976-03-26,102.849998,103.650002,102.199997,102.849998,102.849998,18510000
1976-03-29,102.849998,103.360001,101.989998,102.410004,102.410004,16100000
1976-03-30,102.410004,103.360001,101.250000,102.010002,102.010002,17930000
1976-03-31,102.010002,103.080002,101.599998,102.769997,102.769997,17520000
1976-04-01,102.769997,103.239998,101.500000,102.239998,102.239998,17910000
1976-04-02,102.239998,102.760002,101.230003,102.250000,102.250000,17420000
1976-04-05,102.320000,104.129997,102.320000,103.510002,103.510002,21940000
1976-04-06,103.510002,104.629997,102.930000,103.360001,103.360001,24170000
1976-04-07,103.360001,103.849998,101.919998,102.209999,102.209999,20190000
1976-04-08,102.209999,102.379997,100.529999,101.279999,101.279999,20860000
1976-04-09,101.279999,101.739998,99.870003,100.349998,100.349998,19050000
1976-04-12,100.349998,101.300003,99.570000,100.199997,100.199997,16030000
1976-04-13,100.199997,101.389999,99.639999,101.050003,101.050003,15990000
1976-04-14,101.050003,101.769997,99.980003,100.309998,100.309998,18440000
1976-04-15,100.309998,101.180000,99.730003,100.669998,100.669998,15100000
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1977-12-28,94.690002,95.199997,93.989998,94.750000,94.750000,19630000
1977-12-29,94.750000,95.430000,94.099998,94.940002,94.940002,23610000
1977-12-30,94.940002,95.669998,94.440002,95.099998,95.099998,23560000
1978-01-03,95.099998,95.150002,93.489998,93.820000,93.820000,17720000
1978-01-04,93.820000,94.099998,92.570000,93.519997,93.519997,24090000
1978-01-05,93.519997,94.529999,92.510002,92.739998,92.739998,23570000
1978-01-06,92.660004,92.660004,91.050003,91.620003,91.620003,26150000
1978-01-09,91.480003,91.480003,89.970001,90.639999,90.639999,27990000
1978-01-10,90.639999,91.290001,89.720001,90.169998,90.169998,25180000
1978-01-11,90.169998,90.699997,89.230003,89.739998,89.739998,22880000
1978-01-12,89.739998,90.599998,89.250000,89.820000,89.820000,22730000
1978-01-13,89.820000,90.470001,89.260002,89.690002,89.690002,18010000
1978-01-16,89.690002,90.110001,88.879997,89.430000,89.430000,18760000
1978-01-17,89.430000,90.309998,89.050003,89.879997,89.879997,19360000
1978-01-18,89.879997,90.860001,89.589996,90.559998,90.559998,21390000
1978-01-19,90.559998,91.040001,89.739998,90.089996,90.089996,21500000
1978-01-20,90.089996,90.269997,89.410004,89.889999,89.889999,7580000
1978-01-23,89.889999,90.080002,88.809998,89.239998,89.239998,19380000
1978-01-24,89.239998,89.800003,88.669998,89.250000,89.250000,18690000
1978-01-25,89.250000,89.940002,88.830002,89.389999,89.389999,18690000
1978-01-26,89.389999,89.790001,88.309998,88.580002,88.580002,19600000
1978-01-27,88.580002,89.099998,88.019997,88.580002,88.580002,17600000
1978-01-30,88.580002,89.669998,88.260002,89.339996,89.339996,17400000
1978-01-31,89.339996,89.919998,88.610001,89.250000,89.250000,19870000
1978-02-01,89.250000,90.239998,88.820000,89.930000,89.930000,22240000
1978-02-02,89.930000,90.910004,89.540001,90.129997,90.129997,23050000
1978-02-03,90.129997,90.320000,89.190002,89.620003,89.620003,19400000
1978-02-06,89.620003,89.849998,88.949997,89.500000,89.500000,11630000
1978-02-07,89.500000,90.529999,89.379997,90.330002,90.330002,14730000
1978-02-08,90.330002,91.320000,90.089996,90.830002,90.830002,21300000
1978-02-09,90.830002,90.959999,89.839996,90.300003,90.300003,17940000
1978-02-10,90.300003,90.690002,89.559998,90.080002,90.080002,19480000
1978-02-13,90.080002,90.300003,89.379997,89.860001,89.860001,16810000
1978-02-14,89.860001,89.889999,88.699997,89.040001,89.040001,20470000
1978-02-15,89.040001,89.400002,88.300003,88.830002,88.830002,20170000
1978-02-16,88.769997,88.769997,87.639999,88.080002,88.080002,21570000
1978-02-17,88.080002,88.699997,87.550003,87.959999,87.959999,18500000
1978-02-21,87.959999,88.190002,87.089996,87.589996,87.589996,21890000
1978-02-22,87.589996,88.150002,87.190002,87.559998,87.559998,18450000
1978-02-23,87.559998,87.919998,86.830002,87.639999,87.639999,18720000
1978-02-24,87.660004,88.870003,87.660004,88.489998,88.489998,22510000
1978-02-27,88.489998,88.970001,87.489998,87.720001,87.720001,19990000
1978-02-28,87.720001,87.760002,86.580002,87.040001,87.040001,19750000
1978-03-01,87.040001,87.629997,86.449997,87.190002,87.190002,21010000
1978-03-02,87.190002,87.809998,86.690002,87.320000,87.320000,20280000
1978-03-03,87.320000,87.980003,86.830002,87.449997,87.449997,20120000
1978-03-06,87.449997,87.519997,86.480003,86.900002,86.900002,17230000
1978-03-07,86.900002,87.629997,86.550003,87.360001,87.360001,19900000
1978-03-08,87.360001,88.080002,86.970001,87.839996,87.839996,22030000
1978-03-09,87.839996,88.489998,87.339996,87.889999,87.889999,21820000
1978-03-10,87.889999,89.250000,87.820000,88.879997,88.879997,27090000
1978-03-13,88.879997,89.769997,88.480003,88.949997,88.949997,24070000
1978-03-14,88.949997,89.620003,88.209999,89.349998,89.349998,24300000
1978-03-15,89.349998,89.730003,88.519997,89.120003,89.120003,23340000
1978-03-16,89.120003,89.769997,88.580002,89.510002,89.510002,25400000
1978-03-17,89.510002,90.519997,89.169998,90.199997,90.199997,28470000
1978-03-20,90.199997,91.349998,90.099998,90.820000,90.820000,28360000
1978-03-21,90.820000,91.059998,89.500000,89.790001,89.790001,24410000
1978-03-22,89.790001,90.070000,88.989998,89.470001,89.470001,21950000
1978-03-23,89.470001,89.900002,88.830002,89.360001,89.360001,21290000
1978-03-27,89.360001,89.500000,88.510002,88.870003,88.870003,18870000
1978-03-28,88.870003,89.760002,88.470001,89.500000,89.500000,21600000
1978-03-29,89.500000,90.169998,89.139999,89.639999,89.639999,25450000
1978-03-30,89.639999,89.889999,88.970001,89.410004,89.410004,20460000
1978-03-31,89.410004,89.639999,88.680000,89.209999,89.209999,20130000
1978-04-03,89.199997,89.199997,88.070000,88.459999,88.459999,20230000
1978-04-04,88.459999,89.180000,88.160004,88.860001,88.860001,20130000
1978-04-05,88.860001,89.910004,88.620003,89.639999,89.639999,27260000
1978-04-06,89.639999,90.459999,89.309998,89.790001,89.790001,27360000
1978-04-07,89.790001,90.589996,89.389999,90.169998,90.169998,25160000
1978-04-10,90.169998,90.879997,89.730003,90.489998,90.489998,25740000
1978-04-11,90.489998,90.790001,89.769997,90.250000,90.250000,24300000
1978-04-12,90.250000,90.779999,89.650002,90.110001,90.110001,26210000
1978-04-13,90.110001,91.269997,89.820000,90.980003,90.980003,31580000
1978-04-14,91.400002,93.309998,91.400002,92.919998,92.919998,52280000
1978-04-17,93.599998,95.889999,93.599998,94.449997,94.449997,63510000
1978-04-18,94.449997,94.720001,92.870003,93.430000,93.430000,38950000
1978-04-19,93.430000,94.480003,92.750000,93.860001,93.860001,35060000
1978-04-20,93.970001,95.709999,93.970001,94.540001,94.540001,43230000
1978-04-21,94.540001,95.089996,93.709999,94.339996,94.339996,31540000
1978-04-24,94.339996,96.000000,94.080002,95.769997,95.769997,34510000
1978-04-25,96.050003,97.910004,96.050003,96.639999,96.639999,55800000
1978-04-26,96.639999,97.750000,95.959999,96.820000,96.820000,44430000
1978-04-27,96.820000,96.930000,95.300003,95.860001,95.860001,35470000
1978-04-28,95.860001,97.099998,95.239998,96.830002,96.830002,32850000
1978-05-01,96.830002,98.300003,96.410004,97.669998,97.669998,37020000
1978-05-02,97.669998,98.110001,96.440002,97.250000,97.250000,41400000
1978-05-03,97.250000,97.610001,95.839996,96.260002,96.260002,37560000
1978-05-04,96.260002,96.430000,94.570000,95.930000,95.930000,37520000
1978-05-05,95.930000,97.440002,95.559998,96.529999,96.529999,42680000
1978-05-08,96.529999,97.500000,95.820000,96.190002,96.190002,34680000
1978-05-09,96.190002,96.680000,95.330002,95.900002,95.900002,30860000
1978-05-10,95.900002,96.690002,95.349998,95.919998,95.919998,33330000
1978-05-11,95.919998,97.470001,95.599998,97.199997,97.199997,36630000
1978-05-12,97.199997,98.889999,97.139999,98.070000,98.070000,46600000
1978-05-15,98.070000,99.110001,97.400002,98.760002,98.760002,33890000
1978-05-16,98.760002,100.160004,98.610001,99.349998,99.349998,48170000
1978-05-17,99.349998,100.320000,98.629997,99.599998,99.599998,45490000
1978-05-18,99.599998,100.040001,98.190002,98.620003,98.620003,42270000
1978-05-19,98.620003,99.059998,97.419998,98.120003,98.120003,34360000
1978-05-22,98.120003,99.430000,97.650002,99.089996,99.089996,28680000
1978-05-23,99.089996,99.169998,97.529999,98.050003,98.050003,33230000
1978-05-24,97.739998,97.739998,96.269997,97.080002,97.080002,31450000
1978-05-25,97.080002,97.800003,96.300003,96.800003,96.800003,28410000
1978-05-26,96.800003,97.139999,96.010002,96.580002,96.580002,21410000
1978-05-30,96.580002,97.230003,95.949997,96.860001,96.860001,21040000
1978-05-31,96.860001,97.970001,96.500000,97.239998,97.239998,29070000
1978-06-01,97.239998,97.949997,96.629997,97.349998,97.349998,28750000
1978-06-02,97.349998,98.519997,97.010002,98.139999,98.139999,31860000
1978-06-05,98.139999,100.269997,97.970001,99.949997,99.949997,39580000
1978-06-06,99.949997,101.839996,99.900002,100.320000,100.320000,51970000
1978-06-07,100.320000,100.809998,99.360001,100.120003,100.120003,33060000
1978-06-08,100.120003,101.209999,99.550003,100.209999,100.209999,39380000
1978-06-09,100.209999,100.709999,99.300003,99.930000,99.930000,32470000
1978-06-12,99.930000,100.599998,99.160004,99.550003,99.550003,24440000
1978-06-13,99.550003,99.980003,98.430000,99.570000,99.570000,30760000
1978-06-14,99.570000,100.680000,98.889999,99.480003,99.480003,37290000
1978-06-15,99.480003,99.540001,97.970001,98.339996,98.339996,29280000
1978-06-16,98.339996,98.589996,97.099998,97.419998,97.419998,27690000
1978-06-19,97.419998,97.940002,96.529999,97.489998,97.489998,25500000
1978-06-20,97.489998,97.779999,96.150002,96.510002,96.510002,27920000
1978-06-21,96.510002,96.739998,95.419998,96.010002,96.010002,29100000
1978-06-22,96.010002,96.760002,95.519997,96.239998,96.239998,27160000
1978-06-23,96.239998,96.980003,95.489998,95.849998,95.849998,28530000
1978-06-26,95.849998,96.059998,94.309998,94.599998,94.599998,29250000
1978-06-27,94.599998,95.480003,93.989998,94.980003,94.980003,29280000
1978-06-28,94.980003,95.790001,94.440002,95.400002,95.400002,23260000
1978-06-29,95.400002,96.260002,95.000000,95.570000,95.570000,21660000
1978-06-30,95.570000,95.959999,94.870003,95.529999,95.529999,18100000
1978-07-03,95.529999,95.650002,94.620003,95.089996,95.089996,11560000
1978-07-05,95.089996,95.199997,93.779999,94.269997,94.269997,23730000
1978-07-06,94.269997,94.830002,93.589996,94.320000,94.320000,24990000
1978-07-07,94.320000,95.320000,94.019997,94.889999,94.889999,23480000
1978-07-10,94.889999,95.669998,94.279999,95.269997,95.269997,22470000
1978-07-11,95.269997,96.489998,94.919998,95.930000,95.930000,27470000
1978-07-12,95.930000,96.830002,95.500000,96.239998,96.239998,26640000
1978-07-13,96.239998,96.660004,95.419998,96.250000,96.250000,23620000
1978-07-14,96.250000,97.879997,95.889999,97.580002,97.580002,28370000
1978-07-17,97.580002,98.839996,97.239998,97.779999,97.779999,29180000
1978-07-18,97.779999,97.980003,96.519997,96.870003,96.870003,22860000
1978-07-19,96.870003,98.410004,96.709999,98.120003,98.120003,30850000
1978-07-20,98.120003,99.180000,97.489998,98.029999,98.029999,33350000
1978-07-21,98.029999,98.570000,97.019997,97.750000,97.750000,26060000
1978-07-24,97.750000,98.129997,96.720001,97.720001,97.720001,23280000
1978-07-25,97.720001,98.730003,97.199997,98.440002,98.440002,25400000
1978-07-26,99.080002,99.080002,99.080002,99.080002,99.080002,36830000
1978-07-27,99.080002,100.169998,98.599998,99.540001,99.540001,33970000
1978-07-28,99.540001,100.510002,98.900002,100.000000,100.000000,33390000
1978-07-31,100.000000,101.180000,99.370003,100.680000,100.680000,33990000
1978-08-01,100.680000,101.459999,99.949997,100.660004,100.660004,34810000
1978-08-02,100.660004,103.209999,100.180000,102.919998,102.919998,47470000
1978-08-03,102.919998,105.410004,102.820000,103.510002,103.510002,66370000
1978-08-04,103.510002,104.669998,102.750000,103.919998,103.919998,37910000
1978-08-07,103.919998,104.839996,103.029999,103.550003,103.550003,33350000
1978-08-08,103.550003,104.349998,102.599998,104.010002,104.010002,34290000
1978-08-09,104.010002,105.720001,103.699997,104.500000,104.500000,48800000
1978-08-10,104.500000,105.110001,103.099998,103.660004,103.660004,39760000
1978-08-11,103.660004,104.669998,102.849998,103.959999,103.959999,33550000
1978-08-14,103.959999,104.980003,103.400002,103.970001,103.970001,32320000
1978-08-15,103.970001,104.379997,102.860001,103.849998,103.849998,29760000
1978-08-16,103.849998,105.150002,103.410004,104.650002,104.650002,36120000
1978-08-17,104.650002,106.269997,104.339996,105.080002,105.080002,45270000
1978-08-18,105.080002,105.980003,104.230003,104.730003,104.730003,34650000
1978-08-21,104.730003,105.199997,103.440002,103.889999,103.889999,29440000
1978-08-22,103.889999,104.790001,103.139999,104.309998,104.309998,29620000
1978-08-23,104.309998,105.680000,104.120003,104.910004,104.910004,39630000
1978-08-24,104.910004,105.860001,104.290001,105.080002,105.080002,38500000
1978-08-25,105.080002,105.680000,104.239998,104.900002,104.900002,36190000
1978-08-28,104.900002,105.139999,103.610001,103.959999,103.959999,31760000
1978-08-29,103.959999,104.339996,102.919998,103.389999,103.389999,33780000
1978-08-30,103.389999,104.260002,102.699997,103.500000,103.500000,37750000
1978-08-31,103.500000,104.050003,102.629997,103.290001,103.290001,33850000
1978-09-01,103.290001,104.269997,102.730003,103.680000,103.680000,35070000
1978-09-05,103.680000,104.830002,103.309998,104.489998,104.489998,32170000
1978-09-06,104.510002,106.190002,104.510002,105.379997,105.379997,42600000
1978-09-07,105.379997,106.489998,104.760002,105.419998,105.419998,40310000
1978-09-08,105.500000,107.190002,105.500000,106.790001,106.790001,42170000
1978-09-11,106.790001,108.050003,106.419998,106.980003,106.980003,39670000
1978-09-12,106.980003,107.480003,106.019997,106.989998,106.989998,34400000
1978-09-13,106.989998,107.849998,105.870003,106.339996,106.339996,43340000
1978-09-14,106.339996,106.620003,104.769997,105.099998,105.099998,37400000
1978-09-15,105.099998,105.120003,103.559998,104.120003,104.120003,37290000
1978-09-18,104.120003,105.029999,102.750000,103.209999,103.209999,35860000
1978-09-19,103.209999,103.820000,102.120003,102.529999,102.529999,31660000
1978-09-20,102.529999,103.290001,101.279999,101.730003,101.730003,35080000
1978-09-21,101.730003,102.540001,100.660004,101.900002,101.900002,33640000
1978-09-22,101.900002,102.690002,101.129997,101.839996,101.839996,27960000
1978-09-25,101.839996,102.360001,101.050003,101.860001,101.860001,20970000
1978-09-26,101.860001,103.150002,101.580002,102.620003,102.620003,26330000
1978-09-27,102.620003,103.440002,101.330002,101.660004,101.660004,28370000
1978-09-28,101.660004,102.379997,100.940002,101.959999,101.959999,24390000
1978-09-29,101.959999,103.080002,101.650002,102.540001,102.540001,23610000
1978-10-02,102.540001,103.419998,102.129997,102.959999,102.959999,18700000
1978-10-03,102.959999,103.559998,102.180000,102.599998,102.599998,22540000
1978-10-04,102.599998,103.360001,101.760002,103.059998,103.059998,25090000
1978-10-05,103.059998,104.099998,102.540001,103.269997,103.269997,27820000
1978-10-06,103.269997,104.230003,102.820000,103.519997,103.519997,27380000
1978-10-09,103.519997,104.889999,103.309998,104.589996,104.589996,19720000
1978-10-10,104.589996,105.360001,103.900002,104.459999,104.459999,25470000
1978-10-11,104.459999,105.639999,103.800003,105.389999,105.389999,21740000
1978-10-12,105.389999,106.230003,104.419998,104.879997,104.879997,30170000
1978-10-13,104.879997,105.339996,104.070000,104.660004,104.660004,21920000
1978-10-16,104.629997,104.629997,102.430000,102.610001,102.610001,24600000
1978-10-17,102.349998,102.349998,100.470001,101.260002,101.260002,37870000
1978-10-18,101.260002,101.760002,99.889999,100.489998,100.489998,32940000
1978-10-19,100.489998,101.029999,99.040001,99.330002,99.330002,31810000
1978-10-20,99.260002,99.260002,97.120003,97.949997,97.949997,43670000
1978-10-23,97.949997,98.839996,96.629997,98.180000,98.180000,36090000
1978-10-24,98.180000,98.949997,97.129997,97.489998,97.489998,28880000
1978-10-25,97.489998,98.559998,96.330002,97.309998,97.309998,31380000
1978-10-26,97.309998,97.709999,95.589996,96.029999,96.029999,31990000
1978-10-27,96.029999,96.620003,94.300003,94.589996,94.589996,40360000
1978-10-30,94.589996,95.489998,91.650002,95.059998,95.059998,59480000
1978-10-31,95.059998,95.800003,92.720001,93.150002,93.150002,42720000
1978-11-01,94.129997,97.410004,94.129997,96.849998,96.849998,50450000
1978-11-02,96.849998,97.309998,94.839996,95.610001,95.610001,41030000
1978-11-03,95.610001,96.980003,94.779999,96.180000,96.180000,25990000
1978-11-06,96.180000,96.489998,94.839996,95.190002,95.190002,20450000
1978-11-07,94.750000,94.750000,93.139999,93.849998,93.849998,25320000
1978-11-08,93.849998,94.739998,92.889999,94.449997,94.449997,23560000
1978-11-09,94.449997,95.500000,93.809998,94.419998,94.419998,23320000
1978-11-10,94.419998,95.389999,93.940002,94.769997,94.769997,16750000
1978-11-13,94.769997,94.900002,92.959999,93.129997,93.129997,20960000
1978-11-14,93.129997,93.529999,91.769997,92.489998,92.489998,30610000
1978-11-15,92.489998,94.000000,92.290001,92.709999,92.709999,26280000
1978-11-16,92.709999,94.080002,92.589996,93.709999,93.709999,21340000
1978-11-17,93.709999,95.029999,93.589996,94.419998,94.419998,25170000
1978-11-20,94.419998,95.860001,94.290001,95.250000,95.250000,24440000
1978-11-21,95.250000,95.830002,94.489998,95.010002,95.010002,20750000
1978-11-22,95.010002,95.910004,94.540001,95.480003,95.480003,20010000
1978-11-24,95.480003,96.169998,94.980003,95.790001,95.790001,14590000
1978-11-27,95.790001,96.519997,95.169998,95.989998,95.989998,19790000
1978-11-28,95.989998,96.510002,94.879997,95.150002,95.150002,22740000
1978-11-29,94.919998,94.919998,93.480003,93.750000,93.750000,21160000
1978-11-30,93.750000,94.940002,93.290001,94.699997,94.699997,19900000
1978-12-01,95.010002,96.690002,95.010002,96.279999,96.279999,26830000
1978-12-04,96.279999,96.959999,95.370003,96.150002,96.150002,22020000
1978-12-05,96.150002,97.699997,95.879997,97.440002,97.440002,25670000
1978-12-06,97.440002,98.580002,96.830002,97.489998,97.489998,29680000
1978-12-07,97.489998,98.099998,96.580002,97.080002,97.080002,21170000
1978-12-08,97.080002,97.480003,96.139999,96.629997,96.629997,18560000
1978-12-11,96.629997,97.559998,96.070000,97.110001,97.110001,21000000
1978-12-12,97.110001,97.580002,96.269997,96.589996,96.589996,22210000
1978-12-13,96.589996,97.070000,95.589996,96.059998,96.059998,22480000
1978-12-14,96.059998,96.440002,95.199997,96.040001,96.040001,20840000
1978-12-15,96.040001,96.279999,94.879997,95.330002,95.330002,23620000
1978-12-18,94.330002,94.330002,92.639999,93.440002,93.440002,32900000
1978-12-19,93.440002,94.849998,93.050003,94.239998,94.239998,25960000
1978-12-20,94.239998,95.199997,93.699997,94.680000,94.680000,26520000
1978-12-21,94.680000,95.660004,94.110001,94.709999,94.709999,28670000
1978-12-22,94.769997,96.620003,94.769997,96.309998,96.309998,23790000
1978-12-26,96.309998,97.889999,95.989998,97.519997,97.519997,21470000
1978-12-27,97.510002,97.510002,96.150002,96.660004,96.660004,23580000
1978-12-28,96.660004,97.190002,95.820000,96.279999,96.279999,25440000
1978-12-29,96.279999,97.029999,95.480003,96.110001,96.110001,30030000
1979-01-02,96.110001,96.959999,95.220001,96.730003,96.730003,18340000
1979-01-03,96.809998,98.540001,96.809998,97.800003,97.800003,29180000
1979-01-04,97.800003,99.419998,97.519997,98.580002,98.580002,33290000
1979-01-05,98.580002,99.790001,98.250000,99.129997,99.129997,28890000
1979-01-08,99.129997,99.300003,97.830002,98.800003,98.800003,21440000
1979-01-09,98.800003,99.959999,98.620003,99.330002,99.330002,27340000
1979-01-10,99.330002,99.750000,98.279999,98.769997,98.769997,24990000
1979-01-11,98.769997,99.410004,97.949997,99.099998,99.099998,24580000
1979-01-12,99.320000,100.910004,99.320000,99.930000,99.930000,37120000
1979-01-15,99.930000,101.129997,99.580002,100.690002,100.690002,27520000
1979-01-16,100.690002,100.879997,99.110001,99.459999,99.459999,30340000
1979-01-17,99.459999,100.000000,98.330002,99.480003,99.480003,25310000
1979-01-18,99.480003,100.349998,98.910004,99.720001,99.720001,27260000
1979-01-19,99.720001,100.570000,99.220001,99.750000,99.750000,26800000
1979-01-22,99.750000,100.349998,98.900002,99.900002,99.900002,24390000
1979-01-23,99.900002,101.050003,99.349998,100.599998,100.599998,30130000
1979-01-24,100.599998,101.309998,99.669998,100.160004,100.160004,31730000
1979-01-25,100.160004,101.660004,99.989998,101.190002,101.190002,31440000
1979-01-26,101.190002,102.589996,101.029999,101.860001,101.860001,34230000
1979-01-29,101.860001,102.330002,100.989998,101.550003,101.550003,24170000
1979-01-30,101.550003,102.070000,100.680000,101.050003,101.050003,26910000
1979-01-31,101.050003,101.410004,99.470001,99.930000,99.930000,30330000
1979-02-01,99.930000,100.379997,99.010002,99.959999,99.959999,27930000
1979-02-02,99.959999,100.519997,99.099998,99.500000,99.500000,25350000
1979-02-05,99.070000,99.070000,97.570000,98.089996,98.089996,26490000
1979-02-06,98.089996,98.739998,97.480003,98.050003,98.050003,23570000
1979-02-07,98.050003,98.070000,96.510002,97.160004,97.160004,28450000
1979-02-08,97.160004,98.110001,96.820000,97.650002,97.650002,23360000
1979-02-09,97.650002,98.500000,97.279999,97.870003,97.870003,24320000
1979-02-12,97.870003,98.550003,97.050003,98.199997,98.199997,20610000
1979-02-13,98.250000,99.580002,98.250000,98.930000,98.930000,28470000
1979-02-14,98.930000,99.639999,98.209999,98.870003,98.870003,27220000
1979-02-15,98.870003,99.129997,97.959999,98.730003,98.730003,22550000
1979-02-16,98.730003,99.230003,98.110001,98.669998,98.669998,21110000
1979-02-20,98.669998,99.669998,98.260002,99.419998,99.419998,22010000
1979-02-21,99.419998,100.070000,98.690002,99.070000,99.070000,26050000
1979-02-22,99.070000,99.209999,97.879997,98.330002,98.330002,26290000
1979-02-23,98.330002,98.500000,97.290001,97.779999,97.779999,22750000
1979-02-26,97.779999,98.279999,97.199997,97.669998,97.669998,22620000
1979-02-27,97.650002,97.650002,95.690002,96.129997,96.129997,31470000
1979-02-28,96.129997,96.690002,95.379997,96.279999,96.279999,25090000
1979-03-01,96.279999,97.279999,95.980003,96.900002,96.900002,23830000
1979-03-02,96.900002,97.550003,96.440002,96.970001,96.970001,23130000
1979-03-05,97.029999,98.639999,97.029999,98.059998,98.059998,25690000
1979-03-06,98.059998,98.529999,97.360001,97.870003,97.870003,24490000
1979-03-07,97.870003,99.230003,97.669998,98.440002,98.440002,28930000
1979-03-08,98.440002,99.820000,98.099998,99.580002,99.580002,32000000
1979-03-09,99.580002,100.580002,99.120003,99.540001,99.540001,33410000
1979-03-12,99.540001,100.040001,98.559998,99.669998,99.669998,25740000
1979-03-13,99.669998,100.660004,99.129997,99.839996,99.839996,31170000
1979-03-14,99.839996,100.430000,99.230003,99.709999,99.709999,24630000
1979-03-15,99.709999,100.570000,99.110001,99.860001,99.860001,29370000
1979-03-16,99.860001,101.160004,99.529999,100.690002,100.690002,31770000
1979-03-19,100.690002,101.940002,100.349998,101.059998,101.059998,34620000
1979-03-20,101.059998,101.339996,100.010002,100.500000,100.500000,27180000
1979-03-21,100.500000,101.480003,99.870003,101.250000,101.250000,31120000
1979-03-22,101.250000,102.410004,101.040001,101.669998,101.669998,34380000
1979-03-23,101.669998,102.370003,101.019997,101.599998,101.599998,33570000
1979-03-26,101.599998,101.769997,100.599998,101.040001,101.040001,23430000
1979-03-27,101.040001,102.709999,100.809998,102.480003,102.480003,32940000
1979-03-28,102.480003,103.309998,101.739998,102.120003,102.120003,39920000
1979-03-29,102.120003,102.779999,101.430000,102.029999,102.029999,28510000
1979-03-30,102.029999,102.510002,101.029999,101.589996,101.589996,29970000
1979-04-02,101.559998,101.559998,100.139999,100.900002,100.900002,28990000
1979-04-03,100.900002,102.669998,100.809998,102.400002,102.400002,33530000
1979-04-04,102.400002,103.730003,102.160004,102.650002,102.650002,41940000
1979-04-05,102.650002,103.599998,102.160004,103.260002,103.260002,34520000
1979-04-06,103.260002,103.949997,102.580002,103.180000,103.180000,34710000
1979-04-09,103.180000,103.559998,102.279999,102.870003,102.870003,27230000
1979-04-10,102.870003,103.830002,102.419998,103.339996,103.339996,31900000
1979-04-11,103.339996,103.769997,101.919998,102.309998,102.309998,32900000
1979-04-12,102.309998,102.769997,101.510002,102.000000,102.000000,26780000
1979-04-16,102.000000,102.019997,100.669998,101.120003,101.120003,28050000
1979-04-17,101.120003,101.940002,100.650002,101.239998,101.239998,29260000
1979-04-18,101.239998,102.230003,100.959999,101.699997,101.699997,29510000
1979-04-19,101.699997,102.400002,100.879997,101.279999,101.279999,31150000
1979-04-20,101.279999,101.809998,100.459999,101.230003,101.230003,28830000
1979-04-23,101.230003,102.000000,100.680000,101.570000,101.570000,25610000
1979-04-24,101.570000,103.019997,101.389999,102.199997,102.199997,35540000
1979-04-25,102.199997,103.070000,101.790001,102.500000,102.500000,31750000
1979-04-26,102.500000,102.910004,101.580002,102.010002,102.010002,32400000
1979-04-27,102.010002,102.320000,101.040001,101.800003,101.800003,29610000
1979-04-30,101.800003,102.239998,100.910004,101.760002,101.760002,26440000
1979-05-01,101.760002,102.500000,101.220001,101.680000,101.680000,31040000
1979-05-02,101.680000,102.279999,101.000000,101.720001,101.720001,30510000
1979-05-03,101.720001,102.570000,101.250000,101.809998,101.809998,30870000
1979-05-04,101.809998,102.080002,100.419998,100.690002,100.690002,30630000
1979-05-07,100.370003,100.370003,98.779999,99.019997,99.019997,30480000
1979-05-08,99.019997,99.559998,97.980003,99.169998,99.169998,32720000
1979-05-09,99.169998,100.010002,98.500000,99.459999,99.459999,27670000
1979-05-10,99.459999,99.629997,98.220001,98.519997,98.519997,25230000
1979-05-11,98.519997,99.029999,97.919998,98.519997,98.519997,24010000
1979-05-14,98.519997,98.949997,97.709999,98.059998,98.059998,22450000
1979-05-15,98.059998,98.900002,97.599998,98.139999,98.139999,26190000
1979-05-16,98.139999,98.800003,97.489998,98.419998,98.419998,28350000
1979-05-17,98.419998,100.220001,98.290001,99.940002,99.940002,30550000
1979-05-18,99.940002,100.730003,99.330002,99.930000,99.930000,26590000
1979-05-21,99.930000,100.750000,99.370003,100.139999,100.139999,25550000
1979-05-22,100.139999,100.930000,99.449997,100.510002,100.510002,30400000
1979-05-23,100.510002,101.309998,99.629997,99.889999,99.889999,30390000
1979-05-24,99.889999,100.440002,99.139999,99.930000,99.930000,25710000
1979-05-25,99.930000,100.680000,99.519997,100.220001,100.220001,27810000
1979-05-29,100.220001,100.760002,99.559998,100.050003,100.050003,27040000
1979-05-30,100.050003,100.250000,98.790001,99.110001,99.110001,29250000
1979-05-31,99.110001,99.610001,98.290001,99.080002,99.080002,30300000
1979-06-01,99.080002,99.699997,98.570000,99.169998,99.169998,24560000
1979-06-04,99.169998,99.760002,98.610001,99.320000,99.320000,24040000
1979-06-05,99.320000,101.070000,99.169998,100.620003,100.620003,35050000
1979-06-06,100.620003,101.959999,100.379997,101.300003,101.300003,39830000
1979-06-07,101.300003,102.540001,101.150002,101.790001,101.790001,43380000
1979-06-08,101.790001,102.230003,100.910004,101.489998,101.489998,31470000
1979-06-11,101.489998,102.239998,100.910004,101.910004,101.910004,28270000
1979-06-12,101.910004,103.639999,101.809998,102.849998,102.849998,45450000
1979-06-13,102.849998,103.580002,101.830002,102.309998,102.309998,40740000
1979-06-14,102.309998,102.629997,101.040001,102.199997,102.199997,37850000
1979-06-15,102.199997,102.779999,101.379997,102.089996,102.089996,40740000
1979-06-18,102.089996,102.480003,101.050003,101.559998,101.559998,30970000
1979-06-19,101.559998,102.279999,100.910004,101.580002,101.580002,30780000
1979-06-20,101.580002,102.190002,100.930000,101.629997,101.629997,33790000
1979-06-21,101.629997,102.739998,101.199997,102.089996,102.089996,36490000
1979-06-22,102.089996,103.160004,101.910004,102.639999,102.639999,36410000
1979-06-25,102.639999,102.910004,101.449997,102.089996,102.089996,31330000
1979-06-26,102.089996,102.089996,101.220001,101.660004,101.660004,34680000
1979-06-27,101.660004,102.949997,101.290001,102.269997,102.269997,36720000
1979-06-28,102.269997,103.459999,101.910004,102.800003,102.800003,38470000
1979-06-29,102.800003,103.669998,102.040001,102.910004,102.910004,34690000
1979-07-02,102.910004,103.000000,101.449997,101.989998,101.989998,32060000
1979-07-03,101.989998,102.570000,101.309998,102.089996,102.089996,31670000
1979-07-05,102.089996,102.879997,101.589996,102.430000,102.430000,30290000
1979-07-06,102.430000,103.910004,102.120003,103.620003,103.620003,38570000
1979-07-09,103.620003,105.070000,103.360001,104.470001,104.470001,42460000
1979-07-10,104.470001,105.169998,103.519997,104.199997,104.199997,39730000
1979-07-11,104.199997,104.339996,102.870003,103.639999,103.639999,36650000
1979-07-12,103.639999,103.720001,102.220001,102.690002,102.690002,31780000
1979-07-13,102.690002,102.989998,101.489998,102.320000,102.320000,33080000
1979-07-16,102.320000,103.199997,101.809998,102.739998,102.739998,26620000
1979-07-17,102.739998,103.059998,101.269997,101.830002,101.830002,34270000
1979-07-18,101.830002,102.059998,100.349998,101.690002,101.690002,35950000
1979-07-19,101.690002,102.419998,101.040001,101.610001,101.610001,26780000
1979-07-20,101.610001,102.320000,101.059998,101.820000,101.820000,26360000
1979-07-23,101.820000,102.129997,100.839996,101.589996,101.589996,26860000
1979-07-24,101.589996,102.500000,101.139999,101.970001,101.970001,29690000
1979-07-25,101.970001,103.440002,101.849998,103.080002,103.080002,34890000
1979-07-26,103.080002,103.629997,102.339996,103.099998,103.099998,32270000
1979-07-27,103.099998,103.500000,102.290001,103.099998,103.099998,27760000
1979-07-30,103.099998,103.629997,102.419998,103.150002,103.150002,28640000
1979-07-31,103.150002,104.260002,102.889999,103.809998,103.809998,34360000
1979-08-01,103.809998,104.570000,103.139999,104.169998,104.169998,36570000
1979-08-02,104.169998,105.019997,103.589996,104.099998,104.099998,37720000
1979-08-03,104.099998,104.559998,103.360001,104.040001,104.040001,28160000
1979-08-06,104.040001,104.660004,103.269997,104.300003,104.300003,27190000
1979-08-07,104.300003,106.230003,104.120003,105.650002,105.650002,45410000
1979-08-08,105.650002,106.839996,105.199997,105.980003,105.980003,44970000
1979-08-09,105.980003,106.250000,104.889999,105.489998,105.489998,34630000
1979-08-10,105.489998,106.790001,104.809998,106.400002,106.400002,36740000
1979-08-13,106.400002,107.900002,106.279999,107.419998,107.419998,41980000
1979-08-14,107.419998,108.029999,106.599998,107.519997,107.519997,40910000
1979-08-15,107.519997,108.639999,106.750000,108.250000,108.250000,46130000
1979-08-16,108.250000,109.180000,107.379997,108.089996,108.089996,47000000
1979-08-17,108.089996,108.940002,107.250000,108.300003,108.300003,31630000
1979-08-20,108.300003,109.320000,107.690002,108.830002,108.830002,32300000
1979-08-21,108.830002,109.680000,108.169998,108.910004,108.910004,38860000
1979-08-22,108.910004,109.559998,108.089996,108.989998,108.989998,38450000
1979-08-23,108.989998,109.589996,108.120003,108.629997,108.629997,35710000
1979-08-24,108.629997,109.110001,107.650002,108.599998,108.599998,32730000
1979-08-27,108.599998,109.839996,108.120003,109.139999,109.139999,32050000
1979-08-28,109.139999,109.650002,108.470001,109.019997,109.019997,29430000
1979-08-29,109.019997,109.589996,108.360001,109.019997,109.019997,30810000
1979-08-30,109.019997,109.589996,108.400002,109.019997,109.019997,29300000
1979-08-31,109.019997,109.800003,108.580002,109.320000,109.320000,26370000
1979-09-04,109.320000,109.410004,107.220001,107.440002,107.440002,33350000
1979-09-05,107.190002,107.190002,105.379997,106.400002,106.400002,41650000
1979-09-06,106.400002,107.610001,105.970001,106.849998,106.849998,30330000
1979-09-07,106.849998,108.089996,106.300003,107.660004,107.660004,34360000
1979-09-10,107.660004,108.709999,107.209999,108.169998,108.169998,32980000
1979-09-11,108.169998,108.830002,106.800003,107.510002,107.510002,42530000
1979-09-12,107.510002,108.410004,106.720001,107.820000,107.820000,39350000
1979-09-13,107.820000,108.529999,107.059998,107.849998,107.849998,35240000
1979-09-14,107.849998,109.480003,107.419998,108.760002,108.760002,41980000
1979-09-17,108.760002,110.059998,108.400002,108.839996,108.839996,37610000
1979-09-18,108.839996,109.000000,107.320000,108.000000,108.000000,38750000
1979-09-19,108.000000,109.019997,107.519997,108.279999,108.279999,35370000
1979-09-20,108.279999,110.690002,107.589996,110.510002,110.510002,45100000
1979-09-21,110.510002,111.580002,109.459999,110.470001,110.470001,52380000
1979-09-24,110.470001,110.900002,109.160004,109.610001,109.610001,33790000
1979-09-25,109.610001,110.190002,108.269997,109.680000,109.680000,32410000
1979-09-26,109.680000,111.250000,109.370003,109.959999,109.959999,37700000
1979-09-27,109.959999,110.750000,109.190002,110.209999,110.209999,33110000
1979-09-28,110.209999,110.669998,108.699997,109.320000,109.320000,35950000
1979-10-01,109.190002,109.190002,107.699997,108.559998,108.559998,24980000
1979-10-02,108.559998,110.080002,108.029999,109.589996,109.589996,38310000
1979-10-03,109.589996,110.430000,108.879997,109.589996,109.589996,36470000
1979-10-04,109.589996,110.809998,109.139999,110.169998,110.169998,38800000
1979-10-05,110.169998,112.160004,110.160004,111.269997,111.269997,48250000
1979-10-08,111.269997,111.830002,109.650002,109.879997,109.879997,32610000
1979-10-09,109.430000,109.430000,106.040001,106.629997,106.629997,55560000
1979-10-10,106.230003,106.230003,102.309998,105.300003,105.300003,81620000
1979-10-11,105.300003,106.330002,103.699997,105.050003,105.050003,47530000
1979-10-12,105.050003,106.199997,104.010002,104.489998,104.489998,36390000
1979-10-15,104.489998,104.739998,102.690002,103.360001,103.360001,34850000
1979-10-16,103.360001,104.370003,102.519997,103.190002,103.190002,33770000
1979-10-17,103.190002,104.540001,102.739998,103.389999,103.389999,29650000
1979-10-18,103.389999,104.620003,102.919998,103.610001,103.610001,29590000
1979-10-19,103.580002,103.580002,101.239998,101.599998,101.599998,42430000
1979-10-22,101.379997,101.379997,99.059998,100.709999,100.709999,45240000
1979-10-23,100.709999,101.440002,99.610001,100.279999,100.279999,32910000
1979-10-24,100.279999,101.449997,99.660004,100.440002,100.440002,31480000
1979-10-25,100.440002,101.389999,99.559998,100.000000,100.000000,28440000
1979-10-26,100.000000,101.309998,99.589996,100.570000,100.570000,29660000
1979-10-29,100.570000,101.559998,100.129997,100.709999,100.709999,22720000
1979-10-30,100.709999,102.830002,100.410004,102.669998,102.669998,28890000
1979-10-31,102.669998,103.160004,101.379997,101.820000,101.820000,27780000
1979-11-01,101.820000,103.070000,101.099998,102.570000,102.570000,25880000
1979-11-02,102.570000,103.209999,101.919998,102.510002,102.510002,23670000
1979-11-05,102.510002,102.660004,101.239998,101.820000,101.820000,20470000
1979-11-06,101.820000,102.010002,100.769997,101.199997,101.199997,21960000
1979-11-07,100.970001,100.970001,99.419998,99.870003,99.870003,30830000
1979-11-08,99.870003,101.000000,99.489998,100.300003,100.300003,26270000
1979-11-09,100.580002,102.180000,100.580002,101.510002,101.510002,30060000
1979-11-12,101.510002,103.720001,101.269997,103.510002,103.510002,26640000
1979-11-13,103.510002,104.209999,102.419998,102.940002,102.940002,29240000
1979-11-14,102.940002,104.129997,101.910004,103.389999,103.389999,30970000
1979-11-15,103.389999,104.940002,103.099998,104.129997,104.129997,32380000
1979-11-16,104.129997,104.720001,103.070000,103.790001,103.790001,30060000
1979-11-19,103.790001,105.080002,103.169998,104.230003,104.230003,33090000
1979-11-20,104.230003,105.110001,103.139999,103.690002,103.690002,35010000
1979-11-21,103.690002,104.230003,102.040001,103.889999,103.889999,37020000
1979-11-23,103.889999,105.129997,103.559998,104.669998,104.669998,23300000
1979-11-26,104.830002,107.440002,104.830002,106.800003,106.800003,47940000
1979-11-27,106.800003,107.889999,105.639999,106.379997,106.379997,45140000
1979-11-28,106.379997,107.550003,105.290001,106.769997,106.769997,39690000
1979-11-29,106.769997,107.839996,106.169998,106.809998,106.809998,33550000
1979-11-30,106.809998,107.160004,105.559998,106.160004,106.160004,30480000
1979-12-03,106.160004,106.650002,105.070000,105.830002,105.830002,29030000
1979-12-04,105.830002,107.250000,105.660004,106.790001,106.790001,33510000
1979-12-05,106.790001,108.360001,106.599998,107.250000,107.250000,39300000
1979-12-06,107.250000,108.470001,106.709999,108.000000,108.000000,37510000
1979-12-07,108.000000,109.239998,106.550003,107.519997,107.519997,42370000
1979-12-10,107.519997,108.269997,106.650002,107.669998,107.669998,32270000
1979-12-11,107.669998,108.580002,106.790001,107.489998,107.489998,36160000
1979-12-12,107.489998,108.320000,106.779999,107.519997,107.519997,34630000
1979-12-13,107.519997,108.290001,106.680000,107.669998,107.669998,36690000
1979-12-14,107.669998,109.489998,107.370003,108.919998,108.919998,41800000
1979-12-17,108.919998,110.330002,108.360001,109.330002,109.330002,43830000
1979-12-18,109.330002,109.830002,107.830002,108.300003,108.300003,43310000
1979-12-19,108.300003,108.790001,107.019997,108.199997,108.199997,41780000
1979-12-20,108.199997,109.239998,107.400002,108.260002,108.260002,40380000
1979-12-21,108.260002,108.760002,106.989998,107.589996,107.589996,36160000
1979-12-24,107.589996,108.080002,106.800003,107.660004,107.660004,19150000
1979-12-26,107.660004,108.370003,107.059998,107.779999,107.779999,24960000
1979-12-27,107.779999,108.500000,107.139999,107.959999,107.959999,31410000
1979-12-28,107.959999,108.610001,107.160004,107.839996,107.839996,34430000
1979-12-31,107.839996,108.529999,107.260002,107.940002,107.940002,31530000
1980-01-02,107.940002,108.430000,105.290001,105.760002,105.760002,40610000
1980-01-03,105.760002,106.080002,103.260002,105.220001,105.220001,50480000
1980-01-04,105.220001,107.080002,105.089996,106.519997,106.519997,39130000
1980-01-07,106.519997,107.800003,105.800003,106.809998,106.809998,44500000
1980-01-08,106.809998,109.290001,106.290001,108.949997,108.949997,53390000
1980-01-09,108.949997,111.089996,108.410004,109.050003,109.050003,65260000
1980-01-10,109.050003,110.860001,108.470001,109.889999,109.889999,55980000
1980-01-11,109.889999,111.160004,108.889999,109.919998,109.919998,52890000
1980-01-14,109.919998,111.440002,109.339996,110.379997,110.379997,52930000
1980-01-15,110.379997,111.930000,109.449997,111.139999,111.139999,52320000
1980-01-16,111.139999,112.900002,110.379997,111.050003,111.050003,67700000
1980-01-17,111.050003,112.010002,109.809998,110.699997,110.699997,54170000
1980-01-18,110.699997,111.739998,109.879997,111.070000,111.070000,47150000
1980-01-21,111.070000,112.900002,110.660004,112.099998,112.099998,48040000
1980-01-22,112.099998,113.099998,110.919998,111.510002,111.510002,50620000
1980-01-23,111.510002,113.930000,110.930000,113.440002,113.440002,50730000
1980-01-24,113.440002,115.269997,112.949997,113.699997,113.699997,59070000
1980-01-25,113.699997,114.449997,112.360001,113.610001,113.610001,47100000
1980-01-28,113.610001,115.650002,112.930000,114.849998,114.849998,53620000
1980-01-29,114.849998,115.769997,113.029999,114.070000,114.070000,55480000
1980-01-30,114.070000,115.849998,113.370003,115.199997,115.199997,51170000
1980-01-31,115.199997,117.169998,113.779999,114.160004,114.160004,65900000
1980-02-01,114.160004,115.540001,113.129997,115.120003,115.120003,46610000
1980-02-04,115.120003,116.010002,113.830002,114.370003,114.370003,43070000
1980-02-05,114.370003,115.250000,112.150002,114.660004,114.660004,41880000
1980-02-06,114.660004,116.570000,113.650002,115.720001,115.720001,51950000
1980-02-07,115.720001,117.870003,115.220001,116.279999,116.279999,57690000
1980-02-08,116.279999,118.660004,115.720001,117.949997,117.949997,57860000
1980-02-11,117.949997,119.050003,116.309998,117.120003,117.120003,58660000
1980-02-12,117.120003,118.410004,115.750000,117.900002,117.900002,48090000
1980-02-13,117.900002,120.220001,117.570000,118.440002,118.440002,65230000
1980-02-14,118.440002,119.300003,116.040001,116.720001,116.720001,50540000
1980-02-15,116.699997,116.699997,114.120003,115.410004,115.410004,46680000
1980-02-19,115.410004,115.669998,113.349998,114.599998,114.599998,39480000
1980-02-20,114.599998,117.180000,114.059998,116.470001,116.470001,44340000
1980-02-21,116.470001,117.900002,114.440002,115.279999,115.279999,51530000
1980-02-22,115.279999,116.459999,113.430000,115.040001,115.040001,48210000
1980-02-25,114.930000,114.930000,112.620003,113.330002,113.330002,39140000
1980-02-26,113.330002,114.760002,112.300003,113.980003,113.980003,40000000
1980-02-27,113.980003,115.120003,111.910004,112.379997,112.379997,46430000
1980-02-28,112.379997,113.699997,111.330002,112.349998,112.349998,40330000
1980-02-29,112.349998,114.120003,111.769997,113.660004,113.660004,38810000
1980-03-03,113.660004,114.339996,112.010002,112.500000,112.500000,38690000
1980-03-04,112.500000,113.410004,110.830002,112.779999,112.779999,44310000
1980-03-05,112.779999,113.940002,110.580002,111.129997,111.129997,49240000
1980-03-06,111.129997,111.290001,107.849998,108.650002,108.650002,49610000
1980-03-07,108.650002,108.959999,105.989998,106.900002,106.900002,50950000
1980-03-10,106.900002,107.860001,104.919998,106.510002,106.510002,43750000
1980-03-11,106.510002,108.540001,106.180000,107.779999,107.779999,41350000
1980-03-12,107.779999,108.400002,105.419998,106.870003,106.870003,37990000
1980-03-13,106.870003,107.550003,105.099998,105.620003,105.620003,33070000
1980-03-14,105.620003,106.489998,104.010002,105.430000,105.430000,35180000
1980-03-17,105.230003,105.230003,101.820000,102.260002,102.260002,37020000
1980-03-18,102.260002,104.709999,101.139999,104.099998,104.099998,47340000
1980-03-19,104.099998,105.720001,103.349998,104.309998,104.309998,36520000
1980-03-20,104.309998,105.169998,102.519997,103.120003,103.120003,32580000
1980-03-21,103.120003,103.730003,101.550003,102.309998,102.309998,32220000
1980-03-24,102.180000,102.180000,98.879997,99.279999,99.279999,39230000
1980-03-25,99.279999,100.580002,97.889999,99.190002,99.190002,43790000
1980-03-26,99.190002,101.220001,98.099998,98.680000,98.680000,37370000
1980-03-27,98.680000,99.580002,94.230003,98.220001,98.220001,63680000
1980-03-28,98.220001,101.430000,97.720001,100.680000,100.680000,46720000
1980-03-31,100.680000,102.650002,100.019997,102.089996,102.089996,35840000
1980-04-01,102.089996,103.279999,100.849998,102.180000,102.180000,32230000
1980-04-02,102.180000,103.870003,101.449997,102.680000,102.680000,35210000
1980-04-03,102.680000,103.339996,101.309998,102.150002,102.150002,27970000
1980-04-07,102.150002,102.269997,99.730003,100.190002,100.190002,29130000
1980-04-08,100.190002,101.879997,99.230003,101.199997,101.199997,31700000
1980-04-09,101.199997,103.599998,101.010002,103.110001,103.110001,33020000
1980-04-10,103.110001,105.000000,102.809998,104.080002,104.080002,33940000
1980-04-11,104.080002,105.150002,103.199997,103.790001,103.790001,29960000
1980-04-14,103.790001,103.919998,102.080002,102.839996,102.839996,23060000
1980-04-15,102.839996,103.940002,101.849998,102.629997,102.629997,26670000
1980-04-16,102.629997,104.419998,101.129997,101.540001,101.540001,39730000
1980-04-17,101.540001,102.209999,100.120003,101.050003,101.050003,32770000
1980-04-18,101.050003,102.070000,99.970001,100.550003,100.550003,26880000
1980-04-21,100.550003,101.260002,98.949997,99.800003,99.800003,27560000
1980-04-22,100.809998,104.019997,100.809998,103.430000,103.430000,47920000
1980-04-23,103.430000,105.110001,102.809998,103.730003,103.730003,42620000
1980-04-24,103.730003,105.430000,102.930000,104.400002,104.400002,35790000
1980-04-25,104.400002,105.570000,103.019997,105.160004,105.160004,28590000
1980-04-28,105.160004,106.790001,104.639999,105.639999,105.639999,30600000
1980-04-29,105.639999,106.699997,104.860001,105.860001,105.860001,27940000
1980-04-30,105.860001,106.720001,104.500000,106.290001,106.290001,30850000
1980-05-01,106.290001,106.860001,104.720001,105.459999,105.459999,32480000
1980-05-02,105.459999,106.250000,104.610001,105.580002,105.580002,28040000
1980-05-05,105.580002,106.830002,104.639999,106.379997,106.379997,34090000
1980-05-06,106.379997,107.830002,105.360001,106.250000,106.250000,40160000
1980-05-07,106.250000,108.120003,105.830002,107.180000,107.180000,42600000
1980-05-08,107.180000,108.019997,105.500000,106.129997,106.129997,39280000
1980-05-09,106.129997,106.199997,104.180000,104.720001,104.720001,30280000
1980-05-12,104.720001,105.480003,103.500000,104.779999,104.779999,28220000
1980-05-13,104.779999,106.760002,104.440002,106.300003,106.300003,35460000
1980-05-14,106.300003,107.889999,106.000000,106.849998,106.849998,40840000
1980-05-15,106.849998,107.989998,106.070000,106.989998,106.989998,41120000
1980-05-16,106.989998,107.889999,106.250000,107.349998,107.349998,31710000
1980-05-19,107.349998,108.430000,106.510002,107.669998,107.669998,30970000
1980-05-20,107.669998,108.389999,106.750000,107.620003,107.620003,31800000
1980-05-21,107.620003,108.309998,106.540001,107.720001,107.720001,34830000
1980-05-22,107.720001,109.730003,107.339996,109.010002,109.010002,41040000
1980-05-23,109.010002,111.370003,109.010002,110.620003,110.620003,45790000
1980-05-27,110.620003,112.300003,110.349998,111.400002,111.400002,40810000
1980-05-28,111.400002,112.720001,110.419998,112.059998,112.059998,38580000
1980-05-29,112.059998,112.639999,109.860001,110.269997,110.269997,42000000
1980-05-30,110.269997,111.550003,108.870003,111.239998,111.239998,34820000
1980-06-02,111.239998,112.150002,110.059998,110.760002,110.760002,32710000
1980-06-03,110.760002,111.629997,109.769997,110.510002,110.510002,33150000
1980-06-04,110.510002,113.449997,110.220001,112.610001,112.610001,44180000
1980-06-05,112.610001,114.379997,111.889999,112.779999,112.779999,49070000
1980-06-06,112.779999,114.010002,112.110001,113.199997,113.199997,37230000
1980-06-09,113.199997,114.510002,112.680000,113.709999,113.709999,36820000
1980-06-10,113.709999,115.500000,113.169998,114.660004,114.660004,42030000
1980-06-11,114.660004,116.639999,114.220001,116.019997,116.019997,43800000
1980-06-12,116.019997,117.010002,114.279999,115.519997,115.519997,47300000
1980-06-13,115.519997,116.940002,114.669998,115.809998,115.809998,41880000
1980-06-16,115.809998,116.800003,114.779999,116.089996,116.089996,36190000
1980-06-17,116.089996,117.160004,115.129997,116.029999,116.029999,41990000
1980-06-18,116.029999,116.839996,114.769997,116.260002,116.260002,41960000
1980-06-19,116.260002,116.809998,114.360001,114.660004,114.660004,38280000
1980-06-20,114.660004,114.900002,113.120003,114.059998,114.059998,36530000
1980-06-23,114.059998,115.279999,113.349998,114.510002,114.510002,34180000
1980-06-24,114.510002,115.750000,113.760002,115.139999,115.139999,37730000
1980-06-25,115.139999,117.370003,115.070000,116.720001,116.720001,46500000
1980-06-26,116.720001,117.980003,115.580002,116.190002,116.190002,45110000
1980-06-27,116.190002,116.930000,115.059998,116.000000,116.000000,33110000
1980-06-30,116.000000,116.040001,113.550003,114.239998,114.239998,29910000
1980-07-01,114.239998,115.449997,113.540001,114.930000,114.930000,34340000
1980-07-02,114.930000,116.440002,114.360001,115.680000,115.680000,42950000
1980-07-03,115.680000,117.800003,115.489998,117.459999,117.459999,47230000
1980-07-07,117.459999,118.849998,116.959999,118.290001,118.290001,42540000
1980-07-08,118.290001,119.110001,117.070000,117.839996,117.839996,45830000
1980-07-09,117.839996,119.519997,117.099998,117.980003,117.980003,52010000
1980-07-10,117.980003,118.570000,116.379997,116.949997,116.949997,43730000
1980-07-11,116.949997,118.379997,116.290001,117.839996,117.839996,38310000
1980-07-14,117.839996,120.370003,117.449997,120.010002,120.010002,45500000
1980-07-15,120.010002,121.559998,118.849998,119.300003,119.300003,60920000
1980-07-16,119.300003,120.870003,118.540001,119.629997,119.629997,49140000
1980-07-17,119.629997,121.839996,119.430000,121.440002,121.440002,48850000
1980-07-18,121.440002,123.190002,120.879997,122.040001,122.040001,58040000
1980-07-21,122.040001,123.150002,120.849998,122.510002,122.510002,42750000
1980-07-22,122.510002,123.900002,121.379997,122.190002,122.190002,52230000
1980-07-23,122.190002,123.260002,120.930000,121.930000,121.930000,45890000
1980-07-24,121.930000,122.980003,120.830002,121.790001,121.790001,42420000
1980-07-25,121.790001,121.959999,119.940002,120.779999,120.779999,36250000
1980-07-28,120.779999,122.019997,119.779999,121.430000,121.430000,35330000
1980-07-29,121.430000,122.989998,120.760002,122.400002,122.400002,44840000
1980-07-30,122.400002,123.930000,121.160004,122.230003,122.230003,58060000
1980-07-31,122.230003,122.339996,119.400002,121.669998,121.669998,54610000
1980-08-01,121.669998,122.379997,120.080002,121.209999,121.209999,46440000
1980-08-04,121.209999,121.629997,119.419998,120.980003,120.980003,41550000
1980-08-05,120.980003,122.089996,119.959999,120.739998,120.739998,45510000
1980-08-06,120.739998,122.010002,119.940002,121.550003,121.550003,45050000
1980-08-07,121.660004,123.839996,121.660004,123.300003,123.300003,61820000
1980-08-08,123.300003,125.230003,122.820000,123.610001,123.610001,58860000
1980-08-11,123.610001,125.309998,122.849998,124.779999,124.779999,44690000
1980-08-12,124.779999,125.779999,123.290001,123.790001,123.790001,52050000
1980-08-13,123.790001,124.669998,122.489998,123.279999,123.279999,44350000
1980-08-14,123.279999,125.620003,122.680000,125.250000,125.250000,47700000
1980-08-15,125.250000,126.610001,124.570000,125.720001,125.720001,47780000
1980-08-18,125.279999,125.279999,122.820000,123.389999,123.389999,41890000
1980-08-19,123.389999,124.000000,121.970001,122.599998,122.599998,41930000
1980-08-20,122.599998,124.269997,121.910004,123.769997,123.769997,42560000
1980-08-21,123.769997,125.989998,123.610001,125.459999,125.459999,50770000
1980-08-22,125.459999,127.779999,125.180000,126.019997,126.019997,58210000
1980-08-25,126.019997,126.279999,124.650002,125.160004,125.160004,35400000
1980-08-26,125.160004,126.290001,124.010002,124.839996,124.839996,41700000
1980-08-27,124.839996,124.980003,122.930000,123.519997,123.519997,44000000
1980-08-28,123.519997,123.910004,121.610001,122.080002,122.080002,39890000
1980-08-29,122.080002,123.010002,121.059998,122.379997,122.379997,33510000
1980-09-02,122.379997,124.360001,121.790001,123.739998,123.739998,35290000
1980-09-03,123.870003,126.430000,123.870003,126.120003,126.120003,52370000
1980-09-04,126.120003,127.699997,124.419998,125.419998,125.419998,59030000
1980-09-05,125.419998,126.120003,124.080002,124.879997,124.879997,37990000
1980-09-08,124.879997,125.669998,122.779999,123.309998,123.309998,42050000
1980-09-09,123.309998,124.519997,121.940002,124.070000,124.070000,44460000
1980-09-10,124.070000,125.949997,123.599998,124.809998,124.809998,51430000
1980-09-11,124.809998,126.480003,124.190002,125.660004,125.660004,44770000
1980-09-12,125.660004,126.750000,124.720001,125.540001,125.540001,47180000
1980-09-15,125.540001,126.349998,124.089996,125.669998,125.669998,44630000
1980-09-16,125.669998,127.779999,125.150002,126.739998,126.739998,57290000
1980-09-17,126.739998,129.679993,126.370003,128.869995,128.869995,63990000
1980-09-18,128.869995,130.380005,127.629997,128.399994,128.399994,63390000
1980-09-19,128.399994,130.330002,127.570000,129.250000,129.250000,53780000
1980-09-22,129.250000,130.990005,127.889999,130.399994,130.399994,53140000
1980-09-23,130.399994,132.169998,128.550003,129.429993,129.429993,64390000
1980-09-24,129.429993,131.339996,128.449997,130.369995,130.369995,56860000
1980-09-25,130.369995,131.529999,128.130005,128.720001,128.720001,49510000
1980-09-26,128.169998,128.169998,125.290001,126.349998,126.349998,49460000
1980-09-29,125.410004,125.410004,122.870003,123.540001,123.540001,46410000
1980-09-30,123.540001,126.089996,123.540001,125.459999,125.459999,40290000
1980-10-01,125.459999,127.879997,124.660004,127.129997,127.129997,48720000
1980-10-02,127.129997,128.820007,126.040001,128.089996,128.089996,46160000
1980-10-03,128.089996,130.440002,127.650002,129.330002,129.330002,47510000
1980-10-06,129.350006,132.380005,129.350006,131.729996,131.729996,50130000
1980-10-07,131.729996,132.880005,130.100006,131.000000,131.000000,50310000
1980-10-08,131.000000,132.779999,130.279999,131.649994,131.649994,46580000
1980-10-09,131.649994,132.649994,130.250000,131.039993,131.039993,43980000
1980-10-10,131.039993,132.149994,129.580002,130.289993,130.289993,44040000
1980-10-13,130.289993,132.460007,129.369995,132.029999,132.029999,31360000
1980-10-14,132.029999,133.570007,131.160004,132.020004,132.020004,48830000
1980-10-15,132.020004,134.350006,131.589996,133.699997,133.699997,48260000
1980-10-16,133.699997,135.880005,131.639999,132.220001,132.220001,65450000
1980-10-17,132.220001,133.070007,130.220001,131.520004,131.520004,43920000
1980-10-20,131.520004,133.210007,130.039993,132.610001,132.610001,40910000
1980-10-21,132.610001,134.009995,130.779999,131.839996,131.839996,51220000
1980-10-22,131.839996,132.970001,130.619995,131.919998,131.919998,43060000
1980-10-23,131.919998,132.539993,128.869995,129.529999,129.529999,49200000
1980-10-24,129.529999,130.550003,128.039993,129.850006,129.850006,41050000
1980-10-27,129.850006,129.940002,127.339996,127.879997,127.879997,34430000
1980-10-28,127.879997,128.860001,126.360001,128.050003,128.050003,40300000
1980-10-29,128.050003,129.910004,127.070000,127.910004,127.910004,37200000
1980-10-30,127.910004,128.710007,125.779999,126.290001,126.290001,39060000
1980-10-31,126.290001,128.240005,125.290001,127.470001,127.470001,40110000
1980-11-03,127.470001,129.850006,127.230003,129.039993,129.039993,35820000
1980-11-05,130.770004,135.649994,130.770004,131.330002,131.330002,84080000
1980-11-06,131.300003,131.300003,128.229996,128.910004,128.910004,48890000
1980-11-07,128.910004,130.080002,127.739998,129.179993,129.179993,40070000
1980-11-10,129.179993,130.509995,128.190002,129.479996,129.479996,35720000
1980-11-11,129.479996,132.300003,129.479996,131.259995,131.259995,41520000
1980-11-12,131.330002,135.119995,131.330002,134.589996,134.589996,58500000
1980-11-13,134.589996,137.210007,134.119995,136.490005,136.490005,69340000
1980-11-14,136.490005,138.960007,135.119995,137.149994,137.149994,71630000
1980-11-17,137.149994,138.460007,134.899994,137.750000,137.750000,50260000
1980-11-18,137.910004,140.919998,137.910004,139.699997,139.699997,70380000
1980-11-19,139.699997,141.759995,138.059998,139.059998,139.059998,69230000
1980-11-20,139.059998,141.240005,137.789993,140.399994,140.399994,60180000
1980-11-21,140.399994,141.240005,138.100006,139.110001,139.110001,55950000
1980-11-24,139.110001,139.360001,136.360001,138.309998,138.309998,51120000
1980-11-25,138.309998,140.830002,137.419998,139.330002,139.330002,55840000
1980-11-26,139.330002,141.960007,138.600006,140.169998,140.169998,55340000
1980-11-28,140.169998,141.539993,139.000000,140.520004,140.520004,34240000
1980-12-01,140.520004,140.660004,136.750000,137.210007,137.210007,48180000
1980-12-02,137.210007,138.110001,134.369995,136.970001,136.970001,52340000
1980-12-03,136.970001,138.089996,135.429993,136.710007,136.710007,43430000
1980-12-04,136.710007,138.399994,135.089996,136.479996,136.479996,51170000
1980-12-05,136.369995,136.369995,132.910004,134.029999,134.029999,51990000
1980-12-08,133.190002,133.190002,129.710007,130.610001,130.610001,53390000
1980-12-09,130.610001,131.919998,128.770004,130.479996,130.479996,53220000
1980-12-10,130.479996,131.990005,127.940002,128.259995,128.259995,49860000
1980-12-11,128.259995,128.729996,125.320000,127.360001,127.360001,60220000
1980-12-12,127.360001,129.979996,127.150002,129.229996,129.229996,39530000
1980-12-15,129.229996,131.330002,128.639999,129.449997,129.449997,39700000
1980-12-16,129.449997,131.220001,128.330002,130.600006,130.600006,41630000
1980-12-17,130.600006,133.589996,130.220001,132.889999,132.889999,50800000
1980-12-18,132.889999,135.899994,131.889999,133.000000,133.000000,69570000
1980-12-19,133.000000,134.000000,131.800003,133.699997,133.699997,50770000
1980-12-22,133.699997,136.679993,132.880005,135.779999,135.779999,51950000
1980-12-23,135.779999,137.479996,134.009995,135.300003,135.300003,55260000
1980-12-24,135.300003,136.550003,134.149994,135.880005,135.880005,29490000
1980-12-26,135.880005,137.020004,135.199997,136.570007,136.570007,16130000
1980-12-29,136.570007,137.509995,134.360001,135.029999,135.029999,36060000
1980-12-30,135.029999,136.509995,134.039993,135.330002,135.330002,39750000
1980-12-31,135.330002,136.759995,134.289993,135.759995,135.759995,41210000
1981-01-02,135.759995,137.100006,134.610001,136.339996,136.339996,28870000
1981-01-05,136.339996,139.240005,135.860001,137.970001,137.970001,58710000
1981-01-06,137.970001,140.320007,135.779999,138.119995,138.119995,67400000
1981-01-07,136.020004,136.020004,132.300003,135.080002,135.080002,92890000
1981-01-08,135.080002,136.100006,131.960007,133.059998,133.059998,55350000
1981-01-09,133.059998,134.759995,131.710007,133.479996,133.479996,50190000
1981-01-12,133.479996,135.880005,132.789993,133.520004,133.520004,48760000
1981-01-13,133.520004,134.270004,131.690002,133.289993,133.289993,40890000
1981-01-14,133.289993,135.250000,132.649994,133.470001,133.470001,41390000
1981-01-15,133.470001,135.149994,132.440002,134.220001,134.220001,39640000
1981-01-16,134.220001,135.910004,133.350006,134.770004,134.770004,43260000
1981-01-19,134.770004,135.860001,133.509995,134.369995,134.369995,36470000
1981-01-20,134.369995,135.300003,131.259995,131.649994,131.649994,41750000
1981-01-21,131.649994,132.479996,129.929993,131.360001,131.360001,39190000
1981-01-22,131.360001,132.080002,129.229996,130.259995,130.259995,39880000
1981-01-23,130.259995,131.339996,129.000000,130.229996,130.229996,37220000
1981-01-26,130.229996,131.179993,128.570007,129.839996,129.839996,35380000
1981-01-27,129.839996,131.949997,129.320007,131.119995,131.119995,42260000
1981-01-28,131.119995,132.410004,129.820007,130.339996,130.339996,36690000
1981-01-29,130.339996,131.779999,128.970001,130.240005,130.240005,38170000
1981-01-30,130.240005,131.649994,128.610001,129.550003,129.550003,41160000
1981-02-02,129.479996,129.479996,125.820000,126.910004,126.910004,44070000
1981-02-03,126.910004,128.919998,125.889999,128.460007,128.460007,45950000
1981-02-04,128.460007,129.710007,127.290001,128.589996,128.589996,45520000
1981-02-05,128.589996,130.490005,127.989998,129.630005,129.630005,45320000
1981-02-06,129.630005,131.809998,129.029999,130.600006,130.600006,45820000
1981-02-09,130.600006,131.389999,128.610001,129.270004,129.270004,38330000
1981-02-10,129.270004,130.190002,128.050003,129.240005,129.240005,40820000
1981-02-11,129.240005,129.919998,127.599998,128.240005,128.240005,37770000
1981-02-12,128.240005,128.949997,126.779999,127.480003,127.480003,34700000
1981-02-13,127.480003,128.339996,126.040001,126.980003,126.980003,33360000
1981-02-17,126.980003,128.750000,126.430000,127.809998,127.809998,37940000
1981-02-18,127.809998,129.250000,127.089996,128.479996,128.479996,40410000
1981-02-19,128.479996,129.070007,125.980003,126.610001,126.610001,41630000
1981-02-20,126.610001,127.650002,124.660004,126.580002,126.580002,41900000
1981-02-23,126.580002,128.279999,125.690002,127.349998,127.349998,39590000
1981-02-24,127.349998,128.759995,126.489998,127.389999,127.389999,43960000
1981-02-25,127.389999,129.210007,125.769997,128.520004,128.520004,45710000
1981-02-26,128.520004,130.929993,128.020004,130.100006,130.100006,60300000
1981-02-27,130.100006,132.020004,129.350006,131.270004,131.270004,53210000
1981-03-02,131.270004,132.960007,130.149994,132.009995,132.009995,47710000
1981-03-03,132.009995,132.720001,129.660004,130.559998,130.559998,48730000
1981-03-04,130.559998,132.070007,129.570007,130.860001,130.860001,47260000
1981-03-05,130.860001,131.820007,129.250000,129.929993,129.929993,45380000
1981-03-06,129.929993,131.179993,128.559998,129.850006,129.850006,43940000
1981-03-09,129.850006,131.940002,129.389999,131.119995,131.119995,46180000
1981-03-10,131.119995,132.639999,129.720001,130.460007,130.460007,56610000
1981-03-11,130.460007,131.199997,128.720001,129.949997,129.949997,47390000
1981-03-12,129.949997,133.559998,129.759995,133.190002,133.190002,54640000
1981-03-13,133.190002,135.529999,132.389999,133.110001,133.110001,68290000
1981-03-16,133.110001,135.350006,132.100006,134.679993,134.679993,49940000
1981-03-17,134.679993,136.089996,132.800003,133.919998,133.919998,65920000
1981-03-18,133.919998,135.660004,132.800003,134.220001,134.220001,55740000
1981-03-19,134.220001,135.369995,132.369995,133.460007,133.460007,62440000
1981-03-20,133.460007,135.289993,132.500000,134.080002,134.080002,61980000
1981-03-23,134.080002,136.500000,133.410004,135.690002,135.690002,57880000
1981-03-24,135.690002,137.399994,134.100006,134.669998,134.669998,66400000
1981-03-25,134.669998,137.320007,133.919998,137.110001,137.110001,56320000
1981-03-26,137.110001,138.380005,135.289993,136.270004,136.270004,60370000
1981-03-27,136.270004,136.889999,133.910004,134.649994,134.649994,46930000
1981-03-30,134.649994,135.869995,133.509995,134.279999,134.279999,33500000
1981-03-31,134.679993,137.149994,134.679993,136.000000,136.000000,50980000
1981-04-01,136.000000,137.559998,135.039993,136.570007,136.570007,54880000
1981-04-02,136.570007,137.720001,135.160004,136.320007,136.320007,52570000
1981-04-03,136.320007,137.039993,134.669998,135.490005,135.490005,48680000
1981-04-06,135.490005,135.610001,132.910004,133.929993,133.929993,43190000
1981-04-07,133.929993,135.270004,132.960007,133.910004,133.910004,44540000
1981-04-08,133.910004,135.339996,133.259995,134.309998,134.309998,48000000
1981-04-09,134.309998,135.800003,132.589996,134.669998,134.669998,59520000
1981-04-10,134.669998,136.229996,133.179993,134.509995,134.509995,58130000
1981-04-13,134.509995,134.910004,132.240005,133.149994,133.149994,49860000
1981-04-14,133.149994,134.029999,131.580002,132.679993,132.679993,48350000
1981-04-15,132.679993,134.789993,132.199997,134.169998,134.169998,56040000
1981-04-16,134.169998,135.820007,133.429993,134.699997,134.699997,52950000
1981-04-20,134.699997,136.250000,133.190002,135.449997,135.449997,51020000
1981-04-21,135.449997,136.380005,133.490005,134.229996,134.229996,60280000
1981-04-22,134.229996,135.539993,132.720001,134.139999,134.139999,60660000
1981-04-23,134.139999,135.899994,132.899994,133.940002,133.940002,64200000
1981-04-24,133.940002,136.000000,132.880005,135.139999,135.139999,60000000
1981-04-27,135.139999,136.559998,134.130005,135.479996,135.479996,51080000
1981-04-28,135.479996,136.089996,133.100006,134.330002,134.330002,58210000
1981-04-29,134.330002,134.690002,131.820007,133.050003,133.050003,53340000
1981-04-30,133.050003,134.440002,131.850006,132.809998,132.809998,47970000
1981-05-01,132.809998,134.169998,131.429993,132.720001,132.720001,48360000
1981-05-04,131.779999,131.779999,129.610001,130.669998,130.669998,40430000
1981-05-05,130.669998,131.330002,128.929993,130.320007,130.320007,49000000
1981-05-06,130.320007,132.380005,130.089996,130.779999,130.779999,47100000
1981-05-07,130.779999,132.410004,130.210007,131.669998,131.669998,42590000
1981-05-08,131.669998,132.690002,130.839996,131.660004,131.660004,41860000
1981-05-11,131.660004,132.229996,129.110001,129.710007,129.710007,37640000
1981-05-12,129.710007,131.169998,128.779999,130.720001,130.720001,40440000
1981-05-13,130.720001,131.960007,129.529999,130.550003,130.550003,42600000
1981-05-14,130.550003,132.149994,129.910004,131.279999,131.279999,42750000
1981-05-15,131.279999,133.210007,130.750000,132.169998,132.169998,45460000
1981-05-18,132.169998,133.649994,131.490005,132.539993,132.539993,42510000
1981-05-19,132.539993,133.220001,130.779999,132.089996,132.089996,42220000
1981-05-20,132.089996,133.029999,130.589996,132.000000,132.000000,42370000
1981-05-21,132.000000,133.029999,130.699997,131.750000,131.750000,46820000
1981-05-22,131.750000,132.649994,130.419998,131.330002,131.330002,40710000
1981-05-26,131.330002,133.300003,130.639999,132.770004,132.770004,42760000
1981-05-27,132.770004,134.649994,131.850006,133.770004,133.770004,58730000
1981-05-28,133.770004,134.919998,132.000000,133.449997,133.449997,59500000
1981-05-29,133.449997,134.360001,131.520004,132.589996,132.589996,51580000
1981-06-01,132.589996,134.619995,131.490005,132.410004,132.410004,62170000
1981-06-02,132.410004,132.960007,129.839996,130.619995,130.619995,53930000
1981-06-03,130.619995,131.369995,128.770004,130.710007,130.710007,54700000
1981-06-04,130.710007,132.210007,129.720001,130.960007,130.960007,48940000
1981-06-05,130.960007,132.979996,130.169998,132.220001,132.220001,47180000
1981-06-08,132.220001,133.679993,131.289993,132.240005,132.240005,41580000
1981-06-09,132.240005,133.300003,130.940002,131.970001,131.970001,44600000
1981-06-10,131.970001,133.490005,131.039993,132.320007,132.320007,53200000
1981-06-11,132.320007,134.309998,131.580002,133.750000,133.750000,59530000
1981-06-12,133.750000,135.089996,132.399994,133.490005,133.490005,60790000
1981-06-15,133.490005,135.669998,132.779999,133.610001,133.610001,63350000
1981-06-16,133.610001,134.000000,131.289993,132.149994,132.149994,57780000
1981-06-17,132.149994,133.979996,130.809998,133.320007,133.320007,55470000
1981-06-18,133.320007,133.979996,130.940002,131.639999,131.639999,48400000
1981-06-19,131.639999,133.270004,130.490005,132.270004,132.270004,46430000
1981-06-22,132.270004,133.539993,131.100006,131.949997,131.949997,41790000
1981-06-23,131.949997,133.979996,131.160004,133.350006,133.350006,51840000
1981-06-24,133.350006,133.899994,131.649994,132.660004,132.660004,46650000
1981-06-25,132.660004,134.300003,131.779999,132.809998,132.809998,43920000
1981-06-26,132.809998,133.750000,131.710007,132.559998,132.559998,39240000
1981-06-29,132.559998,133.500000,131.199997,131.889999,131.889999,37930000
1981-06-30,131.889999,132.669998,130.309998,131.210007,131.210007,41550000
1981-07-01,131.210007,131.690002,129.039993,129.770004,129.770004,49080000
1981-07-02,129.770004,130.479996,127.839996,128.639999,128.639999,45100000
1981-07-06,128.639999,128.990005,126.440002,127.370003,127.370003,44590000
1981-07-07,127.370003,129.600006,126.389999,128.240005,128.240005,53560000
1981-07-08,128.240005,129.570007,126.949997,128.320007,128.320007,46000000
1981-07-09,128.320007,130.080002,127.570000,129.300003,129.300003,45510000
1981-07-10,129.300003,130.429993,128.380005,129.369995,129.369995,39950000
1981-07-13,129.369995,130.820007,128.789993,129.639999,129.639999,38100000
1981-07-14,129.639999,130.779999,128.139999,129.649994,129.649994,45230000
1981-07-15,129.649994,131.589996,128.889999,130.229996,130.229996,48950000
1981-07-16,130.229996,131.410004,129.300003,130.339996,130.339996,39010000
1981-07-17,130.339996,131.600006,129.490005,130.759995,130.759995,42780000
1981-07-20,130.600006,130.600006,127.980003,128.720001,128.720001,40240000
1981-07-21,128.720001,129.600006,127.080002,128.339996,128.339996,47280000
1981-07-22,128.339996,129.720001,126.699997,127.129997,127.129997,47500000
1981-07-23,127.129997,128.259995,125.959999,127.400002,127.400002,41790000
1981-07-24,127.400002,129.309998,127.110001,128.460007,128.460007,38880000
1981-07-27,128.460007,130.610001,128.429993,129.899994,129.899994,39610000
1981-07-28,129.899994,130.440002,128.279999,129.139999,129.139999,38160000
1981-07-29,129.139999,130.089996,128.369995,129.160004,129.160004,37610000
1981-07-30,129.160004,130.679993,128.559998,130.009995,130.009995,41560000
1981-07-31,130.009995,131.779999,129.600006,130.919998,130.919998,43480000
1981-08-03,130.919998,131.740005,129.419998,130.479996,130.479996,39650000
1981-08-04,130.479996,131.660004,129.429993,131.179993,131.179993,39460000
1981-08-05,131.179993,133.389999,130.759995,132.669998,132.669998,54290000
1981-08-06,132.669998,134.039993,131.740005,132.639999,132.639999,52070000
1981-08-07,132.639999,133.039993,130.960007,131.750000,131.750000,38370000
1981-08-10,131.750000,133.320007,130.830002,132.539993,132.539993,38370000
1981-08-11,132.539993,134.630005,132.089996,133.850006,133.850006,52600000
1981-08-12,133.850006,135.179993,132.729996,133.399994,133.399994,53650000
1981-08-13,133.399994,134.580002,132.529999,133.509995,133.509995,42460000
1981-08-14,133.509995,134.330002,131.910004,132.490005,132.490005,42580000
1981-08-17,132.490005,133.020004,130.750000,131.220001,131.220001,40840000
1981-08-18,131.220001,131.729996,129.100006,130.110001,130.110001,47270000
1981-08-19,130.110001,131.199997,128.990005,130.490005,130.490005,39390000
1981-08-20,130.490005,131.740005,129.839996,130.690002,130.690002,38270000
1981-08-21,130.690002,131.059998,128.699997,129.229996,129.229996,37670000
1981-08-24,128.589996,128.589996,125.019997,125.500000,125.500000,46750000
1981-08-25,125.500000,125.769997,123.000000,125.129997,125.129997,54600000
1981-08-26,125.129997,126.169998,123.989998,124.959999,124.959999,39980000
1981-08-27,124.959999,125.309998,122.900002,123.510002,123.510002,43900000
1981-08-28,123.510002,125.089996,122.849998,124.080002,124.080002,38020000
1981-08-31,124.080002,125.580002,122.290001,122.790001,122.790001,40360000
1981-09-01,122.790001,123.919998,121.589996,123.019997,123.019997,45110000
1981-09-02,123.019997,124.580002,122.540001,123.489998,123.489998,37570000
1981-09-03,123.489998,124.160004,120.820000,121.239998,121.239998,41730000
1981-09-04,121.239998,121.540001,119.239998,120.070000,120.070000,42760000
1981-09-08,120.070000,120.120003,116.849998,117.980003,117.980003,47340000
1981-09-09,117.980003,119.489998,116.870003,118.400002,118.400002,43910000
1981-09-10,118.400002,122.180000,118.330002,120.139999,120.139999,47430000
1981-09-11,120.139999,122.129997,119.290001,121.610001,121.610001,42170000
1981-09-14,121.610001,122.000000,119.669998,120.660004,120.660004,34040000
1981-09-15,120.660004,121.769997,119.269997,119.769997,119.769997,38580000
1981-09-16,119.769997,120.000000,117.889999,118.870003,118.870003,43660000
1981-09-17,118.870003,119.870003,116.629997,117.150002,117.150002,48300000
1981-09-18,117.150002,117.690002,115.180000,116.260002,116.260002,47350000
1981-09-21,116.260002,118.070000,115.040001,117.239998,117.239998,44570000
1981-09-22,117.239998,118.190002,115.930000,116.680000,116.680000,46830000
1981-09-23,116.680000,116.680000,113.599998,115.650002,115.650002,52700000
1981-09-24,115.650002,117.470001,114.320000,115.010002,115.010002,48880000
1981-09-25,114.690002,114.690002,111.639999,112.769997,112.769997,54390000
1981-09-28,112.769997,115.830002,110.190002,115.529999,115.529999,61320000
1981-09-29,115.529999,117.750000,114.750000,115.940002,115.940002,49800000
1981-09-30,115.940002,117.050003,114.599998,116.180000,116.180000,40700000
1981-10-01,116.180000,117.660004,115.000000,117.080002,117.080002,41600000
1981-10-02,117.080002,120.160004,117.070000,119.360001,119.360001,54540000
1981-10-05,119.360001,121.540001,118.610001,119.510002,119.510002,51290000
1981-10-06,119.510002,121.389999,118.080002,119.389999,119.389999,45460000
1981-10-07,119.389999,121.870003,119.089996,121.309998,121.309998,50030000
1981-10-08,121.309998,123.080002,120.230003,122.309998,122.309998,47090000
1981-10-09,122.309998,123.279999,120.629997,121.449997,121.449997,50060000
1981-10-12,121.449997,122.370003,120.169998,121.209999,121.209999,30030000
1981-10-13,121.209999,122.370003,119.959999,120.779999,120.779999,43360000
1981-10-14,120.779999,120.970001,118.379997,118.800003,118.800003,40260000
1981-10-15,118.800003,120.580002,118.010002,119.709999,119.709999,42830000
1981-10-16,119.709999,120.459999,118.379997,119.190002,119.190002,37800000
1981-10-19,119.190002,119.849998,117.580002,118.980003,118.980003,41590000
1981-10-20,118.980003,121.290001,118.779999,120.279999,120.279999,51530000
1981-10-21,120.279999,121.940002,119.349998,120.099998,120.099998,48490000
1981-10-22,120.099998,120.779999,118.480003,119.639999,119.639999,40630000
1981-10-23,119.639999,119.919998,117.779999,118.599998,118.599998,41990000
1981-10-26,118.599998,119.000000,116.809998,118.160004,118.160004,38210000
1981-10-27,118.160004,120.430000,117.800003,119.290001,119.290001,53030000
1981-10-28,119.290001,120.959999,118.389999,119.449997,119.449997,48100000
1981-10-29,119.449997,120.370003,118.139999,119.059998,119.059998,40070000
1981-10-30,119.059998,122.529999,118.430000,121.889999,121.889999,59570000
1981-11-02,122.349998,125.139999,122.349998,124.199997,124.199997,65100000
1981-11-03,124.199997,125.519997,123.139999,124.800003,124.800003,54620000
1981-11-04,124.800003,126.000000,123.639999,124.739998,124.739998,53450000
1981-11-05,124.739998,125.800003,122.980003,123.540001,123.540001,50860000
1981-11-06,123.540001,124.029999,121.849998,122.669998,122.669998,43270000
1981-11-09,122.669998,124.129997,121.589996,123.290001,123.290001,48310000
1981-11-10,123.290001,124.690002,122.010002,122.699997,122.699997,53940000
1981-11-11,122.699997,123.820000,121.510002,122.919998,122.919998,41920000
1981-11-12,122.919998,124.709999,122.190002,123.190002,123.190002,55720000
1981-11-13,123.190002,123.610001,121.059998,121.669998,121.669998,45550000
1981-11-16,121.639999,121.639999,119.129997,120.239998,120.239998,43740000
1981-11-17,120.239998,121.779999,119.500000,121.150002,121.150002,43190000
1981-11-18,121.150002,121.660004,119.610001,120.260002,120.260002,49980000
1981-11-19,120.260002,121.669998,119.419998,120.709999,120.709999,48890000
1981-11-20,120.709999,122.589996,120.129997,121.709999,121.709999,52010000
1981-11-23,121.709999,123.089996,120.760002,121.599998,121.599998,45250000
1981-11-24,121.599998,124.040001,121.220001,123.510002,123.510002,53200000
1981-11-25,123.510002,125.290001,123.070000,124.050003,124.050003,58570000
1981-11-27,124.050003,125.709999,123.629997,125.089996,125.089996,32770000
1981-11-30,125.089996,126.970001,124.180000,126.349998,126.349998,47580000
1981-12-01,126.349998,127.300003,124.839996,126.099998,126.099998,53980000
1981-12-02,126.099998,126.449997,124.180000,124.690002,124.690002,44510000
1981-12-03,124.690002,125.839996,123.629997,125.120003,125.120003,43770000
1981-12-04,125.120003,127.320000,125.120003,126.260002,126.260002,55040000
1981-12-07,126.260002,126.910004,124.669998,125.190002,125.190002,45720000
1981-12-08,125.190002,125.750000,123.519997,124.820000,124.820000,45140000
1981-12-09,124.820000,126.080002,124.089996,125.480003,125.480003,44810000
1981-12-10,125.480003,126.540001,124.599998,125.709999,125.709999,47020000
1981-12-11,125.709999,126.260002,124.320000,124.930000,124.930000,45850000
1981-12-14,124.370003,124.370003,122.169998,122.779999,122.779999,44740000
1981-12-15,122.779999,123.779999,121.830002,122.989998,122.989998,44130000
1981-12-16,122.989998,123.660004,121.730003,122.419998,122.419998,42770000
1981-12-17,122.419998,123.790001,121.820000,123.120003,123.120003,47230000
1981-12-18,123.120003,124.870003,122.559998,124.000000,124.000000,50940000
1981-12-21,124.000000,124.709999,122.669998,123.339996,123.339996,41290000
1981-12-22,123.339996,124.169998,122.190002,122.879997,122.879997,48320000
1981-12-23,122.879997,123.589996,121.580002,122.309998,122.309998,42910000
1981-12-24,122.309998,123.059998,121.570000,122.540001,122.540001,23940000
1981-12-28,122.540001,123.360001,121.730003,122.269997,122.269997,28320000
1981-12-29,122.269997,122.900002,121.120003,121.669998,121.669998,35300000
1981-12-30,121.669998,123.110001,121.040001,122.300003,122.300003,42960000
1981-12-31,122.300003,123.419998,121.570000,122.550003,122.550003,40780000
1982-01-04,122.550003,123.720001,121.480003,122.739998,122.739998,36760000
1982-01-05,122.610001,122.610001,119.570000,120.050003,120.050003,47510000
1982-01-06,120.050003,120.449997,117.989998,119.180000,119.180000,51510000
1982-01-07,119.180000,119.879997,117.699997,118.930000,118.930000,43410000
1982-01-08,118.930000,120.589996,118.550003,119.550003,119.550003,42050000
1982-01-11,119.550003,120.339996,116.470001,116.779999,116.779999,51900000
1982-01-12,116.779999,117.489998,115.180000,116.300003,116.300003,49800000
1982-01-13,116.300003,117.459999,114.239998,114.879997,114.879997,49130000
1982-01-14,114.879997,116.300003,114.070000,115.540001,115.540001,42940000
1982-01-15,115.540001,117.139999,115.099998,116.330002,116.330002,43310000
1982-01-18,116.330002,117.690002,114.849998,117.220001,117.220001,44920000
1982-01-19,117.220001,118.150002,115.519997,115.970001,115.970001,45070000
1982-01-20,115.970001,116.639999,114.290001,115.269997,115.269997,48860000
1982-01-21,115.269997,116.919998,114.599998,115.750000,115.750000,48610000
1982-01-22,115.750000,116.529999,114.580002,115.379997,115.379997,44370000
1982-01-25,115.379997,115.930000,113.629997,115.410004,115.410004,43170000
1982-01-26,115.410004,116.599998,114.489998,115.190002,115.190002,44870000
1982-01-27,115.190002,116.599998,114.379997,115.739998,115.739998,50060000
1982-01-28,116.099998,119.349998,116.099998,118.919998,118.919998,66690000
1982-01-29,118.919998,121.379997,118.639999,120.400002,120.400002,73400000
1982-02-01,119.809998,119.809998,117.139999,117.779999,117.779999,47720000
1982-02-02,117.779999,119.150002,116.910004,118.010002,118.010002,45020000
1982-02-03,118.010002,118.669998,116.040001,116.480003,116.480003,49560000
1982-02-04,116.480003,117.489998,114.879997,116.419998,116.419998,53300000
1982-02-05,116.419998,118.260002,115.739998,117.260002,117.260002,53350000
1982-02-08,117.040001,117.040001,114.199997,114.629997,114.629997,48500000
1982-02-09,114.629997,115.150002,112.820000,113.680000,113.680000,54420000
1982-02-10,113.680000,115.620003,113.449997,114.660004,114.660004,46620000
1982-02-11,114.660004,115.589996,113.410004,114.430000,114.430000,46730000
1982-02-12,114.430000,115.389999,113.699997,114.379997,114.379997,37070000
1982-02-16,114.379997,114.629997,112.059998,114.059998,114.059998,48880000
1982-02-17,114.059998,115.089996,112.970001,113.690002,113.690002,47660000
1982-02-18,113.690002,115.040001,112.970001,113.820000,113.820000,60810000
1982-02-19,113.820000,114.580002,112.330002,113.220001,113.220001,51340000
1982-02-22,113.220001,114.900002,111.199997,111.589996,111.589996,58310000
1982-02-23,111.589996,112.459999,110.029999,111.510002,111.510002,60100000
1982-02-24,111.510002,113.879997,110.709999,113.470001,113.470001,64800000
1982-02-25,113.470001,114.860001,112.440002,113.209999,113.209999,54160000
1982-02-26,113.209999,114.010002,112.040001,113.110001,113.110001,43840000
1982-03-01,113.110001,114.320000,111.860001,113.309998,113.309998,53010000
1982-03-02,113.309998,114.800003,112.029999,112.680000,112.680000,63800000
1982-03-03,112.510002,112.510002,109.980003,110.919998,110.919998,70230000
1982-03-04,110.919998,111.779999,108.769997,109.879997,109.879997,74340000
1982-03-05,109.879997,110.900002,108.309998,109.339996,109.339996,67440000
1982-03-08,109.339996,111.059998,107.029999,107.339996,107.339996,67330000
1982-03-09,107.339996,109.879997,106.169998,108.830002,108.830002,76060000
1982-03-10,108.830002,110.980003,108.089996,109.410004,109.410004,59440000
1982-03-11,109.410004,110.870003,108.379997,109.360001,109.360001,52960000
1982-03-12,109.360001,109.720001,104.459999,108.610001,108.610001,49600000
1982-03-15,108.610001,109.989998,107.470001,109.449997,109.449997,43370000
1982-03-16,109.449997,110.919998,108.570000,109.279999,109.279999,48900000
1982-03-17,109.279999,110.099998,108.110001,109.080002,109.080002,48900000
1982-03-18,109.080002,111.019997,108.849998,110.300003,110.300003,54270000
1982-03-19,110.300003,111.589996,109.639999,110.610001,110.610001,46250000
1982-03-22,110.709999,113.349998,110.709999,112.769997,112.769997,57610000
1982-03-23,112.769997,114.510002,112.290001,113.550003,113.550003,67130000
1982-03-24,113.550003,114.309998,112.230003,112.970001,112.970001,49380000
1982-03-25,112.970001,114.260002,112.019997,113.209999,113.209999,51970000
1982-03-26,113.209999,113.430000,111.260002,111.940002,111.940002,42400000
1982-03-29,111.940002,112.820000,110.900002,112.300003,112.300003,37100000
1982-03-30,112.300003,113.089996,111.300003,112.269997,112.269997,43900000
1982-03-31,112.269997,113.169998,111.320000,111.959999,111.959999,43300000
1982-04-01,111.959999,114.220001,111.480003,113.790001,113.790001,57100000
1982-04-02,113.790001,115.790001,113.650002,115.120003,115.120003,59800000
1982-04-05,115.120003,115.900002,113.940002,114.730003,114.730003,46900000
1982-04-06,114.730003,115.919998,113.699997,115.360001,115.360001,43200000
1982-04-07,115.360001,116.449997,114.580002,115.459999,115.459999,53130000
1982-04-08,115.459999,116.940002,114.940002,116.220001,116.220001,60190000
1982-04-12,116.220001,117.019997,115.160004,116.000000,116.000000,46520000
1982-04-13,116.000000,117.120003,115.160004,115.989998,115.989998,48660000
1982-04-14,115.989998,116.690002,114.800003,115.830002,115.830002,45150000
1982-04-15,115.830002,116.860001,115.019997,116.349998,116.349998,45700000
1982-04-16,116.349998,117.699997,115.680000,116.809998,116.809998,55890000
1982-04-19,116.809998,118.160004,115.830002,116.699997,116.699997,58470000
1982-04-20,115.800003,117.139999,114.830002,115.440002,115.440002,54610000
1982-04-21,115.480003,115.870003,115.300003,115.720001,115.720001,57820000
1982-04-22,115.720001,117.250000,115.720001,117.190002,117.190002,64470000
1982-04-23,118.019997,118.639999,117.190002,118.639999,118.639999,71840000
1982-04-26,118.940002,119.330002,118.250000,119.260002,119.260002,60500000
1982-04-27,119.070000,119.260002,117.730003,118.000000,118.000000,56480000
1982-04-28,117.830002,118.050003,116.940002,117.260002,117.260002,50530000
1982-04-29,116.400002,117.239998,116.110001,116.139999,116.139999,51330000
1982-04-30,116.209999,116.779999,116.070000,116.440002,116.440002,48200000
1982-05-03,115.959999,116.820000,115.910004,116.820000,116.820000,46490000
1982-05-04,117.410004,117.639999,116.849998,117.459999,117.459999,58720000
1982-05-05,117.849998,118.050003,117.309998,117.669998,117.669998,58860000
1982-05-06,118.820000,118.830002,117.680000,118.680000,118.680000,67540000
1982-05-07,119.080002,119.889999,118.709999,119.470001,119.470001,67130000
1982-05-10,119.080002,119.489998,118.370003,118.379997,118.379997,46300000
1982-05-11,118.540001,119.589996,118.320000,119.419998,119.419998,54680000
1982-05-12,119.889999,119.919998,118.760002,119.169998,119.169998,59210000
1982-05-13,119.080002,119.199997,118.129997,118.220001,118.220001,58230000
1982-05-14,118.199997,118.400002,118.010002,118.010002,118.010002,49900000
1982-05-17,117.620003,118.019997,116.660004,116.709999,116.709999,45600000
1982-05-18,116.349998,116.699997,115.709999,115.839996,115.839996,48970000
1982-05-19,115.610001,115.959999,114.820000,114.889999,114.889999,48840000
1982-05-20,114.849998,115.070000,114.370003,114.589996,114.589996,48330000
1982-05-21,115.029999,115.129997,114.599998,114.889999,114.889999,45260000
1982-05-24,114.459999,114.860001,114.239998,114.790001,114.790001,38510000
1982-05-25,115.500000,115.510002,114.400002,114.400002,114.400002,44010000
1982-05-26,113.680000,114.400002,112.879997,113.110001,113.110001,51250000
1982-05-27,113.110001,113.120003,112.580002,112.660004,112.660004,44730000
1982-05-28,112.790001,112.800003,111.660004,111.879997,111.879997,43900000
1982-06-01,111.970001,112.070000,111.660004,111.680000,111.680000,41650000
1982-06-02,111.739998,112.190002,111.550003,112.040001,112.040001,49220000
1982-06-03,112.040001,112.480003,111.449997,111.860001,111.860001,48450000
1982-06-04,111.660004,111.849998,110.019997,110.089996,110.089996,44110000
1982-06-07,109.589996,110.589996,109.419998,110.120003,110.120003,44630000
1982-06-08,110.330002,110.330002,109.599998,109.629997,109.629997,46820000
1982-06-09,109.459999,109.629997,108.529999,108.989998,108.989998,55770000
1982-06-10,109.349998,109.699997,108.959999,109.610001,109.610001,50950000
1982-06-11,111.110001,111.480003,109.650002,111.239998,111.239998,68610000
1982-06-14,110.500000,111.220001,109.900002,109.959999,109.959999,40100000
1982-06-15,109.629997,109.959999,108.980003,109.690002,109.690002,44970000
1982-06-16,110.099998,110.129997,108.820000,108.870003,108.870003,56280000
1982-06-17,108.010002,108.849998,107.480003,107.599998,107.599998,49230000
1982-06-18,107.599998,107.599998,107.070000,107.279999,107.279999,53800000
1982-06-21,107.279999,107.879997,107.010002,107.199997,107.199997,50370000
1982-06-22,107.250000,108.300003,107.169998,108.300003,108.300003,55290000
1982-06-23,108.589996,110.139999,108.089996,110.139999,110.139999,62710000
1982-06-24,110.250000,110.919998,109.790001,109.830002,109.830002,55860000
1982-06-25,109.559998,109.830002,109.089996,109.139999,109.139999,38740000
1982-06-28,109.300003,110.449997,109.169998,110.260002,110.260002,40700000
1982-06-29,110.260002,110.570000,109.680000,110.209999,110.209999,46990000
1982-06-30,110.949997,111.000000,109.500000,109.610001,109.610001,65280000
1982-07-01,109.519997,109.629997,108.620003,108.709999,108.709999,47900000
1982-07-02,108.099998,108.709999,107.599998,107.650002,107.650002,43760000
1982-07-06,107.269997,107.669998,106.739998,107.290001,107.290001,44350000
1982-07-07,107.080002,107.610001,106.989998,107.220001,107.220001,46920000
1982-07-08,106.849998,107.529999,105.570000,107.529999,107.529999,63270000
1982-07-09,108.230003,108.970001,107.559998,108.830002,108.830002,65870000
1982-07-12,109.480003,109.620003,108.889999,109.570000,109.570000,74690000
1982-07-13,109.190002,110.070000,109.190002,109.449997,109.449997,66170000
1982-07-14,109.680000,110.440002,109.080002,110.440002,110.440002,58160000
1982-07-15,110.830002,110.949997,110.269997,110.470001,110.470001,61090000
1982-07-16,110.160004,111.480003,110.160004,111.070000,111.070000,58740000
1982-07-19,111.750000,111.779999,110.660004,110.730003,110.730003,53030000
1982-07-20,111.110001,111.559998,110.349998,111.540001,111.540001,61060000
1982-07-21,112.150002,112.389999,111.379997,111.419998,111.419998,66770000
1982-07-22,110.949997,112.019997,110.940002,111.480003,111.480003,53870000
1982-07-23,111.459999,111.580002,111.050003,111.169998,111.169998,47280000
1982-07-26,110.660004,111.160004,110.290001,110.360001,110.360001,37740000
1982-07-27,110.260002,110.349998,109.360001,109.430000,109.430000,45740000
1982-07-28,109.419998,109.419998,107.529999,107.739998,107.739998,53830000
1982-07-29,107.419998,107.919998,106.620003,107.720001,107.720001,55680000
1982-07-30,107.349998,107.949997,107.010002,107.089996,107.089996,39270000
1982-08-02,107.709999,109.089996,107.110001,108.980003,108.980003,53460000
1982-08-03,108.980003,109.430000,107.809998,107.830002,107.830002,60480000
1982-08-04,107.830002,107.830002,106.110001,106.139999,106.139999,53440000
1982-08-05,106.099998,106.099998,104.760002,105.160004,105.160004,54700000
1982-08-06,105.160004,105.160004,103.669998,103.709999,103.709999,48660000
1982-08-09,103.690002,103.690002,102.199997,103.080002,103.080002,54560000
1982-08-10,103.110001,103.839996,102.820000,102.839996,102.839996,52680000
1982-08-11,102.830002,103.010002,102.480003,102.599998,102.599998,49040000
1982-08-12,102.599998,103.220001,102.389999,102.419998,102.419998,50080000
1982-08-13,102.419998,103.849998,102.400002,103.849998,103.849998,44720000
1982-08-16,103.860001,105.519997,103.860001,104.089996,104.089996,55420000
1982-08-17,105.400002,109.040001,104.089996,109.040001,109.040001,92860000
1982-08-18,109.040001,111.580002,108.459999,108.540001,108.540001,132690000
1982-08-19,108.529999,109.860001,108.339996,109.160004,109.160004,78270000
1982-08-20,109.190002,113.019997,109.190002,113.019997,113.019997,95890000
1982-08-23,113.019997,116.110001,112.650002,116.110001,116.110001,110310000
1982-08-24,116.110001,116.389999,115.080002,115.349998,115.349998,121650000
1982-08-25,115.349998,118.120003,115.110001,117.580002,117.580002,106200000
1982-08-26,117.570000,120.260002,117.570000,118.550003,118.550003,137330000
1982-08-27,117.379997,118.559998,116.629997,117.110001,117.110001,74410000
1982-08-30,117.050003,117.660004,115.790001,117.660004,117.660004,59560000
1982-08-31,117.650002,119.599998,117.650002,119.510002,119.510002,86360000
1982-09-01,119.519997,120.050003,117.980003,118.250000,118.250000,82830000
1982-09-02,118.239998,120.320000,117.839996,120.290001,120.290001,74740000
1982-09-03,120.309998,123.639999,120.309998,122.680000,122.680000,130910000
1982-09-07,122.680000,122.680000,121.190002,121.370003,121.370003,68960000
1982-09-08,121.330002,123.110001,121.190002,122.199997,122.199997,77960000
1982-09-09,122.190002,123.220001,121.900002,121.970001,121.970001,73090000
1982-09-10,121.970001,121.980003,120.269997,120.970001,120.970001,71080000
1982-09-13,120.940002,122.239998,120.250000,122.239998,122.239998,59520000
1982-09-14,122.269997,123.690002,122.269997,123.099998,123.099998,83070000
1982-09-15,123.089996,124.809998,122.720001,124.290001,124.290001,69680000
1982-09-16,124.279999,124.879997,123.650002,123.769997,123.769997,78900000
1982-09-17,123.760002,123.760002,122.339996,122.550003,122.550003,63950000
1982-09-20,122.540001,122.540001,121.480003,122.510002,122.510002,58520000
1982-09-21,122.510002,124.910004,122.510002,124.879997,124.879997,82920000
1982-09-22,124.900002,126.430000,123.989998,123.989998,123.989998,113150000
1982-09-23,123.989998,124.190002,122.959999,123.809998,123.809998,68260000
1982-09-24,123.790001,123.800003,123.110001,123.320000,123.320000,54600000
1982-09-27,123.320000,123.620003,122.750000,123.620003,123.620003,44840000
1982-09-28,123.620003,124.160004,123.209999,123.239998,123.239998,65900000
1982-09-29,123.239998,123.239998,121.279999,121.629997,121.629997,62550000
1982-09-30,121.620003,121.620003,120.139999,120.419998,120.419998,62610000
1982-10-01,120.400002,121.970001,120.150002,121.970001,121.970001,65000000
1982-10-04,121.970001,121.970001,120.559998,121.510002,121.510002,55650000
1982-10-05,121.599998,122.730003,121.599998,121.980003,121.980003,69770000
1982-10-06,122.000000,125.970001,122.000000,125.970001,125.970001,93570000
1982-10-07,125.989998,128.960007,125.989998,128.800003,128.800003,147070000
1982-10-08,128.789993,131.110001,128.789993,131.050003,131.050003,122250000
1982-10-11,131.059998,135.529999,131.059998,134.470001,134.470001,138530000
1982-10-12,134.479996,135.850006,133.589996,134.440002,134.440002,126310000
1982-10-13,134.419998,137.970001,134.139999,136.710007,136.710007,139800000
1982-10-14,136.710007,136.889999,134.550003,134.570007,134.570007,107530000
1982-10-15,134.550003,134.610001,133.279999,133.570007,133.570007,80290000
1982-10-18,133.589996,136.729996,133.589996,136.729996,136.729996,83790000
1982-10-19,136.729996,137.960007,135.720001,136.580002,136.580002,100850000
1982-10-20,136.580002,139.229996,136.369995,139.229996,139.229996,98680000
1982-10-21,139.229996,140.270004,137.630005,139.059998,139.059998,122460000
1982-10-22,139.059998,140.399994,138.750000,138.830002,138.830002,101120000
1982-10-25,138.809998,138.809998,133.320007,133.320007,133.320007,83720000
1982-10-26,133.289993,134.479996,131.500000,134.479996,134.479996,102080000
1982-10-27,134.479996,135.919998,134.479996,135.289993,135.289993,81670000
1982-10-28,135.279999,135.419998,133.589996,133.589996,133.589996,73590000
1982-10-29,133.539993,134.020004,132.639999,133.720001,133.720001,74830000
1982-11-01,133.720001,136.029999,133.220001,135.470001,135.470001,73530000
1982-11-02,135.479996,138.509995,135.479996,137.490005,137.490005,104770000
1982-11-03,137.529999,142.880005,137.529999,142.869995,142.869995,137010000
1982-11-04,142.850006,143.990005,141.649994,141.850006,141.850006,149350000
1982-11-05,141.850006,142.429993,141.320007,142.160004,142.160004,96550000
1982-11-08,142.119995,142.119995,139.979996,140.440002,140.440002,75240000
1982-11-09,140.479996,143.160004,140.460007,143.020004,143.020004,111220000
1982-11-10,143.039993,144.360001,140.800003,141.160004,141.160004,113240000
1982-11-11,141.149994,141.750000,139.880005,141.750000,141.750000,78410000
1982-11-12,141.750000,141.850006,139.529999,139.529999,139.529999,95080000
1982-11-15,139.539993,139.539993,137.000000,137.029999,137.029999,78900000
1982-11-16,136.970001,136.970001,134.050003,135.419998,135.419998,102910000
1982-11-17,135.470001,137.929993,135.470001,137.929993,137.929993,84440000
1982-11-18,137.929993,138.779999,137.470001,138.339996,138.339996,77620000
1982-11-19,138.350006,138.929993,137.000000,137.020004,137.020004,70310000
1982-11-22,137.029999,137.100006,134.210007,134.220001,134.220001,74960000
1982-11-23,134.210007,134.279999,132.889999,132.929993,132.929993,72920000
1982-11-24,132.919998,133.880005,132.919998,133.880005,133.880005,67220000
1982-11-26,133.889999,134.880005,133.889999,134.880005,134.880005,38810000
1982-11-29,134.889999,135.289993,133.690002,134.199997,134.199997,61080000
1982-11-30,134.199997,138.529999,134.190002,138.529999,138.529999,93470000
1982-12-01,138.559998,140.369995,138.350006,138.720001,138.720001,107850000
1982-12-02,138.720001,139.630005,138.660004,138.820007,138.820007,77600000
1982-12-03,138.869995,139.589996,138.589996,138.690002,138.690002,71540000
1982-12-06,138.699997,141.770004,138.009995,141.770004,141.770004,83880000
1982-12-07,141.789993,143.679993,141.789993,142.720001,142.720001,111620000
1982-12-08,142.710007,143.580002,141.820007,141.820007,141.820007,97430000
1982-12-09,141.800003,141.800003,139.919998,140.000000,140.000000,90320000
1982-12-10,139.990005,141.149994,139.350006,139.570007,139.570007,86430000
1982-12-13,139.570007,140.119995,139.500000,139.949997,139.949997,63140000
1982-12-14,139.990005,142.500000,137.339996,137.399994,137.399994,98380000
1982-12-15,137.399994,137.399994,135.119995,135.240005,135.240005,81030000
1982-12-16,135.220001,135.779999,134.789993,135.300003,135.300003,73680000
1982-12-17,135.350006,137.710007,135.350006,137.490005,137.490005,76010000
1982-12-20,137.490005,137.839996,136.190002,136.250000,136.250000,62210000
1982-12-21,136.240005,139.270004,136.070007,138.610001,138.610001,78010000
1982-12-22,138.630005,139.690002,138.600006,138.830002,138.830002,83470000
1982-12-23,138.839996,139.940002,138.839996,139.720001,139.720001,62880000
1982-12-27,139.729996,142.320007,139.720001,142.169998,142.169998,64690000
1982-12-28,142.179993,142.339996,140.750000,140.770004,140.770004,58610000
1982-12-29,140.770004,141.729996,140.679993,141.240005,141.240005,54810000
1982-12-30,141.240005,141.679993,140.220001,140.330002,140.330002,56380000
1982-12-31,140.339996,140.779999,140.270004,140.639999,140.639999,42110000
1983-01-03,140.649994,141.330002,138.199997,138.339996,138.339996,59080000
1983-01-04,138.330002,141.360001,138.080002,141.360001,141.360001,75530000
1983-01-05,141.350006,142.600006,141.149994,141.960007,141.960007,95390000
1983-01-06,142.009995,145.770004,142.009995,145.270004,145.270004,129410000
1983-01-07,145.270004,146.460007,145.149994,145.179993,145.179993,127290000
1983-01-10,145.190002,147.250000,144.580002,146.779999,146.779999,101890000
1983-01-11,146.789993,146.830002,145.380005,145.779999,145.779999,98250000
1983-01-12,145.759995,148.360001,145.759995,146.690002,146.690002,109850000
1983-01-13,146.669998,146.940002,145.669998,145.729996,145.729996,77030000
1983-01-14,145.720001,147.119995,145.720001,146.649994,146.649994,86480000
1983-01-17,146.649994,147.899994,146.639999,146.720001,146.720001,89210000
1983-01-18,146.710007,146.740005,145.520004,146.399994,146.399994,78380000
1983-01-19,146.399994,146.449997,144.509995,145.270004,145.270004,80900000
1983-01-20,145.289993,146.619995,145.289993,146.289993,146.289993,82790000
1983-01-21,146.300003,146.300003,143.250000,143.850006,143.850006,77110000
1983-01-24,143.839996,143.839996,139.100006,139.970001,139.970001,90800000
1983-01-25,139.979996,141.750000,139.979996,141.750000,141.750000,79740000
1983-01-26,141.770004,142.160004,141.160004,141.539993,141.539993,73720000
1983-01-27,141.539993,144.300003,141.539993,144.270004,144.270004,88120000
1983-01-28,144.309998,145.470001,144.250000,144.509995,144.509995,89490000
1983-01-31,144.509995,145.300003,143.929993,145.300003,145.300003,67140000
1983-02-01,145.289993,145.289993,142.960007,142.960007,142.960007,82750000
1983-02-02,142.949997,143.520004,141.899994,143.229996,143.229996,77220000
1983-02-03,143.250000,144.429993,143.250000,144.259995,144.259995,78890000
1983-02-04,144.259995,146.139999,144.139999,146.139999,146.139999,87000000
1983-02-07,146.139999,147.419998,146.139999,146.929993,146.929993,86030000
1983-02-08,146.929993,147.210007,145.520004,145.699997,145.699997,76580000
1983-02-09,145.699997,145.830002,144.089996,145.000000,145.000000,84520000
1983-02-10,145.039993,147.750000,145.039993,147.500000,147.500000,93510000
1983-02-11,147.509995,148.809998,147.179993,147.649994,147.649994,86700000
1983-02-14,147.710007,149.139999,147.399994,148.929993,148.929993,72640000
1983-02-15,148.940002,149.410004,148.130005,148.300003,148.300003,89040000
1983-02-16,148.309998,148.660004,147.410004,147.429993,147.429993,82100000
1983-02-17,147.429993,147.570007,143.839996,147.440002,147.440002,74930000
1983-02-18,147.440002,148.289993,147.210007,148.000000,148.000000,77420000
1983-02-22,148.009995,148.110001,145.419998,145.479996,145.479996,84080000
1983-02-23,145.470001,146.789993,145.399994,146.789993,146.789993,84100000
1983-02-24,146.800003,149.669998,146.800003,149.600006,149.600006,113220000
1983-02-25,149.600006,150.880005,149.600006,149.740005,149.740005,100970000
1983-02-28,149.740005,149.740005,147.809998,148.059998,148.059998,83750000
1983-03-01,148.070007,150.880005,148.070007,150.880005,150.880005,103750000
1983-03-02,150.910004,152.630005,150.910004,152.300003,152.300003,112600000
1983-03-03,152.309998,154.160004,152.309998,153.479996,153.479996,114440000
1983-03-04,153.470001,153.669998,152.529999,153.669998,153.669998,90930000
1983-03-07,153.669998,154.000000,152.649994,153.669998,153.669998,84020000
1983-03-08,153.630005,153.630005,151.259995,151.259995,151.259995,79410000
1983-03-09,151.250000,152.869995,150.839996,152.869995,152.869995,84250000
1983-03-10,152.869995,154.009995,151.750000,151.800003,151.800003,95410000
1983-03-11,151.750000,151.750000,150.649994,151.240005,151.240005,67240000
1983-03-14,151.279999,151.300003,150.240005,150.830002,150.830002,61890000
1983-03-15,150.830002,151.369995,150.399994,151.369995,151.369995,62410000
1983-03-16,151.360001,151.619995,149.779999,149.809998,149.809998,83570000
1983-03-17,149.800003,149.800003,149.119995,149.589996,149.589996,70290000
1983-03-18,149.589996,150.289993,149.559998,149.899994,149.899994,75110000
1983-03-21,149.820007,151.199997,149.320007,151.190002,151.190002,72160000
1983-03-22,151.210007,151.589996,150.600006,150.660004,150.660004,79610000
1983-03-23,150.649994,152.979996,150.649994,152.809998,152.809998,94980000
1983-03-24,152.820007,153.779999,152.820007,153.369995,153.369995,92340000
1983-03-25,153.369995,153.710007,152.300003,152.669998,152.669998,77330000
1983-03-28,152.669998,152.669998,151.559998,151.850006,151.850006,58510000
1983-03-29,151.850006,152.460007,151.419998,151.589996,151.589996,65300000
1983-03-30,151.600006,153.389999,151.600006,153.389999,153.389999,75800000
1983-03-31,153.410004,155.020004,152.860001,152.960007,152.960007,100570000
1983-04-04,152.919998,153.020004,152.229996,153.020004,153.020004,66010000
1983-04-05,153.039993,153.919998,151.809998,151.899994,151.899994,76810000
1983-04-06,151.899994,151.899994,150.169998,151.039993,151.039993,77140000
1983-04-07,151.039993,151.759995,150.809998,151.759995,151.759995,69480000
1983-04-08,151.770004,152.850006,151.389999,152.850006,152.850006,67710000
1983-04-11,152.869995,155.139999,152.869995,155.139999,155.139999,81440000
1983-04-12,155.149994,155.820007,154.779999,155.820007,155.820007,79900000
1983-04-13,155.820007,157.220001,155.820007,156.770004,156.770004,100520000
1983-04-14,156.800003,158.119995,156.550003,158.119995,158.119995,90160000
1983-04-15,158.110001,158.750000,158.110001,158.750000,158.750000,89590000
1983-04-18,158.750000,159.750000,158.410004,159.740005,159.740005,88560000
1983-04-19,159.740005,159.740005,158.539993,158.710007,158.710007,91210000
1983-04-20,158.710007,160.830002,158.710007,160.710007,160.710007,110240000
1983-04-21,160.729996,161.080002,159.960007,160.050003,160.050003,106170000
1983-04-22,160.039993,160.759995,160.020004,160.419998,160.419998,92270000
1983-04-25,160.429993,160.830002,158.720001,158.809998,158.809998,90150000
1983-04-26,158.809998,161.809998,158.070007,161.809998,161.809998,91210000
1983-04-27,161.850006,162.770004,160.759995,161.440002,161.440002,118140000
1983-04-28,161.440002,162.960007,161.440002,162.949997,162.949997,94410000
1983-04-29,162.970001,164.429993,162.720001,164.429993,164.429993,105750000
1983-05-02,164.410004,164.419998,161.990005,162.110001,162.110001,88170000
1983-05-03,162.100006,162.350006,160.800003,162.339996,162.339996,89550000
1983-05-04,162.380005,163.639999,162.380005,163.309998,163.309998,101690000
1983-05-05,163.350006,164.300003,163.350006,164.279999,164.279999,107860000
1983-05-06,164.300003,166.990005,164.300003,166.100006,166.100006,128200000
1983-05-09,166.100006,166.460007,164.899994,165.809998,165.809998,93670000
1983-05-10,165.820007,166.399994,165.740005,165.949997,165.949997,104010000
1983-05-11,165.949997,166.300003,164.529999,164.960007,164.960007,99820000
1983-05-12,164.979996,165.350006,163.820007,164.250000,164.250000,84060000
1983-05-13,164.259995,165.229996,164.259995,164.910004,164.910004,83110000
1983-05-16,164.899994,164.899994,162.330002,163.399994,163.399994,76250000
1983-05-17,163.399994,163.710007,162.550003,163.710007,163.710007,79510000
1983-05-18,163.729996,165.179993,163.160004,163.270004,163.270004,99780000
1983-05-19,163.270004,163.610001,161.979996,161.990005,161.990005,83260000
1983-05-20,161.970001,162.139999,161.250000,162.139999,162.139999,73150000
1983-05-23,162.059998,163.500000,160.289993,163.429993,163.429993,84960000
1983-05-24,163.449997,165.589996,163.449997,165.539993,165.539993,109850000
1983-05-25,165.539993,166.210007,164.789993,166.210007,166.210007,121050000
1983-05-26,166.220001,166.389999,165.270004,165.479996,165.479996,94980000
1983-05-27,165.490005,165.490005,164.330002,164.460007,164.460007,76290000
1983-05-31,164.440002,164.440002,162.119995,162.389999,162.389999,73910000
1983-06-01,162.380005,162.639999,161.330002,162.550003,162.550003,84460000
1983-06-02,162.559998,164.000000,162.559998,163.979996,163.979996,89750000
1983-06-03,163.960007,164.789993,163.960007,164.419998,164.419998,83110000
1983-06-06,164.429993,165.089996,163.750000,164.830002,164.830002,87670000
1983-06-07,164.839996,164.929993,162.770004,162.770004,162.770004,88550000
1983-06-08,162.779999,162.779999,161.350006,161.360001,161.360001,96600000
1983-06-09,161.369995,161.919998,160.800003,161.830002,161.830002,87440000
1983-06-10,161.860001,162.759995,161.860001,162.679993,162.679993,78470000
1983-06-13,162.699997,164.839996,162.699997,164.839996,164.839996,90700000
1983-06-14,164.869995,165.929993,164.869995,165.529999,165.529999,97710000
1983-06-15,165.520004,167.119995,165.070007,167.119995,167.119995,93410000
1983-06-16,167.110001,169.380005,167.110001,169.139999,169.139999,124560000
1983-06-17,169.110001,169.639999,168.600006,169.130005,169.130005,93630000
1983-06-20,169.130005,170.100006,168.589996,169.020004,169.020004,84270000
1983-06-21,169.029999,170.600006,168.250000,170.529999,170.529999,102880000
1983-06-22,170.529999,171.600006,170.419998,170.990005,170.990005,110270000
1983-06-23,170.990005,171.000000,170.130005,170.570007,170.570007,89590000
1983-06-24,170.570007,170.690002,170.029999,170.410004,170.410004,80810000
1983-06-27,170.399994,170.460007,168.320007,168.460007,168.460007,69360000
1983-06-28,168.449997,168.809998,165.669998,165.679993,165.679993,82730000
1983-06-29,165.779999,166.639999,165.429993,166.639999,166.639999,81580000
1983-06-30,167.639999,167.639999,167.639999,167.639999,167.639999,76310000
1983-07-01,168.110001,168.639999,167.770004,168.639999,168.639999,65110000
1983-07-05,166.550003,168.800003,165.800003,166.600006,166.600006,67320000
1983-07-06,166.710007,168.880005,166.490005,168.479996,168.479996,85670000
1983-07-07,168.479996,169.149994,167.080002,167.559998,167.559998,97130000
1983-07-08,167.559998,167.979996,166.949997,167.080002,167.080002,66520000
1983-07-11,167.089996,168.110001,167.089996,168.110001,168.110001,61610000
1983-07-12,168.050003,168.050003,165.509995,165.529999,165.529999,70220000
1983-07-13,165.000000,165.679993,164.770004,165.460007,165.460007,68900000
1983-07-14,165.610001,166.960007,165.610001,166.009995,166.009995,83500000
1983-07-15,166.009995,166.039993,164.029999,164.289993,164.289993,63160000
1983-07-18,164.279999,164.289993,163.300003,163.949997,163.949997,69110000
1983-07-19,163.949997,165.179993,163.949997,164.820007,164.820007,74030000
1983-07-20,164.889999,169.289993,164.889999,169.289993,169.289993,109310000
1983-07-21,169.289993,169.800003,168.330002,169.059998,169.059998,101830000
1983-07-22,168.509995,169.080002,168.399994,168.889999,168.889999,68850000
1983-07-25,167.669998,169.740005,167.630005,169.529999,169.529999,73680000
1983-07-26,169.619995,170.630005,169.259995,170.529999,170.529999,91280000
1983-07-27,170.679993,170.720001,167.490005,167.589996,167.589996,99290000
1983-07-28,167.320007,167.789993,164.990005,165.039993,165.039993,78410000
1983-07-29,165.029999,165.029999,161.500000,162.559998,162.559998,95240000
1983-08-01,162.339996,162.779999,161.550003,162.039993,162.039993,77210000
1983-08-02,162.059998,163.039993,161.970001,162.009995,162.009995,74460000
1983-08-03,162.009995,163.440002,161.520004,163.440002,163.440002,80370000
1983-08-04,163.279999,163.419998,159.630005,161.330002,161.330002,100870000
1983-08-05,161.330002,161.880005,160.889999,161.740005,161.740005,67850000
1983-08-08,161.729996,161.729996,159.179993,159.179993,159.179993,71460000
1983-08-09,159.199997,160.139999,158.500000,160.130005,160.130005,81420000
1983-08-10,160.110001,161.770004,159.470001,161.539993,161.539993,82900000
1983-08-11,161.550003,162.139999,161.410004,161.539993,161.539993,70630000
1983-08-12,161.550003,162.600006,161.550003,162.160004,162.160004,71840000
1983-08-15,162.220001,164.759995,162.220001,163.699997,163.699997,83200000
1983-08-16,163.740005,163.839996,162.720001,163.410004,163.410004,71780000
1983-08-17,163.580002,165.399994,163.429993,165.289993,165.289993,87800000
1983-08-18,165.289993,165.910004,163.550003,163.550003,163.550003,82280000
1983-08-19,163.580002,164.270004,163.220001,163.979996,163.979996,58950000
1983-08-22,164.179993,165.639999,163.770004,164.339996,164.339996,76420000
1983-08-23,164.330002,164.330002,162.539993,162.770004,162.770004,66800000
1983-08-24,162.770004,162.770004,161.199997,161.250000,161.250000,72200000
1983-08-25,161.270004,161.279999,159.960007,160.839996,160.839996,70140000
1983-08-26,160.850006,162.160004,160.250000,162.139999,162.139999,61650000
1983-08-29,162.139999,162.320007,160.970001,162.250000,162.250000,53030000
1983-08-30,162.250000,163.130005,162.110001,162.580002,162.580002,62370000
1983-08-31,162.550003,164.399994,162.320007,164.399994,164.399994,80800000
1983-09-01,164.399994,164.660004,163.949997,164.229996,164.229996,76120000
1983-09-02,164.250000,165.070007,164.210007,165.000000,165.000000,59300000
1983-09-06,165.199997,167.899994,165.029999,167.889999,167.889999,87500000
1983-09-07,167.899994,168.479996,167.460007,167.960007,167.960007,94240000
1983-09-08,167.960007,168.139999,167.119995,167.770004,167.770004,79250000
1983-09-09,167.770004,167.770004,166.910004,166.919998,166.919998,77990000
1983-09-12,166.949997,169.199997,165.270004,165.479996,165.479996,114020000
1983-09-13,165.479996,165.479996,164.169998,164.800003,164.800003,73970000
1983-09-14,164.800003,165.419998,164.630005,165.350006,165.350006,73370000
1983-09-15,165.389999,165.580002,164.380005,164.380005,164.380005,70420000
1983-09-16,164.419998,166.570007,164.389999,166.250000,166.250000,75530000
1983-09-19,166.270004,168.089996,166.259995,167.619995,167.619995,85630000
1983-09-20,167.639999,169.380005,167.639999,169.240005,169.240005,103050000
1983-09-21,169.270004,169.300003,168.210007,168.410004,168.410004,91280000
1983-09-22,168.399994,169.779999,168.220001,169.759995,169.759995,97050000
1983-09-23,169.759995,170.169998,168.880005,169.509995,169.509995,93180000
1983-09-26,169.529999,170.410004,169.160004,170.070007,170.070007,86400000
1983-09-27,170.020004,170.020004,167.949997,168.429993,168.429993,81100000
1983-09-28,168.419998,168.529999,167.520004,168.000000,168.000000,75820000
1983-09-29,168.020004,168.350006,167.229996,167.229996,167.229996,73730000
1983-09-30,167.229996,167.229996,165.630005,166.070007,166.070007,70860000
1983-10-03,165.990005,166.070007,164.929993,165.809998,165.809998,77230000
1983-10-04,165.809998,166.800003,165.809998,166.270004,166.270004,90270000
1983-10-05,166.289993,167.740005,165.919998,167.740005,167.740005,101710000
1983-10-06,167.759995,170.279999,167.759995,170.279999,170.279999,118270000
1983-10-07,170.320007,171.100006,170.309998,170.800003,170.800003,103630000
1983-10-10,170.770004,172.649994,170.050003,172.649994,172.649994,67050000
1983-10-11,172.589996,172.589996,170.339996,170.339996,170.339996,79510000
1983-10-12,170.339996,170.839996,169.339996,169.619995,169.619995,75630000
1983-10-13,169.630005,170.119995,169.130005,169.869995,169.869995,67750000
1983-10-14,169.880005,169.990005,169.179993,169.860001,169.860001,71600000
1983-10-17,169.850006,171.179993,169.630005,170.429993,170.429993,77730000
1983-10-18,170.410004,170.410004,167.669998,167.809998,167.809998,91080000
1983-10-19,167.809998,167.809998,165.669998,166.729996,166.729996,107790000
1983-10-20,166.770004,167.350006,166.440002,166.979996,166.979996,86000000
1983-10-21,166.970001,167.229996,164.979996,165.949997,165.949997,91640000
1983-10-24,165.850006,165.990005,163.850006,165.990005,165.990005,85420000
1983-10-25,166.000000,167.149994,166.000000,166.470001,166.470001,82530000
1983-10-26,166.490005,166.649994,165.360001,165.380005,165.380005,79570000
1983-10-27,165.309998,165.380005,164.410004,164.839996,164.839996,79570000
1983-10-28,164.889999,165.190002,163.229996,163.369995,163.369995,81180000
1983-10-31,163.369995,164.580002,162.860001,163.550003,163.550003,79460000
1983-11-01,163.550003,163.660004,162.369995,163.660004,163.660004,84460000
1983-11-02,165.210007,165.210007,163.550003,164.839996,164.839996,95210000
1983-11-03,164.839996,164.850006,163.419998,163.449997,163.449997,85350000
1983-11-04,162.679993,163.449997,162.220001,162.440002,162.440002,72080000
1983-11-07,162.419998,162.559998,161.839996,161.910004,161.910004,69400000
1983-11-08,161.910004,162.149994,161.630005,161.759995,161.759995,64900000
1983-11-09,161.740005,163.970001,161.740005,163.970001,163.970001,83100000
1983-11-10,163.990005,164.710007,163.970001,164.410004,164.410004,88730000
1983-11-11,164.410004,166.300003,164.339996,166.289993,166.289993,74270000
1983-11-14,166.289993,167.580002,166.270004,166.580002,166.580002,86880000
1983-11-15,166.580002,166.589996,165.279999,165.360001,165.360001,77840000
1983-11-16,165.360001,166.410004,165.339996,166.080002,166.080002,83380000
1983-11-17,166.080002,166.490005,165.509995,166.130005,166.130005,80740000
1983-11-18,166.080002,166.130005,164.500000,165.089996,165.089996,88280000
1983-11-21,165.039993,166.050003,165.000000,166.050003,166.050003,97740000
1983-11-22,166.050003,167.259995,166.050003,166.839996,166.839996,117550000
1983-11-23,166.880005,167.210007,166.259995,166.960007,166.960007,108080000
1983-11-25,167.020004,167.199997,166.729996,167.179993,167.179993,57820000
1983-11-28,167.199997,167.220001,166.210007,166.539993,166.539993,78210000
1983-11-29,166.539993,167.919998,166.169998,167.910004,167.910004,100460000
1983-11-30,167.910004,168.070007,166.330002,166.399994,166.399994,120130000
1983-12-01,166.369995,166.770004,166.080002,166.490005,166.490005,106970000
1983-12-02,166.490005,166.699997,165.250000,165.440002,165.440002,93960000
1983-12-05,165.440002,165.789993,164.710007,165.759995,165.759995,88330000
1983-12-06,165.770004,165.929993,165.339996,165.470001,165.470001,89690000
1983-12-07,165.470001,166.339996,165.350006,165.910004,165.910004,105670000
1983-12-08,165.910004,166.009995,164.860001,165.199997,165.199997,96530000
1983-12-09,165.199997,165.289993,164.500000,165.080002,165.080002,98280000
1983-12-12,165.130005,165.619995,164.990005,165.619995,165.619995,77340000
1983-12-13,165.619995,165.630005,164.850006,164.929993,164.929993,93500000
1983-12-14,164.929993,164.929993,163.250000,163.330002,163.330002,85430000
1983-12-15,163.330002,163.330002,161.660004,161.660004,161.660004,88300000
1983-12-16,161.690002,162.389999,161.580002,162.389999,162.389999,81030000
1983-12-19,162.339996,162.880005,162.270004,162.320007,162.320007,75180000
1983-12-20,162.330002,162.800003,161.639999,162.000000,162.000000,83740000
1983-12-21,162.000000,163.570007,161.990005,163.559998,163.559998,108080000
1983-12-22,163.559998,164.179993,163.169998,163.529999,163.529999,106260000
1983-12-23,163.270004,163.309998,162.899994,163.220001,163.220001,62710000
1983-12-27,163.220001,164.759995,163.220001,164.759995,164.759995,63800000
1983-12-28,164.690002,165.339996,164.300003,165.339996,165.339996,85660000
1983-12-29,165.330002,165.839996,164.830002,164.860001,164.860001,86560000
1983-12-30,164.860001,165.050003,164.580002,164.929993,164.929993,71840000
1984-01-03,164.929993,164.929993,163.979996,164.039993,164.039993,71340000
1984-01-04,164.089996,166.779999,164.039993,166.779999,166.779999,112980000
1984-01-05,166.779999,169.100006,166.779999,168.809998,168.809998,159990000
1984-01-06,168.809998,169.309998,168.490005,169.279999,169.279999,137590000
1984-01-09,169.179993,169.460007,168.479996,168.899994,168.899994,107100000
1984-01-10,168.899994,169.539993,167.869995,167.949997,167.949997,109570000
1984-01-11,167.949997,168.070007,167.270004,167.800003,167.800003,98660000
1984-01-12,167.789993,168.399994,167.679993,167.750000,167.750000,99410000
1984-01-13,167.750000,168.589996,166.639999,167.020004,167.020004,101790000
1984-01-16,167.020004,167.550003,166.770004,167.179993,167.179993,93790000
1984-01-17,167.179993,167.839996,167.009995,167.830002,167.830002,92750000
1984-01-18,167.830002,168.339996,167.020004,167.550003,167.550003,109010000
1984-01-19,167.550003,167.649994,166.669998,167.039993,167.039993,98340000
1984-01-20,167.039993,167.059998,165.869995,166.210007,166.210007,93360000
1984-01-23,166.210007,166.210007,164.830002,164.869995,164.869995,82010000
1984-01-24,164.869995,166.350006,164.839996,165.940002,165.940002,103050000
1984-01-25,165.940002,167.119995,164.740005,164.839996,164.839996,113470000
1984-01-26,164.839996,165.550003,164.119995,164.240005,164.240005,111100000
1984-01-27,164.240005,164.330002,163.070007,163.940002,163.940002,103720000
1984-01-30,164.399994,164.669998,162.399994,162.869995,162.869995,103120000
1984-01-31,162.869995,163.600006,162.029999,163.410004,163.410004,113510000
1984-02-01,163.410004,164.000000,162.270004,162.740005,162.740005,107100000
1984-02-02,162.740005,163.360001,162.240005,163.360001,163.360001,111330000
1984-02-03,163.440002,163.979996,160.820007,160.910004,160.910004,109100000
1984-02-06,160.910004,160.910004,158.020004,158.080002,158.080002,109090000
1984-02-07,157.910004,158.809998,157.009995,158.740005,158.740005,107640000
1984-02-08,158.740005,159.070007,155.669998,155.850006,155.850006,96890000
1984-02-09,155.850006,156.169998,154.300003,155.419998,155.419998,128190000
1984-02-10,155.419998,156.520004,155.419998,156.300003,156.300003,92220000
1984-02-13,156.300003,156.320007,154.130005,154.949997,154.949997,78460000
1984-02-14,154.949997,156.610001,154.949997,156.610001,156.610001,91800000
1984-02-15,156.610001,157.479996,156.100006,156.250000,156.250000,94870000
1984-02-16,155.940002,156.440002,155.440002,156.130005,156.130005,81750000
1984-02-17,156.130005,156.800003,155.509995,155.740005,155.740005,76600000
1984-02-21,155.710007,155.740005,154.470001,154.639999,154.639999,71890000
1984-02-22,154.520004,155.100006,153.940002,154.309998,154.309998,90080000
1984-02-23,154.020004,154.449997,152.130005,154.289993,154.289993,100220000
1984-02-24,154.309998,157.509995,154.289993,157.509995,157.509995,102620000
1984-02-27,157.509995,159.580002,157.080002,159.300003,159.300003,99140000
1984-02-28,159.300003,159.300003,156.589996,156.820007,156.820007,91010000
1984-02-29,156.820007,158.270004,156.410004,157.059998,157.059998,92810000
1984-03-01,157.059998,158.190002,156.770004,158.190002,158.190002,82010000
1984-03-02,158.190002,159.899994,158.190002,159.240005,159.240005,108270000
1984-03-05,159.240005,159.240005,157.589996,157.889999,157.889999,69870000
1984-03-06,157.889999,158.369995,156.210007,156.250000,156.250000,83590000
1984-03-07,156.250000,156.250000,153.809998,154.570007,154.570007,90080000
1984-03-08,154.570007,155.800003,154.350006,155.190002,155.190002,80630000
1984-03-09,155.119995,155.190002,153.770004,154.350006,154.350006,73170000
1984-03-12,154.350006,156.350006,154.350006,156.339996,156.339996,84470000
1984-03-13,156.339996,157.929993,156.339996,156.779999,156.779999,102600000
1984-03-14,156.779999,157.169998,156.220001,156.770004,156.770004,77250000
1984-03-15,156.779999,158.050003,156.729996,157.410004,157.410004,79520000
1984-03-16,157.410004,160.449997,157.410004,159.270004,159.270004,118000000
1984-03-19,159.270004,159.270004,157.279999,157.779999,157.779999,64060000
1984-03-20,157.779999,159.169998,157.779999,158.860001,158.860001,86460000
1984-03-21,158.860001,159.259995,158.589996,158.660004,158.660004,87170000
1984-03-22,158.660004,158.669998,156.610001,156.690002,156.690002,87340000
1984-03-23,156.690002,156.919998,156.020004,156.860001,156.860001,79760000
1984-03-26,156.860001,157.179993,156.309998,156.669998,156.669998,69070000
1984-03-27,156.669998,157.300003,156.610001,157.300003,157.300003,73670000
1984-03-28,157.300003,159.899994,157.300003,159.880005,159.880005,104870000
1984-03-29,159.880005,160.460007,159.520004,159.520004,159.520004,81470000
1984-03-30,159.520004,159.520004,158.919998,159.179993,159.179993,71590000
1984-04-02,159.179993,159.869995,157.630005,157.979996,157.979996,85680000
1984-04-03,157.990005,158.270004,157.169998,157.660004,157.660004,87980000
1984-04-04,157.660004,158.110001,157.289993,157.539993,157.539993,92860000
1984-04-05,157.539993,158.100006,154.960007,155.039993,155.039993,101750000
1984-04-06,155.039993,155.479996,154.119995,155.479996,155.479996,86620000
1984-04-09,155.479996,155.860001,154.710007,155.449997,155.449997,71570000
1984-04-10,155.449997,156.570007,155.449997,155.869995,155.869995,78990000
1984-04-11,155.929993,156.309998,154.899994,155.000000,155.000000,80280000
1984-04-12,155.000000,157.740005,154.169998,157.729996,157.729996,96330000
1984-04-13,157.729996,158.869995,157.130005,157.309998,157.309998,99620000
1984-04-16,157.309998,158.350006,156.490005,158.320007,158.320007,73870000
1984-04-17,158.320007,159.589996,158.320007,158.970001,158.970001,98150000
1984-04-18,158.970001,158.970001,157.639999,157.899994,157.899994,85040000
1984-04-19,157.899994,158.020004,157.100006,158.020004,158.020004,75860000
1984-04-23,158.020004,158.050003,156.789993,156.800003,156.800003,73080000
1984-04-24,156.800003,158.380005,156.610001,158.070007,158.070007,87060000
1984-04-25,158.070007,158.770004,157.800003,158.649994,158.649994,83520000
1984-04-26,158.649994,160.500000,158.649994,160.300003,160.300003,98000000
1984-04-27,160.300003,160.690002,159.770004,159.889999,159.889999,88530000
1984-04-30,159.889999,160.429993,159.300003,160.050003,160.050003,72740000
1984-05-01,160.050003,161.690002,160.050003,161.679993,161.679993,110550000
1984-05-02,161.679993,162.110001,161.410004,161.899994,161.899994,107080000
1984-05-03,161.899994,161.899994,160.949997,161.199997,161.199997,91910000
1984-05-04,161.199997,161.199997,158.929993,159.110001,159.110001,98580000
1984-05-07,159.110001,159.479996,158.630005,159.470001,159.470001,72760000
1984-05-08,159.470001,160.520004,159.139999,160.520004,160.520004,81610000
1984-05-09,160.520004,161.309998,159.389999,160.110001,160.110001,100590000
1984-05-10,160.110001,160.449997,159.610001,160.000000,160.000000,101810000
1984-05-11,160.000000,160.000000,157.419998,158.490005,158.490005,82780000
1984-05-14,158.490005,158.490005,157.199997,157.500000,157.500000,64900000
1984-05-15,157.500000,158.270004,157.289993,158.000000,158.000000,88250000
1984-05-16,158.000000,158.410004,157.830002,157.990005,157.990005,89210000
1984-05-17,157.990005,157.990005,156.149994,156.570007,156.570007,90310000
1984-05-18,156.570007,156.770004,155.240005,155.779999,155.779999,81270000
1984-05-21,155.779999,156.110001,154.630005,154.729996,154.729996,73380000
1984-05-22,154.729996,154.729996,152.990005,153.880005,153.880005,88030000
1984-05-23,153.880005,154.020004,153.100006,153.149994,153.149994,82690000
1984-05-24,153.149994,153.149994,150.800003,151.229996,151.229996,99040000
1984-05-25,151.229996,152.020004,150.850006,151.619995,151.619995,78190000
1984-05-29,151.619995,151.860001,149.949997,150.289993,150.289993,69060000
1984-05-30,150.289993,151.429993,148.679993,150.350006,150.350006,105660000
1984-05-31,150.350006,150.690002,149.759995,150.550003,150.550003,81890000
1984-06-01,150.550003,153.240005,150.550003,153.240005,153.240005,96040000
1984-06-04,153.240005,155.100006,153.240005,154.339996,154.339996,96740000
1984-06-05,154.339996,154.339996,153.279999,153.649994,153.649994,84840000
1984-06-06,153.649994,155.029999,153.380005,155.009995,155.009995,83440000
1984-06-07,155.009995,155.110001,154.360001,154.919998,154.919998,82120000
1984-06-08,154.919998,155.399994,154.570007,155.169998,155.169998,67840000
1984-06-11,155.169998,155.169998,153.000000,153.059998,153.059998,69050000
1984-06-12,153.059998,153.070007,151.610001,152.190002,152.190002,84660000
1984-06-13,152.190002,152.850006,151.860001,152.130005,152.130005,67510000
1984-06-14,152.119995,152.139999,150.309998,150.389999,150.389999,79120000
1984-06-15,150.490005,150.710007,149.020004,149.029999,149.029999,85460000
1984-06-18,149.029999,151.919998,148.529999,151.729996,151.729996,94900000
1984-06-19,151.729996,153.000000,151.729996,152.610001,152.610001,98000000
1984-06-20,151.889999,154.839996,150.960007,154.839996,154.839996,99090000
1984-06-21,154.839996,155.639999,154.050003,154.509995,154.509995,123380000
1984-06-22,154.509995,154.919998,153.889999,154.460007,154.460007,98400000
1984-06-25,154.460007,154.669998,153.860001,153.970001,153.970001,72850000
1984-06-26,153.970001,153.970001,152.470001,152.710007,152.710007,82600000
1984-06-27,152.710007,152.880005,151.300003,151.639999,151.639999,78400000
1984-06-28,151.639999,153.070007,151.619995,152.839996,152.839996,77660000
1984-06-29,152.839996,154.080002,152.820007,153.179993,153.179993,90770000
1984-07-02,153.160004,153.220001,152.440002,153.199997,153.199997,69230000
1984-07-03,153.199997,153.860001,153.100006,153.699997,153.699997,69960000
1984-07-05,153.699997,153.869995,152.710007,152.759995,152.759995,66100000
1984-07-06,152.759995,152.759995,151.630005,152.240005,152.240005,65850000
1984-07-09,152.240005,153.529999,151.440002,153.360001,153.360001,74830000
1984-07-10,153.360001,153.529999,152.570007,152.889999,152.889999,74010000
1984-07-11,152.889999,152.889999,150.550003,150.559998,150.559998,89540000
1984-07-12,150.559998,151.059998,149.630005,150.029999,150.029999,86050000
1984-07-13,150.029999,151.160004,150.029999,150.880005,150.880005,75480000
1984-07-16,150.880005,151.600006,150.009995,151.600006,151.600006,73420000
1984-07-17,151.600006,152.600006,151.259995,152.380005,152.380005,82890000
1984-07-18,152.380005,152.380005,151.110001,151.399994,151.399994,76640000
1984-07-19,151.399994,151.399994,150.270004,150.369995,150.369995,85230000
1984-07-20,150.369995,150.580002,149.070007,149.550003,149.550003,79090000
1984-07-23,149.550003,149.550003,147.850006,148.949997,148.949997,77990000
1984-07-24,148.949997,149.279999,147.779999,147.820007,147.820007,74370000
1984-07-25,147.820007,149.300003,147.259995,148.830002,148.830002,90520000
1984-07-26,148.830002,150.160004,148.830002,150.080002,150.080002,90410000
1984-07-27,150.080002,151.380005,149.990005,151.190002,151.190002,101350000
1984-07-30,151.190002,151.190002,150.139999,150.190002,150.190002,72330000
1984-07-31,150.190002,150.770004,149.649994,150.660004,150.660004,86910000
1984-08-01,150.660004,154.080002,150.660004,154.080002,154.080002,127500000
1984-08-02,154.080002,157.990005,154.080002,157.990005,157.990005,172800000
1984-08-03,160.279999,162.559998,158.000000,162.350006,162.350006,236500000
1984-08-06,162.350006,165.270004,162.089996,162.600006,162.600006,203000000
1984-08-07,162.600006,163.580002,160.809998,162.720001,162.720001,127900000
1984-08-08,162.710007,163.869995,161.750000,161.750000,161.750000,121200000
1984-08-09,161.750000,165.880005,161.470001,165.539993,165.539993,131100000
1984-08-10,165.539993,168.589996,165.240005,165.419998,165.419998,171000000
1984-08-13,164.839996,165.490005,163.979996,165.429993,165.429993,77960000
1984-08-14,165.429993,166.089996,164.279999,164.419998,164.419998,81470000
1984-08-15,164.419998,164.419998,162.750000,162.800003,162.800003,91880000
1984-08-16,162.800003,164.419998,162.750000,163.770004,163.770004,93610000
1984-08-17,164.300003,164.610001,163.779999,164.139999,164.139999,71500000
1984-08-20,164.139999,164.940002,163.759995,164.940002,164.940002,75450000
1984-08-21,164.940002,168.220001,164.929993,167.830002,167.830002,128100000
1984-08-22,167.830002,168.800003,166.919998,167.059998,167.059998,116000000
1984-08-23,167.059998,167.779999,166.610001,167.119995,167.119995,83130000
1984-08-24,167.119995,167.520004,167.119995,167.509995,167.509995,69640000
1984-08-27,167.509995,167.509995,165.809998,166.440002,166.440002,57660000
1984-08-28,166.440002,167.429993,166.210007,167.399994,167.399994,70560000
1984-08-29,167.399994,168.210007,167.029999,167.089996,167.089996,90660000
1984-08-30,167.100006,167.190002,166.550003,166.600006,166.600006,70840000
1984-08-31,166.600006,166.679993,165.779999,166.679993,166.679993,57460000
1984-09-04,166.679993,166.679993,164.729996,164.880005,164.880005,62110000
1984-09-05,164.880005,164.880005,163.839996,164.289993,164.289993,69250000
1984-09-06,164.289993,165.949997,164.289993,165.649994,165.649994,91920000
1984-09-07,165.649994,166.309998,164.220001,164.369995,164.369995,84110000
1984-09-10,164.369995,165.050003,163.059998,164.259995,164.259995,74410000
1984-09-11,165.220001,166.169998,164.279999,164.449997,164.449997,101300000
1984-09-12,164.449997,164.809998,164.139999,164.679993,164.679993,77980000
1984-09-13,164.679993,167.940002,164.679993,167.940002,167.940002,110500000
1984-09-14,167.940002,169.649994,167.940002,168.779999,168.779999,137400000
1984-09-17,168.779999,169.369995,167.990005,168.869995,168.869995,88790000
1984-09-18,168.869995,168.869995,167.639999,167.649994,167.649994,107700000
1984-09-19,167.649994,168.759995,166.889999,166.940002,166.940002,119900000
1984-09-20,166.940002,167.470001,166.699997,167.470001,167.470001,92030000
1984-09-21,167.470001,168.669998,165.660004,165.669998,165.669998,120600000
1984-09-24,165.669998,166.119995,164.979996,165.279999,165.279999,76380000
1984-09-25,165.279999,165.970001,164.449997,165.619995,165.619995,86250000
1984-09-26,165.619995,167.199997,165.610001,166.279999,166.279999,100200000
1984-09-27,166.750000,167.179993,166.330002,166.960007,166.960007,88880000
1984-09-28,166.960007,166.960007,165.770004,166.100006,166.100006,78950000
1984-10-01,166.100006,166.100006,164.479996,164.619995,164.619995,73630000
1984-10-02,164.619995,165.240005,163.550003,163.589996,163.589996,89360000
1984-10-03,163.589996,163.589996,162.199997,162.440002,162.440002,92400000
1984-10-04,162.440002,163.220001,162.440002,162.919998,162.919998,76700000
1984-10-05,162.919998,163.320007,162.509995,162.679993,162.679993,82950000
1984-10-08,162.679993,162.679993,161.800003,162.130005,162.130005,46360000
1984-10-09,162.130005,162.839996,161.619995,161.669998,161.669998,76840000
1984-10-10,161.669998,162.119995,160.020004,162.110001,162.110001,94270000
1984-10-11,162.110001,162.869995,162.000000,162.779999,162.779999,87020000
1984-10-12,162.779999,164.470001,162.779999,164.179993,164.179993,92190000
1984-10-15,164.179993,166.149994,164.089996,165.770004,165.770004,87590000
1984-10-16,165.779999,165.779999,164.660004,164.779999,164.779999,82930000
1984-10-17,164.779999,165.039993,163.710007,164.139999,164.139999,99740000
1984-10-18,164.139999,168.100006,163.800003,168.100006,168.100006,149500000
1984-10-19,168.080002,169.619995,167.309998,167.960007,167.960007,186900000
1984-10-22,167.960007,168.360001,167.259995,167.360001,167.360001,81020000
1984-10-23,167.360001,168.270004,166.830002,167.089996,167.089996,92260000
1984-10-24,167.089996,167.539993,166.820007,167.199997,167.199997,91620000
1984-10-25,167.199997,167.619995,166.169998,166.309998,166.309998,92760000
1984-10-26,166.309998,166.309998,164.929993,165.289993,165.289993,83900000
1984-10-29,165.289993,165.289993,164.669998,164.779999,164.779999,63200000
1984-10-30,164.779999,167.330002,164.779999,166.839996,166.839996,95200000
1984-10-31,166.740005,166.949997,165.990005,166.089996,166.089996,91890000
1984-11-01,166.089996,167.830002,166.089996,167.490005,167.490005,107300000
1984-11-02,167.490005,167.949997,167.240005,167.419998,167.419998,96810000
1984-11-05,167.419998,168.649994,167.330002,168.580002,168.580002,84730000
1984-11-06,168.580002,170.410004,168.580002,170.410004,170.410004,101200000
1984-11-07,170.410004,170.410004,168.440002,169.169998,169.169998,110800000
1984-11-08,169.190002,169.270004,168.270004,168.679993,168.679993,88580000
1984-11-09,168.679993,169.460007,167.440002,167.600006,167.600006,83620000
1984-11-12,167.649994,167.649994,166.669998,167.360001,167.360001,55610000
1984-11-13,167.360001,167.380005,165.789993,165.970001,165.970001,69790000
1984-11-14,165.970001,166.429993,165.389999,165.990005,165.990005,73940000
1984-11-15,165.990005,166.490005,165.610001,165.889999,165.889999,81530000
1984-11-16,165.889999,166.240005,164.089996,164.100006,164.100006,83140000
1984-11-19,164.100006,164.339996,163.029999,163.089996,163.089996,69730000
1984-11-20,163.100006,164.470001,163.100006,164.179993,164.179993,83240000
1984-11-21,164.179993,164.679993,163.289993,164.509995,164.509995,81620000
1984-11-23,164.520004,166.919998,164.520004,166.919998,166.919998,73910000
1984-11-26,166.919998,166.919998,165.369995,165.550003,165.550003,76520000
1984-11-27,165.550003,166.850006,165.070007,166.289993,166.289993,95470000
1984-11-28,166.289993,166.899994,164.970001,165.020004,165.020004,86300000
1984-11-29,165.020004,165.020004,163.779999,163.910004,163.910004,75860000
1984-11-30,163.910004,163.910004,162.990005,163.580002,163.580002,77580000
1984-12-03,163.580002,163.580002,162.289993,162.820007,162.820007,95300000
1984-12-04,162.820007,163.910004,162.820007,163.380005,163.380005,81250000
1984-12-05,163.380005,163.399994,161.929993,162.100006,162.100006,88700000
1984-12-06,162.100006,163.110001,161.759995,162.759995,162.759995,96560000
1984-12-07,162.759995,163.309998,162.259995,162.259995,162.259995,81000000
1984-12-10,162.259995,163.320007,161.539993,162.830002,162.830002,81140000
1984-12-11,162.830002,163.179993,162.559998,163.070007,163.070007,80240000
1984-12-12,163.070007,163.179993,162.550003,162.630005,162.630005,78710000
1984-12-13,162.630005,162.919998,161.539993,161.809998,161.809998,80850000
1984-12-14,161.809998,163.529999,161.630005,162.690002,162.690002,95060000
1984-12-17,162.690002,163.630005,162.440002,163.610001,163.610001,89490000
1984-12-18,163.610001,168.110001,163.610001,168.110001,168.110001,169000000
1984-12-19,168.110001,169.029999,166.839996,167.160004,167.160004,139600000
1984-12-20,167.160004,167.580002,166.289993,166.380005,166.380005,93220000
1984-12-21,166.339996,166.380005,164.619995,165.509995,165.509995,101200000
1984-12-24,165.509995,166.929993,165.500000,166.759995,166.759995,55550000
1984-12-26,166.759995,166.759995,166.289993,166.470001,166.470001,46700000
1984-12-27,166.470001,166.500000,165.619995,165.750000,165.750000,70100000
1984-12-28,165.750000,166.320007,165.669998,166.259995,166.259995,77070000
1984-12-31,166.259995,167.339996,166.059998,167.240005,167.240005,80260000
1985-01-02,167.199997,167.199997,165.190002,165.369995,165.369995,67820000
1985-01-03,165.369995,166.110001,164.380005,164.570007,164.570007,88880000
1985-01-04,164.550003,164.550003,163.360001,163.679993,163.679993,77480000
1985-01-07,163.679993,164.710007,163.679993,164.240005,164.240005,86190000
1985-01-08,164.240005,164.589996,163.910004,163.990005,163.990005,92110000
1985-01-09,163.990005,165.570007,163.990005,165.179993,165.179993,99230000
1985-01-10,165.179993,168.309998,164.990005,168.309998,168.309998,124700000
1985-01-11,168.309998,168.720001,167.580002,167.910004,167.910004,107600000
1985-01-14,167.910004,170.550003,167.580002,170.509995,170.509995,124900000
1985-01-15,170.509995,171.820007,170.399994,170.809998,170.809998,155300000
1985-01-16,170.809998,171.940002,170.410004,171.190002,171.190002,135500000
1985-01-17,171.190002,171.339996,170.220001,170.729996,170.729996,113600000
1985-01-18,170.729996,171.419998,170.660004,171.320007,171.320007,104700000
1985-01-21,171.320007,175.449997,171.309998,175.229996,175.229996,146800000
1985-01-22,175.229996,176.630005,175.139999,175.479996,175.479996,174800000
1985-01-23,175.479996,177.300003,175.149994,177.300003,177.300003,144400000
1985-01-24,177.300003,178.160004,176.559998,176.710007,176.710007,160700000
1985-01-25,176.710007,177.750000,176.539993,177.350006,177.350006,122400000
1985-01-28,177.350006,178.190002,176.559998,177.399994,177.399994,128400000
1985-01-29,177.399994,179.190002,176.580002,179.179993,179.179993,115700000
1985-01-30,179.179993,180.270004,179.050003,179.389999,179.389999,170000000
1985-01-31,179.389999,179.830002,178.559998,179.630005,179.630005,132500000
1985-02-01,179.630005,179.630005,178.440002,178.630005,178.630005,105400000
1985-02-04,178.630005,180.350006,177.750000,180.350006,180.350006,113700000
1985-02-05,180.350006,181.529999,180.070007,180.610001,180.610001,143900000
1985-02-06,180.610001,181.500000,180.320007,180.429993,180.429993,141000000
1985-02-07,180.429993,181.960007,180.429993,181.820007,181.820007,151700000
1985-02-08,181.820007,182.389999,181.669998,182.190002,182.190002,116500000
1985-02-11,182.190002,182.190002,180.110001,180.509995,180.509995,104000000
1985-02-12,180.509995,180.750000,179.449997,180.559998,180.559998,111100000
1985-02-13,180.559998,183.860001,180.500000,183.350006,183.350006,142500000
1985-02-14,183.350006,183.949997,182.389999,182.410004,182.410004,139700000
1985-02-15,182.410004,182.649994,181.229996,181.600006,181.600006,106500000
1985-02-19,181.600006,181.610001,180.949997,181.330002,181.330002,90400000
1985-02-20,181.330002,182.100006,180.639999,181.179993,181.179993,118200000
1985-02-21,181.179993,181.179993,180.020004,180.190002,180.190002,104000000
1985-02-22,180.190002,180.410004,179.229996,179.360001,179.360001,93680000
1985-02-25,179.360001,179.360001,178.130005,179.229996,179.229996,89740000
1985-02-26,179.229996,181.580002,179.160004,181.169998,181.169998,114200000
1985-02-27,181.169998,181.869995,180.500000,180.710007,180.710007,107700000
1985-02-28,180.710007,181.210007,180.330002,181.179993,181.179993,100700000
1985-03-01,181.179993,183.889999,181.160004,183.229996,183.229996,139900000
1985-03-04,183.229996,183.410004,181.399994,182.059998,182.059998,102100000
1985-03-05,182.059998,182.649994,181.419998,182.229996,182.229996,116400000
1985-03-06,182.229996,182.250000,180.589996,180.649994,180.649994,116900000
1985-03-07,180.649994,180.649994,179.440002,179.509995,179.509995,112100000
1985-03-08,179.509995,179.970001,179.070007,179.100006,179.100006,96390000
1985-03-11,179.100006,179.460007,178.149994,178.789993,178.789993,84110000
1985-03-12,178.789993,180.139999,178.699997,179.660004,179.660004,92840000
1985-03-13,179.660004,179.960007,178.020004,178.190002,178.190002,101700000
1985-03-14,178.190002,178.529999,177.610001,177.839996,177.839996,103400000
1985-03-15,177.839996,178.410004,176.529999,176.529999,176.529999,105200000
1985-03-18,176.529999,177.660004,176.529999,176.880005,176.880005,94020000
1985-03-19,176.880005,179.559998,176.869995,179.539993,179.539993,119200000
1985-03-20,179.539993,179.779999,178.789993,179.080002,179.080002,107500000
1985-03-21,179.080002,180.220001,178.889999,179.350006,179.350006,95930000
1985-03-22,179.350006,179.919998,178.860001,179.039993,179.039993,99250000
1985-03-25,179.039993,179.039993,177.850006,177.970001,177.970001,74040000
1985-03-26,177.970001,178.860001,177.880005,178.429993,178.429993,89930000
1985-03-27,178.429993,179.800003,178.429993,179.539993,179.539993,101000000
1985-03-28,179.539993,180.600006,179.429993,179.539993,179.539993,99780000
1985-03-29,179.539993,180.660004,179.539993,180.660004,180.660004,101400000
1985-04-01,180.660004,181.270004,180.429993,181.270004,181.270004,89900000
1985-04-02,181.270004,181.860001,180.279999,180.529999,180.529999,101700000
1985-04-03,180.529999,180.529999,178.639999,179.110001,179.110001,95480000
1985-04-04,179.110001,179.130005,178.289993,179.029999,179.029999,86910000
1985-04-08,179.029999,179.460007,177.860001,178.029999,178.029999,79960000
1985-04-09,178.029999,178.669998,177.970001,178.210007,178.210007,83980000
1985-04-10,178.210007,179.899994,178.210007,179.419998,179.419998,108200000
1985-04-11,179.419998,180.910004,179.419998,180.190002,180.190002,108400000
1985-04-12,180.190002,180.550003,180.059998,180.539993,180.539993,86220000
1985-04-15,180.539993,181.149994,180.449997,180.919998,180.919998,80660000
1985-04-16,180.919998,181.779999,180.190002,181.199997,181.199997,98480000
1985-04-17,181.199997,181.910004,181.139999,181.679993,181.679993,96020000
1985-04-18,181.679993,182.559998,180.750000,180.839996,180.839996,100600000
1985-04-19,180.839996,181.250000,180.419998,181.110001,181.110001,81110000
1985-04-22,181.110001,181.229996,180.250000,180.699997,180.699997,79930000
1985-04-23,180.699997,181.970001,180.339996,181.880005,181.880005,108900000
1985-04-24,181.880005,182.270004,181.740005,182.259995,182.259995,99600000
1985-04-25,182.259995,183.429993,182.119995,183.429993,183.429993,108600000
1985-04-26,183.429993,183.610001,182.110001,182.179993,182.179993,86570000
1985-04-29,182.179993,182.339996,180.619995,180.630005,180.630005,88860000
1985-04-30,180.630005,180.630005,178.860001,179.830002,179.830002,111800000
1985-05-01,179.830002,180.039993,178.350006,178.369995,178.369995,101600000
1985-05-02,178.369995,179.009995,178.369995,179.009995,179.009995,107700000
1985-05-03,179.009995,180.300003,179.009995,180.080002,180.080002,94870000
1985-05-06,180.080002,180.559998,179.820007,179.990005,179.990005,85650000
1985-05-07,179.990005,181.089996,179.869995,180.759995,180.759995,100200000
1985-05-08,180.759995,180.759995,179.960007,180.619995,180.619995,101300000
1985-05-09,180.619995,181.970001,180.619995,181.919998,181.919998,111000000
1985-05-10,181.919998,184.740005,181.919998,184.279999,184.279999,140300000
1985-05-13,184.279999,184.610001,184.190002,184.610001,184.610001,85830000
1985-05-14,184.610001,185.169998,183.649994,183.869995,183.869995,97360000
1985-05-15,183.869995,185.429993,183.860001,184.539993,184.539993,106100000
1985-05-16,184.539993,185.740005,184.539993,185.660004,185.660004,99420000
1985-05-17,185.660004,187.940002,185.470001,187.419998,187.419998,124600000
1985-05-20,187.419998,189.979996,187.419998,189.720001,189.720001,146300000
1985-05-21,189.720001,189.809998,188.779999,189.639999,189.639999,130200000
1985-05-22,189.639999,189.639999,187.710007,188.559998,188.559998,101400000
1985-05-23,188.559998,188.559998,187.449997,187.600006,187.600006,101000000
1985-05-24,187.600006,188.289993,187.289993,188.289993,188.289993,85970000
1985-05-28,188.289993,188.940002,187.380005,187.860001,187.860001,90600000
1985-05-29,187.860001,187.860001,187.110001,187.679993,187.679993,96540000
1985-05-30,187.679993,188.039993,187.089996,187.750000,187.750000,108300000
1985-05-31,187.750000,189.589996,187.449997,189.550003,189.550003,134100000
1985-06-03,189.550003,190.360001,188.929993,189.320007,189.320007,125000000
1985-06-04,189.320007,190.270004,188.880005,190.039993,190.039993,115400000
1985-06-05,190.039993,191.020004,190.039993,190.160004,190.160004,143900000
1985-06-06,189.750000,191.059998,189.130005,191.059998,191.059998,117200000
1985-06-07,191.059998,191.289993,189.550003,189.679993,189.679993,99630000
1985-06-10,189.679993,189.679993,188.820007,189.509995,189.509995,87940000
1985-06-11,189.509995,189.610001,188.779999,189.039993,189.039993,102100000
1985-06-12,189.039993,189.039993,187.589996,187.610001,187.610001,97700000
1985-06-13,187.610001,187.610001,185.029999,185.330002,185.330002,107000000
1985-06-14,185.330002,187.100006,185.330002,187.100006,187.100006,93090000
1985-06-17,187.100006,187.100006,185.979996,186.529999,186.529999,82170000
1985-06-18,186.529999,187.649994,186.509995,187.339996,187.339996,106900000
1985-06-19,187.339996,187.979996,186.630005,186.630005,186.630005,108300000
1985-06-20,186.630005,186.740005,185.970001,186.729996,186.729996,87500000
1985-06-21,186.729996,189.660004,186.429993,189.610001,189.610001,125400000
1985-06-24,188.770004,189.610001,187.839996,189.149994,189.149994,96040000
1985-06-25,189.149994,190.960007,189.149994,189.740005,189.740005,115700000
1985-06-26,189.740005,190.259995,189.440002,190.059998,190.059998,94130000
1985-06-27,190.059998,191.360001,190.059998,191.229996,191.229996,106700000
1985-06-28,191.229996,191.850006,191.039993,191.850006,191.850006,105200000
1985-07-01,191.850006,192.429993,191.169998,192.429993,192.429993,96080000
1985-07-02,192.429993,192.630005,191.839996,192.009995,192.009995,111100000
1985-07-03,192.009995,192.080002,191.369995,191.449997,191.449997,98410000
1985-07-05,191.449997,192.669998,191.449997,192.520004,192.520004,62450000
1985-07-08,192.470001,192.520004,191.259995,191.929993,191.929993,83670000
1985-07-09,191.929993,191.929993,190.809998,191.050003,191.050003,99060000
1985-07-10,191.050003,192.369995,190.990005,192.369995,192.369995,108200000
1985-07-11,192.369995,192.949997,192.279999,192.940002,192.940002,122800000
1985-07-12,192.940002,193.320007,192.639999,193.289993,193.289993,120300000
1985-07-15,193.289993,193.839996,192.550003,192.720001,192.720001,103900000
1985-07-16,192.720001,194.720001,192.720001,194.720001,194.720001,132500000
1985-07-17,194.860001,196.070007,194.720001,195.649994,195.649994,159900000
1985-07-18,195.649994,195.649994,194.339996,194.380005,194.380005,131400000
1985-07-19,194.380005,195.130005,194.279999,195.130005,195.130005,114800000
1985-07-22,195.130005,195.130005,193.580002,194.350006,194.350006,93540000
1985-07-23,194.350006,194.979996,192.279999,192.550003,192.550003,143600000
1985-07-24,192.550003,192.550003,190.660004,191.580002,191.580002,128600000
1985-07-25,191.580002,192.229996,191.169998,192.059998,192.059998,123300000
1985-07-26,192.059998,192.779999,191.580002,192.399994,192.399994,107000000
1985-07-29,192.399994,192.419998,189.529999,189.600006,189.600006,95960000
1985-07-30,189.619995,190.050003,189.300003,189.929993,189.929993,102300000
1985-07-31,189.929993,191.330002,189.929993,190.919998,190.919998,124200000
1985-08-01,190.919998,192.169998,190.910004,192.110001,192.110001,121500000
1985-08-02,192.110001,192.110001,191.270004,191.479996,191.479996,87860000
1985-08-05,191.479996,191.479996,189.949997,190.619995,190.619995,79610000
1985-08-06,190.619995,190.720001,187.869995,187.929993,187.929993,104000000
1985-08-07,187.929993,187.929993,187.389999,187.679993,187.679993,100000000
1985-08-08,187.679993,188.960007,187.679993,188.949997,188.949997,102900000
1985-08-09,188.949997,189.050003,188.110001,188.320007,188.320007,81750000
1985-08-12,188.320007,188.320007,187.429993,187.630005,187.630005,77340000
1985-08-13,187.630005,188.149994,186.509995,187.300003,187.300003,80300000
1985-08-14,187.300003,187.869995,187.300003,187.410004,187.410004,85780000
1985-08-15,187.410004,187.740005,186.619995,187.259995,187.259995,86100000
1985-08-16,187.259995,187.259995,186.100006,186.100006,186.100006,87910000
1985-08-19,186.100006,186.820007,186.100006,186.380005,186.380005,67930000
1985-08-20,186.380005,188.270004,186.380005,188.080002,188.080002,91230000
1985-08-21,188.080002,189.160004,188.080002,189.160004,189.160004,94880000
1985-08-22,189.110001,189.229996,187.199997,187.360001,187.360001,90600000
1985-08-23,187.220001,187.350006,186.589996,187.169998,187.169998,75270000
1985-08-26,187.169998,187.440002,186.460007,187.309998,187.309998,70290000
1985-08-27,187.309998,188.100006,187.309998,188.100006,188.100006,82140000
1985-08-28,188.100006,188.830002,187.899994,188.830002,188.830002,88530000
1985-08-29,188.729996,188.940002,188.380005,188.929993,188.929993,85660000
1985-08-30,188.929993,189.130005,188.000000,188.630005,188.630005,81620000
1985-09-03,188.630005,188.630005,187.380005,187.910004,187.910004,81190000
1985-09-04,187.910004,187.919998,186.970001,187.369995,187.369995,85510000
1985-09-05,187.369995,187.520004,186.889999,187.270004,187.270004,94480000
1985-09-06,187.270004,188.429993,187.270004,188.240005,188.240005,95040000
1985-09-09,188.240005,188.800003,187.899994,188.250000,188.250000,89850000
1985-09-10,188.250000,188.259995,186.500000,186.899994,186.899994,104700000
1985-09-11,186.899994,186.899994,184.789993,185.029999,185.029999,100400000
1985-09-12,185.029999,185.210007,183.490005,183.690002,183.690002,107100000
1985-09-13,183.690002,184.190002,182.050003,182.910004,182.910004,111400000
1985-09-16,182.910004,182.910004,182.449997,182.880005,182.880005,66700000
1985-09-17,182.880005,182.880005,180.779999,181.360001,181.360001,111900000
1985-09-18,181.360001,181.830002,180.809998,181.710007,181.710007,105700000
1985-09-19,181.710007,183.399994,181.710007,183.389999,183.389999,100300000
1985-09-20,183.389999,183.990005,182.039993,182.050003,182.050003,101400000
1985-09-23,182.050003,184.649994,182.050003,184.300003,184.300003,104800000
1985-09-24,184.300003,184.300003,182.419998,182.619995,182.619995,97870000
1985-09-25,182.619995,182.619995,180.619995,180.660004,180.660004,92120000
1985-09-26,180.660004,181.289993,179.449997,181.289993,181.289993,106100000
1985-09-30,181.300003,182.080002,181.220001,182.080002,182.080002,103600000
1985-10-01,182.059998,185.080002,182.020004,185.070007,185.070007,130200000
1985-10-02,185.070007,185.940002,184.059998,184.059998,184.059998,147300000
1985-10-03,184.059998,185.169998,183.589996,184.360001,184.360001,127500000
1985-10-04,184.360001,184.360001,182.649994,183.220001,183.220001,101200000
1985-10-07,183.220001,183.220001,181.300003,181.869995,181.869995,95550000
1985-10-08,181.869995,182.300003,181.160004,181.869995,181.869995,97170000
1985-10-09,181.869995,183.270004,181.869995,182.520004,182.520004,99140000
1985-10-10,182.520004,182.789993,182.050003,182.779999,182.779999,90910000
1985-10-11,182.779999,184.279999,182.610001,184.279999,184.279999,96370000
1985-10-14,184.309998,186.369995,184.279999,186.369995,186.369995,78540000
1985-10-15,186.369995,187.160004,185.660004,186.080002,186.080002,110400000
1985-10-16,186.080002,187.979996,186.080002,187.979996,187.979996,117400000
1985-10-17,187.979996,188.520004,187.419998,187.660004,187.660004,140500000
1985-10-18,187.660004,188.110001,186.889999,187.039993,187.039993,107100000
1985-10-21,187.039993,187.300003,186.789993,186.960007,186.960007,95680000
1985-10-22,186.960007,188.559998,186.960007,188.039993,188.039993,111300000
1985-10-23,188.039993,189.089996,188.039993,189.089996,189.089996,121700000
1985-10-24,189.089996,189.449997,188.410004,188.500000,188.500000,123100000
1985-10-25,188.500000,188.509995,187.320007,187.520004,187.520004,101800000
1985-10-28,187.520004,187.759995,186.929993,187.759995,187.759995,97880000
1985-10-29,187.759995,189.779999,187.759995,189.229996,189.229996,110600000
1985-10-30,189.229996,190.089996,189.139999,190.070007,190.070007,120400000
1985-10-31,190.070007,190.149994,189.350006,189.820007,189.820007,121500000
1985-11-01,189.820007,191.529999,189.369995,191.529999,191.529999,129400000
1985-11-04,191.449997,191.960007,190.660004,191.250000,191.250000,104900000
1985-11-05,191.250000,192.429993,190.990005,192.369995,192.369995,119200000
1985-11-06,192.369995,193.009995,191.830002,192.759995,192.759995,129500000
1985-11-07,192.779999,192.960007,192.160004,192.619995,192.619995,119000000
1985-11-08,192.619995,193.970001,192.529999,193.720001,193.720001,115000000
1985-11-11,193.720001,197.289993,193.699997,197.279999,197.279999,126500000
1985-11-12,197.279999,198.660004,196.970001,198.080002,198.080002,170800000
1985-11-13,198.080002,198.110001,196.910004,197.100006,197.100006,109700000
1985-11-14,197.100006,199.190002,196.880005,199.059998,199.059998,124900000
1985-11-15,199.059998,199.580002,197.899994,198.110001,198.110001,130200000
1985-11-18,198.110001,198.710007,197.509995,198.710007,198.710007,108400000
1985-11-19,198.710007,199.520004,198.009995,198.669998,198.669998,126100000
1985-11-20,198.669998,199.199997,198.520004,198.990005,198.990005,105100000
1985-11-21,198.990005,201.429993,198.990005,201.410004,201.410004,150300000
1985-11-22,201.410004,202.009995,201.050003,201.520004,201.520004,133800000
1985-11-25,201.520004,201.520004,200.080002,200.350006,200.350006,91710000
1985-11-26,200.350006,201.160004,200.110001,200.669998,200.669998,123100000
1985-11-27,200.669998,202.649994,200.669998,202.539993,202.539993,143700000
1985-11-29,202.539993,203.399994,201.919998,202.169998,202.169998,84060000
1985-12-02,202.169998,202.190002,200.199997,200.460007,200.460007,103500000
1985-12-03,200.460007,200.979996,200.100006,200.860001,200.860001,109700000
1985-12-04,200.860001,204.229996,200.860001,204.229996,204.229996,153200000
1985-12-05,204.229996,205.860001,203.789993,203.880005,203.880005,181000000
1985-12-06,203.880005,203.880005,202.449997,202.990005,202.990005,125500000
1985-12-09,202.990005,204.649994,202.979996,204.250000,204.250000,144000000
1985-12-10,204.250000,205.160004,203.679993,204.389999,204.389999,156500000
1985-12-11,204.389999,206.679993,204.169998,206.309998,206.309998,178500000
1985-12-12,206.309998,207.649994,205.830002,206.729996,206.729996,170500000
1985-12-13,206.729996,210.309998,206.729996,209.940002,209.940002,177900000
1985-12-16,209.940002,213.080002,209.910004,212.020004,212.020004,176000000
1985-12-17,212.020004,212.449997,210.580002,210.649994,210.649994,155200000
1985-12-18,210.649994,211.229996,209.240005,209.809998,209.809998,137900000
1985-12-19,209.809998,210.130005,209.250000,210.020004,210.020004,130200000
1985-12-20,210.020004,211.770004,210.020004,210.940002,210.940002,170300000
1985-12-23,210.570007,210.940002,208.440002,208.570007,208.570007,107900000
1985-12-24,208.570007,208.570007,206.440002,207.139999,207.139999,78300000
1985-12-26,207.139999,207.759995,207.050003,207.649994,207.649994,62050000
1985-12-27,207.649994,209.619995,207.649994,209.610001,209.610001,81560000
1985-12-30,209.610001,210.699997,209.169998,210.679993,210.679993,91970000
1985-12-31,210.679993,211.610001,210.679993,211.279999,211.279999,112700000
1986-01-02,211.279999,211.279999,208.929993,209.589996,209.589996,98960000
1986-01-03,209.589996,210.880005,209.509995,210.880005,210.880005,105000000
1986-01-06,210.880005,210.979996,209.929993,210.649994,210.649994,99610000
1986-01-07,210.649994,213.800003,210.649994,213.800003,213.800003,153000000
1986-01-08,213.800003,214.570007,207.490005,207.970001,207.970001,180300000
1986-01-09,207.970001,207.970001,204.509995,206.110001,206.110001,176500000
1986-01-10,206.110001,207.330002,205.520004,205.960007,205.960007,122800000
1986-01-13,205.960007,206.830002,205.520004,206.720001,206.720001,108700000
1986-01-14,206.720001,207.369995,206.059998,206.639999,206.639999,113900000
1986-01-15,206.639999,208.270004,206.639999,208.259995,208.259995,122400000
1986-01-16,208.259995,209.179993,207.610001,209.169998,209.169998,130500000
1986-01-17,209.169998,209.399994,207.589996,208.429993,208.429993,132100000
1986-01-20,208.429993,208.429993,206.619995,207.529999,207.529999,85340000
1986-01-21,207.529999,207.779999,205.050003,205.789993,205.789993,128300000
1986-01-22,205.789993,206.029999,203.410004,203.490005,203.490005,131200000
1986-01-23,203.490005,204.429993,202.600006,204.250000,204.250000,130300000
1986-01-24,204.250000,206.429993,204.250000,206.429993,206.429993,128900000
1986-01-27,206.429993,207.690002,206.429993,207.389999,207.389999,122900000
1986-01-28,207.419998,209.820007,207.399994,209.809998,209.809998,145700000
1986-01-29,209.809998,212.360001,209.809998,210.289993,210.289993,193800000
1986-01-30,210.289993,211.539993,209.149994,209.330002,209.330002,125300000
1986-01-31,209.330002,212.419998,209.190002,211.779999,211.779999,143500000
1986-02-03,211.779999,214.179993,211.600006,213.960007,213.960007,145300000
1986-02-04,213.960007,214.570007,210.820007,212.789993,212.789993,175700000
1986-02-05,212.839996,213.029999,211.210007,212.960007,212.960007,134300000
1986-02-06,212.960007,214.509995,212.600006,213.470001,213.470001,146100000
1986-02-07,213.470001,215.270004,211.130005,214.559998,214.559998,144400000
1986-02-10,214.559998,216.240005,214.470001,216.240005,216.240005,129900000
1986-02-11,216.240005,216.669998,215.539993,215.919998,215.919998,141300000
1986-02-12,215.919998,216.279999,215.130005,215.970001,215.970001,136400000
1986-02-13,215.970001,217.410004,215.380005,217.399994,217.399994,136500000
1986-02-14,217.399994,219.759995,217.220001,219.759995,219.759995,155600000
1986-02-18,219.759995,222.449997,219.259995,222.449997,222.449997,160200000
1986-02-19,222.449997,222.960007,219.729996,219.759995,219.759995,152000000
1986-02-20,219.759995,222.220001,219.220001,222.220001,222.220001,139700000
1986-02-21,222.220001,224.619995,222.220001,224.619995,224.619995,177600000
1986-02-24,224.580002,225.289993,223.309998,224.339996,224.339996,144700000
1986-02-25,224.339996,224.399994,222.630005,223.789993,223.789993,148000000
1986-02-26,223.720001,224.589996,223.149994,224.039993,224.039993,158000000
1986-02-27,224.039993,226.880005,223.410004,226.770004,226.770004,181700000
1986-02-28,226.770004,227.919998,225.419998,226.919998,226.919998,191700000
1986-03-03,226.919998,226.919998,224.410004,225.419998,225.419998,142700000
1986-03-04,225.419998,227.330002,223.940002,224.380005,224.380005,174500000
1986-03-05,224.139999,224.369995,222.179993,224.339996,224.339996,154600000
1986-03-06,224.389999,225.500000,224.130005,225.130005,225.130005,159000000
1986-03-07,225.130005,226.330002,224.440002,225.570007,225.570007,163200000
1986-03-10,225.570007,226.979996,225.360001,226.580002,226.580002,129900000
1986-03-11,226.580002,231.809998,226.580002,231.690002,231.690002,187300000
1986-03-12,231.690002,234.699997,231.679993,232.539993,232.539993,210300000
1986-03-13,232.539993,233.889999,231.270004,233.190002,233.190002,171500000
1986-03-14,233.190002,236.550003,232.580002,236.550003,236.550003,181900000
1986-03-17,236.550003,236.550003,233.690002,234.669998,234.669998,137500000
1986-03-18,234.669998,236.520004,234.139999,235.779999,235.779999,148000000
1986-03-19,235.779999,236.520004,235.130005,235.600006,235.600006,150000000
1986-03-20,235.600006,237.089996,235.600006,236.539993,236.539993,148000000
1986-03-21,236.539993,237.350006,233.289993,233.339996,233.339996,199100000
1986-03-24,233.339996,235.330002,232.919998,235.330002,235.330002,143800000
1986-03-25,235.330002,235.330002,233.619995,234.720001,234.720001,139300000
1986-03-26,234.720001,237.789993,234.710007,237.300003,237.300003,161500000
1986-03-27,237.300003,240.110001,237.300003,238.970001,238.970001,178100000
1986-03-31,238.970001,239.860001,238.080002,238.899994,238.899994,134400000
1986-04-01,238.899994,239.100006,234.570007,235.139999,235.139999,167400000
1986-04-02,235.139999,235.710007,233.399994,235.710007,235.710007,145300000
1986-04-03,235.710007,236.419998,232.070007,232.470001,232.470001,148200000
1986-04-04,232.470001,232.559998,228.320007,228.690002,228.690002,147300000
1986-04-07,228.690002,228.830002,226.300003,228.630005,228.630005,129800000
1986-04-08,228.630005,233.699997,228.630005,233.520004,233.520004,146300000
1986-04-09,233.520004,235.570007,232.130005,233.750000,233.750000,156300000
1986-04-10,233.750000,236.539993,233.750000,236.440002,236.440002,184800000
1986-04-11,236.440002,237.850006,235.130005,235.970001,235.970001,139400000
1986-04-14,235.970001,237.479996,235.429993,237.279999,237.279999,106700000
1986-04-15,237.279999,238.089996,236.639999,237.729996,237.729996,123700000
1986-04-16,237.729996,242.570007,237.729996,242.220001,242.220001,173800000
1986-04-17,242.220001,243.360001,241.889999,243.029999,243.029999,161400000
1986-04-18,243.029999,243.470001,241.740005,242.380005,242.380005,153600000
1986-04-21,242.380005,244.779999,241.880005,244.740005,244.740005,136100000
1986-04-22,244.740005,245.470001,241.300003,242.419998,242.419998,161500000
1986-04-23,242.419998,242.419998,240.080002,241.750000,241.750000,149700000
1986-04-24,241.750000,243.130005,241.649994,242.020004,242.020004,146600000
1986-04-25,242.020004,242.800003,240.910004,242.289993,242.289993,142300000
1986-04-28,242.289993,243.080002,241.229996,243.080002,243.080002,123900000
1986-04-29,243.080002,243.570007,239.229996,240.509995,240.509995,148800000
1986-04-30,240.520004,240.520004,235.259995,235.520004,235.520004,147500000
1986-05-01,235.520004,236.009995,234.210007,235.160004,235.160004,146500000
1986-05-02,235.160004,236.520004,234.149994,234.789993,234.789993,126300000
1986-05-05,234.789993,237.729996,234.789993,237.729996,237.729996,102400000
1986-05-06,237.729996,238.279999,236.259995,237.240005,237.240005,121200000
1986-05-07,236.559998,237.240005,233.979996,236.080002,236.080002,129900000
1986-05-08,236.080002,237.960007,236.080002,237.130005,237.130005,136000000
1986-05-09,237.130005,238.009995,235.850006,237.850006,237.850006,137400000
1986-05-12,237.850006,238.529999,237.020004,237.580002,237.580002,125400000
1986-05-13,237.580002,237.869995,236.020004,236.410004,236.410004,119200000
1986-05-14,236.410004,237.539993,235.850006,237.539993,237.539993,132100000
1986-05-15,237.539993,237.539993,233.929993,234.429993,234.429993,131600000
1986-05-16,234.429993,234.429993,232.259995,232.759995,232.759995,113500000
1986-05-19,232.759995,233.539993,232.410004,233.199997,233.199997,85840000
1986-05-20,233.199997,236.119995,232.580002,236.110001,236.110001,113000000
1986-05-21,236.110001,236.830002,235.449997,235.449997,235.449997,117100000
1986-05-22,235.449997,240.250000,235.449997,240.119995,240.119995,144900000
1986-05-23,240.119995,242.160004,240.119995,241.350006,241.350006,130200000
1986-05-27,241.350006,244.759995,241.350006,244.750000,244.750000,121200000
1986-05-28,244.750000,247.399994,244.750000,246.630005,246.630005,159600000
1986-05-29,246.630005,248.320007,245.289993,247.979996,247.979996,135700000
1986-05-30,247.979996,249.190002,246.429993,247.350006,247.350006,151200000
1986-06-02,246.039993,247.740005,243.830002,245.039993,245.039993,120600000
1986-06-03,245.039993,245.509995,243.669998,245.509995,245.509995,114700000
1986-06-04,245.509995,246.300003,242.589996,243.940002,243.940002,117000000
1986-06-05,243.940002,245.660004,243.410004,245.649994,245.649994,110900000
1986-06-06,245.649994,246.070007,244.429993,245.669998,245.669998,110900000
1986-06-09,245.669998,245.669998,239.679993,239.960007,239.960007,123300000
1986-06-10,239.960007,240.080002,238.229996,239.580002,239.580002,125000000
1986-06-11,239.580002,241.130005,239.210007,241.130005,241.130005,127400000
1986-06-12,241.240005,241.639999,240.699997,241.490005,241.490005,109100000
1986-06-13,241.710007,245.910004,241.710007,245.729996,245.729996,141200000
1986-06-16,245.729996,246.500000,245.169998,246.130005,246.130005,112100000
1986-06-17,246.130005,246.259995,243.600006,244.350006,244.350006,123100000
1986-06-18,244.350006,245.250000,242.570007,244.990005,244.990005,117000000
1986-06-19,244.990005,245.800003,244.050003,244.059998,244.059998,129000000
1986-06-20,244.059998,247.600006,243.979996,247.580002,247.580002,149100000
1986-06-23,247.580002,247.580002,244.449997,245.259995,245.259995,123800000
1986-06-24,245.259995,248.259995,244.529999,247.029999,247.029999,140600000
1986-06-25,247.029999,250.130005,247.029999,248.929993,248.929993,161800000
1986-06-26,248.929993,249.429993,247.720001,248.740005,248.740005,134100000
1986-06-27,248.740005,249.740005,248.740005,249.600006,249.600006,123800000
1986-06-30,249.600006,251.809998,249.600006,250.839996,250.839996,135100000
1986-07-01,250.669998,252.039993,250.529999,252.039993,252.039993,147700000
1986-07-02,252.039993,253.199997,251.789993,252.699997,252.699997,150000000
1986-07-03,252.699997,252.940002,251.229996,251.789993,251.789993,108300000
1986-07-07,251.789993,251.809998,243.630005,244.050003,244.050003,138200000
1986-07-08,244.050003,244.059998,239.070007,241.589996,241.589996,174100000
1986-07-09,241.589996,243.070007,241.460007,242.820007,242.820007,142900000
1986-07-10,242.820007,243.440002,239.660004,243.009995,243.009995,146200000
1986-07-11,243.009995,243.479996,241.679993,242.220001,242.220001,124500000
1986-07-14,242.220001,242.220001,238.039993,238.110001,238.110001,123200000
1986-07-15,238.089996,238.119995,233.600006,233.660004,233.660004,184000000
1986-07-16,233.660004,236.190002,233.660004,235.009995,235.009995,160800000
1986-07-17,235.009995,236.649994,235.009995,236.070007,236.070007,132400000
1986-07-18,236.070007,238.220001,233.940002,236.360001,236.360001,149700000
1986-07-21,236.360001,236.449997,235.529999,236.240005,236.240005,106300000
1986-07-22,236.240005,238.419998,235.919998,238.179993,238.179993,138500000
1986-07-23,238.190002,239.250000,238.169998,238.669998,238.669998,133300000
1986-07-24,238.690002,239.050003,237.320007,237.949997,237.949997,134700000
1986-07-25,237.990005,240.360001,237.949997,240.220001,240.220001,132000000
1986-07-28,240.199997,240.250000,235.229996,236.009995,236.009995,128000000
1986-07-29,235.720001,236.009995,234.399994,234.550003,234.550003,115700000
1986-07-30,234.570007,237.380005,233.070007,236.589996,236.589996,146700000
1986-07-31,236.589996,236.919998,235.889999,236.119995,236.119995,112700000
1986-08-01,236.119995,236.889999,234.589996,234.910004,234.910004,114900000
1986-08-04,234.910004,236.860001,231.919998,235.990005,235.990005,130000000
1986-08-05,235.990005,238.309998,235.970001,237.029999,237.029999,153100000
1986-08-06,237.029999,237.350006,235.479996,236.839996,236.839996,127500000
1986-08-07,236.839996,238.020004,236.309998,237.039993,237.039993,122400000
1986-08-08,237.039993,238.059998,236.369995,236.880005,236.880005,106300000
1986-08-11,236.880005,241.199997,236.869995,240.679993,240.679993,125600000
1986-08-12,240.679993,243.369995,240.350006,243.339996,243.339996,131700000
1986-08-13,243.339996,246.509995,243.059998,245.669998,245.669998,156400000
1986-08-14,245.669998,246.789993,245.529999,246.250000,246.250000,123800000
1986-08-15,246.250000,247.149994,245.699997,247.149994,247.149994,123500000
1986-08-18,247.149994,247.830002,245.479996,247.380005,247.380005,112800000
1986-08-19,247.380005,247.419998,245.820007,246.509995,246.509995,109300000
1986-08-20,246.529999,249.770004,246.509995,249.770004,249.770004,156600000
1986-08-21,249.770004,250.449997,249.110001,249.669998,249.669998,135200000
1986-08-22,249.669998,250.610001,249.270004,250.190002,250.190002,118100000
1986-08-25,250.190002,250.259995,247.759995,247.809998,247.809998,104400000
1986-08-26,247.809998,252.910004,247.809998,252.839996,252.839996,156600000
1986-08-27,252.839996,254.240005,252.660004,253.300003,253.300003,143300000
1986-08-28,253.300003,253.669998,251.910004,252.839996,252.839996,125100000
1986-08-29,252.839996,254.070007,251.729996,252.929993,252.929993,125300000
1986-09-02,252.929993,253.300003,248.139999,248.520004,248.520004,135500000
1986-09-03,248.520004,250.080002,247.589996,250.080002,250.080002,154300000
1986-09-04,250.080002,254.009995,250.029999,253.830002,253.830002,189400000
1986-09-05,253.830002,254.130005,250.330002,250.470001,250.470001,180600000
1986-09-08,250.470001,250.470001,247.020004,248.139999,248.139999,153300000
1986-09-09,248.139999,250.210007,246.940002,247.669998,247.669998,137500000
1986-09-10,247.669998,247.759995,246.110001,247.059998,247.059998,140300000
1986-09-11,247.059998,247.059998,234.669998,235.179993,235.179993,237600000
1986-09-12,235.179993,235.449997,228.740005,230.669998,230.669998,240500000
1986-09-15,230.669998,232.820007,229.440002,231.940002,231.940002,155600000
1986-09-16,231.929993,231.940002,228.320007,231.720001,231.720001,131200000
1986-09-17,231.729996,233.809998,231.380005,231.679993,231.679993,141000000
1986-09-18,231.669998,232.869995,230.570007,232.309998,232.309998,132200000
1986-09-19,232.300003,232.309998,230.690002,232.210007,232.210007,153900000
1986-09-22,232.199997,234.929993,232.199997,234.929993,234.929993,126100000
1986-09-23,234.960007,235.880005,234.500000,235.669998,235.669998,132600000
1986-09-24,235.660004,237.059998,235.529999,236.279999,236.279999,134600000
1986-09-25,231.830002,236.279999,230.669998,231.830002,231.830002,134300000
1986-09-26,231.830002,233.679993,230.639999,232.229996,232.229996,115300000
1986-09-29,232.229996,232.229996,228.080002,229.910004,229.910004,115600000
1986-09-30,229.910004,233.009995,229.910004,231.320007,231.320007,124900000
1986-10-01,231.320007,234.619995,231.320007,233.600006,233.600006,143600000
1986-10-02,233.600006,234.330002,232.770004,233.919998,233.919998,128100000
1986-10-03,233.919998,236.160004,232.789993,233.710007,233.710007,128100000
1986-10-06,233.710007,235.339996,233.169998,234.779999,234.779999,88250000
1986-10-07,234.740005,235.179993,233.460007,234.410004,234.410004,125100000
1986-10-08,234.410004,236.839996,233.679993,236.679993,236.679993,141700000
1986-10-09,236.669998,238.199997,235.720001,235.850006,235.850006,153400000
1986-10-10,235.839996,236.270004,235.309998,235.479996,235.479996,105100000
1986-10-13,235.520004,235.910004,235.020004,235.910004,235.910004,54990000
1986-10-14,235.899994,236.369995,234.369995,235.369995,235.369995,116800000
1986-10-15,235.360001,239.029999,235.270004,238.800003,238.800003,144300000
1986-10-16,238.830002,240.179993,238.800003,239.529999,239.529999,156900000
1986-10-17,239.500000,239.529999,237.710007,238.839996,238.839996,124100000
1986-10-20,238.839996,238.839996,234.779999,235.970001,235.970001,109000000
1986-10-21,236.029999,236.490005,234.949997,235.880005,235.880005,110000000
1986-10-22,235.889999,236.639999,235.820007,236.259995,236.259995,114000000
1986-10-23,236.279999,239.759995,236.259995,239.279999,239.279999,150900000
1986-10-24,239.300003,239.649994,238.250000,238.259995,238.259995,137500000
1986-10-27,238.220001,238.770004,236.720001,238.770004,238.770004,133200000
1986-10-28,238.809998,240.580002,238.770004,239.259995,239.259995,145900000
1986-10-29,239.229996,241.000000,238.979996,240.940002,240.940002,164400000
1986-10-30,240.970001,244.080002,240.940002,243.710007,243.710007,194200000
1986-10-31,243.699997,244.509995,242.949997,243.979996,243.979996,147200000
1986-11-03,243.970001,245.800003,243.929993,245.800003,245.800003,138200000
1986-11-04,245.800003,246.429993,244.419998,246.199997,246.199997,163200000
1986-11-05,246.089996,247.050003,245.210007,246.580002,246.580002,183200000
1986-11-06,246.539993,246.899994,244.300003,245.869995,245.869995,165300000
1986-11-07,245.850006,246.130005,244.919998,245.770004,245.770004,142300000
1986-11-10,245.750000,246.220001,244.679993,246.130005,246.130005,120200000
1986-11-11,246.149994,247.100006,246.119995,247.080002,247.080002,118500000
1986-11-12,247.059998,247.669998,245.679993,246.639999,246.639999,162200000
1986-11-13,246.630005,246.660004,242.979996,243.020004,243.020004,164000000
1986-11-14,243.009995,244.509995,241.960007,244.500000,244.500000,172100000
1986-11-17,244.500000,244.800003,242.289993,243.210007,243.210007,133300000
1986-11-18,243.199997,243.229996,236.649994,236.779999,236.779999,185300000
1986-11-19,236.770004,237.940002,235.509995,237.660004,237.660004,183300000
1986-11-20,237.660004,242.050003,237.660004,242.050003,242.050003,158100000
1986-11-21,242.029999,246.380005,241.970001,245.860001,245.860001,200700000
1986-11-24,245.860001,248.000000,245.210007,247.449997,247.449997,150800000
1986-11-25,247.440002,248.179993,246.300003,248.169998,248.169998,154600000
1986-11-26,248.139999,248.899994,247.729996,248.770004,248.770004,152000000
1986-11-28,248.820007,249.220001,248.070007,249.220001,249.220001,93530000
1986-12-01,249.220001,249.220001,245.720001,249.050003,249.050003,133800000
1986-12-02,249.059998,254.000000,249.050003,254.000000,254.000000,230400000
1986-12-03,254.000000,254.869995,253.240005,253.850006,253.850006,200100000
1986-12-04,253.850006,254.419998,252.880005,253.039993,253.039993,156900000
1986-12-05,253.050003,253.889999,250.710007,251.169998,251.169998,139800000
1986-12-08,251.160004,252.360001,248.820007,251.160004,251.160004,159000000
1986-12-09,251.160004,251.270004,249.250000,249.279999,249.279999,128700000
1986-12-10,249.279999,251.529999,248.940002,250.960007,250.960007,139700000
1986-12-11,250.970001,250.979996,247.149994,248.169998,248.169998,136000000
1986-12-12,248.169998,248.309998,247.020004,247.350006,247.350006,126600000
1986-12-15,247.309998,248.229996,244.919998,248.210007,248.210007,148200000
1986-12-16,248.210007,250.039993,247.399994,250.039993,250.039993,157000000
1986-12-17,250.009995,250.039993,247.190002,247.559998,247.559998,148800000
1986-12-18,247.559998,247.809998,246.449997,246.779999,246.779999,155400000
1986-12-19,246.789993,249.960007,245.889999,249.729996,249.729996,244700000
1986-12-22,249.729996,249.729996,247.449997,248.750000,248.750000,157600000
1986-12-23,248.750000,248.750000,245.850006,246.339996,246.339996,188700000
1986-12-24,246.339996,247.220001,246.020004,246.750000,246.750000,95410000
1986-12-26,246.750000,247.089996,246.729996,246.919998,246.919998,48860000
1986-12-29,246.899994,246.919998,244.309998,244.669998,244.669998,99800000
1986-12-30,244.660004,244.669998,243.039993,243.369995,243.369995,126200000
1986-12-31,243.369995,244.029999,241.279999,242.169998,242.169998,139200000
1987-01-02,242.169998,246.449997,242.169998,246.449997,246.449997,91880000
1987-01-05,246.449997,252.570007,246.449997,252.190002,252.190002,181900000
1987-01-06,252.199997,253.990005,252.139999,252.779999,252.779999,189300000
1987-01-07,252.779999,255.720001,252.649994,255.330002,255.330002,190900000
1987-01-08,255.360001,257.279999,254.970001,257.279999,257.279999,194500000
1987-01-09,257.260010,259.200012,256.109985,258.730011,258.730011,193000000
1987-01-12,258.720001,261.359985,257.920013,260.299988,260.299988,184200000
1987-01-13,260.299988,260.450012,259.209991,259.950012,259.950012,170900000
1987-01-14,259.950012,262.720001,259.619995,262.640015,262.640015,214200000
1987-01-15,262.649994,266.679993,262.640015,265.489990,265.489990,253100000
1987-01-16,265.459991,267.239990,264.309998,266.279999,266.279999,218400000
1987-01-19,266.260010,269.339996,264.000000,269.339996,269.339996,162800000
1987-01-20,269.339996,271.029999,267.649994,269.040009,269.040009,224800000
1987-01-21,269.040009,270.869995,267.350006,267.839996,267.839996,184200000
1987-01-22,267.839996,274.049988,267.320007,273.910004,273.910004,188700000
1987-01-23,273.910004,280.959991,268.410004,270.100006,270.100006,302400000
1987-01-26,270.100006,270.399994,267.730011,269.609985,269.609985,138900000
1987-01-27,269.609985,274.309998,269.609985,273.750000,273.750000,192300000
1987-01-28,273.750000,275.709991,273.029999,275.399994,275.399994,195800000
1987-01-29,275.399994,276.850006,272.540009,274.239990,274.239990,205300000
1987-01-30,274.239990,274.239990,271.380005,274.079987,274.079987,163400000
1987-02-02,274.079987,277.350006,273.160004,276.450012,276.450012,177400000
1987-02-03,276.450012,277.829987,275.839996,275.989990,275.989990,198100000
1987-02-04,275.989990,279.649994,275.350006,279.640015,279.640015,222400000
1987-02-05,279.640015,282.260010,278.660004,281.160004,281.160004,256700000
1987-02-06,281.160004,281.790009,279.869995,280.040009,280.040009,184100000
1987-02-09,280.040009,280.040009,277.239990,278.160004,278.160004,143300000
1987-02-10,278.160004,278.160004,273.489990,275.070007,275.070007,168300000
1987-02-11,275.070007,277.709991,274.709991,277.540009,277.540009,172400000
1987-02-12,277.540009,278.040009,273.890015,275.619995,275.619995,200400000
1987-02-13,275.619995,280.910004,275.010010,279.700012,279.700012,184400000
1987-02-17,279.700012,285.489990,279.700012,285.489990,285.489990,187800000
1987-02-18,285.489990,287.549988,282.970001,285.420013,285.420013,218200000
1987-02-19,285.420013,286.239990,283.839996,285.570007,285.570007,181500000
1987-02-20,285.570007,285.980011,284.309998,285.480011,285.480011,175800000
1987-02-23,285.480011,285.500000,279.369995,282.380005,282.380005,170500000
1987-02-24,282.380005,283.329987,281.450012,282.880005,282.880005,151300000
1987-02-25,282.880005,285.350006,282.140015,284.000000,284.000000,184100000
1987-02-26,284.000000,284.399994,280.730011,282.959991,282.959991,165800000
1987-02-27,282.959991,284.549988,282.769989,284.200012,284.200012,142800000
1987-03-02,284.170013,284.829987,282.299988,283.000000,283.000000,156700000
1987-03-03,283.000000,284.190002,282.920013,284.119995,284.119995,149200000
1987-03-04,284.119995,288.619995,284.119995,288.619995,288.619995,198400000
1987-03-05,288.619995,291.239990,288.600006,290.519989,290.519989,205400000
1987-03-06,290.519989,290.670013,288.769989,290.660004,290.660004,181600000
1987-03-09,290.660004,290.660004,287.119995,288.299988,288.299988,165400000
1987-03-10,288.299988,290.869995,287.890015,290.859985,290.859985,174800000
1987-03-11,290.869995,292.510010,289.329987,290.309998,290.309998,186900000
1987-03-12,290.329987,291.910004,289.660004,291.220001,291.220001,174500000
1987-03-13,291.220001,291.790009,289.880005,289.890015,289.890015,150900000
1987-03-16,289.880005,289.890015,286.640015,288.230011,288.230011,134900000
1987-03-17,288.089996,292.470001,287.959991,292.470001,292.470001,177300000
1987-03-18,292.489990,294.579987,290.869995,292.779999,292.779999,198100000
1987-03-19,292.730011,294.459991,292.260010,294.079987,294.079987,166100000
1987-03-20,294.079987,298.170013,294.079987,298.170013,298.170013,234000000
1987-03-23,298.160004,301.170013,297.500000,301.160004,301.160004,189100000
1987-03-24,301.170013,301.920013,300.140015,301.640015,301.640015,189900000
1987-03-25,301.519989,301.850006,299.359985,300.380005,300.380005,171300000
1987-03-26,300.390015,302.720001,300.380005,300.929993,300.929993,196000000
1987-03-27,300.959991,301.410004,296.059998,296.130005,296.130005,184400000
1987-03-30,296.100006,296.130005,286.690002,289.200012,289.200012,208400000
1987-03-31,289.209991,291.869995,289.070007,291.700012,291.700012,171800000
1987-04-01,291.589996,292.380005,288.339996,292.380005,292.380005,182600000
1987-04-02,292.410004,294.470001,292.019989,293.630005,293.630005,183000000
1987-04-03,293.640015,301.299988,292.299988,300.410004,300.410004,213400000
1987-04-06,300.459991,302.209991,300.410004,301.950012,301.950012,173700000
1987-04-07,301.940002,303.649994,296.670013,296.690002,296.690002,186400000
1987-04-08,296.720001,299.200012,295.179993,297.260010,297.260010,179800000
1987-04-09,297.250000,297.709991,291.500000,292.859985,292.859985,180300000
1987-04-10,292.820007,293.739990,290.940002,292.489990,292.489990,169500000
1987-04-13,292.480011,293.359985,285.619995,285.619995,285.619995,181000000
1987-04-14,285.609985,285.619995,275.670013,279.160004,279.160004,266500000
1987-04-15,279.170013,285.140015,279.160004,284.440002,284.440002,198200000
1987-04-16,284.450012,289.570007,284.440002,286.910004,286.910004,189600000
1987-04-20,286.910004,288.359985,284.549988,286.089996,286.089996,139100000
1987-04-21,285.880005,293.070007,282.890015,293.070007,293.070007,191300000
1987-04-22,293.049988,293.459991,286.980011,287.190002,287.190002,185900000
1987-04-23,287.190002,289.119995,284.279999,286.820007,286.820007,173900000
1987-04-24,286.809998,286.820007,281.179993,281.519989,281.519989,178000000
1987-04-27,281.519989,284.450012,276.220001,281.829987,281.829987,222700000
1987-04-28,281.829987,285.950012,281.829987,282.510010,282.510010,180100000
1987-04-29,282.579987,286.420013,282.579987,284.570007,284.570007,173600000
1987-04-30,284.579987,290.079987,284.570007,288.359985,288.359985,183100000
1987-05-01,286.989990,289.709991,286.519989,288.029999,288.029999,160100000
1987-05-04,288.019989,289.989990,286.390015,289.359985,289.359985,140600000
1987-05-05,289.359985,295.399994,289.339996,295.339996,295.339996,192300000
1987-05-06,295.350006,296.190002,293.600006,295.470001,295.470001,196600000
1987-05-07,295.450012,296.799988,294.070007,294.709991,294.709991,215200000
1987-05-08,294.730011,296.179993,291.730011,293.369995,293.369995,161900000
1987-05-11,293.369995,298.690002,291.549988,291.570007,291.570007,203700000
1987-05-12,291.570007,293.299988,290.179993,293.299988,293.299988,155300000
1987-05-13,293.309998,294.540009,290.739990,293.980011,293.980011,171000000
1987-05-14,293.980011,295.100006,292.950012,294.239990,294.239990,152000000
1987-05-15,294.230011,294.239990,287.109985,287.429993,287.429993,180800000
1987-05-18,287.429993,287.429993,282.570007,286.649994,286.649994,174200000
1987-05-19,286.660004,287.390015,278.829987,279.619995,279.619995,175400000
1987-05-20,279.619995,280.890015,277.010010,278.209991,278.209991,206800000
1987-05-21,278.230011,282.309998,278.209991,280.170013,280.170013,164800000
1987-05-22,280.170013,283.329987,280.170013,282.160004,282.160004,135800000
1987-05-26,282.160004,289.109985,282.160004,289.109985,289.109985,152500000
1987-05-27,289.070007,290.779999,288.190002,288.730011,288.730011,171400000
1987-05-28,288.730011,291.500000,286.329987,290.760010,290.760010,153800000
1987-05-29,290.769989,292.869995,289.700012,290.100006,290.100006,153500000
1987-06-01,290.119995,291.959991,289.230011,289.829987,289.829987,149300000
1987-06-02,289.820007,290.940002,286.929993,288.459991,288.459991,153400000
1987-06-03,288.559998,293.470001,288.559998,293.470001,293.470001,164200000
1987-06-04,293.459991,295.089996,292.760010,295.089996,295.089996,140300000
1987-06-05,295.109985,295.109985,292.799988,293.450012,293.450012,129100000
1987-06-08,293.459991,297.029999,291.549988,296.720001,296.720001,136400000
1987-06-09,296.720001,297.589996,295.899994,297.279999,297.279999,164200000
1987-06-10,297.279999,300.809998,295.660004,297.470001,297.470001,197400000
1987-06-11,297.500000,298.940002,297.470001,298.730011,298.730011,138900000
1987-06-12,298.769989,302.260010,298.730011,301.619995,301.619995,175100000
1987-06-15,301.619995,304.109985,301.619995,303.140015,303.140015,156900000
1987-06-16,303.119995,304.859985,302.600006,304.760010,304.760010,157800000
1987-06-17,304.769989,305.739990,304.029999,304.809998,304.809998,184700000
1987-06-18,304.779999,306.130005,303.380005,305.690002,305.690002,168600000
1987-06-19,305.709991,306.970001,305.549988,306.970001,306.970001,220500000
1987-06-22,306.980011,310.200012,306.970001,309.649994,309.649994,178200000
1987-06-23,309.660004,310.269989,307.480011,308.429993,308.429993,194200000
1987-06-24,308.440002,308.910004,306.320007,306.859985,306.859985,153800000
1987-06-25,306.869995,309.440002,306.859985,308.959991,308.959991,173500000
1987-06-26,308.940002,308.959991,306.359985,307.160004,307.160004,150500000
1987-06-29,307.149994,308.149994,306.750000,307.899994,307.899994,142500000
1987-06-30,307.890015,308.000000,303.010010,304.000000,304.000000,165500000
1987-07-01,303.989990,304.000000,302.529999,302.940002,302.940002,157000000
1987-07-02,302.959991,306.339996,302.940002,305.630005,305.630005,154900000
1987-07-06,305.640015,306.750000,304.230011,304.920013,304.920013,155000000
1987-07-07,304.910004,308.630005,304.730011,307.399994,307.399994,200700000
1987-07-08,307.410004,308.480011,306.010010,308.290009,308.290009,207500000
1987-07-09,308.299988,309.559998,307.420013,307.519989,307.519989,195400000
1987-07-10,307.549988,308.399994,306.959991,308.369995,308.369995,172100000
1987-07-13,308.410004,308.410004,305.489990,307.630005,307.630005,152500000
1987-07-14,307.670013,310.690002,307.459991,310.679993,310.679993,185900000
1987-07-15,310.670013,312.079987,309.070007,310.420013,310.420013,202300000
1987-07-16,311.000000,312.829987,310.420013,312.700012,312.700012,210900000
1987-07-17,312.709991,314.589996,312.380005,314.589996,314.589996,210000000
1987-07-20,314.559998,314.589996,311.239990,311.390015,311.390015,168100000
1987-07-21,311.359985,312.410004,307.510010,308.549988,308.549988,186600000
1987-07-22,308.559998,309.119995,307.220001,308.470001,308.470001,174700000
1987-07-23,308.500000,309.630005,306.100006,307.809998,307.809998,163700000
1987-07-24,307.820007,309.279999,307.779999,309.269989,309.269989,158400000
1987-07-27,309.299988,310.700012,308.609985,310.649994,310.649994,152000000
1987-07-28,310.649994,312.329987,310.279999,312.329987,312.329987,172600000
1987-07-29,312.339996,315.649994,311.730011,315.649994,315.649994,196200000
1987-07-30,315.690002,318.529999,315.649994,318.049988,318.049988,208000000
1987-07-31,318.049988,318.850006,317.559998,318.660004,318.660004,181900000
1987-08-03,318.619995,320.260010,316.519989,317.570007,317.570007,207800000
1987-08-04,317.589996,318.250000,314.510010,316.230011,316.230011,166500000
1987-08-05,316.250000,319.739990,316.230011,318.450012,318.450012,192700000
1987-08-06,318.489990,322.089996,317.500000,322.089996,322.089996,192000000
1987-08-07,322.100006,324.149994,321.820007,323.000000,323.000000,212700000
1987-08-10,322.980011,328.000000,322.950012,328.000000,328.000000,187200000
1987-08-11,328.019989,333.399994,328.000000,333.329987,333.329987,278100000
1987-08-12,333.320007,334.570007,331.059998,332.390015,332.390015,235800000
1987-08-13,332.380005,335.519989,332.380005,334.649994,334.649994,217100000
1987-08-14,334.630005,336.079987,332.630005,333.989990,333.989990,196100000
1987-08-17,333.980011,335.429993,332.880005,334.109985,334.109985,166100000
1987-08-18,334.100006,334.109985,326.429993,329.250000,329.250000,198400000
1987-08-19,329.260010,329.890015,326.540009,329.829987,329.829987,180900000
1987-08-20,331.489990,335.190002,329.829987,334.839996,334.839996,196600000
1987-08-21,334.850006,336.369995,334.299988,335.899994,335.899994,189600000
1987-08-24,335.890015,335.899994,331.920013,333.329987,333.329987,149400000
1987-08-25,333.369995,337.890015,333.329987,336.769989,336.769989,213500000
1987-08-26,336.769989,337.390015,334.459991,334.570007,334.570007,196200000
1987-08-27,334.559998,334.570007,331.100006,331.380005,331.380005,163600000
1987-08-28,331.369995,331.380005,327.029999,327.040009,327.040009,156300000
1987-08-31,327.029999,330.089996,326.989990,329.799988,329.799988,165800000
1987-09-01,329.809998,332.179993,322.829987,323.399994,323.399994,193500000
1987-09-02,323.399994,324.529999,318.760010,321.679993,321.679993,199900000
1987-09-03,321.470001,324.290009,317.390015,320.209991,320.209991,165200000
1987-09-04,320.209991,322.029999,316.529999,316.700012,316.700012,129100000
1987-09-08,316.679993,316.700012,308.559998,313.559998,313.559998,242900000
1987-09-09,313.600006,315.410004,312.290009,313.920013,313.920013,164900000
1987-09-10,313.920013,317.589996,313.920013,317.130005,317.130005,179800000
1987-09-11,317.140015,322.450012,317.130005,321.980011,321.980011,178000000
1987-09-14,322.019989,323.809998,320.399994,323.079987,323.079987,154400000
1987-09-15,323.070007,323.079987,317.630005,317.739990,317.739990,136200000
1987-09-16,317.750000,319.500000,314.609985,314.859985,314.859985,195700000
1987-09-17,314.940002,316.079987,313.450012,314.929993,314.929993,150700000
1987-09-18,314.980011,316.989990,314.859985,314.859985,314.859985,188100000
1987-09-21,314.920013,317.660004,310.119995,310.540009,310.540009,170100000
1987-09-22,310.540009,319.510010,308.690002,319.500000,319.500000,209500000
1987-09-23,319.489990,321.829987,319.119995,321.190002,321.190002,220300000
1987-09-24,321.089996,322.010010,319.119995,319.720001,319.720001,162200000
1987-09-25,319.720001,320.549988,318.100006,320.160004,320.160004,138000000
1987-09-28,320.160004,325.329987,320.160004,323.200012,323.200012,188100000
1987-09-29,323.200012,324.630005,320.269989,321.690002,321.690002,173500000
1987-09-30,321.690002,322.529999,320.160004,321.829987,321.829987,183100000
1987-10-01,321.829987,327.339996,321.829987,327.329987,327.329987,193200000
1987-10-02,327.329987,328.940002,327.220001,328.070007,328.070007,189100000
1987-10-05,328.070007,328.570007,326.089996,328.079987,328.079987,159700000
1987-10-06,328.079987,328.079987,319.170013,319.220001,319.220001,175600000
1987-10-07,319.220001,319.390015,315.779999,318.540009,318.540009,186300000
1987-10-08,318.540009,319.339996,312.019989,314.160004,314.160004,198700000
1987-10-09,314.160004,315.040009,310.970001,311.070007,311.070007,158300000
1987-10-12,311.070007,311.070007,306.760010,309.390015,309.390015,141900000
1987-10-13,309.390015,314.529999,309.390015,314.519989,314.519989,172900000
1987-10-14,314.519989,314.519989,304.779999,305.230011,305.230011,207400000
1987-10-15,305.209991,305.230011,298.070007,298.079987,298.079987,263200000
1987-10-16,298.079987,298.920013,281.519989,282.700012,282.700012,338500000
1987-10-19,282.700012,282.700012,224.830002,224.839996,224.839996,604300000
1987-10-20,225.059998,245.619995,216.460007,236.830002,236.830002,608100000
1987-10-21,236.830002,259.269989,236.830002,258.380005,258.380005,449600000
1987-10-22,258.239990,258.380005,242.990005,248.250000,248.250000,392200000
1987-10-23,248.289993,250.699997,242.759995,248.220001,248.220001,245600000
1987-10-26,248.199997,248.220001,227.259995,227.669998,227.669998,308800000
1987-10-27,227.669998,237.809998,227.669998,233.190002,233.190002,260200000
1987-10-28,233.190002,238.580002,226.259995,233.279999,233.279999,279400000
1987-10-29,233.309998,246.690002,233.279999,244.770004,244.770004,258100000
1987-10-30,244.770004,254.039993,244.770004,251.789993,251.789993,303400000
1987-11-02,251.729996,255.750000,249.149994,255.750000,255.750000,176000000
1987-11-03,255.750000,255.750000,242.779999,250.820007,250.820007,227800000
1987-11-04,250.809998,251.000000,246.339996,248.960007,248.960007,202500000
1987-11-05,248.929993,256.089996,247.720001,254.479996,254.479996,226000000
1987-11-06,254.490005,257.209991,249.679993,250.410004,250.410004,228290000
1987-11-09,250.410004,250.410004,243.009995,243.169998,243.169998,160690000
1987-11-10,243.139999,243.169998,237.639999,239.000000,239.000000,184310000
1987-11-11,239.009995,243.860001,239.000000,241.899994,241.899994,147850000
1987-11-12,241.929993,249.899994,241.899994,248.520004,248.520004,206280000
1987-11-13,248.539993,249.419998,245.639999,245.639999,245.639999,174920000
1987-11-16,245.690002,249.539993,244.979996,246.759995,246.759995,164340000
1987-11-17,246.729996,246.759995,240.809998,243.039993,243.039993,148240000
1987-11-18,243.089996,245.550003,240.669998,245.550003,245.550003,158270000
1987-11-19,245.539993,245.550003,239.699997,240.050003,240.050003,157140000
1987-11-20,240.039993,242.009995,235.889999,242.000000,242.000000,189170000
1987-11-23,242.000000,242.990005,240.500000,242.990005,242.990005,143160000
1987-11-24,242.979996,247.899994,242.979996,246.389999,246.389999,199520000
1987-11-25,246.419998,246.539993,244.080002,244.100006,244.100006,139780000
1987-11-27,244.110001,244.119995,240.339996,240.339996,240.339996,86360000
1987-11-30,240.270004,240.339996,225.750000,230.300003,230.300003,268910000
1987-12-01,230.320007,234.020004,230.300003,232.000000,232.000000,149870000
1987-12-02,232.009995,234.559998,230.309998,233.449997,233.449997,148890000
1987-12-03,233.460007,233.899994,225.210007,225.210007,225.210007,204160000
1987-12-04,225.199997,225.770004,221.240005,223.919998,223.919998,184800000
1987-12-07,223.979996,228.770004,223.919998,228.759995,228.759995,146660000
1987-12-08,228.770004,234.919998,228.690002,234.910004,234.910004,227310000
1987-12-09,234.910004,240.089996,233.830002,238.889999,238.889999,231430000
1987-12-10,238.889999,240.050003,233.399994,233.570007,233.570007,188960000
1987-12-11,233.600006,235.479996,233.350006,235.320007,235.320007,151680000
1987-12-14,235.300003,242.339996,235.039993,242.190002,242.190002,187680000
1987-12-15,242.190002,245.589996,241.309998,242.809998,242.809998,214970000
1987-12-16,242.809998,248.110001,242.800003,248.080002,248.080002,193820000
1987-12-17,248.080002,248.600006,242.960007,242.979996,242.979996,191780000
1987-12-18,243.009995,249.179993,243.009995,249.160004,249.160004,276220000
1987-12-21,249.139999,250.250000,248.300003,249.539993,249.539993,161790000
1987-12-22,249.559998,249.970001,247.009995,249.949997,249.949997,192650000
1987-12-23,249.960007,253.350006,249.949997,253.160004,253.160004,203110000
1987-12-24,253.130005,253.160004,251.679993,252.029999,252.029999,108800000
1987-12-28,252.009995,252.020004,244.190002,245.570007,245.570007,131220000
1987-12-29,245.580002,245.880005,244.279999,244.589996,244.589996,111580000
1987-12-30,244.630005,248.059998,244.589996,247.860001,247.860001,149230000
1987-12-31,247.839996,247.860001,245.220001,247.080002,247.080002,170140000
1988-01-04,247.100006,256.440002,247.080002,255.940002,255.940002,181810000
1988-01-05,255.949997,261.779999,255.949997,258.630005,258.630005,209520000
1988-01-06,258.640015,259.790009,257.179993,258.890015,258.890015,169730000
1988-01-07,258.869995,261.320007,256.179993,261.070007,261.070007,175360000
1988-01-08,261.049988,261.070007,242.949997,243.399994,243.399994,197300000
1988-01-11,243.380005,247.509995,241.070007,247.490005,247.490005,158980000
1988-01-12,247.440002,247.490005,240.460007,245.419998,245.419998,165730000
1988-01-13,245.410004,249.250000,241.410004,245.809998,245.809998,154020000
1988-01-14,245.830002,247.000000,243.970001,245.880005,245.880005,140570000
1988-01-15,246.020004,253.649994,245.880005,252.050003,252.050003,197940000
1988-01-18,252.050003,252.860001,249.979996,251.880005,251.880005,135100000
1988-01-19,251.839996,253.330002,248.750000,249.320007,249.320007,153550000
1988-01-20,249.309998,249.320007,241.139999,242.630005,242.630005,181660000
1988-01-21,242.649994,244.250000,240.169998,243.139999,243.139999,158080000
1988-01-22,243.139999,246.500000,243.139999,246.500000,246.500000,147050000
1988-01-25,246.529999,252.869995,246.500000,252.169998,252.169998,275250000
1988-01-26,252.130005,252.169998,249.100006,249.570007,249.570007,138380000
1988-01-27,249.580002,253.020004,248.500000,249.380005,249.380005,176360000
1988-01-28,249.389999,253.660004,249.380005,253.289993,253.289993,166430000
1988-01-29,253.309998,257.070007,252.699997,257.070007,257.070007,211880000
1988-02-01,257.049988,258.269989,254.929993,255.039993,255.039993,210660000
1988-02-02,255.050003,256.079987,252.800003,255.570007,255.570007,164920000
1988-02-03,255.559998,256.980011,250.559998,252.210007,252.210007,237270000
1988-02-04,252.199997,253.029999,250.339996,252.210007,252.210007,186490000
1988-02-05,252.220001,253.850006,250.899994,250.960007,250.960007,161310000
1988-02-08,250.949997,250.960007,247.820007,249.100006,249.100006,168850000
1988-02-09,249.110001,251.720001,248.660004,251.720001,251.720001,162350000
1988-02-10,251.740005,256.920013,251.720001,256.660004,256.660004,187980000
1988-02-11,256.630005,257.769989,255.119995,255.949997,255.949997,200760000
1988-02-12,255.949997,258.859985,255.850006,257.630005,257.630005,177190000
1988-02-16,257.609985,259.839996,256.570007,259.829987,259.829987,135380000
1988-02-17,259.940002,261.470001,257.829987,259.209991,259.209991,176830000
1988-02-18,258.820007,259.600006,256.899994,257.910004,257.910004,151430000
1988-02-19,257.899994,261.609985,257.619995,261.609985,261.609985,180300000
1988-02-22,261.600006,266.059998,260.880005,265.640015,265.640015,178930000
1988-02-23,265.619995,266.119995,263.109985,265.019989,265.019989,192260000
1988-02-24,265.010010,266.250000,263.869995,264.429993,264.429993,212730000
1988-02-25,264.390015,267.750000,261.049988,261.579987,261.579987,213490000
1988-02-26,261.559998,263.000000,261.380005,262.459991,262.459991,158060000
1988-02-29,262.459991,267.820007,262.459991,267.820007,267.820007,236050000
1988-03-01,267.820007,267.950012,265.390015,267.220001,267.220001,199990000
1988-03-02,267.230011,268.750000,267.000000,267.980011,267.980011,199630000
1988-03-03,267.980011,268.399994,266.820007,267.880005,267.880005,203310000
1988-03-04,267.869995,268.399994,264.720001,267.299988,267.299988,201410000
1988-03-07,267.279999,267.690002,265.940002,267.380005,267.380005,152980000
1988-03-08,267.380005,270.059998,267.380005,269.429993,269.429993,237680000
1988-03-09,269.459991,270.760010,268.649994,269.059998,269.059998,210900000
1988-03-10,269.070007,269.350006,263.799988,263.839996,263.839996,197260000
1988-03-11,263.850006,264.940002,261.269989,264.940002,264.940002,200020000
1988-03-14,264.929993,266.549988,264.519989,266.369995,266.369995,131890000
1988-03-15,266.339996,266.410004,264.920013,266.130005,266.130005,133170000
1988-03-16,266.109985,268.679993,264.809998,268.649994,268.649994,153590000
1988-03-17,268.660004,271.220001,268.649994,271.220001,271.220001,211920000
1988-03-18,271.220001,272.640015,269.760010,271.119995,271.119995,245750000
1988-03-21,271.100006,271.119995,267.420013,268.739990,268.739990,128830000
1988-03-22,268.730011,269.609985,267.899994,268.839996,268.839996,142000000
1988-03-23,268.809998,269.790009,268.010010,268.910004,268.910004,167370000
1988-03-24,268.910004,268.910004,262.480011,263.350006,263.350006,184910000
1988-03-25,263.339996,263.440002,258.119995,258.510010,258.510010,163170000
1988-03-28,258.500000,258.510010,256.070007,258.059998,258.059998,142820000
1988-03-29,258.109985,260.859985,258.059998,260.070007,260.070007,152690000
1988-03-30,260.059998,261.589996,257.920013,258.070007,258.070007,151810000
1988-03-31,258.029999,259.029999,256.160004,258.890015,258.890015,139870000
1988-04-04,258.890015,259.059998,255.679993,256.089996,256.089996,182240000
1988-04-05,256.100006,258.519989,256.029999,258.510010,258.510010,135290000
1988-04-06,258.519989,265.500000,258.220001,265.489990,265.489990,189760000
1988-04-07,265.510010,267.320007,265.220001,266.160004,266.160004,177840000
1988-04-08,266.149994,270.220001,266.109985,269.429993,269.429993,169300000
1988-04-11,269.429993,270.410004,268.609985,270.160004,270.160004,146370000
1988-04-12,269.880005,272.049988,269.660004,271.369995,271.369995,146400000
1988-04-13,271.329987,271.700012,269.230011,271.579987,271.579987,185120000
1988-04-14,271.549988,271.570007,259.369995,259.750000,259.750000,211810000
1988-04-15,259.739990,260.390015,255.970001,259.769989,259.769989,234160000
1988-04-18,259.750000,259.809998,258.029999,259.209991,259.209991,144650000
1988-04-19,259.239990,262.380005,257.910004,257.920013,257.920013,161910000
1988-04-20,257.910004,258.540009,256.119995,256.130005,256.130005,147590000
1988-04-21,256.149994,260.440002,254.710007,256.420013,256.420013,168440000
1988-04-22,256.450012,261.160004,256.420013,260.140015,260.140015,152520000
1988-04-25,260.149994,263.290009,260.140015,262.510010,262.510010,156950000
1988-04-26,262.450012,265.059998,262.179993,263.929993,263.929993,152300000
1988-04-27,263.940002,265.089996,263.450012,263.799988,263.799988,133810000
1988-04-28,263.790009,263.799988,262.220001,262.609985,262.609985,128680000
1988-04-29,262.589996,262.609985,259.970001,261.329987,261.329987,135620000
1988-05-02,261.359985,261.559998,259.989990,261.559998,261.559998,136470000
1988-05-03,261.549988,263.700012,261.549988,263.000000,263.000000,176920000
1988-05-04,263.049988,263.230011,260.309998,260.320007,260.320007,141320000
1988-05-05,260.299988,260.320007,258.130005,258.790009,258.790009,171840000
1988-05-06,258.799988,260.309998,257.029999,257.480011,257.480011,129080000
1988-05-09,257.470001,258.220001,255.449997,256.540009,256.540009,166320000
1988-05-10,256.529999,258.299988,255.929993,257.619995,257.619995,131200000
1988-05-11,257.600006,257.619995,252.320007,253.309998,253.309998,176720000
1988-05-12,253.320007,254.869995,253.309998,253.850006,253.850006,143880000
1988-05-13,253.880005,256.829987,253.850006,256.779999,256.779999,147240000
1988-05-16,256.750000,258.709991,256.279999,258.709991,258.709991,155010000
1988-05-17,258.720001,260.200012,255.350006,255.389999,255.389999,133850000
1988-05-18,255.399994,255.669998,250.729996,251.350006,251.350006,209420000
1988-05-19,251.360001,252.570007,248.850006,252.570007,252.570007,165160000
1988-05-20,252.610001,253.699997,251.789993,253.020004,253.020004,120600000
1988-05-23,253.000000,253.020004,249.820007,250.830002,250.830002,102640000
1988-05-24,250.839996,253.509995,250.830002,253.509995,253.509995,139930000
1988-05-25,253.520004,255.339996,253.509995,253.759995,253.759995,138310000
1988-05-26,253.750000,254.979996,253.520004,254.630005,254.630005,164260000
1988-05-27,254.619995,254.630005,252.740005,253.419998,253.419998,133590000
1988-05-31,253.440002,262.160004,253.419998,262.160004,262.160004,247610000
1988-06-01,262.160004,267.429993,262.100006,266.690002,266.690002,234560000
1988-06-02,266.649994,266.709991,264.119995,265.329987,265.329987,193540000
1988-06-03,265.339996,267.109985,264.420013,266.450012,266.450012,189600000
1988-06-06,266.459991,267.049988,264.970001,267.049988,267.049988,152460000
1988-06-07,267.019989,267.279999,264.500000,265.170013,265.170013,168710000
1988-06-08,265.320007,272.010010,265.170013,271.519989,271.519989,310030000
1988-06-09,271.500000,272.290009,270.190002,270.200012,270.200012,235160000
1988-06-10,270.220001,273.209991,270.200012,271.260010,271.260010,155710000
1988-06-13,271.279999,271.940002,270.529999,271.429993,271.429993,125310000
1988-06-14,271.579987,276.140015,271.440002,274.299988,274.299988,227150000
1988-06-15,274.290009,274.450012,272.750000,274.450012,274.450012,150260000
1988-06-16,274.440002,274.450012,268.760010,269.769989,269.769989,161550000
1988-06-17,269.790009,270.769989,268.089996,270.679993,270.679993,343920000
1988-06-20,270.670013,270.679993,268.589996,268.940002,268.940002,116750000
1988-06-21,268.950012,271.670013,267.519989,271.670013,271.670013,155060000
1988-06-22,271.690002,276.880005,271.670013,275.660004,275.660004,217510000
1988-06-23,275.619995,275.890015,274.260010,274.820007,274.820007,185770000
1988-06-24,274.809998,275.190002,273.529999,273.779999,273.779999,179880000
1988-06-27,273.779999,273.790009,268.850006,269.059998,269.059998,264410000
1988-06-28,269.070007,272.799988,269.059998,272.309998,272.309998,152370000
1988-06-29,272.320007,273.010010,269.489990,270.980011,270.980011,159590000
1988-06-30,271.000000,273.510010,270.970001,273.500000,273.500000,227410000
1988-07-01,273.500000,273.799988,270.779999,271.779999,271.779999,238330000
1988-07-05,271.779999,275.809998,270.510010,275.809998,275.809998,171790000
1988-07-06,275.799988,276.359985,269.920013,272.019989,272.019989,189630000
1988-07-07,272.000000,272.049988,269.309998,271.779999,271.779999,156100000
1988-07-08,271.760010,272.309998,269.859985,270.019989,270.019989,136070000
1988-07-11,270.029999,271.640015,270.019989,270.549988,270.549988,123300000
1988-07-12,270.540009,270.700012,266.959991,267.850006,267.850006,161650000
1988-07-13,267.869995,269.459991,266.119995,269.320007,269.320007,218930000
1988-07-14,269.329987,270.690002,268.579987,270.260010,270.260010,172410000
1988-07-15,270.230011,272.059998,269.529999,272.049988,272.049988,199710000
1988-07-18,271.989990,272.049988,268.660004,270.510010,270.510010,156210000
1988-07-19,270.489990,271.209991,267.010010,268.470001,268.470001,144110000
1988-07-20,268.519989,270.239990,268.470001,270.000000,270.000000,151990000
1988-07-21,269.989990,270.000000,266.660004,266.660004,266.660004,149460000
1988-07-22,266.649994,266.660004,263.290009,263.500000,263.500000,148880000
1988-07-25,263.489990,265.170013,263.029999,264.679993,264.679993,215140000
1988-07-26,264.700012,266.089996,264.320007,265.190002,265.190002,121960000
1988-07-27,265.179993,265.829987,262.480011,262.500000,262.500000,135890000
1988-07-28,262.519989,266.549988,262.500000,266.019989,266.019989,154570000
1988-07-29,266.040009,272.019989,266.019989,272.019989,272.019989,192340000
1988-08-01,272.029999,272.799988,271.209991,272.209991,272.209991,138170000
1988-08-02,272.190002,273.679993,270.369995,272.059998,272.059998,166660000
1988-08-03,272.029999,273.420013,271.149994,272.980011,272.980011,203590000
1988-08-04,273.000000,274.200012,271.769989,271.929993,271.929993,157240000
1988-08-05,271.700012,271.929993,270.079987,271.149994,271.149994,113400000
1988-08-08,271.130005,272.470001,269.929993,269.980011,269.980011,148800000
1988-08-09,270.000000,270.200012,265.059998,266.489990,266.489990,200710000
1988-08-10,266.429993,266.489990,261.029999,261.899994,261.899994,200950000
1988-08-11,261.920013,262.769989,260.339996,262.750000,262.750000,173000000
1988-08-12,262.700012,262.940002,261.369995,262.549988,262.549988,176960000
1988-08-15,262.489990,262.549988,258.679993,258.690002,258.690002,128560000
1988-08-16,258.679993,262.609985,257.500000,260.559998,260.559998,162790000
1988-08-17,260.570007,261.839996,259.329987,260.769989,260.769989,169500000
1988-08-18,260.760010,262.760010,260.750000,261.029999,261.029999,139820000
1988-08-19,261.049988,262.269989,260.230011,260.239990,260.239990,122370000
1988-08-22,260.239990,260.709991,256.940002,256.980011,256.980011,122250000
1988-08-23,256.989990,257.859985,256.529999,257.089996,257.089996,119540000
1988-08-24,257.160004,261.130005,257.089996,261.130005,261.130005,127800000
1988-08-25,261.100006,261.130005,257.559998,259.179993,259.179993,127640000
1988-08-26,259.179993,260.149994,258.869995,259.679993,259.679993,89240000
1988-08-29,259.679993,262.559998,259.679993,262.329987,262.329987,99280000
1988-08-30,262.329987,263.179993,261.529999,262.510010,262.510010,108720000
1988-08-31,262.510010,263.799988,261.209991,261.519989,261.519989,130480000
1988-09-01,261.519989,261.519989,256.980011,258.350006,258.350006,144090000
1988-09-02,258.350006,264.899994,258.350006,264.480011,264.480011,159840000
1988-09-06,264.420013,265.940002,264.399994,265.589996,265.589996,122250000
1988-09-07,265.619995,266.980011,264.929993,265.869995,265.869995,139590000
1988-09-08,265.869995,266.540009,264.880005,265.880005,265.880005,149380000
1988-09-09,265.880005,268.260010,263.660004,266.839996,266.839996,141540000
1988-09-12,266.850006,267.640015,266.220001,266.470001,266.470001,114880000
1988-09-13,266.450012,267.429993,265.220001,267.429993,267.429993,162490000
1988-09-14,267.500000,269.470001,267.410004,269.309998,269.309998,177220000
1988-09-15,269.299988,269.779999,268.029999,268.130005,268.130005,161210000
1988-09-16,268.130005,270.809998,267.329987,270.649994,270.649994,211110000
1988-09-19,270.640015,270.649994,267.410004,268.820007,268.820007,135770000
1988-09-20,268.829987,270.070007,268.500000,269.730011,269.730011,142220000
1988-09-21,269.760010,270.640015,269.480011,270.160004,270.160004,127400000
1988-09-22,270.190002,270.579987,268.260010,269.179993,269.179993,150670000
1988-09-23,269.160004,270.309998,268.279999,269.760010,269.760010,145100000
1988-09-26,269.769989,269.799988,268.609985,268.880005,268.880005,116420000
1988-09-27,268.890015,269.359985,268.010010,268.260010,268.260010,113010000
1988-09-28,268.220001,269.079987,267.769989,269.079987,269.079987,113720000
1988-09-29,269.089996,273.019989,269.079987,272.589996,272.589996,155790000
1988-09-30,272.549988,274.869995,271.660004,271.910004,271.910004,175750000
1988-10-03,271.890015,271.910004,268.839996,271.380005,271.380005,130380000
1988-10-04,271.369995,271.790009,270.339996,270.619995,270.619995,157760000
1988-10-05,270.630005,272.450012,270.079987,271.859985,271.859985,175130000
1988-10-06,271.869995,272.390015,271.299988,272.390015,272.390015,153570000
1988-10-07,272.380005,278.070007,272.369995,278.070007,278.070007,216390000
1988-10-10,278.059998,278.690002,277.100006,278.239990,278.239990,124660000
1988-10-11,278.149994,278.239990,276.329987,277.929993,277.929993,140900000
1988-10-12,277.910004,277.929993,273.049988,273.980011,273.980011,154840000
1988-10-13,273.950012,275.829987,273.390015,275.220001,275.220001,154530000
1988-10-14,275.269989,277.010010,274.079987,275.500000,275.500000,160240000
1988-10-17,275.480011,276.649994,275.010010,276.410004,276.410004,119290000
1988-10-18,276.429993,279.390015,276.410004,279.380005,279.380005,162500000
1988-10-19,279.399994,280.529999,274.410004,276.970001,276.970001,186350000
1988-10-20,276.970001,282.880005,276.929993,282.880005,282.880005,189580000
1988-10-21,282.880005,283.660004,281.160004,283.660004,283.660004,195410000
1988-10-24,283.630005,283.950012,282.279999,282.279999,282.279999,170590000
1988-10-25,282.279999,282.839996,281.869995,282.380005,282.380005,155190000
1988-10-26,282.369995,282.519989,280.540009,281.380005,281.380005,181550000
1988-10-27,281.350006,281.380005,276.000000,277.279999,277.279999,196540000
1988-10-28,277.290009,279.480011,277.279999,278.529999,278.529999,146300000
1988-10-31,278.540009,279.390015,277.140015,278.970001,278.970001,143460000
1988-11-01,278.970001,279.570007,278.010010,279.059998,279.059998,151250000
1988-11-02,279.070007,279.450012,277.079987,279.059998,279.059998,161300000
1988-11-03,279.040009,280.369995,279.040009,279.200012,279.200012,152980000
1988-11-04,279.109985,279.200012,276.309998,276.309998,276.309998,143580000
1988-11-07,276.299988,276.309998,273.619995,273.929993,273.929993,133870000
1988-11-08,273.950012,275.799988,273.929993,275.149994,275.149994,141660000
1988-11-09,275.140015,275.149994,272.149994,273.329987,273.329987,153140000
1988-11-10,273.320007,274.369995,272.980011,273.690002,273.690002,128920000
1988-11-11,273.649994,273.690002,267.920013,267.920013,267.920013,135500000
1988-11-14,267.929993,269.250000,266.790009,267.720001,267.720001,142900000
1988-11-15,267.730011,268.750000,267.720001,268.339996,268.339996,115170000
1988-11-16,268.410004,268.410004,262.850006,263.820007,263.820007,161710000
1988-11-17,264.609985,265.630005,263.450012,264.600006,264.600006,141280000
1988-11-18,264.600006,266.619995,264.600006,266.470001,266.470001,119320000
1988-11-21,266.350006,266.470001,263.410004,266.220001,266.220001,120430000
1988-11-22,266.190002,267.850006,265.420013,267.209991,267.209991,127000000
1988-11-23,267.220001,269.559998,267.209991,269.000000,269.000000,112010000
1988-11-25,268.989990,269.000000,266.470001,267.230011,267.230011,72090000
1988-11-28,267.220001,268.980011,266.970001,268.640015,268.640015,123480000
1988-11-29,268.600006,271.309998,268.130005,270.910004,270.910004,127420000
1988-11-30,270.910004,274.359985,270.899994,273.700012,273.700012,157810000
1988-12-01,273.679993,273.700012,272.269989,272.489990,272.489990,129380000
1988-12-02,272.489990,272.489990,270.470001,271.809998,271.809998,124610000
1988-12-05,274.929993,275.619995,271.809998,274.929993,274.929993,144660000
1988-12-06,274.929993,277.890015,274.619995,277.589996,277.589996,158340000
1988-12-07,277.589996,279.010010,277.339996,278.130005,278.130005,148360000
1988-12-08,278.130005,278.130005,276.549988,276.589996,276.589996,124150000
1988-12-09,276.570007,277.820007,276.339996,277.029999,277.029999,133770000
1988-12-12,277.029999,278.820007,276.519989,276.519989,276.519989,124160000
1988-12-13,276.519989,276.519989,274.579987,276.309998,276.309998,132340000
1988-12-14,276.309998,276.309998,274.579987,275.309998,275.309998,132350000
1988-12-15,275.320007,275.619995,274.010010,274.279999,274.279999,136820000
1988-12-16,274.279999,276.290009,274.279999,276.290009,276.290009,196480000
1988-12-19,276.290009,279.309998,275.609985,278.910004,278.910004,162250000
1988-12-20,278.910004,280.450012,277.470001,277.470001,277.470001,161090000
1988-12-21,277.470001,277.829987,276.299988,277.380005,277.380005,147250000
1988-12-22,277.380005,277.890015,276.859985,276.869995,276.869995,150510000
1988-12-23,276.869995,277.989990,276.869995,277.869995,277.869995,81760000
1988-12-27,277.869995,278.089996,276.739990,276.829987,276.829987,87490000
1988-12-28,276.829987,277.549988,276.170013,277.079987,277.079987,110630000
1988-12-29,277.079987,279.420013,277.079987,279.399994,279.399994,131290000
1988-12-30,279.390015,279.779999,277.720001,277.720001,277.720001,127210000
1989-01-03,277.720001,277.720001,273.809998,275.309998,275.309998,128500000
1989-01-04,275.309998,279.750000,275.309998,279.429993,279.429993,149700000
1989-01-05,279.429993,281.510010,279.429993,280.010010,280.010010,174040000
1989-01-06,280.010010,282.059998,280.010010,280.670013,280.670013,161330000
1989-01-09,280.670013,281.890015,280.320007,280.980011,280.980011,163180000
1989-01-10,280.980011,281.579987,279.440002,280.380005,280.380005,140420000
1989-01-11,280.380005,282.010010,280.209991,282.010010,282.010010,148950000
1989-01-12,282.010010,284.630005,282.010010,283.170013,283.170013,183000000
1989-01-13,283.170013,284.119995,282.709991,283.869995,283.869995,132320000
1989-01-16,283.869995,284.880005,283.630005,284.140015,284.140015,117380000
1989-01-17,284.140015,284.140015,283.059998,283.549988,283.549988,143930000
1989-01-18,283.549988,286.869995,282.649994,286.529999,286.529999,187450000
1989-01-19,286.529999,287.899994,286.140015,286.910004,286.910004,192030000
1989-01-20,286.899994,287.040009,285.750000,286.630005,286.630005,166120000
1989-01-23,287.850006,287.980011,284.500000,284.500000,284.500000,141640000
1989-01-24,284.500000,288.489990,284.500000,288.489990,288.489990,189620000
1989-01-25,288.489990,289.149994,287.970001,289.140015,289.140015,183610000
1989-01-26,289.140015,292.619995,288.130005,291.690002,291.690002,212250000
1989-01-27,291.690002,296.079987,291.690002,293.820007,293.820007,254870000
1989-01-30,293.820007,295.130005,293.540009,294.989990,294.989990,167830000
1989-01-31,294.989990,297.510010,293.570007,297.470001,297.470001,194050000
1989-02-01,297.470001,298.329987,296.220001,297.089996,297.089996,215640000
1989-02-02,297.089996,297.920013,295.809998,296.839996,296.839996,183430000
1989-02-03,296.839996,297.660004,296.149994,296.970001,296.970001,172980000
1989-02-06,296.970001,296.989990,294.959991,296.040009,296.040009,150980000
1989-02-07,296.040009,300.339996,295.779999,299.630005,299.630005,217260000
1989-02-08,299.619995,300.570007,298.410004,298.649994,298.649994,189420000
1989-02-09,298.649994,298.790009,295.160004,296.059998,296.059998,224220000
1989-02-10,296.059998,296.059998,291.959991,292.019989,292.019989,173560000
1989-02-13,292.019989,293.070007,290.880005,292.540009,292.540009,143520000
1989-02-14,292.540009,294.369995,291.410004,291.809998,291.809998,150610000
1989-02-15,291.809998,294.420013,291.489990,294.239990,294.239990,154220000
1989-02-16,294.239990,295.149994,294.220001,294.809998,294.809998,177450000
1989-02-17,294.809998,297.119995,294.690002,296.760010,296.760010,159520000
1989-02-21,296.760010,297.040009,295.160004,295.980011,295.980011,141950000
1989-02-22,295.980011,295.980011,290.760010,290.910004,290.910004,163140000
1989-02-23,290.910004,292.049988,289.829987,292.049988,292.049988,150370000
1989-02-24,292.049988,292.049988,287.130005,287.130005,287.130005,160680000
1989-02-27,287.130005,288.119995,286.260010,287.820007,287.820007,139900000
1989-02-28,287.820007,289.420013,287.630005,288.859985,288.859985,147430000
1989-03-01,288.859985,290.279999,286.459991,287.109985,287.109985,177210000
1989-03-02,287.109985,290.320007,287.109985,289.950012,289.950012,161980000
1989-03-03,289.940002,291.179993,289.440002,291.179993,291.179993,151790000
1989-03-06,291.200012,294.809998,291.179993,294.809998,294.809998,168880000
1989-03-07,294.809998,295.160004,293.500000,293.869995,293.869995,172500000
1989-03-08,293.869995,295.619995,293.510010,294.079987,294.079987,167620000
1989-03-09,294.079987,294.690002,293.850006,293.929993,293.929993,143160000
1989-03-10,293.929993,293.929993,291.600006,292.880005,292.880005,146830000
1989-03-13,292.880005,296.179993,292.880005,295.320007,295.320007,140460000
1989-03-14,295.320007,296.290009,294.630005,295.140015,295.140015,139970000
1989-03-15,295.140015,296.779999,295.140015,296.670013,296.670013,167070000
1989-03-16,296.670013,299.989990,296.660004,299.440002,299.440002,196040000
1989-03-17,299.440002,299.440002,291.079987,292.690002,292.690002,242900000
1989-03-20,292.690002,292.690002,288.559998,289.920013,289.920013,151260000
1989-03-21,289.920013,292.380005,289.920013,291.329987,291.329987,142010000
1989-03-22,291.329987,291.459991,289.899994,290.489990,290.489990,146570000
1989-03-23,290.489990,291.510010,288.559998,288.980011,288.980011,153750000
1989-03-27,288.980011,290.570007,288.070007,290.570007,290.570007,112960000
1989-03-28,290.570007,292.320007,290.570007,291.589996,291.589996,146420000
1989-03-29,291.589996,292.750000,291.420013,292.350006,292.350006,144240000
1989-03-30,292.350006,293.799988,291.500000,292.519989,292.519989,159950000
1989-03-31,292.519989,294.959991,292.519989,294.869995,294.869995,170960000
1989-04-03,294.869995,297.040009,294.619995,296.390015,296.390015,164660000
1989-04-04,296.399994,296.399994,294.720001,295.309998,295.309998,160680000
1989-04-05,295.309998,296.429993,295.279999,296.239990,296.239990,165880000
1989-04-06,296.220001,296.239990,294.519989,295.290009,295.290009,146530000
1989-04-07,295.290009,297.619995,294.350006,297.160004,297.160004,156950000
1989-04-10,297.160004,297.940002,296.850006,297.109985,297.109985,123990000
1989-04-11,297.109985,298.869995,297.109985,298.489990,298.489990,146830000
1989-04-12,298.489990,299.809998,298.489990,298.989990,298.989990,165200000
1989-04-13,298.989990,299.000000,296.269989,296.399994,296.399994,141590000
1989-04-14,296.399994,301.380005,296.399994,301.359985,301.359985,169780000
1989-04-17,301.359985,302.010010,300.709991,301.720001,301.720001,128540000
1989-04-18,301.720001,306.250000,301.720001,306.019989,306.019989,208650000
1989-04-19,306.019989,307.679993,305.359985,307.149994,307.149994,191510000
1989-04-20,307.149994,307.959991,304.529999,306.190002,306.190002,175970000
1989-04-21,306.190002,309.609985,306.190002,309.609985,309.609985,187310000
1989-04-24,309.609985,309.609985,307.829987,308.690002,308.690002,142100000
1989-04-25,308.690002,309.649994,306.739990,306.750000,306.750000,165430000
1989-04-26,306.779999,307.299988,306.070007,306.929993,306.929993,146090000
1989-04-27,306.929993,310.450012,306.929993,309.579987,309.579987,191170000
1989-04-28,309.579987,309.649994,308.480011,309.640015,309.640015,158390000
1989-05-01,309.640015,309.640015,307.399994,309.119995,309.119995,138050000
1989-05-02,309.130005,310.450012,308.119995,308.119995,308.119995,172560000
1989-05-03,308.119995,308.519989,307.109985,308.160004,308.160004,171690000
1989-05-04,308.160004,308.399994,307.320007,307.769989,307.769989,153130000
1989-05-05,307.769989,310.690002,306.980011,307.609985,307.609985,180810000
1989-05-08,307.609985,307.609985,304.739990,306.000000,306.000000,135130000
1989-05-09,306.000000,306.989990,304.059998,305.190002,305.190002,150090000
1989-05-10,305.190002,306.250000,304.850006,305.799988,305.799988,146000000
1989-05-11,305.799988,307.339996,305.799988,306.950012,306.950012,151620000
1989-05-12,306.950012,313.839996,306.950012,313.839996,313.839996,221490000
1989-05-15,313.839996,316.160004,313.839996,316.160004,316.160004,179350000
1989-05-16,316.160004,316.160004,314.989990,315.279999,315.279999,173100000
1989-05-17,315.279999,317.940002,315.109985,317.480011,317.480011,191210000
1989-05-18,317.480011,318.519989,316.540009,317.970001,317.970001,177480000
1989-05-19,317.970001,321.380005,317.970001,321.239990,321.239990,242410000
1989-05-22,321.239990,323.059998,320.450012,321.980011,321.980011,185010000
1989-05-23,321.980011,321.980011,318.200012,318.320007,318.320007,187690000
1989-05-24,318.320007,319.140015,317.579987,319.140015,319.140015,178600000
1989-05-25,319.140015,319.600006,318.420013,319.170013,319.170013,154470000
1989-05-26,319.170013,321.589996,319.140015,321.589996,321.589996,143120000
1989-05-30,321.589996,322.529999,317.829987,319.049988,319.049988,151780000
1989-05-31,319.049988,321.299988,318.679993,320.519989,320.519989,162530000
1989-06-01,320.510010,322.570007,320.010010,321.970001,321.970001,223160000
1989-06-02,321.970001,325.630005,321.970001,325.519989,325.519989,229140000
1989-06-05,325.519989,325.929993,322.019989,322.029999,322.029999,163420000
1989-06-06,322.029999,324.480011,321.269989,324.239990,324.239990,187570000
1989-06-07,324.239990,327.390015,324.239990,326.950012,326.950012,213710000
1989-06-08,326.950012,327.369995,325.920013,326.750000,326.750000,212310000
1989-06-09,326.750000,327.320007,325.160004,326.690002,326.690002,173240000
1989-06-12,326.690002,326.690002,323.730011,326.239990,326.239990,151460000
1989-06-13,326.239990,326.239990,322.959991,323.910004,323.910004,164870000
1989-06-14,323.910004,324.890015,322.799988,323.829987,323.829987,170540000
1989-06-15,323.829987,323.829987,319.209991,320.079987,320.079987,179480000
1989-06-16,319.959991,321.359985,318.690002,321.350006,321.350006,244510000
1989-06-19,321.350006,321.890015,320.399994,321.890015,321.890015,130720000
1989-06-20,321.890015,322.779999,321.029999,321.250000,321.250000,167650000
1989-06-21,321.250000,321.869995,319.250000,320.480011,320.480011,168830000
1989-06-22,320.480011,322.339996,320.200012,322.320007,322.320007,176510000
1989-06-23,322.320007,328.000000,322.320007,328.000000,328.000000,198720000
1989-06-26,328.000000,328.149994,326.309998,326.600006,326.600006,143600000
1989-06-27,326.600006,329.190002,326.589996,328.440002,328.440002,171090000
1989-06-28,328.440002,328.440002,324.299988,325.809998,325.809998,158470000
1989-06-29,325.809998,325.809998,319.540009,319.679993,319.679993,167100000
1989-06-30,319.670013,319.970001,314.380005,317.980011,317.980011,170490000
1989-07-03,317.980011,319.269989,317.269989,319.230011,319.230011,68870000
1989-07-05,319.230011,321.220001,317.260010,320.640015,320.640015,127710000
1989-07-06,320.640015,321.549988,320.450012,321.549988,321.549988,140450000
1989-07-07,321.549988,325.869995,321.079987,324.910004,324.910004,166430000
1989-07-10,324.929993,327.070007,324.910004,327.070007,327.070007,131870000
1989-07-11,327.070007,330.420013,327.070007,328.779999,328.779999,171590000
1989-07-12,328.779999,330.390015,327.920013,329.809998,329.809998,160550000
1989-07-13,329.809998,330.369995,329.079987,329.950012,329.950012,153820000
1989-07-14,329.959991,331.890015,327.130005,331.839996,331.839996,183480000
1989-07-17,331.779999,333.019989,331.019989,332.440002,332.440002,131960000
1989-07-18,332.420013,332.440002,330.750000,331.350006,331.350006,152350000
1989-07-19,331.369995,335.730011,331.350006,335.730011,335.730011,215740000
1989-07-20,335.739990,337.399994,333.220001,333.510010,333.510010,204590000
1989-07-21,333.500000,335.910004,332.459991,335.899994,335.899994,174880000
1989-07-24,335.899994,335.899994,333.440002,333.670013,333.670013,136260000
1989-07-25,333.670013,336.290009,332.600006,333.880005,333.880005,179270000
1989-07-26,333.880005,338.049988,333.190002,338.049988,338.049988,188270000
1989-07-27,338.049988,342.000000,338.049988,341.989990,341.989990,213680000
1989-07-28,341.940002,342.959991,341.299988,342.149994,342.149994,180610000
1989-07-31,342.130005,346.079987,342.019989,346.079987,346.079987,166650000
1989-08-01,346.079987,347.989990,342.929993,343.750000,343.750000,225280000
1989-08-02,343.750000,344.339996,342.470001,344.339996,344.339996,181760000
1989-08-03,344.339996,345.220001,343.809998,344.739990,344.739990,168690000
1989-08-04,344.739990,345.420013,342.600006,343.920013,343.920013,169750000
1989-08-07,343.920013,349.420013,343.910004,349.410004,349.410004,197580000
1989-08-08,349.410004,349.839996,348.279999,349.350006,349.350006,200340000
1989-08-09,349.299988,351.000000,346.859985,346.940002,346.940002,209900000
1989-08-10,346.940002,349.779999,345.309998,348.250000,348.250000,198660000
1989-08-11,348.279999,351.179993,344.010010,344.739990,344.739990,197550000
1989-08-14,344.709991,345.440002,341.959991,343.059998,343.059998,142010000
1989-08-15,343.059998,345.029999,343.049988,344.709991,344.709991,148770000
1989-08-16,344.709991,346.369995,344.709991,345.660004,345.660004,150060000
1989-08-17,345.660004,346.390015,342.970001,344.450012,344.450012,157560000
1989-08-18,344.450012,346.029999,343.890015,346.029999,346.029999,145810000
1989-08-21,346.029999,346.250000,340.549988,340.670013,340.670013,136800000
1989-08-22,340.670013,341.250000,339.000000,341.190002,341.190002,141930000
1989-08-23,341.190002,344.799988,341.190002,344.700012,344.700012,159640000
1989-08-24,344.700012,351.519989,344.700012,351.519989,351.519989,225520000
1989-08-25,351.519989,352.730011,350.089996,350.519989,350.519989,165930000
1989-08-28,350.519989,352.089996,349.079987,352.089996,352.089996,131180000
1989-08-29,352.089996,352.119995,348.859985,349.839996,349.839996,175210000
1989-08-30,349.839996,352.269989,348.660004,350.649994,350.649994,174350000
1989-08-31,350.649994,351.450012,350.209991,351.450012,351.450012,144820000
1989-09-01,351.450012,353.899994,350.880005,353.730011,353.730011,133300000
1989-09-05,353.730011,354.130005,351.820007,352.559998,352.559998,145180000
1989-09-06,352.559998,352.559998,347.980011,349.239990,349.239990,161800000
1989-09-07,349.239990,350.309998,348.149994,348.350006,348.350006,160160000
1989-09-08,348.350006,349.179993,345.739990,348.760010,348.760010,154090000
1989-09-11,348.760010,348.760010,345.910004,347.660004,347.660004,126020000
1989-09-12,347.660004,349.459991,347.500000,348.700012,348.700012,142140000
1989-09-13,348.700012,350.100006,345.459991,345.459991,345.459991,175330000
1989-09-14,345.459991,345.609985,342.549988,343.160004,343.160004,149250000
1989-09-15,343.160004,345.059998,341.369995,345.059998,345.059998,234860000
1989-09-18,345.059998,346.839996,344.600006,346.730011,346.730011,136940000
1989-09-19,346.730011,348.170013,346.440002,346.549988,346.549988,141610000
1989-09-20,346.549988,347.269989,346.179993,346.470001,346.470001,136640000
1989-09-21,346.470001,348.459991,344.959991,345.700012,345.700012,146930000
1989-09-22,345.700012,347.570007,345.690002,347.049988,347.049988,133350000
1989-09-25,347.049988,347.049988,343.700012,344.230011,344.230011,121130000
1989-09-26,344.230011,347.019989,344.130005,344.329987,344.329987,158350000
1989-09-27,344.329987,345.470001,342.850006,345.100006,345.100006,158400000
1989-09-28,345.100006,348.609985,345.100006,348.600006,348.600006,164240000
1989-09-29,348.600006,350.309998,348.119995,349.149994,349.149994,155300000
1989-10-02,349.149994,350.989990,348.350006,350.869995,350.869995,127410000
1989-10-03,350.869995,354.730011,350.850006,354.709991,354.709991,182550000
1989-10-04,354.709991,357.489990,354.709991,356.940002,356.940002,194590000
1989-10-05,356.940002,357.630005,356.279999,356.970001,356.970001,177890000
1989-10-06,356.970001,359.049988,356.970001,358.779999,358.779999,172520000
1989-10-09,358.760010,359.859985,358.059998,359.799988,359.799988,86810000
1989-10-10,359.799988,360.440002,358.109985,359.130005,359.130005,147560000
1989-10-11,359.130005,359.130005,356.079987,356.989990,356.989990,164070000
1989-10-12,356.989990,356.989990,354.910004,355.390015,355.390015,160120000
1989-10-13,355.390015,355.529999,332.809998,333.649994,333.649994,251170000
1989-10-16,333.649994,342.869995,327.119995,342.850006,342.850006,416290000
1989-10-17,342.839996,342.850006,335.690002,341.160004,341.160004,224070000
1989-10-18,341.160004,343.390015,339.029999,341.760010,341.760010,166900000
1989-10-19,341.760010,348.820007,341.760010,347.130005,347.130005,198120000
1989-10-20,347.040009,347.570007,344.470001,347.160004,347.160004,164830000
1989-10-23,347.109985,348.190002,344.220001,344.829987,344.829987,135860000
1989-10-24,344.829987,344.829987,335.130005,343.700012,343.700012,237960000
1989-10-25,343.700012,344.510010,341.959991,342.500000,342.500000,155650000
1989-10-26,342.500000,342.500000,337.200012,337.929993,337.929993,175240000
1989-10-27,337.929993,337.970001,333.260010,335.059998,335.059998,170330000
1989-10-30,335.059998,337.040009,334.480011,335.070007,335.070007,126630000
1989-10-31,335.079987,340.859985,335.070007,340.359985,340.359985,176100000
1989-11-01,340.359985,341.739990,339.790009,341.200012,341.200012,154240000
1989-11-02,341.200012,341.200012,336.609985,338.480011,338.480011,152440000
1989-11-03,338.480011,339.670013,337.369995,337.619995,337.619995,131500000
1989-11-06,337.609985,337.619995,332.329987,332.609985,332.609985,135480000
1989-11-07,332.609985,334.820007,330.910004,334.809998,334.809998,163000000
1989-11-08,334.809998,339.410004,334.809998,338.149994,338.149994,170150000
1989-11-09,338.149994,338.730011,336.209991,336.570007,336.570007,143390000
1989-11-10,336.570007,339.100006,336.570007,339.100006,339.100006,131800000
1989-11-13,339.079987,340.510010,337.929993,339.549988,339.549988,140750000
1989-11-14,339.549988,340.410004,337.059998,337.989990,337.989990,143170000
1989-11-15,338.000000,340.540009,337.140015,340.540009,340.540009,155130000
1989-11-16,340.540009,341.019989,338.929993,340.579987,340.579987,148370000
1989-11-17,340.579987,342.239990,339.850006,341.609985,341.609985,151020000
1989-11-20,341.609985,341.899994,338.290009,339.350006,339.350006,128170000
1989-11-21,339.350006,340.209991,337.529999,339.589996,339.589996,147900000
1989-11-22,339.589996,341.920013,339.589996,341.910004,341.910004,145730000
1989-11-24,341.920013,344.239990,341.910004,343.970001,343.970001,86290000
1989-11-27,343.980011,346.239990,343.970001,345.609985,345.609985,149390000
1989-11-28,345.609985,346.329987,344.410004,345.769989,345.769989,153770000
1989-11-29,345.769989,345.769989,343.359985,343.600006,343.600006,147270000
1989-11-30,343.600006,346.500000,343.570007,345.989990,345.989990,153200000
1989-12-01,346.010010,351.880005,345.989990,350.630005,350.630005,199200000
1989-12-04,350.630005,351.510010,350.320007,351.410004,351.410004,150360000
1989-12-05,351.410004,352.239990,349.579987,349.579987,349.579987,154640000
1989-12-06,349.579987,349.940002,347.910004,348.549988,348.549988,145850000
1989-12-07,348.549988,349.839996,346.000000,347.589996,347.589996,161980000
1989-12-08,347.600006,349.600006,347.589996,348.690002,348.690002,144910000
1989-12-11,348.679993,348.739990,346.390015,348.559998,348.559998,147130000
1989-12-12,348.559998,352.209991,348.410004,351.730011,351.730011,176820000
1989-12-13,351.700012,354.100006,351.649994,352.750000,352.750000,184660000
1989-12-14,352.739990,352.750000,350.079987,350.929993,350.929993,178700000
1989-12-15,350.970001,351.859985,346.079987,350.140015,350.140015,240390000
1989-12-18,350.140015,350.880005,342.190002,343.690002,343.690002,184750000
1989-12-19,343.690002,343.739990,339.630005,342.459991,342.459991,186060000
1989-12-20,342.500000,343.700012,341.790009,342.839996,342.839996,176520000
1989-12-21,342.839996,345.029999,342.839996,344.779999,344.779999,175150000
1989-12-22,344.779999,347.529999,344.760010,347.420013,347.420013,120980000
1989-12-26,347.420013,347.869995,346.529999,346.809998,346.809998,77610000
1989-12-27,346.839996,349.119995,346.809998,348.809998,348.809998,133740000
1989-12-28,348.799988,350.679993,348.760010,350.670013,350.670013,128030000
1989-12-29,350.679993,353.410004,350.670013,353.399994,353.399994,145940000
1990-01-02,353.399994,359.690002,351.980011,359.690002,359.690002,162070000
1990-01-03,359.690002,360.589996,357.890015,358.760010,358.760010,192330000
1990-01-04,358.760010,358.760010,352.890015,355.670013,355.670013,177000000
1990-01-05,355.670013,355.670013,351.350006,352.200012,352.200012,158530000
1990-01-08,352.200012,354.239990,350.540009,353.790009,353.790009,140110000
1990-01-09,353.829987,354.170013,349.609985,349.619995,349.619995,155210000
1990-01-10,349.619995,349.619995,344.320007,347.309998,347.309998,175990000
1990-01-11,347.309998,350.140015,347.309998,348.529999,348.529999,154390000
1990-01-12,348.529999,348.529999,339.489990,339.929993,339.929993,183880000
1990-01-15,339.929993,339.940002,336.570007,337.000000,337.000000,140590000
1990-01-16,337.000000,340.750000,333.369995,340.750000,340.750000,186070000
1990-01-17,340.769989,342.010010,336.260010,337.399994,337.399994,170470000
1990-01-18,337.399994,338.380005,333.980011,338.190002,338.190002,178590000
1990-01-19,338.190002,340.480011,338.190002,339.149994,339.149994,185590000
1990-01-22,339.140015,339.959991,330.279999,330.380005,330.380005,148380000
1990-01-23,330.380005,332.760010,328.670013,331.609985,331.609985,179300000
1990-01-24,331.609985,331.709991,324.170013,330.260010,330.260010,207830000
1990-01-25,330.260010,332.329987,325.329987,326.079987,326.079987,172270000
1990-01-26,326.089996,328.579987,321.440002,325.799988,325.799988,198190000
1990-01-29,325.799988,327.309998,321.790009,325.200012,325.200012,150770000
1990-01-30,325.200012,325.730011,319.829987,322.980011,322.980011,186030000
1990-01-31,322.980011,329.079987,322.980011,329.079987,329.079987,189660000
1990-02-01,329.079987,329.859985,327.760010,328.790009,328.790009,154580000
1990-02-02,328.790009,332.100006,328.089996,330.920013,330.920013,164400000
1990-02-05,330.920013,332.160004,330.450012,331.850006,331.850006,130950000
1990-02-06,331.850006,331.859985,328.200012,329.660004,329.660004,134070000
1990-02-07,329.660004,333.760010,326.549988,333.750000,333.750000,186710000
1990-02-08,333.750000,336.089996,332.000000,332.959991,332.959991,176240000
1990-02-09,333.019989,334.600006,332.410004,333.619995,333.619995,146910000
1990-02-12,333.619995,333.619995,329.970001,330.079987,330.079987,118390000
1990-02-13,330.079987,331.609985,327.920013,331.019989,331.019989,144490000
1990-02-14,331.019989,333.200012,330.640015,332.010010,332.010010,138530000
1990-02-15,332.010010,335.209991,331.609985,334.890015,334.890015,174620000
1990-02-16,334.890015,335.640015,332.420013,332.720001,332.720001,166840000
1990-02-20,332.720001,332.720001,326.260010,327.989990,327.989990,147300000
1990-02-21,327.910004,328.170013,324.470001,327.670013,327.670013,159240000
1990-02-22,327.670013,330.980011,325.700012,325.700012,325.700012,184320000
1990-02-23,325.700012,326.149994,322.100006,324.149994,324.149994,148490000
1990-02-26,324.160004,328.670013,323.980011,328.670013,328.670013,148900000
1990-02-27,328.679993,331.940002,328.470001,330.260010,330.260010,152590000
1990-02-28,330.260010,333.480011,330.160004,331.890015,331.890015,184400000
1990-03-01,331.890015,334.399994,331.079987,332.739990,332.739990,157930000
1990-03-02,332.739990,335.540009,332.720001,335.540009,335.540009,164330000
1990-03-05,335.540009,336.380005,333.489990,333.739990,333.739990,140110000
1990-03-06,333.739990,337.929993,333.570007,337.929993,337.929993,143640000
1990-03-07,337.929993,338.839996,336.329987,336.950012,336.950012,163580000
1990-03-08,336.950012,340.660004,336.950012,340.269989,340.269989,170900000
1990-03-09,340.119995,340.269989,336.839996,337.929993,337.929993,150410000
1990-03-12,337.929993,339.079987,336.140015,338.670013,338.670013,114790000
1990-03-13,338.670013,338.670013,335.359985,336.000000,336.000000,145440000
1990-03-14,336.000000,337.630005,334.929993,336.869995,336.869995,145060000
1990-03-15,336.869995,338.910004,336.869995,338.070007,338.070007,144410000
1990-03-16,338.070007,341.910004,338.070007,341.910004,341.910004,222520000
1990-03-19,341.910004,343.760010,339.119995,343.529999,343.529999,142300000
1990-03-20,343.529999,344.489990,340.869995,341.570007,341.570007,177320000
1990-03-21,341.570007,342.339996,339.559998,339.739990,339.739990,130990000
1990-03-22,339.739990,339.769989,333.619995,335.690002,335.690002,175930000
1990-03-23,335.690002,337.579987,335.690002,337.220001,337.220001,132070000
1990-03-26,337.220001,339.739990,337.220001,337.630005,337.630005,116110000
1990-03-27,337.630005,341.500000,337.029999,341.500000,341.500000,131610000
1990-03-28,341.500000,342.579987,340.600006,342.000000,342.000000,142300000
1990-03-29,342.000000,342.070007,339.769989,340.790009,340.790009,132190000
1990-03-30,340.790009,341.410004,338.209991,339.940002,339.940002,139340000
1990-04-02,339.940002,339.940002,336.329987,338.700012,338.700012,124360000
1990-04-03,338.700012,343.760010,338.700012,343.640015,343.640015,154310000
1990-04-04,343.640015,344.119995,340.399994,341.089996,341.089996,159530000
1990-04-05,341.089996,342.850006,340.630005,340.730011,340.730011,144170000
1990-04-06,340.730011,341.730011,338.940002,340.079987,340.079987,137490000
1990-04-09,340.079987,341.829987,339.880005,341.369995,341.369995,114970000
1990-04-10,341.369995,342.410004,340.619995,342.070007,342.070007,136020000
1990-04-11,342.070007,343.000000,341.260010,341.920013,341.920013,141080000
1990-04-12,341.920013,344.790009,341.910004,344.339996,344.339996,142470000
1990-04-16,344.339996,347.299988,344.100006,344.739990,344.739990,142810000
1990-04-17,344.739990,345.190002,342.059998,344.679993,344.679993,127990000
1990-04-18,344.679993,345.329987,340.109985,340.720001,340.720001,147130000
1990-04-19,340.720001,340.720001,337.589996,338.089996,338.089996,152930000
1990-04-20,338.089996,338.519989,333.410004,335.119995,335.119995,174260000
1990-04-23,335.119995,335.119995,330.089996,331.049988,331.049988,136150000
1990-04-24,331.049988,332.970001,329.709991,330.359985,330.359985,137360000
1990-04-25,330.359985,332.739990,330.359985,332.029999,332.029999,133480000
1990-04-26,332.029999,333.760010,330.670013,332.920013,332.920013,141330000
1990-04-27,332.920013,333.570007,328.709991,329.109985,329.109985,130630000
1990-04-30,329.109985,331.309998,327.760010,330.799988,330.799988,122750000
1990-05-01,330.799988,332.829987,330.799988,332.250000,332.250000,149020000
1990-05-02,332.250000,334.480011,332.149994,334.480011,334.480011,141610000
1990-05-03,334.480011,337.019989,334.470001,335.570007,335.570007,145560000
1990-05-04,335.579987,338.459991,335.170013,338.390015,338.390015,140550000
1990-05-07,338.390015,341.070007,338.109985,340.529999,340.529999,132760000
1990-05-08,340.529999,342.029999,340.170013,342.010010,342.010010,144230000
1990-05-09,342.010010,343.079987,340.899994,342.859985,342.859985,152220000
1990-05-10,342.869995,344.980011,342.769989,343.820007,343.820007,158460000
1990-05-11,343.820007,352.309998,343.820007,352.000000,352.000000,234040000
1990-05-14,352.000000,358.410004,351.950012,354.750000,354.750000,225410000
1990-05-15,354.750000,355.089996,352.839996,354.279999,354.279999,165730000
1990-05-16,354.269989,354.679993,351.950012,354.000000,354.000000,159810000
1990-05-17,354.000000,356.920013,354.000000,354.470001,354.470001,164770000
1990-05-18,354.470001,354.640015,352.519989,354.640015,354.640015,162520000
1990-05-21,354.640015,359.070007,353.779999,358.000000,358.000000,166280000
1990-05-22,358.000000,360.500000,356.089996,358.429993,358.429993,203350000
1990-05-23,358.429993,359.290009,356.989990,359.290009,359.290009,172330000
1990-05-24,359.290009,359.559998,357.869995,358.410004,358.410004,155140000
1990-05-25,358.410004,358.410004,354.320007,354.579987,354.579987,120250000
1990-05-29,354.579987,360.649994,354.549988,360.649994,360.649994,137410000
1990-05-30,360.649994,362.260010,360.000000,360.859985,360.859985,199540000
1990-05-31,360.859985,361.839996,360.230011,361.230011,361.230011,165690000
1990-06-01,361.260010,363.519989,361.209991,363.160004,363.160004,187860000
1990-06-04,363.160004,367.850006,362.429993,367.399994,367.399994,175520000
1990-06-05,367.399994,368.779999,365.489990,366.640015,366.640015,199720000
1990-06-06,366.640015,366.640015,364.420013,364.959991,364.959991,164030000
1990-06-07,365.920013,365.920013,361.600006,363.149994,363.149994,160360000
1990-06-08,363.149994,363.489990,357.679993,358.709991,358.709991,142600000
1990-06-11,358.709991,361.630005,357.700012,361.630005,361.630005,119550000
1990-06-12,361.630005,367.269989,361.149994,366.250000,366.250000,157100000
1990-06-13,366.250000,367.089996,364.510010,364.899994,364.899994,158910000
1990-06-14,364.899994,364.899994,361.640015,362.899994,362.899994,135770000
1990-06-15,362.890015,363.140015,360.709991,362.910004,362.910004,205130000
1990-06-18,362.910004,362.910004,356.880005,356.880005,356.880005,133470000
1990-06-19,356.880005,358.899994,356.179993,358.470001,358.470001,134930000
1990-06-20,358.470001,359.910004,357.000000,359.100006,359.100006,137420000
1990-06-21,359.100006,360.880005,357.630005,360.470001,360.470001,138570000
1990-06-22,360.519989,363.200012,355.309998,355.429993,355.429993,172570000
1990-06-25,355.420013,356.410004,351.910004,352.309998,352.309998,133100000
1990-06-26,352.320007,356.089996,351.850006,352.059998,352.059998,141420000
1990-06-27,352.059998,355.890015,351.230011,355.140015,355.140015,146620000
1990-06-28,355.160004,357.630005,355.160004,357.630005,357.630005,136120000
1990-06-29,357.640015,359.089996,357.299988,358.019989,358.019989,145510000
1990-07-02,358.019989,359.579987,357.540009,359.540009,359.540009,130200000
1990-07-03,359.540009,360.730011,359.440002,360.160004,360.160004,130050000
1990-07-05,360.160004,360.160004,354.859985,355.679993,355.679993,128320000
1990-07-06,355.690002,359.019989,354.640015,358.420013,358.420013,111730000
1990-07-09,358.420013,360.049988,358.109985,359.519989,359.519989,119390000
1990-07-10,359.519989,359.739990,356.410004,356.489990,356.489990,147630000
1990-07-11,356.489990,361.230011,356.489990,361.230011,361.230011,162220000
1990-07-12,361.230011,365.459991,360.570007,365.440002,365.440002,213180000
1990-07-13,365.450012,369.679993,365.450012,367.309998,367.309998,215600000
1990-07-16,367.309998,369.779999,367.309998,368.950012,368.950012,149430000
1990-07-17,368.950012,369.399994,364.989990,367.519989,367.519989,176790000
1990-07-18,367.519989,367.519989,362.950012,364.220001,364.220001,168760000
1990-07-19,364.220001,365.320007,361.290009,365.320007,365.320007,161990000
1990-07-20,365.320007,366.640015,361.579987,361.609985,361.609985,177810000
1990-07-23,361.609985,361.609985,350.089996,355.309998,355.309998,209030000
1990-07-24,355.309998,356.089996,351.459991,355.790009,355.790009,181920000
1990-07-25,355.790009,357.519989,354.799988,357.089996,357.089996,163530000
1990-07-26,357.089996,357.470001,353.950012,355.910004,355.910004,155040000
1990-07-27,355.899994,355.940002,352.140015,353.440002,353.440002,149070000
1990-07-30,353.440002,355.549988,351.149994,355.549988,355.549988,146470000
1990-07-31,355.549988,357.540009,353.910004,356.149994,356.149994,175380000
1990-08-01,356.149994,357.350006,353.820007,355.519989,355.519989,178260000
1990-08-02,355.519989,355.519989,349.730011,351.480011,351.480011,253090000
1990-08-03,351.480011,351.480011,338.200012,344.859985,344.859985,295880000
1990-08-06,344.859985,344.859985,333.269989,334.429993,334.429993,240400000
1990-08-07,334.429993,338.630005,332.220001,334.829987,334.829987,231580000
1990-08-08,334.829987,339.209991,334.829987,338.350006,338.350006,190400000
1990-08-09,338.350006,340.559998,337.559998,339.940002,339.940002,155810000
1990-08-10,339.899994,339.899994,334.220001,335.519989,335.519989,145340000
1990-08-13,335.390015,338.880005,332.019989,338.839996,338.839996,122820000
1990-08-14,338.839996,340.959991,337.190002,339.390015,339.390015,130320000
1990-08-15,339.390015,341.920013,339.380005,340.059998,340.059998,136710000
1990-08-16,340.059998,340.059998,332.390015,332.390015,332.390015,138850000
1990-08-17,332.359985,332.359985,324.630005,327.829987,327.829987,212560000
1990-08-20,327.829987,329.899994,327.070007,328.510010,328.510010,129630000
1990-08-21,328.510010,328.510010,318.779999,321.859985,321.859985,194630000
1990-08-22,321.859985,324.149994,316.549988,316.549988,316.549988,175550000
1990-08-23,316.549988,316.549988,306.559998,307.059998,307.059998,250440000
1990-08-24,307.059998,311.649994,306.179993,311.510010,311.510010,199040000
1990-08-27,311.549988,323.109985,311.549988,321.440002,321.440002,160150000
1990-08-28,321.440002,322.200012,320.250000,321.339996,321.339996,127660000
1990-08-29,321.339996,325.829987,320.869995,324.190002,324.190002,134240000
1990-08-30,324.190002,324.570007,317.820007,318.709991,318.709991,120890000
1990-08-31,318.709991,322.570007,316.589996,322.559998,322.559998,96480000
1990-09-04,322.559998,323.089996,319.109985,323.089996,323.089996,92940000
1990-09-05,323.089996,324.519989,320.989990,324.390015,324.390015,120610000
1990-09-06,324.390015,324.390015,319.369995,320.459991,320.459991,125620000
1990-09-07,320.459991,324.179993,319.709991,323.399994,323.399994,123800000
1990-09-10,323.420013,326.529999,320.309998,321.630005,321.630005,119730000
1990-09-11,321.630005,322.179993,319.600006,321.040009,321.040009,113220000
1990-09-12,321.040009,322.549988,319.600006,322.540009,322.540009,129890000
1990-09-13,322.510010,322.510010,318.019989,318.649994,318.649994,123390000
1990-09-14,318.649994,318.649994,314.760010,316.829987,316.829987,133390000
1990-09-17,316.829987,318.049988,315.209991,317.769989,317.769989,110600000
1990-09-18,317.769989,318.850006,314.269989,318.600006,318.600006,141130000
1990-09-19,318.600006,319.350006,316.250000,316.600006,316.600006,147530000
1990-09-20,316.600006,316.600006,310.549988,311.480011,311.480011,145100000
1990-09-21,311.529999,312.170013,307.980011,311.320007,311.320007,201050000
1990-09-24,311.299988,311.299988,303.579987,304.589996,304.589996,164070000
1990-09-25,305.459991,308.269989,304.230011,308.260010,308.260010,155940000
1990-09-26,308.260010,308.279999,303.049988,305.059998,305.059998,155570000
1990-09-27,305.059998,307.470001,299.100006,300.970001,300.970001,182690000
1990-09-28,300.970001,306.049988,295.980011,306.049988,306.049988,201010000
1990-10-01,306.100006,314.940002,306.100006,314.940002,314.940002,202210000
1990-10-02,314.940002,319.690002,314.940002,315.209991,315.209991,188360000
1990-10-03,315.209991,316.260010,310.700012,311.399994,311.399994,135490000
1990-10-04,311.399994,313.399994,308.589996,312.690002,312.690002,145410000
1990-10-05,312.690002,314.790009,305.760010,311.500000,311.500000,153380000
1990-10-08,311.500000,315.029999,311.500000,313.480011,313.480011,99470000
1990-10-09,313.459991,313.459991,305.089996,305.100006,305.100006,145610000
1990-10-10,305.089996,306.429993,299.209991,300.390015,300.390015,169190000
1990-10-11,300.390015,301.450012,294.510010,295.459991,295.459991,180060000
1990-10-12,295.450012,301.679993,295.220001,300.029999,300.029999,187940000
1990-10-15,300.029999,304.790009,296.410004,303.230011,303.230011,164980000
1990-10-16,303.230011,304.339996,298.119995,298.920013,298.920013,149570000
1990-10-17,298.920013,301.500000,297.790009,298.760010,298.760010,161260000
1990-10-18,298.750000,305.739990,298.750000,305.739990,305.739990,204110000
1990-10-19,305.739990,312.480011,305.739990,312.480011,312.480011,221480000
1990-10-22,312.480011,315.829987,310.470001,314.760010,314.760010,152650000
1990-10-23,314.760010,315.059998,312.059998,312.359985,312.359985,146300000
1990-10-24,312.359985,313.510010,310.739990,312.600006,312.600006,149290000
1990-10-25,312.600006,313.709991,309.700012,310.170013,310.170013,141460000
1990-10-26,310.170013,310.170013,304.709991,304.709991,304.709991,130190000
1990-10-29,304.739990,307.410004,300.690002,301.880005,301.880005,133980000
1990-10-30,301.880005,304.359985,299.440002,304.059998,304.059998,153450000
1990-10-31,304.059998,305.700012,302.329987,304.000000,304.000000,156060000
1990-11-01,303.989990,307.269989,301.609985,307.019989,307.019989,159270000
1990-11-02,307.019989,311.940002,306.880005,311.850006,311.850006,168700000
1990-11-05,311.850006,314.609985,311.410004,314.589996,314.589996,147510000
1990-11-06,314.589996,314.760010,311.429993,311.619995,311.619995,142660000
1990-11-07,311.619995,311.619995,305.790009,306.010010,306.010010,149130000
1990-11-08,306.010010,309.769989,305.029999,307.609985,307.609985,155570000
1990-11-09,307.609985,313.779999,307.609985,313.739990,313.739990,145160000
1990-11-12,313.739990,319.769989,313.730011,319.480011,319.480011,161390000
1990-11-13,319.480011,319.480011,317.260010,317.670013,317.670013,160240000
1990-11-14,317.660004,321.700012,317.230011,320.399994,320.399994,179310000
1990-11-15,320.399994,320.399994,316.130005,317.019989,317.019989,151370000
1990-11-16,317.019989,318.799988,314.989990,317.119995,317.119995,165440000
1990-11-19,317.149994,319.390015,317.149994,319.339996,319.339996,140950000
1990-11-20,319.339996,319.339996,315.309998,315.309998,315.309998,161170000
1990-11-21,315.309998,316.149994,312.420013,316.029999,316.029999,140660000
1990-11-23,316.029999,317.299988,315.059998,315.100006,315.100006,63350000
1990-11-26,315.079987,316.510010,311.480011,316.510010,316.510010,131540000
1990-11-27,316.510010,318.690002,315.799988,318.100006,318.100006,147590000
1990-11-28,318.109985,319.959991,317.619995,317.950012,317.950012,145490000
1990-11-29,317.950012,317.950012,315.029999,316.420013,316.420013,140920000
1990-11-30,316.420013,323.019989,315.420013,322.220001,322.220001,192350000
1990-12-03,322.230011,324.899994,322.230011,324.100006,324.100006,177000000
1990-12-04,324.109985,326.769989,321.970001,326.350006,326.350006,185820000
1990-12-05,326.359985,329.920013,325.660004,329.920013,329.920013,205820000
1990-12-06,329.940002,333.980011,328.369995,329.070007,329.070007,256380000
1990-12-07,329.089996,329.390015,326.390015,327.750000,327.750000,164950000
1990-12-10,327.750000,328.970001,326.149994,328.890015,328.890015,138650000
1990-12-11,328.880005,328.880005,325.649994,326.440002,326.440002,145330000
1990-12-12,326.440002,330.359985,326.440002,330.190002,330.190002,182270000
1990-12-13,330.140015,330.579987,328.769989,329.339996,329.339996,162110000
1990-12-14,329.339996,329.339996,325.160004,326.820007,326.820007,151010000
1990-12-17,326.820007,326.820007,324.459991,326.019989,326.019989,118560000
1990-12-18,326.019989,330.429993,325.750000,330.049988,330.049988,176460000
1990-12-19,330.040009,330.799988,329.390015,330.200012,330.200012,180380000
1990-12-20,330.200012,330.739990,326.940002,330.119995,330.119995,174700000
1990-12-21,330.119995,332.470001,330.119995,331.750000,331.750000,233400000
1990-12-24,331.739990,331.739990,329.160004,329.899994,329.899994,57200000
1990-12-26,329.890015,331.690002,329.890015,330.850006,330.850006,78730000
1990-12-27,330.850006,331.040009,328.230011,328.290009,328.290009,102900000
1990-12-28,328.290009,328.720001,327.239990,328.720001,328.720001,111030000
1990-12-31,328.709991,330.230011,327.500000,330.220001,330.220001,114130000
1991-01-02,330.200012,330.750000,326.450012,326.450012,326.450012,126280000
1991-01-03,326.459991,326.529999,321.899994,321.910004,321.910004,141450000
1991-01-04,321.910004,322.350006,318.869995,321.000000,321.000000,140820000
1991-01-07,320.970001,320.970001,315.440002,315.440002,315.440002,130610000
1991-01-08,315.440002,316.970001,313.790009,314.899994,314.899994,143390000
1991-01-09,314.899994,320.730011,310.929993,311.489990,311.489990,191100000
1991-01-10,311.510010,314.769989,311.510010,314.529999,314.529999,124510000
1991-01-11,314.529999,315.239990,313.589996,315.230011,315.230011,123050000
1991-01-14,315.230011,315.230011,309.350006,312.489990,312.489990,120830000
1991-01-15,312.489990,313.730011,311.839996,313.730011,313.730011,110000000
1991-01-16,313.730011,316.940002,312.940002,316.170013,316.170013,134560000
1991-01-17,316.250000,327.970001,316.250000,327.970001,327.970001,319080000
1991-01-18,327.929993,332.230011,327.079987,332.230011,332.230011,226770000
1991-01-21,332.230011,332.230011,328.869995,331.059998,331.059998,136290000
1991-01-22,331.059998,331.260010,327.829987,328.309998,328.309998,177060000
1991-01-23,328.299988,331.040009,327.929993,330.209991,330.209991,168620000
1991-01-24,330.209991,335.829987,330.190002,334.779999,334.779999,223150000
1991-01-25,334.779999,336.920013,334.200012,336.070007,336.070007,194350000
1991-01-28,336.059998,337.410004,335.809998,336.029999,336.029999,141270000
1991-01-29,336.029999,336.029999,334.260010,335.839996,335.839996,155740000
1991-01-30,335.799988,340.910004,335.709991,340.910004,340.910004,226790000
1991-01-31,340.920013,343.929993,340.470001,343.929993,343.929993,204520000
1991-02-01,343.910004,344.899994,340.369995,343.049988,343.049988,246670000
1991-02-04,343.049988,348.709991,342.959991,348.339996,348.339996,250750000
1991-02-05,348.339996,351.839996,347.209991,351.260010,351.260010,290570000
1991-02-06,351.260010,358.070007,349.579987,358.070007,358.070007,276940000
1991-02-07,358.070007,363.429993,355.529999,356.519989,356.519989,292190000
1991-02-08,356.519989,359.350006,356.019989,359.350006,359.350006,187830000
1991-02-11,359.359985,368.579987,359.320007,368.579987,368.579987,265350000
1991-02-12,368.579987,370.540009,365.500000,365.500000,365.500000,256160000
1991-02-13,365.500000,369.489990,364.640015,369.019989,369.019989,209960000
1991-02-14,369.019989,370.260010,362.769989,364.220001,364.220001,230750000
1991-02-15,364.230011,369.489990,364.230011,369.059998,369.059998,228480000
1991-02-19,369.059998,370.109985,367.049988,369.390015,369.390015,189900000
1991-02-20,369.369995,369.369995,364.380005,365.140015,365.140015,185680000
1991-02-21,365.140015,366.790009,364.500000,364.970001,364.970001,180770000
1991-02-22,364.970001,370.959991,364.230011,365.649994,365.649994,218760000
1991-02-25,365.649994,370.190002,365.160004,367.260010,367.260010,193820000
1991-02-26,367.260010,367.260010,362.190002,362.809998,362.809998,164170000
1991-02-27,362.809998,368.380005,362.809998,367.739990,367.739990,211410000
1991-02-28,367.730011,369.910004,365.950012,367.070007,367.070007,223010000
1991-03-01,367.070007,370.470001,363.730011,370.470001,370.470001,221510000
1991-03-04,370.470001,371.989990,369.070007,369.329987,369.329987,199830000
1991-03-05,369.329987,377.890015,369.329987,376.720001,376.720001,253700000
1991-03-06,376.720001,379.660004,375.019989,376.170013,376.170013,262290000
1991-03-07,376.160004,377.489990,375.579987,375.910004,375.910004,197060000
1991-03-08,375.910004,378.690002,374.429993,374.950012,374.950012,206850000
1991-03-11,374.940002,375.100006,372.519989,372.959991,372.959991,161600000
1991-03-12,372.959991,374.350006,369.549988,370.029999,370.029999,176440000
1991-03-13,370.029999,374.649994,370.029999,374.570007,374.570007,176000000
1991-03-14,374.589996,378.279999,371.760010,373.500000,373.500000,232070000
1991-03-15,373.500000,374.579987,370.209991,373.589996,373.589996,237650000
1991-03-18,373.589996,374.089996,369.459991,372.109985,372.109985,163100000
1991-03-19,372.109985,372.109985,366.540009,366.589996,366.589996,177070000
1991-03-20,366.589996,368.850006,365.799988,367.920013,367.920013,196810000
1991-03-21,367.940002,371.010010,366.510010,366.579987,366.579987,199830000
1991-03-22,366.579987,368.220001,365.579987,367.480011,367.480011,160890000
1991-03-25,367.480011,371.309998,367.459991,369.829987,369.829987,153920000
1991-03-26,369.829987,376.299988,369.369995,376.299988,376.299988,198720000
1991-03-27,376.279999,378.480011,374.730011,375.350006,375.350006,201830000
1991-03-28,375.350006,376.600006,374.399994,375.220001,375.220001,150750000
1991-04-01,375.220001,375.220001,370.269989,371.299988,371.299988,144010000
1991-04-02,371.299988,379.500000,371.299988,379.500000,379.500000,189530000
1991-04-03,379.500000,381.559998,378.489990,378.940002,378.940002,213720000
1991-04-04,378.940002,381.880005,377.049988,379.769989,379.769989,198120000
1991-04-05,379.779999,381.119995,374.149994,375.359985,375.359985,187410000
1991-04-08,375.350006,378.760010,374.690002,378.660004,378.660004,138580000
1991-04-09,378.649994,379.019989,373.109985,373.559998,373.559998,169940000
1991-04-10,373.570007,374.829987,371.209991,373.149994,373.149994,167940000
1991-04-11,373.149994,379.529999,373.149994,377.630005,377.630005,196570000
1991-04-12,377.649994,381.070007,376.890015,380.399994,380.399994,198610000
1991-04-15,380.399994,382.320007,378.779999,381.190002,381.190002,161800000
1991-04-16,381.190002,387.619995,379.640015,387.619995,387.619995,214480000
1991-04-17,387.619995,391.260010,387.299988,390.450012,390.450012,246930000
1991-04-18,390.450012,390.970001,388.130005,388.459991,388.459991,217410000
1991-04-19,388.459991,388.459991,383.899994,384.200012,384.200012,195520000
1991-04-22,384.190002,384.190002,380.160004,380.950012,380.950012,164410000
1991-04-23,380.950012,383.549988,379.670013,381.760010,381.760010,167840000
1991-04-24,381.760010,383.019989,379.989990,382.760010,382.760010,166800000
1991-04-25,382.890015,382.890015,378.429993,379.250000,379.250000,166940000
1991-04-26,379.250000,380.109985,376.769989,379.019989,379.019989,154550000
1991-04-29,379.010010,380.959991,373.660004,373.660004,373.660004,149860000
1991-04-30,373.660004,377.859985,373.010010,375.339996,375.339996,206230000
1991-05-01,375.350006,380.459991,375.269989,380.290009,380.290009,181900000
1991-05-02,380.290009,382.140015,379.820007,380.519989,380.519989,187090000
1991-05-03,380.519989,381.000000,378.820007,380.799988,380.799988,158150000
1991-05-06,380.779999,380.779999,377.859985,380.079987,380.079987,129110000
1991-05-07,380.079987,380.910004,377.309998,377.320007,377.320007,153290000
1991-05-08,377.329987,379.260010,376.209991,378.510010,378.510010,157240000
1991-05-09,378.510010,383.559998,378.510010,383.250000,383.250000,180460000
1991-05-10,383.260010,383.910004,375.609985,375.739990,375.739990,172730000
1991-05-13,375.739990,377.019989,374.619995,376.760010,376.760010,129620000
1991-05-14,375.510010,375.529999,370.820007,371.619995,371.619995,207890000
1991-05-15,371.549988,372.470001,365.829987,368.570007,368.570007,193110000
1991-05-16,368.570007,372.510010,368.570007,372.190002,372.190002,154460000
1991-05-17,372.190002,373.010010,369.440002,372.390015,372.390015,174210000
1991-05-20,372.390015,373.649994,371.260010,372.279999,372.279999,109510000
1991-05-21,372.279999,376.660004,372.279999,375.350006,375.350006,176620000
1991-05-22,375.350006,376.500000,374.399994,376.190002,376.190002,159310000
1991-05-23,376.190002,378.070007,373.549988,374.959991,374.959991,173080000
1991-05-24,374.970001,378.079987,374.970001,377.489990,377.489990,124640000
1991-05-28,377.489990,382.100006,377.119995,381.940002,381.940002,162350000
1991-05-29,381.940002,383.660004,381.369995,382.790009,382.790009,188450000
1991-05-30,382.790009,388.170013,382.500000,386.959991,386.959991,234440000
1991-05-31,386.959991,389.850006,385.010010,389.829987,389.829987,232040000
1991-06-03,389.809998,389.809998,386.970001,388.059998,388.059998,173990000
1991-06-04,388.059998,388.059998,385.140015,387.739990,387.739990,180450000
1991-06-05,387.739990,388.230011,384.450012,385.089996,385.089996,186560000
1991-06-06,385.100006,385.850006,383.130005,383.630005,383.630005,168260000
1991-06-07,383.630005,383.630005,378.760010,379.429993,379.429993,169570000
1991-06-10,379.429993,379.750000,377.950012,378.570007,378.570007,127720000
1991-06-11,378.570007,381.630005,378.570007,381.049988,381.049988,161610000
1991-06-12,381.049988,381.049988,374.459991,376.649994,376.649994,166140000
1991-06-13,376.649994,377.899994,376.079987,377.630005,377.630005,145650000
1991-06-14,377.630005,382.299988,377.630005,382.290009,382.290009,167950000
1991-06-17,382.299988,382.309998,380.130005,380.130005,380.130005,134230000
1991-06-18,380.130005,381.829987,377.989990,378.589996,378.589996,155200000
1991-06-19,378.570007,378.570007,374.359985,375.089996,375.089996,156440000
1991-06-20,375.089996,376.290009,373.869995,375.420013,375.420013,163980000
1991-06-21,375.420013,377.750000,375.329987,377.750000,377.750000,193310000
1991-06-24,377.739990,377.739990,370.730011,370.940002,370.940002,137940000
1991-06-25,370.940002,372.619995,369.559998,370.649994,370.649994,155710000
1991-06-26,370.649994,372.730011,368.339996,371.589996,371.589996,187170000
1991-06-27,371.589996,374.399994,371.589996,374.399994,374.399994,163080000
1991-06-28,374.399994,374.399994,367.980011,371.160004,371.160004,163770000
1991-07-01,371.179993,377.920013,371.179993,377.920013,377.920013,167480000
1991-07-02,377.920013,377.929993,376.619995,377.470001,377.470001,157290000
1991-07-03,377.470001,377.470001,372.079987,373.329987,373.329987,140580000
1991-07-05,373.339996,375.510010,372.170013,374.079987,374.079987,69910000
1991-07-08,374.089996,377.940002,370.920013,377.940002,377.940002,138330000
1991-07-09,377.940002,378.579987,375.369995,376.109985,376.109985,151820000
1991-07-10,376.109985,380.350006,375.200012,375.739990,375.739990,178290000
1991-07-11,375.730011,377.679993,375.510010,376.970001,376.970001,157930000
1991-07-12,376.970001,381.410004,375.790009,380.250000,380.250000,174770000
1991-07-15,380.279999,383.000000,380.239990,382.390015,382.390015,161750000
1991-07-16,382.390015,382.940002,380.799988,381.540009,381.540009,182990000
1991-07-17,381.500000,382.859985,381.130005,381.179993,381.179993,195460000
1991-07-18,381.179993,385.369995,381.179993,385.369995,385.369995,200930000
1991-07-19,385.380005,385.829987,383.649994,384.220001,384.220001,190700000
1991-07-22,384.209991,384.549988,381.839996,382.880005,382.880005,149050000
1991-07-23,382.880005,384.859985,379.390015,379.420013,379.420013,160190000
1991-07-24,379.420013,380.459991,378.290009,378.640015,378.640015,158700000
1991-07-25,378.640015,381.130005,378.149994,380.959991,380.959991,145800000
1991-07-26,380.959991,381.760010,379.809998,380.929993,380.929993,127760000
1991-07-29,380.929993,383.149994,380.450012,383.149994,383.149994,136000000
1991-07-30,383.149994,386.920013,383.149994,386.690002,386.690002,169010000
1991-07-31,386.690002,387.809998,386.190002,387.809998,387.809998,166830000
1991-08-01,387.809998,387.950012,386.480011,387.119995,387.119995,170610000
1991-08-02,387.119995,389.559998,386.049988,387.179993,387.179993,162270000
1991-08-05,387.170013,387.170013,384.480011,385.059998,385.059998,128050000
1991-08-06,385.059998,390.799988,384.290009,390.619995,390.619995,174460000
1991-08-07,390.619995,391.589996,389.859985,390.559998,390.559998,172220000
1991-08-08,390.559998,391.799988,388.149994,389.320007,389.320007,163890000
1991-08-09,389.320007,389.890015,387.040009,387.119995,387.119995,143740000
1991-08-12,387.109985,388.170013,385.899994,388.019989,388.019989,145440000
1991-08-13,388.019989,392.119995,388.019989,389.619995,389.619995,212760000
1991-08-14,389.619995,391.850006,389.130005,389.899994,389.899994,124230000
1991-08-15,389.910004,391.920013,389.290009,389.329987,389.329987,174690000
1991-08-16,389.329987,390.410004,383.160004,385.579987,385.579987,189480000
1991-08-19,385.579987,385.579987,374.089996,376.470001,376.470001,230350000
1991-08-20,376.470001,380.350006,376.470001,379.429993,379.429993,184260000
1991-08-21,379.549988,390.589996,379.549988,390.589996,390.589996,232690000
1991-08-22,390.589996,391.980011,390.209991,391.329987,391.329987,173090000
1991-08-23,391.329987,395.339996,390.690002,394.170013,394.170013,188870000
1991-08-26,394.170013,394.390015,392.750000,393.850006,393.850006,130570000
1991-08-27,393.850006,393.869995,391.769989,393.059998,393.059998,144670000
1991-08-28,393.059998,396.640015,393.049988,396.640015,396.640015,169890000
1991-08-29,396.649994,396.820007,395.140015,396.470001,396.470001,154150000
1991-08-30,396.470001,396.470001,393.600006,395.429993,395.429993,143440000
1991-09-03,395.429993,397.619995,392.100006,392.149994,392.149994,153600000
1991-09-04,392.149994,392.619995,388.679993,389.970001,389.970001,157520000
1991-09-05,389.970001,390.970001,388.489990,389.140015,389.140015,162380000
1991-09-06,389.140015,390.709991,387.359985,389.100006,389.100006,166560000
1991-09-09,389.109985,389.339996,387.880005,388.570007,388.570007,115100000
1991-09-10,388.570007,388.630005,383.779999,384.559998,384.559998,143390000
1991-09-11,384.559998,385.600006,383.589996,385.089996,385.089996,148000000
1991-09-12,385.089996,387.339996,385.089996,387.339996,387.339996,160420000
1991-09-13,387.160004,387.950012,382.850006,383.589996,383.589996,169630000
1991-09-16,383.589996,385.790009,382.769989,385.779999,385.779999,172560000
1991-09-17,385.779999,387.130005,384.970001,385.500000,385.500000,168340000
1991-09-18,385.489990,386.940002,384.279999,386.940002,386.940002,141340000
1991-09-19,386.940002,389.420013,386.269989,387.559998,387.559998,211010000
1991-09-20,387.559998,388.820007,386.489990,387.920013,387.920013,254520000
1991-09-23,387.899994,388.549988,385.760010,385.920013,385.920013,145940000
1991-09-24,385.920013,388.130005,384.459991,387.709991,387.709991,170350000
1991-09-25,387.720001,388.250000,385.989990,386.880005,386.880005,153910000
1991-09-26,386.869995,388.390015,385.299988,386.489990,386.489990,158980000
1991-09-27,386.489990,389.089996,384.869995,385.899994,385.899994,160660000
1991-09-30,385.910004,388.290009,384.320007,387.859985,387.859985,146780000
1991-10-01,387.859985,389.559998,387.859985,389.200012,389.200012,163570000
1991-10-02,389.200012,390.029999,387.619995,388.260010,388.260010,166380000
1991-10-03,388.230011,388.230011,384.470001,384.470001,384.470001,174360000
1991-10-04,384.470001,385.190002,381.239990,381.250000,381.250000,164000000
1991-10-07,381.220001,381.269989,379.070007,379.500000,379.500000,148430000
1991-10-08,379.500000,381.230011,379.179993,380.670013,380.670013,177120000
1991-10-09,380.570007,380.570007,376.350006,376.799988,376.799988,186710000
1991-10-10,376.799988,380.549988,376.109985,380.549988,380.549988,164240000
1991-10-11,380.549988,381.459991,379.899994,381.450012,381.450012,148850000
1991-10-14,381.450012,386.470001,381.450012,386.470001,386.470001,130120000
1991-10-15,386.470001,391.500000,385.950012,391.010010,391.010010,213540000
1991-10-16,391.010010,393.290009,390.140015,392.799988,392.799988,225380000
1991-10-17,392.790009,393.809998,390.320007,391.920013,391.920013,206030000
1991-10-18,391.920013,392.799988,391.769989,392.500000,392.500000,204090000
1991-10-21,392.489990,392.489990,388.959991,390.019989,390.019989,154140000
1991-10-22,390.019989,391.200012,387.399994,387.829987,387.829987,194160000
1991-10-23,387.829987,389.079987,386.519989,387.940002,387.940002,185390000
1991-10-24,387.940002,388.320007,383.450012,385.070007,385.070007,179040000
1991-10-25,385.070007,386.130005,382.970001,384.200012,384.200012,167310000
1991-10-28,384.200012,389.519989,384.200012,389.519989,389.519989,161630000
1991-10-29,389.519989,391.700012,386.880005,391.480011,391.480011,192810000
1991-10-30,391.480011,393.109985,390.779999,392.959991,392.959991,195400000
1991-10-31,392.959991,392.959991,391.579987,392.450012,392.450012,179680000
1991-11-01,392.459991,395.100006,389.670013,391.320007,391.320007,205780000
1991-11-04,391.290009,391.290009,388.089996,390.279999,390.279999,155660000
1991-11-05,390.279999,392.170013,388.190002,388.709991,388.709991,172090000
1991-11-06,388.709991,389.970001,387.579987,389.970001,389.970001,167440000
1991-11-07,389.970001,393.720001,389.970001,393.720001,393.720001,205480000
1991-11-08,393.720001,396.429993,392.420013,392.890015,392.890015,183260000
1991-11-11,392.899994,393.570007,392.320007,393.119995,393.119995,128920000
1991-11-12,393.119995,397.130005,393.119995,396.739990,396.739990,198610000
1991-11-13,396.739990,397.420013,394.010010,397.410004,397.410004,184480000
1991-11-14,397.410004,398.220001,395.850006,397.149994,397.149994,200030000
1991-11-15,397.149994,397.160004,382.619995,382.619995,382.619995,239690000
1991-11-18,382.619995,385.399994,379.700012,385.239990,385.239990,241940000
1991-11-19,385.239990,385.239990,374.899994,379.420013,379.420013,243880000
1991-11-20,379.420013,381.510010,377.839996,378.529999,378.529999,192760000
1991-11-21,378.529999,381.119995,377.410004,380.059998,380.059998,195130000
1991-11-22,380.049988,380.049988,374.519989,376.140015,376.140015,188240000
1991-11-25,376.140015,377.070007,374.000000,375.339996,375.339996,175870000
1991-11-26,375.339996,378.290009,371.630005,377.959991,377.959991,213810000
1991-11-27,377.959991,378.109985,375.980011,376.549988,376.549988,167720000
1991-11-29,376.549988,376.549988,374.649994,375.220001,375.220001,76830000
1991-12-02,375.109985,381.399994,371.359985,381.399994,381.399994,188410000
1991-12-03,381.399994,381.480011,379.920013,380.959991,380.959991,187230000
1991-12-04,380.959991,381.510010,378.070007,380.070007,380.070007,187960000
1991-12-05,380.070007,380.070007,376.579987,377.390015,377.390015,166350000
1991-12-06,377.390015,382.390015,375.410004,379.100006,379.100006,199160000
1991-12-09,379.089996,381.420013,377.670013,378.260010,378.260010,174760000
1991-12-10,378.260010,379.570007,376.640015,377.899994,377.899994,192920000
1991-12-11,377.899994,379.420013,374.779999,377.700012,377.700012,207430000
1991-12-12,377.700012,381.619995,377.700012,381.549988,381.549988,192950000
1991-12-13,381.549988,385.040009,381.549988,384.470001,384.470001,194470000
1991-12-16,384.480011,385.839996,384.369995,384.459991,384.459991,173080000
1991-12-17,384.459991,385.049988,382.600006,382.739990,382.739990,191310000
1991-12-18,382.739990,383.510010,380.880005,383.480011,383.480011,192410000
1991-12-19,383.459991,383.459991,380.640015,382.519989,382.519989,199330000
1991-12-20,382.519989,388.239990,382.519989,387.040009,387.040009,316140000
1991-12-23,387.049988,397.440002,386.959991,396.820007,396.820007,228900000
1991-12-24,396.820007,401.790009,396.820007,399.329987,399.329987,162640000
1991-12-26,399.329987,404.920013,399.309998,404.839996,404.839996,149230000
1991-12-27,404.839996,406.579987,404.589996,406.459991,406.459991,157950000
1991-12-30,406.489990,415.140015,406.489990,415.140015,415.140015,245600000
1991-12-31,415.140015,418.320007,412.730011,417.089996,417.089996,247080000
1992-01-02,417.029999,417.269989,411.040009,417.260010,417.260010,207570000
1992-01-03,417.269989,419.790009,416.160004,419.339996,419.339996,224270000
1992-01-06,419.309998,419.440002,416.920013,417.959991,417.959991,251210000
1992-01-07,417.959991,417.959991,415.200012,417.399994,417.399994,252780000
1992-01-08,417.359985,420.230011,415.019989,418.100006,418.100006,290750000
1992-01-09,418.089996,420.500000,415.850006,417.609985,417.609985,292350000
1992-01-10,417.619995,417.619995,413.309998,415.100006,415.100006,236130000
1992-01-13,415.049988,415.359985,413.540009,414.339996,414.339996,200270000
1992-01-14,414.339996,420.440002,414.320007,420.440002,420.440002,265900000
1992-01-15,420.450012,421.179993,418.790009,420.769989,420.769989,314830000
1992-01-16,420.769989,420.850006,415.369995,418.209991,418.209991,336240000
1992-01-17,418.200012,419.450012,416.000000,418.859985,418.859985,287370000
1992-01-20,418.859985,418.859985,415.799988,416.359985,416.359985,180900000
1992-01-21,416.359985,416.390015,411.320007,412.640015,412.640015,218750000
1992-01-22,412.649994,418.130005,412.489990,418.130005,418.130005,228140000
1992-01-23,418.130005,419.779999,414.359985,414.959991,414.959991,234580000
1992-01-24,414.959991,417.269989,414.290009,415.480011,415.480011,213630000
1992-01-27,415.440002,416.839996,414.480011,414.989990,414.989990,190970000
1992-01-28,414.980011,416.410004,414.540009,414.959991,414.959991,218400000
1992-01-29,414.959991,417.829987,409.170013,410.339996,410.339996,248940000
1992-01-30,410.339996,412.170013,409.260010,411.619995,411.619995,194680000
1992-01-31,411.649994,412.630005,408.640015,408.779999,408.779999,197620000
1992-02-03,408.790009,409.950012,407.450012,409.529999,409.529999,185290000
1992-02-04,409.600006,413.850006,409.279999,413.850006,413.850006,233680000
1992-02-05,413.880005,416.170013,413.179993,413.839996,413.839996,262440000
1992-02-06,413.869995,414.549988,411.929993,413.820007,413.820007,242050000
1992-02-07,413.820007,415.290009,408.040009,411.089996,411.089996,231120000
1992-02-10,411.070007,413.769989,411.070007,413.769989,413.769989,184410000
1992-02-11,413.769989,414.380005,412.239990,413.760010,413.760010,200130000
1992-02-12,413.769989,418.079987,413.359985,417.130005,417.130005,237630000
1992-02-13,417.130005,417.769989,412.070007,413.690002,413.690002,229360000
1992-02-14,413.690002,413.839996,411.200012,412.480011,412.480011,215110000
1992-02-18,412.480011,413.269989,406.339996,407.380005,407.380005,234300000
1992-02-19,407.380005,408.700012,406.540009,408.260010,408.260010,232970000
1992-02-20,408.260010,413.899994,408.260010,413.899994,413.899994,270650000
1992-02-21,413.899994,414.260010,409.720001,411.429993,411.429993,261650000
1992-02-24,411.459991,412.940002,410.339996,412.269989,412.269989,177540000
1992-02-25,412.269989,412.269989,408.019989,410.450012,410.450012,210350000
1992-02-26,410.480011,415.350006,410.480011,415.350006,415.350006,241500000
1992-02-27,415.350006,415.989990,413.470001,413.859985,413.859985,215110000
1992-02-28,413.859985,416.070007,411.799988,412.700012,412.700012,202320000
1992-03-02,412.679993,413.739990,411.519989,412.450012,412.450012,180380000
1992-03-03,412.450012,413.779999,411.880005,412.850006,412.850006,200890000
1992-03-04,412.859985,413.269989,409.329987,409.329987,409.329987,206860000
1992-03-05,409.329987,409.329987,405.420013,406.510010,406.510010,205770000
1992-03-06,406.510010,407.510010,403.649994,404.440002,404.440002,185190000
1992-03-09,404.450012,405.640015,404.250000,405.209991,405.209991,160650000
1992-03-10,405.209991,409.160004,405.209991,406.890015,406.890015,203000000
1992-03-11,406.880005,407.019989,402.640015,404.029999,404.029999,186330000
1992-03-12,404.029999,404.720001,401.940002,403.890015,403.890015,180310000
1992-03-13,403.920013,406.690002,403.920013,405.839996,405.839996,177900000
1992-03-16,405.850006,406.399994,403.549988,406.390015,406.390015,155950000
1992-03-17,406.390015,409.720001,406.390015,409.579987,409.579987,187250000
1992-03-18,409.579987,410.839996,408.230011,409.149994,409.149994,191720000
1992-03-19,409.149994,410.570007,409.119995,409.799988,409.799988,197310000
1992-03-20,409.799988,411.299988,408.529999,411.299988,411.299988,246210000
1992-03-23,411.290009,411.290009,408.869995,409.910004,409.910004,157050000
1992-03-24,409.910004,411.429993,407.989990,408.880005,408.880005,191610000
1992-03-25,408.880005,409.869995,407.519989,407.519989,407.519989,192650000
1992-03-26,407.519989,409.440002,406.750000,407.859985,407.859985,176720000
1992-03-27,407.859985,407.859985,402.869995,403.500000,403.500000,166140000
1992-03-30,403.500000,404.299988,402.970001,403.000000,403.000000,133990000
1992-03-31,403.000000,405.209991,402.220001,403.690002,403.690002,182360000
1992-04-01,403.670013,404.500000,400.750000,404.230011,404.230011,186530000
1992-04-02,404.170013,404.630005,399.279999,400.500000,400.500000,185210000
1992-04-03,400.500000,401.589996,398.209991,401.549988,401.549988,188580000
1992-04-06,401.540009,405.929993,401.519989,405.589996,405.589996,179910000
1992-04-07,405.589996,405.750000,397.970001,398.059998,398.059998,205210000
1992-04-08,398.049988,398.049988,392.410004,394.500000,394.500000,249280000
1992-04-09,394.500000,401.040009,394.500000,400.640015,400.640015,231430000
1992-04-10,400.589996,405.119995,400.589996,404.290009,404.290009,199530000
1992-04-13,404.279999,406.079987,403.899994,406.079987,406.079987,143140000
1992-04-14,406.079987,413.859985,406.079987,412.390015,412.390015,231130000
1992-04-15,412.390015,416.279999,412.390015,416.279999,416.279999,229710000
1992-04-16,416.279999,416.279999,413.399994,416.040009,416.040009,233230000
1992-04-20,416.049988,416.049988,407.929993,410.179993,410.179993,191980000
1992-04-21,410.160004,411.089996,408.200012,410.260010,410.260010,214460000
1992-04-22,410.260010,411.299988,409.230011,409.809998,409.809998,218850000
1992-04-23,409.809998,411.600006,406.859985,411.600006,411.600006,235860000
1992-04-24,411.600006,412.480011,408.739990,409.019989,409.019989,199310000
1992-04-27,409.029999,409.600006,407.640015,408.450012,408.450012,172900000
1992-04-28,408.450012,409.690002,406.329987,409.109985,409.109985,189220000
1992-04-29,409.109985,412.309998,409.109985,412.019989,412.019989,206780000
1992-04-30,412.019989,414.950012,412.019989,414.950012,414.950012,223590000
1992-05-01,414.950012,415.209991,409.869995,412.529999,412.529999,177390000
1992-05-04,412.540009,417.839996,412.540009,416.910004,416.910004,174540000
1992-05-05,416.910004,418.529999,415.769989,416.839996,416.839996,200550000
1992-05-06,416.839996,418.480011,416.399994,416.790009,416.790009,199950000
1992-05-07,416.790009,416.839996,415.380005,415.850006,415.850006,168980000
1992-05-08,415.869995,416.850006,414.410004,416.049988,416.049988,168720000
1992-05-11,416.049988,418.750000,416.049988,418.489990,418.489990,155730000
1992-05-12,418.489990,418.679993,414.690002,416.290009,416.290009,192870000
1992-05-13,416.290009,417.040009,415.859985,416.450012,416.450012,175850000
1992-05-14,416.450012,416.519989,411.820007,413.140015,413.140015,189150000
1992-05-15,413.140015,413.140015,409.850006,410.089996,410.089996,192740000
1992-05-18,410.130005,413.339996,410.130005,412.809998,412.809998,151380000
1992-05-19,412.820007,416.510010,412.260010,416.369995,416.369995,187130000
1992-05-20,416.369995,416.829987,415.369995,415.390015,415.390015,198180000
1992-05-21,415.399994,415.410004,411.570007,412.600006,412.600006,184860000
1992-05-22,412.609985,414.820007,412.600006,414.019989,414.019989,146710000
1992-05-26,414.019989,414.019989,410.230011,411.410004,411.410004,197700000
1992-05-27,411.410004,412.679993,411.059998,412.170013,412.170013,182240000
1992-05-28,412.170013,416.769989,411.809998,416.739990,416.739990,195300000
1992-05-29,416.739990,418.359985,415.350006,415.350006,415.350006,204010000
1992-06-01,415.350006,417.299988,412.440002,417.299988,417.299988,180800000
1992-06-02,417.299988,417.299988,413.500000,413.500000,413.500000,202560000
1992-06-03,413.500000,416.540009,413.040009,414.589996,414.589996,215770000
1992-06-04,414.600006,414.980011,412.970001,413.260010,413.260010,204450000
1992-06-05,413.260010,413.850006,410.970001,413.480011,413.480011,199050000
1992-06-08,413.480011,413.950012,412.029999,413.359985,413.359985,161240000
1992-06-09,413.399994,413.559998,409.299988,410.059998,410.059998,191170000
1992-06-10,410.059998,410.100006,406.809998,407.250000,407.250000,210750000
1992-06-11,407.250000,409.049988,406.109985,409.049988,409.049988,204780000
1992-06-12,409.079987,411.859985,409.079987,409.760010,409.760010,181860000
1992-06-15,409.760010,411.679993,408.130005,410.290009,410.290009,164080000
1992-06-16,410.290009,411.399994,408.320007,408.320007,408.320007,194400000
1992-06-17,408.329987,408.329987,401.980011,402.260010,402.260010,227760000
1992-06-18,402.260010,402.679993,400.510010,400.959991,400.959991,225600000
1992-06-19,400.959991,404.230011,400.959991,403.670013,403.670013,233460000
1992-06-22,403.640015,403.640015,399.920013,403.399994,403.399994,169370000
1992-06-23,403.399994,405.410004,403.399994,404.040009,404.040009,189190000
1992-06-24,404.049988,404.760010,403.260010,403.839996,403.839996,193870000
1992-06-25,403.829987,405.529999,402.010010,403.119995,403.119995,182960000
1992-06-26,403.119995,403.510010,401.940002,403.450012,403.450012,154430000
1992-06-29,403.470001,408.959991,403.470001,408.940002,408.940002,176750000
1992-06-30,408.940002,409.630005,407.850006,408.140015,408.140015,195530000
1992-07-01,408.200012,412.880005,408.200012,412.880005,412.880005,214250000
1992-07-02,412.880005,415.709991,410.070007,411.769989,411.769989,220200000
1992-07-06,411.769989,413.839996,410.459991,413.839996,413.839996,186920000
1992-07-07,413.829987,415.329987,408.579987,409.160004,409.160004,226050000
1992-07-08,409.149994,410.279999,407.200012,410.279999,410.279999,201030000
1992-07-09,410.279999,414.690002,410.260010,414.230011,414.230011,207980000
1992-07-10,414.230011,415.880005,413.339996,414.619995,414.619995,164770000
1992-07-13,414.619995,415.859985,413.929993,414.869995,414.869995,148870000
1992-07-14,414.859985,417.690002,414.329987,417.679993,417.679993,195570000
1992-07-15,417.679993,417.809998,416.290009,417.100006,417.100006,206560000
1992-07-16,417.040009,417.929993,414.790009,417.540009,417.540009,206900000
1992-07-17,417.540009,417.540009,412.959991,415.619995,415.619995,192120000
1992-07-20,415.619995,415.619995,410.720001,413.750000,413.750000,165760000
1992-07-21,413.750000,414.920013,413.100006,413.760010,413.760010,173760000
1992-07-22,413.739990,413.739990,409.950012,410.929993,410.929993,190160000
1992-07-23,410.929993,412.079987,409.809998,412.079987,412.079987,175490000
1992-07-24,412.070007,412.070007,409.929993,411.600006,411.600006,163890000
1992-07-27,411.600006,412.670013,411.269989,411.540009,411.540009,164700000
1992-07-28,411.549988,417.549988,411.549988,417.519989,417.519989,218060000
1992-07-29,417.519989,423.019989,417.519989,422.230011,422.230011,275850000
1992-07-30,422.200012,423.940002,421.570007,423.920013,423.920013,193410000
1992-07-31,423.920013,424.799988,422.459991,424.209991,424.209991,172920000
1992-08-03,424.190002,425.089996,422.839996,425.089996,425.089996,164460000
1992-08-04,425.089996,425.140015,423.100006,424.359985,424.359985,166760000
1992-08-05,424.350006,424.350006,421.920013,422.190002,422.190002,172450000
1992-08-06,422.190002,422.359985,420.260010,420.589996,420.589996,181440000
1992-08-07,420.589996,423.450012,418.510010,418.880005,418.880005,190640000
1992-08-10,418.869995,419.420013,417.040009,419.420013,419.420013,142480000
1992-08-11,419.450012,419.720001,416.529999,418.899994,418.899994,173940000
1992-08-12,418.890015,419.750000,416.429993,417.779999,417.779999,176560000
1992-08-13,417.779999,419.880005,416.399994,417.730011,417.730011,185750000
1992-08-14,417.739990,420.399994,417.739990,419.910004,419.910004,166820000
1992-08-17,419.890015,421.890015,419.440002,420.739990,420.739990,152830000
1992-08-18,420.739990,421.399994,419.779999,421.339996,421.339996,171750000
1992-08-19,421.339996,421.619995,418.190002,418.190002,418.190002,187070000
1992-08-20,418.190002,418.850006,416.929993,418.260010,418.260010,183420000
1992-08-21,418.269989,420.350006,413.579987,414.850006,414.850006,204800000
1992-08-24,414.799988,414.799988,410.070007,410.720001,410.720001,165690000
1992-08-25,410.730011,411.640015,408.299988,411.609985,411.609985,202760000
1992-08-26,411.649994,413.609985,410.529999,413.510010,413.510010,171860000
1992-08-27,413.510010,415.829987,413.510010,413.529999,413.529999,178600000
1992-08-28,413.540009,414.950012,413.380005,414.839996,414.839996,152260000
1992-08-31,414.869995,415.290009,413.760010,414.029999,414.029999,161480000
1992-09-01,414.029999,416.070007,413.350006,416.070007,416.070007,172680000
1992-09-02,416.070007,418.279999,415.309998,417.980011,417.980011,187480000
1992-09-03,417.980011,420.309998,417.489990,417.980011,417.980011,212500000
1992-09-04,417.980011,418.619995,416.760010,417.079987,417.079987,124380000
1992-09-08,417.079987,417.179993,414.299988,414.440002,414.440002,161440000
1992-09-09,414.440002,416.440002,414.440002,416.359985,416.359985,178800000
1992-09-10,416.339996,420.519989,416.339996,419.950012,419.950012,221990000
1992-09-11,419.950012,420.579987,419.130005,419.579987,419.579987,180560000
1992-09-14,419.649994,425.269989,419.649994,425.269989,425.269989,250940000
1992-09-15,425.220001,425.220001,419.540009,419.769989,419.769989,211860000
1992-09-16,419.709991,422.440002,417.769989,419.920013,419.920013,231450000
1992-09-17,419.920013,421.429993,419.619995,419.929993,419.929993,188270000
1992-09-18,419.920013,422.929993,419.920013,422.929993,422.929993,237440000
1992-09-21,422.899994,422.899994,421.179993,422.140015,422.140015,153940000
1992-09-22,422.140015,422.140015,417.130005,417.140015,417.140015,188810000
1992-09-23,417.140015,417.880005,416.000000,417.440002,417.440002,205700000
1992-09-24,417.459991,419.010010,417.459991,418.470001,418.470001,187960000
1992-09-25,418.470001,418.630005,412.709991,414.350006,414.350006,213670000
1992-09-28,414.350006,416.619995,413.000000,416.619995,416.619995,158760000
1992-09-29,416.619995,417.380005,415.339996,416.799988,416.799988,170750000
1992-09-30,416.790009,418.579987,416.670013,417.799988,417.799988,184470000
1992-10-01,417.799988,418.670013,415.459991,416.290009,416.290009,204780000
1992-10-02,416.290009,416.350006,410.450012,410.470001,410.470001,188030000
1992-10-05,410.470001,410.470001,396.799988,407.570007,407.570007,286550000
1992-10-06,407.570007,408.559998,404.839996,407.179993,407.179993,203500000
1992-10-07,407.170013,408.600006,403.910004,404.250000,404.250000,184380000
1992-10-08,404.290009,408.040009,404.290009,407.750000,407.750000,205000000
1992-10-09,407.750000,407.750000,402.420013,402.660004,402.660004,178940000
1992-10-12,402.660004,407.440002,402.660004,407.440002,407.440002,126670000
1992-10-13,407.440002,410.640015,406.829987,409.299988,409.299988,186650000
1992-10-14,409.299988,411.519989,407.859985,409.369995,409.369995,175900000
1992-10-15,409.339996,411.029999,407.920013,409.600006,409.600006,213590000
1992-10-16,409.600006,411.730011,407.429993,411.730011,411.730011,235920000
1992-10-19,411.730011,414.980011,410.660004,414.980011,414.980011,222150000
1992-10-20,414.980011,417.980011,414.489990,415.480011,415.480011,258210000
1992-10-21,415.529999,416.149994,414.540009,415.670013,415.670013,219100000
1992-10-22,415.670013,416.809998,413.100006,414.899994,414.899994,216400000
1992-10-23,414.899994,416.230011,413.679993,414.100006,414.100006,199060000
1992-10-26,414.089996,418.170013,413.709991,418.160004,418.160004,188060000
1992-10-27,418.179993,419.200012,416.970001,418.489990,418.489990,201730000
1992-10-28,418.489990,420.130005,417.559998,420.130005,420.130005,203910000
1992-10-29,420.149994,421.160004,419.829987,420.859985,420.859985,206550000
1992-10-30,420.859985,421.130005,418.540009,418.679993,418.679993,201930000
1992-11-02,418.660004,422.750000,418.119995,422.750000,422.750000,203280000
1992-11-03,422.750000,422.809998,418.589996,419.920013,419.920013,208140000
1992-11-04,419.910004,421.070007,416.609985,417.109985,417.109985,194400000
1992-11-05,417.079987,418.399994,415.579987,418.339996,418.339996,219730000
1992-11-06,418.350006,418.350006,417.010010,417.579987,417.579987,205310000
1992-11-09,417.579987,420.130005,416.790009,418.589996,418.589996,197560000
1992-11-10,418.589996,419.709991,417.980011,418.619995,418.619995,223180000
1992-11-11,418.619995,422.329987,418.399994,422.200012,422.200012,243750000
1992-11-12,422.200012,423.100006,421.700012,422.869995,422.869995,226010000
1992-11-13,422.890015,422.910004,421.040009,422.429993,422.429993,192950000
1992-11-16,422.440002,422.440002,420.350006,420.679993,420.679993,173600000
1992-11-17,420.630005,420.970001,418.309998,419.269989,419.269989,187660000
1992-11-18,419.269989,423.489990,419.239990,422.850006,422.850006,219080000
1992-11-19,422.859985,423.609985,422.500000,423.609985,423.609985,218720000
1992-11-20,423.609985,426.980011,423.609985,426.649994,426.649994,257460000
1992-11-23,426.649994,426.649994,424.950012,425.119995,425.119995,192530000
1992-11-24,425.140015,429.309998,424.829987,427.589996,427.589996,241540000
1992-11-25,427.589996,429.410004,427.579987,429.190002,429.190002,207700000
1992-11-27,429.190002,431.929993,429.170013,430.160004,430.160004,106020000
1992-11-30,430.190002,431.529999,429.359985,431.350006,431.350006,230150000
1992-12-01,431.350006,431.470001,429.200012,430.779999,430.779999,259050000
1992-12-02,430.779999,430.869995,428.609985,429.890015,429.890015,247010000
1992-12-03,429.980011,430.989990,428.799988,429.910004,429.910004,238050000
1992-12-04,429.929993,432.890015,429.739990,432.059998,432.059998,234960000
1992-12-07,432.059998,435.309998,432.059998,435.309998,435.309998,217700000
1992-12-08,435.309998,436.989990,434.679993,436.989990,436.989990,234330000
1992-12-09,436.989990,436.989990,433.980011,435.649994,435.649994,230060000
1992-12-10,435.660004,435.660004,432.649994,434.640015,434.640015,240640000
1992-12-11,434.640015,434.640015,433.339996,433.730011,433.730011,164510000
1992-12-14,433.730011,435.260010,432.829987,432.839996,432.839996,187040000
1992-12-15,432.820007,433.660004,431.920013,432.570007,432.570007,227770000
1992-12-16,432.579987,434.220001,430.880005,431.519989,431.519989,242130000
1992-12-17,431.519989,435.440002,431.459991,435.429993,435.429993,251640000
1992-12-18,435.459991,441.290009,435.459991,441.279999,441.279999,389300000
1992-12-21,441.260010,441.260010,439.649994,440.700012,440.700012,224680000
1992-12-22,440.700012,441.640015,438.250000,440.309998,440.309998,250430000
1992-12-23,440.290009,441.109985,439.029999,439.029999,439.029999,234140000
1992-12-24,439.029999,439.809998,439.029999,439.769989,439.769989,95240000
1992-12-28,439.769989,439.769989,437.260010,439.149994,439.149994,143970000
1992-12-29,439.149994,442.649994,437.600006,437.980011,437.980011,213660000
1992-12-30,437.980011,439.369995,437.119995,438.820007,438.820007,183930000
1992-12-31,438.820007,439.589996,435.709991,435.709991,435.709991,165910000
1993-01-04,435.700012,437.320007,434.480011,435.380005,435.380005,201210000
1993-01-05,435.380005,435.399994,433.549988,434.339996,434.339996,240350000
1993-01-06,434.339996,435.170013,432.519989,434.519989,434.519989,295240000
1993-01-07,434.519989,435.459991,429.760010,430.730011,430.730011,304850000
1993-01-08,430.730011,430.730011,426.880005,429.049988,429.049988,263470000
1993-01-11,429.040009,431.040009,429.010010,430.950012,430.950012,217150000
1993-01-12,430.950012,431.390015,428.190002,431.040009,431.040009,239410000
1993-01-13,431.029999,433.440002,429.989990,433.029999,433.029999,245360000
1993-01-14,433.079987,435.959991,433.079987,435.940002,435.940002,281040000
1993-01-15,435.869995,439.489990,435.839996,437.149994,437.149994,309720000
1993-01-18,437.130005,437.130005,435.920013,436.839996,436.839996,196030000
1993-01-19,436.839996,437.700012,434.589996,435.130005,435.130005,283240000
1993-01-20,435.140015,436.230011,433.369995,433.369995,433.369995,268790000
1993-01-21,433.369995,435.750000,432.480011,435.489990,435.489990,257620000
1993-01-22,435.489990,437.809998,435.489990,436.109985,436.109985,293320000
1993-01-25,436.109985,440.529999,436.109985,440.010010,440.010010,288740000
1993-01-26,440.049988,442.660004,439.540009,439.950012,439.950012,314110000
1993-01-27,439.950012,440.040009,436.820007,438.109985,438.109985,277020000
1993-01-28,438.130005,439.140015,437.299988,438.660004,438.660004,256980000
1993-01-29,438.670013,438.929993,436.910004,438.779999,438.779999,247200000
1993-02-01,438.779999,442.519989,438.779999,442.519989,442.519989,238570000
1993-02-02,442.519989,442.869995,440.760010,442.549988,442.549988,271560000
1993-02-03,442.559998,447.350006,442.559998,447.200012,447.200012,345410000
1993-02-04,447.200012,449.859985,447.200012,449.559998,449.559998,351140000
1993-02-05,449.559998,449.559998,446.950012,448.929993,448.929993,324710000
1993-02-08,448.940002,450.040009,447.700012,447.850006,447.850006,243400000
1993-02-09,448.040009,448.040009,444.519989,445.329987,445.329987,240410000
1993-02-10,445.329987,446.369995,444.239990,446.230011,446.230011,251910000
1993-02-11,446.209991,449.359985,446.209991,447.660004,447.660004,257190000
1993-02-12,447.660004,447.700012,444.579987,444.579987,444.579987,216810000
1993-02-16,444.529999,444.529999,433.470001,433.910004,433.910004,332850000
1993-02-17,433.929993,433.970001,430.920013,433.299988,433.299988,302210000
1993-02-18,433.299988,437.790009,428.250000,431.899994,431.899994,311180000
1993-02-19,431.929993,434.260010,431.679993,434.220001,434.220001,310700000
1993-02-22,434.209991,436.489990,433.529999,435.239990,435.239990,311570000
1993-02-23,435.339996,436.839996,432.410004,434.799988,434.799988,329060000
1993-02-24,434.760010,440.869995,434.679993,440.869995,440.869995,316750000
1993-02-25,440.700012,442.339996,439.670013,442.339996,442.339996,252860000
1993-02-26,442.339996,443.769989,440.980011,443.380005,443.380005,234160000
1993-03-01,443.380005,444.179993,441.339996,442.010010,442.010010,232460000
1993-03-02,442.000000,447.910004,441.070007,447.899994,447.899994,269750000
1993-03-03,447.899994,450.000000,447.730011,449.260010,449.260010,277380000
1993-03-04,449.260010,449.519989,446.720001,447.339996,447.339996,234220000
1993-03-05,447.339996,449.589996,445.559998,446.109985,446.109985,253480000
1993-03-08,446.119995,454.709991,446.119995,454.709991,454.709991,275290000
1993-03-09,454.670013,455.519989,453.679993,454.399994,454.399994,290670000
1993-03-10,454.399994,456.339996,452.700012,456.329987,456.329987,255610000
1993-03-11,456.350006,456.760010,453.480011,453.720001,453.720001,257060000
1993-03-12,453.700012,453.700012,447.040009,449.829987,449.829987,255420000
1993-03-15,449.829987,451.429993,449.399994,451.429993,451.429993,195930000
1993-03-16,451.429993,452.359985,451.010010,451.369995,451.369995,218820000
1993-03-17,451.359985,451.359985,447.989990,448.309998,448.309998,241270000
1993-03-18,448.359985,452.390015,448.359985,451.890015,451.890015,241180000
1993-03-19,451.899994,453.320007,449.910004,450.179993,450.179993,339660000
1993-03-22,450.170013,450.170013,446.079987,448.880005,448.880005,233190000
1993-03-23,448.880005,449.799988,448.299988,448.760010,448.760010,232730000
1993-03-24,448.709991,450.899994,446.100006,448.070007,448.070007,274300000
1993-03-25,448.089996,451.750000,447.929993,450.880005,450.880005,251530000
1993-03-26,450.910004,452.089996,447.690002,447.779999,447.779999,226650000
1993-03-29,447.760010,452.809998,447.750000,450.769989,450.769989,199970000
1993-03-30,450.790009,452.059998,449.630005,451.970001,451.970001,231190000
1993-03-31,451.970001,454.880005,451.670013,451.670013,451.670013,279190000
1993-04-01,451.670013,452.630005,449.600006,450.299988,450.299988,234530000
1993-04-02,450.279999,450.279999,440.709991,441.390015,441.390015,323330000
1993-04-05,441.420013,442.429993,440.529999,442.290009,442.290009,296080000
1993-04-06,442.290009,443.380005,439.480011,441.160004,441.160004,293680000
1993-04-07,441.160004,442.730011,440.500000,442.730011,442.730011,300000000
1993-04-08,442.709991,443.769989,440.019989,441.839996,441.839996,284370000
1993-04-12,441.839996,448.369995,441.839996,448.369995,448.369995,259690000
1993-04-13,448.410004,450.399994,447.660004,449.220001,449.220001,286690000
1993-04-14,449.220001,450.000000,448.019989,448.660004,448.660004,257340000
1993-04-15,448.600006,449.109985,446.390015,448.399994,448.399994,259500000
1993-04-16,448.410004,449.390015,447.670013,448.940002,448.940002,305160000
1993-04-19,448.940002,449.140015,445.850006,447.459991,447.459991,244710000
1993-04-20,447.459991,447.459991,441.809998,445.100006,445.100006,317990000
1993-04-21,445.089996,445.769989,443.079987,443.630005,443.630005,287300000
1993-04-22,443.549988,445.730011,439.459991,439.459991,439.459991,310390000
1993-04-23,439.489990,439.489990,436.820007,437.029999,437.029999,259810000
1993-04-26,437.029999,438.350006,432.299988,433.540009,433.540009,283260000
1993-04-27,433.519989,438.019989,433.140015,438.010010,438.010010,284140000
1993-04-28,438.010010,438.799988,436.679993,438.019989,438.019989,267980000
1993-04-29,438.019989,438.959991,435.589996,438.890015,438.890015,249760000
1993-04-30,438.890015,442.290009,438.890015,440.190002,440.190002,247460000
1993-05-03,440.190002,442.589996,438.250000,442.459991,442.459991,224970000
1993-05-04,442.579987,445.190002,442.450012,444.049988,444.049988,268310000
1993-05-05,443.980011,446.089996,443.760010,444.519989,444.519989,274240000
1993-05-06,444.600006,444.809998,442.899994,443.260010,443.260010,255460000
1993-05-07,443.279999,443.700012,441.690002,442.309998,442.309998,223570000
1993-05-10,442.339996,445.420013,442.049988,442.799988,442.799988,235580000
1993-05-11,442.799988,444.570007,441.519989,444.359985,444.359985,218480000
1993-05-12,444.320007,445.160004,442.869995,444.799988,444.799988,255680000
1993-05-13,444.750000,444.750000,439.230011,439.230011,439.230011,293920000
1993-05-14,439.220001,439.820007,438.100006,439.559998,439.559998,252910000
1993-05-17,439.559998,440.380005,437.829987,440.369995,440.369995,227580000
1993-05-18,440.390015,441.260010,437.950012,440.320007,440.320007,264300000
1993-05-19,440.320007,447.859985,436.859985,447.570007,447.570007,342420000
1993-05-20,447.570007,450.589996,447.359985,450.589996,450.589996,289160000
1993-05-21,450.589996,450.589996,444.890015,445.839996,445.839996,279120000
1993-05-24,445.839996,448.440002,445.260010,448.000000,448.000000,197990000
1993-05-25,448.000000,449.040009,447.700012,448.850006,448.850006,222090000
1993-05-26,448.850006,453.510010,448.820007,453.440002,453.440002,274230000
1993-05-27,453.440002,454.549988,451.140015,452.410004,452.410004,300810000
1993-05-28,452.410004,452.410004,447.670013,450.190002,450.190002,207820000
1993-06-01,450.230011,455.630005,450.230011,453.829987,453.829987,229690000
1993-06-02,453.829987,454.529999,452.679993,453.850006,453.850006,295560000
1993-06-03,453.839996,453.850006,451.119995,452.489990,452.489990,285570000
1993-06-04,452.429993,452.429993,448.920013,450.059998,450.059998,226440000
1993-06-07,450.070007,450.750000,447.320007,447.690002,447.690002,236920000
1993-06-08,447.649994,447.649994,444.309998,444.709991,444.709991,240640000
1993-06-09,444.709991,447.390015,444.660004,445.779999,445.779999,249030000
1993-06-10,445.779999,446.220001,444.089996,445.380005,445.380005,232600000
1993-06-11,445.380005,448.190002,445.380005,447.260010,447.260010,256750000
1993-06-14,447.260010,448.640015,447.230011,447.709991,447.709991,210440000
1993-06-15,447.730011,448.279999,446.179993,446.269989,446.269989,234110000
1993-06-16,446.269989,447.429993,443.609985,447.429993,447.429993,267500000
1993-06-17,447.429993,448.980011,446.910004,448.540009,448.540009,239810000
1993-06-18,448.540009,448.589996,443.679993,443.679993,443.679993,300500000
1993-06-21,443.679993,446.220001,443.679993,446.220001,446.220001,223650000
1993-06-22,446.250000,446.290009,444.940002,445.929993,445.929993,259530000
1993-06-23,445.959991,445.959991,443.190002,443.190002,443.190002,278260000
1993-06-24,443.040009,447.209991,442.500000,446.619995,446.619995,267450000
1993-06-25,446.619995,448.640015,446.619995,447.600006,447.600006,210430000
1993-06-28,447.600006,451.899994,447.600006,451.850006,451.850006,242090000
1993-06-29,451.890015,451.899994,449.670013,450.690002,450.690002,276310000
1993-06-30,450.690002,451.470001,450.149994,450.529999,450.529999,281120000
1993-07-01,450.540009,451.149994,448.709991,449.019989,449.019989,292040000
1993-07-02,449.019989,449.019989,445.200012,445.839996,445.839996,220750000
1993-07-06,445.859985,446.869995,441.420013,441.429993,441.429993,234810000
1993-07-07,441.399994,443.630005,441.399994,442.829987,442.829987,253170000
1993-07-08,442.839996,448.640015,442.839996,448.640015,448.640015,282910000
1993-07-09,448.640015,448.940002,446.739990,448.109985,448.109985,235210000
1993-07-12,448.130005,449.109985,447.709991,448.980011,448.980011,202310000
1993-07-13,449.000000,450.700012,448.070007,448.089996,448.089996,236720000
1993-07-14,448.079987,451.119995,448.079987,450.079987,450.079987,297430000
1993-07-15,450.089996,450.119995,447.260010,449.220001,449.220001,277810000
1993-07-16,449.070007,449.079987,445.660004,445.750000,445.750000,263100000
1993-07-19,445.750000,446.779999,444.829987,446.029999,446.029999,216370000
1993-07-20,446.029999,447.630005,443.709991,447.309998,447.309998,277420000
1993-07-21,447.279999,447.500000,445.839996,447.179993,447.179993,278590000
1993-07-22,447.179993,447.230011,443.720001,444.510010,444.510010,249630000
1993-07-23,444.540009,447.100006,444.540009,447.100006,447.100006,222170000
1993-07-26,447.059998,449.500000,447.040009,449.089996,449.089996,222580000
1993-07-27,449.000000,449.440002,446.760010,448.239990,448.239990,256750000
1993-07-28,448.250000,448.609985,446.589996,447.190002,447.190002,273100000
1993-07-29,447.190002,450.769989,447.190002,450.239990,450.239990,261240000
1993-07-30,450.190002,450.220001,446.980011,448.130005,448.130005,254420000
1993-08-02,448.130005,450.149994,448.029999,450.149994,450.149994,230380000
1993-08-03,450.149994,450.429993,447.589996,449.269989,449.269989,253110000
1993-08-04,449.269989,449.720001,447.929993,448.540009,448.540009,230040000
1993-08-05,448.549988,449.609985,446.940002,448.130005,448.130005,261900000
1993-08-06,448.130005,449.260010,447.869995,448.679993,448.679993,221170000
1993-08-09,448.679993,451.510010,448.309998,450.720001,450.720001,232750000
1993-08-10,450.709991,450.709991,449.100006,449.450012,449.450012,255520000
1993-08-11,449.600006,451.000000,449.600006,450.459991,450.459991,268330000
1993-08-12,450.470001,451.630005,447.529999,448.959991,448.959991,278530000
1993-08-13,448.970001,450.250000,448.970001,450.140015,450.140015,214370000
1993-08-16,450.250000,453.410004,450.250000,452.380005,452.380005,233640000
1993-08-17,452.380005,453.700012,451.959991,453.130005,453.130005,261320000
1993-08-18,453.209991,456.989990,453.209991,456.040009,456.040009,312940000
1993-08-19,456.010010,456.760010,455.200012,456.429993,456.429993,293330000
1993-08-20,456.510010,456.679993,454.600006,456.160004,456.160004,276800000
1993-08-23,456.119995,456.119995,454.290009,455.230011,455.230011,212500000
1993-08-24,455.230011,459.769989,455.040009,459.769989,459.769989,270700000
1993-08-25,459.750000,462.040009,459.299988,460.130005,460.130005,301650000
1993-08-26,460.040009,462.869995,458.820007,461.040009,461.040009,254070000
1993-08-27,461.049988,461.049988,459.190002,460.540009,460.540009,196140000
1993-08-30,460.540009,462.579987,460.279999,461.899994,461.899994,194180000
1993-08-31,461.899994,463.559998,461.290009,463.559998,463.559998,252830000
1993-09-01,463.549988,463.799988,461.769989,463.149994,463.149994,245040000
1993-09-02,463.130005,463.540009,461.070007,461.299988,461.299988,259870000
1993-09-03,461.299988,462.049988,459.910004,461.339996,461.339996,197160000
1993-09-07,461.339996,462.070007,457.950012,458.519989,458.519989,229500000
1993-09-08,458.519989,458.529999,453.750000,456.649994,456.649994,283100000
1993-09-09,456.649994,458.109985,455.170013,457.500000,457.500000,258070000
1993-09-10,457.489990,461.859985,457.489990,461.720001,461.720001,269950000
1993-09-13,461.700012,463.380005,461.410004,462.059998,462.059998,244970000
1993-09-14,461.929993,461.929993,458.149994,459.899994,459.899994,258650000
1993-09-15,459.899994,461.959991,456.309998,461.600006,461.600006,294410000
1993-09-16,461.540009,461.540009,459.000000,459.429993,459.429993,229700000
1993-09-17,459.429993,459.429993,457.089996,458.829987,458.829987,381370000
1993-09-20,458.839996,459.910004,455.000000,455.049988,455.049988,231130000
1993-09-21,455.049988,455.799988,449.640015,452.950012,452.950012,300310000
1993-09-22,452.940002,456.920013,452.940002,456.200012,456.200012,298960000
1993-09-23,456.250000,458.690002,456.250000,457.739990,457.739990,275350000
1993-09-24,457.739990,458.559998,456.920013,457.630005,457.630005,248270000
1993-09-27,457.630005,461.809998,457.630005,461.799988,461.799988,244920000
1993-09-28,461.839996,462.079987,460.910004,461.529999,461.529999,243320000
1993-09-29,461.600006,462.170013,459.510010,460.109985,460.109985,277690000
1993-09-30,460.109985,460.559998,458.279999,458.929993,458.929993,280980000
1993-10-01,458.929993,461.480011,458.350006,461.279999,461.279999,256880000
1993-10-04,461.279999,461.799988,460.019989,461.339996,461.339996,229380000
1993-10-05,461.339996,463.149994,459.450012,461.200012,461.200012,294570000
1993-10-06,461.239990,462.600006,460.260010,460.739990,460.739990,277070000
1993-10-07,460.709991,461.130005,459.079987,459.179993,459.179993,255210000
1993-10-08,459.179993,460.989990,456.399994,460.309998,460.309998,243600000
1993-10-11,460.309998,461.869995,460.309998,460.880005,460.880005,183060000
1993-10-12,461.040009,462.470001,460.730011,461.119995,461.119995,263970000
1993-10-13,461.119995,461.980011,460.760010,461.489990,461.489990,290930000
1993-10-14,461.549988,466.829987,461.549988,466.829987,466.829987,352530000
1993-10-15,466.829987,471.100006,466.829987,469.500000,469.500000,366110000
1993-10-18,469.500000,470.040009,468.019989,468.450012,468.450012,329580000
1993-10-19,468.410004,468.640015,464.799988,466.209991,466.209991,304400000
1993-10-20,466.209991,466.869995,464.540009,466.070007,466.070007,305670000
1993-10-21,466.059998,466.640015,464.380005,465.359985,465.359985,289600000
1993-10-22,465.359985,467.820007,463.269989,463.269989,463.269989,301440000
1993-10-25,463.269989,464.489990,462.049988,464.200012,464.200012,260310000
1993-10-26,464.200012,464.320007,462.649994,464.299988,464.299988,284530000
1993-10-27,464.299988,464.609985,463.359985,464.609985,464.609985,279830000
1993-10-28,464.519989,468.760010,464.519989,467.730011,467.730011,301220000
1993-10-29,467.720001,468.200012,467.369995,467.829987,467.829987,270570000
1993-11-01,467.829987,469.109985,467.329987,469.100006,469.100006,256030000
1993-11-02,469.100006,469.100006,466.200012,468.440002,468.440002,304780000
1993-11-03,468.440002,468.609985,460.950012,463.019989,463.019989,342110000
1993-11-04,463.019989,463.160004,457.260010,457.489990,457.489990,323430000
1993-11-05,457.489990,459.630005,454.359985,459.570007,459.570007,336890000
1993-11-08,459.570007,461.540009,458.779999,460.209991,460.209991,234340000
1993-11-09,460.209991,463.420013,460.209991,460.329987,460.329987,276360000
1993-11-10,460.399994,463.720001,459.570007,463.720001,463.720001,283450000
1993-11-11,463.720001,464.959991,462.489990,462.640015,462.640015,283820000
1993-11-12,462.640015,465.839996,462.640015,465.390015,465.390015,326240000
1993-11-15,465.390015,466.130005,463.010010,463.750000,463.750000,251030000
1993-11-16,463.750000,466.739990,462.970001,466.739990,466.739990,303980000
1993-11-17,466.739990,467.239990,462.730011,464.809998,464.809998,316940000
1993-11-18,464.829987,464.880005,461.730011,463.619995,463.619995,313490000
1993-11-19,463.589996,463.600006,460.029999,462.600006,462.600006,302970000
1993-11-22,462.600006,462.600006,457.079987,459.130005,459.130005,280130000
1993-11-23,459.130005,461.769989,458.470001,461.029999,461.029999,260400000
1993-11-24,461.029999,462.899994,461.029999,462.359985,462.359985,230630000
1993-11-26,462.359985,463.630005,462.359985,463.059998,463.059998,90220000
1993-11-29,463.059998,464.829987,461.829987,461.899994,461.899994,272710000
1993-11-30,461.899994,463.619995,460.450012,461.790009,461.790009,286660000
1993-12-01,461.929993,464.470001,461.630005,461.890015,461.890015,293870000
1993-12-02,461.890015,463.220001,461.450012,463.109985,463.109985,256370000
1993-12-03,463.130005,464.890015,462.670013,464.890015,464.890015,268360000
1993-12-06,464.890015,466.890015,464.399994,466.429993,466.429993,292370000
1993-12-07,466.429993,466.769989,465.440002,466.760010,466.760010,285690000
1993-12-08,465.880005,466.730011,465.420013,466.290009,466.290009,314460000
1993-12-09,466.290009,466.540009,463.869995,464.179993,464.179993,287570000
1993-12-10,464.179993,464.869995,462.660004,463.929993,463.929993,245620000
1993-12-13,463.929993,465.709991,462.709991,465.700012,465.700012,256580000
1993-12-14,465.730011,466.119995,462.459991,463.059998,463.059998,275050000
1993-12-15,463.059998,463.690002,461.839996,461.839996,461.839996,331770000
1993-12-16,461.859985,463.980011,461.859985,463.339996,463.339996,284620000
1993-12-17,463.339996,466.380005,463.339996,466.380005,466.380005,363750000
1993-12-20,466.380005,466.899994,465.529999,465.850006,465.850006,255900000
1993-12-21,465.839996,465.920013,464.029999,465.299988,465.299988,273370000
1993-12-22,465.079987,467.380005,465.079987,467.320007,467.320007,272440000
1993-12-23,467.299988,468.970001,467.299988,467.380005,467.380005,227240000
1993-12-27,467.399994,470.549988,467.350006,470.540009,470.540009,171200000
1993-12-28,470.609985,471.049988,469.429993,470.940002,470.940002,200960000
1993-12-29,470.880005,471.290009,469.869995,470.579987,470.579987,269570000
1993-12-30,470.579987,470.579987,468.089996,468.640015,468.640015,195860000
1993-12-31,468.660004,470.750000,466.450012,466.450012,466.450012,168590000
1994-01-03,466.510010,466.940002,464.359985,465.440002,465.440002,270140000
1994-01-04,465.440002,466.890015,464.440002,466.890015,466.890015,326600000
1994-01-05,466.890015,467.820007,465.920013,467.549988,467.549988,400030000
1994-01-06,467.549988,469.000000,467.019989,467.119995,467.119995,365960000
1994-01-07,467.089996,470.260010,467.029999,469.899994,469.899994,324920000
1994-01-10,469.899994,475.269989,469.549988,475.269989,475.269989,319490000
1994-01-11,475.269989,475.279999,473.269989,474.130005,474.130005,305490000
1994-01-12,474.130005,475.059998,472.140015,474.170013,474.170013,310690000
1994-01-13,474.170013,474.170013,471.799988,472.470001,472.470001,277970000
1994-01-14,472.500000,475.320007,472.500000,474.910004,474.910004,304920000
1994-01-17,474.910004,474.910004,472.839996,473.299988,473.299988,233980000
1994-01-18,473.299988,475.190002,473.290009,474.250000,474.250000,308840000
1994-01-19,474.250000,474.700012,472.209991,474.299988,474.299988,311370000
1994-01-20,474.299988,475.000000,473.420013,474.980011,474.980011,310450000
1994-01-21,474.980011,475.559998,473.720001,474.720001,474.720001,346350000
1994-01-24,474.720001,475.200012,471.489990,471.970001,471.970001,296900000
1994-01-25,471.970001,472.559998,470.269989,470.920013,470.920013,326120000
1994-01-26,470.920013,473.440002,470.720001,473.200012,473.200012,304660000
1994-01-27,473.200012,477.519989,473.200012,477.049988,477.049988,346500000
1994-01-28,477.049988,479.750000,477.049988,478.700012,478.700012,313140000
1994-01-31,478.700012,482.850006,478.700012,481.609985,481.609985,322870000
1994-02-01,481.600006,481.640015,479.179993,479.619995,479.619995,322510000
1994-02-02,479.619995,482.230011,479.570007,482.000000,482.000000,328960000
1994-02-03,481.959991,481.959991,478.709991,480.709991,480.709991,318350000
1994-02-04,480.679993,481.019989,469.279999,469.809998,469.809998,378380000
1994-02-07,469.809998,472.089996,467.570007,471.760010,471.760010,348270000
1994-02-08,471.760010,472.329987,469.500000,471.049988,471.049988,318180000
1994-02-09,471.049988,473.410004,471.049988,472.769989,472.769989,332670000
1994-02-10,472.809998,473.130005,468.910004,468.929993,468.929993,327250000
1994-02-11,468.929993,471.130005,466.890015,470.179993,470.179993,213740000
1994-02-14,470.179993,471.989990,469.049988,470.230011,470.230011,263190000
1994-02-15,470.230011,473.410004,470.230011,472.519989,472.519989,306790000
1994-02-16,472.529999,474.160004,471.940002,472.790009,472.790009,295450000
1994-02-17,472.790009,475.119995,468.440002,470.339996,470.339996,340030000
1994-02-18,470.290009,471.089996,466.070007,467.690002,467.690002,293210000
1994-02-22,467.690002,471.649994,467.579987,471.459991,471.459991,270900000
1994-02-23,471.480011,472.410004,469.470001,470.690002,470.690002,309910000
1994-02-24,470.649994,470.649994,464.260010,464.260010,464.260010,342940000
1994-02-25,464.329987,466.480011,464.329987,466.070007,466.070007,273680000
1994-02-28,466.070007,469.160004,466.070007,467.140015,467.140015,268690000
1994-03-01,467.190002,467.429993,462.019989,464.440002,464.440002,304450000
1994-03-02,464.399994,464.869995,457.489990,464.809998,464.809998,361130000
1994-03-03,464.809998,464.829987,462.500000,463.010010,463.010010,291790000
1994-03-04,463.029999,466.160004,462.410004,464.739990,464.739990,311850000
1994-03-07,464.739990,468.070007,464.739990,466.910004,466.910004,285590000
1994-03-08,466.920013,467.790009,465.019989,465.880005,465.880005,298110000
1994-03-09,465.940002,467.420013,463.399994,467.059998,467.059998,309810000
1994-03-10,467.079987,467.290009,462.459991,463.899994,463.899994,369370000
1994-03-11,463.859985,466.609985,462.540009,466.440002,466.440002,303890000
1994-03-14,466.440002,467.600006,466.079987,467.390015,467.390015,260150000
1994-03-15,467.390015,468.989990,466.040009,467.010010,467.010010,303750000
1994-03-16,467.040009,469.850006,465.480011,469.420013,469.420013,307640000
1994-03-17,469.420013,471.049988,468.619995,470.899994,470.899994,303930000
1994-03-18,470.890015,471.089996,467.829987,471.059998,471.059998,462240000
1994-03-21,471.059998,471.059998,467.230011,468.540009,468.540009,247380000
1994-03-22,468.399994,470.470001,467.880005,468.799988,468.799988,282240000
1994-03-23,468.890015,470.380005,468.519989,468.540009,468.540009,281500000
1994-03-24,468.570007,468.570007,462.410004,464.350006,464.350006,303740000
1994-03-25,464.350006,465.290009,460.579987,460.579987,460.579987,249640000
1994-03-28,460.579987,461.119995,456.100006,460.000000,460.000000,287350000
1994-03-29,460.000000,460.320007,452.429993,452.480011,452.480011,305360000
1994-03-30,452.480011,452.489990,445.549988,445.549988,445.549988,390520000
1994-03-31,445.549988,447.160004,436.160004,445.769989,445.769989,403580000
1994-04-04,445.660004,445.660004,435.859985,438.920013,438.920013,344390000
1994-04-05,439.140015,448.290009,439.140015,448.290009,448.290009,365990000
1994-04-06,448.290009,449.630005,444.980011,448.049988,448.049988,302000000
1994-04-07,448.109985,451.100006,446.380005,450.880005,450.880005,289280000
1994-04-08,450.890015,450.890015,445.510010,447.100006,447.100006,264090000
1994-04-11,447.119995,450.339996,447.100006,449.869995,449.869995,243180000
1994-04-12,449.829987,450.799988,447.329987,447.570007,447.570007,257990000
1994-04-13,447.630005,448.570007,442.619995,446.260010,446.260010,278030000
1994-04-14,446.260010,447.549988,443.570007,446.380005,446.380005,275130000
1994-04-15,446.380005,447.850006,445.809998,446.179993,446.179993,309550000
1994-04-18,446.269989,447.869995,441.480011,442.459991,442.459991,271470000
1994-04-19,442.540009,444.820007,438.829987,442.540009,442.540009,323280000
1994-04-20,442.540009,445.010010,439.399994,441.959991,441.959991,366540000
1994-04-21,441.959991,449.140015,441.959991,448.730011,448.730011,378770000
1994-04-22,448.730011,449.959991,447.160004,447.630005,447.630005,295710000
1994-04-25,447.640015,452.709991,447.579987,452.709991,452.709991,262320000
1994-04-26,452.709991,452.790009,450.660004,451.869995,451.869995,288120000
1994-04-28,451.839996,452.230011,447.970001,449.100006,449.100006,325200000
1994-04-29,449.070007,451.350006,447.910004,450.910004,450.910004,293970000
1994-05-02,450.910004,453.570007,449.049988,453.019989,453.019989,296130000
1994-05-03,453.059998,453.980011,450.510010,453.029999,453.029999,288270000
1994-05-04,453.040009,453.109985,449.869995,451.720001,451.720001,267940000
1994-05-05,451.720001,452.820007,450.720001,451.380005,451.380005,255690000
1994-05-06,451.369995,451.369995,445.640015,447.820007,447.820007,291910000
1994-05-09,447.820007,447.820007,441.839996,442.320007,442.320007,250870000
1994-05-10,442.369995,446.839996,442.369995,446.010010,446.010010,297660000
1994-05-11,446.029999,446.029999,440.779999,441.489990,441.489990,277400000
1994-05-12,441.500000,444.799988,441.500000,443.750000,443.750000,272770000
1994-05-13,443.619995,444.720001,441.209991,444.140015,444.140015,252070000
1994-05-16,444.149994,445.820007,443.619995,444.489990,444.489990,234700000
1994-05-17,444.489990,449.369995,443.700012,449.369995,449.369995,311280000
1994-05-18,449.390015,454.450012,448.869995,453.690002,453.690002,337670000
1994-05-19,453.690002,456.880005,453.000000,456.480011,456.480011,303680000
1994-05-20,456.480011,456.480011,454.220001,454.920013,454.920013,295180000
1994-05-23,454.920013,454.920013,451.790009,453.200012,453.200012,249420000
1994-05-24,453.209991,456.769989,453.209991,454.809998,454.809998,280040000
1994-05-25,454.839996,456.339996,452.200012,456.339996,456.339996,254420000
1994-05-26,456.329987,457.769989,455.790009,457.059998,457.059998,255740000
1994-05-27,457.029999,457.329987,454.670013,457.329987,457.329987,186430000
1994-05-31,457.320007,457.609985,455.160004,456.500000,456.500000,216700000
1994-06-01,456.500000,458.290009,453.989990,457.630005,457.630005,279910000
1994-06-02,457.619995,458.500000,457.260010,457.649994,457.649994,271630000
1994-06-03,457.649994,460.859985,456.269989,460.130005,460.130005,271490000
1994-06-06,460.130005,461.869995,458.850006,458.880005,458.880005,259080000
1994-06-07,458.880005,459.459991,457.649994,458.209991,458.209991,234680000
1994-06-08,458.209991,459.739990,455.429993,457.059998,457.059998,256000000
1994-06-09,457.059998,457.869995,455.859985,457.859985,457.859985,252870000
1994-06-10,457.859985,459.480011,457.359985,458.670013,458.670013,222480000
1994-06-13,458.670013,459.359985,457.179993,459.100006,459.100006,243640000
1994-06-14,459.100006,462.519989,459.100006,462.369995,462.369995,288550000
1994-06-15,462.380005,463.230011,459.950012,460.609985,460.609985,269740000
1994-06-16,460.609985,461.929993,459.799988,461.929993,461.929993,256390000
1994-06-17,461.929993,462.160004,458.440002,458.450012,458.450012,373450000
1994-06-20,458.450012,458.450012,454.459991,455.480011,455.480011,229520000
1994-06-21,455.480011,455.480011,449.450012,451.339996,451.339996,298730000
1994-06-22,451.399994,453.910004,451.399994,453.089996,453.089996,251110000
1994-06-23,453.089996,454.160004,449.429993,449.630005,449.630005,256480000
1994-06-24,449.630005,449.630005,442.510010,442.799988,442.799988,261260000
1994-06-27,442.779999,447.760010,439.829987,447.309998,447.309998,250080000
1994-06-28,447.359985,448.470001,443.079987,446.070007,446.070007,267740000
1994-06-29,446.049988,449.829987,446.040009,447.630005,447.630005,264430000
1994-06-30,447.630005,448.609985,443.660004,444.269989,444.269989,293410000
1994-07-01,444.269989,446.450012,443.579987,446.200012,446.200012,199030000
1994-07-05,446.200012,447.619995,445.140015,446.369995,446.369995,195410000
1994-07-06,446.290009,447.279999,444.179993,446.130005,446.130005,236230000
1994-07-07,446.149994,448.640015,446.149994,448.380005,448.380005,259740000
1994-07-08,448.380005,449.750000,446.529999,449.549988,449.549988,236520000
1994-07-11,449.559998,450.239990,445.269989,448.059998,448.059998,222970000
1994-07-12,448.019989,448.160004,444.649994,447.950012,447.950012,252250000
1994-07-13,448.029999,450.059998,447.970001,448.730011,448.730011,265840000
1994-07-14,448.730011,454.329987,448.730011,453.410004,453.410004,322330000
1994-07-15,453.279999,454.329987,452.799988,454.160004,454.160004,275860000
1994-07-18,454.410004,455.709991,453.260010,455.220001,455.220001,227460000
1994-07-19,455.220001,455.299988,453.859985,453.859985,453.859985,251530000
1994-07-20,453.890015,454.160004,450.690002,451.600006,451.600006,267840000
1994-07-21,451.600006,453.220001,451.000000,452.609985,452.609985,292120000
1994-07-22,452.609985,454.029999,452.329987,453.109985,453.109985,261600000
1994-07-25,453.100006,454.320007,452.760010,454.250000,454.250000,213470000
1994-07-26,454.250000,454.250000,452.779999,453.359985,453.359985,232670000
1994-07-27,453.359985,453.380005,451.359985,452.570007,452.570007,251680000
1994-07-28,452.570007,454.929993,452.299988,454.239990,454.239990,245990000
1994-07-29,454.250000,459.329987,454.250000,458.260010,458.260010,269560000
1994-08-01,458.279999,461.010010,458.079987,461.010010,461.010010,258180000
1994-08-02,461.010010,462.769989,459.700012,460.559998,460.559998,294740000
1994-08-03,460.649994,461.459991,459.510010,461.450012,461.450012,283840000
1994-08-04,461.450012,461.489990,458.399994,458.399994,458.399994,289150000
1994-08-05,458.339996,458.339996,456.079987,457.089996,457.089996,230270000
1994-08-08,457.079987,458.299988,457.010010,457.890015,457.890015,217680000
1994-08-09,457.890015,458.160004,456.660004,457.920013,457.920013,259140000
1994-08-10,457.980011,460.480011,457.980011,460.299988,460.299988,279500000
1994-08-11,460.309998,461.410004,456.880005,458.880005,458.880005,275690000
1994-08-12,458.880005,462.269989,458.880005,461.940002,461.940002,249280000
1994-08-15,461.970001,463.339996,461.209991,461.230011,461.230011,223210000
1994-08-16,461.220001,465.200012,459.890015,465.010010,465.010010,306640000
1994-08-17,465.109985,465.910004,464.570007,465.170013,465.170013,309250000
1994-08-18,465.100006,465.100006,462.299988,463.170013,463.170013,287330000
1994-08-19,463.250000,464.369995,461.809998,463.679993,463.679993,276630000
1994-08-22,463.609985,463.609985,461.459991,462.320007,462.320007,235870000
1994-08-23,462.390015,466.579987,462.390015,464.510010,464.510010,307240000
1994-08-24,464.510010,469.049988,464.510010,469.029999,469.029999,310510000
1994-08-25,469.070007,470.119995,467.640015,468.079987,468.079987,284230000
1994-08-26,468.079987,474.649994,468.079987,473.799988,473.799988,305120000
1994-08-29,473.890015,477.140015,473.890015,474.589996,474.589996,266080000
1994-08-30,474.589996,476.609985,473.559998,476.070007,476.070007,294520000
1994-08-31,476.070007,477.589996,474.429993,475.489990,475.489990,354650000
1994-09-01,475.489990,475.489990,471.739990,473.170013,473.170013,282830000
1994-09-02,473.200012,474.890015,470.670013,470.989990,470.989990,216150000
1994-09-06,471.000000,471.920013,469.640015,471.859985,471.859985,199670000
1994-09-07,471.859985,472.410004,470.200012,470.989990,470.989990,290330000
1994-09-08,470.959991,473.399994,470.859985,473.140015,473.140015,295010000
1994-09-09,473.130005,473.130005,466.549988,468.179993,468.179993,293360000
1994-09-12,468.179993,468.420013,466.149994,466.209991,466.209991,244680000
1994-09-13,466.269989,468.760010,466.269989,467.510010,467.510010,293370000
1994-09-14,467.549988,468.859985,466.820007,468.799988,468.799988,297480000
1994-09-15,468.799988,474.809998,468.790009,474.809998,474.809998,281920000
1994-09-16,474.809998,474.809998,470.059998,471.190002,471.190002,410750000
1994-09-19,471.209991,473.149994,470.679993,470.850006,470.850006,277110000
1994-09-20,470.829987,470.829987,463.359985,463.359985,463.359985,326050000
1994-09-21,463.420013,464.010010,458.470001,461.459991,461.459991,351830000
1994-09-22,461.450012,463.220001,460.959991,461.269989,461.269989,305210000
1994-09-23,461.269989,462.140015,459.010010,459.670013,459.670013,300060000
1994-09-26,459.649994,460.869995,459.309998,460.820007,460.820007,272530000
1994-09-27,460.820007,462.750000,459.829987,462.049988,462.049988,290330000
1994-09-28,462.100006,465.549988,462.100006,464.839996,464.839996,330020000
1994-09-29,464.839996,464.839996,461.510010,462.239990,462.239990,302280000
1994-09-30,462.269989,465.299988,461.910004,462.709991,462.709991,291900000
1994-10-03,462.690002,463.309998,460.329987,461.739990,461.739990,269130000
1994-10-04,461.769989,462.459991,454.029999,454.589996,454.589996,325620000
1994-10-05,454.589996,454.589996,449.269989,453.519989,453.519989,359670000
1994-10-06,453.519989,454.489990,452.130005,452.359985,452.359985,272620000
1994-10-07,452.369995,455.670013,452.130005,455.100006,455.100006,284230000
1994-10-10,455.119995,459.290009,455.119995,459.040009,459.040009,213110000
1994-10-11,459.040009,466.339996,459.040009,465.790009,465.790009,355540000
1994-10-12,465.779999,466.700012,464.790009,465.470001,465.470001,269550000
1994-10-13,465.559998,471.299988,465.559998,467.790009,467.790009,337900000
1994-10-14,467.779999,469.529999,466.109985,469.100006,469.100006,251770000
1994-10-17,469.109985,469.880005,468.160004,468.959991,468.959991,238490000
1994-10-18,469.019989,469.190002,466.540009,467.660004,467.660004,259730000
1994-10-19,467.690002,471.429993,465.959991,470.279999,470.279999,317030000
1994-10-20,470.369995,470.369995,465.390015,466.850006,466.850006,331460000
1994-10-21,466.690002,466.690002,463.829987,464.890015,464.890015,315310000
1994-10-24,464.890015,466.369995,460.799988,460.829987,460.829987,282800000
1994-10-25,460.829987,461.950012,458.260010,461.529999,461.529999,326110000
1994-10-26,461.549988,463.769989,461.220001,462.619995,462.619995,322570000
1994-10-27,462.679993,465.850006,462.619995,465.850006,465.850006,327790000
1994-10-28,465.839996,473.779999,465.799988,473.769989,473.769989,381450000
1994-10-31,473.760010,474.739990,472.329987,472.350006,472.350006,302820000
1994-11-01,472.260010,472.260010,467.640015,468.420013,468.420013,314940000
1994-11-02,468.410004,470.920013,466.359985,466.510010,466.510010,331360000
1994-11-03,466.500000,468.640015,466.399994,467.910004,467.910004,285170000
1994-11-04,467.959991,469.279999,462.279999,462.279999,462.279999,280560000
1994-11-07,462.309998,463.559998,461.250000,463.070007,463.070007,255030000
1994-11-08,463.079987,467.540009,463.070007,465.649994,465.649994,290860000
1994-11-09,465.649994,469.950012,463.459991,465.399994,465.399994,337780000
1994-11-10,465.399994,467.790009,463.730011,464.369995,464.369995,280910000
1994-11-11,464.170013,464.170013,461.450012,462.350006,462.350006,220800000
1994-11-14,462.440002,466.290009,462.350006,466.040009,466.040009,260380000
1994-11-15,466.040009,468.510010,462.950012,465.029999,465.029999,336450000
1994-11-16,465.059998,466.250000,464.279999,465.619995,465.619995,296980000
1994-11-17,465.709991,465.829987,461.470001,463.570007,463.570007,323190000
1994-11-18,463.600006,463.839996,460.250000,461.470001,461.470001,356730000
1994-11-21,461.690002,463.410004,457.549988,458.299988,458.299988,293030000
1994-11-22,457.950012,458.029999,450.079987,450.089996,450.089996,387270000
1994-11-23,450.010010,450.609985,444.179993,449.929993,449.929993,430760000
1994-11-25,449.940002,452.869995,449.940002,452.290009,452.290009,118290000
1994-11-28,452.260010,454.190002,451.040009,454.160004,454.160004,265480000
1994-11-29,454.230011,455.170013,452.140015,455.170013,455.170013,286620000
1994-11-30,455.170013,457.130005,453.269989,453.690002,453.690002,298650000
1994-12-01,453.549988,453.910004,447.970001,448.920013,448.920013,285920000
1994-12-02,448.920013,453.309998,448.000000,453.299988,453.299988,284750000
1994-12-05,453.299988,455.040009,452.059998,453.320007,453.320007,258490000
1994-12-06,453.290009,453.929993,450.350006,453.109985,453.109985,298930000
1994-12-07,453.109985,453.109985,450.010010,451.230011,451.230011,283490000
1994-12-08,451.230011,452.059998,444.589996,445.450012,445.450012,362290000
1994-12-09,445.450012,446.980011,442.880005,446.959991,446.959991,336440000
1994-12-12,446.950012,449.480011,445.619995,449.470001,449.470001,285730000
1994-12-13,449.519989,451.690002,449.429993,450.149994,450.149994,307110000
1994-12-14,450.049988,456.160004,450.049988,454.970001,454.970001,355000000
1994-12-15,454.970001,456.839996,454.500000,455.339996,455.339996,332790000
1994-12-16,455.350006,458.799988,455.350006,458.799988,458.799988,481860000
1994-12-19,458.779999,458.779999,456.640015,457.910004,457.910004,271850000
1994-12-20,458.079987,458.450012,456.369995,457.100006,457.100006,326530000
1994-12-21,457.239990,461.700012,457.170013,459.609985,459.609985,379130000
1994-12-22,459.619995,461.209991,459.329987,459.679993,459.679993,340330000
1994-12-23,459.700012,461.320007,459.390015,459.829987,459.829987,196540000
1994-12-27,459.850006,462.730011,459.850006,462.470001,462.470001,211180000
1994-12-28,462.470001,462.489990,459.000000,460.859985,460.859985,246260000
1994-12-29,460.920013,461.809998,460.359985,461.170013,461.170013,250650000
1994-12-30,461.170013,462.119995,459.239990,459.269989,459.269989,256260000
1995-01-03,459.209991,459.269989,457.200012,459.109985,459.109985,262450000
1995-01-04,459.130005,460.720001,457.559998,460.709991,460.709991,319510000
1995-01-05,460.730011,461.299988,459.750000,460.339996,460.339996,309050000
1995-01-06,460.380005,462.489990,459.470001,460.679993,460.679993,308070000
1995-01-09,460.670013,461.769989,459.739990,460.829987,460.829987,278790000
1995-01-10,460.899994,464.589996,460.899994,461.679993,461.679993,352450000
1995-01-11,461.679993,463.609985,458.649994,461.660004,461.660004,346310000
1995-01-12,461.640015,461.929993,460.630005,461.640015,461.640015,313040000
1995-01-13,461.640015,466.429993,461.640015,465.970001,465.970001,336740000
1995-01-16,465.970001,470.390015,465.970001,469.380005,469.380005,315810000
1995-01-17,469.380005,470.149994,468.190002,470.049988,470.049988,331520000
1995-01-18,470.049988,470.429993,468.029999,469.709991,469.709991,344660000
1995-01-19,469.720001,469.720001,466.399994,466.950012,466.950012,297220000
1995-01-20,466.950012,466.989990,463.989990,464.779999,464.779999,378190000
1995-01-23,464.779999,466.230011,461.140015,465.820007,465.820007,325830000
1995-01-24,465.809998,466.880005,465.470001,465.859985,465.859985,315430000
1995-01-25,465.859985,469.510010,464.399994,467.440002,467.440002,342610000
1995-01-26,467.440002,468.619995,466.899994,468.320007,468.320007,304730000
1995-01-27,468.320007,471.359985,468.320007,470.390015,470.390015,339510000
1995-01-30,470.390015,470.519989,467.489990,468.510010,468.510010,318550000
1995-01-31,468.510010,471.029999,468.179993,470.420013,470.420013,411590000
1995-02-01,470.420013,472.750000,469.290009,470.399994,470.399994,395310000
1995-02-02,470.399994,472.790009,469.950012,472.790009,472.790009,322110000
1995-02-03,472.779999,479.910004,472.779999,478.649994,478.649994,441000000
1995-02-06,478.640015,481.950012,478.359985,481.140015,481.140015,325660000
1995-02-07,481.140015,481.320007,479.690002,480.809998,480.809998,314660000
1995-02-08,480.809998,482.600006,480.399994,481.190002,481.190002,318430000
1995-02-09,481.190002,482.000000,479.910004,480.190002,480.190002,325570000
1995-02-10,480.190002,481.959991,479.529999,481.459991,481.459991,295600000
1995-02-13,481.459991,482.859985,481.070007,481.649994,481.649994,256270000
1995-02-14,481.649994,482.940002,480.890015,482.549988,482.549988,300720000
1995-02-15,482.549988,485.540009,481.769989,484.540009,484.540009,378040000
1995-02-16,484.559998,485.220001,483.049988,485.220001,485.220001,360990000
1995-02-17,485.149994,485.220001,481.970001,481.970001,481.970001,347970000
1995-02-21,481.950012,483.260010,481.940002,482.720001,482.720001,308090000
1995-02-22,482.739990,486.149994,482.450012,485.070007,485.070007,339460000
1995-02-23,485.070007,489.190002,485.070007,486.910004,486.910004,394280000
1995-02-24,486.820007,488.279999,485.700012,488.109985,488.109985,302930000
1995-02-27,488.260010,488.260010,483.179993,483.809998,483.809998,285790000
1995-02-28,483.809998,487.440002,483.769989,487.390015,487.390015,317220000
1995-03-01,487.390015,487.829987,484.920013,485.649994,485.649994,362600000
1995-03-02,485.649994,485.709991,483.190002,485.130005,485.130005,330030000
1995-03-03,485.130005,485.420013,483.070007,485.420013,485.420013,330840000
1995-03-06,485.420013,485.700012,481.519989,485.630005,485.630005,298870000
1995-03-07,485.630005,485.630005,479.700012,482.119995,482.119995,355550000
1995-03-08,482.119995,484.079987,481.570007,483.140015,483.140015,349780000
1995-03-09,483.140015,483.739990,482.049988,483.160004,483.160004,319320000
1995-03-10,483.160004,490.369995,483.160004,489.570007,489.570007,382940000
1995-03-13,489.570007,491.279999,489.350006,490.049988,490.049988,275280000
1995-03-14,490.049988,493.690002,490.049988,492.890015,492.890015,346160000
1995-03-15,492.890015,492.890015,490.829987,491.880005,491.880005,309540000
1995-03-16,491.869995,495.739990,491.779999,495.410004,495.410004,336670000
1995-03-17,495.429993,496.670013,494.950012,495.519989,495.519989,417380000
1995-03-20,495.519989,496.609985,495.269989,496.140015,496.140015,301740000
1995-03-21,496.149994,499.190002,494.040009,495.070007,495.070007,367110000
1995-03-22,495.070007,495.670013,493.670013,495.670013,495.670013,313120000
1995-03-23,495.670013,496.769989,494.190002,495.950012,495.950012,318530000
1995-03-24,496.070007,500.970001,496.070007,500.970001,500.970001,358370000
1995-03-27,500.970001,503.200012,500.929993,503.200012,503.200012,296270000
1995-03-28,503.190002,503.910004,501.829987,503.899994,503.899994,320360000
1995-03-29,503.920013,508.149994,500.959991,503.119995,503.119995,385940000
1995-03-30,503.170013,504.660004,501.000000,502.220001,502.220001,362940000
1995-03-31,501.940002,502.220001,495.700012,500.709991,500.709991,353060000
1995-04-03,500.700012,501.910004,500.200012,501.850006,501.850006,296430000
1995-04-04,501.850006,505.260010,501.850006,505.239990,505.239990,330580000
1995-04-05,505.269989,505.570007,503.170013,505.570007,505.570007,315170000
1995-04-06,505.630005,507.100006,505.000000,506.079987,506.079987,320460000
1995-04-07,506.130005,507.190002,503.589996,506.420013,506.420013,314760000
1995-04-10,506.299988,507.010010,504.609985,507.010010,507.010010,260980000
1995-04-11,507.239990,508.850006,505.290009,505.529999,505.529999,310660000
1995-04-12,505.589996,507.170013,505.070007,507.170013,507.170013,327880000
1995-04-13,507.190002,509.829987,507.170013,509.230011,509.230011,301580000
1995-04-17,509.230011,512.030029,505.429993,506.130005,506.130005,333930000
1995-04-18,506.429993,507.649994,504.119995,505.369995,505.369995,344680000
1995-04-19,505.369995,505.890015,501.190002,504.920013,504.920013,378050000
1995-04-20,504.920013,506.500000,503.440002,505.290009,505.290009,368450000
1995-04-21,505.630005,508.489990,505.630005,508.489990,508.489990,403250000
1995-04-24,508.489990,513.020020,507.440002,512.890015,512.890015,326280000
1995-04-25,512.799988,513.539978,511.320007,512.099976,512.099976,351790000
1995-04-26,511.989990,513.039978,510.470001,512.659973,512.659973,350810000
1995-04-27,512.700012,513.619995,511.630005,513.549988,513.549988,350850000
1995-04-28,513.640015,515.289978,510.899994,514.710022,514.710022,320440000
1995-05-01,514.760010,515.599976,513.419983,514.260010,514.260010,296830000
1995-05-02,514.229980,515.179993,513.030029,514.859985,514.859985,302560000
1995-05-03,514.929993,520.539978,514.859985,520.479980,520.479980,392370000
1995-05-04,520.479980,525.400024,519.440002,520.539978,520.539978,434990000
1995-05-05,520.750000,522.349976,518.280029,520.119995,520.119995,342380000
1995-05-08,520.090027,525.150024,519.140015,523.960022,523.960022,291810000
1995-05-09,523.960022,525.989990,521.789978,523.559998,523.559998,361300000
1995-05-10,523.739990,524.400024,521.530029,524.359985,524.359985,381990000
1995-05-11,524.330017,524.890015,522.700012,524.369995,524.369995,339900000
1995-05-12,524.369995,527.049988,523.299988,525.549988,525.549988,361000000
1995-05-15,525.549988,527.739990,525.000000,527.739990,527.739990,316240000
1995-05-16,527.739990,529.080017,526.450012,528.190002,528.190002,366180000
1995-05-17,528.190002,528.419983,525.380005,527.070007,527.070007,347930000
1995-05-18,526.880005,526.880005,519.580017,519.580017,519.580017,351900000
1995-05-19,519.580017,519.580017,517.070007,519.190002,519.190002,354010000
1995-05-22,519.190002,524.340027,519.190002,523.650024,523.650024,285600000
1995-05-23,523.650024,528.590027,523.650024,528.590027,528.590027,362690000
1995-05-24,528.590027,531.909973,525.570007,528.609985,528.609985,391770000
1995-05-25,528.369995,529.039978,524.890015,528.590027,528.590027,341820000
1995-05-26,528.590027,528.590027,522.510010,523.650024,523.650024,291220000
1995-05-30,523.650024,525.580017,521.380005,523.580017,523.580017,283020000
1995-05-31,523.700012,533.409973,522.169983,533.400024,533.400024,358180000
1995-06-01,533.400024,534.210022,530.049988,533.489990,533.489990,345920000
1995-06-02,533.489990,536.909973,529.549988,532.510010,532.510010,366000000
1995-06-05,532.510010,537.729980,532.469971,535.599976,535.599976,337520000
1995-06-06,535.599976,537.090027,535.140015,535.549988,535.549988,340490000
1995-06-07,535.549988,535.549988,531.659973,533.130005,533.130005,327790000
1995-06-08,533.130005,533.559998,531.650024,532.349976,532.349976,289880000
1995-06-09,532.349976,532.349976,526.000000,527.940002,527.940002,327570000
1995-06-12,527.940002,532.539978,527.940002,530.880005,530.880005,289920000
1995-06-13,530.880005,536.229980,530.880005,536.049988,536.049988,339660000
1995-06-14,536.049988,536.479980,533.830017,536.469971,536.469971,330770000
1995-06-15,536.479980,539.070007,535.559998,537.119995,537.119995,334700000
1995-06-16,537.510010,539.979980,537.119995,539.830017,539.830017,442740000
1995-06-19,539.830017,545.219971,539.830017,545.219971,545.219971,322990000
1995-06-20,545.219971,545.440002,543.429993,544.979980,544.979980,382370000
1995-06-21,544.979980,545.929993,543.900024,543.979980,543.979980,398210000
1995-06-22,543.979980,551.070007,543.979980,551.070007,551.070007,421000000
1995-06-23,551.070007,551.070007,548.229980,549.710022,549.710022,321660000
1995-06-26,549.710022,549.789978,544.059998,544.130005,544.130005,296720000
1995-06-27,544.109985,547.070007,542.190002,542.429993,542.429993,346950000
1995-06-28,542.429993,546.330017,540.719971,544.729980,544.729980,368060000
1995-06-29,544.729980,546.250000,540.789978,543.869995,543.869995,313080000
1995-06-30,543.869995,546.820007,543.510010,544.750000,544.750000,311650000
1995-07-03,544.750000,547.099976,544.429993,547.090027,547.090027,117900000
1995-07-05,547.090027,549.979980,546.280029,547.260010,547.260010,357850000
1995-07-06,547.260010,553.989990,546.590027,553.989990,553.989990,420500000
1995-07-07,553.900024,556.570007,553.049988,556.369995,556.369995,466540000
1995-07-10,556.369995,558.479980,555.770020,557.190002,557.190002,409700000
1995-07-11,556.780029,557.190002,553.799988,554.780029,554.780029,376770000
1995-07-12,555.270020,561.559998,554.270020,560.890015,560.890015,416360000
1995-07-13,560.890015,562.000000,559.070007,561.000000,561.000000,387500000
1995-07-14,561.000000,561.000000,556.409973,559.890015,559.890015,312930000
1995-07-17,560.340027,562.940002,559.450012,562.719971,562.719971,322540000
1995-07-18,562.549988,562.719971,556.859985,558.460022,558.460022,372230000
1995-07-19,556.580017,558.460022,542.510010,550.979980,550.979980,489850000
1995-07-20,550.979980,554.429993,549.099976,553.539978,553.539978,383380000
1995-07-21,553.340027,554.729980,550.909973,553.619995,553.619995,431830000
1995-07-24,553.619995,557.210022,553.619995,556.630005,556.630005,315300000
1995-07-25,556.630005,561.750000,556.340027,561.099976,561.099976,373200000
1995-07-26,561.099976,563.780029,560.849976,561.609985,561.609985,393470000
1995-07-27,561.609985,565.330017,561.609985,565.219971,565.219971,356570000
1995-07-28,565.219971,565.400024,562.039978,562.929993,562.929993,311590000
1995-07-31,562.929993,563.489990,560.059998,562.059998,562.059998,291950000
1995-08-01,562.059998,562.109985,556.669983,559.640015,559.640015,332210000
1995-08-02,559.640015,565.619995,557.869995,558.799988,558.799988,374330000
1995-08-03,558.799988,558.799988,554.099976,558.750000,558.750000,353110000
1995-08-04,558.750000,559.570007,557.909973,558.940002,558.940002,314740000
1995-08-07,558.940002,561.239990,558.940002,560.030029,560.030029,277050000
1995-08-08,560.030029,561.530029,558.320007,560.390015,560.390015,306090000
1995-08-09,560.390015,561.590027,559.289978,559.710022,559.710022,303390000
1995-08-10,559.710022,560.630005,556.049988,557.450012,557.450012,306660000
1995-08-11,557.450012,558.500000,553.039978,555.109985,555.109985,267850000
1995-08-14,555.109985,559.739990,554.760010,559.739990,559.739990,264920000
1995-08-15,559.739990,559.979980,555.219971,558.570007,558.570007,330070000
1995-08-16,558.570007,559.979980,557.369995,559.969971,559.969971,390170000
1995-08-17,559.969971,559.969971,557.419983,559.039978,559.039978,354460000
1995-08-18,559.039978,561.239990,558.340027,559.210022,559.210022,320490000
1995-08-21,559.210022,563.340027,557.890015,558.109985,558.109985,303200000
1995-08-22,558.109985,559.520020,555.869995,559.520020,559.520020,290890000
1995-08-23,559.520020,560.000000,557.080017,557.140015,557.140015,291890000
1995-08-24,557.140015,558.630005,555.200012,557.460022,557.460022,299200000
1995-08-25,557.460022,561.309998,557.460022,560.099976,560.099976,255990000
1995-08-28,560.099976,562.219971,557.989990,559.049988,559.049988,267860000
1995-08-29,559.049988,560.010010,555.710022,560.000000,560.000000,311290000
1995-08-30,560.000000,561.520020,559.489990,560.919983,560.919983,329840000
1995-08-31,561.090027,562.359985,560.489990,561.880005,561.880005,300920000
1995-09-01,561.880005,564.619995,561.010010,563.840027,563.840027,256730000
1995-09-05,563.859985,569.200012,563.840027,569.169983,569.169983,332670000
1995-09-06,569.169983,570.530029,569.000000,570.169983,570.169983,369540000
1995-09-07,570.169983,571.109985,569.229980,570.289978,570.289978,321720000
1995-09-08,570.289978,572.679993,569.270020,572.679993,572.679993,317940000
1995-09-11,572.679993,575.150024,572.679993,573.909973,573.909973,296840000
1995-09-12,573.909973,576.510010,573.109985,576.510010,576.510010,344540000
1995-09-13,576.510010,579.719971,575.469971,578.770020,578.770020,384380000
1995-09-14,578.770020,583.989990,578.770020,583.609985,583.609985,382880000
1995-09-15,583.609985,585.070007,581.789978,583.349976,583.349976,459370000
1995-09-18,583.349976,583.369995,579.359985,582.770020,582.770020,326090000
1995-09-19,582.780029,584.239990,580.750000,584.200012,584.200012,371170000
1995-09-20,584.200012,586.770020,584.179993,586.770020,586.770020,400050000
1995-09-21,586.770020,586.789978,580.909973,583.000000,583.000000,367100000
1995-09-22,583.000000,583.000000,578.250000,581.729980,581.729980,370790000
1995-09-25,581.729980,582.140015,579.500000,581.809998,581.809998,273120000
1995-09-26,581.809998,584.659973,580.650024,581.409973,581.409973,363630000
1995-09-27,581.409973,581.419983,574.679993,581.039978,581.039978,411300000
1995-09-28,581.039978,585.880005,580.690002,585.869995,585.869995,367720000
1995-09-29,585.869995,587.609985,584.000000,584.409973,584.409973,335250000
1995-10-02,584.409973,585.049988,580.539978,581.719971,581.719971,304990000
1995-10-03,581.719971,582.340027,578.479980,582.340027,582.340027,385940000
1995-10-04,582.340027,582.340027,579.909973,581.469971,581.469971,339380000
1995-10-05,581.469971,582.630005,579.580017,582.630005,582.630005,367480000
1995-10-06,582.630005,584.539978,582.099976,582.489990,582.489990,313680000
1995-10-09,582.489990,582.489990,576.349976,578.369995,578.369995,275320000
1995-10-10,578.369995,578.369995,571.549988,577.520020,577.520020,412710000
1995-10-11,577.520020,579.520020,577.080017,579.460022,579.460022,340740000
1995-10-12,579.460022,583.119995,579.460022,583.099976,583.099976,344060000
1995-10-13,583.099976,587.390015,583.099976,584.500000,584.500000,374680000
1995-10-16,584.500000,584.859985,582.630005,583.030029,583.030029,300750000
1995-10-17,583.030029,586.780029,581.900024,586.780029,586.780029,356380000
1995-10-18,586.780029,589.770020,586.270020,587.440002,587.440002,411270000
1995-10-19,587.440002,590.659973,586.340027,590.650024,590.650024,406620000
1995-10-20,590.650024,590.659973,586.780029,587.460022,587.460022,389360000
1995-10-23,587.460022,587.460022,583.729980,585.059998,585.059998,330750000
1995-10-24,585.059998,587.309998,584.750000,586.539978,586.539978,415540000
1995-10-25,586.539978,587.190002,581.409973,582.469971,582.469971,433620000
1995-10-26,582.469971,582.630005,572.530029,576.719971,576.719971,464270000
1995-10-27,576.719971,579.710022,573.210022,579.700012,579.700012,379230000
1995-10-30,579.700012,583.789978,579.700012,583.250000,583.250000,319160000
1995-10-31,583.250000,586.710022,581.500000,581.500000,581.500000,377390000
1995-11-01,581.500000,584.239990,581.039978,584.219971,584.219971,378090000
1995-11-02,584.219971,589.719971,584.219971,589.719971,589.719971,397070000
1995-11-03,589.719971,590.570007,588.650024,590.570007,590.570007,348500000
1995-11-06,590.570007,590.640015,588.309998,588.460022,588.460022,309100000
1995-11-07,588.460022,588.460022,584.239990,586.320007,586.320007,364680000
1995-11-08,586.320007,591.710022,586.320007,591.710022,591.710022,359780000
1995-11-09,591.710022,593.900024,590.890015,593.260010,593.260010,380760000
1995-11-10,593.260010,593.260010,590.390015,592.719971,592.719971,298690000
1995-11-13,592.719971,593.719971,590.580017,592.299988,592.299988,295840000
1995-11-14,592.299988,592.299988,588.979980,589.289978,589.289978,354420000
1995-11-15,589.289978,593.969971,588.359985,593.960022,593.960022,376100000
1995-11-16,593.960022,597.909973,593.520020,597.340027,597.340027,423280000
1995-11-17,597.340027,600.140015,597.299988,600.070007,600.070007,437200000
1995-11-20,600.070007,600.400024,596.169983,596.849976,596.849976,333150000
1995-11-21,596.849976,600.280029,595.419983,600.239990,600.239990,408320000
1995-11-22,600.239990,600.710022,598.400024,598.400024,598.400024,404980000
1995-11-24,598.400024,600.239990,598.400024,599.969971,599.969971,125870000
1995-11-27,599.969971,603.349976,599.969971,601.320007,601.320007,359130000
1995-11-28,601.320007,606.450012,599.020020,606.450012,606.450012,408860000
1995-11-29,606.450012,607.659973,605.469971,607.640015,607.640015,398280000
1995-11-30,607.640015,608.690002,605.369995,605.369995,605.369995,440050000
1995-12-01,605.369995,608.109985,605.369995,606.979980,606.979980,393310000
1995-12-04,606.979980,613.830017,606.840027,613.679993,613.679993,405480000
1995-12-05,613.679993,618.479980,613.140015,617.679993,617.679993,437360000
1995-12-06,617.679993,621.109985,616.690002,620.179993,620.179993,417780000
1995-12-07,620.179993,620.190002,615.210022,616.169983,616.169983,379260000
1995-12-08,616.169983,617.820007,614.320007,617.479980,617.479980,327900000
1995-12-11,617.479980,620.900024,617.140015,619.520020,619.520020,342070000
1995-12-12,619.520020,619.549988,617.679993,618.780029,618.780029,349860000
1995-12-13,618.780029,622.020020,618.270020,621.690002,621.690002,415290000
1995-12-14,621.690002,622.880005,616.130005,616.919983,616.919983,465300000
1995-12-15,616.919983,617.719971,614.460022,616.340027,616.340027,636800000
1995-12-18,616.340027,616.340027,606.130005,606.809998,606.809998,426270000
1995-12-19,606.809998,611.940002,605.049988,611.929993,611.929993,478280000
1995-12-20,611.929993,614.270020,605.929993,605.940002,605.940002,437680000
1995-12-21,605.940002,610.520020,605.940002,610.489990,610.489990,415810000
1995-12-22,610.489990,613.500000,610.450012,611.950012,611.950012,289600000
1995-12-26,611.960022,614.500000,611.960022,614.299988,614.299988,217280000
1995-12-27,614.299988,615.729980,613.750000,614.530029,614.530029,252300000
1995-12-28,614.530029,615.500000,612.400024,614.119995,614.119995,288660000
1995-12-29,614.119995,615.929993,612.359985,615.929993,615.929993,321250000
1996-01-02,615.929993,620.739990,613.169983,620.729980,620.729980,364180000
1996-01-03,620.729980,623.250000,619.559998,621.320007,621.320007,468950000
1996-01-04,621.320007,624.489990,613.960022,617.700012,617.700012,512580000
1996-01-05,617.700012,617.700012,612.020020,616.710022,616.710022,437110000
1996-01-08,616.710022,618.460022,616.489990,618.460022,618.460022,130360000
1996-01-09,618.460022,619.150024,608.210022,609.450012,609.450012,417400000
1996-01-10,609.450012,609.450012,597.289978,598.479980,598.479980,496830000
1996-01-11,598.479980,602.710022,597.539978,602.690002,602.690002,408800000
1996-01-12,602.690002,604.799988,597.460022,601.809998,601.809998,383400000
1996-01-15,601.809998,603.429993,598.469971,599.820007,599.820007,306180000
1996-01-16,599.820007,608.440002,599.049988,608.440002,608.440002,425220000
1996-01-17,608.440002,609.929993,604.700012,606.369995,606.369995,458720000
1996-01-18,606.369995,608.270020,604.119995,608.239990,608.239990,450410000
1996-01-19,608.239990,612.919983,606.760010,611.830017,611.830017,497720000
1996-01-22,611.830017,613.450012,610.950012,613.400024,613.400024,398040000
1996-01-23,613.400024,613.400024,610.650024,612.789978,612.789978,416910000
1996-01-24,612.789978,619.960022,612.789978,619.960022,619.960022,476380000
1996-01-25,619.960022,620.150024,616.619995,617.030029,617.030029,453270000
1996-01-26,617.030029,621.700012,615.260010,621.619995,621.619995,385700000
1996-01-29,621.619995,624.219971,621.419983,624.219971,624.219971,363330000
1996-01-30,624.219971,630.289978,624.219971,630.150024,630.150024,464350000
1996-01-31,630.150024,636.179993,629.479980,636.020020,636.020020,472210000
1996-02-01,636.020020,638.460022,634.539978,638.460022,638.460022,461430000
1996-02-02,638.460022,639.260010,634.289978,635.840027,635.840027,420020000
1996-02-05,635.840027,641.429993,633.710022,641.429993,641.429993,377760000
1996-02-06,641.429993,646.669983,639.679993,646.330017,646.330017,465940000
1996-02-07,646.330017,649.929993,645.590027,649.929993,649.929993,462730000
1996-02-08,649.929993,656.539978,647.929993,656.070007,656.070007,474970000
1996-02-09,656.070007,661.080017,653.640015,656.369995,656.369995,477640000
1996-02-12,656.369995,662.950012,656.340027,661.450012,661.450012,397890000
1996-02-13,661.450012,664.229980,657.919983,660.510010,660.510010,441540000
1996-02-14,660.510010,661.530029,654.359985,655.580017,655.580017,421790000
1996-02-15,655.580017,656.840027,651.150024,651.320007,651.320007,415320000
1996-02-16,651.320007,651.419983,646.989990,647.979980,647.979980,445570000
1996-02-20,647.979980,647.979980,638.789978,640.650024,640.650024,395910000
1996-02-21,640.650024,648.109985,640.650024,648.099976,648.099976,431220000
1996-02-22,648.099976,659.750000,648.099976,658.859985,658.859985,485470000
1996-02-23,658.859985,663.000000,652.250000,659.080017,659.080017,443130000
1996-02-26,659.080017,659.080017,650.159973,650.460022,650.460022,399330000
1996-02-27,650.460022,650.619995,643.869995,647.239990,647.239990,431340000
1996-02-28,647.239990,654.390015,643.989990,644.750000,644.750000,447790000
1996-02-29,644.750000,646.950012,639.010010,640.429993,640.429993,453170000
1996-03-01,640.429993,644.380005,635.000000,644.369995,644.369995,471480000
1996-03-04,644.369995,653.539978,644.369995,650.809998,650.809998,417270000
1996-03-05,650.809998,655.799988,648.770020,655.789978,655.789978,445700000
1996-03-06,655.789978,656.969971,651.609985,652.000000,652.000000,428220000
1996-03-07,652.000000,653.650024,649.539978,653.650024,653.650024,425790000
1996-03-08,653.650024,653.650024,627.630005,633.500000,633.500000,546550000
1996-03-11,633.500000,640.409973,629.950012,640.020020,640.020020,449500000
1996-03-12,640.020020,640.020020,628.820007,637.090027,637.090027,454980000
1996-03-13,637.090027,640.520020,635.190002,638.549988,638.549988,413030000
1996-03-14,638.549988,644.169983,638.549988,640.869995,640.869995,492630000
1996-03-15,640.869995,642.869995,638.349976,641.429993,641.429993,529970000
1996-03-18,641.429993,652.650024,641.429993,652.650024,652.650024,437100000
1996-03-19,652.650024,656.179993,649.799988,651.690002,651.690002,438300000
1996-03-20,651.690002,653.130005,645.570007,649.979980,649.979980,409780000
1996-03-21,649.979980,651.539978,648.099976,649.190002,649.190002,367180000
1996-03-22,649.190002,652.080017,649.190002,650.619995,650.619995,329390000
1996-03-25,650.619995,655.500000,648.820007,650.039978,650.039978,336700000
1996-03-26,650.039978,654.309998,648.150024,652.969971,652.969971,400090000
1996-03-27,652.969971,653.940002,647.599976,648.909973,648.909973,406280000
1996-03-28,648.909973,649.580017,646.359985,648.940002,648.940002,370750000
1996-03-29,648.940002,650.960022,644.890015,645.500000,645.500000,413510000
1996-04-01,645.500000,653.869995,645.500000,653.729980,653.729980,392120000
1996-04-02,653.729980,655.270020,652.809998,655.260010,655.260010,406640000
1996-04-03,655.260010,655.890015,651.809998,655.880005,655.880005,386620000
1996-04-04,655.880005,656.679993,654.890015,655.859985,655.859985,383400000
1996-04-08,655.859985,655.859985,638.039978,644.239990,644.239990,411810000
1996-04-09,644.239990,646.330017,640.840027,642.190002,642.190002,426790000
1996-04-10,642.190002,642.780029,631.760010,633.500000,633.500000,475150000
1996-04-11,633.500000,635.260010,624.140015,631.179993,631.179993,519710000
1996-04-12,631.179993,637.140015,631.179993,636.710022,636.710022,413270000
1996-04-15,636.710022,642.489990,636.710022,642.489990,642.489990,346370000
1996-04-16,642.489990,645.570007,642.150024,645.000000,645.000000,453310000
1996-04-17,645.000000,645.000000,638.710022,641.609985,641.609985,465200000
1996-04-18,641.609985,644.659973,640.760010,643.609985,643.609985,415150000
1996-04-19,643.609985,647.320007,643.609985,645.070007,645.070007,435690000
1996-04-22,645.070007,650.909973,645.070007,647.890015,647.890015,395370000
1996-04-23,647.890015,651.590027,647.700012,651.580017,651.580017,452690000
1996-04-24,651.580017,653.369995,648.250000,650.169983,650.169983,494220000
1996-04-25,650.169983,654.179993,647.059998,652.869995,652.869995,462120000
1996-04-26,652.869995,656.429993,651.960022,653.460022,653.460022,402530000
1996-04-29,653.460022,654.710022,651.599976,654.159973,654.159973,344030000
1996-04-30,654.159973,654.590027,651.049988,654.169983,654.169983,393390000
1996-05-01,654.169983,656.440002,652.260010,654.580017,654.580017,404620000
1996-05-02,654.580017,654.580017,642.130005,643.380005,643.380005,442960000
1996-05-03,643.380005,648.450012,640.229980,641.630005,641.630005,434010000
1996-05-06,641.630005,644.640015,636.190002,640.809998,640.809998,375820000
1996-05-07,640.809998,641.400024,636.960022,638.260010,638.260010,410770000
1996-05-08,638.260010,644.789978,630.070007,644.770020,644.770020,495460000
1996-05-09,644.770020,647.950012,643.179993,645.440002,645.440002,404310000
1996-05-10,645.440002,653.000000,645.440002,652.090027,652.090027,428370000
1996-05-13,652.090027,662.159973,652.090027,661.510010,661.510010,394180000
1996-05-14,661.510010,666.960022,661.510010,665.599976,665.599976,460440000
1996-05-15,665.599976,669.820007,664.460022,665.419983,665.419983,447790000
1996-05-16,665.419983,667.109985,662.789978,664.849976,664.849976,392070000
1996-05-17,664.849976,669.840027,664.849976,668.909973,668.909973,429140000
1996-05-20,668.909973,673.659973,667.640015,673.150024,673.150024,385000000
1996-05-21,673.150024,675.559998,672.260010,672.760010,672.760010,409610000
1996-05-22,672.760010,678.419983,671.229980,678.419983,678.419983,423670000
1996-05-23,678.419983,681.099976,673.450012,676.000000,676.000000,431850000
1996-05-24,676.000000,679.719971,676.000000,678.510010,678.510010,329150000
1996-05-28,678.510010,679.979980,671.520020,672.229980,672.229980,341480000
1996-05-29,672.229980,673.729980,666.090027,667.929993,667.929993,346730000
1996-05-30,667.929993,673.510010,664.559998,671.700012,671.700012,381960000
1996-05-31,671.700012,673.460022,667.000000,669.119995,669.119995,351750000
1996-06-03,669.119995,669.119995,665.190002,667.679993,667.679993,318470000
1996-06-04,667.679993,672.599976,667.679993,672.559998,672.559998,386040000
1996-06-05,672.559998,678.450012,672.090027,678.440002,678.440002,380360000
1996-06-06,678.440002,680.320007,673.020020,673.030029,673.030029,466940000
1996-06-07,673.030029,673.309998,662.479980,673.309998,673.309998,445710000
1996-06-10,673.309998,673.609985,670.150024,672.159973,672.159973,337480000
1996-06-11,672.159973,676.719971,669.940002,670.969971,670.969971,405390000
1996-06-12,670.969971,673.669983,668.770020,669.039978,669.039978,397190000
1996-06-13,669.039978,670.539978,665.489990,667.919983,667.919983,397620000
1996-06-14,667.919983,668.400024,664.349976,665.849976,665.849976,390630000
1996-06-17,665.849976,668.270020,664.090027,665.159973,665.159973,298410000
1996-06-18,665.159973,666.359985,661.340027,662.059998,662.059998,373290000
1996-06-19,662.059998,665.619995,661.210022,661.960022,661.960022,383610000
1996-06-20,661.960022,664.960022,658.750000,662.099976,662.099976,441060000
1996-06-21,662.099976,666.840027,662.099976,666.840027,666.840027,520340000
1996-06-24,666.840027,671.070007,666.840027,668.849976,668.849976,333840000
1996-06-25,668.849976,670.650024,667.289978,668.479980,668.479980,391900000
1996-06-26,668.479980,668.489990,663.669983,664.390015,664.390015,386520000
1996-06-27,664.390015,668.900024,661.559998,668.549988,668.549988,405580000
1996-06-28,668.549988,672.679993,668.549988,670.630005,670.630005,470460000
1996-07-01,670.630005,675.880005,670.630005,675.880005,675.880005,345750000
1996-07-02,675.880005,675.880005,672.549988,673.609985,673.609985,388000000
1996-07-03,673.609985,673.640015,670.210022,672.400024,672.400024,336260000
1996-07-05,672.400024,672.400024,657.409973,657.440002,657.440002,181470000
1996-07-08,657.440002,657.650024,651.130005,652.539978,652.539978,367560000
1996-07-09,652.539978,656.599976,652.539978,654.750000,654.750000,400170000
1996-07-10,654.750000,656.270020,648.390015,656.059998,656.059998,421350000
1996-07-11,656.059998,656.059998,639.520020,645.669983,645.669983,520470000
1996-07-12,645.669983,647.640015,640.210022,646.190002,646.190002,396740000
1996-07-15,646.190002,646.190002,629.690002,629.799988,629.799988,419020000
1996-07-16,629.799988,631.989990,605.880005,628.369995,628.369995,682980000
1996-07-17,628.369995,636.609985,628.369995,634.070007,634.070007,513830000
1996-07-18,634.070007,644.440002,633.289978,643.559998,643.559998,474460000
1996-07-19,643.510010,643.510010,635.500000,638.729980,638.729980,408070000
1996-07-22,638.729980,638.729980,630.380005,633.770020,633.770020,327300000
1996-07-23,633.789978,637.700012,625.650024,626.869995,626.869995,421900000
1996-07-24,626.190002,629.099976,616.429993,626.650024,626.650024,463030000
1996-07-25,626.650024,633.570007,626.650024,631.169983,631.169983,405390000
1996-07-26,631.169983,636.229980,631.169983,635.900024,635.900024,349900000
1996-07-29,635.900024,635.900024,630.900024,630.909973,630.909973,281560000
1996-07-30,630.909973,635.260010,629.219971,635.260010,635.260010,341090000
1996-07-31,635.260010,640.539978,633.739990,639.950012,639.950012,403560000
1996-08-01,639.950012,650.659973,639.489990,650.020020,650.020020,439110000
1996-08-02,650.020020,662.489990,650.020020,662.489990,662.489990,442080000
1996-08-05,662.489990,663.640015,659.030029,660.229980,660.229980,307240000
1996-08-06,660.229980,662.750000,656.830017,662.380005,662.380005,347290000
1996-08-07,662.380005,664.609985,660.000000,664.159973,664.159973,394340000
1996-08-08,664.159973,664.169983,661.280029,662.590027,662.590027,334570000
1996-08-09,662.590027,665.369995,660.309998,662.099976,662.099976,327280000
1996-08-12,662.099976,665.770020,658.950012,665.770020,665.770020,312170000
1996-08-13,665.770020,665.770020,659.130005,660.200012,660.200012,362470000
1996-08-14,660.200012,662.419983,658.469971,662.049988,662.049988,343460000
1996-08-15,662.049988,664.179993,660.640015,662.280029,662.280029,323950000
1996-08-16,662.280029,666.340027,662.260010,665.210022,665.210022,337650000
1996-08-19,665.210022,667.119995,665.000000,666.580017,666.580017,294080000
1996-08-20,666.580017,666.989990,665.150024,665.690002,665.690002,334960000
1996-08-21,665.690002,665.690002,662.159973,665.070007,665.070007,348820000
1996-08-22,665.070007,670.679993,664.880005,670.679993,670.679993,354950000
1996-08-23,670.679993,670.679993,664.929993,667.030029,667.030029,308010000
1996-08-26,667.030029,667.030029,662.359985,663.880005,663.880005,281430000
1996-08-27,663.880005,666.400024,663.500000,666.400024,666.400024,310520000
1996-08-28,666.400024,667.409973,664.390015,664.809998,664.809998,296440000
1996-08-29,664.809998,664.809998,655.349976,657.400024,657.400024,321120000
1996-08-30,657.400024,657.710022,650.520020,651.989990,651.989990,258380000
1996-09-03,651.989990,655.130005,643.969971,654.719971,654.719971,345740000
1996-09-04,654.719971,655.820007,652.929993,655.609985,655.609985,351290000
1996-09-05,655.609985,655.609985,648.890015,649.440002,649.440002,361430000
1996-09-06,649.440002,658.210022,649.440002,655.679993,655.679993,348710000
1996-09-09,655.679993,663.770020,655.679993,663.760010,663.760010,311530000
1996-09-10,663.760010,665.570007,661.549988,663.809998,663.809998,372960000
1996-09-11,663.809998,667.729980,661.789978,667.280029,667.280029,376880000
1996-09-12,667.280029,673.070007,667.280029,671.150024,671.150024,398820000
1996-09-13,671.150024,681.390015,671.150024,680.539978,680.539978,488360000
1996-09-16,680.539978,686.479980,680.530029,683.979980,683.979980,430080000
1996-09-17,683.979980,685.799988,679.960022,682.940002,682.940002,449850000
1996-09-18,682.940002,683.770020,679.750000,681.469971,681.469971,396600000
1996-09-19,681.469971,684.070007,679.059998,683.000000,683.000000,398580000
1996-09-20,683.000000,687.070007,683.000000,687.030029,687.030029,519420000
1996-09-23,687.030029,687.030029,681.010010,686.479980,686.479980,297760000
1996-09-24,686.479980,690.880005,683.539978,685.609985,685.609985,460150000
1996-09-25,685.609985,688.260010,684.919983,685.830017,685.830017,451710000
1996-09-26,685.830017,690.150024,683.770020,685.859985,685.859985,500870000
1996-09-27,685.859985,687.109985,683.729980,686.190002,686.190002,414760000
1996-09-30,686.190002,690.109985,686.030029,687.330017,687.330017,388570000
1996-10-01,687.309998,689.539978,684.440002,689.080017,689.080017,421550000
1996-10-02,689.080017,694.820007,689.080017,694.010010,694.010010,440130000
1996-10-03,694.010010,694.809998,691.780029,692.780029,692.780029,386500000
1996-10-04,692.780029,701.739990,692.780029,701.460022,701.460022,463940000
1996-10-07,701.460022,704.169983,701.390015,703.340027,703.340027,380750000
1996-10-08,703.340027,705.760010,699.880005,700.640015,700.640015,435070000
1996-10-09,700.640015,702.359985,694.419983,696.739990,696.739990,408450000
1996-10-10,696.739990,696.820007,693.340027,694.609985,694.609985,394950000
1996-10-11,694.609985,700.669983,694.609985,700.659973,700.659973,396050000
1996-10-14,700.659973,705.159973,700.659973,703.539978,703.539978,322000000
1996-10-15,703.539978,708.070007,699.070007,702.570007,702.570007,458980000
1996-10-16,702.570007,704.419983,699.150024,704.409973,704.409973,441410000
1996-10-17,705.000000,708.520020,704.760010,706.989990,706.989990,478550000
1996-10-18,706.989990,711.039978,706.109985,710.820007,710.820007,473020000
1996-10-21,710.820007,714.099976,707.710022,709.849976,709.849976,414630000
1996-10-22,709.849976,709.849976,704.549988,706.570007,706.570007,410790000
1996-10-23,706.570007,707.309998,700.979980,707.270020,707.270020,442170000
1996-10-24,707.270020,708.250000,702.109985,702.289978,702.289978,418970000
1996-10-25,702.289978,704.109985,700.530029,700.919983,700.919983,367640000
1996-10-28,700.919983,705.400024,697.250000,697.260010,697.260010,383620000
1996-10-29,697.260010,703.250000,696.219971,701.500000,701.500000,443890000
1996-10-30,701.500000,703.440002,700.049988,700.900024,700.900024,437770000
1996-10-31,700.900024,706.609985,700.349976,705.270020,705.270020,482840000
1996-11-01,705.270020,708.599976,701.299988,703.770020,703.770020,465510000
1996-11-04,703.770020,707.020020,702.840027,706.729980,706.729980,398790000
1996-11-05,706.729980,714.559998,706.729980,714.140015,714.140015,486660000
1996-11-06,714.140015,724.599976,712.830017,724.590027,724.590027,509600000
1996-11-07,724.590027,729.489990,722.229980,727.650024,727.650024,502530000
1996-11-08,727.650024,730.820007,725.219971,730.820007,730.820007,402320000
1996-11-11,730.820007,732.599976,729.940002,731.869995,731.869995,353960000
1996-11-12,731.869995,733.039978,728.200012,729.559998,729.559998,471740000
1996-11-13,729.559998,732.109985,728.030029,731.130005,731.130005,429840000
1996-11-14,731.130005,735.989990,729.200012,735.880005,735.880005,480350000
1996-11-15,735.880005,741.919983,735.150024,737.619995,737.619995,529100000
1996-11-18,737.619995,739.239990,734.390015,737.020020,737.020020,388520000
1996-11-19,737.020020,742.179993,736.869995,742.159973,742.159973,461980000
1996-11-20,742.159973,746.989990,740.400024,743.950012,743.950012,497900000
1996-11-21,743.950012,745.200012,741.080017,742.750000,742.750000,464430000
1996-11-22,742.750000,748.729980,742.750000,748.729980,748.729980,525210000
1996-11-25,748.729980,757.049988,747.989990,757.030029,757.030029,475260000
1996-11-26,757.030029,762.119995,752.830017,755.960022,755.960022,527380000
1996-11-27,755.960022,757.299988,753.179993,755.000000,755.000000,377780000
1996-11-29,755.000000,758.270020,755.000000,757.020020,757.020020,14990000
1996-12-02,757.020020,757.030029,751.489990,756.559998,756.559998,412520000
1996-12-03,756.559998,761.750000,747.580017,748.280029,748.280029,516160000
1996-12-04,748.280029,748.400024,738.460022,745.099976,745.099976,498240000
1996-12-05,745.099976,747.650024,742.609985,744.380005,744.380005,483710000
1996-12-06,744.380005,744.380005,726.890015,739.599976,739.599976,500860000
1996-12-09,739.599976,749.760010,739.599976,749.760010,749.760010,381570000
1996-12-10,749.760010,753.429993,747.020020,747.539978,747.539978,446120000
1996-12-11,747.539978,747.539978,732.750000,740.729980,740.729980,494210000
1996-12-12,740.729980,744.859985,729.299988,729.299988,729.299988,492920000
1996-12-13,729.330017,731.400024,721.969971,728.640015,728.640015,458540000
1996-12-16,728.640015,732.679993,719.400024,720.979980,720.979980,447560000
1996-12-17,720.979980,727.669983,716.690002,726.039978,726.039978,519840000
1996-12-18,726.039978,732.760010,726.039978,731.539978,731.539978,500490000
1996-12-19,731.539978,746.059998,731.539978,745.760010,745.760010,526410000
1996-12-20,745.760010,755.409973,745.760010,748.869995,748.869995,654340000
1996-12-23,748.869995,750.400024,743.280029,746.919983,746.919983,343280000
1996-12-24,746.919983,751.030029,746.919983,751.030029,751.030029,165140000
1996-12-26,751.030029,757.070007,751.020020,755.820007,755.820007,254630000
1996-12-27,755.820007,758.750000,754.820007,756.789978,756.789978,253810000
1996-12-30,756.789978,759.200012,752.729980,753.849976,753.849976,339060000
1996-12-31,753.849976,753.950012,740.739990,740.739990,740.739990,399760000
1997-01-02,740.739990,742.809998,729.549988,737.010010,737.010010,463230000
1997-01-03,737.010010,748.239990,737.010010,748.030029,748.030029,452970000
1997-01-06,748.030029,753.309998,743.820007,747.650024,747.650024,531350000
1997-01-07,747.650024,753.260010,742.179993,753.229980,753.229980,538220000
1997-01-08,753.229980,755.719971,747.710022,748.409973,748.409973,557510000
1997-01-09,748.409973,757.679993,748.409973,754.849976,754.849976,555370000
1997-01-10,754.849976,759.650024,746.919983,759.500000,759.500000,545850000
1997-01-13,759.500000,762.849976,756.690002,759.510010,759.510010,445400000
1997-01-14,759.510010,772.039978,759.510010,768.859985,768.859985,531600000
1997-01-15,768.859985,770.950012,763.719971,767.200012,767.200012,524990000
1997-01-16,767.200012,772.049988,765.250000,769.750000,769.750000,537290000
1997-01-17,769.750000,776.369995,769.719971,776.169983,776.169983,534640000
1997-01-20,776.169983,780.080017,774.190002,776.700012,776.700012,440470000
1997-01-21,776.700012,783.719971,772.000000,782.719971,782.719971,571280000
1997-01-22,782.719971,786.229980,779.559998,786.229980,786.229980,589230000
1997-01-23,786.229980,794.669983,776.640015,777.559998,777.559998,685070000
1997-01-24,777.559998,778.210022,768.169983,770.520020,770.520020,542920000
1997-01-27,770.520020,771.429993,764.179993,765.020020,765.020020,445760000
1997-01-28,765.020020,776.320007,761.750000,765.020020,765.020020,541580000
1997-01-29,765.020020,772.700012,765.020020,772.500000,772.500000,498390000
1997-01-30,772.500000,784.169983,772.500000,784.169983,784.169983,524160000
1997-01-31,784.169983,791.859985,784.169983,786.159973,786.159973,578550000
1997-02-03,786.159973,787.140015,783.119995,786.729980,786.729980,463600000
1997-02-04,786.729980,789.280029,783.679993,789.260010,789.260010,506530000
1997-02-05,789.260010,792.710022,773.429993,778.280029,778.280029,580520000
1997-02-06,778.280029,780.349976,774.450012,780.150024,780.150024,519660000
1997-02-07,780.150024,789.719971,778.190002,789.559998,789.559998,540910000
1997-02-10,789.559998,793.460022,784.690002,785.429993,785.429993,471590000
1997-02-11,785.429993,789.599976,780.950012,789.590027,789.590027,483090000
1997-02-12,789.590027,802.770020,789.590027,802.770020,802.770020,563890000
1997-02-13,802.770020,812.929993,802.770020,811.820007,811.820007,593710000
1997-02-14,811.820007,812.200012,808.150024,808.479980,808.479980,491540000
1997-02-18,808.479980,816.289978,806.340027,816.289978,816.289978,474110000
1997-02-19,816.289978,817.679993,811.200012,812.489990,812.489990,519350000
1997-02-20,812.489990,812.489990,800.349976,802.799988,802.799988,492220000
1997-02-21,802.799988,804.940002,799.989990,801.770020,801.770020,478450000
1997-02-24,801.770020,810.640015,798.419983,810.280029,810.280029,462450000
1997-02-25,810.280029,812.849976,807.650024,812.030029,812.030029,527450000
1997-02-26,812.099976,812.700012,798.130005,805.679993,805.679993,573920000
1997-02-27,805.679993,805.679993,795.059998,795.070007,795.070007,464660000
1997-02-28,795.070007,795.700012,788.500000,790.820007,790.820007,508280000
1997-03-03,790.820007,795.309998,785.659973,795.309998,795.309998,437220000
1997-03-04,795.309998,798.929993,789.979980,790.950012,790.950012,537890000
1997-03-05,790.950012,801.989990,790.950012,801.989990,801.989990,532500000
1997-03-06,801.989990,804.109985,797.500000,798.559998,798.559998,540310000
1997-03-07,798.559998,808.190002,798.559998,804.969971,804.969971,508270000
1997-03-10,804.969971,813.659973,803.659973,813.650024,813.650024,468780000
1997-03-11,813.650024,814.900024,810.770020,811.340027,811.340027,493250000
1997-03-12,811.340027,811.340027,801.070007,804.260010,804.260010,490200000
1997-03-13,804.260010,804.260010,789.440002,789.559998,789.559998,507560000
1997-03-14,789.559998,796.880005,789.559998,793.169983,793.169983,491540000
1997-03-17,793.169983,796.280029,782.979980,795.710022,795.710022,495260000
1997-03-18,795.710022,797.179993,785.469971,789.659973,789.659973,467330000
1997-03-19,789.659973,791.590027,780.030029,785.770020,785.770020,535580000
1997-03-20,785.770020,786.289978,778.039978,782.650024,782.650024,497480000
1997-03-21,782.650024,786.440002,782.650024,784.099976,784.099976,638760000
1997-03-24,784.099976,791.010010,780.789978,790.890015,790.890015,451970000
1997-03-25,790.890015,798.109985,788.390015,789.070007,789.070007,487520000
1997-03-26,789.070007,794.890015,786.770020,790.500000,790.500000,506670000
1997-03-27,790.500000,792.580017,767.320007,773.880005,773.880005,476790000
1997-03-31,773.880005,773.880005,756.130005,757.119995,757.119995,555880000
1997-04-01,757.119995,761.489990,751.260010,759.640015,759.640015,515770000
1997-04-02,759.640015,759.650024,747.590027,750.109985,750.109985,478210000
1997-04-03,750.109985,751.039978,744.400024,750.320007,750.320007,498010000
1997-04-04,750.320007,757.900024,744.039978,757.900024,757.900024,544580000
1997-04-07,757.900024,764.820007,757.900024,762.130005,762.130005,453790000
1997-04-08,762.130005,766.250000,758.359985,766.119995,766.119995,450790000
1997-04-09,766.119995,769.530029,759.150024,760.599976,760.599976,451500000
1997-04-10,760.599976,763.729980,757.650024,758.340027,758.340027,421790000
1997-04-11,758.340027,758.340027,737.640015,737.650024,737.650024,444380000
1997-04-14,737.650024,743.729980,733.539978,743.729980,743.729980,406800000
1997-04-15,743.729980,754.719971,743.729980,754.719971,754.719971,507370000
1997-04-16,754.719971,763.530029,751.989990,763.530029,763.530029,498820000
1997-04-17,763.530029,768.549988,760.489990,761.770020,761.770020,503760000
1997-04-18,761.770020,767.929993,761.770020,766.340027,766.340027,468940000
1997-04-21,766.340027,767.390015,756.380005,760.369995,760.369995,397300000
1997-04-22,760.369995,774.640015,759.900024,774.609985,774.609985,507500000
1997-04-23,774.609985,778.190002,771.900024,773.640015,773.640015,489350000
1997-04-24,773.640015,779.890015,769.719971,771.179993,771.179993,493640000
1997-04-25,771.179993,771.179993,764.630005,765.369995,765.369995,414350000
1997-04-28,765.369995,773.890015,763.299988,772.960022,772.960022,404470000
1997-04-29,772.960022,794.440002,772.960022,794.049988,794.049988,547690000
1997-04-30,794.049988,804.130005,791.210022,801.340027,801.340027,556070000
1997-05-01,801.340027,802.950012,793.210022,798.530029,798.530029,460380000
1997-05-02,798.530029,812.989990,798.530029,812.969971,812.969971,499770000
1997-05-05,812.969971,830.289978,811.799988,830.289978,830.289978,549410000
1997-05-06,830.239990,832.289978,824.700012,827.760010,827.760010,603680000
1997-05-07,827.760010,827.760010,814.700012,815.619995,815.619995,500580000
1997-05-08,815.619995,829.090027,811.840027,820.260010,820.260010,534120000
1997-05-09,820.260010,827.690002,815.780029,824.780029,824.780029,455690000
1997-05-12,824.780029,838.559998,824.780029,837.659973,837.659973,459370000
1997-05-13,837.659973,838.489990,829.119995,833.130005,833.130005,489760000
1997-05-14,833.130005,841.289978,833.130005,836.039978,836.039978,504960000
1997-05-15,836.039978,842.450012,833.340027,841.880005,841.880005,458170000
1997-05-16,841.880005,841.880005,829.150024,829.750000,829.750000,486780000
1997-05-19,829.750000,835.919983,828.869995,833.270020,833.270020,345140000
1997-05-20,833.270020,841.960022,826.409973,841.659973,841.659973,450850000
1997-05-21,841.659973,846.869995,835.219971,839.349976,839.349976,540730000
1997-05-22,839.349976,841.909973,833.859985,835.659973,835.659973,426940000
1997-05-23,835.659973,848.489990,835.659973,847.030029,847.030029,417030000
1997-05-27,847.030029,851.530029,840.960022,849.710022,849.710022,436150000
1997-05-28,849.710022,850.950012,843.210022,847.210022,847.210022,487340000
1997-05-29,847.210022,848.960022,842.609985,844.080017,844.080017,462600000
1997-05-30,844.080017,851.869995,831.869995,848.280029,848.280029,537200000
1997-06-02,848.280029,851.340027,844.609985,846.359985,846.359985,435950000
1997-06-03,846.359985,850.559998,841.510010,845.479980,845.479980,527120000
1997-06-04,845.479980,845.549988,838.820007,840.109985,840.109985,466690000
1997-06-05,840.109985,848.890015,840.109985,843.429993,843.429993,452610000
1997-06-06,843.429993,859.239990,843.359985,858.010010,858.010010,488940000
1997-06-09,858.010010,865.140015,858.010010,862.909973,862.909973,465810000
1997-06-10,862.909973,870.049988,862.179993,865.270020,865.270020,526980000
1997-06-11,865.270020,870.659973,865.150024,869.570007,869.570007,513740000
1997-06-12,869.570007,884.340027,869.010010,883.460022,883.460022,592730000
1997-06-13,883.479980,894.690002,883.479980,893.270020,893.270020,575810000
1997-06-16,893.270020,895.169983,891.210022,893.900024,893.900024,414280000
1997-06-17,893.900024,897.599976,886.190002,894.419983,894.419983,543010000
1997-06-18,894.419983,894.419983,887.030029,889.059998,889.059998,491740000
1997-06-19,889.059998,900.090027,888.989990,897.989990,897.989990,536940000
1997-06-20,897.989990,901.770020,897.770020,898.700012,898.700012,653110000
1997-06-23,898.700012,898.700012,878.429993,878.619995,878.619995,492940000
1997-06-24,878.619995,896.750000,878.619995,896.340027,896.340027,542650000
1997-06-25,896.340027,902.090027,882.239990,888.989990,888.989990,603040000
1997-06-26,888.989990,893.210022,879.320007,883.679993,883.679993,499780000
1997-06-27,883.679993,894.700012,883.679993,887.299988,887.299988,472540000
1997-06-30,887.299988,892.619995,879.820007,885.140015,885.140015,561540000
1997-07-01,885.140015,893.880005,884.539978,891.030029,891.030029,544190000
1997-07-02,891.030029,904.049988,891.030029,904.030029,904.030029,526970000
1997-07-03,904.030029,917.820007,904.030029,916.919983,916.919983,374680000
1997-07-07,916.919983,923.260010,909.690002,912.200012,912.200012,518780000
1997-07-08,912.200012,918.760010,911.559998,918.750000,918.750000,526010000
1997-07-09,918.750000,922.030029,902.479980,907.539978,907.539978,589110000
1997-07-10,907.539978,916.539978,904.309998,913.780029,913.780029,551340000
1997-07-11,913.780029,919.739990,913.109985,916.679993,916.679993,500050000
1997-07-14,916.679993,921.780029,912.020020,918.380005,918.380005,485960000
1997-07-15,918.380005,926.150024,914.520020,925.760010,925.760010,598370000
1997-07-16,925.760010,939.320007,925.760010,936.590027,936.590027,647390000
1997-07-17,936.590027,936.960022,927.900024,931.609985,931.609985,629250000
1997-07-18,931.609985,931.609985,912.900024,915.299988,915.299988,589710000
1997-07-21,915.299988,915.380005,907.119995,912.940002,912.940002,459500000
1997-07-22,912.940002,934.380005,912.940002,933.979980,933.979980,579590000
1997-07-23,933.979980,941.799988,933.979980,936.559998,936.559998,616930000
1997-07-24,936.559998,941.510010,926.909973,940.299988,940.299988,571020000
1997-07-25,940.299988,945.650024,936.090027,938.789978,938.789978,521510000
1997-07-28,938.789978,942.969971,935.190002,936.450012,936.450012,466920000
1997-07-29,936.450012,942.960022,932.559998,942.289978,942.289978,544540000
1997-07-30,942.289978,953.979980,941.979980,952.289978,952.289978,568470000
1997-07-31,952.289978,957.729980,948.890015,954.309998,954.309998,547830000
1997-08-01,954.289978,955.349976,939.039978,947.140015,947.140015,513750000
1997-08-04,947.140015,953.179993,943.599976,950.299988,950.299988,456000000
1997-08-05,950.299988,954.210022,948.919983,952.369995,952.369995,525710000
1997-08-06,952.369995,962.429993,949.450012,960.320007,960.320007,565200000
1997-08-07,960.320007,964.169983,950.869995,951.190002,951.190002,576030000
1997-08-08,951.190002,951.190002,925.739990,933.539978,933.539978,563420000
1997-08-11,933.539978,938.500000,925.390015,937.000000,937.000000,480340000
1997-08-12,937.000000,942.989990,925.659973,926.530029,926.530029,499310000
1997-08-13,926.530029,935.770020,916.539978,922.020020,922.020020,587210000
1997-08-14,922.020020,930.070007,916.919983,924.770020,924.770020,530460000
1997-08-15,924.770020,924.770020,900.809998,900.809998,900.809998,537820000
1997-08-18,900.809998,912.570007,893.340027,912.489990,912.489990,514330000
1997-08-19,912.489990,926.010010,912.489990,926.010010,926.010010,545630000
1997-08-20,926.010010,939.349976,924.580017,939.349976,939.349976,521270000
1997-08-21,939.349976,939.469971,921.349976,925.049988,925.049988,499000000
1997-08-22,925.049988,925.049988,905.419983,923.539978,923.539978,460160000
1997-08-25,923.549988,930.929993,917.289978,920.159973,920.159973,388990000
1997-08-26,920.159973,922.469971,911.719971,913.020020,913.020020,449110000
1997-08-27,913.020020,916.229980,903.830017,913.700012,913.700012,492150000
1997-08-28,913.700012,915.900024,898.650024,903.669983,903.669983,486300000
1997-08-29,903.669983,907.280029,896.820007,899.469971,899.469971,413910000
1997-09-02,899.469971,927.580017,899.469971,927.580017,927.580017,491870000
1997-09-03,927.580017,935.900024,926.869995,927.859985,927.859985,549060000
1997-09-04,927.859985,933.359985,925.590027,930.869995,930.869995,559310000
1997-09-05,930.869995,940.369995,924.049988,929.049988,929.049988,536400000
1997-09-08,929.049988,936.500000,929.049988,931.200012,931.200012,466430000
1997-09-09,931.200012,938.900024,927.280029,933.619995,933.619995,502200000
1997-09-10,933.619995,933.619995,918.760010,919.030029,919.030029,517620000
1997-09-11,919.030029,919.030029,902.559998,912.590027,912.590027,575020000
1997-09-12,912.590027,925.049988,906.700012,923.909973,923.909973,544150000
1997-09-15,923.909973,928.900024,919.409973,919.770020,919.770020,468030000
1997-09-16,919.770020,947.659973,919.770020,945.640015,945.640015,636380000
1997-09-17,945.640015,950.289978,941.989990,943.000000,943.000000,590550000
1997-09-18,943.000000,958.190002,943.000000,947.289978,947.289978,566830000
1997-09-19,947.289978,952.349976,943.900024,950.510010,950.510010,631040000
1997-09-22,950.510010,960.590027,950.510010,955.429993,955.429993,490900000
1997-09-23,955.429993,955.780029,948.070007,951.929993,951.929993,522930000
1997-09-24,951.929993,959.780029,944.070007,944.479980,944.479980,639460000
1997-09-25,944.479980,947.000000,937.380005,937.909973,937.909973,524880000
1997-09-26,937.909973,946.440002,937.909973,945.219971,945.219971,505340000
1997-09-29,945.219971,953.960022,941.940002,953.340027,953.340027,477100000
1997-09-30,953.340027,955.169983,947.280029,947.280029,947.280029,587500000
1997-10-01,947.280029,956.710022,947.280029,955.409973,955.409973,598660000
1997-10-02,955.409973,960.460022,952.940002,960.460022,960.460022,474760000
1997-10-03,960.460022,975.469971,955.130005,965.030029,965.030029,623370000
1997-10-06,965.030029,974.159973,965.030029,972.690002,972.690002,495620000
1997-10-07,972.690002,983.119995,971.950012,983.119995,983.119995,551970000
1997-10-08,983.119995,983.119995,968.650024,973.840027,973.840027,573110000
1997-10-09,973.840027,974.719971,963.340027,970.619995,970.619995,551840000
1997-10-10,970.619995,970.619995,963.419983,966.979980,966.979980,500680000
1997-10-13,966.979980,973.460022,966.950012,968.099976,968.099976,354800000
1997-10-14,968.099976,972.859985,961.869995,970.280029,970.280029,510330000
1997-10-15,970.280029,970.280029,962.750000,965.719971,965.719971,505310000
1997-10-16,965.719971,973.380005,950.770020,955.250000,955.250000,597010000
1997-10-17,955.229980,955.229980,931.580017,944.159973,944.159973,624980000
1997-10-20,944.159973,955.719971,941.429993,955.609985,955.609985,483880000
1997-10-21,955.609985,972.559998,955.609985,972.280029,972.280029,582310000
1997-10-22,972.280029,972.609985,965.659973,968.489990,968.489990,613490000
1997-10-23,968.489990,968.489990,944.159973,950.690002,950.690002,673270000
1997-10-24,950.690002,960.039978,937.549988,941.640015,941.640015,677630000
1997-10-27,941.640015,941.640015,876.729980,876.989990,876.989990,693730000
1997-10-28,876.989990,923.090027,855.270020,921.849976,921.849976,1202550000
1997-10-29,921.849976,935.239990,913.880005,919.159973,919.159973,777660000
1997-10-30,919.159973,923.280029,903.679993,903.679993,903.679993,712230000
1997-10-31,903.679993,919.929993,903.679993,914.619995,914.619995,638070000
1997-11-03,914.619995,939.020020,914.619995,938.989990,938.989990,564740000
1997-11-04,938.989990,941.400024,932.659973,940.760010,940.760010,541590000
1997-11-05,940.760010,949.619995,938.159973,942.760010,942.760010,565680000
1997-11-06,942.760010,942.849976,934.159973,938.030029,938.030029,522890000
1997-11-07,938.030029,938.030029,915.390015,927.510010,927.510010,569980000
1997-11-10,927.510010,935.900024,920.260010,921.130005,921.130005,464140000
1997-11-11,921.130005,928.289978,919.630005,923.780029,923.780029,435660000
1997-11-12,923.780029,923.880005,905.340027,905.960022,905.960022,585340000
1997-11-13,905.960022,917.789978,900.609985,916.659973,916.659973,653960000
1997-11-14,916.659973,930.440002,915.340027,928.349976,928.349976,635760000
1997-11-17,928.349976,949.659973,928.349976,946.200012,946.200012,576540000
1997-11-18,946.200012,947.650024,937.429993,938.229980,938.229980,521380000
1997-11-19,938.229980,947.280029,934.830017,944.590027,944.590027,542720000
1997-11-20,944.590027,961.830017,944.590027,958.979980,958.979980,602610000
1997-11-21,958.979980,964.549988,954.599976,963.090027,963.090027,611000000
1997-11-24,963.090027,963.090027,945.219971,946.669983,946.669983,514920000
1997-11-25,946.669983,954.469971,944.710022,950.820007,950.820007,587890000
1997-11-26,950.820007,956.469971,950.820007,951.640015,951.640015,487750000
1997-11-28,951.640015,959.130005,951.640015,955.400024,955.400024,189070000
1997-12-01,955.400024,974.770020,955.400024,974.770020,974.770020,590300000
1997-12-02,974.780029,976.200012,969.830017,971.679993,971.679993,576120000
1997-12-03,971.679993,980.809998,966.159973,976.770020,976.770020,624610000
1997-12-04,976.770020,983.359985,971.369995,973.099976,973.099976,633470000
1997-12-05,973.099976,986.250000,969.099976,983.789978,983.789978,563590000
1997-12-08,983.789978,985.669983,979.570007,982.369995,982.369995,490320000
1997-12-09,982.369995,982.369995,973.809998,975.780029,975.780029,539130000
1997-12-10,975.780029,975.780029,962.679993,969.789978,969.789978,602290000
1997-12-11,969.789978,969.789978,951.890015,954.940002,954.940002,631770000
1997-12-12,954.940002,961.320007,947.000000,953.390015,953.390015,579280000
1997-12-15,953.390015,965.960022,953.390015,963.390015,963.390015,597150000
1997-12-16,963.390015,973.000000,963.390015,968.039978,968.039978,623320000
1997-12-17,968.039978,974.299988,964.250000,965.539978,965.539978,618900000
1997-12-18,965.539978,965.539978,950.549988,955.299988,955.299988,618870000
1997-12-19,955.299988,955.299988,924.919983,946.780029,946.780029,793200000
1997-12-22,946.780029,956.729980,946.250000,953.700012,953.700012,530670000
1997-12-23,953.700012,954.510010,938.909973,939.130005,939.130005,515070000
1997-12-24,939.130005,942.880005,932.700012,932.700012,932.700012,265980000
1997-12-26,932.700012,939.989990,932.700012,936.460022,936.460022,154900000
1997-12-29,936.460022,953.950012,936.460022,953.349976,953.349976,443160000
1997-12-30,953.349976,970.840027,953.349976,970.840027,970.840027,499500000
1997-12-31,970.840027,975.020020,967.409973,970.429993,970.429993,467280000
1998-01-02,970.429993,975.039978,965.729980,975.039978,975.039978,366730000
1998-01-05,975.039978,982.630005,969.000000,977.070007,977.070007,628070000
1998-01-06,977.070007,977.070007,962.679993,966.580017,966.580017,618360000
1998-01-07,966.580017,966.580017,952.669983,964.000000,964.000000,667390000
1998-01-08,964.000000,964.000000,955.039978,956.049988,956.049988,652140000
1998-01-09,956.049988,956.049988,921.719971,927.690002,927.690002,746420000
1998-01-12,927.690002,939.250000,912.830017,939.210022,939.210022,705450000
1998-01-13,939.210022,952.140015,939.210022,952.119995,952.119995,646740000
1998-01-14,952.119995,958.119995,948.000000,957.940002,957.940002,603280000
1998-01-15,957.940002,957.940002,950.270020,950.729980,950.729980,569050000
1998-01-16,950.729980,965.119995,950.729980,961.510010,961.510010,670080000
1998-01-20,961.510010,978.599976,961.479980,978.599976,978.599976,644790000
1998-01-21,978.599976,978.599976,963.289978,970.809998,970.809998,626160000
1998-01-22,970.809998,970.809998,959.489990,963.039978,963.039978,646570000
1998-01-23,963.039978,966.440002,950.859985,957.590027,957.590027,635770000
1998-01-26,957.590027,963.039978,954.239990,956.950012,956.950012,555080000
1998-01-27,956.950012,973.229980,956.260010,969.020020,969.020020,679140000
1998-01-28,969.020020,978.630005,969.020020,977.460022,977.460022,708470000
1998-01-29,977.460022,992.650024,975.210022,985.489990,985.489990,750760000
1998-01-30,985.489990,987.409973,979.630005,980.280029,980.280029,613380000
1998-02-02,980.280029,1002.479980,980.280029,1001.270020,1001.270020,724320000
1998-02-03,1001.270020,1006.130005,996.900024,1006.000000,1006.000000,692120000
1998-02-04,1006.000000,1009.520020,999.429993,1006.900024,1006.900024,695420000
1998-02-05,1006.900024,1013.510010,1000.270020,1003.539978,1003.539978,703980000
1998-02-06,1003.539978,1013.070007,1003.359985,1012.460022,1012.460022,569650000
1998-02-09,1012.460022,1015.330017,1006.280029,1010.739990,1010.739990,524810000
1998-02-10,1010.739990,1022.150024,1010.710022,1019.010010,1019.010010,642800000
1998-02-11,1019.010010,1020.710022,1016.380005,1020.010010,1020.010010,599300000
1998-02-12,1020.010010,1026.300049,1008.549988,1024.140015,1024.140015,611480000
1998-02-13,1024.140015,1024.140015,1017.710022,1020.090027,1020.090027,531940000
1998-02-17,1020.090027,1028.020020,1020.090027,1022.760010,1022.760010,605890000
1998-02-18,1022.760010,1032.079956,1021.700012,1032.079956,1032.079956,606000000
1998-02-19,1032.079956,1032.930054,1026.619995,1028.280029,1028.280029,581820000
1998-02-20,1028.280029,1034.209961,1022.690002,1034.209961,1034.209961,594300000
1998-02-23,1034.209961,1038.680054,1031.760010,1038.140015,1038.140015,550730000
1998-02-24,1038.140015,1038.729980,1028.890015,1030.560059,1030.560059,589880000
1998-02-25,1030.560059,1045.790039,1030.560059,1042.900024,1042.900024,611350000
1998-02-26,1042.900024,1048.680054,1039.849976,1048.670044,1048.670044,646280000
1998-02-27,1048.670044,1051.660034,1044.400024,1049.339966,1049.339966,574480000
1998-03-02,1049.339966,1053.979980,1044.699951,1047.699951,1047.699951,591470000
1998-03-03,1047.699951,1052.020020,1043.410034,1052.020020,1052.020020,612360000
1998-03-04,1052.020020,1052.020020,1042.739990,1047.329956,1047.329956,644280000
1998-03-05,1047.329956,1047.329956,1030.869995,1035.050049,1035.050049,648270000
1998-03-06,1035.050049,1055.689941,1035.050049,1055.689941,1055.689941,665500000
1998-03-09,1055.689941,1058.550049,1050.020020,1052.310059,1052.310059,624700000
1998-03-10,1052.310059,1064.589966,1052.310059,1064.250000,1064.250000,631920000
1998-03-11,1064.250000,1069.180054,1064.219971,1068.469971,1068.469971,655260000
1998-03-12,1068.469971,1071.869995,1063.540039,1069.920044,1069.920044,594940000
1998-03-13,1069.920044,1075.859985,1066.569946,1068.609985,1068.609985,597800000
1998-03-16,1068.609985,1079.459961,1068.609985,1079.270020,1079.270020,548980000
1998-03-17,1079.270020,1080.520020,1073.290039,1080.449951,1080.449951,680960000
1998-03-18,1080.449951,1085.520020,1077.770020,1085.520020,1085.520020,632690000
1998-03-19,1085.520020,1089.739990,1084.300049,1089.739990,1089.739990,598240000
1998-03-20,1089.739990,1101.040039,1089.390015,1099.160034,1099.160034,717310000
1998-03-23,1099.160034,1101.160034,1094.250000,1095.550049,1095.550049,631350000
1998-03-24,1095.550049,1106.750000,1095.550049,1105.650024,1105.650024,605720000
1998-03-25,1105.650024,1113.069946,1092.839966,1101.930054,1101.930054,676550000
1998-03-26,1101.930054,1106.280029,1097.000000,1100.800049,1100.800049,606770000
1998-03-27,1100.800049,1107.180054,1091.140015,1095.439941,1095.439941,582190000
1998-03-30,1095.439941,1099.099976,1090.020020,1093.599976,1093.599976,497400000
1998-03-31,1093.550049,1110.130005,1093.550049,1101.750000,1101.750000,674930000
1998-04-01,1101.750000,1109.189941,1095.290039,1108.150024,1108.150024,677310000
1998-04-02,1108.150024,1121.010010,1107.890015,1120.010010,1120.010010,674340000
1998-04-03,1120.010010,1126.359985,1118.119995,1122.699951,1122.699951,653880000
1998-04-06,1122.699951,1131.989990,1121.369995,1121.380005,1121.380005,625810000
1998-04-07,1121.380005,1121.380005,1102.439941,1109.550049,1109.550049,670760000
1998-04-08,1109.550049,1111.599976,1098.209961,1101.650024,1101.650024,616330000
1998-04-09,1101.650024,1111.449951,1101.650024,1110.670044,1110.670044,548940000
1998-04-13,1110.670044,1110.750000,1100.599976,1109.689941,1109.689941,564480000
1998-04-14,1109.689941,1115.949951,1109.479980,1115.750000,1115.750000,613730000
1998-04-15,1115.750000,1119.900024,1112.239990,1119.319946,1119.319946,685020000
1998-04-16,1119.319946,1119.319946,1105.270020,1108.170044,1108.170044,699570000
1998-04-17,1108.170044,1122.719971,1104.949951,1122.719971,1122.719971,672290000
1998-04-20,1122.719971,1124.880005,1118.430054,1123.650024,1123.650024,595190000
1998-04-21,1123.650024,1129.650024,1119.540039,1126.670044,1126.670044,675640000
1998-04-22,1126.670044,1132.979980,1126.290039,1130.540039,1130.540039,696740000
1998-04-23,1130.540039,1130.540039,1117.489990,1119.579956,1119.579956,653190000
1998-04-24,1119.579956,1122.810059,1104.770020,1107.900024,1107.900024,633890000
1998-04-27,1107.900024,1107.900024,1076.699951,1086.540039,1086.540039,685960000
1998-04-28,1086.540039,1095.939941,1081.489990,1085.109985,1085.109985,678600000
1998-04-29,1085.109985,1098.239990,1084.650024,1094.619995,1094.619995,638790000
1998-04-30,1094.630005,1116.969971,1094.630005,1111.750000,1111.750000,695600000
1998-05-01,1111.750000,1121.020020,1111.750000,1121.000000,1121.000000,581970000
1998-05-04,1121.000000,1130.520020,1121.000000,1122.069946,1122.069946,551700000
1998-05-05,1122.069946,1122.069946,1111.160034,1115.500000,1115.500000,583630000
1998-05-06,1115.500000,1118.390015,1104.640015,1104.920044,1104.920044,606540000
1998-05-07,1104.920044,1105.579956,1094.589966,1095.140015,1095.140015,582240000
1998-05-08,1095.140015,1111.420044,1094.530029,1108.140015,1108.140015,567890000
1998-05-11,1108.140015,1119.130005,1103.719971,1106.640015,1106.640015,560840000
1998-05-12,1106.640015,1115.959961,1102.780029,1115.790039,1115.790039,604420000
1998-05-13,1115.790039,1122.219971,1114.930054,1118.859985,1118.859985,600010000
1998-05-14,1118.859985,1124.030029,1112.430054,1117.369995,1117.369995,578380000
1998-05-15,1117.369995,1118.660034,1107.109985,1108.729980,1108.729980,621990000
1998-05-18,1108.729980,1112.439941,1097.989990,1105.819946,1105.819946,519100000
1998-05-19,1105.819946,1113.500000,1105.819946,1109.520020,1109.520020,566020000
1998-05-20,1109.520020,1119.079956,1107.510010,1119.060059,1119.060059,587240000
1998-05-21,1119.060059,1124.449951,1111.939941,1114.640015,1114.640015,551970000
1998-05-22,1114.640015,1116.890015,1107.989990,1110.469971,1110.469971,444070000
1998-05-26,1110.469971,1116.790039,1094.010010,1094.020020,1094.020020,541410000
1998-05-27,1094.020020,1094.439941,1074.390015,1092.229980,1092.229980,682040000
1998-05-28,1092.229980,1099.729980,1089.060059,1097.599976,1097.599976,588900000
1998-05-29,1097.599976,1104.160034,1090.819946,1090.819946,1090.819946,556780000
1998-06-01,1090.819946,1097.849976,1084.219971,1090.979980,1090.979980,537660000
1998-06-02,1090.979980,1098.709961,1089.670044,1093.219971,1093.219971,590930000
1998-06-03,1093.219971,1097.430054,1081.089966,1082.729980,1082.729980,584480000
1998-06-04,1082.729980,1095.930054,1078.099976,1094.829956,1094.829956,577470000
1998-06-05,1095.099976,1113.880005,1094.829956,1113.859985,1113.859985,558440000
1998-06-08,1113.859985,1119.699951,1113.310059,1115.719971,1115.719971,543390000
1998-06-09,1115.719971,1119.920044,1111.310059,1118.410034,1118.410034,563610000
1998-06-10,1118.410034,1126.000000,1110.270020,1112.280029,1112.280029,609410000
1998-06-11,1112.280029,1114.199951,1094.280029,1094.579956,1094.579956,627470000
1998-06-12,1094.579956,1098.839966,1080.829956,1098.839966,1098.839966,633300000
1998-06-15,1098.839966,1098.839966,1077.010010,1077.010010,1077.010010,595820000
1998-06-16,1077.010010,1087.589966,1074.670044,1087.589966,1087.589966,664600000
1998-06-17,1087.589966,1112.869995,1087.579956,1107.109985,1107.109985,744400000
1998-06-18,1107.109985,1109.359985,1103.709961,1106.369995,1106.369995,590440000
1998-06-19,1106.369995,1111.250000,1097.099976,1100.650024,1100.650024,715500000
1998-06-22,1100.650024,1109.010010,1099.420044,1103.209961,1103.209961,531550000
1998-06-23,1103.209961,1119.489990,1103.209961,1119.489990,1119.489990,657100000
1998-06-24,1119.489990,1134.400024,1115.099976,1132.880005,1132.880005,714900000
1998-06-25,1132.880005,1142.040039,1127.599976,1129.280029,1129.280029,669900000
1998-06-26,1129.280029,1136.829956,1129.280029,1133.199951,1133.199951,520050000
1998-06-29,1133.199951,1145.150024,1133.199951,1138.489990,1138.489990,564350000
1998-06-30,1138.489990,1140.800049,1131.979980,1133.839966,1133.839966,757200000
1998-07-01,1133.839966,1148.560059,1133.839966,1148.560059,1148.560059,701600000
1998-07-02,1148.560059,1148.560059,1142.989990,1146.420044,1146.420044,510210000
1998-07-06,1146.420044,1157.329956,1145.030029,1157.329956,1157.329956,514750000
1998-07-07,1157.329956,1159.810059,1152.849976,1154.660034,1154.660034,624890000
1998-07-08,1154.660034,1166.890015,1154.660034,1166.380005,1166.380005,607230000
1998-07-09,1166.380005,1166.380005,1156.030029,1158.560059,1158.560059,663600000
1998-07-10,1158.569946,1166.930054,1150.880005,1164.329956,1164.329956,576080000
1998-07-13,1164.329956,1166.979980,1160.209961,1165.189941,1165.189941,574880000
1998-07-14,1165.189941,1179.760010,1165.189941,1177.579956,1177.579956,700300000
1998-07-15,1177.579956,1181.479980,1174.729980,1174.810059,1174.810059,723900000
1998-07-16,1174.810059,1184.020020,1170.400024,1183.989990,1183.989990,677800000
1998-07-17,1183.989990,1188.099976,1182.420044,1186.750000,1186.750000,618030000
1998-07-20,1186.750000,1190.579956,1179.189941,1184.099976,1184.099976,560580000
1998-07-21,1184.099976,1187.369995,1163.050049,1165.069946,1165.069946,659700000
1998-07-22,1165.069946,1167.670044,1155.199951,1164.079956,1164.079956,739800000
1998-07-23,1164.079956,1164.349976,1139.750000,1139.750000,1139.750000,741600000
1998-07-24,1139.750000,1150.140015,1129.109985,1140.800049,1140.800049,698600000
1998-07-27,1140.800049,1147.270020,1128.189941,1147.270020,1147.270020,619990000
1998-07-28,1147.270020,1147.270020,1119.439941,1130.239990,1130.239990,703600000
1998-07-29,1130.239990,1138.560059,1121.979980,1125.209961,1125.209961,644350000
1998-07-30,1125.209961,1143.069946,1125.209961,1142.949951,1142.949951,687400000
1998-07-31,1142.949951,1142.969971,1114.300049,1120.670044,1120.670044,645910000
1998-08-03,1120.670044,1121.790039,1110.390015,1112.439941,1112.439941,620400000
1998-08-04,1112.439941,1119.729980,1071.819946,1072.119995,1072.119995,852600000
1998-08-05,1072.119995,1084.800049,1057.349976,1081.430054,1081.430054,851600000
1998-08-06,1081.430054,1090.949951,1074.939941,1089.630005,1089.630005,768400000
1998-08-07,1089.630005,1102.540039,1084.719971,1089.449951,1089.449951,759100000
1998-08-10,1089.449951,1092.819946,1081.760010,1083.140015,1083.140015,579180000
1998-08-11,1083.140015,1083.140015,1054.000000,1068.979980,1068.979980,774400000
1998-08-12,1068.979980,1084.699951,1068.979980,1084.219971,1084.219971,711700000
1998-08-13,1084.219971,1091.500000,1074.910034,1074.910034,1074.910034,660700000
1998-08-14,1074.910034,1083.920044,1057.219971,1062.750000,1062.750000,644030000
1998-08-17,1062.750000,1083.670044,1055.079956,1083.670044,1083.670044,584380000
1998-08-18,1083.670044,1101.719971,1083.670044,1101.199951,1101.199951,690600000
1998-08-19,1101.199951,1106.319946,1094.930054,1098.060059,1098.060059,633630000
1998-08-20,1098.060059,1098.790039,1089.550049,1091.599976,1091.599976,621630000
1998-08-21,1091.599976,1091.599976,1054.920044,1081.239990,1081.239990,725700000
1998-08-24,1081.239990,1093.819946,1081.239990,1088.140015,1088.140015,558100000
1998-08-25,1088.140015,1106.640015,1085.530029,1092.849976,1092.849976,664900000
1998-08-26,1092.849976,1092.849976,1075.910034,1084.189941,1084.189941,674100000
1998-08-27,1084.189941,1084.189941,1037.609985,1042.589966,1042.589966,938600000
1998-08-28,1042.589966,1051.800049,1021.039978,1027.140015,1027.140015,840300000
1998-08-31,1027.140015,1033.469971,957.280029,957.280029,957.280029,917500000
1998-09-01,957.280029,1000.710022,939.979980,994.260010,994.260010,1216600000
1998-09-02,994.260010,1013.190002,988.400024,990.479980,990.479980,894600000
1998-09-03,990.469971,990.469971,969.320007,982.260010,982.260010,880500000
1998-09-04,982.260010,991.409973,956.510010,973.890015,973.890015,780300000
1998-09-08,973.890015,1023.460022,973.890015,1023.460022,1023.460022,814800000
1998-09-09,1023.460022,1027.719971,1004.559998,1006.200012,1006.200012,704300000
1998-09-10,1006.200012,1006.200012,968.640015,980.190002,980.190002,880300000
1998-09-11,980.190002,1009.059998,969.710022,1009.059998,1009.059998,819100000
1998-09-14,1009.059998,1038.380005,1009.059998,1029.719971,1029.719971,714400000
1998-09-15,1029.719971,1037.900024,1021.419983,1037.680054,1037.680054,724600000
1998-09-16,1037.680054,1046.069946,1029.310059,1045.479980,1045.479980,797500000
1998-09-17,1045.479980,1045.479980,1016.049988,1018.869995,1018.869995,694500000
1998-09-18,1018.869995,1022.010010,1011.859985,1020.090027,1020.090027,794700000
1998-09-21,1020.090027,1026.020020,993.820007,1023.890015,1023.890015,609880000
1998-09-22,1023.890015,1033.890015,1021.960022,1029.630005,1029.630005,694900000
1998-09-23,1029.630005,1066.089966,1029.630005,1066.089966,1066.089966,899700000
1998-09-24,1066.089966,1066.109985,1033.040039,1042.719971,1042.719971,805900000
1998-09-25,1042.719971,1051.890015,1028.489990,1044.750000,1044.750000,736800000
1998-09-28,1044.750000,1061.459961,1042.229980,1048.689941,1048.689941,690500000
1998-09-29,1048.689941,1056.310059,1039.880005,1049.020020,1049.020020,760100000
1998-09-30,1049.020020,1049.020020,1015.729980,1017.010010,1017.010010,800100000
1998-10-01,1017.010010,1017.010010,981.289978,986.390015,986.390015,899700000
1998-10-02,986.390015,1005.450012,971.690002,1002.599976,1002.599976,902900000
1998-10-05,1002.599976,1002.599976,964.719971,988.559998,988.559998,817500000
1998-10-06,988.559998,1008.770020,974.809998,984.590027,984.590027,845700000
1998-10-07,984.590027,995.659973,957.150024,970.679993,970.679993,977000000
1998-10-08,970.679993,970.679993,923.320007,959.440002,959.440002,1114600000
1998-10-09,959.440002,984.419983,953.039978,984.390015,984.390015,878100000
1998-10-12,984.390015,1010.710022,984.390015,997.710022,997.710022,691100000
1998-10-13,997.710022,1000.780029,987.549988,994.799988,994.799988,733300000
1998-10-14,994.799988,1014.419983,987.799988,1005.530029,1005.530029,791200000
1998-10-15,1005.530029,1053.089966,1000.119995,1047.489990,1047.489990,937600000
1998-10-16,1047.489990,1062.650024,1047.489990,1056.420044,1056.420044,1042200000
1998-10-19,1056.420044,1065.209961,1054.229980,1062.390015,1062.390015,738600000
1998-10-20,1062.390015,1084.060059,1060.609985,1063.930054,1063.930054,958200000
1998-10-21,1063.930054,1073.609985,1058.079956,1069.920044,1069.920044,745100000
1998-10-22,1069.920044,1080.430054,1061.469971,1078.479980,1078.479980,754900000
1998-10-23,1078.479980,1078.479980,1067.430054,1070.670044,1070.670044,637640000
1998-10-26,1070.670044,1081.229980,1068.170044,1072.319946,1072.319946,609910000
1998-10-27,1072.319946,1087.079956,1063.060059,1065.339966,1065.339966,764500000
1998-10-28,1065.339966,1072.790039,1059.650024,1068.089966,1068.089966,677500000
1998-10-29,1068.089966,1086.109985,1065.949951,1085.930054,1085.930054,699400000
1998-10-30,1085.930054,1103.780029,1085.930054,1098.670044,1098.670044,785000000
1998-11-02,1098.670044,1114.439941,1098.670044,1111.599976,1111.599976,753800000
1998-11-03,1111.599976,1115.020020,1106.420044,1110.839966,1110.839966,704300000
1998-11-04,1110.839966,1127.180054,1110.589966,1118.670044,1118.670044,861100000
1998-11-05,1118.670044,1133.880005,1109.550049,1133.849976,1133.849976,770200000
1998-11-06,1133.849976,1141.300049,1131.180054,1141.010010,1141.010010,683100000
1998-11-09,1141.010010,1141.010010,1123.170044,1130.199951,1130.199951,592990000
1998-11-10,1130.199951,1135.369995,1122.800049,1128.260010,1128.260010,671300000
1998-11-11,1128.260010,1136.250000,1117.400024,1120.969971,1120.969971,715700000
1998-11-12,1120.969971,1126.569946,1115.550049,1117.689941,1117.689941,662300000
1998-11-13,1117.689941,1126.339966,1116.760010,1125.719971,1125.719971,602270000
1998-11-16,1125.719971,1138.719971,1125.719971,1135.869995,1135.869995,615580000
1998-11-17,1135.869995,1151.709961,1129.670044,1139.319946,1139.319946,705200000
1998-11-18,1139.319946,1144.520020,1133.069946,1144.479980,1144.479980,652510000
1998-11-19,1144.479980,1155.099976,1144.420044,1152.609985,1152.609985,671000000
1998-11-20,1152.609985,1163.550049,1152.609985,1163.550049,1163.550049,721200000
1998-11-23,1163.550049,1188.209961,1163.550049,1188.209961,1188.209961,774100000
1998-11-24,1188.209961,1191.300049,1181.810059,1182.989990,1182.989990,766200000
1998-11-25,1182.989990,1187.160034,1179.369995,1186.869995,1186.869995,583580000
1998-11-27,1186.869995,1192.969971,1186.829956,1192.329956,1192.329956,256950000
1998-11-30,1192.329956,1192.719971,1163.630005,1163.630005,1163.630005,687900000
1998-12-01,1163.630005,1175.890015,1150.310059,1175.280029,1175.280029,789200000
1998-12-02,1175.280029,1175.280029,1157.760010,1171.250000,1171.250000,727400000
1998-12-03,1171.250000,1176.989990,1149.609985,1150.140015,1150.140015,799100000
1998-12-04,1150.140015,1176.739990,1150.140015,1176.739990,1176.739990,709700000
1998-12-07,1176.739990,1188.959961,1176.709961,1187.699951,1187.699951,671200000
1998-12-08,1187.699951,1193.530029,1172.780029,1181.380005,1181.380005,727700000
1998-12-09,1181.380005,1185.219971,1175.890015,1183.489990,1183.489990,694200000
1998-12-10,1183.489990,1183.770020,1163.750000,1165.020020,1165.020020,748600000
1998-12-11,1165.020020,1167.890015,1153.189941,1166.459961,1166.459961,688900000
1998-12-14,1166.459961,1166.459961,1136.890015,1141.199951,1141.199951,741800000
1998-12-15,1141.199951,1162.829956,1141.199951,1162.829956,1162.829956,777900000
1998-12-16,1162.829956,1166.290039,1154.689941,1161.939941,1161.939941,725500000
1998-12-17,1161.939941,1180.030029,1161.939941,1179.979980,1179.979980,739400000
1998-12-18,1179.979980,1188.890015,1178.270020,1188.030029,1188.030029,839600000
1998-12-21,1188.030029,1210.880005,1188.030029,1202.839966,1202.839966,744800000
1998-12-22,1202.839966,1209.219971,1192.719971,1203.569946,1203.569946,680500000
1998-12-23,1203.569946,1229.890015,1203.569946,1228.540039,1228.540039,697500000
1998-12-24,1228.540039,1229.719971,1224.849976,1226.270020,1226.270020,246980000
1998-12-28,1226.270020,1231.520020,1221.170044,1225.489990,1225.489990,531560000
1998-12-29,1225.489990,1241.859985,1220.780029,1241.810059,1241.810059,586490000
1998-12-30,1241.810059,1244.930054,1231.199951,1231.930054,1231.930054,594220000
1998-12-31,1231.930054,1237.180054,1224.959961,1229.229980,1229.229980,719200000
1999-01-04,1229.229980,1248.810059,1219.099976,1228.099976,1228.099976,877000000
1999-01-05,1228.099976,1246.109985,1228.099976,1244.780029,1244.780029,775000000
1999-01-06,1244.780029,1272.500000,1244.780029,1272.339966,1272.339966,986900000
1999-01-07,1272.339966,1272.339966,1257.680054,1269.729980,1269.729980,863000000
1999-01-08,1269.729980,1278.239990,1261.819946,1275.089966,1275.089966,937800000
1999-01-11,1275.089966,1276.219971,1253.339966,1263.880005,1263.880005,818000000
1999-01-12,1263.880005,1264.449951,1238.290039,1239.510010,1239.510010,800200000
1999-01-13,1239.510010,1247.750000,1205.459961,1234.400024,1234.400024,931500000
1999-01-14,1234.400024,1236.810059,1209.540039,1212.189941,1212.189941,797200000
1999-01-15,1212.189941,1243.260010,1212.189941,1243.260010,1243.260010,798100000
1999-01-19,1243.260010,1253.270020,1234.910034,1252.000000,1252.000000,785500000
1999-01-20,1252.000000,1274.069946,1251.540039,1256.619995,1256.619995,905700000
1999-01-21,1256.619995,1256.939941,1232.189941,1235.160034,1235.160034,871800000
1999-01-22,1235.160034,1236.410034,1217.969971,1225.189941,1225.189941,785900000
1999-01-25,1225.189941,1233.979980,1219.459961,1233.979980,1233.979980,723900000
1999-01-26,1233.979980,1253.250000,1233.979980,1252.310059,1252.310059,896400000
1999-01-27,1252.310059,1262.609985,1242.819946,1243.170044,1243.170044,893800000
1999-01-28,1243.170044,1266.400024,1243.170044,1265.369995,1265.369995,848800000
1999-01-29,1265.369995,1280.369995,1255.180054,1279.640015,1279.640015,917000000
1999-02-01,1279.640015,1283.750000,1271.310059,1273.000000,1273.000000,799400000
1999-02-02,1273.000000,1273.489990,1247.560059,1261.989990,1261.989990,845500000
1999-02-03,1261.989990,1276.040039,1255.270020,1272.069946,1272.069946,876500000
1999-02-04,1272.069946,1272.229980,1248.359985,1248.489990,1248.489990,854400000
1999-02-05,1248.489990,1251.859985,1232.280029,1239.400024,1239.400024,872000000
1999-02-08,1239.400024,1246.930054,1231.979980,1243.770020,1243.770020,705400000
1999-02-09,1243.770020,1243.969971,1215.630005,1216.140015,1216.140015,736000000
1999-02-10,1216.140015,1226.780029,1211.890015,1223.550049,1223.550049,721400000
1999-02-11,1223.550049,1254.050049,1223.189941,1254.040039,1254.040039,815800000
1999-02-12,1254.040039,1254.040039,1225.530029,1230.130005,1230.130005,691500000
1999-02-16,1230.130005,1252.170044,1230.130005,1241.869995,1241.869995,653760000
1999-02-17,1241.869995,1249.310059,1220.920044,1224.030029,1224.030029,735100000
1999-02-18,1224.030029,1239.130005,1220.699951,1237.280029,1237.280029,742400000
1999-02-19,1237.280029,1247.910034,1232.030029,1239.219971,1239.219971,700000000
1999-02-22,1239.219971,1272.219971,1239.219971,1272.140015,1272.140015,718500000
1999-02-23,1272.140015,1280.380005,1263.359985,1271.180054,1271.180054,781100000
1999-02-24,1271.180054,1283.839966,1251.939941,1253.410034,1253.410034,782000000
1999-02-25,1253.410034,1253.410034,1225.010010,1245.020020,1245.020020,740500000
1999-02-26,1245.020020,1246.729980,1226.239990,1238.329956,1238.329956,784600000
1999-03-01,1238.329956,1238.699951,1221.880005,1236.160034,1236.160034,699500000
1999-03-02,1236.160034,1248.310059,1221.869995,1225.500000,1225.500000,753600000
1999-03-03,1225.500000,1231.630005,1216.030029,1227.699951,1227.699951,751700000
1999-03-04,1227.699951,1247.739990,1227.699951,1246.640015,1246.640015,770900000
1999-03-05,1246.640015,1275.729980,1246.640015,1275.469971,1275.469971,834900000
1999-03-08,1275.469971,1282.739990,1271.579956,1282.729980,1282.729980,714600000
1999-03-09,1282.729980,1293.739990,1275.109985,1279.839966,1279.839966,803700000
1999-03-10,1279.839966,1287.020020,1275.160034,1286.839966,1286.839966,841900000
1999-03-11,1286.839966,1306.430054,1286.839966,1297.680054,1297.680054,904800000
1999-03-12,1297.680054,1304.420044,1289.170044,1294.589966,1294.589966,825800000
1999-03-15,1294.589966,1307.469971,1291.030029,1307.260010,1307.260010,727200000
1999-03-16,1307.260010,1311.109985,1302.290039,1306.380005,1306.380005,751900000
1999-03-17,1306.380005,1306.550049,1292.630005,1297.819946,1297.819946,752300000
1999-03-18,1297.819946,1317.619995,1294.750000,1316.550049,1316.550049,831000000
1999-03-19,1316.550049,1323.819946,1298.920044,1299.290039,1299.290039,914700000
1999-03-22,1299.290039,1303.839966,1294.260010,1297.010010,1297.010010,658200000
1999-03-23,1297.010010,1297.010010,1257.459961,1262.140015,1262.140015,811300000
1999-03-24,1262.140015,1269.020020,1256.430054,1268.589966,1268.589966,761900000
1999-03-25,1268.589966,1289.989990,1268.589966,1289.989990,1289.989990,784200000
1999-03-26,1289.989990,1289.989990,1277.250000,1282.800049,1282.800049,707200000
1999-03-29,1282.800049,1311.760010,1282.800049,1310.170044,1310.170044,747900000
1999-03-30,1310.170044,1310.170044,1295.469971,1300.750000,1300.750000,729000000
1999-03-31,1300.750000,1313.599976,1285.869995,1286.369995,1286.369995,924300000
1999-04-01,1286.369995,1294.540039,1282.560059,1293.719971,1293.719971,703000000
1999-04-05,1293.719971,1321.119995,1293.719971,1321.119995,1321.119995,695800000
1999-04-06,1321.119995,1326.760010,1311.069946,1317.890015,1317.890015,787500000
1999-04-07,1317.890015,1329.579956,1312.589966,1326.890015,1326.890015,816400000
1999-04-08,1326.890015,1344.079956,1321.599976,1343.979980,1343.979980,850500000
1999-04-09,1343.979980,1351.219971,1335.239990,1348.349976,1348.349976,716100000
1999-04-12,1348.349976,1358.689941,1333.479980,1358.630005,1358.630005,810800000
1999-04-13,1358.640015,1362.380005,1344.030029,1349.819946,1349.819946,810900000
1999-04-14,1349.819946,1357.239990,1326.410034,1328.439941,1328.439941,952000000
1999-04-15,1328.439941,1332.410034,1308.380005,1322.849976,1322.849976,1089800000
1999-04-16,1322.859985,1325.030029,1311.400024,1319.000000,1319.000000,1002300000
1999-04-19,1319.000000,1340.099976,1284.479980,1289.479980,1289.479980,1214400000
1999-04-20,1289.479980,1306.300049,1284.209961,1306.170044,1306.170044,985400000
1999-04-21,1306.170044,1336.119995,1301.839966,1336.119995,1336.119995,920000000
1999-04-22,1336.119995,1358.839966,1336.119995,1358.819946,1358.819946,927900000
1999-04-23,1358.829956,1363.650024,1348.449951,1356.849976,1356.849976,744900000
1999-04-26,1356.849976,1363.560059,1353.719971,1360.040039,1360.040039,712000000
1999-04-27,1360.040039,1371.560059,1356.550049,1362.800049,1362.800049,891700000
1999-04-28,1362.800049,1368.619995,1348.290039,1350.910034,1350.910034,951700000
1999-04-29,1350.910034,1356.750000,1336.810059,1342.829956,1342.829956,1003600000
1999-04-30,1342.829956,1351.829956,1314.579956,1335.180054,1335.180054,936500000
1999-05-03,1335.180054,1354.630005,1329.010010,1354.630005,1354.630005,811400000
1999-05-04,1354.630005,1354.640015,1330.640015,1332.000000,1332.000000,933100000
1999-05-05,1332.000000,1347.319946,1317.439941,1347.310059,1347.310059,913500000
1999-05-06,1347.310059,1348.359985,1322.560059,1332.050049,1332.050049,875400000
1999-05-07,1332.050049,1345.989990,1332.050049,1345.000000,1345.000000,814900000
1999-05-10,1345.000000,1352.010010,1334.000000,1340.300049,1340.300049,773300000
1999-05-11,1340.300049,1360.000000,1340.300049,1355.609985,1355.609985,836100000
1999-05-12,1355.609985,1367.359985,1333.099976,1364.000000,1364.000000,825500000
1999-05-13,1364.000000,1375.979980,1364.000000,1367.560059,1367.560059,796900000
1999-05-14,1367.560059,1367.560059,1332.630005,1337.800049,1337.800049,727800000
1999-05-17,1337.800049,1339.949951,1321.189941,1339.489990,1339.489990,665500000
1999-05-18,1339.489990,1345.439941,1323.459961,1333.319946,1333.319946,753400000
1999-05-19,1333.319946,1344.229980,1327.050049,1344.229980,1344.229980,801100000
1999-05-20,1344.229980,1350.489990,1338.829956,1338.829956,1338.829956,752200000
1999-05-21,1338.829956,1340.880005,1326.189941,1330.290039,1330.290039,686600000
1999-05-24,1330.290039,1333.020020,1303.530029,1306.650024,1306.650024,754700000
1999-05-25,1306.650024,1317.520020,1284.380005,1284.400024,1284.400024,826700000
1999-05-26,1284.400024,1304.849976,1278.430054,1304.760010,1304.760010,870800000
1999-05-27,1304.760010,1304.760010,1277.310059,1281.410034,1281.410034,811400000
1999-05-28,1281.410034,1304.000000,1281.410034,1301.839966,1301.839966,649960000
1999-06-01,1301.839966,1301.839966,1281.439941,1294.260010,1294.260010,683800000
1999-06-02,1294.260010,1297.099976,1277.469971,1294.810059,1294.810059,728000000
1999-06-03,1294.810059,1304.150024,1294.199951,1299.540039,1299.540039,719600000
1999-06-04,1299.540039,1327.750000,1299.540039,1327.750000,1327.750000,694500000
1999-06-07,1327.750000,1336.420044,1325.890015,1334.520020,1334.520020,664300000
1999-06-08,1334.520020,1334.520020,1312.829956,1317.329956,1317.329956,685900000
1999-06-09,1317.329956,1326.010010,1314.729980,1318.640015,1318.640015,662000000
1999-06-10,1318.640015,1318.640015,1293.280029,1302.819946,1302.819946,716500000
1999-06-11,1302.819946,1311.969971,1287.880005,1293.640015,1293.640015,698200000
1999-06-14,1293.640015,1301.989990,1292.199951,1294.000000,1294.000000,669400000
1999-06-15,1294.000000,1310.760010,1294.000000,1301.160034,1301.160034,696600000
1999-06-16,1301.160034,1332.829956,1301.160034,1330.410034,1330.410034,806800000
1999-06-17,1330.410034,1343.540039,1322.750000,1339.900024,1339.900024,700300000
1999-06-18,1339.900024,1344.479980,1333.520020,1342.839966,1342.839966,914500000
1999-06-21,1342.839966,1349.060059,1337.630005,1349.000000,1349.000000,686600000
1999-06-22,1349.000000,1351.119995,1335.520020,1335.880005,1335.880005,716500000
1999-06-23,1335.869995,1335.880005,1322.550049,1333.060059,1333.060059,731800000
1999-06-24,1333.060059,1333.060059,1308.469971,1315.780029,1315.780029,690400000
1999-06-25,1315.780029,1329.130005,1312.640015,1315.310059,1315.310059,623460000
1999-06-28,1315.310059,1333.680054,1315.310059,1331.349976,1331.349976,652910000
1999-06-29,1331.349976,1351.510010,1328.400024,1351.449951,1351.449951,820100000
1999-06-30,1351.449951,1372.930054,1338.780029,1372.709961,1372.709961,1117000000
1999-07-01,1372.709961,1382.800049,1360.800049,1380.959961,1380.959961,843400000
1999-07-02,1380.959961,1391.219971,1379.569946,1391.219971,1391.219971,613570000
1999-07-06,1391.219971,1405.290039,1387.079956,1388.119995,1388.119995,722900000
1999-07-07,1388.119995,1395.880005,1384.949951,1395.859985,1395.859985,791200000
1999-07-08,1395.859985,1403.250000,1386.689941,1394.420044,1394.420044,830600000
1999-07-09,1394.420044,1403.280029,1394.420044,1403.280029,1403.280029,701000000
1999-07-12,1403.280029,1406.819946,1394.699951,1399.099976,1399.099976,685300000
1999-07-13,1399.099976,1399.099976,1386.839966,1393.560059,1393.560059,736000000
1999-07-14,1393.560059,1400.050049,1386.510010,1398.170044,1398.170044,756100000
1999-07-15,1398.170044,1409.839966,1398.170044,1409.619995,1409.619995,818800000
1999-07-16,1409.619995,1418.780029,1407.069946,1418.780029,1418.780029,714100000
1999-07-19,1418.780029,1420.329956,1404.560059,1407.650024,1407.650024,642330000
1999-07-20,1407.650024,1407.650024,1375.150024,1377.099976,1377.099976,754800000
1999-07-21,1377.099976,1386.660034,1372.630005,1379.290039,1379.290039,785500000
1999-07-22,1379.290039,1379.290039,1353.979980,1360.969971,1360.969971,771700000
1999-07-23,1360.969971,1367.410034,1349.910034,1356.939941,1356.939941,630580000
1999-07-26,1356.939941,1358.609985,1346.199951,1347.760010,1347.760010,613450000
1999-07-27,1347.750000,1368.699951,1347.750000,1362.839966,1362.839966,723800000
1999-07-28,1362.839966,1370.530029,1355.540039,1365.400024,1365.400024,690900000
1999-07-29,1365.400024,1365.400024,1332.819946,1341.030029,1341.030029,770100000
1999-07-30,1341.030029,1350.920044,1328.489990,1328.719971,1328.719971,736800000
1999-08-02,1328.719971,1344.689941,1325.209961,1328.050049,1328.050049,649550000
1999-08-03,1328.050049,1336.130005,1314.910034,1322.180054,1322.180054,739600000
1999-08-04,1322.180054,1330.160034,1304.500000,1305.329956,1305.329956,789300000
1999-08-05,1305.329956,1313.709961,1287.229980,1313.709961,1313.709961,859300000
1999-08-06,1313.709961,1316.739990,1293.189941,1300.290039,1300.290039,698900000
1999-08-09,1300.290039,1306.680054,1295.989990,1297.800049,1297.800049,684300000
1999-08-10,1297.800049,1298.619995,1267.729980,1281.430054,1281.430054,836200000
1999-08-11,1281.430054,1301.930054,1281.430054,1301.930054,1301.930054,792300000
1999-08-12,1301.930054,1313.609985,1298.060059,1298.160034,1298.160034,745600000
1999-08-13,1298.160034,1327.719971,1298.160034,1327.680054,1327.680054,691700000
1999-08-16,1327.680054,1331.050049,1320.510010,1330.770020,1330.770020,583550000
1999-08-17,1330.770020,1344.160034,1328.760010,1344.160034,1344.160034,691500000
1999-08-18,1344.160034,1344.160034,1332.130005,1332.839966,1332.839966,682800000
1999-08-19,1332.839966,1332.839966,1315.349976,1323.589966,1323.589966,684200000
1999-08-20,1323.589966,1336.609985,1323.589966,1336.609985,1336.609985,661200000
1999-08-23,1336.609985,1360.239990,1336.609985,1360.219971,1360.219971,682600000
1999-08-24,1360.219971,1373.319946,1353.630005,1363.500000,1363.500000,732700000
1999-08-25,1363.500000,1382.839966,1359.199951,1381.790039,1381.790039,864600000
1999-08-26,1381.790039,1381.790039,1361.530029,1362.010010,1362.010010,719000000
1999-08-27,1362.010010,1365.630005,1347.349976,1348.270020,1348.270020,570050000
1999-08-30,1348.270020,1350.699951,1322.800049,1324.020020,1324.020020,597900000
1999-08-31,1324.020020,1333.270020,1306.959961,1320.410034,1320.410034,861700000
1999-09-01,1320.410034,1331.180054,1320.390015,1331.069946,1331.069946,708200000
1999-09-02,1331.069946,1331.069946,1304.880005,1319.109985,1319.109985,687100000
1999-09-03,1319.109985,1357.739990,1319.109985,1357.239990,1357.239990,663200000
1999-09-07,1357.239990,1361.390015,1349.589966,1350.449951,1350.449951,715300000
1999-09-08,1350.449951,1355.180054,1337.359985,1344.150024,1344.150024,791200000
1999-09-09,1344.150024,1347.660034,1333.910034,1347.660034,1347.660034,773900000
1999-09-10,1347.660034,1357.619995,1346.199951,1351.660034,1351.660034,808500000
1999-09-13,1351.660034,1351.660034,1341.699951,1344.130005,1344.130005,657900000
1999-09-14,1344.130005,1344.180054,1330.609985,1336.290039,1336.290039,734500000
1999-09-15,1336.290039,1347.209961,1317.969971,1317.969971,1317.969971,787300000
1999-09-16,1317.969971,1322.510010,1299.969971,1318.479980,1318.479980,739000000
1999-09-17,1318.479980,1337.589966,1318.479980,1335.420044,1335.420044,861900000
1999-09-20,1335.420044,1338.380005,1330.609985,1335.530029,1335.530029,568000000
1999-09-21,1335.520020,1335.530029,1301.969971,1307.579956,1307.579956,817300000
1999-09-22,1307.579956,1316.180054,1297.810059,1310.510010,1310.510010,822200000
1999-09-23,1310.510010,1315.250000,1277.300049,1280.410034,1280.410034,890800000
1999-09-24,1280.410034,1281.170044,1263.839966,1277.359985,1277.359985,872800000
1999-09-27,1277.359985,1295.030029,1277.359985,1283.310059,1283.310059,780600000
1999-09-28,1283.310059,1285.550049,1256.260010,1282.199951,1282.199951,885400000
1999-09-29,1282.199951,1288.829956,1268.160034,1268.369995,1268.369995,856000000
1999-09-30,1268.369995,1291.310059,1268.369995,1282.709961,1282.709961,1017600000
1999-10-01,1282.709961,1283.170044,1265.780029,1282.810059,1282.810059,896200000
1999-10-04,1282.810059,1304.599976,1282.810059,1304.599976,1304.599976,803300000
1999-10-05,1304.599976,1316.410034,1286.439941,1301.349976,1301.349976,965700000
1999-10-06,1301.349976,1325.459961,1301.349976,1325.400024,1325.400024,895200000
1999-10-07,1325.400024,1328.050049,1314.130005,1317.640015,1317.640015,827800000
1999-10-08,1317.640015,1336.609985,1311.880005,1336.020020,1336.020020,897300000
1999-10-11,1336.020020,1339.229980,1332.959961,1335.209961,1335.209961,655900000
1999-10-12,1335.209961,1335.209961,1311.800049,1313.040039,1313.040039,778300000
1999-10-13,1313.040039,1313.040039,1282.800049,1285.550049,1285.550049,821500000
1999-10-14,1285.550049,1289.630005,1267.619995,1283.420044,1283.420044,892300000
1999-10-15,1283.420044,1283.420044,1245.390015,1247.410034,1247.410034,912600000
1999-10-18,1247.410034,1254.130005,1233.699951,1254.130005,1254.130005,818700000
1999-10-19,1254.130005,1279.319946,1254.130005,1261.319946,1261.319946,905700000
1999-10-20,1261.319946,1289.439941,1261.319946,1289.430054,1289.430054,928800000
1999-10-21,1289.430054,1289.430054,1265.609985,1283.609985,1283.609985,1012500000
1999-10-22,1283.609985,1308.810059,1283.609985,1301.650024,1301.650024,959200000
1999-10-25,1301.650024,1301.680054,1286.069946,1293.630005,1293.630005,777000000
1999-10-26,1293.630005,1303.459961,1281.859985,1281.910034,1281.910034,878300000
1999-10-27,1281.910034,1299.390015,1280.479980,1296.709961,1296.709961,950100000
1999-10-28,1296.709961,1342.469971,1296.709961,1342.439941,1342.439941,1135100000
1999-10-29,1342.439941,1373.170044,1342.439941,1362.930054,1362.930054,1120500000
1999-11-01,1362.930054,1367.300049,1354.050049,1354.119995,1354.119995,861000000
1999-11-02,1354.119995,1369.319946,1346.410034,1347.739990,1347.739990,904500000
1999-11-03,1347.739990,1360.329956,1347.739990,1354.930054,1354.930054,914400000
1999-11-04,1354.930054,1369.410034,1354.930054,1362.640015,1362.640015,981700000
1999-11-05,1362.640015,1387.479980,1362.640015,1370.229980,1370.229980,1007300000
1999-11-08,1370.229980,1380.780029,1365.869995,1377.010010,1377.010010,806800000
1999-11-09,1377.010010,1383.810059,1361.449951,1365.280029,1365.280029,854300000
1999-11-10,1365.280029,1379.180054,1359.979980,1373.459961,1373.459961,984700000
1999-11-11,1373.459961,1382.119995,1372.189941,1381.459961,1381.459961,891300000
1999-11-12,1381.459961,1396.119995,1368.540039,1396.060059,1396.060059,900200000
1999-11-15,1396.060059,1398.579956,1392.280029,1394.390015,1394.390015,795700000
1999-11-16,1394.390015,1420.359985,1394.390015,1420.069946,1420.069946,942200000
1999-11-17,1420.069946,1423.439941,1410.689941,1410.709961,1410.709961,960000000
1999-11-18,1410.709961,1425.310059,1410.709961,1424.939941,1424.939941,1022800000
1999-11-19,1424.939941,1424.939941,1417.540039,1422.000000,1422.000000,893800000
1999-11-22,1422.000000,1425.000000,1412.400024,1420.939941,1420.939941,873500000
1999-11-23,1420.939941,1423.910034,1402.199951,1404.640015,1404.640015,926100000
1999-11-24,1404.640015,1419.709961,1399.170044,1417.079956,1417.079956,734800000
1999-11-26,1417.079956,1425.239990,1416.140015,1416.619995,1416.619995,312120000
1999-11-29,1416.619995,1416.619995,1404.150024,1407.829956,1407.829956,866100000
1999-11-30,1407.829956,1410.589966,1386.949951,1388.910034,1388.910034,951500000
1999-12-01,1388.910034,1400.119995,1387.380005,1397.719971,1397.719971,884000000
1999-12-02,1397.719971,1409.040039,1397.719971,1409.040039,1409.040039,900700000
1999-12-03,1409.040039,1447.420044,1409.040039,1433.300049,1433.300049,1006400000
1999-12-06,1433.300049,1434.150024,1418.250000,1423.329956,1423.329956,916800000
1999-12-07,1423.329956,1426.810059,1409.170044,1409.170044,1409.170044,1085800000
1999-12-08,1409.170044,1415.660034,1403.880005,1403.880005,1403.880005,957000000
1999-12-09,1403.880005,1418.430054,1391.469971,1408.109985,1408.109985,1122100000
1999-12-10,1408.109985,1421.579956,1405.650024,1417.040039,1417.040039,987200000
1999-12-13,1417.040039,1421.579956,1410.099976,1415.219971,1415.219971,977600000
1999-12-14,1415.219971,1418.300049,1401.589966,1403.170044,1403.170044,1027800000
1999-12-15,1403.170044,1417.400024,1396.199951,1413.329956,1413.329956,1033900000
1999-12-16,1413.319946,1423.109985,1408.349976,1418.780029,1418.780029,1070300000
1999-12-17,1418.780029,1431.770020,1418.780029,1421.030029,1421.030029,1349800000
1999-12-20,1421.030029,1429.160034,1411.099976,1418.089966,1418.089966,904600000
1999-12-21,1418.089966,1436.469971,1414.800049,1433.430054,1433.430054,963500000
1999-12-22,1433.430054,1440.020020,1429.130005,1436.130005,1436.130005,850000000
1999-12-23,1436.130005,1461.439941,1436.130005,1458.339966,1458.339966,728600000
1999-12-27,1458.339966,1463.189941,1450.829956,1457.099976,1457.099976,722600000
1999-12-28,1457.089966,1462.680054,1452.780029,1457.660034,1457.660034,655400000
1999-12-29,1457.660034,1467.469971,1457.660034,1463.459961,1463.459961,567860000
1999-12-30,1463.459961,1473.099976,1462.599976,1464.469971,1464.469971,554680000
1999-12-31,1464.469971,1472.420044,1458.189941,1469.250000,1469.250000,374050000
2000-01-03,1469.250000,1478.000000,1438.359985,1455.219971,1455.219971,931800000
2000-01-04,1455.219971,1455.219971,1397.430054,1399.420044,1399.420044,1009000000
2000-01-05,1399.420044,1413.270020,1377.680054,1402.109985,1402.109985,1085500000
2000-01-06,1402.109985,1411.900024,1392.099976,1403.449951,1403.449951,1092300000
2000-01-07,1403.449951,1441.469971,1400.729980,1441.469971,1441.469971,1225200000
2000-01-10,1441.469971,1464.359985,1441.469971,1457.599976,1457.599976,1064800000
2000-01-11,1457.599976,1458.660034,1434.420044,1438.560059,1438.560059,1014000000
2000-01-12,1438.560059,1442.599976,1427.079956,1432.250000,1432.250000,974600000
2000-01-13,1432.250000,1454.199951,1432.250000,1449.680054,1449.680054,1030400000
2000-01-14,1449.680054,1473.000000,1449.680054,1465.150024,1465.150024,1085900000
2000-01-18,1465.150024,1465.150024,1451.300049,1455.140015,1455.140015,1056700000
2000-01-19,1455.140015,1461.390015,1448.680054,1455.900024,1455.900024,1087800000
2000-01-20,1455.900024,1465.709961,1438.540039,1445.569946,1445.569946,1100700000
2000-01-21,1445.569946,1453.180054,1439.599976,1441.359985,1441.359985,1209800000
2000-01-24,1441.359985,1454.089966,1395.420044,1401.530029,1401.530029,1115800000
2000-01-25,1401.530029,1414.260010,1388.489990,1410.030029,1410.030029,1073700000
2000-01-26,1410.030029,1412.729980,1400.160034,1404.089966,1404.089966,1117300000
2000-01-27,1404.089966,1418.859985,1370.989990,1398.560059,1398.560059,1129500000
2000-01-28,1398.560059,1398.560059,1356.199951,1360.160034,1360.160034,1095800000
2000-01-31,1360.160034,1394.479980,1350.140015,1394.459961,1394.459961,993800000
2000-02-01,1394.459961,1412.489990,1384.790039,1409.280029,1409.280029,981000000
2000-02-02,1409.280029,1420.609985,1403.489990,1409.119995,1409.119995,1038600000
2000-02-03,1409.119995,1425.780029,1398.520020,1424.969971,1424.969971,1146500000
2000-02-04,1424.969971,1435.910034,1420.630005,1424.369995,1424.369995,1045100000
2000-02-07,1424.369995,1427.150024,1413.329956,1424.239990,1424.239990,918100000
2000-02-08,1424.239990,1441.829956,1424.239990,1441.719971,1441.719971,1047700000
2000-02-09,1441.719971,1444.550049,1411.650024,1411.709961,1411.709961,1050500000
2000-02-10,1411.699951,1422.099976,1406.430054,1416.829956,1416.829956,1058800000
2000-02-11,1416.829956,1416.829956,1378.890015,1387.119995,1387.119995,1025700000
2000-02-14,1387.119995,1394.930054,1380.530029,1389.939941,1389.939941,927300000
2000-02-15,1389.939941,1407.719971,1376.250000,1402.050049,1402.050049,1092100000
2000-02-16,1402.050049,1404.550049,1385.579956,1387.670044,1387.670044,1018800000
2000-02-17,1387.670044,1399.880005,1380.069946,1388.260010,1388.260010,1034800000
2000-02-18,1388.260010,1388.589966,1345.319946,1346.089966,1346.089966,1042300000
2000-02-22,1346.089966,1358.109985,1331.880005,1352.170044,1352.170044,980000000
2000-02-23,1352.170044,1370.109985,1342.439941,1360.689941,1360.689941,993700000
2000-02-24,1360.689941,1364.800049,1329.880005,1353.430054,1353.430054,1215000000
2000-02-25,1353.430054,1362.140015,1329.150024,1333.359985,1333.359985,1065200000
2000-02-28,1333.359985,1360.819946,1325.069946,1348.050049,1348.050049,1026500000
2000-02-29,1348.050049,1369.630005,1348.050049,1366.420044,1366.420044,1204300000
2000-03-01,1366.420044,1383.459961,1366.420044,1379.189941,1379.189941,1274100000
2000-03-02,1379.189941,1386.560059,1370.349976,1381.760010,1381.760010,1198600000
2000-03-03,1381.760010,1410.880005,1381.760010,1409.170044,1409.170044,1150300000
2000-03-06,1409.170044,1409.739990,1384.750000,1391.280029,1391.280029,1029000000
2000-03-07,1391.280029,1399.209961,1349.989990,1355.619995,1355.619995,1314100000
2000-03-08,1355.619995,1373.790039,1346.619995,1366.699951,1366.699951,1203000000
2000-03-09,1366.699951,1401.819946,1357.880005,1401.689941,1401.689941,1123000000
2000-03-10,1401.689941,1413.459961,1392.069946,1395.069946,1395.069946,1138800000
2000-03-13,1395.069946,1398.390015,1364.839966,1383.619995,1383.619995,1016100000
2000-03-14,1383.619995,1395.150024,1359.150024,1359.150024,1359.150024,1094000000
2000-03-15,1359.150024,1397.989990,1356.989990,1392.140015,1392.140015,1302800000
2000-03-16,1392.150024,1458.469971,1392.150024,1458.469971,1458.469971,1482300000
2000-03-17,1458.469971,1477.329956,1453.319946,1464.469971,1464.469971,1295100000
2000-03-20,1464.469971,1470.300049,1448.489990,1456.630005,1456.630005,920800000
2000-03-21,1456.630005,1493.920044,1446.060059,1493.869995,1493.869995,1065900000
2000-03-22,1493.869995,1505.079956,1487.329956,1500.640015,1500.640015,1075000000
2000-03-23,1500.640015,1532.500000,1492.390015,1527.349976,1527.349976,1078300000
2000-03-24,1527.349976,1552.869995,1516.829956,1527.459961,1527.459961,1052200000
2000-03-27,1527.459961,1534.630005,1518.459961,1523.859985,1523.859985,901000000
2000-03-28,1523.859985,1527.359985,1507.089966,1507.729980,1507.729980,959100000
2000-03-29,1507.729980,1521.449951,1497.449951,1508.520020,1508.520020,1061900000
2000-03-30,1508.520020,1517.380005,1474.630005,1487.920044,1487.920044,1193400000
2000-03-31,1487.920044,1519.810059,1484.380005,1498.579956,1498.579956,1227400000
2000-04-03,1498.579956,1507.189941,1486.959961,1505.969971,1505.969971,1021700000
2000-04-04,1505.979980,1526.449951,1416.410034,1494.729980,1494.729980,1515460000
2000-04-05,1494.729980,1506.550049,1478.050049,1487.369995,1487.369995,1110300000
2000-04-06,1487.369995,1511.760010,1487.369995,1501.339966,1501.339966,1008000000
2000-04-07,1501.339966,1518.680054,1501.339966,1516.349976,1516.349976,891600000
2000-04-10,1516.349976,1527.189941,1503.349976,1504.459961,1504.459961,853700000
2000-04-11,1504.459961,1512.800049,1486.780029,1500.589966,1500.589966,971400000
2000-04-12,1500.589966,1509.079956,1466.150024,1467.170044,1467.170044,1175900000
2000-04-13,1467.170044,1477.520020,1439.339966,1440.510010,1440.510010,1032000000
2000-04-14,1440.510010,1440.510010,1339.400024,1356.560059,1356.560059,1279700000
2000-04-17,1356.560059,1401.530029,1346.500000,1401.439941,1401.439941,1204700000
2000-04-18,1401.439941,1441.609985,1397.810059,1441.609985,1441.609985,1109400000
2000-04-19,1441.609985,1447.689941,1424.260010,1427.469971,1427.469971,1001400000
2000-04-20,1427.469971,1435.489990,1422.079956,1434.540039,1434.540039,896200000
2000-04-24,1434.540039,1434.540039,1407.130005,1429.859985,1429.859985,868700000
2000-04-25,1429.859985,1477.670044,1429.859985,1477.439941,1477.439941,1071100000
2000-04-26,1477.439941,1482.939941,1456.979980,1460.989990,1460.989990,999600000
2000-04-27,1460.989990,1469.209961,1434.810059,1464.920044,1464.920044,1111000000
2000-04-28,1464.920044,1473.619995,1448.150024,1452.430054,1452.430054,984600000
2000-05-01,1452.430054,1481.510010,1452.430054,1468.250000,1468.250000,966300000
2000-05-02,1468.250000,1468.250000,1445.219971,1446.290039,1446.290039,1011500000
2000-05-03,1446.290039,1446.290039,1398.359985,1415.099976,1415.099976,991600000
2000-05-04,1415.099976,1420.989990,1404.939941,1409.569946,1409.569946,925800000
2000-05-05,1409.569946,1436.030029,1405.079956,1432.630005,1432.630005,805500000
2000-05-08,1432.630005,1432.630005,1417.050049,1424.170044,1424.170044,787600000
2000-05-09,1424.170044,1430.280029,1401.849976,1412.140015,1412.140015,896600000
2000-05-10,1412.140015,1412.140015,1375.140015,1383.050049,1383.050049,1006400000
2000-05-11,1383.050049,1410.260010,1383.050049,1407.810059,1407.810059,953600000
2000-05-12,1407.810059,1430.130005,1407.810059,1420.959961,1420.959961,858200000
2000-05-15,1420.959961,1452.390015,1416.540039,1452.359985,1452.359985,854600000
2000-05-16,1452.359985,1470.400024,1450.760010,1466.040039,1466.040039,955500000
2000-05-17,1466.040039,1466.040039,1441.670044,1447.800049,1447.800049,820500000
2000-05-18,1447.800049,1458.040039,1436.589966,1437.209961,1437.209961,807900000
2000-05-19,1437.209961,1437.209961,1401.739990,1406.949951,1406.949951,853700000
2000-05-22,1406.949951,1410.550049,1368.729980,1400.719971,1400.719971,869000000
2000-05-23,1400.719971,1403.770020,1373.430054,1373.859985,1373.859985,869900000
2000-05-24,1373.859985,1401.750000,1361.089966,1399.050049,1399.050049,1152300000
2000-05-25,1399.050049,1411.650024,1373.930054,1381.520020,1381.520020,984500000
2000-05-26,1381.520020,1391.420044,1369.750000,1378.020020,1378.020020,722600000
2000-05-30,1378.020020,1422.449951,1378.020020,1422.449951,1422.449951,844200000
2000-05-31,1422.439941,1434.489990,1415.500000,1420.599976,1420.599976,960500000
2000-06-01,1420.599976,1448.810059,1420.599976,1448.810059,1448.810059,960100000
2000-06-02,1448.810059,1483.229980,1448.810059,1477.260010,1477.260010,1162400000
2000-06-05,1477.260010,1477.280029,1464.680054,1467.630005,1467.630005,838600000
2000-06-06,1467.630005,1471.359985,1454.739990,1457.839966,1457.839966,950100000
2000-06-07,1457.839966,1474.640015,1455.060059,1471.359985,1471.359985,854600000
2000-06-08,1471.359985,1475.650024,1456.489990,1461.670044,1461.670044,854300000
2000-06-09,1461.670044,1472.670044,1454.959961,1456.949951,1456.949951,786000000
2000-06-12,1456.949951,1462.930054,1445.989990,1446.000000,1446.000000,774100000
2000-06-13,1446.000000,1470.420044,1442.380005,1469.439941,1469.439941,935900000
2000-06-14,1469.439941,1483.619995,1467.709961,1470.540039,1470.540039,929700000
2000-06-15,1470.540039,1482.040039,1464.619995,1478.729980,1478.729980,1011400000
2000-06-16,1478.729980,1480.770020,1460.420044,1464.459961,1464.459961,1250800000
2000-06-19,1464.459961,1488.930054,1459.050049,1486.000000,1486.000000,921700000
2000-06-20,1486.000000,1487.319946,1470.180054,1475.949951,1475.949951,1031500000
2000-06-21,1475.949951,1482.189941,1468.000000,1479.130005,1479.130005,1009600000
2000-06-22,1479.130005,1479.130005,1448.030029,1452.180054,1452.180054,1022700000
2000-06-23,1452.180054,1459.939941,1438.310059,1441.479980,1441.479980,847600000
2000-06-26,1441.479980,1459.660034,1441.479980,1455.310059,1455.310059,889000000
2000-06-27,1455.310059,1463.349976,1450.550049,1450.550049,1450.550049,1042500000
2000-06-28,1450.550049,1467.630005,1450.550049,1454.819946,1454.819946,1095100000
2000-06-29,1454.819946,1455.140015,1434.630005,1442.390015,1442.390015,1110900000
2000-06-30,1442.390015,1454.680054,1438.709961,1454.599976,1454.599976,1459700000
2000-07-03,1454.599976,1469.579956,1450.849976,1469.540039,1469.540039,451900000
2000-07-05,1469.540039,1469.540039,1442.449951,1446.229980,1446.229980,1019300000
2000-07-06,1446.229980,1461.650024,1439.560059,1456.670044,1456.670044,947300000
2000-07-07,1456.670044,1484.119995,1456.670044,1478.900024,1478.900024,931700000
2000-07-10,1478.900024,1486.560059,1474.760010,1475.619995,1475.619995,838700000
2000-07-11,1475.619995,1488.770020,1470.479980,1480.880005,1480.880005,980500000
2000-07-12,1480.880005,1497.689941,1480.880005,1492.920044,1492.920044,1001200000
2000-07-13,1492.920044,1501.390015,1489.650024,1495.839966,1495.839966,1026800000
2000-07-14,1495.839966,1509.989990,1494.560059,1509.979980,1509.979980,960600000
2000-07-17,1509.979980,1517.319946,1505.260010,1510.489990,1510.489990,906000000
2000-07-18,1510.489990,1510.489990,1491.349976,1493.739990,1493.739990,908300000
2000-07-19,1493.739990,1495.630005,1479.920044,1481.959961,1481.959961,909400000
2000-07-20,1481.959961,1501.920044,1481.959961,1495.569946,1495.569946,1064600000
2000-07-21,1495.569946,1495.569946,1477.910034,1480.189941,1480.189941,968300000
2000-07-24,1480.189941,1485.880005,1463.800049,1464.290039,1464.290039,880300000
2000-07-25,1464.290039,1476.229980,1464.290039,1474.469971,1474.469971,969400000
2000-07-26,1474.469971,1474.469971,1452.420044,1452.420044,1452.420044,1235800000
2000-07-27,1452.420044,1464.910034,1445.329956,1449.619995,1449.619995,1156400000
2000-07-28,1449.619995,1456.680054,1413.890015,1419.890015,1419.890015,980000000
2000-07-31,1419.890015,1437.650024,1418.709961,1430.829956,1430.829956,952600000
2000-08-01,1430.829956,1443.540039,1428.959961,1438.099976,1438.099976,938700000
2000-08-02,1438.099976,1451.589966,1433.489990,1438.699951,1438.699951,994500000
2000-08-03,1438.699951,1454.189941,1425.430054,1452.560059,1452.560059,1095600000
2000-08-04,1452.560059,1462.930054,1451.310059,1462.930054,1462.930054,956000000
2000-08-07,1462.930054,1480.800049,1460.719971,1479.319946,1479.319946,854800000
2000-08-08,1479.319946,1484.520020,1472.609985,1482.800049,1482.800049,992200000
2000-08-09,1482.800049,1490.329956,1471.160034,1472.869995,1472.869995,1054000000
2000-08-10,1472.869995,1475.150024,1459.890015,1460.250000,1460.250000,940800000
2000-08-11,1460.250000,1475.719971,1453.060059,1471.839966,1471.839966,835500000
2000-08-14,1471.839966,1491.640015,1468.560059,1491.560059,1491.560059,783800000
2000-08-15,1491.560059,1493.119995,1482.739990,1484.430054,1484.430054,895900000
2000-08-16,1484.430054,1496.089966,1475.739990,1479.849976,1479.849976,929800000
2000-08-17,1479.849976,1499.319946,1479.849976,1496.069946,1496.069946,922400000
2000-08-18,1496.069946,1499.469971,1488.989990,1491.719971,1491.719971,821400000
2000-08-21,1491.719971,1502.839966,1491.130005,1499.479980,1499.479980,731600000
2000-08-22,1499.479980,1508.449951,1497.420044,1498.130005,1498.130005,818800000
2000-08-23,1498.130005,1507.199951,1489.520020,1505.969971,1505.969971,871000000
2000-08-24,1505.969971,1511.160034,1501.250000,1508.310059,1508.310059,837100000
2000-08-25,1508.310059,1513.469971,1505.089966,1506.449951,1506.449951,685600000
2000-08-28,1506.449951,1523.949951,1506.449951,1514.089966,1514.089966,733600000
2000-08-29,1514.089966,1514.810059,1505.459961,1509.839966,1509.839966,795600000
2000-08-30,1509.839966,1510.489990,1500.089966,1502.589966,1502.589966,818400000
2000-08-31,1502.589966,1525.209961,1502.589966,1517.680054,1517.680054,1056600000
2000-09-01,1517.680054,1530.089966,1515.530029,1520.770020,1520.770020,767700000
2000-09-05,1520.770020,1520.770020,1504.209961,1507.079956,1507.079956,838500000
2000-09-06,1507.079956,1512.609985,1492.119995,1492.250000,1492.250000,995100000
2000-09-07,1492.250000,1505.339966,1492.250000,1502.510010,1502.510010,985500000
2000-09-08,1502.510010,1502.510010,1489.880005,1494.500000,1494.500000,961000000
2000-09-11,1494.500000,1506.760010,1483.010010,1489.260010,1489.260010,899300000
2000-09-12,1489.260010,1496.930054,1479.670044,1481.989990,1481.989990,991200000
2000-09-13,1481.989990,1487.449951,1473.609985,1484.910034,1484.910034,1068300000
2000-09-14,1484.910034,1494.160034,1476.729980,1480.869995,1480.869995,1014000000
2000-09-15,1480.869995,1480.959961,1460.219971,1465.810059,1465.810059,1268400000
2000-09-18,1465.810059,1467.770020,1441.920044,1444.510010,1444.510010,962500000
2000-09-19,1444.510010,1461.160034,1444.510010,1459.900024,1459.900024,1024900000
2000-09-20,1459.900024,1460.489990,1430.949951,1451.339966,1451.339966,1104000000
2000-09-21,1451.339966,1452.770020,1436.300049,1449.050049,1449.050049,1105400000
2000-09-22,1449.050049,1449.050049,1421.880005,1448.719971,1448.719971,1185500000
2000-09-25,1448.719971,1457.420044,1435.930054,1439.030029,1439.030029,982400000
2000-09-26,1439.030029,1448.040039,1425.250000,1427.209961,1427.209961,1106600000
2000-09-27,1427.209961,1437.219971,1419.439941,1426.569946,1426.569946,1174700000
2000-09-28,1426.569946,1461.689941,1425.780029,1458.290039,1458.290039,1206200000
2000-09-29,1458.290039,1458.290039,1436.290039,1436.510010,1436.510010,1197100000
2000-10-02,1436.520020,1445.599976,1429.829956,1436.229980,1436.229980,1051200000
2000-10-03,1436.229980,1454.819946,1425.280029,1426.459961,1426.459961,1098100000
2000-10-04,1426.459961,1439.989990,1416.310059,1434.319946,1434.319946,1167400000
2000-10-05,1434.319946,1444.170044,1431.800049,1436.280029,1436.280029,1176100000
2000-10-06,1436.280029,1443.300049,1397.060059,1408.989990,1408.989990,1150100000
2000-10-09,1408.989990,1409.689941,1392.479980,1402.030029,1402.030029,716600000
2000-10-10,1402.030029,1408.829956,1383.849976,1387.020020,1387.020020,1044000000
2000-10-11,1387.020020,1387.020020,1349.670044,1364.589966,1364.589966,1387500000
2000-10-12,1364.589966,1374.930054,1328.060059,1329.780029,1329.780029,1388600000
2000-10-13,1329.780029,1374.170044,1327.079956,1374.170044,1374.170044,1223900000
2000-10-16,1374.170044,1379.479980,1365.060059,1374.619995,1374.619995,1005400000
2000-10-17,1374.619995,1380.989990,1342.339966,1349.969971,1349.969971,1161500000
2000-10-18,1349.969971,1356.650024,1305.790039,1342.130005,1342.130005,1441700000
2000-10-19,1342.130005,1389.930054,1342.130005,1388.760010,1388.760010,1297900000
2000-10-20,1388.760010,1408.469971,1382.189941,1396.930054,1396.930054,1177400000
2000-10-23,1396.930054,1406.959961,1387.750000,1395.780029,1395.780029,1046800000
2000-10-24,1395.780029,1415.640015,1388.130005,1398.130005,1398.130005,1158600000
2000-10-25,1398.130005,1398.130005,1362.209961,1364.900024,1364.900024,1315600000
2000-10-26,1364.900024,1372.719971,1337.810059,1364.439941,1364.439941,1303800000
2000-10-27,1364.439941,1384.569946,1364.130005,1379.579956,1379.579956,1086300000
2000-10-30,1379.579956,1406.359985,1376.859985,1398.660034,1398.660034,1186500000
2000-10-31,1398.660034,1432.219971,1398.660034,1429.400024,1429.400024,1366400000
2000-11-01,1429.400024,1429.599976,1410.449951,1421.219971,1421.219971,1206800000
2000-11-02,1421.219971,1433.400024,1421.219971,1428.319946,1428.319946,1167700000
2000-11-03,1428.319946,1433.209961,1420.920044,1426.689941,1426.689941,997700000
2000-11-06,1428.760010,1438.459961,1427.719971,1432.189941,1432.189941,930900000
2000-11-07,1432.189941,1436.219971,1423.260010,1431.869995,1431.869995,880900000
2000-11-08,1431.869995,1437.280029,1408.780029,1409.280029,1409.280029,909300000
2000-11-09,1409.280029,1409.280029,1369.680054,1400.140015,1400.140015,1111000000
2000-11-10,1400.140015,1400.140015,1365.969971,1365.979980,1365.979980,962500000
2000-11-13,1365.979980,1365.979980,1328.619995,1351.260010,1351.260010,1129300000
2000-11-14,1351.260010,1390.060059,1351.260010,1382.949951,1382.949951,1118800000
2000-11-15,1382.949951,1395.959961,1374.750000,1389.810059,1389.810059,1066800000
2000-11-16,1389.810059,1394.760010,1370.390015,1372.319946,1372.319946,956300000
2000-11-17,1372.319946,1384.849976,1355.550049,1367.719971,1367.719971,1070400000
2000-11-20,1367.719971,1367.719971,1341.670044,1342.619995,1342.619995,955800000
2000-11-21,1342.619995,1355.869995,1333.619995,1347.349976,1347.349976,1137100000
2000-11-22,1347.349976,1347.349976,1321.890015,1322.359985,1322.359985,963200000
2000-11-24,1322.359985,1343.829956,1322.359985,1341.770020,1341.770020,404870000
2000-11-27,1341.770020,1362.500000,1341.770020,1348.969971,1348.969971,946100000
2000-11-28,1348.969971,1358.810059,1334.969971,1336.089966,1336.089966,1028200000
2000-11-29,1336.089966,1352.380005,1329.280029,1341.930054,1341.930054,402100000
2000-11-30,1341.910034,1341.910034,1294.900024,1314.949951,1314.949951,1186530000
2000-12-01,1314.949951,1334.670044,1307.020020,1315.229980,1315.229980,1195200000
2000-12-04,1315.180054,1332.060059,1310.229980,1324.969971,1324.969971,1103000000
2000-12-05,1324.969971,1376.560059,1324.969971,1376.540039,1376.540039,900300000
2000-12-06,1376.540039,1376.540039,1346.150024,1351.459961,1351.459961,1399300000
2000-12-07,1351.459961,1353.500000,1339.260010,1343.550049,1343.550049,1128000000
2000-12-08,1343.550049,1380.329956,1343.550049,1369.890015,1369.890015,1358300000
2000-12-11,1369.890015,1389.050049,1364.140015,1380.199951,1380.199951,1202400000
2000-12-12,1380.199951,1380.270020,1370.270020,1371.180054,1371.180054,1083400000
2000-12-13,1371.180054,1385.819946,1358.479980,1359.989990,1359.989990,1195100000
2000-12-14,1359.989990,1359.989990,1340.479980,1340.930054,1340.930054,1061300000
2000-12-15,1340.930054,1340.930054,1305.380005,1312.150024,1312.150024,1561100000
2000-12-18,1312.150024,1332.319946,1312.150024,1322.739990,1322.739990,1189900000
2000-12-19,1322.959961,1346.439941,1305.199951,1305.599976,1305.599976,1324900000
2000-12-20,1305.599976,1305.599976,1261.160034,1264.739990,1264.739990,1421600000
2000-12-21,1264.739990,1285.310059,1254.069946,1274.859985,1274.859985,1449900000
2000-12-22,1274.859985,1305.969971,1274.859985,1305.949951,1305.949951,1087100000
2000-12-26,1305.969971,1315.939941,1301.640015,1315.189941,1315.189941,806500000
2000-12-27,1315.189941,1332.030029,1310.959961,1328.920044,1328.920044,1092700000
2000-12-28,1328.920044,1335.930054,1325.780029,1334.219971,1334.219971,1015300000
2000-12-29,1334.219971,1340.099976,1317.510010,1320.280029,1320.280029,1035500000
2001-01-02,1320.280029,1320.280029,1276.050049,1283.270020,1283.270020,1129400000
2001-01-03,1283.270020,1347.760010,1274.619995,1347.560059,1347.560059,1880700000
2001-01-04,1347.560059,1350.239990,1329.140015,1333.339966,1333.339966,2131000000
2001-01-05,1333.339966,1334.770020,1294.949951,1298.349976,1298.349976,1430800000
2001-01-08,1298.349976,1298.349976,1276.290039,1295.859985,1295.859985,1115500000
2001-01-09,1295.859985,1311.719971,1295.140015,1300.800049,1300.800049,1191300000
2001-01-10,1300.800049,1313.760010,1287.280029,1313.270020,1313.270020,1296500000
2001-01-11,1313.270020,1332.189941,1309.719971,1326.819946,1326.819946,1411200000
2001-01-12,1326.819946,1333.209961,1311.589966,1318.550049,1318.550049,1276000000
2001-01-16,1318.319946,1327.810059,1313.329956,1326.650024,1326.650024,1205700000
2001-01-17,1326.650024,1346.920044,1325.410034,1329.469971,1329.469971,1349100000
2001-01-18,1329.890015,1352.709961,1327.410034,1347.969971,1347.969971,1445000000
2001-01-19,1347.969971,1354.550049,1336.739990,1342.540039,1342.540039,1407800000
2001-01-22,1342.540039,1353.619995,1333.839966,1342.900024,1342.900024,1164000000
2001-01-23,1342.900024,1362.900024,1339.630005,1360.400024,1360.400024,1232600000
2001-01-24,1360.400024,1369.750000,1357.280029,1364.300049,1364.300049,1309000000
2001-01-25,1364.300049,1367.349976,1354.630005,1357.510010,1357.510010,1258000000
2001-01-26,1357.510010,1357.510010,1342.750000,1354.949951,1354.949951,1098000000
2001-01-29,1354.920044,1365.540039,1350.359985,1364.170044,1364.170044,1053100000
2001-01-30,1364.170044,1375.680054,1356.199951,1373.729980,1373.729980,1149800000
2001-01-31,1373.729980,1383.369995,1364.660034,1366.010010,1366.010010,1295300000
2001-02-01,1366.010010,1373.500000,1359.339966,1373.469971,1373.469971,1118800000
2001-02-02,1373.469971,1376.380005,1348.719971,1349.469971,1349.469971,1048400000
2001-02-05,1349.469971,1354.560059,1344.479980,1354.310059,1354.310059,1013000000
2001-02-06,1354.310059,1363.550049,1350.040039,1352.260010,1352.260010,1059600000
2001-02-07,1352.260010,1352.260010,1334.260010,1340.890015,1340.890015,1158300000
2001-02-08,1341.099976,1350.319946,1332.420044,1332.530029,1332.530029,1107200000
2001-02-09,1332.530029,1332.530029,1309.979980,1314.760010,1314.760010,1075500000
2001-02-12,1314.760010,1330.959961,1313.640015,1330.310059,1330.310059,1039100000
2001-02-13,1330.310059,1336.619995,1317.510010,1318.800049,1318.800049,1075200000
2001-02-14,1318.800049,1320.729980,1304.719971,1315.920044,1315.920044,1150300000
2001-02-15,1315.920044,1331.290039,1315.920044,1326.609985,1326.609985,1153700000
2001-02-16,1326.609985,1326.609985,1293.180054,1301.530029,1301.530029,1257200000
2001-02-20,1301.530029,1307.160034,1278.439941,1278.939941,1278.939941,1112200000
2001-02-21,1278.939941,1282.969971,1253.160034,1255.270020,1255.270020,1208500000
2001-02-22,1255.270020,1259.939941,1228.329956,1252.819946,1252.819946,1365900000
2001-02-23,1252.819946,1252.819946,1215.439941,1245.859985,1245.859985,1231300000
2001-02-26,1245.859985,1267.689941,1241.709961,1267.650024,1267.650024,1130800000
2001-02-27,1267.650024,1272.760010,1252.260010,1257.939941,1257.939941,1114100000
2001-02-28,1257.939941,1263.469971,1229.650024,1239.939941,1239.939941,1225300000
2001-03-01,1239.939941,1241.359985,1214.500000,1241.229980,1241.229980,1294900000
2001-03-02,1241.229980,1251.010010,1219.739990,1234.180054,1234.180054,1294000000
2001-03-05,1234.180054,1242.550049,1234.040039,1241.410034,1241.410034,929200000
2001-03-06,1241.410034,1267.420044,1241.410034,1253.800049,1253.800049,1091800000
2001-03-07,1253.800049,1263.859985,1253.800049,1261.890015,1261.890015,1132200000
2001-03-08,1261.890015,1266.500000,1257.599976,1264.739990,1264.739990,1114100000
2001-03-09,1264.739990,1264.739990,1228.420044,1233.420044,1233.420044,1085900000
2001-03-12,1233.420044,1233.420044,1176.780029,1180.160034,1180.160034,1229000000
2001-03-13,1180.160034,1197.829956,1171.500000,1197.660034,1197.660034,1360900000
2001-03-14,1197.660034,1197.660034,1155.349976,1166.709961,1166.709961,1397400000
2001-03-15,1166.709961,1182.040039,1166.709961,1173.560059,1173.560059,1259500000
2001-03-16,1173.560059,1173.560059,1148.640015,1150.530029,1150.530029,1543560000
2001-03-19,1150.530029,1173.500000,1147.180054,1170.810059,1170.810059,1126200000
2001-03-20,1170.810059,1180.560059,1142.189941,1142.619995,1142.619995,1235900000
2001-03-21,1142.619995,1149.390015,1118.739990,1122.140015,1122.140015,1346300000
2001-03-22,1122.140015,1124.270020,1081.189941,1117.579956,1117.579956,1723950000
2001-03-23,1117.579956,1141.829956,1117.579956,1139.829956,1139.829956,1364900000
2001-03-26,1139.829956,1160.020020,1139.829956,1152.689941,1152.689941,1114000000
2001-03-27,1152.689941,1183.349976,1150.959961,1182.170044,1182.170044,1314200000
2001-03-28,1182.170044,1182.170044,1147.829956,1153.290039,1153.290039,1333400000
2001-03-29,1153.290039,1161.689941,1136.260010,1147.949951,1147.949951,1234500000
2001-03-30,1147.949951,1162.800049,1143.829956,1160.329956,1160.329956,1280800000
2001-04-02,1160.329956,1169.510010,1137.510010,1145.869995,1145.869995,1254900000
2001-04-03,1145.869995,1145.869995,1100.189941,1106.459961,1106.459961,1386100000
2001-04-04,1106.459961,1117.500000,1091.989990,1103.250000,1103.250000,1425590000
2001-04-05,1103.250000,1151.469971,1103.250000,1151.439941,1151.439941,1368000000
2001-04-06,1151.439941,1151.439941,1119.290039,1128.430054,1128.430054,1266800000
2001-04-09,1128.430054,1146.130005,1126.380005,1137.589966,1137.589966,1062800000
2001-04-10,1137.589966,1173.920044,1137.589966,1168.380005,1168.380005,1349600000
2001-04-11,1168.380005,1182.239990,1160.260010,1165.890015,1165.890015,1290300000
2001-04-12,1165.890015,1183.510010,1157.729980,1183.500000,1183.500000,1102000000
2001-04-16,1183.500000,1184.640015,1167.380005,1179.680054,1179.680054,913900000
2001-04-17,1179.680054,1192.250000,1168.900024,1191.810059,1191.810059,1109600000
2001-04-18,1191.810059,1248.420044,1191.810059,1238.160034,1238.160034,1918900000
2001-04-19,1238.160034,1253.709961,1233.390015,1253.689941,1253.689941,1486800000
2001-04-20,1253.699951,1253.699951,1234.410034,1242.979980,1242.979980,1338700000
2001-04-23,1242.979980,1242.979980,1217.469971,1224.359985,1224.359985,1012600000
2001-04-24,1224.359985,1233.540039,1208.890015,1209.469971,1209.469971,1216500000
2001-04-25,1209.469971,1232.359985,1207.380005,1228.750000,1228.750000,1203600000
2001-04-26,1228.750000,1248.300049,1228.750000,1234.520020,1234.520020,1345200000
2001-04-27,1234.520020,1253.069946,1234.520020,1253.050049,1253.050049,1091300000
2001-04-30,1253.050049,1269.300049,1243.989990,1249.459961,1249.459961,1266800000
2001-05-01,1249.459961,1266.469971,1243.550049,1266.439941,1266.439941,1181300000
2001-05-02,1266.439941,1272.930054,1257.699951,1267.430054,1267.430054,1342200000
2001-05-03,1267.430054,1267.430054,1239.880005,1248.579956,1248.579956,1137900000
2001-05-04,1248.579956,1267.510010,1232.000000,1266.609985,1266.609985,1082100000
2001-05-07,1266.609985,1270.000000,1259.189941,1263.510010,1263.510010,949000000
2001-05-08,1266.709961,1267.010010,1253.000000,1261.199951,1261.199951,1006300000
2001-05-09,1261.199951,1261.650024,1247.829956,1255.540039,1255.540039,1132400000
2001-05-10,1255.540039,1268.140015,1254.560059,1255.180054,1255.180054,1056700000
2001-05-11,1255.180054,1259.839966,1240.790039,1245.670044,1245.670044,906200000
2001-05-14,1245.670044,1249.680054,1241.020020,1248.920044,1248.920044,858200000
2001-05-15,1248.920044,1257.449951,1245.359985,1249.439941,1249.439941,1071800000
2001-05-16,1249.439941,1286.390015,1243.020020,1284.989990,1284.989990,1405300000
2001-05-17,1284.989990,1296.479980,1282.650024,1288.489990,1288.489990,1355600000
2001-05-18,1288.489990,1292.060059,1281.150024,1291.959961,1291.959961,1130800000
2001-05-21,1291.959961,1312.949951,1287.869995,1312.829956,1312.829956,1174900000
2001-05-22,1312.829956,1315.930054,1306.890015,1309.380005,1309.380005,1260400000
2001-05-23,1309.380005,1309.380005,1288.699951,1289.050049,1289.050049,1134800000
2001-05-24,1289.050049,1295.040039,1281.219971,1293.170044,1293.170044,1100700000
2001-05-25,1293.170044,1293.170044,1276.420044,1277.890015,1277.890015,828100000
2001-05-29,1277.890015,1278.420044,1265.410034,1267.930054,1267.930054,1026000000
2001-05-30,1267.930054,1267.930054,1245.959961,1248.079956,1248.079956,1158600000
2001-05-31,1248.079956,1261.910034,1248.069946,1255.819946,1255.819946,1226600000
2001-06-01,1255.819946,1265.339966,1246.880005,1260.670044,1260.670044,1015000000
2001-06-04,1260.670044,1267.170044,1256.359985,1267.109985,1267.109985,836500000
2001-06-05,1267.109985,1286.619995,1267.109985,1283.569946,1283.569946,1116800000
2001-06-06,1283.569946,1283.849976,1269.010010,1270.030029,1270.030029,1061900000
2001-06-07,1270.030029,1277.079956,1265.079956,1276.959961,1276.959961,1089600000
2001-06-08,1276.959961,1277.109985,1259.989990,1264.959961,1264.959961,726200000
2001-06-11,1264.959961,1264.959961,1249.229980,1254.390015,1254.390015,870100000
2001-06-12,1254.390015,1261.000000,1235.750000,1255.849976,1255.849976,1136500000
2001-06-13,1255.849976,1259.750000,1241.589966,1241.599976,1241.599976,1063600000
2001-06-14,1241.599976,1241.599976,1218.900024,1219.869995,1219.869995,1242900000
2001-06-15,1219.869995,1221.500000,1203.030029,1214.359985,1214.359985,1635550000
2001-06-18,1214.359985,1221.229980,1208.329956,1208.430054,1208.430054,1111600000
2001-06-19,1208.430054,1226.109985,1207.709961,1212.579956,1212.579956,1184900000
2001-06-20,1212.579956,1225.609985,1210.069946,1223.140015,1223.140015,1350100000
2001-06-21,1223.140015,1240.239990,1220.250000,1237.040039,1237.040039,1546820000
2001-06-22,1237.040039,1237.729980,1221.410034,1225.349976,1225.349976,1189200000
2001-06-25,1225.349976,1231.500000,1213.599976,1218.599976,1218.599976,1050100000
2001-06-26,1218.599976,1220.699951,1204.640015,1216.760010,1216.760010,1198900000
2001-06-27,1216.760010,1219.920044,1207.290039,1211.069946,1211.069946,1162100000
2001-06-28,1211.069946,1234.439941,1211.069946,1226.199951,1226.199951,1327300000
2001-06-29,1226.199951,1237.290039,1221.140015,1224.380005,1224.380005,1832360000
2001-07-02,1224.420044,1239.780029,1224.030029,1236.719971,1236.719971,1128300000
2001-07-03,1236.709961,1236.709961,1229.430054,1234.449951,1234.449951,622110000
2001-07-05,1234.449951,1234.449951,1219.150024,1219.239990,1219.239990,934900000
2001-07-06,1219.239990,1219.239990,1188.739990,1190.589966,1190.589966,1056700000
2001-07-09,1190.589966,1201.760010,1189.750000,1198.780029,1198.780029,1045700000
2001-07-10,1198.780029,1203.430054,1179.930054,1181.520020,1181.520020,1263800000
2001-07-11,1181.520020,1184.930054,1168.459961,1180.180054,1180.180054,1384100000
2001-07-12,1180.180054,1210.250000,1180.180054,1208.140015,1208.140015,1394000000
2001-07-13,1208.140015,1218.540039,1203.609985,1215.680054,1215.680054,1121700000
2001-07-16,1215.680054,1219.630005,1200.050049,1202.449951,1202.449951,1039800000
2001-07-17,1202.449951,1215.359985,1196.140015,1214.439941,1214.439941,1238100000
2001-07-18,1214.439941,1214.439941,1198.329956,1207.709961,1207.709961,1316300000
2001-07-19,1207.709961,1225.040039,1205.800049,1215.020020,1215.020020,1343500000
2001-07-20,1215.020020,1215.689941,1207.040039,1210.849976,1210.849976,1170900000
2001-07-23,1210.849976,1215.219971,1190.500000,1191.030029,1191.030029,986900000
2001-07-24,1191.030029,1191.030029,1165.540039,1171.650024,1171.650024,1198700000
2001-07-25,1171.650024,1190.520020,1171.280029,1190.489990,1190.489990,1280700000
2001-07-26,1190.489990,1204.180054,1182.650024,1202.930054,1202.930054,1213900000
2001-07-27,1202.930054,1209.260010,1195.989990,1205.819946,1205.819946,1015300000
2001-07-30,1205.819946,1209.050049,1200.410034,1204.520020,1204.520020,909100000
2001-07-31,1204.520020,1222.739990,1204.520020,1211.229980,1211.229980,1129200000
2001-08-01,1211.229980,1223.040039,1211.229980,1215.930054,1215.930054,1340300000
2001-08-02,1215.930054,1226.270020,1215.310059,1220.750000,1220.750000,1218300000
2001-08-03,1220.750000,1220.750000,1205.310059,1214.349976,1214.349976,939900000
2001-08-06,1214.349976,1214.349976,1197.349976,1200.479980,1200.479980,811700000
2001-08-07,1200.469971,1207.560059,1195.640015,1204.400024,1204.400024,1012000000
2001-08-08,1204.400024,1206.790039,1181.270020,1183.530029,1183.530029,1124600000
2001-08-09,1183.530029,1184.709961,1174.680054,1183.430054,1183.430054,1104200000
2001-08-10,1183.430054,1193.329956,1169.550049,1190.160034,1190.160034,960900000
2001-08-13,1190.160034,1193.819946,1185.119995,1191.290039,1191.290039,837600000
2001-08-14,1191.290039,1198.790039,1184.260010,1186.729980,1186.729980,964600000
2001-08-15,1186.729980,1191.209961,1177.609985,1178.020020,1178.020020,1065600000
2001-08-16,1178.020020,1181.800049,1166.079956,1181.660034,1181.660034,1055400000
2001-08-17,1181.660034,1181.660034,1156.069946,1161.969971,1161.969971,974300000
2001-08-20,1161.969971,1171.410034,1160.939941,1171.410034,1171.410034,897100000
2001-08-21,1171.410034,1179.849976,1156.560059,1157.260010,1157.260010,1041600000
2001-08-22,1157.260010,1168.560059,1153.339966,1165.310059,1165.310059,1110800000
2001-08-23,1165.310059,1169.859985,1160.959961,1162.089966,1162.089966,986200000
2001-08-24,1162.089966,1185.150024,1162.089966,1184.930054,1184.930054,1043600000
2001-08-27,1184.930054,1186.849976,1178.069946,1179.209961,1179.209961,842600000
2001-08-28,1179.209961,1179.660034,1161.170044,1161.510010,1161.510010,987100000
2001-08-29,1161.510010,1166.969971,1147.380005,1148.560059,1148.560059,963700000
2001-08-30,1148.599976,1151.750000,1124.869995,1129.030029,1129.030029,1157000000
2001-08-31,1129.030029,1141.829956,1126.380005,1133.579956,1133.579956,920100000
2001-09-04,1133.579956,1155.400024,1129.060059,1132.939941,1132.939941,1178300000
2001-09-05,1132.939941,1135.520020,1114.859985,1131.739990,1131.739990,1384500000
2001-09-06,1131.739990,1131.739990,1105.829956,1106.400024,1106.400024,1359700000
2001-09-07,1106.400024,1106.400024,1082.119995,1085.780029,1085.780029,1424300000
2001-09-10,1085.780029,1096.939941,1073.150024,1092.540039,1092.540039,1276600000
2001-09-17,1092.540039,1092.540039,1037.459961,1038.770020,1038.770020,2330830000
2001-09-18,1038.770020,1046.420044,1029.250000,1032.739990,1032.739990,1650410000
2001-09-19,1032.739990,1038.910034,984.619995,1016.099976,1016.099976,2120550000
2001-09-20,1016.099976,1016.099976,984.489990,984.539978,984.539978,2004800000
2001-09-21,984.539978,984.539978,944.750000,965.799988,965.799988,2317300000
2001-09-24,965.799988,1008.440002,965.799988,1003.450012,1003.450012,1746600000
2001-09-25,1003.450012,1017.140015,998.330017,1012.270020,1012.270020,1613800000
2001-09-26,1012.270020,1020.289978,1002.619995,1007.039978,1007.039978,1519100000
2001-09-27,1007.039978,1018.919983,998.239990,1018.609985,1018.609985,1467000000
2001-09-28,1018.609985,1040.939941,1018.609985,1040.939941,1040.939941,1631500000
2001-10-01,1040.939941,1040.939941,1026.760010,1038.550049,1038.550049,1175600000
2001-10-02,1038.550049,1051.329956,1034.469971,1051.329956,1051.329956,1289800000
2001-10-03,1051.329956,1075.380005,1041.479980,1072.280029,1072.280029,1650600000
2001-10-04,1072.280029,1084.119995,1067.819946,1069.630005,1069.630005,1609100000
2001-10-05,1069.619995,1072.349976,1053.500000,1071.380005,1071.380005,1301700000
2001-10-08,1071.369995,1071.369995,1056.880005,1062.439941,1062.439941,979000000
2001-10-09,1062.439941,1063.369995,1053.829956,1056.750000,1056.750000,1227800000
2001-10-10,1056.750000,1081.619995,1052.760010,1080.989990,1080.989990,1312400000
2001-10-11,1080.989990,1099.160034,1080.989990,1097.430054,1097.430054,1704580000
2001-10-12,1097.430054,1097.430054,1072.150024,1091.650024,1091.650024,1331400000
2001-10-15,1091.650024,1091.650024,1078.189941,1089.979980,1089.979980,1024700000
2001-10-16,1089.979980,1101.660034,1087.130005,1097.540039,1097.540039,1210500000
2001-10-17,1097.540039,1107.119995,1076.569946,1077.089966,1077.089966,1452200000
2001-10-18,1077.089966,1077.939941,1064.540039,1068.609985,1068.609985,1262900000
2001-10-19,1068.609985,1075.520020,1057.239990,1073.479980,1073.479980,1294900000
2001-10-22,1073.479980,1090.569946,1070.790039,1089.900024,1089.900024,1105700000
2001-10-23,1089.900024,1098.989990,1081.530029,1084.780029,1084.780029,1317300000
2001-10-24,1084.780029,1090.260010,1079.979980,1085.199951,1085.199951,1336200000
2001-10-25,1085.199951,1100.089966,1065.640015,1100.089966,1100.089966,1364400000
2001-10-26,1100.089966,1110.609985,1094.239990,1104.609985,1104.609985,1244500000
2001-10-29,1104.609985,1104.609985,1078.300049,1078.300049,1078.300049,1106100000
2001-10-30,1078.300049,1078.300049,1053.609985,1059.790039,1059.790039,1297400000
2001-10-31,1059.790039,1074.790039,1057.550049,1059.780029,1059.780029,1352500000
2001-11-01,1059.780029,1085.609985,1054.310059,1084.099976,1084.099976,1317400000
2001-11-02,1084.099976,1089.630005,1075.579956,1087.199951,1087.199951,1121900000
2001-11-05,1087.199951,1106.719971,1087.199951,1102.839966,1102.839966,1267700000
2001-11-06,1102.839966,1119.729980,1095.359985,1118.859985,1118.859985,1356000000
2001-11-07,1118.859985,1126.619995,1112.979980,1115.800049,1115.800049,1411300000
2001-11-08,1115.800049,1135.750000,1115.420044,1118.540039,1118.540039,1517500000
2001-11-09,1118.540039,1123.020020,1111.130005,1120.310059,1120.310059,1093800000
2001-11-12,1120.310059,1121.709961,1098.319946,1118.329956,1118.329956,991600000
2001-11-13,1118.329956,1139.140015,1118.329956,1139.089966,1139.089966,1370100000
2001-11-14,1139.089966,1148.280029,1132.869995,1141.209961,1141.209961,1443400000
2001-11-15,1141.209961,1146.459961,1135.060059,1142.239990,1142.239990,1454500000
2001-11-16,1142.239990,1143.520020,1129.920044,1138.650024,1138.650024,1337400000
2001-11-19,1138.650024,1151.060059,1138.650024,1151.060059,1151.060059,1316800000
2001-11-20,1151.060059,1152.449951,1142.170044,1142.660034,1142.660034,1330200000
2001-11-21,1142.660034,1142.660034,1129.780029,1137.030029,1137.030029,1029300000
2001-11-23,1137.030029,1151.050049,1135.900024,1150.339966,1150.339966,410300000
2001-11-26,1150.339966,1157.880005,1146.170044,1157.420044,1157.420044,1129800000
2001-11-27,1157.420044,1163.380005,1140.810059,1149.500000,1149.500000,1288000000
2001-11-28,1149.500000,1149.500000,1128.290039,1128.520020,1128.520020,1423700000
2001-11-29,1128.520020,1140.400024,1125.510010,1140.199951,1140.199951,1375700000
2001-11-30,1140.199951,1143.569946,1135.890015,1139.449951,1139.449951,1343600000
2001-12-03,1139.449951,1139.449951,1125.780029,1129.900024,1129.900024,1202900000
2001-12-04,1129.900024,1144.800049,1128.859985,1144.800049,1144.800049,1318500000
2001-12-05,1143.770020,1173.619995,1143.770020,1170.349976,1170.349976,1765300000
2001-12-06,1170.349976,1173.349976,1164.430054,1167.099976,1167.099976,1487900000
2001-12-07,1167.099976,1167.099976,1152.660034,1158.310059,1158.310059,1248200000
2001-12-10,1158.310059,1158.310059,1139.660034,1139.930054,1139.930054,1218700000
2001-12-11,1139.930054,1150.890015,1134.319946,1136.760010,1136.760010,1367200000
2001-12-12,1136.760010,1141.579956,1126.010010,1137.069946,1137.069946,1449700000
2001-12-13,1137.069946,1137.069946,1117.849976,1119.380005,1119.380005,1511500000
2001-12-14,1119.380005,1128.280029,1114.530029,1123.089966,1123.089966,1306800000
2001-12-17,1123.089966,1137.300049,1122.660034,1134.359985,1134.359985,1260400000
2001-12-18,1134.359985,1145.099976,1134.359985,1142.920044,1142.920044,1354000000
2001-12-19,1142.920044,1152.439941,1134.750000,1149.560059,1149.560059,1484900000
2001-12-20,1149.560059,1151.420044,1139.930054,1139.930054,1139.930054,1490500000
2001-12-21,1139.930054,1147.459961,1139.930054,1144.890015,1144.890015,1694000000
2001-12-24,1144.890015,1147.829956,1144.619995,1144.650024,1144.650024,439670000
2001-12-26,1144.650024,1159.180054,1144.650024,1149.369995,1149.369995,791100000
2001-12-27,1149.369995,1157.130005,1149.369995,1157.130005,1157.130005,876300000
2001-12-28,1157.130005,1164.640015,1157.130005,1161.020020,1161.020020,917400000
2001-12-31,1161.020020,1161.160034,1148.040039,1148.079956,1148.079956,943600000
2002-01-02,1148.079956,1154.670044,1136.229980,1154.670044,1154.670044,1171000000
2002-01-03,1154.670044,1165.270020,1154.010010,1165.270020,1165.270020,1398900000
2002-01-04,1165.270020,1176.550049,1163.420044,1172.510010,1172.510010,1513000000
2002-01-07,1172.510010,1176.969971,1163.550049,1164.890015,1164.890015,1308300000
2002-01-08,1164.890015,1167.599976,1157.459961,1160.709961,1160.709961,1258800000
2002-01-09,1160.709961,1174.260010,1151.890015,1155.140015,1155.140015,1452000000
2002-01-10,1155.140015,1159.930054,1150.849976,1156.550049,1156.550049,1299000000
2002-01-11,1156.550049,1159.410034,1145.449951,1145.599976,1145.599976,1211900000
2002-01-14,1145.599976,1145.599976,1138.150024,1138.410034,1138.410034,1286400000
2002-01-15,1138.410034,1148.810059,1136.880005,1146.189941,1146.189941,1386900000
2002-01-16,1146.189941,1146.189941,1127.489990,1127.569946,1127.569946,1482500000
2002-01-17,1127.569946,1139.270020,1127.569946,1138.880005,1138.880005,1380100000
2002-01-18,1138.880005,1138.880005,1124.449951,1127.579956,1127.579956,1333300000
2002-01-22,1127.579956,1135.260010,1117.910034,1119.310059,1119.310059,1311600000
2002-01-23,1119.310059,1131.939941,1117.430054,1128.180054,1128.180054,1479200000
2002-01-24,1128.180054,1139.500000,1128.180054,1132.150024,1132.150024,1552800000
2002-01-25,1132.150024,1138.310059,1127.819946,1133.280029,1133.280029,1345100000
2002-01-28,1133.280029,1138.630005,1126.660034,1133.060059,1133.060059,1186800000
2002-01-29,1133.060059,1137.469971,1098.739990,1100.640015,1100.640015,1812000000
2002-01-30,1100.640015,1113.790039,1081.660034,1113.569946,1113.569946,2019600000
2002-01-31,1113.569946,1130.209961,1113.300049,1130.199951,1130.199951,1557000000
2002-02-01,1130.199951,1130.199951,1118.510010,1122.199951,1122.199951,1367200000
2002-02-04,1122.199951,1122.199951,1092.250000,1094.439941,1094.439941,1437600000
2002-02-05,1094.439941,1100.959961,1082.579956,1090.020020,1090.020020,1778300000
2002-02-06,1090.020020,1093.579956,1077.780029,1083.510010,1083.510010,1665800000
2002-02-07,1083.510010,1094.030029,1078.439941,1080.170044,1080.170044,1441600000
2002-02-08,1080.170044,1096.300049,1079.910034,1096.219971,1096.219971,1371900000
2002-02-11,1096.219971,1112.010010,1094.680054,1111.939941,1111.939941,1159400000
2002-02-12,1111.939941,1112.680054,1102.979980,1107.500000,1107.500000,1094200000
2002-02-13,1107.500000,1120.560059,1107.500000,1118.510010,1118.510010,1215900000
2002-02-14,1118.510010,1124.719971,1112.300049,1116.479980,1116.479980,1272500000
2002-02-15,1116.479980,1117.089966,1103.229980,1104.180054,1104.180054,1359200000
2002-02-19,1104.180054,1104.180054,1082.239990,1083.339966,1083.339966,1189900000
2002-02-20,1083.339966,1098.319946,1074.359985,1097.979980,1097.979980,1438900000
2002-02-21,1097.979980,1101.500000,1080.239990,1080.949951,1080.949951,1381600000
2002-02-22,1080.949951,1093.930054,1074.390015,1089.839966,1089.839966,1411000000
2002-02-25,1089.839966,1112.709961,1089.839966,1109.430054,1109.430054,1367400000
2002-02-26,1109.430054,1115.050049,1101.719971,1109.380005,1109.380005,1309200000
2002-02-27,1109.380005,1123.060059,1102.260010,1109.890015,1109.890015,1393800000
2002-02-28,1109.890015,1121.569946,1106.729980,1106.729980,1106.729980,1392200000
2002-03-01,1106.729980,1131.790039,1106.729980,1131.780029,1131.780029,1456500000
2002-03-04,1131.780029,1153.839966,1130.930054,1153.839966,1153.839966,1594300000
2002-03-05,1153.839966,1157.739990,1144.780029,1146.140015,1146.140015,1549300000
2002-03-06,1146.140015,1165.290039,1145.109985,1162.770020,1162.770020,1541300000
2002-03-07,1162.770020,1167.939941,1150.689941,1157.540039,1157.540039,1517400000
2002-03-08,1157.540039,1172.760010,1157.540039,1164.310059,1164.310059,1412000000
2002-03-11,1164.310059,1173.030029,1159.579956,1168.260010,1168.260010,1210200000
2002-03-12,1168.260010,1168.260010,1154.339966,1165.579956,1165.579956,1304400000
2002-03-13,1165.579956,1165.579956,1151.010010,1154.089966,1154.089966,1354000000
2002-03-14,1154.089966,1157.829956,1151.079956,1153.040039,1153.040039,1208800000
2002-03-15,1153.040039,1166.479980,1153.040039,1166.160034,1166.160034,1493900000
2002-03-18,1166.160034,1172.729980,1159.140015,1165.550049,1165.550049,1169500000
2002-03-19,1165.550049,1173.939941,1165.550049,1170.290039,1170.290039,1255000000
2002-03-20,1170.290039,1170.290039,1151.609985,1151.849976,1151.849976,1304900000
2002-03-21,1151.849976,1155.099976,1139.479980,1153.589966,1153.589966,1339200000
2002-03-22,1153.589966,1156.489990,1144.599976,1148.699951,1148.699951,1243300000
2002-03-25,1148.699951,1151.040039,1131.869995,1131.869995,1131.869995,1057900000
2002-03-26,1131.869995,1147.000000,1131.609985,1138.489990,1138.489990,1223600000
2002-03-27,1138.489990,1146.949951,1135.329956,1144.579956,1144.579956,1180100000
2002-03-28,1144.579956,1154.449951,1144.579956,1147.390015,1147.390015,1147600000
2002-04-01,1147.390015,1147.839966,1132.869995,1146.540039,1146.540039,1050900000
2002-04-02,1146.540039,1146.540039,1135.709961,1136.760010,1136.760010,1176700000
2002-04-03,1136.760010,1138.849976,1119.680054,1125.400024,1125.400024,1219700000
2002-04-04,1125.400024,1130.449951,1120.060059,1126.339966,1126.339966,1283800000
2002-04-05,1126.339966,1133.310059,1119.489990,1122.729980,1122.729980,1110200000
2002-04-08,1122.729980,1125.410034,1111.790039,1125.290039,1125.290039,1095300000
2002-04-09,1125.290039,1128.290039,1116.729980,1117.800049,1117.800049,1235400000
2002-04-10,1117.800049,1131.760010,1117.800049,1130.469971,1130.469971,1447900000
2002-04-11,1130.469971,1130.469971,1102.420044,1103.689941,1103.689941,1505600000
2002-04-12,1103.689941,1112.770020,1102.739990,1111.010010,1111.010010,1282100000
2002-04-15,1111.010010,1114.859985,1099.410034,1102.550049,1102.550049,1120400000
2002-04-16,1102.550049,1129.400024,1102.550049,1128.369995,1128.369995,1341300000
2002-04-17,1128.369995,1133.000000,1123.369995,1126.069946,1126.069946,1376900000
2002-04-18,1126.069946,1130.489990,1109.290039,1124.469971,1124.469971,1359300000
2002-04-19,1124.469971,1128.819946,1122.589966,1125.170044,1125.170044,1185000000
2002-04-22,1125.170044,1125.170044,1105.619995,1107.829956,1107.829956,1181800000
2002-04-23,1107.829956,1111.170044,1098.939941,1100.959961,1100.959961,1388500000
2002-04-24,1100.959961,1108.459961,1092.510010,1093.140015,1093.140015,1373200000
2002-04-25,1093.140015,1094.359985,1084.810059,1091.479980,1091.479980,1517400000
2002-04-26,1091.479980,1096.770020,1076.310059,1076.319946,1076.319946,1374200000
2002-04-29,1076.319946,1078.949951,1063.619995,1065.449951,1065.449951,1314700000
2002-04-30,1065.449951,1082.619995,1063.459961,1076.920044,1076.920044,1628600000
2002-05-01,1076.920044,1088.319946,1065.290039,1086.459961,1086.459961,1451400000
2002-05-02,1086.459961,1091.420044,1079.459961,1084.560059,1084.560059,1364000000
2002-05-03,1084.560059,1084.560059,1068.890015,1073.430054,1073.430054,1284500000
2002-05-06,1073.430054,1075.959961,1052.650024,1052.670044,1052.670044,1122600000
2002-05-07,1052.670044,1058.670044,1048.959961,1049.489990,1049.489990,1354700000
2002-05-08,1049.489990,1088.920044,1049.489990,1088.849976,1088.849976,1502000000
2002-05-09,1088.849976,1088.849976,1072.229980,1073.010010,1073.010010,1153000000
2002-05-10,1073.010010,1075.430054,1053.930054,1054.989990,1054.989990,1171900000
2002-05-13,1054.989990,1074.839966,1053.900024,1074.560059,1074.560059,1088600000
2002-05-14,1074.560059,1097.709961,1074.560059,1097.280029,1097.280029,1414500000
2002-05-15,1097.280029,1104.229980,1088.939941,1091.069946,1091.069946,1420200000
2002-05-16,1091.069946,1099.290039,1089.170044,1098.229980,1098.229980,1256600000
2002-05-17,1098.229980,1106.589966,1096.770020,1106.589966,1106.589966,1274400000
2002-05-20,1106.589966,1106.589966,1090.609985,1091.880005,1091.880005,989800000
2002-05-21,1091.880005,1099.550049,1079.079956,1079.880005,1079.880005,1200500000
2002-05-22,1079.880005,1086.020020,1075.640015,1086.020020,1086.020020,1136300000
2002-05-23,1086.020020,1097.099976,1080.550049,1097.079956,1097.079956,1192900000
2002-05-24,1097.079956,1097.079956,1082.189941,1083.819946,1083.819946,885400000
2002-05-28,1083.819946,1085.979980,1070.310059,1074.550049,1074.550049,996500000
2002-05-29,1074.550049,1074.829956,1067.660034,1067.660034,1067.660034,1081800000
2002-05-30,1067.660034,1069.500000,1054.260010,1064.660034,1064.660034,1286600000
2002-05-31,1064.660034,1079.930054,1064.660034,1067.140015,1067.140015,1277300000
2002-06-03,1067.140015,1070.739990,1039.900024,1040.680054,1040.680054,1324300000
2002-06-04,1040.680054,1046.060059,1030.520020,1040.689941,1040.689941,1466600000
2002-06-05,1040.689941,1050.109985,1038.839966,1049.900024,1049.900024,1300100000
2002-06-06,1049.900024,1049.900024,1026.910034,1029.150024,1029.150024,1601500000
2002-06-07,1029.150024,1033.020020,1012.489990,1027.530029,1027.530029,1341300000
2002-06-10,1027.530029,1038.180054,1025.449951,1030.739990,1030.739990,1226200000
2002-06-11,1030.739990,1039.040039,1012.940002,1013.599976,1013.599976,1212400000
2002-06-12,1013.260010,1021.849976,1002.580017,1020.260010,1020.260010,1795720000
2002-06-13,1020.260010,1023.469971,1008.119995,1009.559998,1009.559998,1405500000
2002-06-14,1009.559998,1009.559998,981.630005,1007.270020,1007.270020,1549000000
2002-06-17,1007.270020,1036.170044,1007.270020,1036.170044,1036.170044,1236600000
2002-06-18,1036.170044,1040.829956,1030.920044,1037.140015,1037.140015,1193100000
2002-06-19,1037.140015,1037.609985,1017.880005,1019.989990,1019.989990,1336100000
2002-06-20,1019.989990,1023.330017,1004.590027,1006.289978,1006.289978,1389700000
2002-06-21,1006.289978,1006.289978,985.650024,989.140015,989.140015,1497200000
2002-06-24,989.140015,1002.109985,970.849976,992.719971,992.719971,1552600000
2002-06-25,992.719971,1005.880005,974.210022,976.140015,976.140015,1513700000
2002-06-26,976.140015,977.429993,952.919983,973.530029,973.530029,2014290000
2002-06-27,973.530029,990.669983,963.739990,990.640015,990.640015,1908600000
2002-06-28,990.640015,1001.789978,988.309998,989.820007,989.820007,2117000000
2002-07-01,989.820007,994.460022,967.429993,968.650024,968.650024,1425500000
2002-07-02,968.650024,968.650024,945.539978,948.090027,948.090027,1823000000
2002-07-03,948.090027,954.299988,934.869995,953.989990,953.989990,1527800000
2002-07-05,953.989990,989.070007,953.989990,989.030029,989.030029,699400000
2002-07-08,989.030029,993.559998,972.909973,976.979980,976.979980,1184400000
2002-07-09,976.979980,979.630005,951.710022,952.830017,952.830017,1348900000
2002-07-10,952.830017,956.340027,920.289978,920.469971,920.469971,1816900000
2002-07-11,920.469971,929.159973,900.940002,927.369995,927.369995,2080480000
2002-07-12,927.369995,934.309998,913.710022,921.390015,921.390015,1607400000
2002-07-15,921.390015,921.390015,876.460022,917.929993,917.929993,2574800000
2002-07-16,917.929993,918.650024,897.130005,900.940002,900.940002,1843700000
2002-07-17,901.049988,926.520020,895.030029,906.039978,906.039978,2566500000
2002-07-18,905.450012,907.799988,880.599976,881.559998,881.559998,1736300000
2002-07-19,881.559998,881.559998,842.070007,847.750000,847.750000,2654100000
2002-07-22,847.760010,854.130005,813.260010,819.849976,819.849976,2248060000
2002-07-23,819.849976,827.690002,796.130005,797.700012,797.700012,2441020000
2002-07-24,797.710022,844.320007,775.679993,843.429993,843.429993,2775560000
2002-07-25,843.419983,853.830017,816.109985,838.679993,838.679993,2424700000
2002-07-26,838.679993,852.849976,835.919983,852.840027,852.840027,1796100000
2002-07-29,852.840027,898.960022,852.840027,898.960022,898.960022,1778650000
2002-07-30,898.960022,909.809998,884.700012,902.780029,902.780029,1826090000
2002-07-31,902.780029,911.640015,889.880005,911.619995,911.619995,2049360000
2002-08-01,911.619995,911.619995,882.479980,884.659973,884.659973,1672200000
2002-08-02,884.400024,884.719971,853.950012,864.239990,864.239990,1538100000
2002-08-05,864.239990,864.239990,833.440002,834.599976,834.599976,1425500000
2002-08-06,834.599976,874.440002,834.599976,859.570007,859.570007,1514100000
2002-08-07,859.570007,878.739990,854.150024,876.770020,876.770020,1490400000
2002-08-08,876.770020,905.840027,875.169983,905.460022,905.460022,1646700000
2002-08-09,898.729980,913.950012,890.770020,908.640015,908.640015,1294900000
2002-08-12,908.640015,908.640015,892.380005,903.799988,903.799988,1036500000
2002-08-13,903.799988,911.710022,883.619995,884.210022,884.210022,1297700000
2002-08-14,884.210022,920.210022,876.200012,919.619995,919.619995,1533800000
2002-08-15,919.619995,933.289978,918.169983,930.250000,930.250000,1505100000
2002-08-16,930.250000,935.380005,916.210022,928.770020,928.770020,1265300000
2002-08-19,928.770020,951.169983,927.210022,950.700012,950.700012,1299800000
2002-08-20,950.700012,950.700012,931.859985,937.429993,937.429993,1308500000
2002-08-21,937.429993,951.590027,931.320007,949.359985,949.359985,1353100000
2002-08-22,949.359985,965.000000,946.429993,962.700012,962.700012,1373000000
2002-08-23,962.700012,962.700012,937.169983,940.859985,940.859985,1071500000
2002-08-26,940.859985,950.799988,930.419983,947.950012,947.950012,1016900000
2002-08-27,947.950012,955.820007,930.359985,934.820007,934.820007,1307700000
2002-08-28,934.820007,934.820007,913.210022,917.869995,917.869995,1146600000
2002-08-29,917.869995,924.590027,903.330017,917.799988,917.799988,1271100000
2002-08-30,917.799988,928.150024,910.169983,916.070007,916.070007,929900000
2002-09-03,916.070007,916.070007,877.510010,878.020020,878.020020,1289800000
2002-09-04,878.020020,896.099976,875.729980,893.400024,893.400024,1372100000
2002-09-05,893.400024,893.400024,870.500000,879.150024,879.150024,1401300000
2002-09-06,879.150024,899.070007,879.150024,893.919983,893.919983,1184500000
2002-09-09,893.919983,907.340027,882.919983,902.960022,902.960022,1130600000
2002-09-10,902.960022,909.890015,900.500000,909.580017,909.580017,1186400000
2002-09-11,910.630005,924.020020,908.469971,909.450012,909.450012,846600000
2002-09-12,909.450012,909.450012,884.840027,886.909973,886.909973,1191600000
2002-09-13,886.909973,892.750000,877.049988,889.809998,889.809998,1271000000
2002-09-16,889.809998,891.840027,878.909973,891.099976,891.099976,1001400000
2002-09-17,891.099976,902.679993,872.380005,873.520020,873.520020,1448600000
2002-09-18,873.520020,878.450012,857.390015,869.460022,869.460022,1501000000
2002-09-19,869.460022,869.460022,843.090027,843.320007,843.320007,1524000000
2002-09-20,843.320007,849.320007,839.090027,845.390015,845.390015,1792800000
2002-09-23,845.390015,845.390015,825.760010,833.700012,833.700012,1381100000
2002-09-24,833.700012,833.700012,817.380005,819.289978,819.289978,1670240000
2002-09-25,819.270020,844.219971,818.460022,839.659973,839.659973,1651500000
2002-09-26,839.659973,856.599976,839.659973,854.950012,854.950012,1650000000
2002-09-27,854.950012,854.950012,826.840027,827.369995,827.369995,1507300000
2002-09-30,827.369995,827.369995,800.200012,815.280029,815.280029,1721870000
2002-10-01,815.280029,847.929993,812.820007,847.909973,847.909973,1780900000
2002-10-02,843.770020,851.929993,826.500000,827.909973,827.909973,1668900000
2002-10-03,827.909973,840.020020,817.250000,818.950012,818.950012,1674500000
2002-10-04,818.950012,825.900024,794.099976,800.580017,800.580017,1835930000
2002-10-07,800.580017,808.210022,782.960022,785.280029,785.280029,1576500000
2002-10-08,785.280029,808.859985,779.500000,798.549988,798.549988,1938430000
2002-10-09,798.549988,798.549988,775.799988,776.760010,776.760010,1885030000
2002-10-10,776.760010,806.510010,768.630005,803.919983,803.919983,2090230000
2002-10-11,803.919983,843.270020,803.919983,835.320007,835.320007,1854130000
2002-10-14,835.320007,844.390015,828.369995,841.440002,841.440002,1200300000
2002-10-15,841.440002,881.270020,841.440002,881.270020,881.270020,1956000000
2002-10-16,881.270020,881.270020,856.280029,860.020020,860.020020,1585000000
2002-10-17,860.020020,885.349976,860.020020,879.200012,879.200012,1780390000
2002-10-18,879.200012,886.679993,866.580017,884.390015,884.390015,1423100000
2002-10-21,884.390015,900.690002,873.059998,899.719971,899.719971,1447000000
2002-10-22,899.719971,899.719971,882.400024,890.159973,890.159973,1549200000
2002-10-23,890.159973,896.140015,873.820007,896.140015,896.140015,1593900000
2002-10-24,896.140015,902.940002,879.000000,882.500000,882.500000,1700570000
2002-10-25,882.500000,897.710022,877.030029,897.650024,897.650024,1340400000
2002-10-28,897.650024,907.440002,886.150024,890.229980,890.229980,1382600000
2002-10-29,890.229980,890.640015,867.909973,882.150024,882.150024,1529700000
2002-10-30,882.150024,895.280029,879.190002,890.710022,890.710022,1422300000
2002-10-31,890.710022,898.830017,879.750000,885.760010,885.760010,1641300000
2002-11-01,885.760010,903.419983,877.710022,900.960022,900.960022,1450400000
2002-11-04,900.960022,924.580017,900.960022,908.349976,908.349976,1645900000
2002-11-05,908.349976,915.830017,904.909973,915.390015,915.390015,1354100000
2002-11-06,915.390015,925.659973,905.000000,923.760010,923.760010,1674000000
2002-11-07,923.760010,923.760010,898.679993,902.650024,902.650024,1466900000
2002-11-08,902.650024,910.109985,891.619995,894.739990,894.739990,1446500000
2002-11-11,894.739990,894.739990,874.630005,876.190002,876.190002,1113000000
2002-11-12,876.190002,894.299988,876.190002,882.950012,882.950012,1377100000
2002-11-13,882.950012,892.510010,872.049988,882.530029,882.530029,1463400000
2002-11-14,882.530029,904.270020,882.530029,904.270020,904.270020,1519000000
2002-11-15,904.270020,910.210022,895.349976,909.830017,909.830017,1400100000
2002-11-18,909.830017,915.909973,899.479980,900.359985,900.359985,1282600000
2002-11-19,900.359985,905.450012,893.090027,896.739990,896.739990,1337400000
2002-11-20,896.739990,915.010010,894.929993,914.150024,914.150024,1517300000
2002-11-21,914.150024,935.130005,914.150024,933.760010,933.760010,2415100000
2002-11-22,933.760010,937.280029,928.409973,930.549988,930.549988,1626800000
2002-11-25,930.549988,937.150024,923.309998,932.869995,932.869995,1574000000
2002-11-26,932.869995,932.869995,912.099976,913.309998,913.309998,1543600000
2002-11-27,913.309998,940.409973,913.309998,938.869995,938.869995,1350300000
2002-11-29,938.869995,941.820007,935.580017,936.309998,936.309998,643460000
2002-12-02,936.309998,954.280029,927.719971,934.530029,934.530029,1612000000
2002-12-03,934.530029,934.530029,918.729980,920.750000,920.750000,1488400000
2002-12-04,920.750000,925.250000,909.510010,917.580017,917.580017,1588900000
2002-12-05,917.580017,921.489990,905.900024,906.549988,906.549988,1250200000
2002-12-06,906.549988,915.479980,895.960022,912.229980,912.229980,1241100000
2002-12-09,912.229980,912.229980,891.969971,892.000000,892.000000,1320800000
2002-12-10,892.000000,904.950012,892.000000,904.450012,904.450012,1286600000
2002-12-11,904.450012,909.940002,896.479980,904.960022,904.960022,1285100000
2002-12-12,904.960022,908.369995,897.000000,901.580017,901.580017,1255300000
2002-12-13,901.580017,901.580017,888.479980,889.479980,889.479980,1330800000
2002-12-16,889.479980,910.419983,889.479980,910.400024,910.400024,1271600000
2002-12-17,910.400024,911.219971,901.739990,902.989990,902.989990,1251800000
2002-12-18,902.989990,902.989990,887.820007,891.119995,891.119995,1446200000
2002-12-19,890.020020,899.190002,880.320007,884.250000,884.250000,1385900000
2002-12-20,884.250000,897.789978,884.250000,895.760010,895.760010,1782730000
2002-12-23,895.739990,902.429993,892.260010,897.380005,897.380005,1112100000
2002-12-24,897.380005,897.380005,892.289978,892.469971,892.469971,458310000
2002-12-26,892.469971,903.890015,887.479980,889.659973,889.659973,721100000
2002-12-27,889.659973,890.460022,873.619995,875.400024,875.400024,758400000
2002-12-30,875.400024,882.099976,870.229980,879.390015,879.390015,1057800000
2002-12-31,879.390015,881.929993,869.450012,879.820007,879.820007,1088500000
2003-01-02,879.820007,909.030029,879.820007,909.030029,909.030029,1229200000
2003-01-03,909.030029,911.250000,903.070007,908.590027,908.590027,1130800000
2003-01-06,908.590027,931.770020,908.590027,929.010010,929.010010,1435900000
2003-01-07,929.010010,930.809998,919.929993,922.929993,922.929993,1545200000
2003-01-08,922.929993,922.929993,908.320007,909.929993,909.929993,1467600000
2003-01-09,909.929993,928.309998,909.929993,927.570007,927.570007,1560300000
2003-01-10,927.580017,932.890015,917.659973,927.570007,927.570007,1485400000
2003-01-13,927.570007,935.049988,922.049988,926.260010,926.260010,1396300000
2003-01-14,926.260010,931.659973,921.719971,931.659973,931.659973,1379400000
2003-01-15,931.659973,932.590027,916.700012,918.219971,918.219971,1432100000
2003-01-16,918.219971,926.030029,911.979980,914.599976,914.599976,1534600000
2003-01-17,914.599976,914.599976,899.020020,901.780029,901.780029,1358200000
2003-01-21,901.780029,906.000000,887.619995,887.619995,887.619995,1335200000
2003-01-22,887.619995,889.739990,877.640015,878.359985,878.359985,1560800000
2003-01-23,878.359985,890.250000,876.890015,887.340027,887.340027,1744550000
2003-01-24,887.340027,887.340027,859.710022,861.400024,861.400024,1574800000
2003-01-27,861.400024,863.950012,844.250000,847.479980,847.479980,1435900000
2003-01-28,847.479980,860.760010,847.479980,858.539978,858.539978,1459100000
2003-01-29,858.539978,868.719971,845.859985,864.359985,864.359985,1595400000
2003-01-30,864.359985,865.479980,843.739990,844.609985,844.609985,1510300000
2003-01-31,844.609985,858.330017,840.340027,855.700012,855.700012,1578530000
2003-02-03,855.700012,864.640015,855.700012,860.320007,860.320007,1258500000
2003-02-04,860.320007,860.320007,840.190002,848.200012,848.200012,1451600000
2003-02-05,848.200012,861.630005,842.109985,843.590027,843.590027,1450800000
2003-02-06,843.590027,844.229980,833.250000,838.150024,838.150024,1430900000
2003-02-07,838.150024,845.729980,826.700012,829.690002,829.690002,1276800000
2003-02-10,829.690002,837.159973,823.530029,835.969971,835.969971,1238200000
2003-02-11,835.969971,843.020020,825.090027,829.200012,829.200012,1307000000
2003-02-12,829.200012,832.119995,818.489990,818.679993,818.679993,1260500000
2003-02-13,818.679993,821.250000,806.289978,817.369995,817.369995,1489300000
2003-02-14,817.369995,834.890015,815.030029,834.890015,834.890015,1404600000
2003-02-18,834.890015,852.869995,834.890015,851.169983,851.169983,1250800000
2003-02-19,851.169983,851.169983,838.789978,845.130005,845.130005,1075600000
2003-02-20,845.130005,849.369995,836.559998,837.099976,837.099976,1194100000
2003-02-21,837.099976,852.280029,831.479980,848.169983,848.169983,1398200000
2003-02-24,848.169983,848.169983,832.159973,832.580017,832.580017,1229200000
2003-02-25,832.580017,839.549988,818.539978,838.570007,838.570007,1483700000
2003-02-26,838.570007,840.099976,826.679993,827.549988,827.549988,1374400000
2003-02-27,827.549988,842.190002,827.549988,837.280029,837.280029,1287800000
2003-02-28,837.280029,847.000000,837.280029,841.150024,841.150024,1373300000
2003-03-03,841.150024,852.340027,832.739990,834.809998,834.809998,1208900000
2003-03-04,834.809998,835.429993,821.960022,821.989990,821.989990,1256600000
2003-03-05,821.989990,829.869995,819.000000,829.849976,829.849976,1332700000
2003-03-06,829.849976,829.849976,819.849976,822.099976,822.099976,1299200000
2003-03-07,822.099976,829.549988,811.229980,828.890015,828.890015,1368500000
2003-03-10,828.890015,828.890015,806.570007,807.479980,807.479980,1255000000
2003-03-11,807.479980,814.250000,800.299988,800.729980,800.729980,1427700000
2003-03-12,800.729980,804.190002,788.900024,804.190002,804.190002,1620000000
2003-03-13,804.190002,832.020020,804.190002,831.900024,831.900024,1816300000
2003-03-14,831.890015,841.390015,828.260010,833.270020,833.270020,1541900000
2003-03-17,833.270020,862.789978,827.169983,862.789978,862.789978,1700420000
2003-03-18,862.789978,866.940002,857.359985,866.450012,866.450012,1555100000
2003-03-19,866.450012,874.989990,861.210022,874.020020,874.020020,1473400000
2003-03-20,874.020020,879.599976,859.010010,875.669983,875.669983,1439100000
2003-03-21,875.840027,895.900024,875.840027,895.789978,895.789978,1883710000
2003-03-24,895.789978,895.789978,862.020020,864.229980,864.229980,1293000000
2003-03-25,864.229980,879.869995,862.590027,874.739990,874.739990,1333400000
2003-03-26,874.739990,875.799988,866.469971,869.950012,869.950012,1319700000
2003-03-27,869.950012,874.150024,858.090027,868.520020,868.520020,1232900000
2003-03-28,868.520020,869.880005,860.830017,863.500000,863.500000,1227000000
2003-03-31,863.500000,863.500000,843.679993,848.179993,848.179993,1495500000
2003-04-01,848.179993,861.280029,847.849976,858.479980,858.479980,1461600000
2003-04-02,858.479980,884.570007,858.479980,880.900024,880.900024,1589800000
2003-04-03,880.900024,885.890015,876.119995,876.450012,876.450012,1339500000
2003-04-04,876.450012,882.729980,874.229980,878.849976,878.849976,1241200000
2003-04-07,878.849976,904.890015,878.849976,879.929993,879.929993,1494000000
2003-04-08,879.929993,883.109985,874.679993,878.289978,878.289978,1235400000
2003-04-09,878.289978,887.349976,865.719971,865.989990,865.989990,1293700000
2003-04-10,865.989990,871.780029,862.760010,871.580017,871.580017,1275300000
2003-04-11,871.580017,883.340027,865.919983,868.299988,868.299988,1141600000
2003-04-14,868.299988,885.260010,868.299988,885.229980,885.229980,1131000000
2003-04-15,885.229980,891.270020,881.849976,890.809998,890.809998,1460200000
2003-04-16,890.809998,896.770020,877.929993,879.909973,879.909973,1587600000
2003-04-17,879.909973,893.830017,879.200012,893.580017,893.580017,1430600000
2003-04-21,893.580017,898.010010,888.169983,892.010010,892.010010,1118700000
2003-04-22,892.010010,911.739990,886.700012,911.369995,911.369995,1631200000
2003-04-23,911.369995,919.739990,909.890015,919.020020,919.020020,1667200000
2003-04-24,919.020020,919.020020,906.690002,911.429993,911.429993,1648100000
2003-04-25,911.429993,911.429993,897.520020,898.809998,898.809998,1335800000
2003-04-28,898.809998,918.150024,898.809998,914.840027,914.840027,1273000000
2003-04-29,914.840027,924.239990,911.099976,917.840027,917.840027,1525600000
2003-04-30,917.840027,922.010010,911.700012,916.919983,916.919983,1788510000
2003-05-01,916.919983,919.679993,902.830017,916.299988,916.299988,1397500000
2003-05-02,916.299988,930.559998,912.349976,930.080017,930.080017,1554300000
2003-05-05,930.080017,933.880005,924.549988,926.549988,926.549988,1446300000
2003-05-06,926.549988,939.609985,926.380005,934.390015,934.390015,1649600000
2003-05-07,934.390015,937.219971,926.409973,929.619995,929.619995,1531900000
2003-05-08,929.619995,929.619995,919.719971,920.270020,920.270020,1379600000
2003-05-09,920.270020,933.770020,920.270020,933.409973,933.409973,1326100000
2003-05-12,933.409973,946.840027,929.299988,945.109985,945.109985,1378800000
2003-05-13,945.109985,947.510010,938.909973,942.299988,942.299988,1418100000
2003-05-14,942.299988,947.289978,935.239990,939.280029,939.280029,1401800000
2003-05-15,939.280029,948.229980,938.789978,946.669983,946.669983,1508700000
2003-05-16,946.669983,948.650024,938.599976,944.299988,944.299988,1505500000
2003-05-19,944.299988,944.299988,920.229980,920.770020,920.770020,1375700000
2003-05-20,920.770020,925.340027,912.049988,919.729980,919.729980,1505300000
2003-05-21,919.729980,923.849976,914.909973,923.419983,923.419983,1457800000
2003-05-22,923.419983,935.299988,922.539978,931.869995,931.869995,1448500000
2003-05-23,931.869995,935.200012,927.419983,933.219971,933.219971,1201000000
2003-05-27,933.219971,952.760010,927.330017,951.479980,951.479980,1532000000
2003-05-28,951.479980,959.390015,950.119995,953.219971,953.219971,1559000000
2003-05-29,953.219971,962.080017,946.229980,949.640015,949.640015,1685800000
2003-05-30,949.640015,965.380005,949.640015,963.590027,963.590027,1688800000
2003-06-02,963.590027,979.109985,963.590027,967.000000,967.000000,1662500000
2003-06-03,967.000000,973.020020,964.469971,971.559998,971.559998,1450200000
2003-06-04,971.559998,987.849976,970.719971,986.239990,986.239990,1618700000
2003-06-05,986.239990,990.140015,978.130005,990.140015,990.140015,1693100000
2003-06-06,990.140015,1007.690002,986.010010,987.760010,987.760010,1837200000
2003-06-09,987.760010,987.760010,972.590027,975.929993,975.929993,1307000000
2003-06-10,975.929993,984.840027,975.929993,984.840027,984.840027,1275400000
2003-06-11,984.840027,997.479980,981.609985,997.479980,997.479980,1520000000
2003-06-12,997.479980,1002.739990,991.270020,998.510010,998.510010,1553100000
2003-06-13,998.510010,1000.919983,984.270020,988.609985,988.609985,1271600000
2003-06-16,988.609985,1010.859985,988.609985,1010.739990,1010.739990,1345900000
2003-06-17,1010.739990,1015.330017,1007.039978,1011.659973,1011.659973,1479700000
2003-06-18,1011.659973,1015.119995,1004.609985,1010.090027,1010.090027,1488900000
2003-06-19,1010.090027,1011.219971,993.080017,994.700012,994.700012,1530100000
2003-06-20,994.700012,1002.090027,993.359985,995.690002,995.690002,1698000000
2003-06-23,995.690002,995.690002,977.400024,981.640015,981.640015,1398100000
2003-06-24,981.640015,987.840027,979.080017,983.450012,983.450012,1388300000
2003-06-25,983.450012,991.640015,974.859985,975.320007,975.320007,1459200000
2003-06-26,975.320007,986.530029,973.799988,985.820007,985.820007,1387400000
2003-06-27,985.820007,988.880005,974.289978,976.219971,976.219971,1267800000
2003-06-30,976.219971,983.609985,973.599976,974.500000,974.500000,1587200000
2003-07-01,974.500000,983.260010,962.099976,982.320007,982.320007,1460200000
2003-07-02,982.320007,993.780029,982.320007,993.750000,993.750000,1519300000
2003-07-03,993.750000,995.000000,983.340027,985.700012,985.700012,775900000
2003-07-07,985.700012,1005.559998,985.700012,1004.419983,1004.419983,1429100000
2003-07-08,1004.419983,1008.919983,998.729980,1007.840027,1007.840027,1565700000
2003-07-09,1007.840027,1010.429993,998.169983,1002.210022,1002.210022,1618000000
2003-07-10,1002.210022,1002.210022,983.630005,988.700012,988.700012,1465700000
2003-07-11,988.700012,1000.859985,988.700012,998.140015,998.140015,1212700000
2003-07-14,998.140015,1015.409973,998.140015,1003.859985,1003.859985,1448900000
2003-07-15,1003.859985,1009.609985,996.669983,1000.419983,1000.419983,1518600000
2003-07-16,1000.419983,1003.469971,989.299988,994.090027,994.090027,1662000000
2003-07-17,994.000000,994.000000,978.599976,981.729980,981.729980,1661400000
2003-07-18,981.729980,994.250000,981.710022,993.320007,993.320007,1365200000
2003-07-21,993.320007,993.320007,975.630005,978.799988,978.799988,1254200000
2003-07-22,978.799988,990.289978,976.080017,988.109985,988.109985,1439700000
2003-07-23,988.109985,989.859985,979.789978,988.609985,988.609985,1362700000
2003-07-24,988.609985,998.890015,981.070007,981.599976,981.599976,1559000000
2003-07-25,981.599976,998.710022,977.489990,998.679993,998.679993,1397500000
2003-07-28,998.679993,1000.679993,993.590027,996.520020,996.520020,1328600000
2003-07-29,996.520020,998.640015,984.150024,989.280029,989.280029,1508900000
2003-07-30,989.280029,992.619995,985.960022,987.489990,987.489990,1391900000
2003-07-31,987.489990,1004.590027,987.489990,990.309998,990.309998,1608000000
2003-08-01,990.309998,990.309998,978.859985,980.150024,980.150024,1390600000
2003-08-04,980.150024,985.750000,966.789978,982.820007,982.820007,1318700000
2003-08-05,982.820007,982.820007,964.969971,965.460022,965.460022,1351700000
2003-08-06,965.460022,975.739990,960.840027,967.080017,967.080017,1491000000
2003-08-07,967.080017,974.890015,963.820007,974.119995,974.119995,1389300000
2003-08-08,974.119995,980.570007,973.830017,977.590027,977.590027,1086600000
2003-08-11,977.590027,985.460022,974.210022,980.590027,980.590027,1022200000
2003-08-12,980.590027,990.409973,979.900024,990.349976,990.349976,1132300000
2003-08-13,990.349976,992.500000,980.849976,984.030029,984.030029,1208800000
2003-08-14,984.030029,991.909973,980.359985,990.510010,990.510010,1186800000
2003-08-15,990.510010,992.390015,987.099976,990.669983,990.669983,636370000
2003-08-18,990.669983,1000.349976,990.669983,999.739990,999.739990,1127600000
2003-08-19,999.739990,1003.299988,995.299988,1002.349976,1002.349976,1300600000
2003-08-20,1002.349976,1003.539978,996.619995,1000.299988,1000.299988,1210800000
2003-08-21,1000.299988,1009.530029,999.330017,1003.270020,1003.270020,1407100000
2003-08-22,1003.270020,1011.010010,992.619995,993.059998,993.059998,1308900000
2003-08-25,993.059998,993.710022,987.909973,993.710022,993.710022,971700000
2003-08-26,993.710022,997.929993,983.570007,996.729980,996.729980,1178700000
2003-08-27,996.729980,998.049988,993.330017,996.789978,996.789978,1051400000
2003-08-28,996.789978,1004.119995,991.419983,1002.840027,1002.840027,1165200000
2003-08-29,1002.840027,1008.849976,999.520020,1008.010010,1008.010010,945100000
2003-09-02,1008.010010,1022.590027,1005.729980,1021.989990,1021.989990,1470500000
2003-09-03,1021.989990,1029.339966,1021.989990,1026.270020,1026.270020,1675600000
2003-09-04,1026.270020,1029.170044,1022.190002,1027.969971,1027.969971,1453900000
2003-09-05,1027.969971,1029.209961,1018.190002,1021.390015,1021.390015,1465200000
2003-09-08,1021.390015,1032.410034,1021.390015,1031.640015,1031.640015,1299300000
2003-09-09,1031.640015,1031.640015,1021.140015,1023.169983,1023.169983,1414800000
2003-09-10,1023.169983,1023.169983,1009.739990,1010.919983,1010.919983,1582100000
2003-09-11,1010.919983,1020.880005,1010.919983,1016.419983,1016.419983,1335900000
2003-09-12,1016.419983,1019.650024,1007.710022,1018.630005,1018.630005,1236700000
2003-09-15,1018.630005,1019.789978,1013.590027,1014.809998,1014.809998,1151300000
2003-09-16,1014.809998,1029.660034,1014.809998,1029.319946,1029.319946,1403200000
2003-09-17,1029.319946,1031.339966,1024.530029,1025.969971,1025.969971,1338210000
2003-09-18,1025.969971,1040.160034,1025.750000,1039.579956,1039.579956,1498800000
2003-09-19,1039.579956,1040.290039,1031.890015,1036.300049,1036.300049,1518600000
2003-09-22,1036.300049,1036.300049,1018.299988,1022.820007,1022.820007,1278800000
2003-09-23,1022.820007,1030.119995,1021.539978,1029.030029,1029.030029,1301700000
2003-09-24,1029.030029,1029.829956,1008.929993,1009.380005,1009.380005,1556000000
2003-09-25,1009.380005,1015.969971,1003.260010,1003.270020,1003.270020,1530000000
2003-09-26,1003.270020,1003.450012,996.080017,996.849976,996.849976,1472500000
2003-09-29,996.849976,1006.890015,995.309998,1006.580017,1006.580017,1366500000
2003-09-30,1006.580017,1006.580017,990.359985,995.969971,995.969971,1590500000
2003-10-01,995.969971,1018.219971,995.969971,1018.219971,1018.219971,1566300000
2003-10-02,1018.219971,1021.869995,1013.380005,1020.239990,1020.239990,1269300000
2003-10-03,1020.239990,1039.310059,1020.239990,1029.849976,1029.849976,1570500000
2003-10-06,1029.849976,1036.479980,1029.150024,1034.349976,1034.349976,1025800000
2003-10-07,1034.349976,1039.250000,1026.270020,1039.250000,1039.250000,1279500000
2003-10-08,1039.250000,1040.060059,1030.959961,1033.780029,1033.780029,1262500000
2003-10-09,1033.780029,1048.280029,1033.780029,1038.729980,1038.729980,1578700000
2003-10-10,1038.729980,1040.839966,1035.739990,1038.060059,1038.060059,1108100000
2003-10-13,1038.060059,1048.900024,1038.060059,1045.349976,1045.349976,1040500000
2003-10-14,1045.349976,1049.489990,1040.839966,1049.479980,1049.479980,1271900000
2003-10-15,1049.479980,1053.790039,1043.150024,1046.760010,1046.760010,1521100000
2003-10-16,1046.760010,1052.939941,1044.040039,1050.069946,1050.069946,1417700000
2003-10-17,1050.069946,1051.890015,1036.569946,1039.319946,1039.319946,1352000000
2003-10-20,1039.319946,1044.689941,1036.130005,1044.680054,1044.680054,1172600000
2003-10-21,1044.680054,1048.569946,1042.589966,1046.030029,1046.030029,1498000000
2003-10-22,1046.030029,1046.030029,1028.390015,1030.359985,1030.359985,1647200000
2003-10-23,1030.359985,1035.439941,1025.890015,1033.770020,1033.770020,1604300000
2003-10-24,1033.770020,1033.770020,1018.320007,1028.910034,1028.910034,1420300000
2003-10-27,1028.910034,1037.750000,1028.910034,1031.130005,1031.130005,1371800000
2003-10-28,1031.130005,1046.790039,1031.130005,1046.790039,1046.790039,1629200000
2003-10-29,1046.790039,1049.829956,1043.349976,1048.109985,1048.109985,1562600000
2003-10-30,1048.109985,1052.810059,1043.819946,1046.939941,1046.939941,1629700000
2003-10-31,1046.939941,1053.089966,1046.939941,1050.709961,1050.709961,1498900000
2003-11-03,1050.709961,1061.439941,1050.709961,1059.020020,1059.020020,1378200000
2003-11-04,1059.020020,1059.020020,1051.699951,1053.250000,1053.250000,1417600000
2003-11-05,1053.250000,1054.540039,1044.880005,1051.810059,1051.810059,1401800000
2003-11-06,1051.810059,1058.939941,1046.930054,1058.050049,1058.050049,1453900000
2003-11-07,1058.050049,1062.390015,1052.170044,1053.209961,1053.209961,1440500000
2003-11-10,1053.209961,1053.650024,1045.579956,1047.109985,1047.109985,1243600000
2003-11-11,1047.109985,1048.229980,1043.459961,1046.569946,1046.569946,1162500000
2003-11-12,1046.569946,1059.099976,1046.569946,1058.530029,1058.530029,1349300000
2003-11-13,1058.560059,1059.619995,1052.959961,1058.410034,1058.410034,1383000000
2003-11-14,1058.410034,1063.650024,1048.109985,1050.349976,1050.349976,1356100000
2003-11-17,1050.349976,1050.349976,1035.280029,1043.630005,1043.630005,1374300000
2003-11-18,1043.630005,1048.770020,1034.000000,1034.150024,1034.150024,1354300000
2003-11-19,1034.150024,1043.949951,1034.150024,1042.439941,1042.439941,1326200000
2003-11-20,1042.439941,1046.479980,1033.420044,1033.650024,1033.650024,1326700000
2003-11-21,1033.650024,1037.569946,1031.199951,1035.280029,1035.280029,1273800000
2003-11-24,1035.280029,1052.079956,1035.280029,1052.079956,1052.079956,1302800000
2003-11-25,1052.079956,1058.050049,1049.310059,1053.890015,1053.890015,1333700000
2003-11-26,1053.890015,1058.449951,1048.280029,1058.449951,1058.449951,1097700000
2003-11-28,1058.449951,1060.630005,1056.770020,1058.199951,1058.199951,487220000
2003-12-01,1058.199951,1070.469971,1058.199951,1070.119995,1070.119995,1375000000
2003-12-02,1070.119995,1071.219971,1065.219971,1066.619995,1066.619995,1383200000
2003-12-03,1066.619995,1074.300049,1064.630005,1064.729980,1064.729980,1441700000
2003-12-04,1064.729980,1070.369995,1063.150024,1069.719971,1069.719971,1463100000
2003-12-05,1069.719971,1069.719971,1060.089966,1061.500000,1061.500000,1265900000
2003-12-08,1061.500000,1069.589966,1060.930054,1069.300049,1069.300049,1218900000
2003-12-09,1069.300049,1071.939941,1059.160034,1060.180054,1060.180054,1465500000
2003-12-10,1060.180054,1063.020020,1053.410034,1059.050049,1059.050049,1444000000
2003-12-11,1059.050049,1073.630005,1059.050049,1071.209961,1071.209961,1441100000
2003-12-12,1071.209961,1074.760010,1067.640015,1074.140015,1074.140015,1223100000
2003-12-15,1074.140015,1082.790039,1068.000000,1068.040039,1068.040039,1520800000
2003-12-16,1068.040039,1075.939941,1068.040039,1075.130005,1075.130005,1547900000
2003-12-17,1075.130005,1076.540039,1071.140015,1076.479980,1076.479980,1441700000
2003-12-18,1076.479980,1089.500000,1076.479980,1089.180054,1089.180054,1579900000
2003-12-19,1089.180054,1091.060059,1084.189941,1088.660034,1088.660034,1657300000
2003-12-22,1088.660034,1092.939941,1086.140015,1092.939941,1092.939941,1251700000
2003-12-23,1092.939941,1096.949951,1091.729980,1096.020020,1096.020020,1145300000
2003-12-24,1096.020020,1096.400024,1092.729980,1094.040039,1094.040039,518060000
2003-12-26,1094.040039,1098.469971,1094.040039,1095.890015,1095.890015,356070000
2003-12-29,1095.890015,1109.479980,1095.890015,1109.479980,1109.479980,1058800000
2003-12-30,1109.479980,1109.750000,1106.410034,1109.640015,1109.640015,1012600000
2003-12-31,1109.640015,1112.560059,1106.209961,1111.920044,1111.920044,1027500000
2004-01-02,1111.920044,1118.849976,1105.079956,1108.479980,1108.479980,1153200000
2004-01-05,1108.479980,1122.219971,1108.479980,1122.219971,1122.219971,1578200000
2004-01-06,1122.219971,1124.459961,1118.439941,1123.670044,1123.670044,1494500000
2004-01-07,1123.670044,1126.329956,1116.449951,1126.329956,1126.329956,1704900000
2004-01-08,1126.329956,1131.920044,1124.910034,1131.920044,1131.920044,1868400000
2004-01-09,1131.920044,1131.920044,1120.900024,1121.859985,1121.859985,1720700000
2004-01-12,1121.859985,1127.849976,1120.900024,1127.229980,1127.229980,1510200000
2004-01-13,1127.229980,1129.069946,1115.189941,1121.219971,1121.219971,1595900000
2004-01-14,1121.219971,1130.750000,1121.219971,1130.520020,1130.520020,1514600000
2004-01-15,1130.520020,1137.109985,1124.540039,1132.050049,1132.050049,1695000000
2004-01-16,1132.050049,1139.829956,1132.050049,1139.829956,1139.829956,1721100000
2004-01-20,1139.829956,1142.930054,1135.400024,1138.770020,1138.770020,1698200000
2004-01-21,1138.770020,1149.209961,1134.619995,1147.619995,1147.619995,1757600000
2004-01-22,1147.619995,1150.510010,1143.010010,1143.939941,1143.939941,1693700000
2004-01-23,1143.939941,1150.310059,1136.849976,1141.550049,1141.550049,1561200000
2004-01-26,1141.550049,1155.380005,1141.000000,1155.369995,1155.369995,1480600000
2004-01-27,1155.369995,1155.369995,1144.050049,1144.050049,1144.050049,1673100000
2004-01-28,1144.050049,1149.140015,1126.500000,1128.479980,1128.479980,1842000000
2004-01-29,1128.479980,1134.390015,1122.380005,1134.109985,1134.109985,1921900000
2004-01-30,1134.109985,1134.170044,1127.729980,1131.130005,1131.130005,1635000000
2004-02-02,1131.130005,1142.449951,1127.869995,1135.260010,1135.260010,1599200000
2004-02-03,1135.260010,1137.439941,1131.329956,1136.030029,1136.030029,1476900000
2004-02-04,1136.030029,1136.030029,1124.739990,1126.520020,1126.520020,1634800000
2004-02-05,1126.520020,1131.170044,1124.439941,1128.589966,1128.589966,1566600000
2004-02-06,1128.589966,1142.790039,1128.390015,1142.760010,1142.760010,1477600000
2004-02-09,1142.760010,1144.459961,1139.209961,1139.810059,1139.810059,1303500000
2004-02-10,1139.810059,1147.020020,1138.699951,1145.540039,1145.540039,1403900000
2004-02-11,1145.540039,1158.890015,1142.329956,1157.760010,1157.760010,1699300000
2004-02-12,1157.760010,1157.760010,1151.439941,1152.109985,1152.109985,1464300000
2004-02-13,1152.109985,1156.880005,1143.239990,1145.810059,1145.810059,1329200000
2004-02-17,1145.810059,1158.979980,1145.810059,1156.989990,1156.989990,1396500000
2004-02-18,1156.989990,1157.400024,1149.540039,1151.819946,1151.819946,1382400000
2004-02-19,1151.819946,1158.569946,1146.849976,1147.060059,1147.060059,1562800000
2004-02-20,1147.060059,1149.810059,1139.000000,1144.109985,1144.109985,1479600000
2004-02-23,1144.109985,1146.689941,1136.979980,1140.989990,1140.989990,1380400000
2004-02-24,1140.989990,1144.540039,1134.430054,1139.089966,1139.089966,1543600000
2004-02-25,1139.089966,1145.239990,1138.959961,1143.670044,1143.670044,1360700000
2004-02-26,1143.670044,1147.229980,1138.619995,1144.910034,1144.910034,1383900000
2004-02-27,1145.800049,1151.680054,1141.800049,1144.939941,1144.939941,1540400000
2004-03-01,1144.939941,1157.449951,1144.939941,1155.969971,1155.969971,1497100000
2004-03-02,1155.969971,1156.540039,1147.310059,1149.099976,1149.099976,1476000000
2004-03-03,1149.099976,1152.439941,1143.780029,1151.030029,1151.030029,1334500000
2004-03-04,1151.030029,1154.969971,1149.810059,1154.869995,1154.869995,1265800000
2004-03-05,1154.869995,1163.229980,1148.770020,1156.859985,1156.859985,1398200000
2004-03-08,1156.859985,1159.939941,1146.969971,1147.199951,1147.199951,1254400000
2004-03-09,1147.199951,1147.319946,1136.839966,1140.579956,1140.579956,1499400000
2004-03-10,1140.579956,1141.449951,1122.530029,1123.890015,1123.890015,1648400000
2004-03-11,1123.890015,1125.959961,1105.869995,1106.780029,1106.780029,1889900000
2004-03-12,1106.780029,1120.630005,1106.780029,1120.569946,1120.569946,1388500000
2004-03-15,1120.569946,1120.569946,1103.359985,1104.489990,1104.489990,1600600000
2004-03-16,1104.489990,1113.760010,1102.609985,1110.699951,1110.699951,1500700000
2004-03-17,1110.699951,1125.760010,1110.699951,1123.750000,1123.750000,1490100000
2004-03-18,1123.750000,1125.500000,1113.250000,1122.319946,1122.319946,1369200000
2004-03-19,1122.319946,1122.719971,1109.689941,1109.780029,1109.780029,1457400000
2004-03-22,1109.780029,1109.780029,1089.540039,1095.400024,1095.400024,1452300000
2004-03-23,1095.400024,1101.520020,1091.569946,1093.949951,1093.949951,1458200000
2004-03-24,1093.949951,1098.319946,1087.160034,1091.329956,1091.329956,1527800000
2004-03-25,1091.329956,1110.380005,1091.329956,1109.189941,1109.189941,1471700000
2004-03-26,1109.189941,1115.270020,1106.130005,1108.060059,1108.060059,1319100000
2004-03-29,1108.060059,1124.369995,1108.060059,1122.469971,1122.469971,1405500000
2004-03-30,1122.469971,1127.599976,1119.660034,1127.000000,1127.000000,1332400000
2004-03-31,1127.000000,1130.829956,1121.459961,1126.209961,1126.209961,1560700000
2004-04-01,1126.209961,1135.670044,1126.199951,1132.170044,1132.170044,1560700000
2004-04-02,1132.170044,1144.810059,1132.170044,1141.810059,1141.810059,1629200000
2004-04-05,1141.810059,1150.569946,1141.640015,1150.569946,1150.569946,1413700000
2004-04-06,1150.569946,1150.569946,1143.300049,1148.160034,1148.160034,1397700000
2004-04-07,1148.160034,1148.160034,1138.410034,1140.530029,1140.530029,1458800000
2004-04-08,1140.530029,1148.969971,1134.520020,1139.319946,1139.319946,1199800000
2004-04-12,1139.319946,1147.290039,1139.319946,1145.199951,1145.199951,1102400000
2004-04-13,1145.199951,1147.780029,1127.699951,1129.439941,1129.439941,1423200000
2004-04-14,1129.439941,1132.520020,1122.150024,1128.170044,1128.170044,1547700000
2004-04-15,1128.170044,1134.079956,1120.750000,1128.839966,1128.839966,1568700000
2004-04-16,1128.839966,1136.800049,1126.900024,1134.609985,1134.609985,1487800000
2004-04-19,1134.560059,1136.180054,1129.839966,1135.819946,1135.819946,1194900000
2004-04-20,1135.819946,1139.260010,1118.089966,1118.150024,1118.150024,1508500000
2004-04-21,1118.150024,1125.719971,1116.030029,1124.089966,1124.089966,1738100000
2004-04-22,1124.089966,1142.770020,1121.949951,1139.930054,1139.930054,1826700000
2004-04-23,1139.930054,1141.920044,1134.810059,1140.599976,1140.599976,1396100000
2004-04-26,1140.599976,1145.079956,1132.910034,1135.530029,1135.530029,1290600000
2004-04-27,1135.530029,1146.560059,1135.530029,1138.109985,1138.109985,1518000000
2004-04-28,1138.109985,1138.109985,1121.699951,1122.410034,1122.410034,1855600000
2004-04-29,1122.410034,1128.800049,1108.040039,1113.890015,1113.890015,1859000000
2004-04-30,1113.890015,1119.260010,1107.229980,1107.300049,1107.300049,1634700000
2004-05-03,1107.300049,1118.719971,1107.300049,1117.489990,1117.489990,1571600000
2004-05-04,1117.489990,1127.739990,1112.890015,1119.550049,1119.550049,1662100000
2004-05-05,1119.550049,1125.069946,1117.900024,1121.530029,1121.530029,1469000000
2004-05-06,1121.530029,1121.530029,1106.300049,1113.989990,1113.989990,1509300000
2004-05-07,1113.989990,1117.300049,1098.630005,1098.699951,1098.699951,1653600000
2004-05-10,1098.699951,1098.699951,1079.630005,1087.119995,1087.119995,1918400000
2004-05-11,1087.119995,1095.689941,1087.119995,1095.449951,1095.449951,1533800000
2004-05-12,1095.449951,1097.550049,1076.319946,1097.280029,1097.280029,1697600000
2004-05-13,1097.280029,1102.770020,1091.760010,1096.439941,1096.439941,1411100000
2004-05-14,1096.439941,1102.099976,1088.239990,1095.699951,1095.699951,1335900000
2004-05-17,1095.699951,1095.699951,1079.359985,1084.099976,1084.099976,1430100000
2004-05-18,1084.099976,1094.099976,1084.099976,1091.489990,1091.489990,1353000000
2004-05-19,1091.489990,1105.930054,1088.489990,1088.680054,1088.680054,1548600000
2004-05-20,1088.680054,1092.619995,1085.430054,1089.189941,1089.189941,1211000000
2004-05-21,1089.189941,1099.640015,1089.189941,1093.560059,1093.560059,1258600000
2004-05-24,1093.560059,1101.280029,1091.770020,1095.410034,1095.410034,1227500000
2004-05-25,1095.410034,1113.800049,1090.739990,1113.050049,1113.050049,1545700000
2004-05-26,1113.050049,1116.709961,1109.910034,1114.939941,1114.939941,1369400000
2004-05-27,1114.939941,1123.949951,1114.859985,1121.280029,1121.280029,1447500000
2004-05-28,1121.280029,1122.689941,1118.099976,1120.680054,1120.680054,1172600000
2004-06-01,1120.680054,1122.699951,1113.319946,1121.199951,1121.199951,1238000000
2004-06-02,1121.199951,1128.099976,1118.640015,1124.989990,1124.989990,1251700000
2004-06-03,1124.989990,1125.310059,1116.569946,1116.640015,1116.640015,1232400000
2004-06-04,1116.640015,1129.170044,1116.640015,1122.500000,1122.500000,1115300000
2004-06-07,1122.500000,1140.540039,1122.500000,1140.420044,1140.420044,1211800000
2004-06-08,1140.420044,1142.180054,1135.449951,1142.180054,1142.180054,1190300000
2004-06-09,1142.180054,1142.180054,1131.170044,1131.329956,1131.329956,1276800000
2004-06-10,1131.329956,1136.469971,1131.329956,1136.469971,1136.469971,1160600000
2004-06-14,1136.469971,1136.469971,1122.160034,1125.290039,1125.290039,1179400000
2004-06-15,1125.290039,1137.359985,1125.290039,1132.010010,1132.010010,1345900000
2004-06-16,1132.010010,1135.280029,1130.550049,1133.560059,1133.560059,1168400000
2004-06-17,1133.560059,1133.560059,1126.890015,1132.050049,1132.050049,1296700000
2004-06-18,1132.050049,1138.959961,1129.829956,1135.020020,1135.020020,1500600000
2004-06-21,1135.020020,1138.050049,1129.640015,1130.300049,1130.300049,1123900000
2004-06-22,1130.300049,1135.050049,1124.369995,1134.410034,1134.410034,1382300000
2004-06-23,1134.410034,1145.150024,1131.729980,1144.060059,1144.060059,1444200000
2004-06-24,1144.060059,1146.339966,1139.939941,1140.650024,1140.650024,1394900000
2004-06-25,1140.650024,1145.969971,1134.239990,1134.430054,1134.430054,1812900000
2004-06-28,1134.430054,1142.599976,1131.719971,1133.349976,1133.349976,1354600000
2004-06-29,1133.349976,1138.260010,1131.810059,1136.199951,1136.199951,1375000000
2004-06-30,1136.199951,1144.199951,1133.619995,1140.839966,1140.839966,1473800000
2004-07-01,1140.839966,1140.839966,1123.060059,1128.939941,1128.939941,1495700000
2004-07-02,1128.939941,1129.150024,1123.260010,1125.380005,1125.380005,1085000000
2004-07-06,1125.380005,1125.380005,1113.209961,1116.209961,1116.209961,1283300000
2004-07-07,1116.209961,1122.369995,1114.920044,1118.329956,1118.329956,1328600000
2004-07-08,1118.329956,1119.119995,1108.719971,1109.109985,1109.109985,1401100000
2004-07-09,1109.109985,1115.569946,1109.109985,1112.810059,1112.810059,1186300000
2004-07-12,1112.810059,1116.109985,1106.709961,1114.349976,1114.349976,1114600000
2004-07-13,1114.349976,1116.300049,1112.989990,1115.140015,1115.140015,1199700000
2004-07-14,1115.140015,1119.599976,1107.829956,1111.469971,1111.469971,1462000000
2004-07-15,1111.469971,1114.630005,1106.670044,1106.689941,1106.689941,1408700000
2004-07-16,1106.689941,1112.170044,1101.069946,1101.390015,1101.390015,1450300000
2004-07-19,1101.390015,1105.520020,1096.550049,1100.900024,1100.900024,1319900000
2004-07-20,1100.900024,1108.880005,1099.099976,1108.670044,1108.670044,1445800000
2004-07-21,1108.670044,1116.270020,1093.880005,1093.880005,1093.880005,1679500000
2004-07-22,1093.880005,1099.660034,1084.160034,1096.839966,1096.839966,1680800000
2004-07-23,1096.839966,1096.839966,1083.560059,1086.199951,1086.199951,1337500000
2004-07-26,1086.199951,1089.819946,1078.780029,1084.069946,1084.069946,1413400000
2004-07-27,1084.069946,1096.650024,1084.069946,1094.829956,1094.829956,1610800000
2004-07-28,1094.829956,1098.839966,1082.170044,1095.420044,1095.420044,1554300000
2004-07-29,1095.420044,1103.510010,1095.420044,1100.430054,1100.430054,1530100000
2004-07-30,1100.430054,1103.729980,1096.959961,1101.719971,1101.719971,1298200000
2004-08-02,1101.719971,1108.599976,1097.339966,1106.619995,1106.619995,1276000000
2004-08-03,1106.619995,1106.619995,1099.260010,1099.689941,1099.689941,1338300000
2004-08-04,1099.689941,1102.449951,1092.400024,1098.630005,1098.630005,1369200000
2004-08-05,1098.630005,1098.790039,1079.979980,1080.699951,1080.699951,1397400000
2004-08-06,1080.699951,1080.699951,1062.229980,1063.969971,1063.969971,1521000000
2004-08-09,1063.969971,1069.459961,1063.969971,1065.219971,1065.219971,1086000000
2004-08-10,1065.219971,1079.040039,1065.219971,1079.040039,1079.040039,1245600000
2004-08-11,1079.040039,1079.040039,1065.920044,1075.790039,1075.790039,1410400000
2004-08-12,1075.790039,1075.790039,1062.819946,1063.229980,1063.229980,1405100000
2004-08-13,1063.229980,1067.579956,1060.719971,1064.800049,1064.800049,1175100000
2004-08-16,1064.800049,1080.660034,1064.800049,1079.339966,1079.339966,1206200000
2004-08-17,1079.339966,1086.780029,1079.339966,1081.709961,1081.709961,1267800000
2004-08-18,1081.709961,1095.170044,1078.930054,1095.170044,1095.170044,1282500000
2004-08-19,1095.170044,1095.170044,1086.280029,1091.229980,1091.229980,1249400000
2004-08-20,1091.229980,1100.260010,1089.569946,1098.349976,1098.349976,1199900000
2004-08-23,1098.349976,1101.400024,1094.729980,1095.680054,1095.680054,1021900000
2004-08-24,1095.680054,1100.939941,1092.819946,1096.189941,1096.189941,1092500000
2004-08-25,1096.189941,1106.290039,1093.239990,1104.959961,1104.959961,1192200000
2004-08-26,1104.959961,1106.780029,1102.459961,1105.089966,1105.089966,1023600000
2004-08-27,1105.089966,1109.680054,1104.619995,1107.770020,1107.770020,845400000
2004-08-30,1107.770020,1107.770020,1099.150024,1099.150024,1099.150024,843100000
2004-08-31,1099.150024,1104.239990,1094.719971,1104.239990,1104.239990,1138200000
2004-09-01,1104.239990,1109.239990,1099.180054,1105.910034,1105.910034,1142100000
2004-09-02,1105.910034,1119.109985,1105.599976,1118.310059,1118.310059,1118400000
2004-09-03,1118.310059,1120.800049,1113.569946,1113.630005,1113.630005,924170000
2004-09-07,1113.630005,1124.079956,1113.630005,1121.300049,1121.300049,1214400000
2004-09-08,1121.300049,1123.050049,1116.270020,1116.270020,1116.270020,1246300000
2004-09-09,1116.270020,1121.300049,1113.619995,1118.380005,1118.380005,1371300000
2004-09-10,1118.380005,1125.260010,1114.390015,1123.920044,1123.920044,1261200000
2004-09-13,1123.920044,1129.780029,1123.349976,1125.819946,1125.819946,1299800000
2004-09-14,1125.819946,1129.459961,1124.719971,1128.329956,1128.329956,1204500000
2004-09-15,1128.329956,1128.329956,1119.819946,1120.369995,1120.369995,1256000000
2004-09-16,1120.369995,1126.060059,1120.369995,1123.500000,1123.500000,1113900000
2004-09-17,1123.500000,1130.140015,1123.500000,1128.550049,1128.550049,1422600000
2004-09-20,1128.550049,1128.550049,1120.339966,1122.199951,1122.199951,1197600000
2004-09-21,1122.199951,1131.540039,1122.199951,1129.300049,1129.300049,1325000000
2004-09-22,1129.300049,1129.300049,1112.670044,1113.560059,1113.560059,1379900000
2004-09-23,1113.560059,1113.609985,1108.050049,1108.359985,1108.359985,1286300000
2004-09-24,1108.359985,1113.810059,1108.359985,1110.109985,1110.109985,1255400000
2004-09-27,1110.109985,1110.109985,1103.239990,1103.520020,1103.520020,1263500000
2004-09-28,1103.520020,1111.770020,1101.290039,1110.060059,1110.060059,1396600000
2004-09-29,1110.060059,1114.800049,1107.420044,1114.800049,1114.800049,1402900000
2004-09-30,1114.800049,1116.310059,1109.680054,1114.579956,1114.579956,1748000000
2004-10-01,1114.579956,1131.640015,1114.579956,1131.500000,1131.500000,1582200000
2004-10-04,1131.500000,1140.130005,1131.500000,1135.170044,1135.170044,1534000000
2004-10-05,1135.170044,1137.869995,1132.030029,1134.479980,1134.479980,1418400000
2004-10-06,1134.479980,1142.050049,1132.939941,1142.050049,1142.050049,1416700000
2004-10-07,1142.050049,1142.050049,1130.500000,1130.650024,1130.650024,1447500000
2004-10-08,1130.650024,1132.920044,1120.189941,1122.140015,1122.140015,1291600000
2004-10-11,1122.140015,1126.199951,1122.140015,1124.390015,1124.390015,943800000
2004-10-12,1124.390015,1124.390015,1115.770020,1121.839966,1121.839966,1320100000
2004-10-13,1121.839966,1127.010010,1109.630005,1113.650024,1113.650024,1546200000
2004-10-14,1113.650024,1114.959961,1102.060059,1103.290039,1103.290039,1489500000
2004-10-15,1103.290039,1113.170044,1102.140015,1108.199951,1108.199951,1645100000
2004-10-18,1108.199951,1114.459961,1103.329956,1114.020020,1114.020020,1373300000
2004-10-19,1114.020020,1117.959961,1103.150024,1103.229980,1103.229980,1737500000
2004-10-20,1103.229980,1104.089966,1094.250000,1103.660034,1103.660034,1685700000
2004-10-21,1103.660034,1108.869995,1098.469971,1106.489990,1106.489990,1673000000
2004-10-22,1106.489990,1108.140015,1095.469971,1095.739990,1095.739990,1469600000
2004-10-25,1095.739990,1096.810059,1090.290039,1094.800049,1094.800049,1380500000
2004-10-26,1094.810059,1111.099976,1094.810059,1111.089966,1111.089966,1685400000
2004-10-27,1111.089966,1126.290039,1107.430054,1125.400024,1125.400024,1741900000
2004-10-28,1125.339966,1130.670044,1120.599976,1127.439941,1127.439941,1628200000
2004-10-29,1127.439941,1131.400024,1124.619995,1130.199951,1130.199951,1500800000
2004-11-01,1130.199951,1133.410034,1127.599976,1130.510010,1130.510010,1395900000
2004-11-02,1130.510010,1140.479980,1128.119995,1130.560059,1130.560059,1659000000
2004-11-03,1130.540039,1147.569946,1130.540039,1143.199951,1143.199951,1767500000
2004-11-04,1143.199951,1161.670044,1142.339966,1161.670044,1161.670044,1782700000
2004-11-05,1161.670044,1170.869995,1160.660034,1166.170044,1166.170044,1724400000
2004-11-08,1166.170044,1166.770020,1162.319946,1164.890015,1164.890015,1358700000
2004-11-09,1164.890015,1168.959961,1162.479980,1164.079956,1164.079956,1450800000
2004-11-10,1164.079956,1169.250000,1162.510010,1162.910034,1162.910034,1504300000
2004-11-11,1162.910034,1174.800049,1162.910034,1173.479980,1173.479980,1393000000
2004-11-12,1173.479980,1184.170044,1171.430054,1184.170044,1184.170044,1531600000
2004-11-15,1184.170044,1184.479980,1179.849976,1183.810059,1183.810059,1453300000
2004-11-16,1183.810059,1183.810059,1175.319946,1175.430054,1175.430054,1364400000
2004-11-17,1175.430054,1188.459961,1175.430054,1181.939941,1181.939941,1684200000
2004-11-18,1181.939941,1184.900024,1180.150024,1183.550049,1183.550049,1456700000
2004-11-19,1183.550049,1184.000000,1169.189941,1170.339966,1170.339966,1526600000
2004-11-22,1170.339966,1178.180054,1167.890015,1177.239990,1177.239990,1392700000
2004-11-23,1177.239990,1179.520020,1171.410034,1176.939941,1176.939941,1428300000
2004-11-24,1176.939941,1182.459961,1176.939941,1181.760010,1181.760010,1149600000
2004-11-26,1181.760010,1186.619995,1181.079956,1182.650024,1182.650024,504580000
2004-11-29,1182.650024,1186.939941,1172.369995,1178.569946,1178.569946,1378500000
2004-11-30,1178.569946,1178.660034,1173.810059,1173.819946,1173.819946,1553500000
2004-12-01,1173.780029,1191.369995,1173.780029,1191.369995,1191.369995,1772800000
2004-12-02,1191.369995,1194.800049,1186.719971,1190.329956,1190.329956,1774900000
2004-12-03,1190.329956,1197.459961,1187.709961,1191.170044,1191.170044,1566700000
2004-12-06,1191.170044,1192.410034,1185.180054,1190.250000,1190.250000,1354400000
2004-12-07,1190.250000,1192.170044,1177.069946,1177.069946,1177.069946,1533900000
2004-12-08,1177.069946,1184.050049,1177.069946,1182.810059,1182.810059,1525200000
2004-12-09,1182.810059,1190.510010,1173.790039,1189.239990,1189.239990,1624700000
2004-12-10,1189.239990,1191.449951,1185.239990,1188.000000,1188.000000,1443700000
2004-12-13,1188.000000,1198.739990,1188.000000,1198.680054,1198.680054,1436100000
2004-12-14,1198.680054,1205.290039,1197.839966,1203.380005,1203.380005,1544400000
2004-12-15,1203.380005,1206.609985,1199.439941,1205.719971,1205.719971,1695800000
2004-12-16,1205.719971,1207.969971,1198.410034,1203.209961,1203.209961,1793900000
2004-12-17,1203.209961,1203.209961,1193.489990,1194.199951,1194.199951,2335000000
2004-12-20,1194.199951,1203.430054,1193.359985,1194.650024,1194.650024,1422800000
2004-12-21,1194.650024,1205.930054,1194.650024,1205.449951,1205.449951,1483700000
2004-12-22,1205.449951,1211.420044,1203.849976,1209.569946,1209.569946,1390800000
2004-12-23,1209.569946,1213.660034,1208.709961,1210.130005,1210.130005,956100000
2004-12-27,1210.130005,1214.130005,1204.920044,1204.920044,1204.920044,922000000
2004-12-28,1204.920044,1213.540039,1204.920044,1213.540039,1213.540039,983000000
2004-12-29,1213.540039,1213.849976,1210.949951,1213.449951,1213.449951,925900000
2004-12-30,1213.449951,1216.469971,1213.410034,1213.550049,1213.550049,829800000
2004-12-31,1213.550049,1217.329956,1211.650024,1211.920044,1211.920044,786900000
2005-01-03,1211.920044,1217.800049,1200.319946,1202.079956,1202.079956,1510800000
2005-01-04,1202.079956,1205.839966,1185.390015,1188.050049,1188.050049,1721000000
2005-01-05,1188.050049,1192.729980,1183.719971,1183.739990,1183.739990,1738900000
2005-01-06,1183.739990,1191.630005,1183.270020,1187.890015,1187.890015,1569100000
2005-01-07,1187.890015,1192.199951,1182.160034,1186.189941,1186.189941,1477900000
2005-01-10,1186.189941,1194.780029,1184.800049,1190.250000,1190.250000,1490400000
2005-01-11,1190.250000,1190.250000,1180.430054,1182.989990,1182.989990,1488800000
2005-01-12,1182.989990,1187.920044,1175.640015,1187.699951,1187.699951,1562100000
2005-01-13,1187.699951,1187.699951,1175.810059,1177.449951,1177.449951,1510300000
2005-01-14,1177.449951,1185.209961,1177.449951,1184.520020,1184.520020,1335400000
2005-01-18,1184.520020,1195.979980,1180.099976,1195.979980,1195.979980,1596800000
2005-01-19,1195.979980,1195.979980,1184.410034,1184.630005,1184.630005,1498700000
2005-01-20,1184.630005,1184.630005,1173.420044,1175.410034,1175.410034,1692000000
2005-01-21,1175.410034,1179.449951,1167.819946,1167.869995,1167.869995,1643500000
2005-01-24,1167.869995,1173.030029,1163.750000,1163.750000,1163.750000,1494600000
2005-01-25,1163.750000,1174.300049,1163.750000,1168.410034,1168.410034,1610400000
2005-01-26,1168.410034,1175.959961,1168.410034,1174.069946,1174.069946,1635900000
2005-01-27,1174.069946,1177.500000,1170.150024,1174.550049,1174.550049,1600600000
2005-01-28,1174.550049,1175.609985,1166.250000,1171.359985,1171.359985,1641800000
2005-01-31,1171.359985,1182.069946,1171.359985,1181.270020,1181.270020,1679800000
2005-02-01,1181.270020,1190.390015,1180.949951,1189.410034,1189.410034,1681980000
2005-02-02,1189.410034,1195.250000,1188.920044,1193.189941,1193.189941,1561740000
2005-02-03,1193.189941,1193.189941,1185.640015,1189.890015,1189.890015,1554460000
2005-02-04,1189.890015,1203.469971,1189.670044,1203.030029,1203.030029,1648160000
2005-02-07,1203.030029,1204.150024,1199.270020,1201.719971,1201.719971,1347270000
2005-02-08,1201.719971,1205.109985,1200.160034,1202.300049,1202.300049,1416170000
2005-02-09,1202.300049,1203.829956,1191.540039,1191.989990,1191.989990,1511040000
2005-02-10,1191.989990,1198.750000,1191.540039,1197.010010,1197.010010,1491670000
2005-02-11,1197.010010,1208.380005,1193.280029,1205.300049,1205.300049,1562300000
2005-02-14,1205.300049,1206.930054,1203.589966,1206.140015,1206.140015,1290180000
2005-02-15,1206.140015,1212.439941,1205.520020,1210.119995,1210.119995,1527080000
2005-02-16,1210.119995,1212.439941,1205.060059,1210.339966,1210.339966,1490100000
2005-02-17,1210.339966,1211.329956,1200.739990,1200.750000,1200.750000,1580120000
2005-02-18,1200.750000,1202.920044,1197.349976,1201.589966,1201.589966,1551200000
2005-02-22,1201.589966,1202.479980,1184.160034,1184.160034,1184.160034,1744940000
2005-02-23,1184.160034,1193.520020,1184.160034,1190.800049,1190.800049,1501090000
2005-02-24,1190.800049,1200.420044,1187.800049,1200.199951,1200.199951,1518750000
2005-02-25,1200.199951,1212.150024,1199.609985,1211.369995,1211.369995,1523680000
2005-02-28,1211.369995,1211.369995,1198.130005,1203.599976,1203.599976,1795480000
2005-03-01,1203.599976,1212.250000,1203.599976,1210.410034,1210.410034,1708060000
2005-03-02,1210.410034,1215.790039,1204.219971,1210.079956,1210.079956,1568540000
2005-03-03,1210.079956,1215.719971,1204.449951,1210.469971,1210.469971,1616240000
2005-03-04,1210.469971,1224.760010,1210.469971,1222.119995,1222.119995,1636820000
2005-03-07,1222.119995,1229.109985,1222.119995,1225.310059,1225.310059,1488830000
2005-03-08,1225.310059,1225.689941,1218.569946,1219.430054,1219.430054,1523090000
2005-03-09,1219.430054,1219.430054,1206.660034,1207.010010,1207.010010,1704970000
2005-03-10,1207.010010,1211.229980,1201.410034,1209.250000,1209.250000,1604020000
2005-03-11,1209.250000,1213.040039,1198.150024,1200.079956,1200.079956,1449820000
2005-03-14,1200.079956,1206.829956,1199.510010,1206.829956,1206.829956,1437430000
2005-03-15,1206.829956,1210.540039,1197.750000,1197.750000,1197.750000,1513530000
2005-03-16,1197.750000,1197.750000,1185.609985,1188.069946,1188.069946,1653190000
2005-03-17,1188.069946,1193.280029,1186.339966,1190.209961,1190.209961,1581930000
2005-03-18,1190.209961,1191.979980,1182.780029,1189.650024,1189.650024,2344370000
2005-03-21,1189.650024,1189.650024,1178.819946,1183.780029,1183.780029,1819440000
2005-03-22,1183.780029,1189.589966,1171.630005,1171.709961,1171.709961,2114470000
2005-03-23,1171.709961,1176.260010,1168.699951,1172.530029,1172.530029,2246870000
2005-03-24,1172.530029,1180.109985,1171.420044,1171.420044,1171.420044,1721720000
2005-03-28,1171.420044,1179.910034,1171.420044,1174.280029,1174.280029,1746220000
2005-03-29,1174.280029,1179.390015,1163.689941,1165.359985,1165.359985,2223250000
2005-03-30,1165.359985,1181.540039,1165.359985,1181.410034,1181.410034,2097110000
2005-03-31,1181.410034,1184.530029,1179.489990,1180.589966,1180.589966,2214230000
2005-04-01,1180.589966,1189.800049,1169.910034,1172.920044,1172.920044,2168690000
2005-04-04,1172.790039,1178.609985,1167.719971,1176.119995,1176.119995,2079770000
2005-04-05,1176.119995,1183.560059,1176.119995,1181.390015,1181.390015,1870800000
2005-04-06,1181.390015,1189.339966,1181.390015,1184.069946,1184.069946,1797400000
2005-04-07,1184.069946,1191.880005,1183.810059,1191.140015,1191.140015,1900620000
2005-04-08,1191.140015,1191.750000,1181.130005,1181.199951,1181.199951,1661330000
2005-04-11,1181.199951,1184.069946,1178.689941,1181.209961,1181.209961,1525310000
2005-04-12,1181.209961,1190.170044,1170.849976,1187.760010,1187.760010,1979830000
2005-04-13,1187.760010,1187.760010,1171.400024,1173.790039,1173.790039,2049740000
2005-04-14,1173.790039,1174.670044,1161.699951,1162.050049,1162.050049,2355040000
2005-04-15,1162.050049,1162.050049,1141.920044,1142.619995,1142.619995,2689960000
2005-04-18,1142.619995,1148.920044,1139.800049,1145.979980,1145.979980,2180670000
2005-04-19,1145.979980,1154.670044,1145.979980,1152.780029,1152.780029,2142700000
2005-04-20,1152.780029,1155.500000,1136.150024,1137.500000,1137.500000,2217050000
2005-04-21,1137.500000,1159.949951,1137.500000,1159.949951,1159.949951,2308560000
2005-04-22,1159.949951,1159.949951,1142.949951,1152.119995,1152.119995,2045880000
2005-04-25,1152.119995,1164.050049,1152.119995,1162.099976,1162.099976,1795030000
2005-04-26,1162.099976,1164.800049,1151.829956,1151.829956,1151.829956,1959740000
2005-04-27,1151.739990,1159.869995,1144.420044,1156.380005,1156.380005,2151520000
2005-04-28,1156.380005,1156.380005,1143.219971,1143.219971,1143.219971,2182270000
2005-04-29,1143.219971,1156.969971,1139.189941,1156.849976,1156.849976,2362360000
2005-05-02,1156.849976,1162.869995,1154.709961,1162.160034,1162.160034,1980040000
2005-05-03,1162.160034,1166.890015,1156.709961,1161.170044,1161.170044,2167020000
2005-05-04,1161.170044,1176.010010,1161.170044,1175.650024,1175.650024,2306480000
2005-05-05,1175.650024,1178.619995,1166.770020,1172.630005,1172.630005,1997100000
2005-05-06,1172.630005,1177.750000,1170.500000,1171.349976,1171.349976,1707200000
2005-05-09,1171.349976,1178.869995,1169.380005,1178.839966,1178.839966,1857020000
2005-05-10,1178.839966,1178.839966,1162.979980,1166.219971,1166.219971,1889660000
2005-05-11,1166.219971,1171.770020,1157.709961,1171.109985,1171.109985,1834970000
2005-05-12,1171.109985,1173.369995,1157.760010,1159.359985,1159.359985,1995290000
2005-05-13,1159.359985,1163.750000,1146.180054,1154.050049,1154.050049,2188590000
2005-05-16,1154.050049,1165.750000,1153.640015,1165.689941,1165.689941,1856860000
2005-05-17,1165.689941,1174.349976,1159.859985,1173.800049,1173.800049,1887260000
2005-05-18,1173.800049,1187.900024,1173.800049,1185.560059,1185.560059,2266320000
2005-05-19,1185.560059,1191.089966,1184.489990,1191.079956,1191.079956,1775860000
2005-05-20,1191.079956,1191.219971,1185.189941,1189.280029,1189.280029,1631750000
2005-05-23,1189.280029,1197.439941,1188.760010,1193.859985,1193.859985,1681170000
2005-05-24,1193.859985,1195.290039,1189.869995,1194.069946,1194.069946,1681000000
2005-05-25,1194.069946,1194.069946,1185.959961,1190.010010,1190.010010,1742180000
2005-05-26,1190.010010,1198.949951,1190.010010,1197.619995,1197.619995,1654110000
2005-05-27,1197.619995,1199.560059,1195.280029,1198.780029,1198.780029,1381430000
2005-05-31,1198.780029,1198.780029,1191.500000,1191.500000,1191.500000,1840680000
2005-06-01,1191.500000,1205.640015,1191.030029,1202.219971,1202.219971,1810100000
2005-06-02,1202.270020,1204.670044,1198.420044,1204.290039,1204.290039,1813790000
2005-06-03,1204.290039,1205.089966,1194.550049,1196.020020,1196.020020,1627520000
2005-06-06,1196.020020,1198.780029,1192.750000,1197.510010,1197.510010,1547120000
2005-06-07,1197.510010,1208.849976,1197.260010,1197.260010,1197.260010,1851370000
2005-06-08,1197.260010,1201.969971,1193.329956,1194.670044,1194.670044,1715490000
2005-06-09,1194.670044,1201.859985,1191.089966,1200.930054,1200.930054,1824120000
2005-06-10,1200.930054,1202.790039,1192.640015,1198.109985,1198.109985,1664180000
2005-06-13,1198.109985,1206.030029,1194.510010,1200.819946,1200.819946,1661350000
2005-06-14,1200.819946,1207.530029,1200.180054,1203.910034,1203.910034,1698150000
2005-06-15,1203.910034,1208.079956,1198.660034,1206.579956,1206.579956,1840440000
2005-06-16,1206.550049,1212.099976,1205.469971,1210.959961,1210.959961,1776040000
2005-06-17,1210.930054,1219.550049,1210.930054,1216.959961,1216.959961,2407370000
2005-06-20,1216.959961,1219.099976,1210.650024,1216.099976,1216.099976,1714530000
2005-06-21,1216.099976,1217.130005,1211.859985,1213.609985,1213.609985,1720700000
2005-06-22,1213.609985,1219.589966,1211.689941,1213.880005,1213.880005,1823250000
2005-06-23,1213.880005,1216.449951,1200.719971,1200.729980,1200.729980,2029920000
2005-06-24,1200.729980,1200.900024,1191.449951,1191.569946,1191.569946,2418800000
2005-06-27,1191.569946,1194.329956,1188.300049,1190.689941,1190.689941,1738620000
2005-06-28,1190.689941,1202.540039,1190.689941,1201.569946,1201.569946,1772410000
2005-06-29,1201.569946,1204.069946,1198.699951,1199.849976,1199.849976,1769280000
2005-06-30,1199.849976,1203.270020,1190.510010,1191.329956,1191.329956,2109490000
2005-07-01,1191.329956,1197.890015,1191.329956,1194.439941,1194.439941,1593820000
2005-07-05,1194.439941,1206.339966,1192.489990,1204.989990,1204.989990,1805820000
2005-07-06,1204.989990,1206.109985,1194.780029,1194.939941,1194.939941,1883470000
2005-07-07,1194.939941,1198.459961,1183.550049,1197.869995,1197.869995,1952440000
2005-07-08,1197.869995,1212.729980,1197.199951,1211.859985,1211.859985,1900810000
2005-07-11,1211.859985,1220.030029,1211.859985,1219.439941,1219.439941,1846300000
2005-07-12,1219.439941,1225.540039,1216.599976,1222.209961,1222.209961,1932010000
2005-07-13,1222.209961,1224.459961,1219.640015,1223.290039,1223.290039,1812500000
2005-07-14,1223.290039,1233.160034,1223.290039,1226.500000,1226.500000,2048710000
2005-07-15,1226.500000,1229.530029,1223.500000,1227.920044,1227.920044,1716400000
2005-07-18,1227.920044,1227.920044,1221.130005,1221.130005,1221.130005,1582100000
2005-07-19,1221.130005,1230.339966,1221.130005,1229.349976,1229.349976,2041280000
2005-07-20,1229.349976,1236.560059,1222.910034,1235.199951,1235.199951,2063340000
2005-07-21,1235.199951,1235.829956,1224.699951,1227.040039,1227.040039,2129840000
2005-07-22,1227.040039,1234.189941,1226.150024,1233.680054,1233.680054,1766990000
2005-07-25,1233.680054,1238.359985,1228.150024,1229.030029,1229.030029,1717580000
2005-07-26,1229.030029,1234.420044,1229.030029,1231.160034,1231.160034,1934180000
2005-07-27,1231.160034,1237.640015,1230.150024,1236.790039,1236.790039,1945800000
2005-07-28,1236.790039,1245.150024,1235.810059,1243.719971,1243.719971,2001680000
2005-07-29,1243.719971,1245.040039,1234.180054,1234.180054,1234.180054,1789600000
2005-08-01,1234.180054,1239.099976,1233.800049,1235.349976,1235.349976,1716870000
2005-08-02,1235.349976,1244.689941,1235.349976,1244.119995,1244.119995,2043120000
2005-08-03,1244.119995,1245.859985,1240.569946,1245.040039,1245.040039,1999980000
2005-08-04,1245.040039,1245.040039,1235.150024,1235.859985,1235.859985,1981220000
2005-08-05,1235.859985,1235.859985,1225.619995,1226.420044,1226.420044,1930280000
2005-08-08,1226.420044,1232.280029,1222.670044,1223.130005,1223.130005,1804140000
2005-08-09,1223.130005,1234.109985,1223.130005,1231.380005,1231.380005,1897520000
2005-08-10,1231.380005,1242.689941,1226.579956,1229.130005,1229.130005,2172320000
2005-08-11,1229.130005,1237.810059,1228.329956,1237.810059,1237.810059,1941560000
2005-08-12,1237.810059,1237.810059,1225.869995,1230.390015,1230.390015,1709300000
2005-08-15,1230.400024,1236.239990,1226.199951,1233.869995,1233.869995,1562880000
2005-08-16,1233.869995,1233.869995,1219.050049,1219.339966,1219.339966,1820410000
2005-08-17,1219.339966,1225.630005,1218.069946,1220.239990,1220.239990,1859150000
2005-08-18,1220.239990,1222.640015,1215.930054,1219.020020,1219.020020,1808170000
2005-08-19,1219.020020,1225.079956,1219.020020,1219.709961,1219.709961,1558790000
2005-08-22,1219.709961,1228.959961,1216.469971,1221.729980,1221.729980,1621330000
2005-08-23,1221.729980,1223.040039,1214.439941,1217.589966,1217.589966,1678620000
2005-08-24,1217.569946,1224.150024,1209.369995,1209.589966,1209.589966,1930800000
2005-08-25,1209.589966,1213.729980,1209.569946,1212.369995,1212.369995,1571110000
2005-08-26,1212.400024,1212.400024,1204.229980,1205.099976,1205.099976,1541090000
2005-08-29,1205.099976,1214.280029,1201.530029,1212.280029,1212.280029,1599450000
2005-08-30,1212.280029,1212.280029,1201.069946,1208.410034,1208.410034,1916470000
2005-08-31,1208.410034,1220.359985,1204.400024,1220.329956,1220.329956,2365510000
2005-09-01,1220.329956,1227.290039,1216.180054,1221.589966,1221.589966,2229860000
2005-09-02,1221.589966,1224.449951,1217.750000,1218.020020,1218.020020,1640160000
2005-09-06,1218.020020,1233.609985,1218.020020,1233.390015,1233.390015,1932090000
2005-09-07,1233.390015,1237.060059,1230.930054,1236.359985,1236.359985,2067700000
2005-09-08,1236.359985,1236.359985,1229.510010,1231.670044,1231.670044,1955380000
2005-09-09,1231.670044,1243.130005,1231.670044,1241.479980,1241.479980,1992560000
2005-09-12,1241.479980,1242.599976,1239.150024,1240.560059,1240.560059,1938050000
2005-09-13,1240.569946,1240.569946,1231.199951,1231.199951,1231.199951,2082360000
2005-09-14,1231.199951,1234.739990,1226.160034,1227.160034,1227.160034,1986750000
2005-09-15,1227.160034,1231.880005,1224.849976,1227.729980,1227.729980,2079340000
2005-09-16,1228.420044,1237.949951,1228.420044,1237.910034,1237.910034,3152470000
2005-09-19,1237.910034,1237.910034,1227.650024,1231.020020,1231.020020,2076540000
2005-09-20,1231.020020,1236.489990,1220.069946,1221.339966,1221.339966,2319250000
2005-09-21,1221.339966,1221.520020,1209.890015,1210.199951,1210.199951,2548150000
2005-09-22,1210.199951,1216.640015,1205.349976,1214.619995,1214.619995,2424720000
2005-09-23,1214.619995,1218.829956,1209.800049,1215.290039,1215.290039,1973020000
2005-09-26,1215.290039,1222.560059,1211.839966,1215.630005,1215.630005,2022220000
2005-09-27,1215.630005,1220.170044,1211.109985,1215.660034,1215.660034,1976270000
2005-09-28,1215.660034,1220.979980,1212.719971,1216.890015,1216.890015,2106980000
2005-09-29,1216.890015,1228.699951,1211.540039,1227.680054,1227.680054,2176120000
2005-09-30,1227.680054,1229.569946,1225.219971,1228.810059,1228.810059,2097520000
2005-10-03,1228.810059,1233.339966,1225.150024,1226.699951,1226.699951,2097490000
2005-10-04,1226.699951,1229.880005,1214.020020,1214.469971,1214.469971,2341420000
2005-10-05,1214.469971,1214.469971,1196.250000,1196.390015,1196.390015,2546780000
2005-10-06,1196.390015,1202.140015,1181.920044,1191.489990,1191.489990,2792030000
2005-10-07,1191.489990,1199.709961,1191.459961,1195.900024,1195.900024,2126080000
2005-10-10,1195.900024,1196.520020,1186.119995,1187.329956,1187.329956,2195990000
2005-10-11,1187.329956,1193.099976,1183.160034,1184.869995,1184.869995,2299040000
2005-10-12,1184.869995,1190.020020,1173.650024,1177.680054,1177.680054,2491280000
2005-10-13,1177.680054,1179.560059,1168.199951,1176.839966,1176.839966,2351150000
2005-10-14,1176.839966,1187.130005,1175.439941,1186.569946,1186.569946,2188940000
2005-10-17,1186.569946,1191.209961,1184.479980,1190.099976,1190.099976,2054570000
2005-10-18,1190.099976,1190.099976,1178.130005,1178.140015,1178.140015,2197010000
2005-10-19,1178.140015,1195.760010,1170.550049,1195.760010,1195.760010,2703590000
2005-10-20,1195.760010,1197.300049,1173.300049,1177.800049,1177.800049,2617250000
2005-10-21,1177.800049,1186.459961,1174.920044,1179.589966,1179.589966,2470920000
2005-10-24,1179.589966,1199.390015,1179.589966,1199.380005,1199.380005,2197790000
2005-10-25,1199.380005,1201.300049,1189.290039,1196.540039,1196.540039,2312470000
2005-10-26,1196.540039,1204.010010,1191.380005,1191.380005,1191.380005,2467750000
2005-10-27,1191.380005,1192.650024,1178.890015,1178.900024,1178.900024,2395370000
2005-10-28,1178.900024,1198.410034,1178.900024,1198.410034,1198.410034,2379400000
2005-10-31,1198.410034,1211.430054,1198.410034,1207.010010,1207.010010,2567470000
2005-11-01,1207.010010,1207.339966,1201.660034,1202.760010,1202.760010,2457850000
2005-11-02,1202.760010,1215.170044,1201.069946,1214.760010,1214.760010,2648090000
2005-11-03,1214.760010,1224.699951,1214.760010,1219.939941,1219.939941,2716630000
2005-11-04,1219.939941,1222.520020,1214.449951,1220.140015,1220.140015,2050510000
2005-11-07,1220.140015,1224.180054,1217.290039,1222.810059,1222.810059,1987580000
2005-11-08,1222.810059,1222.810059,1216.079956,1218.589966,1218.589966,1965050000
2005-11-09,1218.589966,1226.589966,1216.530029,1220.650024,1220.650024,2214460000
2005-11-10,1220.650024,1232.410034,1215.050049,1230.959961,1230.959961,2378460000
2005-11-11,1230.959961,1235.699951,1230.719971,1234.719971,1234.719971,1773140000
2005-11-14,1234.719971,1237.199951,1231.780029,1233.760010,1233.760010,1899780000
2005-11-15,1233.760010,1237.939941,1226.410034,1229.010010,1229.010010,2359370000
2005-11-16,1229.010010,1232.239990,1227.180054,1231.209961,1231.209961,2121580000
2005-11-17,1231.209961,1242.959961,1231.209961,1242.800049,1242.800049,2298040000
2005-11-18,1242.800049,1249.579956,1240.709961,1248.270020,1248.270020,2453290000
2005-11-21,1248.270020,1255.890015,1246.900024,1254.849976,1254.849976,2117350000
2005-11-22,1254.849976,1261.900024,1251.400024,1261.229980,1261.229980,2291420000
2005-11-23,1261.229980,1270.640015,1259.510010,1265.609985,1265.609985,1985400000
2005-11-25,1265.609985,1268.780029,1265.540039,1268.250000,1268.250000,724940000
2005-11-28,1268.250000,1268.439941,1257.170044,1257.459961,1257.459961,2016900000
2005-11-29,1257.459961,1266.180054,1257.459961,1257.479980,1257.479980,2268340000
2005-11-30,1257.479980,1260.930054,1249.390015,1249.479980,1249.479980,2374690000
2005-12-01,1249.479980,1266.170044,1249.479980,1264.670044,1264.670044,2614830000
2005-12-02,1264.670044,1266.849976,1261.420044,1265.079956,1265.079956,2125580000
2005-12-05,1265.079956,1265.079956,1258.119995,1262.089966,1262.089966,2325840000
2005-12-06,1262.089966,1272.890015,1262.089966,1263.699951,1263.699951,2110740000
2005-12-07,1263.699951,1264.849976,1253.020020,1257.369995,1257.369995,2093830000
2005-12-08,1257.369995,1263.359985,1250.910034,1255.839966,1255.839966,2178300000
2005-12-09,1255.839966,1263.079956,1254.239990,1259.369995,1259.369995,1896290000
2005-12-12,1259.369995,1263.859985,1255.520020,1260.430054,1260.430054,1876550000
2005-12-13,1260.430054,1272.109985,1258.560059,1267.430054,1267.430054,2390020000
2005-12-14,1267.430054,1275.800049,1267.069946,1272.739990,1272.739990,2145520000
2005-12-15,1272.739990,1275.170044,1267.739990,1270.939941,1270.939941,2180590000
2005-12-16,1270.939941,1275.239990,1267.319946,1267.319946,1267.319946,2584190000
2005-12-19,1267.319946,1270.510010,1259.280029,1259.920044,1259.920044,2208810000
2005-12-20,1259.920044,1263.859985,1257.209961,1259.619995,1259.619995,1996690000
2005-12-21,1259.619995,1269.369995,1259.619995,1262.790039,1262.790039,2065170000
2005-12-22,1262.790039,1268.189941,1262.500000,1268.119995,1268.119995,1888500000
2005-12-23,1268.119995,1269.760010,1265.920044,1268.660034,1268.660034,1285810000
2005-12-27,1268.660034,1271.829956,1256.540039,1256.540039,1256.540039,1540470000
2005-12-28,1256.540039,1261.099976,1256.540039,1258.170044,1258.170044,1422360000
2005-12-29,1258.170044,1260.609985,1254.180054,1254.420044,1254.420044,1382540000
2005-12-30,1254.420044,1254.420044,1246.589966,1248.290039,1248.290039,1443500000
2006-01-03,1248.290039,1270.219971,1245.739990,1268.800049,1268.800049,2554570000
2006-01-04,1268.800049,1275.369995,1267.739990,1273.459961,1273.459961,2515330000
2006-01-05,1273.459961,1276.910034,1270.300049,1273.479980,1273.479980,2433340000
2006-01-06,1273.479980,1286.089966,1273.479980,1285.449951,1285.449951,2446560000
2006-01-09,1285.449951,1290.780029,1284.819946,1290.150024,1290.150024,2301490000
2006-01-10,1290.150024,1290.150024,1283.760010,1289.689941,1289.689941,2373080000
2006-01-11,1289.719971,1294.900024,1288.119995,1294.180054,1294.180054,2406130000
2006-01-12,1294.180054,1294.180054,1285.040039,1286.060059,1286.060059,2318350000
2006-01-13,1286.060059,1288.959961,1282.780029,1287.609985,1287.609985,2206510000
2006-01-17,1287.609985,1287.609985,1278.609985,1282.930054,1282.930054,2179970000
2006-01-18,1282.930054,1282.930054,1272.079956,1277.930054,1277.930054,2233200000
2006-01-19,1277.930054,1287.790039,1277.930054,1285.040039,1285.040039,2444020000
2006-01-20,1285.040039,1285.040039,1260.920044,1261.489990,1261.489990,2845810000
2006-01-23,1261.489990,1268.189941,1261.489990,1263.819946,1263.819946,2256070000
2006-01-24,1263.819946,1271.469971,1263.819946,1266.859985,1266.859985,2608720000
2006-01-25,1266.859985,1271.869995,1259.420044,1264.680054,1264.680054,2617060000
2006-01-26,1264.680054,1276.439941,1264.680054,1273.829956,1273.829956,2856780000
2006-01-27,1273.829956,1286.380005,1273.829956,1283.719971,1283.719971,2623620000
2006-01-30,1283.719971,1287.939941,1283.510010,1285.189941,1285.189941,2282730000
2006-01-31,1285.199951,1285.199951,1276.849976,1280.079956,1280.079956,2708310000
2006-02-01,1280.079956,1283.329956,1277.569946,1282.459961,1282.459961,2589410000
2006-02-02,1282.459961,1282.459961,1267.719971,1270.839966,1270.839966,2565300000
2006-02-03,1270.839966,1270.869995,1261.020020,1264.030029,1264.030029,2282210000
2006-02-06,1264.030029,1267.040039,1261.619995,1265.020020,1265.020020,2132360000
2006-02-07,1265.020020,1265.780029,1253.609985,1254.780029,1254.780029,2366370000
2006-02-08,1254.780029,1266.469971,1254.780029,1265.650024,1265.650024,2456860000
2006-02-09,1265.650024,1274.560059,1262.800049,1263.780029,1263.780029,2441920000
2006-02-10,1263.819946,1269.890015,1254.979980,1266.989990,1266.989990,2290050000
2006-02-13,1266.989990,1266.989990,1258.339966,1262.859985,1262.859985,1850080000
2006-02-14,1262.859985,1278.209961,1260.800049,1275.530029,1275.530029,2437940000
2006-02-15,1275.530029,1281.000000,1271.060059,1280.000000,1280.000000,2317590000
2006-02-16,1280.000000,1289.390015,1280.000000,1289.380005,1289.380005,2251490000
2006-02-17,1289.380005,1289.469971,1284.069946,1287.239990,1287.239990,2128260000
2006-02-21,1287.239990,1291.920044,1281.329956,1283.030029,1283.030029,2104320000
2006-02-22,1283.030029,1294.170044,1283.030029,1292.670044,1292.670044,2222380000
2006-02-23,1292.670044,1293.839966,1285.140015,1287.790039,1287.790039,2144210000
2006-02-24,1287.790039,1292.109985,1285.619995,1289.430054,1289.430054,1933010000
2006-02-27,1289.430054,1297.569946,1289.430054,1294.119995,1294.119995,1975320000
2006-02-28,1294.119995,1294.119995,1278.660034,1280.660034,1280.660034,2370860000
2006-03-01,1280.660034,1291.800049,1280.660034,1291.239990,1291.239990,2308320000
2006-03-02,1291.239990,1291.239990,1283.209961,1289.140015,1289.140015,2494590000
2006-03-03,1289.140015,1297.329956,1284.199951,1287.229980,1287.229980,2152950000
2006-03-06,1287.229980,1288.229980,1275.670044,1278.260010,1278.260010,2280190000
2006-03-07,1278.260010,1278.260010,1271.109985,1275.880005,1275.880005,2268050000
2006-03-08,1275.880005,1280.329956,1268.420044,1278.469971,1278.469971,2442870000
2006-03-09,1278.469971,1282.739990,1272.229980,1272.229980,1272.229980,2140110000
2006-03-10,1272.229980,1284.369995,1271.109985,1281.420044,1281.420044,2123450000
2006-03-13,1281.579956,1287.369995,1281.579956,1284.130005,1284.130005,2070330000
2006-03-14,1284.130005,1298.140015,1282.670044,1297.479980,1297.479980,2165270000
2006-03-15,1297.479980,1304.400024,1294.969971,1303.020020,1303.020020,2293000000
2006-03-16,1303.020020,1310.449951,1303.020020,1305.329956,1305.329956,2292180000
2006-03-17,1305.329956,1309.790039,1305.319946,1307.250000,1307.250000,2549620000
2006-03-20,1307.250000,1310.000000,1303.589966,1305.079956,1305.079956,1976830000
2006-03-21,1305.079956,1310.880005,1295.819946,1297.229980,1297.229980,2147370000
2006-03-22,1297.229980,1305.969971,1295.810059,1305.040039,1305.040039,2039810000
2006-03-23,1305.040039,1305.040039,1298.109985,1301.670044,1301.670044,1980940000
2006-03-24,1301.670044,1306.530029,1298.890015,1302.949951,1302.949951,2326070000
2006-03-27,1302.949951,1303.739990,1299.089966,1301.609985,1301.609985,2029700000
2006-03-28,1301.609985,1306.239990,1291.839966,1293.229980,1293.229980,2148580000
2006-03-29,1293.229980,1305.599976,1293.229980,1302.890015,1302.890015,2143540000
2006-03-30,1302.890015,1310.150024,1296.719971,1300.250000,1300.250000,2294560000
2006-03-31,1300.250000,1303.000000,1294.869995,1294.869995,1294.869995,2236710000
2006-04-03,1302.880005,1309.189941,1296.650024,1297.810059,1297.810059,2494080000
2006-04-04,1297.810059,1307.550049,1294.709961,1305.930054,1305.930054,2147660000
2006-04-05,1305.930054,1312.810059,1304.819946,1311.560059,1311.560059,2420020000
2006-04-06,1311.560059,1311.989990,1302.439941,1309.040039,1309.040039,2281680000
2006-04-07,1309.040039,1314.069946,1294.180054,1295.500000,1295.500000,2082470000
2006-04-10,1295.510010,1300.739990,1293.170044,1296.619995,1296.619995,1898320000
2006-04-11,1296.599976,1300.709961,1282.959961,1286.569946,1286.569946,2232880000
2006-04-12,1286.569946,1290.930054,1286.449951,1288.119995,1288.119995,1938100000
2006-04-13,1288.119995,1292.089966,1283.369995,1289.119995,1289.119995,1891940000
2006-04-17,1289.119995,1292.449951,1280.739990,1285.329956,1285.329956,1794650000
2006-04-18,1285.329956,1309.020020,1285.329956,1307.280029,1307.280029,2595440000
2006-04-19,1307.650024,1310.390015,1302.790039,1309.930054,1309.930054,2447310000
2006-04-20,1309.930054,1318.160034,1306.380005,1311.459961,1311.459961,2512920000
2006-04-21,1311.459961,1317.670044,1306.589966,1311.280029,1311.280029,2392630000
2006-04-24,1311.280029,1311.280029,1303.790039,1308.109985,1308.109985,2117330000
2006-04-25,1308.109985,1310.790039,1299.170044,1301.739990,1301.739990,2366380000
2006-04-26,1301.739990,1310.969971,1301.739990,1305.410034,1305.410034,2502690000
2006-04-27,1305.410034,1315.000000,1295.569946,1309.719971,1309.719971,2772010000
2006-04-28,1309.719971,1316.040039,1306.160034,1310.609985,1310.609985,2419920000
2006-05-01,1310.609985,1317.209961,1303.459961,1305.189941,1305.189941,2437040000
2006-05-02,1305.189941,1313.660034,1305.189941,1313.209961,1313.209961,2403470000
2006-05-03,1313.209961,1313.469971,1303.920044,1308.119995,1308.119995,2395230000
2006-05-04,1307.849976,1315.140015,1307.849976,1312.250000,1312.250000,2431450000
2006-05-05,1312.250000,1326.530029,1312.250000,1325.760010,1325.760010,2294760000
2006-05-08,1325.760010,1326.699951,1322.869995,1324.660034,1324.660034,2151300000
2006-05-09,1324.660034,1326.599976,1322.479980,1325.140015,1325.140015,2157290000
2006-05-10,1324.569946,1325.510010,1317.439941,1322.849976,1322.849976,2268550000
2006-05-11,1322.630005,1322.630005,1303.449951,1305.920044,1305.920044,2531520000
2006-05-12,1305.880005,1305.880005,1290.380005,1291.239990,1291.239990,2567970000
2006-05-15,1291.189941,1294.810059,1284.510010,1294.500000,1294.500000,2505660000
2006-05-16,1294.500000,1297.880005,1288.510010,1292.079956,1292.079956,2386210000
2006-05-17,1291.729980,1291.729980,1267.310059,1270.319946,1270.319946,2830200000
2006-05-18,1270.250000,1274.890015,1261.750000,1261.810059,1261.810059,2537490000
2006-05-19,1261.810059,1272.150024,1256.280029,1267.030029,1267.030029,2982300000
2006-05-22,1267.030029,1268.770020,1252.979980,1262.069946,1262.069946,2773010000
2006-05-23,1262.060059,1273.670044,1256.150024,1256.579956,1256.579956,2605250000
2006-05-24,1256.560059,1264.530029,1245.339966,1258.569946,1258.569946,2999030000
2006-05-25,1258.410034,1273.260010,1258.410034,1272.880005,1272.880005,2372730000
2006-05-26,1272.709961,1280.540039,1272.500000,1280.160034,1280.160034,1814020000
2006-05-30,1280.040039,1280.040039,1259.869995,1259.869995,1259.869995,2176190000
2006-05-31,1259.380005,1270.089966,1259.380005,1270.089966,1270.089966,2692160000
2006-06-01,1270.050049,1285.709961,1269.189941,1285.709961,1285.709961,2360160000
2006-06-02,1285.709961,1290.680054,1280.219971,1288.219971,1288.219971,2295540000
2006-06-05,1288.160034,1288.160034,1264.660034,1265.290039,1265.290039,2313470000
2006-06-06,1265.229980,1269.880005,1254.459961,1263.849976,1263.849976,2697650000
2006-06-07,1263.609985,1272.469971,1255.770020,1256.150024,1256.150024,2644170000
2006-06-08,1256.079956,1259.849976,1235.180054,1257.930054,1257.930054,3543790000
2006-06-09,1257.930054,1262.579956,1250.030029,1252.300049,1252.300049,2214000000
2006-06-12,1252.270020,1255.219971,1236.430054,1237.439941,1237.439941,2247010000
2006-06-13,1236.079956,1243.369995,1222.520020,1223.689941,1223.689941,3215770000
2006-06-14,1223.660034,1231.459961,1219.290039,1230.040039,1230.040039,2667990000
2006-06-15,1230.010010,1258.640015,1230.010010,1256.160034,1256.160034,2775480000
2006-06-16,1256.160034,1256.270020,1246.329956,1251.540039,1251.540039,2783390000
2006-06-19,1251.540039,1255.930054,1237.170044,1240.130005,1240.130005,2517200000
2006-06-20,1240.119995,1249.010010,1238.869995,1240.119995,1240.119995,2232950000
2006-06-21,1240.089966,1257.959961,1240.089966,1252.199951,1252.199951,2361230000
2006-06-22,1251.920044,1251.920044,1241.530029,1245.599976,1245.599976,2148180000
2006-06-23,1245.589966,1253.130005,1241.430054,1244.500000,1244.500000,2017270000
2006-06-26,1244.500000,1250.920044,1243.680054,1250.560059,1250.560059,1878580000
2006-06-27,1250.550049,1253.369995,1238.939941,1239.199951,1239.199951,2203130000
2006-06-28,1238.989990,1247.060059,1237.589966,1246.000000,1246.000000,2085490000
2006-06-29,1245.939941,1272.880005,1245.939941,1272.869995,1272.869995,2621250000
2006-06-30,1272.859985,1276.300049,1270.199951,1270.199951,1270.199951,3049560000
2006-07-03,1270.060059,1280.380005,1270.060059,1280.189941,1280.189941,1114470000
2006-07-05,1280.050049,1280.050049,1265.910034,1270.910034,1270.910034,2165070000
2006-07-06,1270.579956,1278.319946,1270.579956,1274.079956,1274.079956,2009160000
2006-07-07,1274.079956,1275.380005,1263.130005,1265.479980,1265.479980,1988150000
2006-07-10,1265.459961,1274.060059,1264.459961,1267.339966,1267.339966,1854590000
2006-07-11,1267.260010,1273.640015,1259.650024,1272.430054,1272.430054,2310850000
2006-07-12,1272.390015,1273.310059,1257.290039,1258.599976,1258.599976,2250450000
2006-07-13,1258.579956,1258.579956,1241.430054,1242.280029,1242.280029,2545760000
2006-07-14,1242.290039,1242.699951,1228.449951,1236.199951,1236.199951,2467120000
2006-07-17,1236.199951,1240.069946,1231.489990,1234.489990,1234.489990,2146410000
2006-07-18,1234.479980,1239.859985,1224.540039,1236.859985,1236.859985,2481750000
2006-07-19,1236.739990,1261.810059,1236.739990,1259.810059,1259.810059,2701980000
2006-07-20,1259.810059,1262.560059,1249.130005,1249.130005,1249.130005,2345580000
2006-07-21,1249.119995,1250.959961,1238.719971,1240.290039,1240.290039,2704090000
2006-07-24,1240.250000,1262.500000,1240.250000,1260.910034,1260.910034,2312720000
2006-07-25,1260.910034,1272.390015,1257.189941,1268.880005,1268.880005,2563930000
2006-07-26,1268.869995,1273.890015,1261.939941,1268.400024,1268.400024,2667710000
2006-07-27,1268.199951,1275.849976,1261.920044,1263.199951,1263.199951,2776710000
2006-07-28,1263.150024,1280.420044,1263.150024,1278.550049,1278.550049,2480420000
2006-07-31,1278.530029,1278.660034,1274.310059,1276.660034,1276.660034,2461300000
2006-08-01,1278.530029,1278.660034,1265.709961,1270.920044,1270.920044,2527690000
2006-08-02,1270.729980,1283.420044,1270.729980,1277.410034,1277.410034,2610750000
2006-08-03,1278.219971,1283.959961,1271.250000,1280.270020,1280.270020,2728440000
2006-08-04,1280.260010,1292.920044,1273.819946,1279.359985,1279.359985,2530970000
2006-08-07,1279.310059,1279.310059,1273.000000,1275.770020,1275.770020,2045660000
2006-08-08,1275.670044,1282.750000,1268.369995,1271.479980,1271.479980,2457840000
2006-08-09,1271.130005,1283.739990,1264.729980,1265.949951,1265.949951,2555180000
2006-08-10,1265.719971,1272.550049,1261.300049,1271.810059,1271.810059,2402190000
2006-08-11,1271.640015,1271.640015,1262.079956,1266.739990,1266.739990,2004540000
2006-08-14,1266.670044,1278.900024,1266.670044,1268.209961,1268.209961,2118020000
2006-08-15,1268.189941,1286.229980,1268.189941,1285.579956,1285.579956,2334100000
2006-08-16,1285.270020,1296.209961,1285.270020,1295.430054,1295.430054,2554570000
2006-08-17,1295.369995,1300.780029,1292.709961,1297.479980,1297.479980,2458340000
2006-08-18,1297.479980,1302.300049,1293.569946,1302.300049,1302.300049,2033910000
2006-08-21,1302.300049,1302.300049,1295.510010,1297.520020,1297.520020,1759240000
2006-08-22,1297.520020,1302.489990,1294.439941,1298.819946,1298.819946,1908740000
2006-08-23,1298.729980,1301.500000,1289.819946,1292.989990,1292.989990,1893670000
2006-08-24,1292.969971,1297.229980,1291.400024,1296.060059,1296.060059,1930320000
2006-08-25,1295.920044,1298.880005,1292.390015,1295.089966,1295.089966,1667580000
2006-08-28,1295.089966,1305.020020,1293.969971,1301.780029,1301.780029,1834920000
2006-08-29,1301.569946,1305.020020,1295.290039,1304.280029,1304.280029,2093720000
2006-08-30,1303.699951,1306.739990,1302.150024,1305.369995,1305.369995,2060690000
2006-08-31,1304.250000,1306.109985,1302.449951,1303.819946,1303.819946,1974540000
2006-09-01,1303.800049,1312.030029,1303.800049,1311.010010,1311.010010,1800520000
2006-09-05,1310.939941,1314.670044,1308.819946,1313.250000,1313.250000,2114480000
2006-09-06,1313.040039,1313.040039,1299.280029,1300.260010,1300.260010,2329870000
2006-09-07,1300.209961,1301.250000,1292.130005,1294.020020,1294.020020,2325850000
2006-09-08,1294.020020,1300.140015,1294.020020,1298.920044,1298.920044,2132890000
2006-09-11,1298.859985,1302.359985,1290.930054,1299.540039,1299.540039,2506430000
2006-09-12,1299.530029,1314.280029,1299.530029,1313.000000,1313.000000,2791580000
2006-09-13,1312.739990,1319.920044,1311.119995,1318.069946,1318.069946,2597220000
2006-09-14,1318.000000,1318.000000,1313.250000,1316.280029,1316.280029,2351220000
2006-09-15,1316.280029,1324.650024,1316.280029,1319.660034,1319.660034,3198030000
2006-09-18,1319.849976,1324.869995,1318.160034,1321.180054,1321.180054,2325080000
2006-09-19,1321.170044,1322.040039,1312.170044,1317.640015,1317.640015,2390850000
2006-09-20,1318.280029,1328.530029,1318.280029,1325.180054,1325.180054,2543070000
2006-09-21,1324.890015,1328.189941,1315.449951,1318.030029,1318.030029,2627440000
2006-09-22,1318.030029,1318.030029,1310.939941,1314.780029,1314.780029,2162880000
2006-09-25,1314.780029,1329.349976,1311.579956,1326.369995,1326.369995,2710240000
2006-09-26,1326.349976,1336.599976,1325.300049,1336.349976,1336.349976,2673350000
2006-09-27,1336.119995,1340.079956,1333.540039,1336.589966,1336.589966,2749190000
2006-09-28,1336.560059,1340.280029,1333.750000,1338.880005,1338.880005,2397820000
2006-09-29,1339.150024,1339.880005,1335.640015,1335.849976,1335.849976,2273430000
2006-10-02,1335.819946,1338.540039,1330.280029,1331.319946,1331.319946,2154480000
2006-10-03,1331.319946,1338.310059,1327.099976,1334.109985,1334.109985,2682690000
2006-10-04,1333.810059,1350.199951,1331.479980,1350.199951,1350.199951,3019880000
2006-10-05,1349.839966,1353.790039,1347.750000,1353.219971,1353.219971,2817240000
2006-10-06,1353.219971,1353.219971,1344.209961,1349.589966,1349.589966,2523000000
2006-10-09,1349.579956,1352.689941,1346.550049,1350.660034,1350.660034,1935170000
2006-10-10,1350.619995,1354.229980,1348.599976,1353.420044,1353.420044,2376140000
2006-10-11,1353.280029,1353.969971,1343.569946,1349.949951,1349.949951,2521000000
2006-10-12,1349.939941,1363.760010,1349.939941,1362.829956,1362.829956,2514350000
2006-10-13,1362.819946,1366.630005,1360.500000,1365.619995,1365.619995,2482920000
2006-10-16,1365.609985,1370.199951,1364.479980,1369.060059,1369.060059,2305920000
2006-10-17,1369.050049,1369.050049,1356.869995,1364.050049,1364.050049,2519620000
2006-10-18,1363.930054,1372.869995,1360.949951,1365.800049,1365.800049,2658840000
2006-10-19,1365.949951,1368.089966,1362.060059,1366.959961,1366.959961,2619830000
2006-10-20,1366.939941,1368.660034,1362.099976,1368.599976,1368.599976,2526410000
2006-10-23,1368.579956,1377.400024,1363.939941,1377.020020,1377.020020,2480430000
2006-10-24,1377.020020,1377.780029,1372.420044,1377.380005,1377.380005,2876890000
2006-10-25,1377.359985,1383.609985,1376.000000,1382.219971,1382.219971,2953540000
2006-10-26,1382.209961,1389.449951,1379.469971,1389.079956,1389.079956,2793350000
2006-10-27,1388.890015,1388.890015,1375.849976,1377.339966,1377.339966,2458450000
2006-10-30,1377.300049,1381.219971,1373.459961,1377.930054,1377.930054,2770440000
2006-10-31,1377.930054,1381.209961,1372.189941,1377.939941,1377.939941,2803030000
2006-11-01,1377.760010,1381.949951,1366.260010,1367.810059,1367.810059,2821160000
2006-11-02,1367.439941,1368.390015,1362.209961,1367.339966,1367.339966,2646180000
2006-11-03,1367.310059,1371.680054,1360.979980,1364.300049,1364.300049,2419730000
2006-11-06,1364.270020,1381.400024,1364.270020,1379.780029,1379.780029,2533550000
2006-11-07,1379.750000,1388.189941,1379.189941,1382.839966,1382.839966,2636390000
2006-11-08,1382.500000,1388.609985,1379.329956,1385.719971,1385.719971,2814820000
2006-11-09,1385.430054,1388.920044,1377.310059,1378.329956,1378.329956,3012050000
2006-11-10,1378.329956,1381.040039,1375.599976,1380.900024,1380.900024,2290200000
2006-11-13,1380.579956,1387.609985,1378.800049,1384.420044,1384.420044,2386340000
2006-11-14,1384.359985,1394.489990,1379.069946,1393.219971,1393.219971,3027480000
2006-11-15,1392.910034,1401.349976,1392.130005,1396.569946,1396.569946,2831130000
2006-11-16,1396.530029,1403.760010,1396.530029,1399.760010,1399.760010,2835730000
2006-11-17,1399.760010,1401.209961,1394.550049,1401.199951,1401.199951,2726100000
2006-11-20,1401.170044,1404.369995,1397.849976,1400.500000,1400.500000,2546710000
2006-11-21,1400.430054,1403.489990,1399.989990,1402.810059,1402.810059,2597940000
2006-11-22,1402.689941,1407.890015,1402.260010,1406.089966,1406.089966,2237710000
2006-11-24,1405.939941,1405.939941,1399.250000,1400.949951,1400.949951,832550000
2006-11-27,1400.949951,1400.949951,1381.439941,1381.959961,1381.959961,2711210000
2006-11-28,1381.609985,1387.910034,1377.829956,1386.719971,1386.719971,2639750000
2006-11-29,1386.109985,1401.140015,1386.109985,1399.479980,1399.479980,2790970000
2006-11-30,1399.469971,1406.300049,1393.829956,1400.630005,1400.630005,4006230000
2006-12-01,1400.630005,1402.459961,1385.930054,1396.709961,1396.709961,2800980000
2006-12-04,1396.670044,1411.229980,1396.670044,1409.119995,1409.119995,2766320000
2006-12-05,1409.099976,1415.270020,1408.780029,1414.760010,1414.760010,2755700000
2006-12-06,1414.400024,1415.930054,1411.050049,1412.900024,1412.900024,2725280000
2006-12-07,1412.859985,1418.270020,1406.800049,1407.290039,1407.290039,2743150000
2006-12-08,1407.270020,1414.089966,1403.670044,1409.839966,1409.839966,2440460000
2006-12-11,1409.810059,1415.599976,1408.560059,1413.040039,1413.040039,2289900000
2006-12-12,1413.000000,1413.780029,1404.750000,1411.560059,1411.560059,2738170000
2006-12-13,1411.319946,1416.640015,1411.050049,1413.209961,1413.209961,2552260000
2006-12-14,1413.160034,1427.229980,1413.160034,1425.489990,1425.489990,2729700000
2006-12-15,1425.479980,1431.630005,1425.479980,1427.089966,1427.089966,3229580000
2006-12-18,1427.079956,1431.810059,1420.650024,1422.479980,1422.479980,2568140000
2006-12-19,1422.420044,1428.300049,1414.880005,1425.550049,1425.550049,2717060000
2006-12-20,1425.510010,1429.050049,1423.510010,1423.530029,1423.530029,2387630000
2006-12-21,1423.199951,1426.400024,1415.900024,1418.300049,1418.300049,2322410000
2006-12-22,1418.099976,1418.819946,1410.280029,1410.760010,1410.760010,1647590000
2006-12-26,1410.750000,1417.910034,1410.449951,1416.900024,1416.900024,1310310000
2006-12-27,1416.630005,1427.719971,1416.630005,1426.839966,1426.839966,1667370000
2006-12-28,1426.770020,1427.260010,1422.050049,1424.729980,1424.729980,1508570000
2006-12-29,1424.709961,1427.000000,1416.839966,1418.300049,1418.300049,1678200000
2007-01-03,1418.030029,1429.420044,1407.859985,1416.599976,1416.599976,3429160000
2007-01-04,1416.599976,1421.839966,1408.430054,1418.339966,1418.339966,3004460000
2007-01-05,1418.339966,1418.339966,1405.750000,1409.709961,1409.709961,2919400000
2007-01-08,1409.260010,1414.979980,1403.969971,1412.839966,1412.839966,2763340000
2007-01-09,1412.839966,1415.609985,1405.420044,1412.109985,1412.109985,3038380000
2007-01-10,1408.699951,1415.989990,1405.319946,1414.849976,1414.849976,2764660000
2007-01-11,1414.839966,1427.119995,1414.839966,1423.819946,1423.819946,2857870000
2007-01-12,1423.819946,1431.229980,1422.579956,1430.729980,1430.729980,2686480000
2007-01-16,1430.729980,1433.930054,1428.619995,1431.900024,1431.900024,2599530000
2007-01-17,1431.770020,1435.270020,1428.569946,1430.619995,1430.619995,2690270000
2007-01-18,1430.589966,1432.959961,1424.209961,1426.369995,1426.369995,2822430000
2007-01-19,1426.349976,1431.569946,1425.189941,1430.500000,1430.500000,2777480000
2007-01-22,1430.469971,1431.390015,1420.400024,1422.949951,1422.949951,2540120000
2007-01-23,1422.949951,1431.329956,1421.660034,1427.989990,1427.989990,2975070000
2007-01-24,1427.959961,1440.140015,1427.959961,1440.130005,1440.130005,2783180000
2007-01-25,1440.119995,1440.689941,1422.339966,1423.900024,1423.900024,2994330000
2007-01-26,1423.900024,1427.270020,1416.959961,1422.180054,1422.180054,2626620000
2007-01-29,1422.030029,1426.939941,1418.459961,1420.619995,1420.619995,2730480000
2007-01-30,1420.609985,1428.819946,1420.609985,1428.819946,1428.819946,2706250000
2007-01-31,1428.650024,1441.609985,1424.780029,1438.239990,1438.239990,2976690000
2007-02-01,1437.900024,1446.640015,1437.900024,1445.939941,1445.939941,2914890000
2007-02-02,1445.939941,1449.329956,1444.489990,1448.390015,1448.390015,2569450000
2007-02-05,1448.329956,1449.380005,1443.849976,1446.989990,1446.989990,2439430000
2007-02-06,1446.979980,1450.189941,1443.400024,1448.000000,1448.000000,2608710000
2007-02-07,1447.410034,1452.989990,1446.439941,1450.020020,1450.020020,2618820000
2007-02-08,1449.989990,1450.449951,1442.810059,1448.310059,1448.310059,2816180000
2007-02-09,1448.250000,1452.449951,1433.439941,1438.060059,1438.060059,2951810000
2007-02-12,1438.000000,1439.109985,1431.439941,1433.369995,1433.369995,2395680000
2007-02-13,1433.219971,1444.410034,1433.219971,1444.260010,1444.260010,2652150000
2007-02-14,1443.910034,1457.650024,1443.910034,1455.300049,1455.300049,2699290000
2007-02-15,1455.150024,1457.969971,1453.189941,1456.810059,1456.810059,2490920000
2007-02-16,1456.770020,1456.770020,1451.569946,1455.540039,1455.540039,2399450000
2007-02-20,1455.530029,1460.530029,1449.199951,1459.680054,1459.680054,2337860000
2007-02-21,1459.599976,1459.599976,1452.020020,1457.630005,1457.630005,2606980000
2007-02-22,1457.290039,1461.569946,1450.510010,1456.380005,1456.380005,1950770000
2007-02-23,1456.219971,1456.219971,1448.359985,1451.189941,1451.189941,2579950000
2007-02-26,1451.040039,1456.949951,1445.479980,1449.369995,1449.369995,2822170000
2007-02-27,1449.250000,1449.250000,1389.420044,1399.040039,1399.040039,4065230000
2007-02-28,1398.640015,1415.890015,1396.650024,1406.819946,1406.819946,3925250000
2007-03-01,1406.800049,1409.459961,1380.869995,1403.170044,1403.170044,3874910000
2007-03-02,1403.160034,1403.400024,1386.869995,1387.170044,1387.170044,3312260000
2007-03-05,1387.109985,1391.859985,1373.969971,1374.119995,1374.119995,3480520000
2007-03-06,1374.060059,1397.900024,1374.060059,1395.410034,1395.410034,3358160000
2007-03-07,1395.020020,1401.160034,1390.640015,1391.969971,1391.969971,3141350000
2007-03-08,1391.880005,1407.930054,1391.880005,1401.890015,1401.890015,3014850000
2007-03-09,1401.890015,1410.150024,1397.300049,1402.839966,1402.839966,2623050000
2007-03-12,1402.800049,1409.339966,1398.400024,1406.599976,1406.599976,2664000000
2007-03-13,1406.229980,1406.229980,1377.709961,1377.949951,1377.949951,3485570000
2007-03-14,1377.859985,1388.089966,1363.979980,1387.170044,1387.170044,3758350000
2007-03-15,1387.109985,1395.729980,1385.160034,1392.280029,1392.280029,2821900000
2007-03-16,1392.280029,1397.510010,1383.630005,1386.949951,1386.949951,3393640000
2007-03-19,1386.949951,1403.199951,1386.949951,1402.060059,1402.060059,2777180000
2007-03-20,1402.040039,1411.530029,1400.699951,1410.939941,1410.939941,2795940000
2007-03-21,1410.920044,1437.770020,1409.750000,1435.040039,1435.040039,3184770000
2007-03-22,1435.040039,1437.660034,1429.880005,1434.540039,1434.540039,3129970000
2007-03-23,1434.540039,1438.890015,1433.209961,1436.109985,1436.109985,2619020000
2007-03-26,1436.109985,1437.650024,1423.280029,1437.500000,1437.500000,2754660000
2007-03-27,1437.489990,1437.489990,1425.540039,1428.609985,1428.609985,2673040000
2007-03-28,1428.349976,1428.349976,1414.069946,1417.229980,1417.229980,3000440000
2007-03-29,1417.170044,1426.239990,1413.270020,1422.530029,1422.530029,2854710000
2007-03-30,1422.520020,1429.219971,1408.900024,1420.859985,1420.859985,2903960000
2007-04-02,1420.829956,1425.489990,1416.369995,1424.550049,1424.550049,2875880000
2007-04-03,1424.270020,1440.569946,1424.270020,1437.770020,1437.770020,2921760000
2007-04-04,1437.750000,1440.160034,1435.079956,1439.369995,1439.369995,2616320000
2007-04-05,1438.939941,1444.880005,1436.670044,1443.760010,1443.760010,2357230000
2007-04-09,1443.770020,1448.099976,1443.280029,1444.609985,1444.609985,2349410000
2007-04-10,1444.579956,1448.729980,1443.989990,1448.390015,1448.390015,2510110000
2007-04-11,1448.229980,1448.390015,1436.150024,1438.869995,1438.869995,2950190000
2007-04-12,1438.869995,1448.020020,1433.910034,1447.800049,1447.800049,2770570000
2007-04-13,1447.800049,1453.109985,1444.150024,1452.849976,1452.849976,2690020000
2007-04-16,1452.839966,1468.619995,1452.839966,1468.329956,1468.329956,2870140000
2007-04-17,1468.469971,1474.349976,1467.150024,1471.479980,1471.479980,2920570000
2007-04-18,1471.469971,1476.569946,1466.410034,1472.500000,1472.500000,2971330000
2007-04-19,1472.479980,1474.229980,1464.469971,1470.729980,1470.729980,2913610000
2007-04-20,1470.689941,1484.739990,1470.689941,1484.349976,1484.349976,3329940000
2007-04-23,1484.329956,1487.319946,1480.189941,1480.930054,1480.930054,2575020000
2007-04-24,1480.930054,1483.819946,1473.739990,1480.410034,1480.410034,3119750000
2007-04-25,1480.280029,1496.589966,1480.280029,1495.420044,1495.420044,3252590000
2007-04-26,1495.270020,1498.020020,1491.170044,1494.250000,1494.250000,3211800000
2007-04-27,1494.209961,1497.319946,1488.670044,1494.069946,1494.069946,2732810000
2007-04-30,1494.069946,1497.160034,1482.290039,1482.369995,1482.369995,3093420000
2007-05-01,1482.369995,1487.270020,1476.699951,1486.300049,1486.300049,3400350000
2007-05-02,1486.130005,1499.099976,1486.130005,1495.920044,1495.920044,3189800000
2007-05-03,1495.560059,1503.339966,1495.560059,1502.390015,1502.390015,3007970000
2007-05-04,1502.349976,1510.339966,1501.800049,1505.619995,1505.619995,2761930000
2007-05-07,1505.569946,1511.000000,1505.540039,1509.479980,1509.479980,2545090000
2007-05-08,1509.359985,1509.359985,1500.660034,1507.719971,1507.719971,2795720000
2007-05-09,1507.319946,1513.800049,1503.770020,1512.579956,1512.579956,2935550000
2007-05-10,1512.329956,1512.329956,1491.420044,1491.469971,1491.469971,3031240000
2007-05-11,1491.469971,1506.239990,1491.469971,1505.849976,1505.849976,2720780000
2007-05-14,1505.760010,1510.900024,1498.339966,1503.150024,1503.150024,2776130000
2007-05-15,1503.109985,1514.829956,1500.430054,1501.189941,1501.189941,3071020000
2007-05-16,1500.750000,1514.150024,1500.750000,1514.140015,1514.140015,2915350000
2007-05-17,1514.010010,1517.140015,1509.290039,1512.750000,1512.750000,2868640000
2007-05-18,1512.739990,1522.750000,1512.739990,1522.750000,1522.750000,2959050000
2007-05-21,1522.750000,1529.869995,1522.709961,1525.099976,1525.099976,3465360000
2007-05-22,1525.099976,1529.239990,1522.050049,1524.119995,1524.119995,2860500000
2007-05-23,1524.089966,1532.430054,1521.900024,1522.280029,1522.280029,3084260000
2007-05-24,1522.099976,1529.310059,1505.180054,1507.510010,1507.510010,3365530000
2007-05-25,1507.500000,1517.410034,1507.500000,1515.729980,1515.729980,2316250000
2007-05-29,1515.550049,1521.800049,1512.020020,1518.109985,1518.109985,2571790000
2007-05-30,1517.599976,1530.229980,1510.060059,1530.229980,1530.229980,2980210000
2007-05-31,1530.189941,1535.560059,1528.260010,1530.619995,1530.619995,3335530000
2007-06-01,1530.619995,1540.560059,1530.619995,1536.339966,1536.339966,2927020000
2007-06-04,1536.280029,1540.530029,1532.310059,1539.180054,1539.180054,2738930000
2007-06-05,1539.119995,1539.119995,1525.619995,1530.949951,1530.949951,2939450000
2007-06-06,1530.569946,1530.569946,1514.130005,1517.380005,1517.380005,2964190000
2007-06-07,1517.359985,1517.359985,1490.369995,1490.719971,1490.719971,3538470000
2007-06-08,1490.709961,1507.760010,1487.410034,1507.670044,1507.670044,2993460000
2007-06-11,1507.640015,1515.530029,1503.349976,1509.119995,1509.119995,2525280000
2007-06-12,1509.119995,1511.329956,1492.969971,1493.000000,1493.000000,3056200000
2007-06-13,1492.650024,1515.699951,1492.650024,1515.670044,1515.670044,3077930000
2007-06-14,1515.579956,1526.449951,1515.579956,1522.969971,1522.969971,2813630000
2007-06-15,1522.969971,1538.709961,1522.969971,1532.910034,1532.910034,3406030000
2007-06-18,1532.900024,1535.439941,1529.310059,1531.050049,1531.050049,2480240000
2007-06-19,1531.020020,1535.849976,1525.670044,1533.699951,1533.699951,2873590000
2007-06-20,1533.680054,1537.319946,1512.359985,1512.839966,1512.839966,3286900000
2007-06-21,1512.500000,1522.900024,1504.750000,1522.189941,1522.189941,3161110000
2007-06-22,1522.189941,1522.189941,1500.739990,1502.560059,1502.560059,4284320000
2007-06-25,1502.560059,1514.290039,1492.680054,1497.739990,1497.739990,3287250000
2007-06-26,1497.680054,1506.119995,1490.540039,1492.890015,1492.890015,3398530000
2007-06-27,1492.619995,1506.800049,1484.180054,1506.339966,1506.339966,3398150000
2007-06-28,1506.319946,1514.839966,1503.410034,1505.709961,1505.709961,3006710000
2007-06-29,1505.699951,1517.530029,1493.609985,1503.349976,1503.349976,3165410000
2007-07-02,1504.660034,1519.449951,1504.660034,1519.430054,1519.430054,2648990000
2007-07-03,1519.119995,1526.010010,1519.119995,1524.869995,1524.869995,1560790000
2007-07-05,1524.859985,1526.569946,1517.719971,1525.400024,1525.400024,2622950000
2007-07-06,1524.959961,1532.400024,1520.469971,1530.439941,1530.439941,2441520000
2007-07-09,1530.430054,1534.260010,1527.449951,1531.849976,1531.849976,2715330000
2007-07-10,1531.849976,1531.849976,1510.010010,1510.119995,1510.119995,3244280000
2007-07-11,1509.930054,1519.339966,1506.099976,1518.760010,1518.760010,3082920000
2007-07-12,1518.739990,1547.920044,1518.739990,1547.699951,1547.699951,3489600000
2007-07-13,1547.680054,1555.099976,1544.849976,1552.500000,1552.500000,2801120000
2007-07-16,1552.500000,1555.900024,1546.689941,1549.520020,1549.520020,2704110000
2007-07-17,1549.520020,1555.319946,1547.739990,1549.369995,1549.369995,3007140000
2007-07-18,1549.199951,1549.199951,1533.670044,1546.170044,1546.170044,3609220000
2007-07-19,1546.130005,1555.199951,1546.130005,1553.079956,1553.079956,3251450000
2007-07-20,1553.189941,1553.189941,1529.199951,1534.099976,1534.099976,3745780000
2007-07-23,1534.060059,1547.229980,1534.060059,1541.569946,1541.569946,3102700000
2007-07-24,1541.569946,1541.569946,1508.619995,1511.040039,1511.040039,4115830000
2007-07-25,1511.030029,1524.310059,1503.729980,1518.089966,1518.089966,4283200000
2007-07-26,1518.089966,1518.089966,1465.300049,1482.660034,1482.660034,4472550000
2007-07-27,1482.439941,1488.530029,1458.949951,1458.949951,1458.949951,4784650000
2007-07-30,1458.930054,1477.880005,1454.319946,1473.910034,1473.910034,4128780000
2007-07-31,1473.900024,1488.300049,1454.250000,1455.270020,1455.270020,4524520000
2007-08-01,1455.180054,1468.380005,1439.589966,1465.810059,1465.810059,5256780000
2007-08-02,1465.459961,1476.430054,1460.579956,1472.199951,1472.199951,4368850000
2007-08-03,1472.180054,1473.229980,1432.800049,1433.060059,1433.060059,4272110000
2007-08-06,1433.040039,1467.670044,1427.390015,1467.670044,1467.670044,5067200000
2007-08-07,1467.619995,1488.300049,1455.800049,1476.709961,1476.709961,4909390000
2007-08-08,1476.219971,1503.890015,1476.219971,1497.489990,1497.489990,5499560000
2007-08-09,1497.209961,1497.209961,1453.089966,1453.089966,1453.089966,5889600000
2007-08-10,1453.089966,1462.020020,1429.739990,1453.640015,1453.640015,5345780000
2007-08-13,1453.420044,1466.290039,1451.540039,1452.920044,1452.920044,3696280000
2007-08-14,1452.869995,1456.739990,1426.199951,1426.540039,1426.540039,3814630000
2007-08-15,1426.150024,1440.780029,1404.359985,1406.699951,1406.699951,4290930000
2007-08-16,1406.640015,1415.969971,1370.599976,1411.270020,1411.270020,6509300000
2007-08-17,1411.260010,1450.329956,1411.260010,1445.939941,1445.939941,3570040000
2007-08-20,1445.939941,1451.750000,1430.540039,1445.550049,1445.550049,3321340000
2007-08-21,1445.550049,1455.319946,1439.760010,1447.119995,1447.119995,3012150000
2007-08-22,1447.030029,1464.859985,1447.030029,1464.069946,1464.069946,3309120000
2007-08-23,1464.050049,1472.060059,1453.880005,1462.500000,1462.500000,3084390000
2007-08-24,1462.339966,1479.400024,1460.540039,1479.369995,1479.369995,2541400000
2007-08-27,1479.359985,1479.359985,1465.979980,1466.790039,1466.790039,2406180000
2007-08-28,1466.719971,1466.719971,1432.010010,1432.359985,1432.359985,3078090000
2007-08-29,1432.010010,1463.760010,1432.010010,1463.760010,1463.760010,2824070000
2007-08-30,1463.670044,1468.430054,1451.250000,1457.640015,1457.640015,2582960000
2007-08-31,1457.609985,1481.469971,1457.609985,1473.989990,1473.989990,2731610000
2007-09-04,1473.959961,1496.400024,1472.150024,1489.420044,1489.420044,2766600000
2007-09-05,1488.760010,1488.760010,1466.339966,1472.290039,1472.290039,2991600000
2007-09-06,1472.030029,1481.489990,1467.410034,1478.550049,1478.550049,2459590000
2007-09-07,1478.550049,1478.550049,1449.069946,1453.550049,1453.550049,3191080000
2007-09-10,1453.500000,1462.250000,1439.290039,1451.699951,1451.699951,2835720000
2007-09-11,1451.689941,1472.479980,1451.689941,1471.489990,1471.489990,3015330000
2007-09-12,1471.099976,1479.500000,1465.750000,1471.560059,1471.560059,2885720000
2007-09-13,1471.469971,1489.579956,1471.469971,1483.949951,1483.949951,2877080000
2007-09-14,1483.949951,1485.989990,1473.180054,1484.250000,1484.250000,2641740000
2007-09-17,1484.239990,1484.239990,1471.819946,1476.650024,1476.650024,2598390000
2007-09-18,1476.630005,1519.890015,1476.630005,1519.780029,1519.780029,3708940000
2007-09-19,1519.750000,1538.739990,1519.750000,1529.030029,1529.030029,3846750000
2007-09-20,1528.689941,1529.140015,1516.420044,1518.750000,1518.750000,2957700000
2007-09-21,1518.750000,1530.890015,1518.750000,1525.750000,1525.750000,3679460000
2007-09-24,1525.750000,1530.180054,1516.150024,1517.729980,1517.729980,3131310000
2007-09-25,1516.339966,1518.270020,1507.130005,1517.209961,1517.209961,3187770000
2007-09-26,1518.619995,1529.390015,1518.619995,1525.420044,1525.420044,3237390000
2007-09-27,1527.319946,1532.459961,1525.810059,1531.380005,1531.380005,2872180000
2007-09-28,1531.239990,1533.739990,1521.989990,1526.750000,1526.750000,2925350000
2007-10-01,1527.290039,1549.020020,1527.250000,1547.040039,1547.040039,3281990000
2007-10-02,1546.959961,1548.010010,1540.369995,1546.630005,1546.630005,3101910000
2007-10-03,1545.800049,1545.839966,1536.339966,1539.589966,1539.589966,3065320000
2007-10-04,1539.910034,1544.020020,1537.630005,1542.839966,1542.839966,2690430000
2007-10-05,1543.839966,1561.910034,1543.839966,1557.589966,1557.589966,2919030000
2007-10-08,1556.510010,1556.510010,1549.000000,1552.579956,1552.579956,2040650000
2007-10-09,1553.180054,1565.260010,1551.819946,1565.150024,1565.150024,2932040000
2007-10-10,1564.979980,1565.420044,1555.459961,1562.469971,1562.469971,3044760000
2007-10-11,1564.719971,1576.089966,1546.719971,1554.410034,1554.410034,3911260000
2007-10-12,1555.410034,1563.030029,1554.089966,1561.800049,1561.800049,2788690000
2007-10-15,1562.250000,1564.739990,1540.810059,1548.709961,1548.709961,3139290000
2007-10-16,1547.810059,1547.810059,1536.290039,1538.530029,1538.530029,3234560000
2007-10-17,1544.439941,1550.660034,1526.010010,1541.239990,1541.239990,3638070000
2007-10-18,1539.290039,1542.790039,1531.760010,1540.079956,1540.079956,3203210000
2007-10-19,1540.000000,1540.000000,1500.260010,1500.630005,1500.630005,4160970000
2007-10-22,1497.790039,1508.060059,1490.400024,1506.329956,1506.329956,3471830000
2007-10-23,1509.300049,1520.010010,1503.609985,1519.589966,1519.589966,3309120000
2007-10-24,1516.609985,1517.229980,1489.560059,1515.880005,1515.880005,4003300000
2007-10-25,1516.150024,1523.239990,1500.459961,1514.400024,1514.400024,4183960000
2007-10-26,1522.170044,1535.530029,1520.180054,1535.280029,1535.280029,3612120000
2007-10-29,1536.920044,1544.670044,1536.430054,1540.979980,1540.979980,3124480000
2007-10-30,1539.420044,1539.420044,1529.550049,1531.020020,1531.020020,3212520000
2007-10-31,1532.150024,1552.760010,1529.400024,1549.380005,1549.380005,3953070000
2007-11-01,1545.790039,1545.790039,1506.660034,1508.439941,1508.439941,4241470000
2007-11-02,1511.069946,1513.150024,1492.530029,1509.650024,1509.650024,4285990000
2007-11-05,1505.609985,1510.839966,1489.949951,1502.170044,1502.170044,3819330000
2007-11-06,1505.329956,1520.770020,1499.069946,1520.270020,1520.270020,3879160000
2007-11-07,1515.459961,1515.459961,1475.040039,1475.619995,1475.619995,4353160000
2007-11-08,1475.270020,1482.500000,1450.310059,1474.770020,1474.770020,5439720000
2007-11-09,1467.589966,1474.089966,1448.510010,1453.699951,1453.699951,4587050000
2007-11-12,1453.660034,1464.939941,1438.530029,1439.180054,1439.180054,4192520000
2007-11-13,1441.349976,1481.369995,1441.349976,1481.050049,1481.050049,4141310000
2007-11-14,1483.400024,1492.140015,1466.469971,1470.579956,1470.579956,4031470000
2007-11-15,1468.040039,1472.670044,1443.489990,1451.150024,1451.150024,3941010000
2007-11-16,1453.089966,1462.180054,1443.989990,1458.739990,1458.739990,4168870000
2007-11-19,1456.699951,1456.699951,1430.420044,1433.270020,1433.270020,4119650000
2007-11-20,1434.510010,1452.640015,1419.280029,1439.699951,1439.699951,4875150000
2007-11-21,1434.709961,1436.400024,1415.640015,1416.770020,1416.770020,4076230000
2007-11-23,1417.619995,1440.859985,1417.619995,1440.699951,1440.699951,1612720000
2007-11-26,1440.739990,1446.089966,1406.099976,1407.219971,1407.219971,3706470000
2007-11-27,1409.589966,1429.489990,1407.430054,1428.229980,1428.229980,4320720000
2007-11-28,1432.949951,1471.619995,1432.949951,1469.020020,1469.020020,4508020000
2007-11-29,1467.410034,1473.810059,1458.359985,1469.719971,1469.719971,3524730000
2007-11-30,1471.829956,1488.939941,1470.890015,1481.140015,1481.140015,4422200000
2007-12-03,1479.630005,1481.160034,1470.079956,1472.420044,1472.420044,3323250000
2007-12-04,1471.339966,1471.339966,1460.660034,1462.790039,1462.790039,3343620000
2007-12-05,1465.219971,1486.089966,1465.219971,1485.010010,1485.010010,3663660000
2007-12-06,1484.589966,1508.020020,1482.189941,1507.339966,1507.339966,3568570000
2007-12-07,1508.599976,1510.630005,1502.660034,1504.660034,1504.660034,3177710000
2007-12-10,1505.109985,1518.270020,1504.959961,1515.959961,1515.959961,2911760000
2007-12-11,1516.680054,1523.569946,1475.989990,1477.650024,1477.650024,4080180000
2007-12-12,1487.579956,1511.959961,1468.229980,1486.589966,1486.589966,4482120000
2007-12-13,1483.270020,1489.400024,1469.209961,1488.410034,1488.410034,3635170000
2007-12-14,1486.189941,1486.670044,1467.780029,1467.949951,1467.949951,3401050000
2007-12-17,1465.050049,1465.050049,1445.430054,1445.900024,1445.900024,3569030000
2007-12-18,1445.920044,1460.160034,1435.650024,1454.979980,1454.979980,3723690000
2007-12-19,1454.699951,1464.420044,1445.310059,1453.000000,1453.000000,3401300000
2007-12-20,1456.420044,1461.530029,1447.219971,1460.119995,1460.119995,3526890000
2007-12-21,1463.189941,1485.400024,1463.189941,1484.459961,1484.459961,4508590000
2007-12-24,1484.550049,1497.630005,1484.550049,1496.449951,1496.449951,1267420000
2007-12-26,1495.119995,1498.849976,1488.199951,1497.660034,1497.660034,2010500000
2007-12-27,1495.050049,1495.050049,1475.859985,1476.270020,1476.270020,2365770000
2007-12-28,1479.829956,1488.010010,1471.699951,1478.489990,1478.489990,2420510000
2007-12-31,1475.250000,1475.829956,1465.130005,1468.359985,1468.359985,2440880000
2008-01-02,1467.969971,1471.770020,1442.069946,1447.160034,1447.160034,3452650000
2008-01-03,1447.550049,1456.800049,1443.729980,1447.160034,1447.160034,3429500000
2008-01-04,1444.010010,1444.010010,1411.189941,1411.630005,1411.630005,4166000000
2008-01-07,1414.069946,1423.869995,1403.449951,1416.180054,1416.180054,4221260000
2008-01-08,1415.709961,1430.280029,1388.300049,1390.189941,1390.189941,4705390000
2008-01-09,1390.250000,1409.189941,1378.699951,1409.130005,1409.130005,5351030000
2008-01-10,1406.780029,1429.089966,1395.310059,1420.329956,1420.329956,5170490000
2008-01-11,1419.910034,1419.910034,1394.829956,1401.020020,1401.020020,4495840000
2008-01-14,1402.910034,1417.890015,1402.910034,1416.250000,1416.250000,3682090000
2008-01-15,1411.880005,1411.880005,1380.599976,1380.949951,1380.949951,4601640000
2008-01-16,1377.410034,1391.989990,1364.270020,1373.199951,1373.199951,5440620000
2008-01-17,1374.790039,1377.719971,1330.670044,1333.250000,1333.250000,5303130000
2008-01-18,1333.900024,1350.280029,1312.510010,1325.189941,1325.189941,6004840000
2008-01-22,1312.939941,1322.089966,1274.290039,1310.500000,1310.500000,6544690000
2008-01-23,1310.410034,1339.089966,1270.050049,1338.599976,1338.599976,3241680000
2008-01-24,1340.130005,1355.150024,1334.310059,1352.069946,1352.069946,5735300000
2008-01-25,1357.319946,1368.560059,1327.500000,1330.609985,1330.609985,4882250000
2008-01-28,1330.699951,1353.969971,1322.260010,1353.959961,1353.959961,4100930000
2008-01-29,1355.939941,1364.930054,1350.189941,1362.300049,1362.300049,4232960000
2008-01-30,1362.219971,1385.859985,1352.949951,1355.810059,1355.810059,4742760000
2008-01-31,1351.979980,1385.619995,1334.079956,1378.550049,1378.550049,4970290000
2008-02-01,1378.599976,1396.020020,1375.930054,1395.420044,1395.420044,4650770000
2008-02-04,1395.380005,1395.380005,1379.689941,1380.819946,1380.819946,3495780000
2008-02-05,1380.280029,1380.280029,1336.640015,1336.640015,1336.640015,4315740000
2008-02-06,1339.479980,1351.959961,1324.339966,1326.449951,1326.449951,4008120000
2008-02-07,1324.010010,1347.160034,1316.750000,1336.910034,1336.910034,4589160000
2008-02-08,1336.880005,1341.219971,1321.060059,1331.290039,1331.290039,3768490000
2008-02-11,1331.920044,1341.400024,1320.319946,1339.130005,1339.130005,3593140000
2008-02-12,1340.550049,1362.099976,1339.359985,1348.859985,1348.859985,4044640000
2008-02-13,1353.119995,1369.229980,1350.780029,1367.209961,1367.209961,3856420000
2008-02-14,1367.329956,1368.160034,1347.310059,1348.859985,1348.859985,3644760000
2008-02-15,1347.520020,1350.000000,1338.130005,1349.989990,1349.989990,3583300000
2008-02-19,1355.859985,1367.280029,1345.050049,1348.780029,1348.780029,3613550000
2008-02-20,1348.390015,1363.709961,1336.550049,1360.030029,1360.030029,3870520000
2008-02-21,1362.209961,1367.939941,1339.339966,1342.530029,1342.530029,3696660000
2008-02-22,1344.219971,1354.300049,1327.040039,1353.109985,1353.109985,3572660000
2008-02-25,1352.750000,1374.359985,1346.030029,1371.800049,1371.800049,3866350000
2008-02-26,1371.760010,1387.339966,1363.290039,1381.290039,1381.290039,4096060000
2008-02-27,1378.949951,1388.339966,1372.000000,1380.020020,1380.020020,3904700000
2008-02-28,1378.160034,1378.160034,1363.160034,1367.680054,1367.680054,3938580000
2008-02-29,1364.069946,1364.069946,1325.420044,1330.630005,1330.630005,4426730000
2008-03-03,1330.449951,1335.130005,1320.040039,1331.339966,1331.339966,4117570000
2008-03-04,1329.579956,1331.030029,1307.390015,1326.750000,1326.750000,4757180000
2008-03-05,1327.689941,1344.189941,1320.219971,1333.699951,1333.699951,4277710000
2008-03-06,1332.199951,1332.199951,1303.420044,1304.339966,1304.339966,4323460000
2008-03-07,1301.530029,1313.239990,1282.430054,1293.369995,1293.369995,4565410000
2008-03-10,1293.160034,1295.010010,1272.660034,1273.369995,1273.369995,4261240000
2008-03-11,1274.400024,1320.650024,1274.400024,1320.650024,1320.650024,5109080000
2008-03-12,1321.130005,1333.260010,1307.859985,1308.770020,1308.770020,4414280000
2008-03-13,1305.260010,1321.680054,1282.109985,1315.479980,1315.479980,5073360000
2008-03-14,1316.050049,1321.469971,1274.859985,1288.140015,1288.140015,5153780000
2008-03-17,1283.209961,1287.500000,1256.979980,1276.599976,1276.599976,5683010000
2008-03-18,1277.160034,1330.739990,1277.160034,1330.739990,1330.739990,5335630000
2008-03-19,1330.969971,1341.510010,1298.420044,1298.420044,1298.420044,5358550000
2008-03-20,1299.670044,1330.670044,1295.219971,1329.510010,1329.510010,6145220000
2008-03-24,1330.290039,1359.680054,1330.290039,1349.880005,1349.880005,4499000000
2008-03-25,1349.069946,1357.469971,1341.209961,1352.989990,1352.989990,4145120000
2008-03-26,1352.449951,1352.449951,1336.410034,1341.130005,1341.130005,4055670000
2008-03-27,1340.339966,1345.619995,1325.660034,1325.760010,1325.760010,4037930000
2008-03-28,1327.020020,1334.869995,1312.949951,1315.219971,1315.219971,3686980000
2008-03-31,1315.920044,1328.520020,1312.810059,1322.699951,1322.699951,4188990000
2008-04-01,1326.410034,1370.180054,1326.410034,1370.180054,1370.180054,4745120000
2008-04-02,1369.959961,1377.949951,1361.550049,1367.530029,1367.530029,4320440000
2008-04-03,1365.689941,1375.660034,1358.680054,1369.310059,1369.310059,3920100000
2008-04-04,1369.849976,1380.910034,1362.829956,1370.400024,1370.400024,3703100000
2008-04-07,1373.689941,1386.739990,1369.020020,1372.540039,1372.540039,3747780000
2008-04-08,1370.160034,1370.160034,1360.619995,1365.540039,1365.540039,3602500000
2008-04-09,1365.500000,1368.390015,1349.969971,1354.489990,1354.489990,3556670000
2008-04-10,1355.369995,1367.239990,1350.109985,1360.550049,1360.550049,3686150000
2008-04-11,1357.979980,1357.979980,1331.209961,1332.829956,1332.829956,3723790000
2008-04-14,1332.199951,1335.640015,1326.160034,1328.319946,1328.319946,3565020000
2008-04-15,1331.719971,1337.719971,1324.349976,1334.430054,1334.430054,3581230000
2008-04-16,1337.020020,1365.489990,1337.020020,1364.709961,1364.709961,4260370000
2008-04-17,1363.369995,1368.599976,1357.250000,1365.560059,1365.560059,3713880000
2008-04-18,1369.000000,1395.900024,1369.000000,1390.329956,1390.329956,4222380000
2008-04-21,1387.719971,1390.229980,1379.250000,1388.170044,1388.170044,3420570000
2008-04-22,1386.430054,1386.430054,1369.839966,1375.939941,1375.939941,3821900000
2008-04-23,1378.400024,1387.869995,1372.239990,1379.930054,1379.930054,4103610000
2008-04-24,1380.520020,1397.719971,1371.089966,1388.819946,1388.819946,4461660000
2008-04-25,1387.880005,1399.109985,1379.979980,1397.839966,1397.839966,3891150000
2008-04-28,1397.959961,1402.900024,1394.400024,1396.369995,1396.369995,3607000000
2008-04-29,1395.609985,1397.000000,1386.699951,1390.939941,1390.939941,3815320000
2008-04-30,1391.219971,1404.569946,1384.250000,1385.589966,1385.589966,4508890000
2008-05-01,1385.969971,1410.069946,1383.069946,1409.339966,1409.339966,4448780000
2008-05-02,1409.160034,1422.719971,1406.250000,1413.900024,1413.900024,3953030000
2008-05-05,1415.339966,1415.339966,1404.369995,1407.489990,1407.489990,3410090000
2008-05-06,1405.599976,1421.569946,1397.099976,1418.260010,1418.260010,3924100000
2008-05-07,1417.489990,1419.540039,1391.160034,1392.569946,1392.569946,4075860000
2008-05-08,1394.290039,1402.349976,1389.390015,1397.680054,1397.680054,3827550000
2008-05-09,1394.900024,1394.900024,1384.109985,1388.280029,1388.280029,3518620000
2008-05-12,1389.400024,1404.060059,1386.199951,1403.579956,1403.579956,3370630000
2008-05-13,1404.400024,1406.300049,1396.260010,1403.040039,1403.040039,4018590000
2008-05-14,1405.650024,1420.189941,1405.650024,1408.660034,1408.660034,3979370000
2008-05-15,1408.359985,1424.400024,1406.869995,1423.569946,1423.569946,3836480000
2008-05-16,1423.890015,1425.819946,1414.349976,1425.349976,1425.349976,3842590000
2008-05-19,1425.280029,1440.239990,1421.630005,1426.630005,1426.630005,3683970000
2008-05-20,1424.489990,1424.489990,1409.089966,1413.400024,1413.400024,3854320000
2008-05-21,1414.060059,1419.119995,1388.810059,1390.709961,1390.709961,4517990000
2008-05-22,1390.829956,1399.069946,1390.229980,1394.349976,1394.349976,3955960000
2008-05-23,1392.199951,1392.199951,1373.719971,1375.930054,1375.930054,3516380000
2008-05-27,1375.969971,1387.400024,1373.069946,1385.349976,1385.349976,3588860000
2008-05-28,1386.540039,1391.250000,1378.160034,1390.839966,1390.839966,3927240000
2008-05-29,1390.500000,1406.319946,1388.589966,1398.260010,1398.260010,3894440000
2008-05-30,1398.359985,1404.459961,1398.079956,1400.380005,1400.380005,3845630000
2008-06-02,1399.619995,1399.619995,1377.790039,1385.670044,1385.670044,3714320000
2008-06-03,1386.420044,1393.119995,1370.119995,1377.650024,1377.650024,4396380000
2008-06-04,1376.260010,1388.180054,1371.739990,1377.199951,1377.199951,4338640000
2008-06-05,1377.479980,1404.050049,1377.479980,1404.050049,1404.050049,4350790000
2008-06-06,1400.060059,1400.060059,1359.900024,1360.680054,1360.680054,4771660000
2008-06-09,1360.829956,1370.630005,1350.619995,1361.760010,1361.760010,4404570000
2008-06-10,1358.979980,1366.839966,1351.560059,1358.439941,1358.439941,4635070000
2008-06-11,1357.089966,1357.089966,1335.469971,1335.489990,1335.489990,4779980000
2008-06-12,1335.780029,1353.030029,1331.290039,1339.869995,1339.869995,4734240000
2008-06-13,1341.810059,1360.030029,1341.709961,1360.030029,1360.030029,4080420000
2008-06-16,1358.849976,1364.699951,1352.069946,1360.140015,1360.140015,3706940000
2008-06-17,1360.709961,1366.589966,1350.540039,1350.930054,1350.930054,3801960000
2008-06-18,1349.589966,1349.589966,1333.400024,1337.810059,1337.810059,4573570000
2008-06-19,1336.890015,1347.660034,1330.500000,1342.829956,1342.829956,4811670000
2008-06-20,1341.020020,1341.020020,1314.459961,1317.930054,1317.930054,5324900000
2008-06-23,1319.770020,1323.780029,1315.310059,1318.000000,1318.000000,4186370000
2008-06-24,1317.229980,1326.020020,1304.420044,1314.290039,1314.290039,4705050000
2008-06-25,1314.540039,1335.630005,1314.540039,1321.969971,1321.969971,4825640000
2008-06-26,1316.290039,1316.290039,1283.150024,1283.150024,1283.150024,5231280000
2008-06-27,1283.599976,1289.449951,1272.000000,1278.380005,1278.380005,6208260000
2008-06-30,1278.060059,1290.310059,1274.859985,1280.000000,1280.000000,5032330000
2008-07-01,1276.689941,1285.310059,1260.680054,1284.910034,1284.910034,5846290000
2008-07-02,1285.819946,1292.170044,1261.510010,1261.520020,1261.520020,5276090000
2008-07-03,1262.959961,1271.479980,1252.010010,1262.900024,1262.900024,3247590000
2008-07-07,1262.900024,1273.949951,1240.680054,1252.310059,1252.310059,5265420000
2008-07-08,1251.839966,1274.170044,1242.839966,1273.699951,1273.699951,6034110000
2008-07-09,1273.380005,1277.359985,1244.569946,1244.689941,1244.689941,5181000000
2008-07-10,1245.250000,1257.650024,1236.760010,1253.390015,1253.390015,5840430000
2008-07-11,1248.660034,1257.270020,1225.349976,1239.489990,1239.489990,6742200000
2008-07-14,1241.609985,1253.500000,1225.010010,1228.300049,1228.300049,5434860000
2008-07-15,1226.829956,1234.349976,1200.439941,1214.910034,1214.910034,7363640000
2008-07-16,1214.650024,1245.520020,1211.390015,1245.359985,1245.359985,6738630000
2008-07-17,1246.310059,1262.310059,1241.489990,1260.319946,1260.319946,7365210000
2008-07-18,1258.219971,1262.229980,1251.810059,1260.680054,1260.680054,5653280000
2008-07-21,1261.819946,1267.739990,1255.699951,1260.000000,1260.000000,4630640000
2008-07-22,1257.079956,1277.420044,1248.829956,1277.000000,1277.000000,6180230000
2008-07-23,1278.869995,1291.170044,1276.060059,1282.189941,1282.189941,6705830000
2008-07-24,1283.219971,1283.219971,1251.479980,1252.540039,1252.540039,6127980000
2008-07-25,1253.510010,1263.229980,1251.750000,1257.760010,1257.760010,4672560000
2008-07-28,1257.760010,1260.089966,1234.369995,1234.369995,1234.369995,4282960000
2008-07-29,1236.380005,1263.199951,1236.380005,1263.199951,1263.199951,5414240000
2008-07-30,1264.520020,1284.329956,1264.520020,1284.260010,1284.260010,5631330000
2008-07-31,1281.369995,1284.930054,1265.969971,1267.380005,1267.380005,5346050000
2008-08-01,1269.420044,1270.520020,1254.540039,1260.310059,1260.310059,4684870000
2008-08-04,1253.270020,1260.489990,1247.449951,1249.010010,1249.010010,4562280000
2008-08-05,1254.869995,1284.880005,1254.670044,1284.880005,1284.880005,1219310000
2008-08-06,1283.989990,1291.670044,1276.000000,1289.189941,1289.189941,4873420000
2008-08-07,1286.510010,1286.510010,1264.290039,1266.069946,1266.069946,5319380000
2008-08-08,1266.290039,1297.849976,1262.109985,1296.319946,1296.319946,4966810000
2008-08-11,1294.420044,1313.150024,1291.410034,1305.319946,1305.319946,5067310000
2008-08-12,1304.790039,1304.790039,1285.640015,1289.589966,1289.589966,4711290000
2008-08-13,1288.640015,1294.030029,1274.859985,1285.829956,1285.829956,4787600000
2008-08-14,1282.109985,1300.109985,1276.839966,1292.930054,1292.930054,4064000000
2008-08-15,1293.849976,1302.050049,1290.739990,1298.199951,1298.199951,4041820000
2008-08-18,1298.140015,1300.219971,1274.510010,1278.599976,1278.599976,3829290000
2008-08-19,1276.650024,1276.650024,1263.109985,1266.689941,1266.689941,4159760000
2008-08-20,1267.339966,1276.010010,1261.160034,1274.540039,1274.540039,4555030000
2008-08-21,1271.069946,1281.400024,1265.219971,1277.719971,1277.719971,4032590000
2008-08-22,1277.589966,1293.089966,1277.589966,1292.199951,1292.199951,3741070000
2008-08-25,1290.469971,1290.469971,1264.869995,1266.839966,1266.839966,3420600000
2008-08-26,1267.030029,1275.650024,1263.209961,1271.510010,1271.510010,3587570000
2008-08-27,1271.290039,1285.050049,1270.030029,1281.660034,1281.660034,3499610000
2008-08-28,1283.790039,1300.680054,1283.790039,1300.680054,1300.680054,3854280000
2008-08-29,1296.489990,1297.589966,1282.739990,1282.829956,1282.829956,3288120000
2008-09-02,1287.829956,1303.040039,1272.199951,1277.579956,1277.579956,4783560000
2008-09-03,1276.609985,1280.599976,1265.589966,1274.979980,1274.979980,5056980000
2008-09-04,1271.800049,1271.800049,1232.829956,1236.829956,1236.829956,5212500000
2008-09-05,1233.209961,1244.939941,1217.229980,1242.310059,1242.310059,5017080000
2008-09-08,1249.500000,1274.420044,1247.119995,1267.790039,1267.790039,7351340000
2008-09-09,1267.979980,1268.660034,1224.510010,1224.510010,1224.510010,7380630000
2008-09-10,1227.500000,1243.900024,1221.599976,1232.040039,1232.040039,6543440000
2008-09-11,1229.040039,1249.979980,1211.540039,1249.050049,1249.050049,6869250000
2008-09-12,1245.880005,1255.089966,1233.810059,1251.699951,1251.699951,6273260000
2008-09-15,1250.920044,1250.920044,1192.699951,1192.699951,1192.699951,8279510000
2008-09-16,1188.310059,1214.839966,1169.280029,1213.599976,1213.599976,9459830000
2008-09-17,1210.339966,1210.339966,1155.880005,1156.390015,1156.390015,9431870000
2008-09-18,1157.079956,1211.140015,1133.500000,1206.510010,1206.510010,10082690000
2008-09-19,1213.109985,1265.119995,1213.109985,1255.079956,1255.079956,9387170000
2008-09-22,1255.369995,1255.369995,1205.609985,1207.089966,1207.089966,5368130000
2008-09-23,1207.609985,1221.150024,1187.060059,1188.219971,1188.219971,5185730000
2008-09-24,1188.790039,1197.410034,1179.790039,1185.869995,1185.869995,4820360000
2008-09-25,1187.869995,1220.030029,1187.869995,1209.180054,1209.180054,5877640000
2008-09-26,1204.469971,1215.770020,1187.540039,1213.270020,1213.270020,5383610000
2008-09-29,1209.069946,1209.069946,1106.420044,1106.420044,1106.420044,7305060000
2008-09-30,1113.780029,1168.030029,1113.780029,1166.359985,1166.359985,4937680000
2008-10-01,1164.170044,1167.030029,1140.770020,1161.060059,1161.060059,5782130000
2008-10-02,1160.640015,1160.640015,1111.430054,1114.280029,1114.280029,6285640000
2008-10-03,1115.160034,1153.819946,1098.140015,1099.229980,1099.229980,6716120000
2008-10-06,1097.560059,1097.560059,1007.969971,1056.890015,1056.890015,7956020000
2008-10-07,1057.599976,1072.910034,996.229980,996.229980,996.229980,7069210000
2008-10-08,988.909973,1021.059998,970.969971,984.940002,984.940002,8716330000
2008-10-09,988.419983,1005.250000,909.190002,909.919983,909.919983,6819000000
2008-10-10,902.309998,936.359985,839.799988,899.219971,899.219971,11456230000
2008-10-13,912.750000,1006.929993,912.750000,1003.349976,1003.349976,7263370000
2008-10-14,1009.969971,1044.310059,972.070007,998.010010,998.010010,8161990000
2008-10-15,994.599976,994.599976,903.989990,907.840027,907.840027,6542330000
2008-10-16,909.530029,947.710022,865.830017,946.429993,946.429993,7984500000
2008-10-17,942.289978,984.640015,918.739990,940.549988,940.549988,6581780000
2008-10-20,943.510010,985.400024,943.510010,985.400024,985.400024,5175640000
2008-10-21,980.400024,985.440002,952.469971,955.049988,955.049988,5121830000
2008-10-22,951.669983,951.669983,875.809998,896.780029,896.780029,6147980000
2008-10-23,899.080017,922.830017,858.440002,908.109985,908.109985,7189900000
2008-10-24,895.219971,896.299988,852.849976,876.770020,876.770020,6550050000
2008-10-27,874.280029,893.780029,846.750000,848.919983,848.919983,5558050000
2008-10-28,848.919983,940.510010,845.270020,940.510010,940.510010,7096950000
2008-10-29,939.510010,969.969971,922.260010,930.090027,930.090027,7077800000
2008-10-30,939.380005,963.229980,928.500000,954.090027,954.090027,6175830000
2008-10-31,953.109985,984.380005,944.590027,968.750000,968.750000,6394350000
2008-11-03,968.669983,975.570007,958.820007,966.299988,966.299988,4492280000
2008-11-04,971.309998,1007.510010,971.309998,1005.750000,1005.750000,5531290000
2008-11-05,1001.840027,1001.840027,949.859985,952.770020,952.770020,5426640000
2008-11-06,952.400024,952.400024,899.729980,904.880005,904.880005,6102230000
2008-11-07,907.440002,931.460022,906.900024,930.989990,930.989990,4931640000
2008-11-10,936.750000,951.950012,907.469971,919.210022,919.210022,4572000000
2008-11-11,917.150024,917.150024,884.900024,898.950012,898.950012,4998340000
2008-11-12,893.390015,893.390015,850.479980,852.299988,852.299988,5764180000
2008-11-13,853.130005,913.010010,818.690002,911.289978,911.289978,7849120000
2008-11-14,904.359985,916.880005,869.880005,873.289978,873.289978,5881030000
2008-11-17,873.229980,882.289978,848.979980,850.750000,850.750000,4927490000
2008-11-18,852.340027,865.900024,826.840027,859.119995,859.119995,6679470000
2008-11-19,859.030029,864.570007,806.179993,806.580017,806.580017,6548600000
2008-11-20,805.869995,820.520020,747.780029,752.440002,752.440002,9093740000
2008-11-21,755.840027,801.200012,741.020020,800.030029,800.030029,9495900000
2008-11-24,801.200012,865.599976,801.200012,851.809998,851.809998,7879440000
2008-11-25,853.400024,868.940002,834.989990,857.390015,857.390015,6952700000
2008-11-26,852.900024,887.679993,841.369995,887.679993,887.679993,5793260000
2008-11-28,886.890015,896.250000,881.210022,896.239990,896.239990,2740860000
2008-12-01,888.609985,888.609985,815.690002,816.210022,816.210022,6052010000
2008-12-02,817.940002,850.539978,817.940002,848.809998,848.809998,6170100000
2008-12-03,843.599976,873.119995,827.599976,870.739990,870.739990,6221880000
2008-12-04,869.750000,875.599976,833.599976,845.219971,845.219971,5860390000
2008-12-05,844.429993,879.419983,818.409973,876.070007,876.070007,6165370000
2008-12-08,882.710022,918.570007,882.710022,909.700012,909.700012,6553600000
2008-12-09,906.479980,916.260010,885.380005,888.669983,888.669983,5693110000
2008-12-10,892.169983,908.270020,885.450012,899.239990,899.239990,5942130000
2008-12-11,898.349976,904.630005,868.729980,873.590027,873.590027,5513840000
2008-12-12,871.789978,883.239990,851.349976,879.729980,879.729980,5959590000
2008-12-15,881.070007,884.630005,857.719971,868.570007,868.570007,4982390000
2008-12-16,871.530029,914.659973,871.530029,913.179993,913.179993,6009780000
2008-12-17,908.159973,918.849976,895.940002,904.419983,904.419983,5907380000
2008-12-18,905.979980,911.020020,877.440002,885.280029,885.280029,5675000000
2008-12-19,886.960022,905.469971,883.020020,887.880005,887.880005,6705310000
2008-12-22,887.200012,887.369995,857.090027,871.630005,871.630005,4869850000
2008-12-23,874.309998,880.440002,860.099976,863.159973,863.159973,4051970000
2008-12-24,863.869995,869.789978,861.440002,868.150024,868.150024,1546550000
2008-12-26,869.510010,873.739990,866.520020,872.799988,872.799988,1880050000
2008-12-29,872.369995,873.700012,857.070007,869.419983,869.419983,3323430000
2008-12-30,870.580017,891.119995,870.580017,890.640015,890.640015,3627800000
2008-12-31,890.590027,910.320007,889.669983,903.250000,903.250000,4172940000
2009-01-02,902.989990,934.729980,899.349976,931.799988,931.799988,4048270000
2009-01-05,929.169983,936.630005,919.530029,927.450012,927.450012,5413910000
2009-01-06,931.169983,943.849976,927.280029,934.700012,934.700012,5392620000
2009-01-07,927.450012,927.450012,902.369995,906.650024,906.650024,4704940000
2009-01-08,905.729980,910.000000,896.809998,909.729980,909.729980,4991550000
2009-01-09,909.909973,911.929993,888.309998,890.349976,890.349976,4716500000
2009-01-12,890.400024,890.400024,864.320007,870.260010,870.260010,4725050000
2009-01-13,869.789978,877.020020,862.020020,871.789978,871.789978,5567460000
2009-01-14,867.280029,867.280029,836.929993,842.619995,842.619995,5407880000
2009-01-15,841.989990,851.590027,817.039978,843.739990,843.739990,7807350000
2009-01-16,844.450012,858.130005,830.659973,850.119995,850.119995,6786040000
2009-01-20,849.640015,849.640015,804.469971,805.219971,805.219971,6375230000
2009-01-21,806.770020,841.719971,804.299988,840.239990,840.239990,6467830000
2009-01-22,839.739990,839.739990,811.289978,827.500000,827.500000,5843830000
2009-01-23,822.159973,838.609985,806.070007,831.950012,831.950012,5832160000
2009-01-26,832.500000,852.530029,827.690002,836.570007,836.570007,6039940000
2009-01-27,837.299988,850.450012,835.400024,845.710022,845.710022,5353260000
2009-01-28,845.729980,877.859985,845.729980,874.090027,874.090027,6199180000
2009-01-29,868.890015,868.890015,844.150024,845.140015,845.140015,5067060000
2009-01-30,845.690002,851.659973,821.669983,825.880005,825.880005,5350580000
2009-02-02,823.090027,830.780029,812.869995,825.440002,825.440002,5673270000
2009-02-03,825.690002,842.599976,821.979980,838.510010,838.510010,5886310000
2009-02-04,837.770020,851.849976,829.179993,832.229980,832.229980,6420450000
2009-02-05,831.750000,850.549988,819.909973,845.849976,845.849976,6624030000
2009-02-06,846.090027,870.750000,845.419983,868.599976,868.599976,6484100000
2009-02-09,868.239990,875.010010,861.650024,869.890015,869.890015,5574370000
2009-02-10,866.869995,868.049988,822.989990,827.159973,827.159973,6770170000
2009-02-11,827.409973,838.219971,822.299988,833.739990,833.739990,5926460000
2009-02-12,829.909973,835.479980,808.059998,835.190002,835.190002,6476460000
2009-02-13,833.950012,839.429993,825.210022,826.840027,826.840027,5296650000
2009-02-17,818.609985,818.609985,789.169983,789.169983,789.169983,5907820000
2009-02-18,791.059998,796.169983,780.429993,788.419983,788.419983,5740710000
2009-02-19,787.909973,797.580017,777.030029,778.940002,778.940002,5746940000
2009-02-20,775.869995,778.690002,754.250000,770.049988,770.049988,8210590000
2009-02-23,773.250000,777.849976,742.369995,743.330017,743.330017,6509300000
2009-02-24,744.690002,775.489990,744.690002,773.140015,773.140015,7234490000
2009-02-25,770.640015,780.119995,752.890015,764.900024,764.900024,7483640000
2009-02-26,765.760010,779.419983,751.750000,752.830017,752.830017,7599970000
2009-02-27,749.929993,751.270020,734.520020,735.090027,735.090027,8926480000
2009-03-02,729.570007,729.570007,699.700012,700.820007,700.820007,7868290000
2009-03-03,704.440002,711.669983,692.299988,696.330017,696.330017,7583230000
2009-03-04,698.599976,724.119995,698.599976,712.869995,712.869995,7673620000
2009-03-05,708.270020,708.270020,677.929993,682.549988,682.549988,7507250000
2009-03-06,684.039978,699.090027,666.789978,683.380005,683.380005,7331830000
2009-03-09,680.760010,695.270020,672.880005,676.530029,676.530029,7277320000
2009-03-10,679.280029,719.599976,679.280029,719.599976,719.599976,8618330000
2009-03-11,719.590027,731.919983,713.849976,721.359985,721.359985,7287810000
2009-03-12,720.890015,752.630005,714.760010,750.739990,750.739990,7326630000
2009-03-13,751.969971,758.289978,742.460022,756.549988,756.549988,6787090000
2009-03-16,758.840027,774.530029,753.369995,753.890015,753.890015,7883540000
2009-03-17,753.880005,778.119995,749.929993,778.119995,778.119995,6156800000
2009-03-18,776.010010,803.039978,765.640015,794.349976,794.349976,9098450000
2009-03-19,797.919983,803.239990,781.820007,784.039978,784.039978,9033870000
2009-03-20,784.580017,788.909973,766.200012,768.539978,768.539978,7643720000
2009-03-23,772.309998,823.369995,772.309998,822.919983,822.919983,7715770000
2009-03-24,820.599976,823.650024,805.479980,806.119995,806.119995,6767980000
2009-03-25,806.809998,826.780029,791.369995,813.880005,813.880005,7687180000
2009-03-26,814.059998,832.979980,814.059998,832.859985,832.859985,6992960000
2009-03-27,828.679993,828.679993,813.429993,815.940002,815.940002,5600210000
2009-03-30,809.070007,809.070007,779.809998,787.530029,787.530029,5912660000
2009-03-31,790.880005,810.479980,790.880005,797.869995,797.869995,6089100000
2009-04-01,793.590027,813.619995,783.320007,811.080017,811.080017,6034140000
2009-04-02,814.530029,845.609985,814.530029,834.380005,834.380005,7542810000
2009-04-03,835.130005,842.500000,826.700012,842.500000,842.500000,5855640000
2009-04-06,839.750000,839.750000,822.789978,835.479980,835.479980,6210000000
2009-04-07,834.119995,834.119995,814.530029,815.549988,815.549988,5155580000
2009-04-08,816.760010,828.419983,814.840027,825.159973,825.159973,5938460000
2009-04-09,829.289978,856.909973,829.289978,856.559998,856.559998,7600710000
2009-04-13,855.330017,864.309998,845.349976,858.729980,858.729980,6434890000
2009-04-14,856.880005,856.880005,840.250000,841.500000,841.500000,7569840000
2009-04-15,839.440002,852.929993,835.580017,852.059998,852.059998,6241100000
2009-04-16,854.539978,870.349976,847.039978,865.299988,865.299988,6598670000
2009-04-17,865.179993,875.630005,860.869995,869.599976,869.599976,7352010000
2009-04-20,868.270020,868.270020,832.390015,832.390015,832.390015,6973960000
2009-04-21,831.250000,850.090027,826.830017,850.080017,850.080017,7436490000
2009-04-22,847.260010,861.780029,840.570007,843.549988,843.549988,7327860000
2009-04-23,844.619995,852.869995,835.450012,851.919983,851.919983,6563100000
2009-04-24,853.909973,871.799988,853.909973,866.229980,866.229980,7114440000
2009-04-27,862.820007,868.830017,854.650024,857.510010,857.510010,5613460000
2009-04-28,854.479980,864.479980,847.119995,855.159973,855.159973,6328000000
2009-04-29,856.849976,882.059998,856.849976,873.640015,873.640015,6101620000
2009-04-30,876.590027,888.700012,868.510010,872.809998,872.809998,6862540000
2009-05-01,872.739990,880.479980,866.099976,877.520020,877.520020,5312170000
2009-05-04,879.210022,907.849976,879.210022,907.239990,907.239990,7038840000
2009-05-05,906.099976,907.700012,897.340027,903.799988,903.799988,6882860000
2009-05-06,903.950012,920.280029,903.950012,919.530029,919.530029,8555040000
2009-05-07,919.580017,929.580017,901.359985,907.390015,907.390015,9120100000
2009-05-08,909.030029,930.169983,909.030029,929.229980,929.229980,8163280000
2009-05-11,922.989990,922.989990,908.679993,909.239990,909.239990,6150600000
2009-05-12,910.520020,915.570007,896.460022,908.349976,908.349976,6871750000
2009-05-13,905.400024,905.400024,882.799988,883.919983,883.919983,7091820000
2009-05-14,884.239990,898.359985,882.520020,893.070007,893.070007,6134870000
2009-05-15,892.760010,896.969971,878.940002,882.880005,882.880005,5439720000
2009-05-18,886.070007,910.000000,886.070007,909.710022,909.710022,5702150000
2009-05-19,909.669983,916.390015,905.219971,908.130005,908.130005,6616270000
2009-05-20,908.619995,924.599976,901.369995,903.469971,903.469971,8205060000
2009-05-21,900.419983,900.419983,879.609985,888.330017,888.330017,6019840000
2009-05-22,888.679993,896.650024,883.750000,887.000000,887.000000,5155320000
2009-05-26,887.000000,911.760010,881.460022,910.330017,910.330017,5667050000
2009-05-27,909.950012,913.840027,891.869995,893.059998,893.059998,5698800000
2009-05-28,892.960022,909.450012,887.599976,906.830017,906.830017,5738980000
2009-05-29,907.020020,920.020020,903.559998,919.140015,919.140015,6050420000
2009-06-01,923.260010,947.770020,923.260010,942.869995,942.869995,6370440000
2009-06-02,942.869995,949.380005,938.460022,944.739990,944.739990,5987340000
2009-06-03,942.510010,942.510010,923.849976,931.760010,931.760010,5323770000
2009-06-04,932.489990,942.469971,929.320007,942.460022,942.460022,5352890000
2009-06-05,945.669983,951.690002,934.130005,940.090027,940.090027,5277910000
2009-06-08,938.119995,946.330017,926.440002,939.140015,939.140015,4483430000
2009-06-09,940.349976,946.919983,936.150024,942.429993,942.429993,4439950000
2009-06-10,942.729980,949.770020,927.969971,939.150024,939.150024,5379420000
2009-06-11,939.039978,956.229980,939.039978,944.890015,944.890015,5500840000
2009-06-12,943.440002,946.299988,935.659973,946.210022,946.210022,4528120000
2009-06-15,942.450012,942.450012,919.650024,923.719971,923.719971,4697880000
2009-06-16,925.599976,928.000000,911.599976,911.969971,911.969971,4951200000
2009-06-17,911.890015,918.440002,903.780029,910.710022,910.710022,5523650000
2009-06-18,910.859985,921.929993,907.940002,918.369995,918.369995,4684010000
2009-06-19,919.960022,927.090027,915.799988,921.229980,921.229980,5713390000
2009-06-22,918.130005,918.130005,893.039978,893.039978,893.039978,4903940000
2009-06-23,893.460022,898.690002,888.859985,895.099976,895.099976,5071020000
2009-06-24,896.309998,910.849976,896.309998,900.940002,900.940002,4636720000
2009-06-25,899.450012,921.419983,896.270020,920.260010,920.260010,4911240000
2009-06-26,918.840027,922.000000,913.030029,918.900024,918.900024,6076660000
2009-06-29,919.859985,927.989990,916.179993,927.229980,927.229980,4211760000
2009-06-30,927.150024,930.010010,912.859985,919.320007,919.320007,4627570000
2009-07-01,920.820007,931.919983,920.820007,923.330017,923.330017,3919400000
2009-07-02,921.239990,921.239990,896.419983,896.419983,896.419983,3931000000
2009-07-06,894.270020,898.719971,886.359985,898.719971,898.719971,4712580000
2009-07-07,898.599976,898.599976,879.929993,881.030029,881.030029,4673300000
2009-07-08,881.900024,886.799988,869.320007,879.559998,879.559998,5721780000
2009-07-09,881.280029,887.859985,878.450012,882.679993,882.679993,4347170000
2009-07-10,880.030029,883.570007,872.809998,879.130005,879.130005,3912080000
2009-07-13,879.570007,901.049988,875.320007,901.049988,901.049988,4499440000
2009-07-14,900.770020,905.840027,896.500000,905.840027,905.840027,4149030000
2009-07-15,910.150024,933.950012,910.150024,932.679993,932.679993,5238830000
2009-07-16,930.169983,943.960022,927.450012,940.739990,940.739990,4898640000
2009-07-17,940.559998,941.890015,934.650024,940.380005,940.380005,5141380000
2009-07-20,942.070007,951.619995,940.989990,951.130005,951.130005,4853150000
2009-07-21,951.969971,956.530029,943.219971,954.580017,954.580017,5309300000
2009-07-22,953.400024,959.830017,947.750000,954.070007,954.070007,4634100000
2009-07-23,954.070007,979.419983,953.270020,976.289978,976.289978,5761650000
2009-07-24,972.159973,979.789978,965.950012,979.260010,979.260010,4458300000
2009-07-27,978.630005,982.489990,972.289978,982.179993,982.179993,4631290000
2009-07-28,981.479980,982.349976,969.349976,979.619995,979.619995,5490350000
2009-07-29,977.659973,977.760010,968.650024,975.150024,975.150024,5178770000
2009-07-30,976.010010,996.679993,976.010010,986.750000,986.750000,6035180000
2009-07-31,986.799988,993.179993,982.849976,987.479980,987.479980,5139070000
2009-08-03,990.219971,1003.609985,990.219971,1002.630005,1002.630005,5603440000
2009-08-04,1001.409973,1007.119995,996.679993,1005.650024,1005.650024,5713700000
2009-08-05,1005.409973,1006.640015,994.309998,1002.719971,1002.719971,7242120000
2009-08-06,1004.059998,1008.000000,992.489990,997.080017,997.080017,6753380000
2009-08-07,999.830017,1018.000000,999.830017,1010.479980,1010.479980,6827090000
2009-08-10,1008.890015,1010.119995,1000.989990,1007.099976,1007.099976,5406080000
2009-08-11,1005.770020,1005.770020,992.400024,994.349976,994.349976,5773160000
2009-08-12,994.000000,1012.780029,993.359985,1005.809998,1005.809998,5498170000
2009-08-13,1005.859985,1013.140015,1000.820007,1012.729980,1012.729980,5250660000
2009-08-14,1012.229980,1012.599976,994.599976,1004.090027,1004.090027,4940750000
2009-08-17,998.179993,998.179993,978.510010,979.729980,979.729980,4088570000
2009-08-18,980.619995,991.200012,980.619995,989.669983,989.669983,4198970000
2009-08-19,986.880005,999.609985,980.619995,996.460022,996.460022,4257000000
2009-08-20,996.409973,1008.919983,996.390015,1007.369995,1007.369995,4893160000
2009-08-21,1009.059998,1027.589966,1009.059998,1026.130005,1026.130005,5885550000
2009-08-24,1026.589966,1035.819946,1022.479980,1025.569946,1025.569946,6302450000
2009-08-25,1026.630005,1037.750000,1026.209961,1028.000000,1028.000000,5768740000
2009-08-26,1027.349976,1032.469971,1021.570007,1028.119995,1028.119995,5080060000
2009-08-27,1027.810059,1033.329956,1016.200012,1030.979980,1030.979980,5785880000
2009-08-28,1031.619995,1039.469971,1023.130005,1028.930054,1028.930054,5785780000
2009-08-31,1025.209961,1025.209961,1014.619995,1020.619995,1020.619995,5004560000
2009-09-01,1019.520020,1028.449951,996.280029,998.039978,998.039978,6862360000
2009-09-02,996.070007,1000.340027,991.969971,994.750000,994.750000,5842730000
2009-09-03,996.119995,1003.429993,992.250000,1003.239990,1003.239990,4624280000
2009-09-04,1003.840027,1016.479980,1001.650024,1016.400024,1016.400024,4097370000
2009-09-08,1018.669983,1026.069946,1018.669983,1025.390015,1025.390015,5235160000
2009-09-09,1025.359985,1036.339966,1023.969971,1033.369995,1033.369995,5202550000
2009-09-10,1032.989990,1044.140015,1028.040039,1044.140015,1044.140015,5191380000
2009-09-11,1043.920044,1048.180054,1038.400024,1042.729980,1042.729980,4922600000
2009-09-14,1040.150024,1049.739990,1035.000000,1049.339966,1049.339966,4979610000
2009-09-15,1049.030029,1056.040039,1043.420044,1052.630005,1052.630005,6185620000
2009-09-16,1053.989990,1068.760010,1052.869995,1068.760010,1068.760010,6793530000
2009-09-17,1067.869995,1074.770020,1061.199951,1065.489990,1065.489990,6668110000
2009-09-18,1066.599976,1071.520020,1064.270020,1068.300049,1068.300049,5607970000
2009-09-21,1067.140015,1067.280029,1057.459961,1064.660034,1064.660034,4615280000
2009-09-22,1066.349976,1073.810059,1066.349976,1071.660034,1071.660034,5246600000
2009-09-23,1072.689941,1080.150024,1060.390015,1060.869995,1060.869995,5531930000
2009-09-24,1062.560059,1066.290039,1045.849976,1050.780029,1050.780029,5505610000
2009-09-25,1049.479980,1053.469971,1041.170044,1044.380005,1044.380005,4507090000
2009-09-28,1045.380005,1065.130005,1045.380005,1062.979980,1062.979980,3726950000
2009-09-29,1063.689941,1069.619995,1057.829956,1060.609985,1060.609985,4949900000
2009-09-30,1061.020020,1063.400024,1046.469971,1057.079956,1057.079956,5998860000
2009-10-01,1054.910034,1054.910034,1029.449951,1029.849976,1029.849976,5791450000
2009-10-02,1029.709961,1030.599976,1019.950012,1025.209961,1025.209961,5583240000
2009-10-05,1026.869995,1042.579956,1025.920044,1040.459961,1040.459961,4313310000
2009-10-06,1042.020020,1060.550049,1042.020020,1054.719971,1054.719971,5029840000
2009-10-07,1053.650024,1058.020020,1050.099976,1057.579956,1057.579956,4238220000
2009-10-08,1060.030029,1070.670044,1060.030029,1065.479980,1065.479980,4988400000
2009-10-09,1065.280029,1071.510010,1063.000000,1071.489990,1071.489990,3763780000
2009-10-12,1071.630005,1079.459961,1071.630005,1076.189941,1076.189941,3710430000
2009-10-13,1074.959961,1075.300049,1066.709961,1073.189941,1073.189941,4320480000
2009-10-14,1078.680054,1093.170044,1078.680054,1092.020020,1092.020020,5406420000
2009-10-15,1090.359985,1096.560059,1086.410034,1096.560059,1096.560059,5369780000
2009-10-16,1094.670044,1094.670044,1081.530029,1087.680054,1087.680054,4894740000
2009-10-19,1088.219971,1100.170044,1086.479980,1097.910034,1097.910034,4619240000
2009-10-20,1098.640015,1098.640015,1086.160034,1091.060059,1091.060059,5396930000
2009-10-21,1090.359985,1101.359985,1080.770020,1081.400024,1081.400024,5616290000
2009-10-22,1080.959961,1095.209961,1074.310059,1092.910034,1092.910034,5192410000
2009-10-23,1095.619995,1095.829956,1075.489990,1079.599976,1079.599976,4767460000
2009-10-26,1080.359985,1091.750000,1065.229980,1066.949951,1066.949951,6363380000
2009-10-27,1067.540039,1072.479980,1060.619995,1063.410034,1063.410034,5337380000
2009-10-28,1061.510010,1063.260010,1042.189941,1042.630005,1042.630005,6600350000
2009-10-29,1043.689941,1066.829956,1043.689941,1066.109985,1066.109985,5595040000
2009-10-30,1065.410034,1065.410034,1033.380005,1036.189941,1036.189941,6512420000
2009-11-02,1036.180054,1052.180054,1029.380005,1042.880005,1042.880005,6202640000
2009-11-03,1040.920044,1046.359985,1033.939941,1045.410034,1045.410034,5487500000
2009-11-04,1047.140015,1061.000000,1045.150024,1046.500000,1046.500000,5635510000
2009-11-05,1047.300049,1066.650024,1047.300049,1066.630005,1066.630005,4848350000
2009-11-06,1064.949951,1071.479980,1059.319946,1069.300049,1069.300049,4277130000
2009-11-09,1072.310059,1093.189941,1072.310059,1093.079956,1093.079956,4460030000
2009-11-10,1091.859985,1096.420044,1087.400024,1093.010010,1093.010010,4394770000
2009-11-11,1096.040039,1105.369995,1093.810059,1098.510010,1098.510010,4286700000
2009-11-12,1098.310059,1101.969971,1084.900024,1087.239990,1087.239990,4160250000
2009-11-13,1087.589966,1097.790039,1085.329956,1093.479980,1093.479980,3792610000
2009-11-16,1094.130005,1113.689941,1094.130005,1109.300049,1109.300049,4565850000
2009-11-17,1109.219971,1110.520020,1102.189941,1110.319946,1110.319946,3824070000
2009-11-18,1109.439941,1111.099976,1102.699951,1109.800049,1109.800049,4293340000
2009-11-19,1106.439941,1106.439941,1088.400024,1094.900024,1094.900024,4178030000
2009-11-20,1094.660034,1094.660034,1086.810059,1091.380005,1091.380005,3751230000
2009-11-23,1094.859985,1112.380005,1094.859985,1106.239990,1106.239990,3827920000
2009-11-24,1105.829956,1107.560059,1097.630005,1105.650024,1105.650024,3700820000
2009-11-25,1106.489990,1111.180054,1104.750000,1110.630005,1110.630005,3036350000
2009-11-27,1105.469971,1105.469971,1083.739990,1091.489990,1091.489990,2362910000
2009-11-30,1091.069946,1097.239990,1086.250000,1095.630005,1095.630005,3895520000
2009-12-01,1098.890015,1112.280029,1098.890015,1108.859985,1108.859985,4249310000
2009-12-02,1109.030029,1115.579956,1105.290039,1109.239990,1109.239990,3941340000
2009-12-03,1110.589966,1117.280029,1098.739990,1099.920044,1099.920044,4810030000
2009-12-04,1100.430054,1119.130005,1096.520020,1105.979980,1105.979980,5781140000
2009-12-07,1105.520020,1110.719971,1100.829956,1103.250000,1103.250000,4103360000
2009-12-08,1103.040039,1103.040039,1088.609985,1091.939941,1091.939941,4748030000
2009-12-09,1091.069946,1097.040039,1085.890015,1095.949951,1095.949951,4115410000
2009-12-10,1098.689941,1106.250000,1098.689941,1102.349976,1102.349976,3996490000
2009-12-11,1103.959961,1108.500000,1101.339966,1106.410034,1106.410034,3791090000
2009-12-14,1107.839966,1114.760010,1107.839966,1114.109985,1114.109985,4548490000
2009-12-15,1114.109985,1114.109985,1105.349976,1107.930054,1107.930054,5045100000
2009-12-16,1108.609985,1116.209961,1107.959961,1109.180054,1109.180054,4829820000
2009-12-17,1106.359985,1106.359985,1095.880005,1096.079956,1096.079956,7615070000
2009-12-18,1097.859985,1103.739990,1093.880005,1102.469971,1102.469971,6325890000
2009-12-21,1105.310059,1117.680054,1105.310059,1114.050049,1114.050049,3977340000
2009-12-22,1114.510010,1120.270020,1114.510010,1118.020020,1118.020020,3641130000
2009-12-23,1118.839966,1121.579956,1116.000000,1120.589966,1120.589966,3166870000
2009-12-24,1121.079956,1126.479980,1121.079956,1126.479980,1126.479980,1267710000
2009-12-28,1127.530029,1130.380005,1123.510010,1127.780029,1127.780029,2716400000
2009-12-29,1128.550049,1130.380005,1126.079956,1126.199951,1126.199951,2491020000
2009-12-30,1125.530029,1126.420044,1121.939941,1126.420044,1126.420044,2277300000
2009-12-31,1126.599976,1127.640015,1114.810059,1115.099976,1115.099976,2076990000
2010-01-04,1116.560059,1133.869995,1116.560059,1132.989990,1132.989990,3991400000
2010-01-05,1132.660034,1136.630005,1129.660034,1136.520020,1136.520020,2491020000
2010-01-06,1135.709961,1139.189941,1133.949951,1137.140015,1137.140015,4972660000
2010-01-07,1136.270020,1142.459961,1131.319946,1141.689941,1141.689941,5270680000
2010-01-08,1140.520020,1145.390015,1136.219971,1144.979980,1144.979980,4389590000
2010-01-11,1145.959961,1149.739990,1142.020020,1146.979980,1146.979980,4255780000
2010-01-12,1143.810059,1143.810059,1131.770020,1136.219971,1136.219971,4716160000
2010-01-13,1137.310059,1148.400024,1133.180054,1145.680054,1145.680054,4170360000
2010-01-14,1145.680054,1150.410034,1143.800049,1148.459961,1148.459961,3915200000
2010-01-15,1147.719971,1147.770020,1131.390015,1136.030029,1136.030029,4758730000
2010-01-19,1136.030029,1150.449951,1135.770020,1150.229980,1150.229980,4724830000
2010-01-20,1147.949951,1147.949951,1129.250000,1138.040039,1138.040039,4810560000
2010-01-21,1138.680054,1141.579956,1114.839966,1116.479980,1116.479980,6874290000
2010-01-22,1115.489990,1115.489990,1090.180054,1091.760010,1091.760010,6208650000
2010-01-25,1092.400024,1102.969971,1092.400024,1096.780029,1096.780029,4481390000
2010-01-26,1095.800049,1103.689941,1089.859985,1092.170044,1092.170044,4731910000
2010-01-27,1091.939941,1099.510010,1083.109985,1097.500000,1097.500000,5319120000
2010-01-28,1096.930054,1100.219971,1078.459961,1084.530029,1084.530029,5452400000
2010-01-29,1087.609985,1096.449951,1071.589966,1073.869995,1073.869995,5412850000
2010-02-01,1073.890015,1089.380005,1073.890015,1089.189941,1089.189941,4077610000
2010-02-02,1090.050049,1104.729980,1087.959961,1103.319946,1103.319946,4749540000
2010-02-03,1100.670044,1102.719971,1093.969971,1097.280029,1097.280029,4285450000
2010-02-04,1097.250000,1097.250000,1062.780029,1063.109985,1063.109985,5859690000
2010-02-05,1064.119995,1067.130005,1044.500000,1066.189941,1066.189941,6438900000
2010-02-08,1065.510010,1071.199951,1056.510010,1056.739990,1056.739990,4089820000
2010-02-09,1060.060059,1079.280029,1060.060059,1070.520020,1070.520020,5114260000
2010-02-10,1069.680054,1073.670044,1059.339966,1068.130005,1068.130005,4251450000
2010-02-11,1067.099976,1080.040039,1060.589966,1078.469971,1078.469971,4400870000
2010-02-12,1075.949951,1077.810059,1062.969971,1075.510010,1075.510010,4160680000
2010-02-16,1079.130005,1095.670044,1079.130005,1094.869995,1094.869995,4080770000
2010-02-17,1096.140015,1101.030029,1094.719971,1099.510010,1099.510010,4259230000
2010-02-18,1099.030029,1108.239990,1097.479980,1106.750000,1106.750000,3878620000
2010-02-19,1105.489990,1112.420044,1100.800049,1109.170044,1109.170044,3944280000
2010-02-22,1110.000000,1112.290039,1105.380005,1108.010010,1108.010010,3814440000
2010-02-23,1107.489990,1108.579956,1092.180054,1094.599976,1094.599976,4521050000
2010-02-24,1095.890015,1106.420044,1095.500000,1105.239990,1105.239990,4168360000
2010-02-25,1101.239990,1103.500000,1086.020020,1102.939941,1102.939941,4521130000
2010-02-26,1103.099976,1107.239990,1097.560059,1104.489990,1104.489990,3945190000
2010-03-01,1105.359985,1116.109985,1105.359985,1115.709961,1115.709961,3847640000
2010-03-02,1117.010010,1123.459961,1116.510010,1118.310059,1118.310059,4134680000
2010-03-03,1119.359985,1125.640015,1116.579956,1118.790039,1118.790039,3951320000
2010-03-04,1119.119995,1123.729980,1116.660034,1122.969971,1122.969971,3945010000
2010-03-05,1125.119995,1139.380005,1125.119995,1138.699951,1138.699951,4133000000
2010-03-08,1138.400024,1141.050049,1136.770020,1138.500000,1138.500000,3774680000
2010-03-09,1137.560059,1145.369995,1134.900024,1140.449951,1140.449951,5185570000
2010-03-10,1140.219971,1148.260010,1140.089966,1145.609985,1145.609985,5469120000
2010-03-11,1143.959961,1150.239990,1138.989990,1150.239990,1150.239990,4669060000
2010-03-12,1151.709961,1153.410034,1146.969971,1149.989990,1149.989990,4928160000
2010-03-15,1148.530029,1150.979980,1141.449951,1150.510010,1150.510010,4164110000
2010-03-16,1150.829956,1160.280029,1150.349976,1159.459961,1159.459961,4369770000
2010-03-17,1159.939941,1169.839966,1159.939941,1166.209961,1166.209961,4963200000
2010-03-18,1166.130005,1167.770020,1161.160034,1165.829956,1165.829956,4234510000
2010-03-19,1166.680054,1169.199951,1155.329956,1159.900024,1159.900024,5212410000
2010-03-22,1157.250000,1167.819946,1152.880005,1165.810059,1165.810059,4261680000
2010-03-23,1166.469971,1174.719971,1163.829956,1174.170044,1174.170044,4411640000
2010-03-24,1172.699951,1173.040039,1166.010010,1167.719971,1167.719971,4705750000
2010-03-25,1170.030029,1180.689941,1165.089966,1165.729980,1165.729980,5668900000
2010-03-26,1167.579956,1173.930054,1161.479980,1166.589966,1166.589966,4708420000
2010-03-29,1167.709961,1174.849976,1167.709961,1173.219971,1173.219971,4375580000
2010-03-30,1173.750000,1177.829956,1168.920044,1173.270020,1173.270020,4085000000
2010-03-31,1171.750000,1174.560059,1165.770020,1169.430054,1169.430054,4484340000
2010-04-01,1171.229980,1181.430054,1170.689941,1178.099976,1178.099976,4006870000
2010-04-05,1178.709961,1187.729980,1178.709961,1187.439941,1187.439941,3881620000
2010-04-06,1186.010010,1191.800049,1182.770020,1189.439941,1189.439941,4086180000
2010-04-07,1188.229980,1189.599976,1177.250000,1182.449951,1182.449951,5101430000
2010-04-08,1181.750000,1188.550049,1175.119995,1186.439941,1186.439941,4726970000
2010-04-09,1187.469971,1194.660034,1187.150024,1194.369995,1194.369995,4511570000
2010-04-12,1194.939941,1199.199951,1194.709961,1196.479980,1196.479980,4607090000
2010-04-13,1195.939941,1199.040039,1188.819946,1197.300049,1197.300049,5403580000
2010-04-14,1198.689941,1210.650024,1198.689941,1210.650024,1210.650024,5760040000
2010-04-15,1210.770020,1213.920044,1208.500000,1211.670044,1211.670044,5995330000
2010-04-16,1210.170044,1210.170044,1186.770020,1192.130005,1192.130005,8108470000
2010-04-19,1192.060059,1197.869995,1183.680054,1197.520020,1197.520020,6597740000
2010-04-20,1199.040039,1208.579956,1199.040039,1207.170044,1207.170044,5316590000
2010-04-21,1207.160034,1210.989990,1198.849976,1205.939941,1205.939941,5724310000
2010-04-22,1202.520020,1210.270020,1190.189941,1208.670044,1208.670044,6035780000
2010-04-23,1207.869995,1217.280029,1205.099976,1217.280029,1217.280029,5326060000
2010-04-26,1217.069946,1219.800049,1211.069946,1212.050049,1212.050049,5647760000
2010-04-27,1209.920044,1211.380005,1181.619995,1183.709961,1183.709961,7454540000
2010-04-28,1184.589966,1195.050049,1181.810059,1191.359985,1191.359985,6342310000
2010-04-29,1193.300049,1209.359985,1193.300049,1206.780029,1206.780029,6059410000
2010-04-30,1206.770020,1207.989990,1186.319946,1186.689941,1186.689941,6048260000
2010-05-03,1188.579956,1205.130005,1188.579956,1202.260010,1202.260010,4938050000
2010-05-04,1197.500000,1197.500000,1168.119995,1173.599976,1173.599976,6594720000
2010-05-05,1169.239990,1175.949951,1158.150024,1165.869995,1165.869995,6795940000
2010-05-06,1164.380005,1167.579956,1065.790039,1128.150024,1128.150024,10617810000
2010-05-07,1127.040039,1135.130005,1094.150024,1110.880005,1110.880005,9472910000
2010-05-10,1122.270020,1163.849976,1122.270020,1159.729980,1159.729980,6893700000
2010-05-11,1156.390015,1170.479980,1147.709961,1155.790039,1155.790039,5842550000
2010-05-12,1155.430054,1172.869995,1155.430054,1171.670044,1171.670044,5225460000
2010-05-13,1170.040039,1173.569946,1156.140015,1157.439941,1157.439941,4870640000
2010-05-14,1157.189941,1157.189941,1126.140015,1135.680054,1135.680054,6126400000
2010-05-17,1136.520020,1141.880005,1114.959961,1136.939941,1136.939941,5922920000
2010-05-18,1138.780029,1148.660034,1117.199951,1120.800049,1120.800049,6170840000
2010-05-19,1119.569946,1124.270020,1100.660034,1115.050049,1115.050049,6765800000
2010-05-20,1107.339966,1107.339966,1071.579956,1071.589966,1071.589966,8328570000
2010-05-21,1067.260010,1090.160034,1055.900024,1087.689941,1087.689941,5452130000
2010-05-24,1084.780029,1089.949951,1072.699951,1073.650024,1073.650024,5224040000
2010-05-25,1067.420044,1074.750000,1040.780029,1074.030029,1074.030029,7329580000
2010-05-26,1075.510010,1090.750000,1065.589966,1067.949951,1067.949951,4521050000
2010-05-27,1074.270020,1103.520020,1074.270020,1103.060059,1103.060059,5698460000
2010-05-28,1102.589966,1102.589966,1084.780029,1089.410034,1089.410034,4871210000
2010-06-01,1087.300049,1094.770020,1069.890015,1070.709961,1070.709961,5271480000
2010-06-02,1073.010010,1098.560059,1072.030029,1098.380005,1098.380005,5026360000
2010-06-03,1098.819946,1105.670044,1091.810059,1102.829956,1102.829956,4995970000
2010-06-04,1098.430054,1098.430054,1060.500000,1064.880005,1064.880005,6180580000
2010-06-07,1065.839966,1071.359985,1049.859985,1050.469971,1050.469971,5467560000
2010-06-08,1050.810059,1063.150024,1042.170044,1062.000000,1062.000000,6192750000
2010-06-09,1062.750000,1077.739990,1052.250000,1055.689941,1055.689941,5983200000
2010-06-10,1058.770020,1087.849976,1058.770020,1086.839966,1086.839966,5144780000
2010-06-11,1082.650024,1092.250000,1077.119995,1091.599976,1091.599976,4059280000
2010-06-14,1095.000000,1105.910034,1089.030029,1089.630005,1089.630005,4425830000
2010-06-15,1091.209961,1115.589966,1091.209961,1115.229980,1115.229980,4644490000
2010-06-16,1114.020020,1118.739990,1107.130005,1114.609985,1114.609985,5002600000
2010-06-17,1115.979980,1117.719971,1105.869995,1116.040039,1116.040039,4557760000
2010-06-18,1116.160034,1121.010010,1113.930054,1117.510010,1117.510010,4555360000
2010-06-21,1122.790039,1131.229980,1108.239990,1113.199951,1113.199951,4514360000
2010-06-22,1113.900024,1118.500000,1094.180054,1095.310059,1095.310059,4514380000
2010-06-23,1095.569946,1099.640015,1085.310059,1092.040039,1092.040039,4526150000
2010-06-24,1090.930054,1090.930054,1071.599976,1073.689941,1073.689941,4814830000
2010-06-25,1075.099976,1083.560059,1067.890015,1076.760010,1076.760010,5128840000
2010-06-28,1077.500000,1082.599976,1071.449951,1074.569946,1074.569946,3896410000
2010-06-29,1071.099976,1071.099976,1035.180054,1041.239990,1041.239990,6136700000
2010-06-30,1040.560059,1048.079956,1028.329956,1030.709961,1030.709961,5067080000
2010-07-01,1031.099976,1033.579956,1010.909973,1027.369995,1027.369995,6435770000
2010-07-02,1027.650024,1032.949951,1015.929993,1022.580017,1022.580017,3968500000
2010-07-06,1028.089966,1042.500000,1018.349976,1028.060059,1028.060059,4691240000
2010-07-07,1028.540039,1060.890015,1028.540039,1060.270020,1060.270020,4931220000
2010-07-08,1062.920044,1071.250000,1058.239990,1070.250000,1070.250000,4548460000
2010-07-09,1070.500000,1078.160034,1068.099976,1077.959961,1077.959961,3506570000
2010-07-12,1077.229980,1080.780029,1070.449951,1078.750000,1078.750000,3426990000
2010-07-13,1080.650024,1099.459961,1080.650024,1095.339966,1095.339966,4640460000
2010-07-14,1095.609985,1099.079956,1087.680054,1095.170044,1095.170044,4521050000
2010-07-15,1094.459961,1098.660034,1080.530029,1096.479980,1096.479980,4552470000
2010-07-16,1093.849976,1093.849976,1063.319946,1064.880005,1064.880005,5297350000
2010-07-19,1066.849976,1074.699951,1061.109985,1071.250000,1071.250000,4089500000
2010-07-20,1064.530029,1083.939941,1056.880005,1083.479980,1083.479980,4713280000
2010-07-21,1086.670044,1088.959961,1065.250000,1069.589966,1069.589966,4747180000
2010-07-22,1072.140015,1097.500000,1072.140015,1093.670044,1093.670044,4826900000
2010-07-23,1092.170044,1103.729980,1087.880005,1102.660034,1102.660034,4524570000
2010-07-26,1102.890015,1115.010010,1101.300049,1115.010010,1115.010010,4009650000
2010-07-27,1117.359985,1120.949951,1109.780029,1113.839966,1113.839966,4725690000
2010-07-28,1112.839966,1114.660034,1103.109985,1106.130005,1106.130005,4002390000
2010-07-29,1108.069946,1115.900024,1092.819946,1101.530029,1101.530029,4612420000
2010-07-30,1098.439941,1106.439941,1088.010010,1101.599976,1101.599976,4006450000
2010-08-02,1107.530029,1127.300049,1107.530029,1125.859985,1125.859985,4144180000
2010-08-03,1125.339966,1125.439941,1116.760010,1120.459961,1120.459961,4071820000
2010-08-04,1121.060059,1128.750000,1119.459961,1127.239990,1127.239990,4057850000
2010-08-05,1125.780029,1126.560059,1118.810059,1125.810059,1125.810059,3685560000
2010-08-06,1122.069946,1123.060059,1107.170044,1121.640015,1121.640015,3857890000
2010-08-09,1122.800049,1129.239990,1120.910034,1127.790039,1127.790039,3979360000
2010-08-10,1122.920044,1127.160034,1111.579956,1121.060059,1121.060059,3979360000
2010-08-11,1116.890015,1116.890015,1088.550049,1089.469971,1089.469971,4511860000
2010-08-12,1081.479980,1086.719971,1076.689941,1083.609985,1083.609985,4521050000
2010-08-13,1082.219971,1086.250000,1079.000000,1079.250000,1079.250000,3328890000
2010-08-16,1077.489990,1082.619995,1069.489990,1079.380005,1079.380005,3142450000
2010-08-17,1081.160034,1100.140015,1081.160034,1092.540039,1092.540039,3968210000
2010-08-18,1092.079956,1099.770020,1085.760010,1094.160034,1094.160034,3724260000
2010-08-19,1092.439941,1092.439941,1070.660034,1075.630005,1075.630005,4290540000
2010-08-20,1075.630005,1075.630005,1063.910034,1071.689941,1071.689941,3761570000
2010-08-23,1073.359985,1081.579956,1067.079956,1067.359985,1067.359985,3210950000
2010-08-24,1063.199951,1063.199951,1046.680054,1051.869995,1051.869995,4436330000
2010-08-25,1048.979980,1059.380005,1039.829956,1055.329956,1055.329956,4360190000
2010-08-26,1056.280029,1061.449951,1045.400024,1047.219971,1047.219971,3646710000
2010-08-27,1049.270020,1065.209961,1039.699951,1064.589966,1064.589966,4102460000
2010-08-30,1062.900024,1064.400024,1048.790039,1048.920044,1048.920044,2917990000
2010-08-31,1046.880005,1055.140015,1040.880005,1049.329956,1049.329956,4038770000
2010-09-01,1049.719971,1081.300049,1049.719971,1080.290039,1080.290039,4396880000
2010-09-02,1080.660034,1090.099976,1080.390015,1090.099976,1090.099976,3704210000
2010-09-03,1093.609985,1105.099976,1093.609985,1104.510010,1104.510010,3534500000
2010-09-07,1102.599976,1102.599976,1091.150024,1091.839966,1091.839966,3107380000
2010-09-08,1092.359985,1103.260010,1092.359985,1098.869995,1098.869995,3224640000
2010-09-09,1101.150024,1110.270020,1101.150024,1104.180054,1104.180054,3387770000
2010-09-10,1104.569946,1110.880005,1103.920044,1109.550049,1109.550049,3061160000
2010-09-13,1113.380005,1123.869995,1113.380005,1121.900024,1121.900024,4521050000
2010-09-14,1121.160034,1127.359985,1115.579956,1121.099976,1121.099976,4521050000
2010-09-15,1119.430054,1126.459961,1114.630005,1125.069946,1125.069946,3369840000
2010-09-16,1123.890015,1125.439941,1118.880005,1124.660034,1124.660034,3364080000
2010-09-17,1126.390015,1131.469971,1122.430054,1125.589966,1125.589966,4086140000
2010-09-20,1126.569946,1144.859985,1126.569946,1142.709961,1142.709961,3364080000
2010-09-21,1142.819946,1148.589966,1136.219971,1139.780029,1139.780029,4175660000
2010-09-22,1139.489990,1144.380005,1131.579956,1134.280029,1134.280029,3911070000
2010-09-23,1131.099976,1136.770020,1122.790039,1124.829956,1124.829956,3847850000
2010-09-24,1131.689941,1148.900024,1131.689941,1148.670044,1148.670044,4123950000
2010-09-27,1148.640015,1149.920044,1142.000000,1142.160034,1142.160034,3587860000
2010-09-28,1142.310059,1150.000000,1132.089966,1147.699951,1147.699951,4025840000
2010-09-29,1146.750000,1148.630005,1140.260010,1144.729980,1144.729980,3990280000
2010-09-30,1145.969971,1157.160034,1136.079956,1141.199951,1141.199951,4284160000
2010-10-01,1143.489990,1150.300049,1139.420044,1146.239990,1146.239990,4298910000
2010-10-04,1144.959961,1148.160034,1131.869995,1137.030029,1137.030029,3604110000
2010-10-05,1140.680054,1162.760010,1140.680054,1160.750000,1160.750000,4068840000
2010-10-06,1159.810059,1162.329956,1154.849976,1159.969971,1159.969971,4073160000
2010-10-07,1161.569946,1163.869995,1151.410034,1158.060059,1158.060059,3910550000
2010-10-08,1158.359985,1167.729980,1155.579956,1165.150024,1165.150024,3871420000
2010-10-11,1165.319946,1168.680054,1162.020020,1165.319946,1165.319946,2505900000
2010-10-12,1164.280029,1172.579956,1155.709961,1169.770020,1169.770020,4076170000
2010-10-13,1171.319946,1184.380005,1171.319946,1178.099976,1178.099976,4969410000
2010-10-14,1177.819946,1178.890015,1166.709961,1173.810059,1173.810059,4969410000
2010-10-15,1177.469971,1181.199951,1167.119995,1176.189941,1176.189941,5724910000
2010-10-18,1176.829956,1185.530029,1174.550049,1184.709961,1184.709961,4450050000
2010-10-19,1178.640015,1178.640015,1159.709961,1165.900024,1165.900024,5600120000
2010-10-20,1166.739990,1182.939941,1166.739990,1178.170044,1178.170044,5027880000
2010-10-21,1179.819946,1189.430054,1171.170044,1180.260010,1180.260010,4625470000
2010-10-22,1180.520020,1183.930054,1178.989990,1183.079956,1183.079956,3177890000
2010-10-25,1184.739990,1196.140015,1184.739990,1185.619995,1185.619995,4221380000
2010-10-26,1184.880005,1187.109985,1177.719971,1185.640015,1185.640015,4203680000
2010-10-27,1183.839966,1183.839966,1171.699951,1182.449951,1182.449951,4335670000
2010-10-28,1184.469971,1189.530029,1177.099976,1183.780029,1183.780029,4283460000
2010-10-29,1183.869995,1185.459961,1179.699951,1183.260010,1183.260010,3537880000
2010-11-01,1185.709961,1195.810059,1177.650024,1184.380005,1184.380005,4129180000
2010-11-02,1187.859985,1195.880005,1187.859985,1193.569946,1193.569946,3866200000
2010-11-03,1193.790039,1198.300049,1183.560059,1197.959961,1197.959961,4665480000
2010-11-04,1198.339966,1221.250000,1198.339966,1221.060059,1221.060059,5695470000
2010-11-05,1221.199951,1227.079956,1220.290039,1225.849976,1225.849976,5637460000
2010-11-08,1223.239990,1224.569946,1217.550049,1223.250000,1223.250000,3937230000
2010-11-09,1223.589966,1226.839966,1208.939941,1213.400024,1213.400024,4848040000
2010-11-10,1213.140015,1218.750000,1204.329956,1218.709961,1218.709961,4561300000
2010-11-11,1213.040039,1215.449951,1204.489990,1213.540039,1213.540039,3931120000
2010-11-12,1209.069946,1210.500000,1194.079956,1199.209961,1199.209961,4213620000
2010-11-15,1200.439941,1207.430054,1197.150024,1197.750000,1197.750000,3503370000
2010-11-16,1194.790039,1194.790039,1173.000000,1178.339966,1178.339966,5116380000
2010-11-17,1178.329956,1183.560059,1175.819946,1178.589966,1178.589966,3904780000
2010-11-18,1183.750000,1200.290039,1183.750000,1196.689941,1196.689941,4687260000
2010-11-19,1196.119995,1199.969971,1189.439941,1199.729980,1199.729980,3675390000
2010-11-22,1198.069946,1198.939941,1184.579956,1197.839966,1197.839966,3689500000
2010-11-23,1192.510010,1192.510010,1176.910034,1180.729980,1180.729980,4133070000
2010-11-24,1183.699951,1198.619995,1183.699951,1198.349976,1198.349976,3384250000
2010-11-26,1194.160034,1194.160034,1186.930054,1189.400024,1189.400024,1613820000
2010-11-29,1189.079956,1190.339966,1173.640015,1187.760010,1187.760010,3673450000
2010-11-30,1182.959961,1187.400024,1174.140015,1180.550049,1180.550049,4284700000
2010-12-01,1186.599976,1207.609985,1186.599976,1206.069946,1206.069946,4548110000
2010-12-02,1206.810059,1221.890015,1206.810059,1221.530029,1221.530029,4970800000
2010-12-03,1219.930054,1225.569946,1216.819946,1224.709961,1224.709961,3735780000
2010-12-06,1223.869995,1225.800049,1220.670044,1223.119995,1223.119995,3527370000
2010-12-07,1227.250000,1235.050049,1223.250000,1223.750000,1223.750000,6970630000
2010-12-08,1225.020020,1228.930054,1219.500000,1228.280029,1228.280029,4607590000
2010-12-09,1230.140015,1234.709961,1226.849976,1233.000000,1233.000000,4522510000
2010-12-10,1233.849976,1240.400024,1232.579956,1240.400024,1240.400024,4547310000
2010-12-13,1242.520020,1246.729980,1240.339966,1240.459961,1240.459961,4361240000
2010-12-14,1241.839966,1246.589966,1238.170044,1241.589966,1241.589966,4132350000
2010-12-15,1241.579956,1244.250000,1234.010010,1235.229980,1235.229980,4407340000
2010-12-16,1236.339966,1243.750000,1232.849976,1242.869995,1242.869995,4736820000
2010-12-17,1243.630005,1245.810059,1239.869995,1243.910034,1243.910034,4632470000
2010-12-20,1245.760010,1250.199951,1241.510010,1247.079956,1247.079956,3548140000
2010-12-21,1249.430054,1255.819946,1249.430054,1254.599976,1254.599976,3479670000
2010-12-22,1254.939941,1259.390015,1254.939941,1258.839966,1258.839966,1285590000
2010-12-23,1257.530029,1258.589966,1254.050049,1256.770020,1256.770020,2515020000
2010-12-27,1254.660034,1258.430054,1251.479980,1257.540039,1257.540039,1992470000
2010-12-28,1259.099976,1259.900024,1256.219971,1258.510010,1258.510010,2478450000
2010-12-29,1258.780029,1262.599976,1258.780029,1259.780029,1259.780029,2214380000
2010-12-30,1259.439941,1261.089966,1256.319946,1257.880005,1257.880005,1970720000
2010-12-31,1256.760010,1259.339966,1254.189941,1257.640015,1257.640015,1799770000
2011-01-03,1257.619995,1276.170044,1257.619995,1271.869995,1271.869995,4286670000
2011-01-04,1272.949951,1274.119995,1262.660034,1270.199951,1270.199951,4796420000
2011-01-05,1268.780029,1277.630005,1265.359985,1276.560059,1276.560059,4764920000
2011-01-06,1276.290039,1278.170044,1270.430054,1273.849976,1273.849976,4844100000
2011-01-07,1274.410034,1276.829956,1261.699951,1271.500000,1271.500000,4963110000
2011-01-10,1270.839966,1271.520020,1262.180054,1269.750000,1269.750000,4036450000
2011-01-11,1272.579956,1277.250000,1269.619995,1274.479980,1274.479980,4050750000
2011-01-12,1275.650024,1286.869995,1275.650024,1285.959961,1285.959961,4226940000
2011-01-13,1285.780029,1286.699951,1280.469971,1283.760010,1283.760010,4310840000
2011-01-14,1282.900024,1293.239990,1281.239990,1293.239990,1293.239990,4661590000
2011-01-18,1293.219971,1296.060059,1290.160034,1295.020020,1295.020020,5284990000
2011-01-19,1294.520020,1294.599976,1278.920044,1281.920044,1281.920044,4743710000
2011-01-20,1280.849976,1283.349976,1271.260010,1280.260010,1280.260010,4935320000
2011-01-21,1283.630005,1291.209961,1282.069946,1283.349976,1283.349976,4935320000
2011-01-24,1283.290039,1291.930054,1282.469971,1290.839966,1290.839966,3902470000
2011-01-25,1288.170044,1291.260010,1281.069946,1291.180054,1291.180054,4595380000
2011-01-26,1291.969971,1299.739990,1291.969971,1296.630005,1296.630005,4730980000
2011-01-27,1297.510010,1301.290039,1294.410034,1299.540039,1299.540039,4309190000
2011-01-28,1299.630005,1302.670044,1275.099976,1276.339966,1276.339966,5618630000
2011-01-31,1276.500000,1287.170044,1276.500000,1286.119995,1286.119995,4167160000
2011-02-01,1289.140015,1308.859985,1289.140015,1307.589966,1307.589966,5164500000
2011-02-02,1305.910034,1307.609985,1302.619995,1304.030029,1304.030029,4098260000
2011-02-03,1302.770020,1308.599976,1294.829956,1307.099976,1307.099976,4370990000
2011-02-04,1307.010010,1311.000000,1301.670044,1310.869995,1310.869995,3925950000
2011-02-07,1311.849976,1322.849976,1311.849976,1319.050049,1319.050049,3902270000
2011-02-08,1318.760010,1324.869995,1316.030029,1324.569946,1324.569946,3881530000
2011-02-09,1322.479980,1324.540039,1314.890015,1320.880005,1320.880005,3922240000
2011-02-10,1318.130005,1322.780029,1311.739990,1321.869995,1321.869995,4184610000
2011-02-11,1318.660034,1330.790039,1316.079956,1329.150024,1329.150024,4219300000
2011-02-14,1328.729980,1332.959961,1326.900024,1332.319946,1332.319946,3567040000
2011-02-15,1330.430054,1330.430054,1324.609985,1328.010010,1328.010010,3926860000
2011-02-16,1329.510010,1337.609985,1329.510010,1336.319946,1336.319946,1966450000
2011-02-17,1334.369995,1341.500000,1331.000000,1340.430054,1340.430054,1966450000
2011-02-18,1340.380005,1344.069946,1338.119995,1343.010010,1343.010010,1162310000
2011-02-22,1338.910034,1338.910034,1312.329956,1315.439941,1315.439941,1322780000
2011-02-23,1315.439941,1317.910034,1299.550049,1307.400024,1307.400024,1330340000
2011-02-24,1307.089966,1310.910034,1294.260010,1306.099976,1306.099976,1222900000
2011-02-25,1307.339966,1320.609985,1307.339966,1319.880005,1319.880005,3836030000
2011-02-28,1321.609985,1329.380005,1320.550049,1327.219971,1327.219971,1252850000
2011-03-01,1328.640015,1332.089966,1306.140015,1306.329956,1306.329956,1180420000
2011-03-02,1305.469971,1314.189941,1302.579956,1308.439941,1308.439941,1025000000
2011-03-03,1312.369995,1332.280029,1312.369995,1330.969971,1330.969971,4340470000
2011-03-04,1330.729980,1331.079956,1312.589966,1321.150024,1321.150024,4223740000
2011-03-07,1322.719971,1327.680054,1303.989990,1310.130005,1310.130005,3964730000
2011-03-08,1311.050049,1325.739990,1306.859985,1321.819946,1321.819946,4531420000
2011-03-09,1319.920044,1323.209961,1312.270020,1320.020020,1320.020020,3709520000
2011-03-10,1315.719971,1315.719971,1294.209961,1295.109985,1295.109985,4723020000
2011-03-11,1293.430054,1308.349976,1291.989990,1304.280029,1304.280029,3740400000
2011-03-14,1301.189941,1301.189941,1286.369995,1296.390015,1296.390015,4050370000
2011-03-15,1288.459961,1288.459961,1261.119995,1281.869995,1281.869995,5201400000
2011-03-16,1279.459961,1280.910034,1249.050049,1256.880005,1256.880005,5833000000
2011-03-17,1261.609985,1278.880005,1261.609985,1273.719971,1273.719971,4134950000
2011-03-18,1276.709961,1288.880005,1276.180054,1279.209961,1279.209961,4685500000
2011-03-21,1281.650024,1300.579956,1281.650024,1298.380005,1298.380005,4223730000
2011-03-22,1298.290039,1299.349976,1292.699951,1293.770020,1293.770020,3576550000
2011-03-23,1292.189941,1300.510010,1284.050049,1297.540039,1297.540039,3842350000
2011-03-24,1300.609985,1311.339966,1297.739990,1309.660034,1309.660034,4223740000
2011-03-25,1311.800049,1319.180054,1310.150024,1313.800049,1313.800049,4223740000
2011-03-28,1315.449951,1319.739990,1310.189941,1310.189941,1310.189941,3215170000
2011-03-29,1309.369995,1319.449951,1305.260010,1319.439941,1319.439941,3482580000
2011-03-30,1321.890015,1331.739990,1321.890015,1328.260010,1328.260010,3809570000
2011-03-31,1327.439941,1329.770020,1325.030029,1325.829956,1325.829956,3566270000
2011-04-01,1329.479980,1337.849976,1328.890015,1332.410034,1332.410034,4223740000
2011-04-04,1333.560059,1336.739990,1329.099976,1332.869995,1332.869995,4223740000
2011-04-05,1332.030029,1338.209961,1330.030029,1332.630005,1332.630005,3852280000
2011-04-06,1335.939941,1339.380005,1331.089966,1335.540039,1335.540039,4223740000
2011-04-07,1334.819946,1338.800049,1326.560059,1333.510010,1333.510010,4005600000
2011-04-08,1336.160034,1339.459961,1322.939941,1328.170044,1328.170044,3582810000
2011-04-11,1329.010010,1333.770020,1321.060059,1324.459961,1324.459961,3478970000
2011-04-12,1321.959961,1321.959961,1309.510010,1314.160034,1314.160034,4275490000
2011-04-13,1314.030029,1321.349976,1309.189941,1314.410034,1314.410034,3850860000
2011-04-14,1311.130005,1316.790039,1302.420044,1314.520020,1314.520020,3872630000
2011-04-15,1314.540039,1322.880005,1313.680054,1319.680054,1319.680054,4223740000
2011-04-18,1313.349976,1313.349976,1294.699951,1305.140015,1305.140015,4223740000
2011-04-19,1305.989990,1312.699951,1303.969971,1312.619995,1312.619995,3886300000
2011-04-20,1319.119995,1332.660034,1319.119995,1330.359985,1330.359985,4236280000
2011-04-21,1333.229980,1337.489990,1332.829956,1337.380005,1337.380005,3587240000
2011-04-25,1337.140015,1337.550049,1331.469971,1335.250000,1335.250000,2142130000
2011-04-26,1336.750000,1349.550049,1336.750000,1347.239990,1347.239990,3908060000
2011-04-27,1348.430054,1357.489990,1344.250000,1355.660034,1355.660034,4051570000
2011-04-28,1353.859985,1361.709961,1353.599976,1360.479980,1360.479980,4036820000
2011-04-29,1360.140015,1364.560059,1358.689941,1363.609985,1363.609985,3479070000
2011-05-02,1365.209961,1370.579956,1358.589966,1361.219971,1361.219971,3846250000
2011-05-03,1359.760010,1360.839966,1349.520020,1356.619995,1356.619995,4223740000
2011-05-04,1355.900024,1355.900024,1341.500000,1347.319946,1347.319946,4223740000
2011-05-05,1344.160034,1348.000000,1329.170044,1335.099976,1335.099976,3846250000
2011-05-06,1340.239990,1354.359985,1335.579956,1340.199951,1340.199951,4223740000
2011-05-09,1340.199951,1349.439941,1338.640015,1346.290039,1346.290039,4265250000
2011-05-10,1348.339966,1359.439941,1348.339966,1357.160034,1357.160034,4223740000
2011-05-11,1354.510010,1354.510010,1336.359985,1342.079956,1342.079956,3846250000
2011-05-12,1339.390015,1351.050049,1332.030029,1348.650024,1348.650024,3777210000
2011-05-13,1348.689941,1350.469971,1333.359985,1337.770020,1337.770020,3426660000
2011-05-16,1334.770020,1343.329956,1327.319946,1329.469971,1329.469971,3846250000
2011-05-17,1326.099976,1330.420044,1318.510010,1328.979980,1328.979980,4053970000
2011-05-18,1328.540039,1341.819946,1326.589966,1340.680054,1340.680054,3922030000
2011-05-19,1342.400024,1346.819946,1336.359985,1343.599976,1343.599976,3626110000
2011-05-20,1342.000000,1342.000000,1330.670044,1333.270020,1333.270020,4066020000
2011-05-23,1333.069946,1333.069946,1312.880005,1317.369995,1317.369995,3255580000
2011-05-24,1317.699951,1323.719971,1313.869995,1316.280029,1316.280029,3846250000
2011-05-25,1316.359985,1325.859985,1311.800049,1320.469971,1320.469971,4109670000
2011-05-26,1320.640015,1328.510010,1314.410034,1325.689941,1325.689941,3259470000
2011-05-27,1325.689941,1334.619995,1325.689941,1331.099976,1331.099976,3124560000
2011-05-31,1331.099976,1345.199951,1331.099976,1345.199951,1345.199951,4696240000
2011-06-01,1345.199951,1345.199951,1313.709961,1314.550049,1314.550049,4241090000
2011-06-02,1314.550049,1318.030029,1305.609985,1312.939941,1312.939941,3762170000
2011-06-03,1312.939941,1312.939941,1297.900024,1300.160034,1300.160034,3505030000
2011-06-06,1300.260010,1300.260010,1284.719971,1286.170044,1286.170044,3555980000
2011-06-07,1286.310059,1296.219971,1284.739990,1284.939941,1284.939941,3846250000
2011-06-08,1284.630005,1287.040039,1277.420044,1279.560059,1279.560059,3970810000
2011-06-09,1279.630005,1294.540039,1279.630005,1289.000000,1289.000000,3332510000
2011-06-10,1288.599976,1288.599976,1268.280029,1270.979980,1270.979980,3846250000
2011-06-13,1271.310059,1277.040039,1265.640015,1271.829956,1271.829956,4132520000
2011-06-14,1272.219971,1292.500000,1272.219971,1287.869995,1287.869995,3500280000
2011-06-15,1287.869995,1287.869995,1261.900024,1265.420044,1265.420044,4070500000
2011-06-16,1265.530029,1274.109985,1258.069946,1267.640015,1267.640015,3846250000
2011-06-17,1268.579956,1279.819946,1267.400024,1271.500000,1271.500000,4916460000
2011-06-20,1271.500000,1280.420044,1267.560059,1278.359985,1278.359985,3464660000
2011-06-21,1278.400024,1297.619995,1278.400024,1295.520020,1295.520020,4056150000
2011-06-22,1295.479980,1298.609985,1286.790039,1287.140015,1287.140015,3718420000
2011-06-23,1286.599976,1286.599976,1262.869995,1283.500000,1283.500000,4983450000
2011-06-24,1283.040039,1283.930054,1267.239990,1268.449951,1268.449951,3665340000
2011-06-27,1268.439941,1284.910034,1267.530029,1280.099976,1280.099976,3479070000
2011-06-28,1280.209961,1296.800049,1280.209961,1296.670044,1296.670044,3681500000
2011-06-29,1296.849976,1309.209961,1296.849976,1307.410034,1307.410034,4347540000
2011-06-30,1307.640015,1321.969971,1307.640015,1320.640015,1320.640015,4200500000
2011-07-01,1320.640015,1341.010010,1318.180054,1339.670044,1339.670044,3796930000
2011-07-05,1339.589966,1340.890015,1334.300049,1337.880005,1337.880005,3722320000
2011-07-06,1337.560059,1340.939941,1330.920044,1339.219971,1339.219971,3564190000
2011-07-07,1339.619995,1356.479980,1339.619995,1353.219971,1353.219971,4069530000
2011-07-08,1352.390015,1352.390015,1333.709961,1343.800049,1343.800049,3594360000
2011-07-11,1343.310059,1343.310059,1316.420044,1319.489990,1319.489990,3879130000
2011-07-12,1319.609985,1327.170044,1313.329956,1313.640015,1313.640015,4227890000
2011-07-13,1314.449951,1331.479980,1314.449951,1317.719971,1317.719971,4060080000
2011-07-14,1317.739990,1326.880005,1306.510010,1308.869995,1308.869995,4358570000
2011-07-15,1308.869995,1317.699951,1307.520020,1316.140015,1316.140015,4242760000
2011-07-18,1315.939941,1315.939941,1295.920044,1305.439941,1305.439941,4118160000
2011-07-19,1307.069946,1328.140015,1307.069946,1326.729980,1326.729980,4304600000
2011-07-20,1328.660034,1330.430054,1323.650024,1325.839966,1325.839966,3767420000
2011-07-21,1325.650024,1347.000000,1325.650024,1343.800049,1343.800049,4837430000
2011-07-22,1343.800049,1346.099976,1336.949951,1345.020020,1345.020020,3522830000
2011-07-25,1344.319946,1344.319946,1331.089966,1337.430054,1337.430054,3536890000
2011-07-26,1337.390015,1338.510010,1329.589966,1331.939941,1331.939941,4007050000
2011-07-27,1331.910034,1331.910034,1303.489990,1304.890015,1304.890015,3479040000
2011-07-28,1304.839966,1316.319946,1299.160034,1300.670044,1300.670044,4951800000
2011-07-29,1300.119995,1304.160034,1282.859985,1292.280029,1292.280029,5061190000
2011-08-01,1292.589966,1307.380005,1274.729980,1286.939941,1286.939941,4967390000
2011-08-02,1286.560059,1286.560059,1254.030029,1254.050049,1254.050049,5206290000
2011-08-03,1254.250000,1261.199951,1234.560059,1260.339966,1260.339966,6446940000
2011-08-04,1260.229980,1260.229980,1199.540039,1200.069946,1200.069946,4266530000
2011-08-05,1200.280029,1218.109985,1168.089966,1199.380005,1199.380005,5454590000
2011-08-08,1198.479980,1198.479980,1119.280029,1119.459961,1119.459961,2615150000
2011-08-09,1120.229980,1172.880005,1101.540039,1172.530029,1172.530029,2366660000
2011-08-10,1171.770020,1171.770020,1118.010010,1120.760010,1120.760010,5018070000
2011-08-11,1121.300049,1186.290039,1121.300049,1172.640015,1172.640015,3685050000
2011-08-12,1172.869995,1189.040039,1170.739990,1178.810059,1178.810059,5640380000
2011-08-15,1178.859985,1204.489990,1178.859985,1204.489990,1204.489990,4272850000
2011-08-16,1204.219971,1204.219971,1180.530029,1192.760010,1192.760010,5071600000
2011-08-17,1192.890015,1208.469971,1184.359985,1193.890015,1193.890015,4388340000
2011-08-18,1189.619995,1189.619995,1131.030029,1140.650024,1140.650024,3234810000
2011-08-19,1140.469971,1154.540039,1122.050049,1123.530029,1123.530029,5167560000
2011-08-22,1123.550049,1145.489990,1121.089966,1123.819946,1123.819946,5436260000
2011-08-23,1124.359985,1162.349976,1124.359985,1162.349976,1162.349976,5013170000
2011-08-24,1162.160034,1178.560059,1156.300049,1177.599976,1177.599976,5315310000
2011-08-25,1176.689941,1190.680054,1155.469971,1159.270020,1159.270020,5748420000
2011-08-26,1158.849976,1181.229980,1135.910034,1176.800049,1176.800049,5035320000
2011-08-29,1177.910034,1210.280029,1177.910034,1210.079956,1210.079956,4228070000
2011-08-30,1209.760010,1220.099976,1195.770020,1212.920044,1212.920044,4572570000
2011-08-31,1213.000000,1230.709961,1209.349976,1218.890015,1218.890015,5267840000
2011-09-01,1219.119995,1229.290039,1203.849976,1204.420044,1204.420044,4780410000
2011-09-02,1203.900024,1203.900024,1170.560059,1173.969971,1173.969971,4401740000
2011-09-06,1173.969971,1173.969971,1140.130005,1165.239990,1165.239990,5103980000
2011-09-07,1165.849976,1198.619995,1165.849976,1198.619995,1198.619995,4441040000
2011-09-08,1197.979980,1204.400024,1183.339966,1185.900024,1185.900024,4465170000
2011-09-09,1185.369995,1185.369995,1148.369995,1154.229980,1154.229980,4586370000
2011-09-12,1153.500000,1162.520020,1136.069946,1162.270020,1162.270020,5168550000
2011-09-13,1162.589966,1176.410034,1157.439941,1172.869995,1172.869995,4681370000
2011-09-14,1173.319946,1202.380005,1162.729980,1188.680054,1188.680054,4986740000
2011-09-15,1189.439941,1209.109985,1189.439941,1209.109985,1209.109985,4479730000
2011-09-16,1209.209961,1220.060059,1204.459961,1216.010010,1216.010010,5248890000
2011-09-19,1214.989990,1214.989990,1188.359985,1204.089966,1204.089966,4254190000
2011-09-20,1204.500000,1220.390015,1201.290039,1202.089966,1202.089966,4315610000
2011-09-21,1203.630005,1206.300049,1166.209961,1166.760010,1166.760010,4728550000
2011-09-22,1164.550049,1164.550049,1114.219971,1129.560059,1129.560059,6703140000
2011-09-23,1128.819946,1141.719971,1121.359985,1136.430054,1136.430054,5639930000
2011-09-26,1136.910034,1164.189941,1131.069946,1162.949951,1162.949951,4762830000
2011-09-27,1163.319946,1195.859985,1163.319946,1175.380005,1175.380005,5548130000
2011-09-28,1175.390015,1184.709961,1150.400024,1151.060059,1151.060059,4787920000
2011-09-29,1151.739990,1175.869995,1139.930054,1160.400024,1160.400024,5285740000
2011-09-30,1159.930054,1159.930054,1131.339966,1131.420044,1131.420044,4416790000
2011-10-03,1131.209961,1138.989990,1098.920044,1099.229980,1099.229980,5670340000
2011-10-04,1097.420044,1125.119995,1074.770020,1123.949951,1123.949951,3714670000
2011-10-05,1124.030029,1146.069946,1115.680054,1144.030029,1144.030029,2510620000
2011-10-06,1144.109985,1165.550049,1134.949951,1164.969971,1164.969971,5098330000
2011-10-07,1165.030029,1171.400024,1150.260010,1155.459961,1155.459961,5580380000
2011-10-10,1158.150024,1194.910034,1158.150024,1194.890015,1194.890015,4446800000
2011-10-11,1194.599976,1199.239990,1187.300049,1195.540039,1195.540039,4424500000
2011-10-12,1196.189941,1220.250000,1196.189941,1207.250000,1207.250000,5355360000
2011-10-13,1206.959961,1207.459961,1190.579956,1203.660034,1203.660034,4436270000
2011-10-14,1205.650024,1224.609985,1205.650024,1224.579956,1224.579956,4116690000
2011-10-17,1224.469971,1224.469971,1198.550049,1200.859985,1200.859985,4300700000
2011-10-18,1200.750000,1233.099976,1191.479980,1225.380005,1225.380005,4840170000
2011-10-19,1223.459961,1229.640015,1206.310059,1209.880005,1209.880005,4846390000
2011-10-20,1209.920044,1219.530029,1197.339966,1215.390015,1215.390015,4870290000
2011-10-21,1215.390015,1239.030029,1215.390015,1238.250000,1238.250000,4980770000
2011-10-24,1238.719971,1256.550049,1238.719971,1254.189941,1254.189941,4309380000
2011-10-25,1254.189941,1254.189941,1226.790039,1229.050049,1229.050049,4473970000
2011-10-26,1229.170044,1246.280029,1221.060059,1242.000000,1242.000000,4873530000
2011-10-27,1243.969971,1292.660034,1243.969971,1284.589966,1284.589966,6367610000
2011-10-28,1284.390015,1287.079956,1277.010010,1285.089966,1285.089966,4536690000
2011-10-31,1284.959961,1284.959961,1253.160034,1253.300049,1253.300049,4310210000
2011-11-01,1251.000000,1251.000000,1215.420044,1218.280029,1218.280029,5645540000
2011-11-02,1219.619995,1242.479980,1219.619995,1237.900024,1237.900024,4110530000
2011-11-03,1238.250000,1263.209961,1234.810059,1261.150024,1261.150024,4849140000
2011-11-04,1260.819946,1260.819946,1238.920044,1253.229980,1253.229980,3830650000
2011-11-07,1253.209961,1261.699951,1240.750000,1261.119995,1261.119995,3429740000
2011-11-08,1261.119995,1277.550049,1254.989990,1275.920044,1275.920044,3908490000
2011-11-09,1275.180054,1275.180054,1226.640015,1229.099976,1229.099976,4659740000
2011-11-10,1229.589966,1246.219971,1227.699951,1239.699951,1239.699951,4002760000
2011-11-11,1240.119995,1266.979980,1240.119995,1263.849976,1263.849976,3370180000
2011-11-14,1263.849976,1263.849976,1246.680054,1251.780029,1251.780029,3219680000
2011-11-15,1251.699951,1264.250000,1244.339966,1257.810059,1257.810059,3599300000
2011-11-16,1257.810059,1259.609985,1235.670044,1236.910034,1236.910034,4085010000
2011-11-17,1236.560059,1237.729980,1209.430054,1216.130005,1216.130005,4596450000
2011-11-18,1216.189941,1223.510010,1211.359985,1215.650024,1215.650024,3827610000
2011-11-21,1215.619995,1215.619995,1183.160034,1192.979980,1192.979980,4050070000
2011-11-22,1192.979980,1196.810059,1181.650024,1188.040039,1188.040039,3911710000
2011-11-23,1187.479980,1187.479980,1161.790039,1161.790039,1161.790039,3798940000
2011-11-25,1161.410034,1172.660034,1158.660034,1158.670044,1158.670044,1664200000
2011-11-28,1158.670044,1197.349976,1158.670044,1192.550049,1192.550049,3920750000
2011-11-29,1192.560059,1203.670044,1191.800049,1195.189941,1195.189941,3992650000
2011-11-30,1196.719971,1247.109985,1196.719971,1246.959961,1246.959961,5801910000
2011-12-01,1246.910034,1251.089966,1239.729980,1244.579956,1244.579956,3818680000
2011-12-02,1246.030029,1260.079956,1243.349976,1244.280029,1244.280029,4144310000
2011-12-05,1244.329956,1266.729980,1244.329956,1257.079956,1257.079956,4148060000
2011-12-06,1257.189941,1266.030029,1253.030029,1258.469971,1258.469971,3734230000
2011-12-07,1258.140015,1267.060059,1244.800049,1261.010010,1261.010010,4160540000
2011-12-08,1260.869995,1260.869995,1231.469971,1234.349976,1234.349976,4298370000
2011-12-09,1234.479980,1258.250000,1234.479980,1255.189941,1255.189941,3830610000
2011-12-12,1255.050049,1255.050049,1227.250000,1236.469971,1236.469971,3600570000
2011-12-13,1236.829956,1249.859985,1219.430054,1225.729980,1225.729980,4121570000
2011-12-14,1225.729980,1225.729980,1209.469971,1211.819946,1211.819946,4298290000
2011-12-15,1212.119995,1225.599976,1212.119995,1215.750000,1215.750000,3810340000
2011-12-16,1216.089966,1231.040039,1215.199951,1219.660034,1219.660034,5345800000
2011-12-19,1219.739990,1224.569946,1202.369995,1205.349976,1205.349976,3659820000
2011-12-20,1205.719971,1242.819946,1205.719971,1241.300049,1241.300049,4055590000
2011-12-21,1241.250000,1245.089966,1229.510010,1243.719971,1243.719971,2959020000
2011-12-22,1243.719971,1255.219971,1243.719971,1254.000000,1254.000000,3492250000
2011-12-23,1254.000000,1265.420044,1254.000000,1265.329956,1265.329956,2233830000
2011-12-27,1265.020020,1269.369995,1262.300049,1265.430054,1265.430054,2130590000
2011-12-28,1265.380005,1265.849976,1248.640015,1249.640015,1249.640015,2349980000
2011-12-29,1249.750000,1263.540039,1249.750000,1263.020020,1263.020020,2278130000
2011-12-30,1262.819946,1264.119995,1257.459961,1257.599976,1257.599976,2271850000
2012-01-03,1258.859985,1284.619995,1258.859985,1277.060059,1277.060059,3943710000
2012-01-04,1277.030029,1278.729980,1268.099976,1277.300049,1277.300049,3592580000
2012-01-05,1277.300049,1283.050049,1265.260010,1281.060059,1281.060059,4315950000
2012-01-06,1280.930054,1281.839966,1273.339966,1277.810059,1277.810059,3656830000
2012-01-09,1277.829956,1281.989990,1274.550049,1280.699951,1280.699951,3371600000
2012-01-10,1280.770020,1296.459961,1280.770020,1292.079956,1292.079956,4221960000
2012-01-11,1292.020020,1293.800049,1285.410034,1292.479980,1292.479980,3968120000
2012-01-12,1292.479980,1296.819946,1285.770020,1295.500000,1295.500000,4019890000
2012-01-13,1294.819946,1294.819946,1277.579956,1289.089966,1289.089966,3692370000
2012-01-17,1290.219971,1303.000000,1290.219971,1293.670044,1293.670044,4010490000
2012-01-18,1293.650024,1308.109985,1290.989990,1308.040039,1308.040039,4096160000
2012-01-19,1308.069946,1315.489990,1308.069946,1314.500000,1314.500000,4465890000
2012-01-20,1314.489990,1315.380005,1309.170044,1315.380005,1315.380005,3912620000
2012-01-23,1315.290039,1322.280029,1309.890015,1316.000000,1316.000000,3770910000
2012-01-24,1315.959961,1315.959961,1306.060059,1314.650024,1314.650024,3693560000
2012-01-25,1314.400024,1328.300049,1307.650024,1326.060059,1326.060059,4410910000
2012-01-26,1326.280029,1333.469971,1313.599976,1318.430054,1318.430054,4522070000
2012-01-27,1318.250000,1320.060059,1311.719971,1316.329956,1316.329956,4007380000
2012-01-30,1316.160034,1316.160034,1300.489990,1313.010010,1313.010010,3659010000
2012-01-31,1313.530029,1321.410034,1306.689941,1312.410034,1312.410034,4235550000
2012-02-01,1312.449951,1330.520020,1312.449951,1324.089966,1324.089966,4504360000
2012-02-02,1324.239990,1329.189941,1321.569946,1325.540039,1325.540039,4120920000
2012-02-03,1326.209961,1345.339966,1326.209961,1344.900024,1344.900024,4608550000
2012-02-06,1344.319946,1344.359985,1337.520020,1344.329956,1344.329956,3379700000
2012-02-07,1344.329956,1349.239990,1335.920044,1347.050049,1347.050049,3742460000
2012-02-08,1347.040039,1351.000000,1341.949951,1349.959961,1349.959961,4096730000
2012-02-09,1349.969971,1354.319946,1344.630005,1351.949951,1351.949951,4209890000
2012-02-10,1351.209961,1351.209961,1337.349976,1342.640015,1342.640015,3877580000
2012-02-13,1343.060059,1353.349976,1343.060059,1351.770020,1351.770020,3618040000
2012-02-14,1351.300049,1351.300049,1340.829956,1350.500000,1350.500000,3889520000
2012-02-15,1350.520020,1355.869995,1340.800049,1343.229980,1343.229980,4080340000
2012-02-16,1342.609985,1359.020020,1341.219971,1358.040039,1358.040039,4108880000
2012-02-17,1358.060059,1363.400024,1357.239990,1361.229980,1361.229980,3717640000
2012-02-21,1361.219971,1367.760010,1358.109985,1362.209961,1362.209961,3795200000
2012-02-22,1362.109985,1362.699951,1355.530029,1357.660034,1357.660034,3633710000
2012-02-23,1357.530029,1364.239990,1352.280029,1363.459961,1363.459961,3786450000
2012-02-24,1363.459961,1368.920044,1363.459961,1365.739990,1365.739990,3505360000
2012-02-27,1365.199951,1371.939941,1354.920044,1367.589966,1367.589966,3648890000
2012-02-28,1367.560059,1373.089966,1365.969971,1372.180054,1372.180054,3579120000
2012-02-29,1372.199951,1378.040039,1363.810059,1365.680054,1365.680054,4482370000
2012-03-01,1365.900024,1376.170044,1365.900024,1374.089966,1374.089966,3919240000
2012-03-02,1374.089966,1374.530029,1366.420044,1369.630005,1369.630005,3283490000
2012-03-05,1369.589966,1369.589966,1359.130005,1364.329956,1364.329956,3429480000
2012-03-06,1363.630005,1363.630005,1340.030029,1343.359985,1343.359985,4191060000
2012-03-07,1343.390015,1354.849976,1343.390015,1352.630005,1352.630005,3580380000
2012-03-08,1352.650024,1368.719971,1352.650024,1365.910034,1365.910034,3543060000
2012-03-09,1365.969971,1374.760010,1365.969971,1370.869995,1370.869995,3639470000
2012-03-12,1370.780029,1373.040039,1366.689941,1371.089966,1371.089966,3081870000
2012-03-13,1371.920044,1396.130005,1371.920044,1395.949951,1395.949951,4386470000
2012-03-14,1395.949951,1399.420044,1389.969971,1394.280029,1394.280029,4502280000
2012-03-15,1394.170044,1402.630005,1392.780029,1402.599976,1402.599976,4271650000
2012-03-16,1402.550049,1405.880005,1401.469971,1404.170044,1404.170044,5163950000
2012-03-19,1404.170044,1414.000000,1402.430054,1409.750000,1409.750000,3932570000
2012-03-20,1409.589966,1409.589966,1397.680054,1405.520020,1405.520020,3695280000
2012-03-21,1405.520020,1407.750000,1400.640015,1402.890015,1402.890015,3573590000
2012-03-22,1402.890015,1402.890015,1388.729980,1392.780029,1392.780029,3740590000
2012-03-23,1392.780029,1399.180054,1386.869995,1397.109985,1397.109985,3472950000
2012-03-26,1397.109985,1416.579956,1397.109985,1416.510010,1416.510010,3576950000
2012-03-27,1416.550049,1419.150024,1411.949951,1412.520020,1412.520020,3513640000
2012-03-28,1412.520020,1413.650024,1397.199951,1405.540039,1405.540039,3892800000
2012-03-29,1405.390015,1405.390015,1391.560059,1403.280029,1403.280029,3832000000
2012-03-30,1403.310059,1410.890015,1401.420044,1408.469971,1408.469971,3676890000
2012-04-02,1408.469971,1422.380005,1404.459961,1419.040039,1419.040039,3572010000
2012-04-03,1418.979980,1419.000000,1404.619995,1413.380005,1413.380005,3822090000
2012-04-04,1413.089966,1413.089966,1394.089966,1398.959961,1398.959961,3938290000
2012-04-05,1398.790039,1401.599976,1392.920044,1398.079956,1398.079956,3303740000
2012-04-09,1397.449951,1397.449951,1378.239990,1382.199951,1382.199951,3468980000
2012-04-10,1382.180054,1383.010010,1357.380005,1358.589966,1358.589966,4631730000
2012-04-11,1358.979980,1374.709961,1358.979980,1368.709961,1368.709961,3743040000
2012-04-12,1368.770020,1388.130005,1368.770020,1387.569946,1387.569946,3618280000
2012-04-13,1387.609985,1387.609985,1369.849976,1370.260010,1370.260010,3631160000
2012-04-16,1370.270020,1379.660034,1365.380005,1369.569946,1369.569946,3574780000
2012-04-17,1369.569946,1392.760010,1369.569946,1390.780029,1390.780029,3456200000
2012-04-18,1390.780029,1390.780029,1383.290039,1385.140015,1385.140015,3463140000
2012-04-19,1385.079956,1390.459961,1370.300049,1376.920044,1376.920044,4180020000
2012-04-20,1376.959961,1387.400024,1376.959961,1378.530029,1378.530029,3833320000
2012-04-23,1378.530029,1378.530029,1358.790039,1366.939941,1366.939941,3654860000
2012-04-24,1366.969971,1375.569946,1366.819946,1371.969971,1371.969971,3617100000
2012-04-25,1372.109985,1391.369995,1372.109985,1390.689941,1390.689941,3998430000
2012-04-26,1390.640015,1402.089966,1387.280029,1399.979980,1399.979980,4034700000
2012-04-27,1400.189941,1406.640015,1397.310059,1403.359985,1403.359985,3645830000
2012-04-30,1403.260010,1403.260010,1394.000000,1397.910034,1397.910034,3574010000
2012-05-01,1397.859985,1415.319946,1395.729980,1405.819946,1405.819946,3807950000
2012-05-02,1405.500000,1405.500000,1393.920044,1402.310059,1402.310059,3803860000
2012-05-03,1402.319946,1403.069946,1388.709961,1391.569946,1391.569946,4004910000
2012-05-04,1391.510010,1391.510010,1367.959961,1369.099976,1369.099976,3975140000
2012-05-07,1368.790039,1373.910034,1363.939941,1369.579956,1369.579956,3559390000
2012-05-08,1369.160034,1369.160034,1347.750000,1363.719971,1363.719971,4261670000
2012-05-09,1363.199951,1363.729980,1343.130005,1354.579956,1354.579956,4288540000
2012-05-10,1354.579956,1365.880005,1354.579956,1357.989990,1357.989990,3727990000
2012-05-11,1358.109985,1365.660034,1348.890015,1353.390015,1353.390015,3869070000
2012-05-14,1351.930054,1351.930054,1336.609985,1338.349976,1338.349976,3688120000
2012-05-15,1338.359985,1344.939941,1328.410034,1330.660034,1330.660034,4114040000
2012-05-16,1330.780029,1341.780029,1324.790039,1324.800049,1324.800049,4280420000
2012-05-17,1324.819946,1326.359985,1304.859985,1304.859985,1304.859985,4664280000
2012-05-18,1305.050049,1312.239990,1291.979980,1295.219971,1295.219971,4512470000
2012-05-21,1295.729980,1316.390015,1295.729980,1315.989990,1315.989990,3786750000
2012-05-22,1316.089966,1328.489990,1310.040039,1316.630005,1316.630005,4123680000
2012-05-23,1316.020020,1320.709961,1296.530029,1318.859985,1318.859985,4108330000
2012-05-24,1318.719971,1324.140015,1310.500000,1320.680054,1320.680054,3937670000
2012-05-25,1320.810059,1324.199951,1314.229980,1317.819946,1317.819946,2872660000
2012-05-29,1318.900024,1334.930054,1318.900024,1332.420044,1332.420044,3441640000
2012-05-30,1331.250000,1331.250000,1310.760010,1313.319946,1313.319946,3534290000
2012-05-31,1313.089966,1319.739990,1298.900024,1310.329956,1310.329956,4557620000
2012-06-01,1309.869995,1309.869995,1277.250000,1278.040039,1278.040039,4669350000
2012-06-04,1278.290039,1282.550049,1266.739990,1278.180054,1278.180054,4011960000
2012-06-05,1277.819946,1287.619995,1274.160034,1285.500000,1285.500000,3403230000
2012-06-06,1285.609985,1315.130005,1285.609985,1315.130005,1315.130005,4268360000
2012-06-07,1316.150024,1329.050049,1312.680054,1314.989990,1314.989990,4258140000
2012-06-08,1314.989990,1325.810059,1307.770020,1325.660034,1325.660034,3497190000
2012-06-11,1325.719971,1335.520020,1307.729980,1308.930054,1308.930054,3537530000
2012-06-12,1309.400024,1324.310059,1306.619995,1324.180054,1324.180054,3442920000
2012-06-13,1324.020020,1327.280029,1310.510010,1314.880005,1314.880005,3506510000
2012-06-14,1314.880005,1333.680054,1314.140015,1329.099976,1329.099976,3687720000
2012-06-15,1329.189941,1343.319946,1329.189941,1342.839966,1342.839966,4401570000
2012-06-18,1342.420044,1348.219971,1334.459961,1344.780029,1344.780029,3259430000
2012-06-19,1344.829956,1363.459961,1344.829956,1357.979980,1357.979980,3815350000
2012-06-20,1358.040039,1361.569946,1346.449951,1355.689941,1355.689941,3695700000
2012-06-21,1355.430054,1358.270020,1324.410034,1325.510010,1325.510010,4094470000
2012-06-22,1325.920044,1337.819946,1325.920044,1335.020020,1335.020020,5271490000
2012-06-25,1334.900024,1334.900024,1309.270020,1313.719971,1313.719971,3501820000
2012-06-26,1314.089966,1324.239990,1310.300049,1319.989990,1319.989990,3412940000
2012-06-27,1320.709961,1334.400024,1320.709961,1331.849976,1331.849976,3286910000
2012-06-28,1331.520020,1331.520020,1313.290039,1329.040039,1329.040039,3969370000
2012-06-29,1330.119995,1362.170044,1330.119995,1362.160034,1362.160034,4590480000
2012-07-02,1362.329956,1366.349976,1355.699951,1365.510010,1365.510010,3301650000
2012-07-03,1365.750000,1374.810059,1363.530029,1374.020020,1374.020020,2116390000
2012-07-05,1373.719971,1373.849976,1363.020020,1367.579956,1367.579956,3041520000
2012-07-06,1367.089966,1367.089966,1348.030029,1354.680054,1354.680054,2745140000
2012-07-09,1354.660034,1354.869995,1346.650024,1352.459961,1352.459961,2904860000
2012-07-10,1352.959961,1361.540039,1336.270020,1341.469971,1341.469971,3470600000
2012-07-11,1341.400024,1345.000000,1333.250000,1341.449951,1341.449951,3426290000
2012-07-12,1341.290039,1341.290039,1325.410034,1334.760010,1334.760010,3654440000
2012-07-13,1334.810059,1357.699951,1334.810059,1356.780029,1356.780029,3212930000
2012-07-16,1356.500000,1357.260010,1348.510010,1353.640015,1353.640015,2862720000
2012-07-17,1353.680054,1365.359985,1345.069946,1363.670044,1363.670044,3566680000
2012-07-18,1363.579956,1375.260010,1358.959961,1372.780029,1372.780029,3642630000
2012-07-19,1373.010010,1380.390015,1371.209961,1376.510010,1376.510010,4043360000
2012-07-20,1376.510010,1376.510010,1362.189941,1362.660034,1362.660034,3925020000
2012-07-23,1362.339966,1362.339966,1337.560059,1350.520020,1350.520020,3717180000
2012-07-24,1350.520020,1351.530029,1329.239990,1338.310059,1338.310059,3891290000
2012-07-25,1338.349976,1343.979980,1331.500000,1337.890015,1337.890015,3719170000
2012-07-26,1338.170044,1363.130005,1338.170044,1360.020020,1360.020020,4429300000
2012-07-27,1360.050049,1389.189941,1360.050049,1385.969971,1385.969971,4399010000
2012-07-30,1385.939941,1391.739990,1381.369995,1385.300049,1385.300049,3212060000
2012-07-31,1385.270020,1387.160034,1379.170044,1379.319946,1379.319946,3821570000
2012-08-01,1379.319946,1385.030029,1373.349976,1375.319946,1375.319946,4440920000
2012-08-02,1375.130005,1375.130005,1354.650024,1365.000000,1365.000000,4193740000
2012-08-03,1365.449951,1394.160034,1365.449951,1390.989990,1390.989990,3751170000
2012-08-06,1391.040039,1399.630005,1391.040039,1394.229980,1394.229980,3122050000
2012-08-07,1394.459961,1407.140015,1394.459961,1401.349976,1401.349976,3682490000
2012-08-08,1401.229980,1404.140015,1396.130005,1402.219971,1402.219971,3221790000
2012-08-09,1402.260010,1405.949951,1398.800049,1402.800049,1402.800049,3119610000
2012-08-10,1402.579956,1405.979980,1395.619995,1405.869995,1405.869995,2767980000
2012-08-13,1405.869995,1405.869995,1397.319946,1404.109985,1404.109985,2499990000
2012-08-14,1404.359985,1410.030029,1400.599976,1403.930054,1403.930054,2930900000
2012-08-15,1403.890015,1407.729980,1401.829956,1405.530029,1405.530029,2655750000
2012-08-16,1405.569946,1417.439941,1404.150024,1415.510010,1415.510010,3114100000
2012-08-17,1415.839966,1418.709961,1414.670044,1418.160034,1418.160034,2922990000
2012-08-20,1417.849976,1418.130005,1412.119995,1418.130005,1418.130005,2766320000
2012-08-21,1418.130005,1426.680054,1410.859985,1413.170044,1413.170044,3282950000
2012-08-22,1413.089966,1416.119995,1406.780029,1413.489990,1413.489990,3062690000
2012-08-23,1413.489990,1413.489990,1400.500000,1402.079956,1402.079956,3008240000
2012-08-24,1401.989990,1413.459961,1398.040039,1411.130005,1411.130005,2598790000
2012-08-27,1411.130005,1416.170044,1409.109985,1410.439941,1410.439941,2472500000
2012-08-28,1410.439941,1413.630005,1405.589966,1409.300049,1409.300049,2629090000
2012-08-29,1409.319946,1413.949951,1406.569946,1410.489990,1410.489990,2571220000
2012-08-30,1410.079956,1410.079956,1397.010010,1399.479980,1399.479980,2530280000
2012-08-31,1400.069946,1413.089966,1398.959961,1406.579956,1406.579956,2938250000
2012-09-04,1406.540039,1409.310059,1396.560059,1404.939941,1404.939941,3200310000
2012-09-05,1404.939941,1408.810059,1401.250000,1403.439941,1403.439941,3389110000
2012-09-06,1403.739990,1432.119995,1403.739990,1432.119995,1432.119995,3952870000
2012-09-07,1432.119995,1437.920044,1431.449951,1437.920044,1437.920044,3717620000
2012-09-10,1437.920044,1438.739990,1428.979980,1429.079956,1429.079956,3223670000
2012-09-11,1429.130005,1437.760010,1429.130005,1433.560059,1433.560059,3509630000
2012-09-12,1433.560059,1439.150024,1432.989990,1436.560059,1436.560059,3641200000
2012-09-13,1436.560059,1463.760010,1435.339966,1459.989990,1459.989990,4606550000
2012-09-14,1460.069946,1474.510010,1460.069946,1465.770020,1465.770020,5041990000
2012-09-17,1465.420044,1465.630005,1457.550049,1461.189941,1461.189941,3482430000
2012-09-18,1461.189941,1461.469971,1456.130005,1459.319946,1459.319946,3377390000
2012-09-19,1459.500000,1465.150024,1457.880005,1461.050049,1461.050049,3451360000
2012-09-20,1461.050049,1461.229980,1449.979980,1460.260010,1460.260010,3382520000
2012-09-21,1460.339966,1467.069946,1459.510010,1460.150024,1460.150024,4833870000
2012-09-24,1459.760010,1460.719971,1452.060059,1456.890015,1456.890015,3008920000
2012-09-25,1456.939941,1463.239990,1441.589966,1441.589966,1441.589966,3739900000
2012-09-26,1441.599976,1441.599976,1430.530029,1433.319946,1433.319946,3565380000
2012-09-27,1433.359985,1450.199951,1433.359985,1447.150024,1447.150024,3150330000
2012-09-28,1447.130005,1447.130005,1435.599976,1440.670044,1440.670044,3509230000
2012-10-01,1440.900024,1457.140015,1440.900024,1444.489990,1444.489990,3505080000
2012-10-02,1444.989990,1451.520020,1439.010010,1445.750000,1445.750000,3321790000
2012-10-03,1446.050049,1454.300049,1441.989990,1450.989990,1450.989990,3531640000
2012-10-04,1451.079956,1463.140015,1451.079956,1461.400024,1461.400024,3615860000
2012-10-05,1461.400024,1470.959961,1456.890015,1460.930054,1460.930054,3172940000
2012-10-08,1460.930054,1460.930054,1453.099976,1455.880005,1455.880005,2328720000
2012-10-09,1455.900024,1455.900024,1441.180054,1441.479980,1441.479980,3216320000
2012-10-10,1441.479980,1442.520020,1430.640015,1432.560059,1432.560059,3225060000
2012-10-11,1432.819946,1443.900024,1432.819946,1432.839966,1432.839966,3672540000
2012-10-12,1432.839966,1438.430054,1425.530029,1428.589966,1428.589966,3134750000
2012-10-15,1428.750000,1441.310059,1427.239990,1440.130005,1440.130005,3483810000
2012-10-16,1440.310059,1455.510010,1440.310059,1454.920044,1454.920044,3568770000
2012-10-17,1454.219971,1462.199951,1453.349976,1460.910034,1460.910034,3655320000
2012-10-18,1460.939941,1464.020020,1452.630005,1457.339966,1457.339966,3880030000
2012-10-19,1457.339966,1457.339966,1429.849976,1433.189941,1433.189941,3875170000
2012-10-22,1433.209961,1435.459961,1422.060059,1433.819946,1433.819946,3216220000
2012-10-23,1433.739990,1433.739990,1407.560059,1413.109985,1413.109985,3587670000
2012-10-24,1413.199951,1420.040039,1407.099976,1408.750000,1408.750000,3385970000
2012-10-25,1409.739990,1421.119995,1405.140015,1412.969971,1412.969971,3512640000
2012-10-26,1412.969971,1417.089966,1403.280029,1411.939941,1411.939941,3284910000
2012-10-31,1410.989990,1418.760010,1405.949951,1412.160034,1412.160034,3577110000
2012-11-01,1412.199951,1428.349976,1412.199951,1427.589966,1427.589966,3929890000
2012-11-02,1427.589966,1434.270020,1412.910034,1414.199951,1414.199951,3732480000
2012-11-05,1414.020020,1419.900024,1408.130005,1417.260010,1417.260010,2921040000
2012-11-06,1417.260010,1433.380005,1417.260010,1428.390015,1428.390015,3306970000
2012-11-07,1428.270020,1428.270020,1388.140015,1394.530029,1394.530029,4356490000
2012-11-08,1394.530029,1401.229980,1377.510010,1377.510010,1377.510010,3779520000
2012-11-09,1377.550049,1391.390015,1373.030029,1379.849976,1379.849976,3647350000
2012-11-12,1379.859985,1384.869995,1377.189941,1380.030029,1380.030029,2567540000
2012-11-13,1380.030029,1388.810059,1371.390015,1374.530029,1374.530029,3455550000
2012-11-14,1374.640015,1380.130005,1352.500000,1355.489990,1355.489990,4109510000
2012-11-15,1355.410034,1360.619995,1348.050049,1353.329956,1353.329956,3928870000
2012-11-16,1353.359985,1362.030029,1343.349976,1359.880005,1359.880005,4045910000
2012-11-19,1359.880005,1386.890015,1359.880005,1386.890015,1386.890015,3374800000
2012-11-20,1386.819946,1389.770020,1377.040039,1387.810059,1387.810059,3207160000
2012-11-21,1387.790039,1391.250000,1386.390015,1391.030029,1391.030029,2667090000
2012-11-23,1391.030029,1409.160034,1391.030029,1409.150024,1409.150024,1504960000
2012-11-26,1409.150024,1409.150024,1397.680054,1406.290039,1406.290039,2948960000
2012-11-27,1406.290039,1409.010010,1398.030029,1398.939941,1398.939941,3323120000
2012-11-28,1398.770020,1410.310059,1385.430054,1409.930054,1409.930054,3359250000
2012-11-29,1409.959961,1419.699951,1409.040039,1415.949951,1415.949951,3356850000
2012-11-30,1415.949951,1418.859985,1411.630005,1416.180054,1416.180054,3966000000
2012-12-03,1416.339966,1423.729980,1408.459961,1409.459961,1409.459961,3074280000
2012-12-04,1409.459961,1413.140015,1403.650024,1407.050049,1407.050049,3247710000
2012-12-05,1407.050049,1415.560059,1398.229980,1409.280029,1409.280029,4253920000
2012-12-06,1409.430054,1413.949951,1405.930054,1413.939941,1413.939941,3229700000
2012-12-07,1413.949951,1420.339966,1410.900024,1418.069946,1418.069946,3125160000
2012-12-10,1418.069946,1421.640015,1415.640015,1418.550049,1418.550049,2999430000
2012-12-11,1418.550049,1434.270020,1418.550049,1427.839966,1427.839966,3650230000
2012-12-12,1427.839966,1438.589966,1426.760010,1428.479980,1428.479980,3709050000
2012-12-13,1428.479980,1431.359985,1416.000000,1419.449951,1419.449951,3349960000
2012-12-14,1419.449951,1419.449951,1411.880005,1413.579956,1413.579956,3210170000
2012-12-17,1413.540039,1430.670044,1413.540039,1430.359985,1430.359985,3455610000
2012-12-18,1430.469971,1448.000000,1430.469971,1446.790039,1446.790039,4302240000
2012-12-19,1446.790039,1447.750000,1435.800049,1435.810059,1435.810059,3869800000
2012-12-20,1435.810059,1443.699951,1432.819946,1443.689941,1443.689941,3686580000
2012-12-21,1443.670044,1443.670044,1422.579956,1430.150024,1430.150024,5229160000
2012-12-24,1430.150024,1430.150024,1424.660034,1426.660034,1426.660034,1248960000
2012-12-26,1426.660034,1429.420044,1416.430054,1419.829956,1419.829956,2285030000
2012-12-27,1419.829956,1422.800049,1401.800049,1418.099976,1418.099976,2830180000
2012-12-28,1418.099976,1418.099976,1401.579956,1402.430054,1402.430054,2426680000
2012-12-31,1402.430054,1426.739990,1398.109985,1426.189941,1426.189941,3204330000
2013-01-02,1426.189941,1462.430054,1426.189941,1462.420044,1462.420044,4202600000
2013-01-03,1462.420044,1465.469971,1455.530029,1459.369995,1459.369995,3829730000
2013-01-04,1459.369995,1467.939941,1458.989990,1466.469971,1466.469971,3424290000
2013-01-07,1466.469971,1466.469971,1456.619995,1461.890015,1461.890015,3304970000
2013-01-08,1461.890015,1461.890015,1451.640015,1457.150024,1457.150024,3601600000
2013-01-09,1457.150024,1464.729980,1457.150024,1461.020020,1461.020020,3674390000
2013-01-10,1461.020020,1472.300049,1461.020020,1472.119995,1472.119995,4081840000
2013-01-11,1472.119995,1472.750000,1467.579956,1472.050049,1472.050049,3340650000
2013-01-14,1472.050049,1472.050049,1465.689941,1470.680054,1470.680054,3003010000
2013-01-15,1470.670044,1473.310059,1463.760010,1472.339966,1472.339966,3135350000
2013-01-16,1472.329956,1473.959961,1467.599976,1472.630005,1472.630005,3384080000
2013-01-17,1472.630005,1485.160034,1472.630005,1480.939941,1480.939941,3706710000
2013-01-18,1480.949951,1485.979980,1475.810059,1485.979980,1485.979980,3795740000
2013-01-22,1485.979980,1492.560059,1481.160034,1492.560059,1492.560059,3570950000
2013-01-23,1492.560059,1496.130005,1489.900024,1494.810059,1494.810059,3552010000
2013-01-24,1494.810059,1502.270020,1489.459961,1494.819946,1494.819946,3699430000
2013-01-25,1494.819946,1503.260010,1494.819946,1502.959961,1502.959961,3476290000
2013-01-28,1502.959961,1503.229980,1496.329956,1500.180054,1500.180054,3388540000
2013-01-29,1500.180054,1509.349976,1498.089966,1507.839966,1507.839966,3949640000
2013-01-30,1507.839966,1509.939941,1500.109985,1501.959961,1501.959961,3726810000
2013-01-31,1501.959961,1504.189941,1496.760010,1498.109985,1498.109985,3999880000
2013-02-01,1498.109985,1514.410034,1498.109985,1513.170044,1513.170044,3836320000
2013-02-04,1513.170044,1513.170044,1495.020020,1495.709961,1495.709961,3390000000
2013-02-05,1495.709961,1514.959961,1495.709961,1511.290039,1511.290039,3618360000
2013-02-06,1511.290039,1512.530029,1504.709961,1512.119995,1512.119995,3611570000
2013-02-07,1512.119995,1512.900024,1498.489990,1509.390015,1509.390015,3614580000
2013-02-08,1509.390015,1518.310059,1509.390015,1517.930054,1517.930054,2986150000
2013-02-11,1517.930054,1518.310059,1513.609985,1517.010010,1517.010010,2684100000
2013-02-12,1517.010010,1522.290039,1515.609985,1519.430054,1519.430054,3414370000
2013-02-13,1519.430054,1524.689941,1515.930054,1520.329956,1520.329956,3385880000
2013-02-14,1520.329956,1523.140015,1514.020020,1521.380005,1521.380005,3759740000
2013-02-15,1521.380005,1524.239990,1514.140015,1519.790039,1519.790039,3838510000
2013-02-19,1519.790039,1530.939941,1519.790039,1530.939941,1530.939941,3748910000
2013-02-20,1530.939941,1530.939941,1511.410034,1511.949951,1511.949951,4240570000
2013-02-21,1511.949951,1511.949951,1497.290039,1502.420044,1502.420044,4274600000
2013-02-22,1502.420044,1515.640015,1502.420044,1515.599976,1515.599976,3419320000
2013-02-25,1515.599976,1525.839966,1487.849976,1487.849976,1487.849976,4011050000
2013-02-26,1487.849976,1498.989990,1485.010010,1496.939941,1496.939941,3975280000
2013-02-27,1496.939941,1520.079956,1494.880005,1515.989990,1515.989990,3551850000
2013-02-28,1515.989990,1525.339966,1514.459961,1514.680054,1514.680054,3912320000
2013-03-01,1514.680054,1519.989990,1501.479980,1518.199951,1518.199951,3695610000
2013-03-04,1518.199951,1525.270020,1512.290039,1525.199951,1525.199951,3414430000
2013-03-05,1525.199951,1543.469971,1525.199951,1539.790039,1539.790039,3610690000
2013-03-06,1539.790039,1545.250000,1538.109985,1541.459961,1541.459961,3676890000
2013-03-07,1541.459961,1545.780029,1541.459961,1544.260010,1544.260010,3634710000
2013-03-08,1544.260010,1552.479980,1542.939941,1551.180054,1551.180054,3652260000
2013-03-11,1551.150024,1556.270020,1547.359985,1556.219971,1556.219971,3091080000
2013-03-12,1556.219971,1556.770020,1548.239990,1552.479980,1552.479980,3274910000
2013-03-13,1552.479980,1556.390015,1548.250000,1554.520020,1554.520020,3073830000
2013-03-14,1554.520020,1563.319946,1554.520020,1563.229980,1563.229980,3459260000
2013-03-15,1563.209961,1563.619995,1555.739990,1560.699951,1560.699951,5175850000
2013-03-18,1560.699951,1560.699951,1545.130005,1552.099976,1552.099976,3164560000
2013-03-19,1552.099976,1557.250000,1538.569946,1548.339966,1548.339966,3796210000
2013-03-20,1548.339966,1561.560059,1548.339966,1558.709961,1558.709961,3349090000
2013-03-21,1558.709961,1558.709961,1543.550049,1545.800049,1545.800049,3243270000
2013-03-22,1545.900024,1557.739990,1545.900024,1556.890015,1556.890015,2948380000
2013-03-25,1556.890015,1564.910034,1546.219971,1551.689941,1551.689941,3178170000
2013-03-26,1551.689941,1563.949951,1551.689941,1563.770020,1563.770020,2869260000
2013-03-27,1563.750000,1564.069946,1551.900024,1562.849976,1562.849976,2914210000
2013-03-28,1562.859985,1570.280029,1561.079956,1569.189941,1569.189941,3304440000
2013-04-01,1569.180054,1570.569946,1558.469971,1562.170044,1562.170044,2753110000
2013-04-02,1562.170044,1573.660034,1562.170044,1570.250000,1570.250000,3312160000
2013-04-03,1570.250000,1571.469971,1549.800049,1553.689941,1553.689941,4060610000
2013-04-04,1553.689941,1562.599976,1552.520020,1559.979980,1559.979980,3350670000
2013-04-05,1559.979980,1559.979980,1539.500000,1553.280029,1553.280029,3515410000
2013-04-08,1553.260010,1563.069946,1548.630005,1563.069946,1563.069946,2887120000
2013-04-09,1563.109985,1573.890015,1560.920044,1568.609985,1568.609985,3252780000
2013-04-10,1568.609985,1589.069946,1568.609985,1587.729980,1587.729980,3453350000
2013-04-11,1587.729980,1597.349976,1586.170044,1593.369995,1593.369995,3393950000
2013-04-12,1593.300049,1593.300049,1579.969971,1588.849976,1588.849976,3206290000
2013-04-15,1588.839966,1588.839966,1552.280029,1552.359985,1552.359985,4660130000
2013-04-16,1552.359985,1575.349976,1552.359985,1574.569946,1574.569946,3654700000
2013-04-17,1574.569946,1574.569946,1543.689941,1552.010010,1552.010010,4250310000
2013-04-18,1552.030029,1554.380005,1536.030029,1541.609985,1541.609985,3890800000
2013-04-19,1541.609985,1555.890015,1539.400024,1555.250000,1555.250000,3569870000
2013-04-22,1555.250000,1565.550049,1548.189941,1562.500000,1562.500000,2979880000
2013-04-23,1562.500000,1579.579956,1562.500000,1578.780029,1578.780029,3565150000
2013-04-24,1578.780029,1583.000000,1575.800049,1578.790039,1578.790039,3598240000
2013-04-25,1578.930054,1592.640015,1578.930054,1585.160034,1585.160034,3908580000
2013-04-26,1585.160034,1585.780029,1577.560059,1582.239990,1582.239990,3198620000
2013-04-29,1582.339966,1596.650024,1582.339966,1593.609985,1593.609985,2891200000
2013-04-30,1593.579956,1597.569946,1586.500000,1597.569946,1597.569946,3745070000
2013-05-01,1597.550049,1597.550049,1581.280029,1582.699951,1582.699951,3530320000
2013-05-02,1582.770020,1598.599976,1582.770020,1597.589966,1597.589966,3366950000
2013-05-03,1597.599976,1618.459961,1597.599976,1614.420044,1614.420044,3603910000
2013-05-06,1614.400024,1619.770020,1614.209961,1617.500000,1617.500000,3062240000
2013-05-07,1617.550049,1626.030029,1616.640015,1625.959961,1625.959961,3309580000
2013-05-08,1625.949951,1632.780029,1622.699951,1632.689941,1632.689941,3554700000
2013-05-09,1632.689941,1635.010010,1623.089966,1626.670044,1626.670044,3457400000
2013-05-10,1626.689941,1633.699951,1623.709961,1633.699951,1633.699951,3086470000
2013-05-13,1632.099976,1636.000000,1626.739990,1633.770020,1633.770020,2910600000
2013-05-14,1633.750000,1651.099976,1633.750000,1650.339966,1650.339966,3457790000
2013-05-15,1649.130005,1661.489990,1646.680054,1658.780029,1658.780029,3657440000
2013-05-16,1658.069946,1660.510010,1648.599976,1650.469971,1650.469971,3513130000
2013-05-17,1652.449951,1667.469971,1652.449951,1667.469971,1667.469971,3440710000
2013-05-20,1665.709961,1672.839966,1663.520020,1666.290039,1666.290039,3275080000
2013-05-21,1666.199951,1674.930054,1662.670044,1669.160034,1669.160034,3513560000
2013-05-22,1669.390015,1687.180054,1648.859985,1655.349976,1655.349976,4361020000
2013-05-23,1651.619995,1655.500000,1635.530029,1650.510010,1650.510010,3945510000
2013-05-24,1646.670044,1649.780029,1636.880005,1649.599976,1649.599976,2758080000
2013-05-28,1652.630005,1674.209961,1652.630005,1660.060059,1660.060059,3457400000
2013-05-29,1656.569946,1656.569946,1640.050049,1648.359985,1648.359985,3587140000
2013-05-30,1649.140015,1661.910034,1648.609985,1654.410034,1654.410034,3498620000
2013-05-31,1652.130005,1658.989990,1630.739990,1630.739990,1630.739990,4099600000
2013-06-03,1631.709961,1640.420044,1622.719971,1640.420044,1640.420044,3952070000
2013-06-04,1640.729980,1646.530029,1623.619995,1631.380005,1631.380005,3653840000
2013-06-05,1629.050049,1629.310059,1607.089966,1608.900024,1608.900024,3632350000
2013-06-06,1609.290039,1622.560059,1598.229980,1622.560059,1622.560059,3547380000
2013-06-07,1625.270020,1644.400024,1625.270020,1643.380005,1643.380005,3371990000
2013-06-10,1644.670044,1648.689941,1639.260010,1642.810059,1642.810059,2978730000
2013-06-11,1638.640015,1640.130005,1622.920044,1626.130005,1626.130005,3435710000
2013-06-12,1629.939941,1637.709961,1610.920044,1612.520020,1612.520020,3202550000
2013-06-13,1612.150024,1639.250000,1608.069946,1636.359985,1636.359985,3378620000
2013-06-14,1635.520020,1640.800049,1623.959961,1626.729980,1626.729980,2939400000
2013-06-17,1630.640015,1646.500000,1630.339966,1639.040039,1639.040039,3137080000
2013-06-18,1639.770020,1654.189941,1639.770020,1651.810059,1651.810059,3120980000
2013-06-19,1651.829956,1652.449951,1628.910034,1628.930054,1628.930054,3545060000
2013-06-20,1624.619995,1624.619995,1584.319946,1588.189941,1588.189941,4858850000
2013-06-21,1588.619995,1599.189941,1577.699951,1592.430054,1592.430054,5797280000
2013-06-24,1588.770020,1588.770020,1560.329956,1573.089966,1573.089966,4733660000
2013-06-25,1577.520020,1593.790039,1577.089966,1588.030029,1588.030029,3761170000
2013-06-26,1592.270020,1606.829956,1592.270020,1603.260010,1603.260010,3558340000
2013-06-27,1606.439941,1620.069946,1606.439941,1613.199951,1613.199951,3364540000
2013-06-28,1611.119995,1615.939941,1601.060059,1606.280029,1606.280029,4977190000
2013-07-01,1609.780029,1626.609985,1609.780029,1614.959961,1614.959961,3104690000
2013-07-02,1614.290039,1624.260010,1606.770020,1614.079956,1614.079956,3317130000
2013-07-03,1611.479980,1618.969971,1604.569946,1615.410034,1615.410034,1966050000
2013-07-05,1618.650024,1632.069946,1614.709961,1631.890015,1631.890015,2634140000
2013-07-08,1634.199951,1644.680054,1634.199951,1640.459961,1640.459961,3514590000
2013-07-09,1642.890015,1654.180054,1642.890015,1652.319946,1652.319946,3155360000
2013-07-10,1651.560059,1657.920044,1647.660034,1652.619995,1652.619995,3011010000
2013-07-11,1657.410034,1676.630005,1657.410034,1675.020020,1675.020020,3446340000
2013-07-12,1675.260010,1680.189941,1672.329956,1680.189941,1680.189941,3039070000
2013-07-15,1679.589966,1684.510010,1677.890015,1682.500000,1682.500000,2623200000
2013-07-16,1682.699951,1683.729980,1671.839966,1676.260010,1676.260010,3081710000
2013-07-17,1677.910034,1684.750000,1677.910034,1680.910034,1680.910034,3153440000
2013-07-18,1681.050049,1693.119995,1681.050049,1689.369995,1689.369995,3452370000
2013-07-19,1686.150024,1692.089966,1684.079956,1692.089966,1692.089966,3302580000
2013-07-22,1694.410034,1697.609985,1690.670044,1695.530029,1695.530029,2779130000
2013-07-23,1696.630005,1698.780029,1691.130005,1692.390015,1692.390015,3096180000
2013-07-24,1696.060059,1698.380005,1682.569946,1685.939941,1685.939941,3336120000
2013-07-25,1685.209961,1690.939941,1680.069946,1690.250000,1690.250000,3322500000
2013-07-26,1687.310059,1691.849976,1676.030029,1691.650024,1691.650024,2762770000
2013-07-29,1690.319946,1690.920044,1681.859985,1685.329956,1685.329956,2840520000
2013-07-30,1687.920044,1693.189941,1682.420044,1685.959961,1685.959961,3320530000
2013-07-31,1687.760010,1698.430054,1684.939941,1685.729980,1685.729980,3847390000
2013-08-01,1689.420044,1707.849976,1689.420044,1706.869995,1706.869995,3775170000
2013-08-02,1706.099976,1709.670044,1700.680054,1709.670044,1709.670044,3136630000
2013-08-05,1708.010010,1709.239990,1703.550049,1707.140015,1707.140015,2529300000
2013-08-06,1705.790039,1705.790039,1693.290039,1697.369995,1697.369995,3141210000
2013-08-07,1695.300049,1695.300049,1684.910034,1690.910034,1690.910034,3010230000
2013-08-08,1693.349976,1700.180054,1688.380005,1697.479980,1697.479980,3271660000
2013-08-09,1696.099976,1699.420044,1686.020020,1691.420044,1691.420044,2957670000
2013-08-12,1688.369995,1691.489990,1683.349976,1689.469971,1689.469971,2789160000
2013-08-13,1690.650024,1696.810059,1682.619995,1694.160034,1694.160034,3035560000
2013-08-14,1693.880005,1695.520020,1684.829956,1685.390015,1685.390015,2871430000
2013-08-15,1679.609985,1679.609985,1658.589966,1661.319946,1661.319946,3426690000
2013-08-16,1661.219971,1663.599976,1652.609985,1655.829956,1655.829956,3211450000
2013-08-19,1655.250000,1659.180054,1645.839966,1646.060059,1646.060059,2904530000
2013-08-20,1646.810059,1658.920044,1646.079956,1652.349976,1652.349976,2994090000
2013-08-21,1650.660034,1656.989990,1639.430054,1642.800049,1642.800049,2932180000
2013-08-22,1645.030029,1659.550049,1645.030029,1656.959961,1656.959961,2537460000
2013-08-23,1659.920044,1664.849976,1654.810059,1663.500000,1663.500000,2582670000
2013-08-26,1664.290039,1669.510010,1656.020020,1656.780029,1656.780029,2430670000
2013-08-27,1652.540039,1652.540039,1629.050049,1630.479980,1630.479980,3219190000
2013-08-28,1630.250000,1641.180054,1627.469971,1634.959961,1634.959961,2784010000
2013-08-29,1633.500000,1646.410034,1630.880005,1638.170044,1638.170044,2527550000
2013-08-30,1638.890015,1640.079956,1628.050049,1632.969971,1632.969971,2734300000
2013-09-03,1635.949951,1651.349976,1633.410034,1639.770020,1639.770020,3731610000
2013-09-04,1640.719971,1655.719971,1637.410034,1653.079956,1653.079956,3312150000
2013-09-05,1653.280029,1659.170044,1653.069946,1655.079956,1655.079956,2957110000
2013-09-06,1657.439941,1664.829956,1640.619995,1655.170044,1655.170044,3123880000
2013-09-09,1656.849976,1672.400024,1656.849976,1671.709961,1671.709961,3102780000
2013-09-10,1675.109985,1684.089966,1675.109985,1683.989990,1683.989990,3691800000
2013-09-11,1681.040039,1689.130005,1678.699951,1689.130005,1689.130005,3135460000
2013-09-12,1689.209961,1689.969971,1681.959961,1683.420044,1683.420044,3106290000
2013-09-13,1685.040039,1688.729980,1682.219971,1687.989990,1687.989990,2736500000
2013-09-16,1691.699951,1704.949951,1691.699951,1697.599976,1697.599976,3079800000
2013-09-17,1697.729980,1705.520020,1697.729980,1704.760010,1704.760010,2774240000
2013-09-18,1705.739990,1729.439941,1700.349976,1725.520020,1725.520020,3989760000
2013-09-19,1727.339966,1729.859985,1720.199951,1722.339966,1722.339966,3740130000
2013-09-20,1722.439941,1725.229980,1708.890015,1709.910034,1709.910034,5074030000
2013-09-23,1711.439941,1711.439941,1697.099976,1701.839966,1701.839966,3126950000
2013-09-24,1702.599976,1707.630005,1694.900024,1697.420044,1697.420044,3268930000
2013-09-25,1698.020020,1701.709961,1691.880005,1692.770020,1692.770020,3148730000
2013-09-26,1694.050049,1703.849976,1693.109985,1698.670044,1698.670044,2813930000
2013-09-27,1695.520020,1695.520020,1687.109985,1691.750000,1691.750000,2951700000
2013-09-30,1687.260010,1687.260010,1674.989990,1681.550049,1681.550049,3308630000
2013-10-01,1682.410034,1696.550049,1682.069946,1695.000000,1695.000000,3238690000
2013-10-02,1691.900024,1693.869995,1680.339966,1693.869995,1693.869995,3148600000
2013-10-03,1692.349976,1692.349976,1670.359985,1678.660034,1678.660034,3279650000
2013-10-04,1678.790039,1691.939941,1677.329956,1690.500000,1690.500000,2880270000
2013-10-07,1687.150024,1687.150024,1674.699951,1676.119995,1676.119995,2678490000
2013-10-08,1676.219971,1676.790039,1655.030029,1655.449951,1655.449951,3569230000
2013-10-09,1656.989990,1662.469971,1646.469971,1656.400024,1656.400024,3577840000
2013-10-10,1660.880005,1692.560059,1660.880005,1692.560059,1692.560059,3362300000
2013-10-11,1691.089966,1703.439941,1688.520020,1703.199951,1703.199951,2944670000
2013-10-14,1699.859985,1711.030029,1692.130005,1710.140015,1710.140015,2580580000
2013-10-15,1709.170044,1711.569946,1695.930054,1698.060059,1698.060059,3327740000
2013-10-16,1700.489990,1721.760010,1700.489990,1721.540039,1721.540039,3486180000
2013-10-17,1720.170044,1733.449951,1714.119995,1733.150024,1733.150024,3453590000
2013-10-18,1736.719971,1745.310059,1735.739990,1744.500000,1744.500000,3664890000
2013-10-21,1745.199951,1747.790039,1740.670044,1744.660034,1744.660034,3052710000
2013-10-22,1746.479980,1759.329956,1746.479980,1754.670044,1754.670044,3850840000
2013-10-23,1752.270020,1752.270020,1740.500000,1746.380005,1746.380005,3713380000
2013-10-24,1747.479980,1753.939941,1745.500000,1752.069946,1752.069946,3671700000
2013-10-25,1756.010010,1759.819946,1752.449951,1759.770020,1759.770020,3175720000
2013-10-28,1759.420044,1764.989990,1757.670044,1762.109985,1762.109985,3282300000
2013-10-29,1762.930054,1772.089966,1762.930054,1771.949951,1771.949951,3358460000
2013-10-30,1772.270020,1775.219971,1757.239990,1763.310059,1763.310059,3523040000
2013-10-31,1763.239990,1768.530029,1755.719971,1756.540039,1756.540039,3826530000
2013-11-01,1758.699951,1765.670044,1752.699951,1761.640015,1761.640015,3686290000
2013-11-04,1763.400024,1768.780029,1761.560059,1767.930054,1767.930054,3194870000
2013-11-05,1765.670044,1767.030029,1755.760010,1762.969971,1762.969971,3516680000
2013-11-06,1765.000000,1773.739990,1764.400024,1770.489990,1770.489990,3322100000
2013-11-07,1770.739990,1774.540039,1746.199951,1747.150024,1747.150024,4143200000
2013-11-08,1748.369995,1770.780029,1747.630005,1770.609985,1770.609985,3837170000
2013-11-11,1769.959961,1773.439941,1767.849976,1771.890015,1771.890015,2534060000
2013-11-12,1769.510010,1771.780029,1762.290039,1767.689941,1767.689941,3221030000
2013-11-13,1764.369995,1782.000000,1760.640015,1782.000000,1782.000000,3327480000
2013-11-14,1782.750000,1791.530029,1780.219971,1790.619995,1790.619995,3139060000
2013-11-15,1790.660034,1798.219971,1790.660034,1798.180054,1798.180054,3254820000
2013-11-18,1798.819946,1802.329956,1788.000000,1791.530029,1791.530029,3168520000
2013-11-19,1790.790039,1795.510010,1784.719971,1787.869995,1787.869995,3224450000
2013-11-20,1789.589966,1795.729980,1777.229980,1781.369995,1781.369995,3109140000
2013-11-21,1783.520020,1797.160034,1783.520020,1795.849976,1795.849976,3256630000
2013-11-22,1797.209961,1804.839966,1794.699951,1804.760010,1804.760010,3055140000
2013-11-25,1806.329956,1808.099976,1800.579956,1802.479980,1802.479980,2998540000
2013-11-26,1802.869995,1808.420044,1800.770020,1802.750000,1802.750000,3427120000
2013-11-27,1803.479980,1808.270020,1802.770020,1807.229980,1807.229980,2613590000
2013-11-29,1808.689941,1813.550049,1803.979980,1805.810059,1805.810059,1598300000
2013-12-02,1806.550049,1810.020020,1798.599976,1800.900024,1800.900024,3095430000
2013-12-03,1800.099976,1800.099976,1787.849976,1795.150024,1795.150024,3475680000
2013-12-04,1793.150024,1799.800049,1779.089966,1792.810059,1792.810059,3610540000
2013-12-05,1792.819946,1792.819946,1783.380005,1785.030029,1785.030029,3336880000
2013-12-06,1788.359985,1806.040039,1788.359985,1805.089966,1805.089966,3150030000
2013-12-09,1806.209961,1811.520020,1806.209961,1808.369995,1808.369995,3129500000
2013-12-10,1807.599976,1808.520020,1801.750000,1802.619995,1802.619995,3117150000
2013-12-11,1802.760010,1802.969971,1780.089966,1782.219971,1782.219971,3472240000
2013-12-12,1781.709961,1782.989990,1772.280029,1775.500000,1775.500000,3306640000
2013-12-13,1777.979980,1780.920044,1772.449951,1775.319946,1775.319946,3061070000
2013-12-16,1777.479980,1792.219971,1777.479980,1786.540039,1786.540039,3209890000
2013-12-17,1786.469971,1786.770020,1777.050049,1781.000000,1781.000000,3270030000
2013-12-18,1781.459961,1811.079956,1767.989990,1810.650024,1810.650024,4327770000
2013-12-19,1809.000000,1810.880005,1801.349976,1809.599976,1809.599976,3497210000
2013-12-20,1810.390015,1823.750000,1810.250000,1818.319946,1818.319946,5097700000
2013-12-23,1822.920044,1829.750000,1822.920044,1827.989990,1827.989990,2851540000
2013-12-24,1828.020020,1833.319946,1828.020020,1833.319946,1833.319946,1307630000
2013-12-26,1834.959961,1842.839966,1834.959961,1842.020020,1842.020020,1982270000
2013-12-27,1842.969971,1844.890015,1839.810059,1841.400024,1841.400024,2052920000
2013-12-30,1841.469971,1842.469971,1838.770020,1841.069946,1841.069946,2293860000
2013-12-31,1842.609985,1849.439941,1842.410034,1848.359985,1848.359985,2312840000
2014-01-02,1845.859985,1845.859985,1827.739990,1831.979980,1831.979980,3080600000
2014-01-03,1833.209961,1838.239990,1829.130005,1831.369995,1831.369995,2774270000
2014-01-06,1832.310059,1837.160034,1823.729980,1826.770020,1826.770020,3294850000
2014-01-07,1828.709961,1840.099976,1828.709961,1837.880005,1837.880005,3511750000
2014-01-08,1837.900024,1840.020020,1831.400024,1837.489990,1837.489990,3652140000
2014-01-09,1839.000000,1843.229980,1830.380005,1838.130005,1838.130005,3581150000
2014-01-10,1840.060059,1843.150024,1832.430054,1842.369995,1842.369995,3335710000
2014-01-13,1841.260010,1843.449951,1815.520020,1819.199951,1819.199951,3591350000
2014-01-14,1821.359985,1839.260010,1821.359985,1838.880005,1838.880005,3353270000
2014-01-15,1840.520020,1850.839966,1840.520020,1848.380005,1848.380005,3777800000
2014-01-16,1847.989990,1847.989990,1840.300049,1845.890015,1845.890015,3491310000
2014-01-17,1844.229980,1846.040039,1835.229980,1838.699951,1838.699951,3626120000
2014-01-21,1841.050049,1849.310059,1832.380005,1843.800049,1843.800049,3782470000
2014-01-22,1844.709961,1846.869995,1840.880005,1844.859985,1844.859985,3374170000
2014-01-23,1842.290039,1842.290039,1820.060059,1828.459961,1828.459961,3972250000
2014-01-24,1826.959961,1826.959961,1790.290039,1790.290039,1790.290039,4618450000
2014-01-27,1791.030029,1795.979980,1772.880005,1781.560059,1781.560059,4045200000
2014-01-28,1783.000000,1793.869995,1779.489990,1792.500000,1792.500000,3437830000
2014-01-29,1790.150024,1790.150024,1770.449951,1774.199951,1774.199951,3964020000
2014-01-30,1777.170044,1798.770020,1777.170044,1794.189941,1794.189941,3547510000
2014-01-31,1790.880005,1793.880005,1772.260010,1782.589966,1782.589966,4059690000
2014-02-03,1782.680054,1784.829956,1739.660034,1741.890015,1741.890015,4726040000
2014-02-04,1743.819946,1758.729980,1743.819946,1755.199951,1755.199951,4068410000
2014-02-05,1753.380005,1755.790039,1737.920044,1751.640015,1751.640015,3984290000
2014-02-06,1752.989990,1774.060059,1752.989990,1773.430054,1773.430054,3825410000
2014-02-07,1776.010010,1798.030029,1776.010010,1797.020020,1797.020020,3775990000
2014-02-10,1796.199951,1799.939941,1791.829956,1799.839966,1799.839966,3312160000
2014-02-11,1800.449951,1823.540039,1800.410034,1819.750000,1819.750000,3699380000
2014-02-12,1820.119995,1826.550049,1815.969971,1819.260010,1819.260010,3326380000
2014-02-13,1814.819946,1830.250000,1809.219971,1829.829956,1829.829956,3289510000
2014-02-14,1828.459961,1841.650024,1825.589966,1838.630005,1838.630005,3114750000
2014-02-18,1839.030029,1842.869995,1835.010010,1840.760010,1840.760010,3421110000
2014-02-19,1838.900024,1847.500000,1826.989990,1828.750000,1828.750000,3661570000
2014-02-20,1829.239990,1842.790039,1824.579956,1839.780029,1839.780029,3404980000
2014-02-21,1841.069946,1846.130005,1835.599976,1836.250000,1836.250000,3403880000
2014-02-24,1836.780029,1858.709961,1836.780029,1847.609985,1847.609985,4014530000
2014-02-25,1847.660034,1852.910034,1840.189941,1845.119995,1845.119995,3515560000
2014-02-26,1845.790039,1852.650024,1840.660034,1845.160034,1845.160034,3716730000
2014-02-27,1844.900024,1854.530029,1841.130005,1854.290039,1854.290039,3547460000
2014-02-28,1855.119995,1867.920044,1847.670044,1859.449951,1859.449951,3917450000
2014-03-03,1857.680054,1857.680054,1834.439941,1845.729980,1845.729980,3428220000
2014-03-04,1849.229980,1876.229980,1849.229980,1873.910034,1873.910034,3765770000
2014-03-05,1874.050049,1876.530029,1871.109985,1873.810059,1873.810059,3392990000
2014-03-06,1874.180054,1881.939941,1874.180054,1877.030029,1877.030029,3360450000
2014-03-07,1878.520020,1883.569946,1870.560059,1878.040039,1878.040039,3564740000
2014-03-10,1877.859985,1877.869995,1867.040039,1877.170044,1877.170044,3021350000
2014-03-11,1878.260010,1882.349976,1863.880005,1867.630005,1867.630005,3392400000
2014-03-12,1866.150024,1868.380005,1854.380005,1868.199951,1868.199951,3270860000
2014-03-13,1869.060059,1874.400024,1841.859985,1846.339966,1846.339966,3670990000
2014-03-14,1845.069946,1852.439941,1839.569946,1841.130005,1841.130005,3285460000
2014-03-17,1842.810059,1862.300049,1842.810059,1858.829956,1858.829956,2860490000
2014-03-18,1858.920044,1873.760010,1858.920044,1872.250000,1872.250000,2930190000
2014-03-19,1872.250000,1874.140015,1850.349976,1860.770020,1860.770020,3289210000
2014-03-20,1860.089966,1873.489990,1854.630005,1872.010010,1872.010010,3327540000
2014-03-21,1874.530029,1883.969971,1863.459961,1866.520020,1866.520020,5270710000
2014-03-24,1867.670044,1873.339966,1849.689941,1857.439941,1857.439941,3409000000
2014-03-25,1859.479980,1871.869995,1855.959961,1865.619995,1865.619995,3200560000
2014-03-26,1867.089966,1875.920044,1852.560059,1852.560059,1852.560059,3480850000
2014-03-27,1852.109985,1855.550049,1842.109985,1849.040039,1849.040039,3733430000
2014-03-28,1850.069946,1866.630005,1850.069946,1857.619995,1857.619995,2955520000
2014-03-31,1859.160034,1875.180054,1859.160034,1872.339966,1872.339966,3274300000
2014-04-01,1873.959961,1885.839966,1873.959961,1885.520020,1885.520020,3336190000
2014-04-02,1886.609985,1893.170044,1883.790039,1890.900024,1890.900024,3131660000
2014-04-03,1891.430054,1893.800049,1882.650024,1888.770020,1888.770020,3055600000
2014-04-04,1890.250000,1897.280029,1863.260010,1865.089966,1865.089966,3583750000
2014-04-07,1863.920044,1864.040039,1841.479980,1845.040039,1845.040039,3801540000
2014-04-08,1845.479980,1854.949951,1837.489990,1851.959961,1851.959961,3721450000
2014-04-09,1852.640015,1872.430054,1852.380005,1872.180054,1872.180054,3308650000
2014-04-10,1872.280029,1872.530029,1830.869995,1833.079956,1833.079956,3758780000
2014-04-11,1830.650024,1835.069946,1814.359985,1815.689941,1815.689941,3743460000
2014-04-14,1818.180054,1834.189941,1815.800049,1830.609985,1830.609985,3111540000
2014-04-15,1831.449951,1844.020020,1816.290039,1842.979980,1842.979980,3736440000
2014-04-16,1846.010010,1862.310059,1846.010010,1862.310059,1862.310059,3155080000
2014-04-17,1861.729980,1869.630005,1856.719971,1864.849976,1864.849976,3341430000
2014-04-21,1865.790039,1871.890015,1863.180054,1871.890015,1871.890015,2642500000
2014-04-22,1872.569946,1884.890015,1872.569946,1879.550049,1879.550049,3215440000
2014-04-23,1879.319946,1879.750000,1873.910034,1875.390015,1875.390015,3085720000
2014-04-24,1881.969971,1884.060059,1870.239990,1878.609985,1878.609985,3191830000
2014-04-25,1877.719971,1877.719971,1859.699951,1863.400024,1863.400024,3213020000
2014-04-28,1865.000000,1877.010010,1850.609985,1869.430054,1869.430054,4034680000
2014-04-29,1870.780029,1880.599976,1870.780029,1878.329956,1878.329956,3647820000
2014-04-30,1877.099976,1885.199951,1872.689941,1883.949951,1883.949951,3779230000
2014-05-01,1884.390015,1888.589966,1878.040039,1883.680054,1883.680054,3416740000
2014-05-02,1885.300049,1891.329956,1878.500000,1881.140015,1881.140015,3159560000
2014-05-05,1879.449951,1885.510010,1866.770020,1884.660034,1884.660034,2733730000
2014-05-06,1883.689941,1883.689941,1867.719971,1867.719971,1867.719971,3327260000
2014-05-07,1868.530029,1878.829956,1859.790039,1878.209961,1878.209961,3632950000
2014-05-08,1877.390015,1889.069946,1870.050049,1875.630005,1875.630005,3393420000
2014-05-09,1875.270020,1878.569946,1867.020020,1878.479980,1878.479980,3025020000
2014-05-12,1880.030029,1897.130005,1880.030029,1896.650024,1896.650024,3005740000
2014-05-13,1896.750000,1902.170044,1896.060059,1897.449951,1897.449951,2915680000
2014-05-14,1897.130005,1897.130005,1885.770020,1888.530029,1888.530029,2822060000
2014-05-15,1888.160034,1888.160034,1862.359985,1870.849976,1870.849976,3552640000
2014-05-16,1871.189941,1878.280029,1864.819946,1877.859985,1877.859985,3173650000
2014-05-19,1876.660034,1886.000000,1872.420044,1885.079956,1885.079956,2664250000
2014-05-20,1884.880005,1884.880005,1868.140015,1872.829956,1872.829956,3007700000
2014-05-21,1873.339966,1888.800049,1873.339966,1888.030029,1888.030029,2777140000
2014-05-22,1888.189941,1896.329956,1885.390015,1892.489990,1892.489990,2759800000
2014-05-23,1893.319946,1901.260010,1893.319946,1900.530029,1900.530029,2396280000
2014-05-27,1902.010010,1912.280029,1902.010010,1911.910034,1911.910034,2911020000
2014-05-28,1911.770020,1914.459961,1907.300049,1909.780029,1909.780029,2976450000
2014-05-29,1910.599976,1920.030029,1909.819946,1920.030029,1920.030029,2709050000
2014-05-30,1920.329956,1924.030029,1916.640015,1923.569946,1923.569946,3263490000
2014-06-02,1923.869995,1925.880005,1915.979980,1924.969971,1924.969971,2509020000
2014-06-03,1923.069946,1925.069946,1918.790039,1924.239990,1924.239990,2867180000
2014-06-04,1923.060059,1928.630005,1918.599976,1927.880005,1927.880005,2793920000
2014-06-05,1928.520020,1941.739990,1922.930054,1940.459961,1940.459961,3113270000
2014-06-06,1942.410034,1949.439941,1942.410034,1949.439941,1949.439941,2864300000
2014-06-09,1948.969971,1955.550049,1947.160034,1951.270020,1951.270020,2812180000
2014-06-10,1950.339966,1950.859985,1944.640015,1950.790039,1950.790039,2702360000
2014-06-11,1949.369995,1949.369995,1940.079956,1943.890015,1943.890015,2710620000
2014-06-12,1943.349976,1943.349976,1925.780029,1930.109985,1930.109985,3040480000
2014-06-13,1930.800049,1937.300049,1927.689941,1936.160034,1936.160034,2598230000
2014-06-16,1934.839966,1941.150024,1930.910034,1937.780029,1937.780029,2926130000
2014-06-17,1937.150024,1943.689941,1933.550049,1941.989990,1941.989990,2971260000
2014-06-18,1942.729980,1957.739990,1939.290039,1956.979980,1956.979980,3065220000
2014-06-19,1957.500000,1959.869995,1952.260010,1959.479980,1959.479980,2952150000
2014-06-20,1960.449951,1963.910034,1959.170044,1962.869995,1962.869995,4336240000
2014-06-23,1962.920044,1963.739990,1958.890015,1962.609985,1962.609985,2717630000
2014-06-24,1961.969971,1968.170044,1948.339966,1949.979980,1949.979980,3089700000
2014-06-25,1949.270020,1960.829956,1947.489990,1959.530029,1959.530029,3106710000
2014-06-26,1959.890015,1959.890015,1944.689941,1957.219971,1957.219971,2778840000
2014-06-27,1956.560059,1961.469971,1952.180054,1960.959961,1960.959961,4290590000
2014-06-30,1960.790039,1964.239990,1958.219971,1960.229980,1960.229980,3037350000
2014-07-01,1962.290039,1978.579956,1962.290039,1973.319946,1973.319946,3188240000
2014-07-02,1973.060059,1976.670044,1972.579956,1974.619995,1974.619995,2851480000
2014-07-03,1975.880005,1985.589966,1975.880005,1985.439941,1985.439941,1998090000
2014-07-07,1984.219971,1984.219971,1974.880005,1977.650024,1977.650024,2681260000
2014-07-08,1976.390015,1976.390015,1959.459961,1963.709961,1963.709961,3302430000
2014-07-09,1965.099976,1974.150024,1965.099976,1972.829956,1972.829956,2858800000
2014-07-10,1966.670044,1969.839966,1952.859985,1964.680054,1964.680054,3165690000
2014-07-11,1965.760010,1968.670044,1959.630005,1967.569946,1967.569946,2684630000
2014-07-14,1969.859985,1979.849976,1969.859985,1977.099976,1977.099976,2744920000
2014-07-15,1977.359985,1982.520020,1965.339966,1973.280029,1973.280029,3328740000
2014-07-16,1976.349976,1983.939941,1975.670044,1981.569946,1981.569946,3390950000
2014-07-17,1979.750000,1981.800049,1955.589966,1958.119995,1958.119995,3381680000
2014-07-18,1961.540039,1979.910034,1960.819946,1978.219971,1978.219971,3106060000
2014-07-21,1976.930054,1976.930054,1965.770020,1973.630005,1973.630005,2611160000
2014-07-22,1975.650024,1986.239990,1975.650024,1983.530029,1983.530029,2890480000
2014-07-23,1985.319946,1989.229980,1982.439941,1987.010010,1987.010010,2869720000
2014-07-24,1988.069946,1991.390015,1985.790039,1987.979980,1987.979980,3203530000
2014-07-25,1984.599976,1984.599976,1974.369995,1978.339966,1978.339966,2638960000
2014-07-28,1978.250000,1981.520020,1967.310059,1978.910034,1978.910034,2803320000
2014-07-29,1980.030029,1984.849976,1969.949951,1969.949951,1969.949951,3183300000
2014-07-30,1973.209961,1978.900024,1962.420044,1970.069946,1970.069946,3448250000
2014-07-31,1965.140015,1965.140015,1930.670044,1930.670044,1930.670044,4193000000
2014-08-01,1929.800049,1937.349976,1916.369995,1925.150024,1925.150024,3789660000
2014-08-04,1926.619995,1942.920044,1921.199951,1938.989990,1938.989990,3072920000
2014-08-05,1936.339966,1936.339966,1913.770020,1920.209961,1920.209961,3462520000
2014-08-06,1917.290039,1927.910034,1911.449951,1920.239990,1920.239990,3539150000
2014-08-07,1923.030029,1928.890015,1904.780029,1909.569946,1909.569946,3230520000
2014-08-08,1910.349976,1932.380005,1909.010010,1931.589966,1931.589966,2902280000
2014-08-11,1933.430054,1944.900024,1933.430054,1936.920044,1936.920044,2784890000
2014-08-12,1935.729980,1939.650024,1928.290039,1933.750000,1933.750000,2611700000
2014-08-13,1935.599976,1948.410034,1935.599976,1946.719971,1946.719971,2718020000
2014-08-14,1947.410034,1955.229980,1947.410034,1955.180054,1955.180054,2609460000
2014-08-15,1958.869995,1964.040039,1941.500000,1955.060059,1955.060059,3023380000
2014-08-18,1958.359985,1971.989990,1958.359985,1971.739990,1971.739990,2638160000
2014-08-19,1972.729980,1982.569946,1972.729980,1981.599976,1981.599976,2656430000
2014-08-20,1980.459961,1988.569946,1977.680054,1986.510010,1986.510010,2579560000
2014-08-21,1986.819946,1994.760010,1986.819946,1992.369995,1992.369995,2638920000
2014-08-22,1992.599976,1993.540039,1984.760010,1988.400024,1988.400024,2301860000
2014-08-25,1991.739990,2001.949951,1991.739990,1997.920044,1997.920044,2233880000
2014-08-26,1998.589966,2005.040039,1998.589966,2000.020020,2000.020020,2451950000
2014-08-27,2000.540039,2002.140015,1996.199951,2000.119995,2000.119995,2344350000
2014-08-28,1997.420044,1998.550049,1990.520020,1996.739990,1996.739990,2282400000
2014-08-29,1998.449951,2003.380005,1994.650024,2003.369995,2003.369995,2259130000
2014-09-02,2004.069946,2006.119995,1994.849976,2002.280029,2002.280029,2819980000
2014-09-03,2003.569946,2009.280029,1998.140015,2000.719971,2000.719971,2809980000
2014-09-04,2001.670044,2011.170044,1992.540039,1997.650024,1997.650024,3072410000
2014-09-05,1998.000000,2007.709961,1990.099976,2007.709961,2007.709961,2818300000
2014-09-08,2007.170044,2007.170044,1995.599976,2001.540039,2001.540039,2789090000
2014-09-09,2000.729980,2001.010010,1984.609985,1988.439941,1988.439941,2882830000
2014-09-10,1988.410034,1996.660034,1982.989990,1995.689941,1995.689941,2912430000
2014-09-11,1992.849976,1997.650024,1985.930054,1997.449951,1997.449951,2941690000
2014-09-12,1996.739990,1996.739990,1980.260010,1985.540039,1985.540039,3206570000
2014-09-15,1986.040039,1987.180054,1978.479980,1984.130005,1984.130005,2776530000
2014-09-16,1981.930054,2002.280029,1979.060059,1998.979980,1998.979980,3160310000
2014-09-17,1999.300049,2010.739990,1993.290039,2001.569946,2001.569946,3209420000
2014-09-18,2003.069946,2012.339966,2003.069946,2011.359985,2011.359985,3235340000
2014-09-19,2012.739990,2019.260010,2006.589966,2010.400024,2010.400024,4880220000
2014-09-22,2009.079956,2009.079956,1991.010010,1994.290039,1994.290039,3349670000
2014-09-23,1992.780029,1995.410034,1982.770020,1982.770020,1982.770020,3279350000
2014-09-24,1983.339966,1999.790039,1978.630005,1998.300049,1998.300049,3313850000
2014-09-25,1997.319946,1997.319946,1965.989990,1965.989990,1965.989990,3273050000
2014-09-26,1966.219971,1986.369995,1966.219971,1982.849976,1982.849976,2929440000
2014-09-29,1978.959961,1981.280029,1964.040039,1977.800049,1977.800049,3094440000
2014-09-30,1978.209961,1985.170044,1968.959961,1972.290039,1972.290039,3951100000
2014-10-01,1971.439941,1971.439941,1941.719971,1946.160034,1946.160034,4188590000
2014-10-02,1945.829956,1952.319946,1926.030029,1946.170044,1946.170044,4012510000
2014-10-03,1948.119995,1971.189941,1948.119995,1967.900024,1967.900024,3560970000
2014-10-06,1970.010010,1977.839966,1958.430054,1964.819946,1964.819946,3358220000
2014-10-07,1962.359985,1962.359985,1934.869995,1935.099976,1935.099976,3687870000
2014-10-08,1935.550049,1970.359985,1925.250000,1968.890015,1968.890015,4441890000
2014-10-09,1967.680054,1967.680054,1927.560059,1928.209961,1928.209961,4344020000
2014-10-10,1925.630005,1936.979980,1906.050049,1906.130005,1906.130005,4550540000
2014-10-13,1905.650024,1912.089966,1874.140015,1874.739990,1874.739990,4352580000
2014-10-14,1877.109985,1898.709961,1871.790039,1877.699951,1877.699951,4812010000
2014-10-15,1874.180054,1874.180054,1820.660034,1862.489990,1862.489990,6090800000
2014-10-16,1855.949951,1876.010010,1835.020020,1862.760010,1862.760010,5073150000
2014-10-17,1864.910034,1898.160034,1864.910034,1886.760010,1886.760010,4482120000
2014-10-20,1885.619995,1905.030029,1882.300049,1904.010010,1904.010010,3331210000
2014-10-21,1909.380005,1942.449951,1909.380005,1941.280029,1941.280029,3987090000
2014-10-22,1941.290039,1949.310059,1926.829956,1927.109985,1927.109985,3761930000
2014-10-23,1931.020020,1961.949951,1931.020020,1950.819946,1950.819946,3789250000
2014-10-24,1951.589966,1965.270020,1946.270020,1964.579956,1964.579956,3078380000
2014-10-27,1962.969971,1964.640015,1951.369995,1961.630005,1961.630005,3538860000
2014-10-28,1964.140015,1985.050049,1964.140015,1985.050049,1985.050049,3653260000
2014-10-29,1983.290039,1991.400024,1969.040039,1982.300049,1982.300049,3740350000
2014-10-30,1979.489990,1999.400024,1974.750000,1994.650024,1994.650024,3586150000
2014-10-31,2001.199951,2018.189941,2001.199951,2018.050049,2018.050049,4292290000
2014-11-03,2018.209961,2024.459961,2013.680054,2017.810059,2017.810059,3555440000
2014-11-04,2015.810059,2015.979980,2001.010010,2012.099976,2012.099976,3956260000
2014-11-05,2015.290039,2023.770020,2014.420044,2023.569946,2023.569946,3766590000
2014-11-06,2023.329956,2031.609985,2015.859985,2031.209961,2031.209961,3669770000
2014-11-07,2032.359985,2034.260010,2025.069946,2031.920044,2031.920044,3704280000
2014-11-10,2032.010010,2038.699951,2030.170044,2038.260010,2038.260010,3284940000
2014-11-11,2038.199951,2041.280029,2035.280029,2039.680054,2039.680054,2958320000
2014-11-12,2037.750000,2040.329956,2031.949951,2038.250000,2038.250000,3246650000
2014-11-13,2039.209961,2046.180054,2030.439941,2039.329956,2039.329956,3455270000
2014-11-14,2039.739990,2042.219971,2035.199951,2039.819946,2039.819946,3227130000
2014-11-17,2038.290039,2043.069946,2034.459961,2041.319946,2041.319946,3152890000
2014-11-18,2041.479980,2056.080078,2041.479980,2051.800049,2051.800049,3416190000
2014-11-19,2051.159912,2052.139893,2040.369995,2048.719971,2048.719971,3390850000
2014-11-20,2045.869995,2053.840088,2040.489990,2052.750000,2052.750000,3128290000
2014-11-21,2057.459961,2071.459961,2056.750000,2063.500000,2063.500000,3916420000
2014-11-24,2065.070068,2070.169922,2065.070068,2069.409912,2069.409912,3128060000
2014-11-25,2070.149902,2074.209961,2064.750000,2067.030029,2067.030029,3392940000
2014-11-26,2067.360107,2073.290039,2066.620117,2072.830078,2072.830078,2745260000
2014-11-28,2074.780029,2075.760010,2065.060059,2067.560059,2067.560059,2504640000
2014-12-01,2065.780029,2065.780029,2049.570068,2053.439941,2053.439941,4159010000
2014-12-02,2053.770020,2068.770020,2053.770020,2066.550049,2066.550049,3686650000
2014-12-03,2067.449951,2076.280029,2066.649902,2074.330078,2074.330078,3612680000
2014-12-04,2073.639893,2077.340088,2062.340088,2071.919922,2071.919922,3408340000
2014-12-05,2072.780029,2079.469971,2070.810059,2075.370117,2075.370117,3419620000
2014-12-08,2074.840088,2075.780029,2054.270020,2060.310059,2060.310059,3800990000
2014-12-09,2056.550049,2060.600098,2034.170044,2059.820068,2059.820068,3970150000
2014-12-10,2058.860107,2058.860107,2024.260010,2026.140015,2026.140015,4114440000
2014-12-11,2027.920044,2055.530029,2027.920044,2035.329956,2035.329956,3917950000
2014-12-12,2030.359985,2032.250000,2002.329956,2002.329956,2002.329956,4157650000
2014-12-15,2005.030029,2018.689941,1982.260010,1989.630005,1989.630005,4361990000
2014-12-16,1986.709961,2016.890015,1972.560059,1972.739990,1972.739990,4958680000
2014-12-17,1973.770020,2016.750000,1973.770020,2012.890015,2012.890015,4942370000
2014-12-18,2018.979980,2061.229980,2018.979980,2061.229980,2061.229980,4703380000
2014-12-19,2061.040039,2077.850098,2061.030029,2070.649902,2070.649902,6465530000
2014-12-22,2069.280029,2078.760010,2069.280029,2078.540039,2078.540039,3369520000
2014-12-23,2081.479980,2086.729980,2079.770020,2082.169922,2082.169922,3043950000
2014-12-24,2083.250000,2087.560059,2081.860107,2081.879883,2081.879883,1416980000
2014-12-26,2084.300049,2092.699951,2084.300049,2088.770020,2088.770020,1735230000
2014-12-29,2087.629883,2093.550049,2085.750000,2090.570068,2090.570068,2452360000
2014-12-30,2088.489990,2088.489990,2079.530029,2080.350098,2080.350098,2440280000
2014-12-31,2082.110107,2085.580078,2057.939941,2058.899902,2058.899902,2606070000
2015-01-02,2058.899902,2072.360107,2046.040039,2058.199951,2058.199951,2708700000
2015-01-05,2054.439941,2054.439941,2017.339966,2020.579956,2020.579956,3799120000
2015-01-06,2022.150024,2030.250000,1992.439941,2002.609985,2002.609985,4460110000
2015-01-07,2005.550049,2029.609985,2005.550049,2025.900024,2025.900024,3805480000
2015-01-08,2030.609985,2064.080078,2030.609985,2062.139893,2062.139893,3934010000
2015-01-09,2063.449951,2064.429932,2038.329956,2044.810059,2044.810059,3364140000
2015-01-12,2046.130005,2049.300049,2022.579956,2028.260010,2028.260010,3456460000
2015-01-13,2031.579956,2056.929932,2008.250000,2023.030029,2023.030029,4107300000
2015-01-14,2018.400024,2018.400024,1988.439941,2011.270020,2011.270020,4378680000
2015-01-15,2013.750000,2021.349976,1991.469971,1992.670044,1992.670044,4276720000
2015-01-16,1992.250000,2020.459961,1988.119995,2019.420044,2019.420044,4056410000
2015-01-20,2020.760010,2028.939941,2004.489990,2022.550049,2022.550049,3944340000
2015-01-21,2020.189941,2038.290039,2012.040039,2032.119995,2032.119995,3730070000
2015-01-22,2034.300049,2064.620117,2026.380005,2063.149902,2063.149902,4176050000
2015-01-23,2062.979980,2062.979980,2050.540039,2051.820068,2051.820068,3573560000
2015-01-26,2050.419922,2057.620117,2040.969971,2057.090088,2057.090088,3465760000
2015-01-27,2047.859985,2047.859985,2019.910034,2029.550049,2029.550049,3329810000
2015-01-28,2032.339966,2042.489990,2001.489990,2002.160034,2002.160034,4067530000
2015-01-29,2002.449951,2024.640015,1989.180054,2021.250000,2021.250000,4127140000
2015-01-30,2019.349976,2023.319946,1993.380005,1994.989990,1994.989990,4568650000
2015-02-02,1996.670044,2021.660034,1980.900024,2020.849976,2020.849976,4008330000
2015-02-03,2022.709961,2050.300049,2022.709961,2050.030029,2050.030029,4615900000
2015-02-04,2048.860107,2054.739990,2036.719971,2041.510010,2041.510010,4141920000
2015-02-05,2043.449951,2063.550049,2043.449951,2062.520020,2062.520020,3821990000
2015-02-06,2062.280029,2072.399902,2049.969971,2055.469971,2055.469971,4232970000
2015-02-09,2053.469971,2056.159912,2041.880005,2046.739990,2046.739990,3549540000
2015-02-10,2049.379883,2070.860107,2048.620117,2068.590088,2068.590088,3669850000
2015-02-11,2068.550049,2073.479980,2057.989990,2068.530029,2068.530029,3596860000
2015-02-12,2069.979980,2088.530029,2069.979980,2088.479980,2088.479980,3788350000
2015-02-13,2088.780029,2097.030029,2086.699951,2096.989990,2096.989990,3527450000
2015-02-17,2096.469971,2101.300049,2089.800049,2100.340088,2100.340088,3361750000
2015-02-18,2099.159912,2100.229980,2092.149902,2099.679932,2099.679932,3370020000
2015-02-19,2099.250000,2102.129883,2090.790039,2097.449951,2097.449951,3247100000
2015-02-20,2097.649902,2110.610107,2085.439941,2110.300049,2110.300049,3281600000
2015-02-23,2109.830078,2110.050049,2103.000000,2109.659912,2109.659912,3093680000
2015-02-24,2109.100098,2117.939941,2105.870117,2115.479980,2115.479980,3199840000
2015-02-25,2115.300049,2119.590088,2109.889893,2113.860107,2113.860107,3312340000
2015-02-26,2113.909912,2113.909912,2103.760010,2110.739990,2110.739990,3408690000
2015-02-27,2110.879883,2112.739990,2103.750000,2104.500000,2104.500000,3547380000
2015-03-02,2105.229980,2117.520020,2104.500000,2117.389893,2117.389893,3409490000
2015-03-03,2115.760010,2115.760010,2098.260010,2107.780029,2107.780029,3262300000
2015-03-04,2107.719971,2107.719971,2094.489990,2098.530029,2098.530029,3421110000
2015-03-05,2098.540039,2104.250000,2095.219971,2101.040039,2101.040039,3103030000
2015-03-06,2100.909912,2100.909912,2067.270020,2071.260010,2071.260010,3853570000
2015-03-09,2072.250000,2083.489990,2072.209961,2079.429932,2079.429932,3349090000
2015-03-10,2076.139893,2076.139893,2044.160034,2044.160034,2044.160034,3668900000
2015-03-11,2044.689941,2050.080078,2039.689941,2040.239990,2040.239990,3406570000
2015-03-12,2041.099976,2066.409912,2041.099976,2065.949951,2065.949951,3405860000
2015-03-13,2064.560059,2064.560059,2041.170044,2053.399902,2053.399902,3498560000
2015-03-16,2055.350098,2081.409912,2055.350098,2081.189941,2081.189941,3295600000
2015-03-17,2080.590088,2080.590088,2065.080078,2074.280029,2074.280029,3221840000
2015-03-18,2072.840088,2106.850098,2061.229980,2099.500000,2099.500000,4128210000
2015-03-19,2098.689941,2098.689941,2085.560059,2089.270020,2089.270020,3305220000
2015-03-20,2090.320068,2113.919922,2090.320068,2108.100098,2108.100098,5554120000
2015-03-23,2107.989990,2114.860107,2104.419922,2104.419922,2104.419922,3267960000
2015-03-24,2103.939941,2107.629883,2091.500000,2091.500000,2091.500000,3189820000
2015-03-25,2093.100098,2097.429932,2061.050049,2061.050049,2061.050049,3521140000
2015-03-26,2059.939941,2067.149902,2045.500000,2056.149902,2056.149902,3510670000
2015-03-27,2055.780029,2062.830078,2052.959961,2061.020020,2061.020020,3008550000
2015-03-30,2064.110107,2088.969971,2064.110107,2086.239990,2086.239990,2917690000
2015-03-31,2084.050049,2084.050049,2067.040039,2067.889893,2067.889893,3376550000
2015-04-01,2067.629883,2067.629883,2048.379883,2059.689941,2059.689941,3543270000
2015-04-02,2060.030029,2072.169922,2057.320068,2066.959961,2066.959961,3095960000
2015-04-06,2064.870117,2086.989990,2056.520020,2080.620117,2080.620117,3302970000
2015-04-07,2080.790039,2089.810059,2076.100098,2076.330078,2076.330078,3065510000
2015-04-08,2076.939941,2086.689941,2073.300049,2081.899902,2081.899902,3265330000
2015-04-09,2081.290039,2093.310059,2074.290039,2091.179932,2091.179932,3172360000
2015-04-10,2091.510010,2102.610107,2091.510010,2102.060059,2102.060059,3156200000
2015-04-13,2102.030029,2107.649902,2092.330078,2092.429932,2092.429932,2908420000
2015-04-14,2092.280029,2098.620117,2083.239990,2095.840088,2095.840088,3301270000
2015-04-15,2097.820068,2111.909912,2097.820068,2106.629883,2106.629883,4013760000
2015-04-16,2105.959961,2111.300049,2100.020020,2104.989990,2104.989990,3434120000
2015-04-17,2102.580078,2102.580078,2072.370117,2081.179932,2081.179932,3627600000
2015-04-20,2084.110107,2103.939941,2084.110107,2100.399902,2100.399902,3000160000
2015-04-21,2102.820068,2109.639893,2094.379883,2097.290039,2097.290039,3243410000
2015-04-22,2098.270020,2109.979980,2091.050049,2107.959961,2107.959961,3348480000
2015-04-23,2107.209961,2120.489990,2103.189941,2112.929932,2112.929932,3636670000
2015-04-24,2112.800049,2120.919922,2112.800049,2117.689941,2117.689941,3375780000
2015-04-27,2119.290039,2125.919922,2107.040039,2108.919922,2108.919922,3438750000
2015-04-28,2108.350098,2116.040039,2094.889893,2114.760010,2114.760010,3546270000
2015-04-29,2112.489990,2113.649902,2097.409912,2106.850098,2106.850098,4074970000
2015-04-30,2105.520020,2105.520020,2077.590088,2085.510010,2085.510010,4509680000
2015-05-01,2087.379883,2108.409912,2087.379883,2108.290039,2108.290039,3379390000
2015-05-04,2110.229980,2120.949951,2110.229980,2114.489990,2114.489990,3091580000
2015-05-05,2112.629883,2115.239990,2088.459961,2089.459961,2089.459961,3793950000
2015-05-06,2091.260010,2098.419922,2067.929932,2080.149902,2080.149902,3792210000
2015-05-07,2079.959961,2092.899902,2074.989990,2088.000000,2088.000000,3676640000
2015-05-08,2092.129883,2117.659912,2092.129883,2116.100098,2116.100098,3399440000
2015-05-11,2115.560059,2117.689941,2104.580078,2105.330078,2105.330078,2992670000
2015-05-12,2102.870117,2105.060059,2085.570068,2099.120117,2099.120117,3139520000
2015-05-13,2099.620117,2110.189941,2096.040039,2098.479980,2098.479980,3374260000
2015-05-14,2100.429932,2121.449951,2100.429932,2121.100098,2121.100098,3225740000
2015-05-15,2122.070068,2123.889893,2116.810059,2122.729980,2122.729980,3092080000
2015-05-18,2121.300049,2131.780029,2120.010010,2129.199951,2129.199951,2888190000
2015-05-19,2129.449951,2133.020020,2124.500000,2127.830078,2127.830078,3296030000
2015-05-20,2127.790039,2134.719971,2122.590088,2125.850098,2125.850098,3025880000
2015-05-21,2125.550049,2134.280029,2122.949951,2130.820068,2130.820068,3070460000
2015-05-22,2130.360107,2132.149902,2126.060059,2126.060059,2126.060059,2571860000
2015-05-26,2125.340088,2125.340088,2099.179932,2104.199951,2104.199951,3342130000
2015-05-27,2105.129883,2126.219971,2105.129883,2123.479980,2123.479980,3127960000
2015-05-28,2122.270020,2122.270020,2112.860107,2120.790039,2120.790039,2980350000
2015-05-29,2120.659912,2120.659912,2104.889893,2107.389893,2107.389893,3927390000
2015-06-01,2108.639893,2119.149902,2102.540039,2111.729980,2111.729980,3011710000
2015-06-02,2110.409912,2117.590088,2099.139893,2109.600098,2109.600098,3049350000
2015-06-03,2110.639893,2121.919922,2109.610107,2114.070068,2114.070068,3099980000
2015-06-04,2112.350098,2112.889893,2093.229980,2095.840088,2095.840088,3200050000
2015-06-05,2095.090088,2100.989990,2085.669922,2092.830078,2092.830078,3243690000
2015-06-08,2092.340088,2093.010010,2079.110107,2079.280029,2079.280029,2917150000
2015-06-09,2079.070068,2085.620117,2072.139893,2080.149902,2080.149902,3034580000
2015-06-10,2081.120117,2108.500000,2081.120117,2105.199951,2105.199951,3414320000
2015-06-11,2106.239990,2115.020020,2106.239990,2108.860107,2108.860107,3128600000
2015-06-12,2107.429932,2107.429932,2091.330078,2094.110107,2094.110107,2719400000
2015-06-15,2091.340088,2091.340088,2072.489990,2084.429932,2084.429932,3061570000
2015-06-16,2084.260010,2097.399902,2082.100098,2096.290039,2096.290039,2919900000
2015-06-17,2097.399902,2106.790039,2088.860107,2100.439941,2100.439941,3222240000
2015-06-18,2101.580078,2126.649902,2101.580078,2121.239990,2121.239990,3520360000
2015-06-19,2121.060059,2121.639893,2109.449951,2109.989990,2109.989990,4449810000
2015-06-22,2112.500000,2129.870117,2112.500000,2122.850098,2122.850098,3030020000
2015-06-23,2123.159912,2128.030029,2119.889893,2124.199951,2124.199951,3091190000
2015-06-24,2123.649902,2125.100098,2108.580078,2108.580078,2108.580078,3102480000
2015-06-25,2109.959961,2116.040039,2101.780029,2102.310059,2102.310059,3214610000
2015-06-26,2102.620117,2108.919922,2095.379883,2101.489990,2101.489990,5025470000
2015-06-29,2098.629883,2098.629883,2056.639893,2057.639893,2057.639893,3678960000
2015-06-30,2061.189941,2074.280029,2056.320068,2063.110107,2063.110107,4078540000
2015-07-01,2067.000000,2082.780029,2067.000000,2077.419922,2077.419922,3727260000
2015-07-02,2078.030029,2085.060059,2071.020020,2076.780029,2076.780029,2996540000
2015-07-06,2073.949951,2078.610107,2058.399902,2068.760010,2068.760010,3486360000
2015-07-07,2069.520020,2083.739990,2044.020020,2081.340088,2081.340088,4458660000
2015-07-08,2077.659912,2077.659912,2044.660034,2046.680054,2046.680054,3608780000
2015-07-09,2049.729980,2074.280029,2049.729980,2051.310059,2051.310059,3446810000
2015-07-10,2052.739990,2081.310059,2052.739990,2076.620117,2076.620117,3065070000
2015-07-13,2080.030029,2100.669922,2080.030029,2099.600098,2099.600098,3096730000
2015-07-14,2099.719971,2111.979980,2098.179932,2108.949951,2108.949951,3002120000
2015-07-15,2109.010010,2114.139893,2102.489990,2107.399902,2107.399902,3261810000
2015-07-16,2110.550049,2124.419922,2110.550049,2124.290039,2124.290039,3227080000
2015-07-17,2126.800049,2128.909912,2119.879883,2126.639893,2126.639893,3362750000
2015-07-20,2126.850098,2132.820068,2123.659912,2128.280029,2128.280029,3245870000
2015-07-21,2127.550049,2128.489990,2115.399902,2119.209961,2119.209961,3343690000
2015-07-22,2118.209961,2118.510010,2110.000000,2114.149902,2114.149902,3694070000
2015-07-23,2114.159912,2116.870117,2098.629883,2102.149902,2102.149902,3772810000
2015-07-24,2102.239990,2106.010010,2077.090088,2079.649902,2079.649902,3870040000
2015-07-27,2078.189941,2078.189941,2063.520020,2067.639893,2067.639893,3836750000
2015-07-28,2070.750000,2095.600098,2069.090088,2093.250000,2093.250000,4117740000
2015-07-29,2094.699951,2110.600098,2094.080078,2108.570068,2108.570068,4038900000
2015-07-30,2106.780029,2110.479980,2094.969971,2108.629883,2108.629883,3579410000
2015-07-31,2111.600098,2114.239990,2102.070068,2103.840088,2103.840088,3681340000
2015-08-03,2104.489990,2105.699951,2087.310059,2098.040039,2098.040039,3476770000
2015-08-04,2097.679932,2102.510010,2088.600098,2093.320068,2093.320068,3546710000
2015-08-05,2095.270020,2112.659912,2095.270020,2099.840088,2099.840088,3968680000
2015-08-06,2100.750000,2103.320068,2075.530029,2083.560059,2083.560059,4246570000
2015-08-07,2082.610107,2082.610107,2067.909912,2077.570068,2077.570068,3602320000
2015-08-10,2080.979980,2105.350098,2080.979980,2104.179932,2104.179932,3514460000
2015-08-11,2102.659912,2102.659912,2076.489990,2084.070068,2084.070068,3708880000
2015-08-12,2081.100098,2089.060059,2052.090088,2086.050049,2086.050049,4269130000
2015-08-13,2086.189941,2092.929932,2078.260010,2083.389893,2083.389893,3221300000
2015-08-14,2083.149902,2092.449951,2080.610107,2091.540039,2091.540039,2795590000
2015-08-17,2089.699951,2102.870117,2079.300049,2102.439941,2102.439941,2867690000
2015-08-18,2101.989990,2103.469971,2094.139893,2096.919922,2096.919922,2949990000
2015-08-19,2095.689941,2096.169922,2070.530029,2079.610107,2079.610107,3512920000
2015-08-20,2076.610107,2076.610107,2035.729980,2035.729980,2035.729980,3922470000
2015-08-21,2034.079956,2034.079956,1970.890015,1970.890015,1970.890015,5018240000
2015-08-24,1965.150024,1965.150024,1867.010010,1893.209961,1893.209961,6612690000
2015-08-25,1898.079956,1948.040039,1867.079956,1867.609985,1867.609985,5183560000
2015-08-26,1872.750000,1943.089966,1872.750000,1940.510010,1940.510010,5338250000
2015-08-27,1942.770020,1989.599976,1942.770020,1987.660034,1987.660034,5006390000
2015-08-28,1986.060059,1993.479980,1975.189941,1988.869995,1988.869995,3949080000
2015-08-31,1986.729980,1986.729980,1965.979980,1972.180054,1972.180054,3915100000
2015-09-01,1970.089966,1970.089966,1903.069946,1913.849976,1913.849976,4371850000
2015-09-02,1916.520020,1948.910034,1916.520020,1948.859985,1948.859985,3742620000
2015-09-03,1950.790039,1975.010010,1944.719971,1951.130005,1951.130005,3520700000
2015-09-04,1947.760010,1947.760010,1911.209961,1921.219971,1921.219971,3167090000
2015-09-08,1927.300049,1970.420044,1927.300049,1969.410034,1969.410034,3548650000
2015-09-09,1971.449951,1988.630005,1937.880005,1942.040039,1942.040039,3652120000
2015-09-10,1941.589966,1965.290039,1937.189941,1952.290039,1952.290039,3626320000
2015-09-11,1951.449951,1961.050049,1939.189941,1961.050049,1961.050049,3218590000
2015-09-14,1963.060059,1963.060059,1948.270020,1953.030029,1953.030029,3000200000
2015-09-15,1955.099976,1983.189941,1954.300049,1978.089966,1978.089966,3239860000
2015-09-16,1978.020020,1997.260010,1977.930054,1995.310059,1995.310059,3630680000
2015-09-17,1995.329956,2020.859985,1986.729980,1990.199951,1990.199951,4183790000
2015-09-18,1989.660034,1989.660034,1953.449951,1958.030029,1958.030029,6021240000
2015-09-21,1960.839966,1979.640015,1955.800049,1966.969971,1966.969971,3269350000
2015-09-22,1961.390015,1961.390015,1929.219971,1942.739990,1942.739990,3808260000
2015-09-23,1943.239990,1949.520020,1932.569946,1938.760010,1938.760010,3190530000
2015-09-24,1934.810059,1937.170044,1908.920044,1932.239990,1932.239990,4091530000
2015-09-25,1935.930054,1952.890015,1921.500000,1931.339966,1931.339966,3721870000
2015-09-28,1929.180054,1929.180054,1879.209961,1881.770020,1881.770020,4326660000
2015-09-29,1881.900024,1899.479980,1871.910034,1884.089966,1884.089966,4132390000
2015-09-30,1887.140015,1920.530029,1887.140015,1920.030029,1920.030029,4525070000
2015-10-01,1919.650024,1927.209961,1900.699951,1923.819946,1923.819946,3983600000
2015-10-02,1921.770020,1951.359985,1893.699951,1951.359985,1951.359985,4378570000
2015-10-05,1954.329956,1989.170044,1954.329956,1987.050049,1987.050049,4334490000
2015-10-06,1986.630005,1991.619995,1971.989990,1979.920044,1979.920044,4202400000
2015-10-07,1982.339966,1999.310059,1976.439941,1995.829956,1995.829956,4666470000
2015-10-08,1994.010010,2016.500000,1987.530029,2013.430054,2013.430054,3939140000
2015-10-09,2013.729980,2020.130005,2007.609985,2014.890015,2014.890015,3706900000
2015-10-12,2015.650024,2018.660034,2010.550049,2017.459961,2017.459961,2893250000
2015-10-13,2015.000000,2022.339966,2001.780029,2003.689941,2003.689941,3401920000
2015-10-14,2003.660034,2009.560059,1990.729980,1994.239990,1994.239990,3644590000
2015-10-15,1996.469971,2024.150024,1996.469971,2023.859985,2023.859985,3746290000
2015-10-16,2024.369995,2033.540039,2020.459961,2033.109985,2033.109985,3595430000
2015-10-19,2031.729980,2034.449951,2022.310059,2033.660034,2033.660034,3287320000
2015-10-20,2033.130005,2039.119995,2026.609985,2030.770020,2030.770020,3331500000
2015-10-21,2033.469971,2037.969971,2017.219971,2018.939941,2018.939941,3627790000
2015-10-22,2021.880005,2055.199951,2021.880005,2052.510010,2052.510010,4430850000
2015-10-23,2058.189941,2079.739990,2058.189941,2075.149902,2075.149902,4108460000
2015-10-26,2075.080078,2075.139893,2066.530029,2071.179932,2071.179932,3385800000
2015-10-27,2068.750000,2070.370117,2058.840088,2065.889893,2065.889893,4216880000
2015-10-28,2066.479980,2090.350098,2063.110107,2090.350098,2090.350098,4698110000
2015-10-29,2088.350098,2092.520020,2082.629883,2089.409912,2089.409912,4008940000
2015-10-30,2090.000000,2094.320068,2079.340088,2079.360107,2079.360107,4256200000
2015-11-02,2080.760010,2106.199951,2080.760010,2104.050049,2104.050049,3760020000
2015-11-03,2102.629883,2116.479980,2097.510010,2109.790039,2109.790039,4272060000
2015-11-04,2110.600098,2114.590088,2096.979980,2102.310059,2102.310059,4078870000
2015-11-05,2101.679932,2108.780029,2090.409912,2099.929932,2099.929932,4051890000
2015-11-06,2098.600098,2101.909912,2083.739990,2099.199951,2099.199951,4369020000
2015-11-09,2096.560059,2096.560059,2068.239990,2078.580078,2078.580078,3882350000
2015-11-10,2077.189941,2083.669922,2069.909912,2081.719971,2081.719971,3821440000
2015-11-11,2083.409912,2086.939941,2074.850098,2075.000000,2075.000000,3692410000
2015-11-12,2072.290039,2072.290039,2045.660034,2045.969971,2045.969971,4016370000
2015-11-13,2044.640015,2044.640015,2022.020020,2023.040039,2023.040039,4278750000
2015-11-16,2022.079956,2053.219971,2019.390015,2053.189941,2053.189941,3741240000
2015-11-17,2053.669922,2066.689941,2045.900024,2050.439941,2050.439941,4427350000
2015-11-18,2051.989990,2085.310059,2051.989990,2083.580078,2083.580078,3926390000
2015-11-19,2083.699951,2086.739990,2078.760010,2081.239990,2081.239990,3628110000
2015-11-20,2082.820068,2097.060059,2082.820068,2089.169922,2089.169922,3929600000
2015-11-23,2089.409912,2095.610107,2081.389893,2086.590088,2086.590088,3587980000
2015-11-24,2084.419922,2094.120117,2070.290039,2089.139893,2089.139893,3884930000
2015-11-25,2089.300049,2093.000000,2086.300049,2088.870117,2088.870117,2852940000
2015-11-27,2088.820068,2093.290039,2084.129883,2090.110107,2090.110107,1466840000
2015-11-30,2090.949951,2093.810059,2080.409912,2080.409912,2080.409912,4275030000
2015-12-01,2082.929932,2103.370117,2082.929932,2102.629883,2102.629883,3712120000
2015-12-02,2101.709961,2104.270020,2077.110107,2079.510010,2079.510010,3950640000
2015-12-03,2080.709961,2085.000000,2042.349976,2049.620117,2049.620117,4306490000
2015-12-04,2051.239990,2093.840088,2051.239990,2091.689941,2091.689941,4214910000
2015-12-07,2090.419922,2090.419922,2066.780029,2077.070068,2077.070068,4043820000
2015-12-08,2073.389893,2073.850098,2052.320068,2063.590088,2063.590088,4173570000
2015-12-09,2061.169922,2080.330078,2036.530029,2047.619995,2047.619995,4385250000
2015-12-10,2047.930054,2067.649902,2045.670044,2052.229980,2052.229980,3715150000
2015-12-11,2047.270020,2047.270020,2008.800049,2012.369995,2012.369995,4301060000
2015-12-14,2013.369995,2022.920044,1993.260010,2021.939941,2021.939941,4612440000
2015-12-15,2025.550049,2053.870117,2025.550049,2043.410034,2043.410034,4353540000
2015-12-16,2046.500000,2076.719971,2042.430054,2073.070068,2073.070068,4635450000
2015-12-17,2073.760010,2076.370117,2041.660034,2041.890015,2041.890015,4327390000
2015-12-18,2040.810059,2040.810059,2005.329956,2005.550049,2005.550049,6683070000
2015-12-21,2010.270020,2022.900024,2005.930054,2021.150024,2021.150024,3760280000
2015-12-22,2023.150024,2042.739990,2020.489990,2038.969971,2038.969971,3520860000
2015-12-23,2042.199951,2064.729980,2042.199951,2064.290039,2064.290039,3484090000
2015-12-24,2063.520020,2067.360107,2058.729980,2060.989990,2060.989990,1411860000
2015-12-28,2057.770020,2057.770020,2044.199951,2056.500000,2056.500000,2492510000
2015-12-29,2060.540039,2081.560059,2060.540039,2078.360107,2078.360107,2542000000
2015-12-30,2077.340088,2077.340088,2061.969971,2063.360107,2063.360107,2367430000
2015-12-31,2060.590088,2062.540039,2043.619995,2043.939941,2043.939941,2655330000
2016-01-04,2038.199951,2038.199951,1989.680054,2012.660034,2012.660034,4304880000
2016-01-05,2013.780029,2021.939941,2004.170044,2016.709961,2016.709961,3706620000
2016-01-06,2011.709961,2011.709961,1979.050049,1990.260010,1990.260010,4336660000
2016-01-07,1985.319946,1985.319946,1938.829956,1943.089966,1943.089966,5076590000
2016-01-08,1945.969971,1960.400024,1918.459961,1922.030029,1922.030029,4664940000
2016-01-11,1926.119995,1935.650024,1901.099976,1923.670044,1923.670044,4607290000
2016-01-12,1927.829956,1947.380005,1914.349976,1938.680054,1938.680054,4887260000
2016-01-13,1940.339966,1950.329956,1886.410034,1890.280029,1890.280029,5087030000
2016-01-14,1891.680054,1934.469971,1878.930054,1921.839966,1921.839966,5241110000
2016-01-15,1916.680054,1916.680054,1857.829956,1880.329956,1880.329956,5468460000
2016-01-19,1888.660034,1901.439941,1864.599976,1881.329956,1881.329956,4928350000
2016-01-20,1876.180054,1876.180054,1812.290039,1859.329956,1859.329956,6416070000
2016-01-21,1861.459961,1889.849976,1848.979980,1868.989990,1868.989990,5078810000
2016-01-22,1877.400024,1908.849976,1877.400024,1906.900024,1906.900024,4901760000
2016-01-25,1906.280029,1906.280029,1875.969971,1877.079956,1877.079956,4401380000
2016-01-26,1878.790039,1906.729980,1878.790039,1903.630005,1903.630005,4357940000
2016-01-27,1902.520020,1916.989990,1872.699951,1882.949951,1882.949951,4754040000
2016-01-28,1885.219971,1902.959961,1873.650024,1893.359985,1893.359985,4693010000
2016-01-29,1894.000000,1940.239990,1894.000000,1940.239990,1940.239990,5497570000
2016-02-01,1936.939941,1947.199951,1920.300049,1939.380005,1939.380005,4322530000
2016-02-02,1935.260010,1935.260010,1897.290039,1903.030029,1903.030029,4463190000
2016-02-03,1907.069946,1918.010010,1872.229980,1912.530029,1912.530029,5172950000
2016-02-04,1911.670044,1927.349976,1900.520020,1915.449951,1915.449951,5193320000
2016-02-05,1913.069946,1913.069946,1872.650024,1880.050049,1880.050049,4929940000
2016-02-08,1873.250000,1873.250000,1828.459961,1853.439941,1853.439941,5636460000
2016-02-09,1848.459961,1868.250000,1834.939941,1852.209961,1852.209961,5183220000
2016-02-10,1857.099976,1881.599976,1850.319946,1851.859985,1851.859985,4471170000
2016-02-11,1847.000000,1847.000000,1810.099976,1829.079956,1829.079956,5500800000
2016-02-12,1833.400024,1864.780029,1833.400024,1864.780029,1864.780029,4696920000
2016-02-16,1871.439941,1895.770020,1871.439941,1895.579956,1895.579956,4570670000
2016-02-17,1898.800049,1930.680054,1898.800049,1926.819946,1926.819946,5011540000
2016-02-18,1927.569946,1930.000000,1915.089966,1917.829956,1917.829956,4436490000
2016-02-19,1916.739990,1918.780029,1902.170044,1917.780029,1917.780029,4142850000
2016-02-22,1924.439941,1946.699951,1924.439941,1945.500000,1945.500000,4054710000
2016-02-23,1942.380005,1942.380005,1919.439941,1921.270020,1921.270020,3890650000
2016-02-24,1917.560059,1932.079956,1891.000000,1929.800049,1929.800049,4317250000
2016-02-25,1931.869995,1951.829956,1925.410034,1951.699951,1951.699951,4118210000
2016-02-26,1954.949951,1962.959961,1945.780029,1948.050049,1948.050049,4348510000
2016-02-29,1947.130005,1958.270020,1931.810059,1932.229980,1932.229980,4588180000
2016-03-01,1937.089966,1978.349976,1937.089966,1978.349976,1978.349976,4819750000
2016-03-02,1976.599976,1986.510010,1968.800049,1986.449951,1986.449951,4666610000
2016-03-03,1985.599976,1993.689941,1977.369995,1993.400024,1993.400024,5081700000
2016-03-04,1994.010010,2009.130005,1986.770020,1999.989990,1999.989990,6049930000
2016-03-07,1996.109985,2006.119995,1989.380005,2001.760010,2001.760010,4968180000
2016-03-08,1996.880005,1996.880005,1977.430054,1979.260010,1979.260010,4641650000
2016-03-09,1981.439941,1992.689941,1979.839966,1989.260010,1989.260010,4038120000
2016-03-10,1990.969971,2005.079956,1969.250000,1989.569946,1989.569946,4376790000
2016-03-11,1994.709961,2022.369995,1994.709961,2022.189941,2022.189941,4078620000
2016-03-14,2019.270020,2024.569946,2012.050049,2019.640015,2019.640015,3487850000
2016-03-15,2015.270020,2015.939941,2005.229980,2015.930054,2015.930054,3560280000
2016-03-16,2014.239990,2032.020020,2010.040039,2027.219971,2027.219971,4057020000
2016-03-17,2026.900024,2046.239990,2022.160034,2040.589966,2040.589966,4530480000
2016-03-18,2041.160034,2052.360107,2041.160034,2049.580078,2049.580078,6503140000
2016-03-21,2047.880005,2053.909912,2043.140015,2051.600098,2051.600098,3376600000
2016-03-22,2048.639893,2056.600098,2040.569946,2049.800049,2049.800049,3418460000
2016-03-23,2048.550049,2048.550049,2034.859985,2036.709961,2036.709961,3639510000
2016-03-24,2032.479980,2036.040039,2022.489990,2035.939941,2035.939941,3407720000
2016-03-28,2037.890015,2042.670044,2031.959961,2037.050049,2037.050049,2809090000
2016-03-29,2035.750000,2055.909912,2028.310059,2055.010010,2055.010010,3822330000
2016-03-30,2058.270020,2072.209961,2058.270020,2063.949951,2063.949951,3590310000
2016-03-31,2063.770020,2067.919922,2057.459961,2059.739990,2059.739990,3715280000
2016-04-01,2056.620117,2075.070068,2043.979980,2072.780029,2072.780029,3749990000
2016-04-04,2073.189941,2074.020020,2062.570068,2066.129883,2066.129883,3485710000
2016-04-05,2062.500000,2062.500000,2042.560059,2045.170044,2045.170044,4154920000
2016-04-06,2045.560059,2067.330078,2043.089966,2066.659912,2066.659912,3750800000
2016-04-07,2063.010010,2063.010010,2033.800049,2041.910034,2041.910034,3801250000
2016-04-08,2045.540039,2060.629883,2041.689941,2047.599976,2047.599976,3359530000
2016-04-11,2050.229980,2062.929932,2041.880005,2041.989990,2041.989990,3567840000
2016-04-12,2043.719971,2065.050049,2039.739990,2061.719971,2061.719971,4239740000
2016-04-13,2065.919922,2083.179932,2065.919922,2082.419922,2082.419922,4191830000
2016-04-14,2082.889893,2087.840088,2078.129883,2082.780029,2082.780029,3765870000
2016-04-15,2083.100098,2083.219971,2076.310059,2080.729980,2080.729980,3701450000
2016-04-18,2078.830078,2094.659912,2073.649902,2094.340088,2094.340088,3316880000
2016-04-19,2096.050049,2104.050049,2091.679932,2100.800049,2100.800049,3896830000
2016-04-20,2101.520020,2111.050049,2096.320068,2102.399902,2102.399902,4184880000
2016-04-21,2102.090088,2103.780029,2088.520020,2091.479980,2091.479980,4175290000
2016-04-22,2091.489990,2094.320068,2081.199951,2091.580078,2091.580078,3790580000
2016-04-25,2089.370117,2089.370117,2077.520020,2087.790039,2087.790039,3319740000
2016-04-26,2089.840088,2096.870117,2085.800049,2091.699951,2091.699951,3557190000
2016-04-27,2092.330078,2099.889893,2082.310059,2095.149902,2095.149902,4100110000
2016-04-28,2090.929932,2099.300049,2071.620117,2075.810059,2075.810059,4309840000
2016-04-29,2071.820068,2073.850098,2052.280029,2065.300049,2065.300049,4704720000
2016-05-02,2067.169922,2083.419922,2066.110107,2081.429932,2081.429932,3841110000
2016-05-03,2077.179932,2077.179932,2054.889893,2063.370117,2063.370117,4173390000
2016-05-04,2060.300049,2060.300049,2045.550049,2051.120117,2051.120117,4058560000
2016-05-05,2052.949951,2060.229980,2045.770020,2050.629883,2050.629883,4008530000
2016-05-06,2047.770020,2057.719971,2039.449951,2057.139893,2057.139893,3796350000
2016-05-09,2057.550049,2064.149902,2054.310059,2058.689941,2058.689941,3788620000
2016-05-10,2062.629883,2084.870117,2062.629883,2084.389893,2084.389893,3600200000
2016-05-11,2083.290039,2083.290039,2064.459961,2064.459961,2064.459961,3821980000
2016-05-12,2067.169922,2073.989990,2053.129883,2064.110107,2064.110107,3782390000
2016-05-13,2062.500000,2066.790039,2043.130005,2046.609985,2046.609985,3579880000
2016-05-16,2046.530029,2071.879883,2046.530029,2066.659912,2066.659912,3501360000
2016-05-17,2065.040039,2065.689941,2040.819946,2047.209961,2047.209961,4108960000
2016-05-18,2044.380005,2060.610107,2034.489990,2047.630005,2047.630005,4101320000
2016-05-19,2044.209961,2044.209961,2025.910034,2040.040039,2040.040039,3846770000
2016-05-20,2041.880005,2058.350098,2041.880005,2052.320068,2052.320068,3507650000
2016-05-23,2052.229980,2055.580078,2047.260010,2048.040039,2048.040039,3055480000
2016-05-24,2052.649902,2079.669922,2052.649902,2076.060059,2076.060059,3627340000
2016-05-25,2078.929932,2094.729980,2078.929932,2090.540039,2090.540039,3859160000
2016-05-26,2091.439941,2094.300049,2087.080078,2090.100098,2090.100098,3230990000
2016-05-27,2090.060059,2099.060059,2090.060059,2099.060059,2099.060059,3079150000
2016-05-31,2100.129883,2103.479980,2088.659912,2096.949951,2096.949951,4514410000
2016-06-01,2093.939941,2100.969971,2085.100098,2099.330078,2099.330078,3525170000
2016-06-02,2097.709961,2105.260010,2088.590088,2105.260010,2105.260010,3632720000
2016-06-03,2104.070068,2104.070068,2085.360107,2099.129883,2099.129883,3627780000
2016-06-06,2100.830078,2113.360107,2100.830078,2109.409912,2109.409912,3442020000
2016-06-07,2110.179932,2119.219971,2110.179932,2112.129883,2112.129883,3534730000
2016-06-08,2112.709961,2120.550049,2112.709961,2119.120117,2119.120117,3562060000
2016-06-09,2115.649902,2117.639893,2107.729980,2115.479980,2115.479980,3290320000
2016-06-10,2109.570068,2109.570068,2089.959961,2096.070068,2096.070068,3515010000
2016-06-13,2091.750000,2098.120117,2078.459961,2079.060059,2079.060059,3392030000
2016-06-14,2076.649902,2081.300049,2064.100098,2075.320068,2075.320068,3759770000
2016-06-15,2077.600098,2085.649902,2069.800049,2071.500000,2071.500000,3544720000
2016-06-16,2066.360107,2079.620117,2050.370117,2077.989990,2077.989990,3628280000
2016-06-17,2078.199951,2078.199951,2062.840088,2071.219971,2071.219971,4952630000
2016-06-20,2075.580078,2100.659912,2075.580078,2083.250000,2083.250000,3467440000
2016-06-21,2085.189941,2093.659912,2083.020020,2088.899902,2088.899902,3232880000
2016-06-22,2089.750000,2099.709961,2084.360107,2085.449951,2085.449951,3168160000
2016-06-23,2092.800049,2113.320068,2092.800049,2113.320068,2113.320068,3297940000
2016-06-24,2103.810059,2103.810059,2032.569946,2037.410034,2037.410034,7597450000
2016-06-27,2031.449951,2031.449951,1991.680054,2000.540039,2000.540039,5431220000
2016-06-28,2006.670044,2036.089966,2006.670044,2036.089966,2036.089966,4385810000
2016-06-29,2042.689941,2073.129883,2042.689941,2070.770020,2070.770020,4241740000
2016-06-30,2073.169922,2098.939941,2070.000000,2098.860107,2098.860107,4622820000
2016-07-01,2099.340088,2108.709961,2097.899902,2102.949951,2102.949951,3458890000
2016-07-05,2095.050049,2095.050049,2080.860107,2088.550049,2088.550049,3658380000
2016-07-06,2084.429932,2100.719971,2074.020020,2099.729980,2099.729980,3909380000
2016-07-07,2100.419922,2109.080078,2089.389893,2097.899902,2097.899902,3604550000
2016-07-08,2106.969971,2131.709961,2106.969971,2129.899902,2129.899902,3607500000
2016-07-11,2131.719971,2143.159912,2131.719971,2137.159912,2137.159912,3253340000
2016-07-12,2139.500000,2155.399902,2139.500000,2152.139893,2152.139893,4097820000
2016-07-13,2153.810059,2156.449951,2146.209961,2152.429932,2152.429932,3502320000
2016-07-14,2157.879883,2168.989990,2157.879883,2163.750000,2163.750000,3465610000
2016-07-15,2165.129883,2169.050049,2155.790039,2161.739990,2161.739990,3122600000
2016-07-18,2162.040039,2168.350098,2159.629883,2166.889893,2166.889893,3009310000
2016-07-19,2163.790039,2164.629883,2159.010010,2163.780029,2163.780029,2968340000
2016-07-20,2166.100098,2175.629883,2164.889893,2173.020020,2173.020020,3211860000
2016-07-21,2172.909912,2174.560059,2159.750000,2165.169922,2165.169922,3438900000
2016-07-22,2166.469971,2175.110107,2163.239990,2175.030029,2175.030029,3023280000
2016-07-25,2173.709961,2173.709961,2161.949951,2168.479980,2168.479980,3057240000
2016-07-26,2168.969971,2173.540039,2160.179932,2169.179932,2169.179932,3442350000
2016-07-27,2169.810059,2174.979980,2159.070068,2166.580078,2166.580078,3995500000
2016-07-28,2166.050049,2172.850098,2159.739990,2170.060059,2170.060059,3664240000
2016-07-29,2168.830078,2177.090088,2163.489990,2173.600098,2173.600098,4038840000
2016-08-01,2173.149902,2178.290039,2166.209961,2170.840088,2170.840088,3505990000
2016-08-02,2169.939941,2170.199951,2147.580078,2157.030029,2157.030029,3848750000
2016-08-03,2156.810059,2163.790039,2152.560059,2163.790039,2163.790039,3786530000
2016-08-04,2163.510010,2168.189941,2159.070068,2164.250000,2164.250000,3709200000
2016-08-05,2168.790039,2182.870117,2168.790039,2182.870117,2182.870117,3663070000
2016-08-08,2183.760010,2185.439941,2177.850098,2180.889893,2180.889893,3327550000
2016-08-09,2182.239990,2187.659912,2178.610107,2181.739990,2181.739990,3334300000
2016-08-10,2182.810059,2183.409912,2172.000000,2175.489990,2175.489990,3254950000
2016-08-11,2177.969971,2188.449951,2177.969971,2185.790039,2185.790039,3423160000
2016-08-12,2183.739990,2186.280029,2179.419922,2184.050049,2184.050049,3000660000
2016-08-15,2186.080078,2193.810059,2186.080078,2190.149902,2190.149902,3078530000
2016-08-16,2186.239990,2186.239990,2178.139893,2178.149902,2178.149902,3196400000
2016-08-17,2177.840088,2183.080078,2168.500000,2182.219971,2182.219971,3388910000
2016-08-18,2181.899902,2187.030029,2180.459961,2187.020020,2187.020020,3300570000
2016-08-19,2184.239990,2185.000000,2175.129883,2183.870117,2183.870117,3084800000
2016-08-22,2181.580078,2185.149902,2175.959961,2182.639893,2182.639893,2777550000
2016-08-23,2187.810059,2193.419922,2186.800049,2186.899902,2186.899902,3041490000
2016-08-24,2185.090088,2186.659912,2171.250000,2175.439941,2175.439941,3148280000
2016-08-25,2173.290039,2179.000000,2169.739990,2172.469971,2172.469971,2969310000
2016-08-26,2175.100098,2187.939941,2160.389893,2169.040039,2169.040039,3342340000
2016-08-29,2170.189941,2183.479980,2170.189941,2180.379883,2180.379883,2654780000
2016-08-30,2179.449951,2182.270020,2170.409912,2176.120117,2176.120117,3006800000
2016-08-31,2173.560059,2173.790039,2161.350098,2170.949951,2170.949951,3766390000
2016-09-01,2171.330078,2173.560059,2157.090088,2170.860107,2170.860107,3392120000
2016-09-02,2177.489990,2184.870117,2173.590088,2179.979980,2179.979980,3091120000
2016-09-06,2181.610107,2186.570068,2175.100098,2186.479980,2186.479980,3447650000
2016-09-07,2185.169922,2187.870117,2179.070068,2186.159912,2186.159912,3319420000
2016-09-08,2182.760010,2184.939941,2177.489990,2181.300049,2181.300049,3727840000
2016-09-09,2169.080078,2169.080078,2127.810059,2127.810059,2127.810059,4233960000
2016-09-12,2120.860107,2163.300049,2119.120117,2159.040039,2159.040039,4010480000
2016-09-13,2150.469971,2150.469971,2120.270020,2127.020020,2127.020020,4141670000
2016-09-14,2127.860107,2141.330078,2119.899902,2125.770020,2125.770020,3664100000
2016-09-15,2125.360107,2151.310059,2122.360107,2147.260010,2147.260010,3373720000
2016-09-16,2146.479980,2146.479980,2131.199951,2139.159912,2139.159912,5014360000
2016-09-19,2143.989990,2153.610107,2135.909912,2139.120117,2139.120117,3163000000
2016-09-20,2145.939941,2150.800049,2139.169922,2139.760010,2139.760010,3140730000
2016-09-21,2144.580078,2165.110107,2139.570068,2163.120117,2163.120117,3712090000
2016-09-22,2170.939941,2179.989990,2170.939941,2177.179932,2177.179932,3552830000
2016-09-23,2173.290039,2173.750000,2163.969971,2164.689941,2164.689941,3317190000
2016-09-26,2158.540039,2158.540039,2145.040039,2146.100098,2146.100098,3216170000
2016-09-27,2146.040039,2161.129883,2141.550049,2159.929932,2159.929932,3437770000
2016-09-28,2161.850098,2172.399902,2151.790039,2171.370117,2171.370117,3891460000
2016-09-29,2168.899902,2172.669922,2145.199951,2151.129883,2151.129883,4249220000
2016-09-30,2156.510010,2175.300049,2156.510010,2168.270020,2168.270020,4173340000
2016-10-03,2164.330078,2164.409912,2154.770020,2161.199951,2161.199951,3137550000
2016-10-04,2163.370117,2165.459961,2144.010010,2150.489990,2150.489990,3750890000
2016-10-05,2155.149902,2163.949951,2155.149902,2159.729980,2159.729980,3906550000
2016-10-06,2158.219971,2162.929932,2150.280029,2160.770020,2160.770020,3461550000
2016-10-07,2164.189941,2165.860107,2144.850098,2153.739990,2153.739990,3619890000
2016-10-10,2160.389893,2169.600098,2160.389893,2163.659912,2163.659912,2916550000
2016-10-11,2161.350098,2161.560059,2128.840088,2136.729980,2136.729980,3438270000
2016-10-12,2137.669922,2145.360107,2132.770020,2139.179932,2139.179932,2977100000
2016-10-13,2130.260010,2138.189941,2114.719971,2132.550049,2132.550049,3580450000
2016-10-14,2139.679932,2149.189941,2132.979980,2132.979980,2132.979980,3228150000
2016-10-17,2132.949951,2135.610107,2124.429932,2126.500000,2126.500000,2830390000
2016-10-18,2138.310059,2144.379883,2135.489990,2139.600098,2139.600098,3170000000
2016-10-19,2140.810059,2148.439941,2138.149902,2144.290039,2144.290039,3362670000
2016-10-20,2142.510010,2147.179932,2133.439941,2141.340088,2141.340088,3337170000
2016-10-21,2139.429932,2142.629883,2130.090088,2141.159912,2141.159912,3448850000
2016-10-24,2148.500000,2154.790039,2146.909912,2151.330078,2151.330078,3357320000
2016-10-25,2149.719971,2151.439941,2141.929932,2143.159912,2143.159912,3751340000
2016-10-26,2136.969971,2145.729980,2131.590088,2139.429932,2139.429932,3775200000
2016-10-27,2144.060059,2147.129883,2132.520020,2133.040039,2133.040039,4204830000
2016-10-28,2132.229980,2140.719971,2119.360107,2126.409912,2126.409912,4019510000
2016-10-31,2129.780029,2133.250000,2125.530029,2126.149902,2126.149902,3922400000
2016-11-01,2128.679932,2131.449951,2097.850098,2111.719971,2111.719971,4532160000
2016-11-02,2109.429932,2111.760010,2094.000000,2097.939941,2097.939941,4248580000
2016-11-03,2098.800049,2102.560059,2085.229980,2088.659912,2088.659912,3886740000
2016-11-04,2083.790039,2099.070068,2083.790039,2085.179932,2085.179932,3837860000
2016-11-07,2100.590088,2132.000000,2100.590088,2131.520020,2131.520020,3736060000
2016-11-08,2129.919922,2146.870117,2123.560059,2139.560059,2139.560059,3916930000
2016-11-09,2131.560059,2170.100098,2125.350098,2163.260010,2163.260010,6264150000
2016-11-10,2167.489990,2182.300049,2151.169922,2167.479980,2167.479980,6451640000
2016-11-11,2162.709961,2165.919922,2152.489990,2164.449951,2164.449951,4988050000
2016-11-14,2165.639893,2171.360107,2156.080078,2164.199951,2164.199951,5367200000
2016-11-15,2168.290039,2180.840088,2166.379883,2180.389893,2180.389893,4543860000
2016-11-16,2177.530029,2179.219971,2172.199951,2176.939941,2176.939941,3830590000
2016-11-17,2178.610107,2188.060059,2176.649902,2187.120117,2187.120117,3809160000
2016-11-18,2186.850098,2189.889893,2180.379883,2181.899902,2181.899902,3572400000
2016-11-21,2186.429932,2198.699951,2186.429932,2198.179932,2198.179932,3607010000
2016-11-22,2201.560059,2204.800049,2194.510010,2202.939941,2202.939941,3957940000
2016-11-23,2198.550049,2204.719971,2194.510010,2204.719971,2204.719971,3418640000
2016-11-25,2206.270020,2213.350098,2206.270020,2213.350098,2213.350098,1584600000
2016-11-28,2210.209961,2211.139893,2200.360107,2201.719971,2201.719971,3505650000
2016-11-29,2200.760010,2210.459961,2198.149902,2204.659912,2204.659912,3706560000
2016-11-30,2204.969971,2214.100098,2198.810059,2198.810059,2198.810059,5533980000
2016-12-01,2200.169922,2202.600098,2187.439941,2191.080078,2191.080078,5063740000
2016-12-02,2191.120117,2197.949951,2188.370117,2191.949951,2191.949951,3779500000
2016-12-05,2200.649902,2209.419922,2199.969971,2204.709961,2204.709961,3895230000
2016-12-06,2207.260010,2212.780029,2202.209961,2212.229980,2212.229980,3855320000
2016-12-07,2210.719971,2241.629883,2208.929932,2241.350098,2241.350098,4501820000
2016-12-08,2241.129883,2251.689941,2237.570068,2246.189941,2246.189941,4200580000
2016-12-09,2249.729980,2259.800049,2249.229980,2259.530029,2259.530029,3884480000
2016-12-12,2258.830078,2264.030029,2252.370117,2256.959961,2256.959961,4034510000
2016-12-13,2263.320068,2277.530029,2263.320068,2271.719971,2271.719971,3857590000
2016-12-14,2268.350098,2276.199951,2248.439941,2253.280029,2253.280029,4406970000
2016-12-15,2253.770020,2272.120117,2253.770020,2262.030029,2262.030029,4168200000
2016-12-16,2266.810059,2268.050049,2254.239990,2258.070068,2258.070068,5920340000
2016-12-19,2259.239990,2267.469971,2258.209961,2262.530029,2262.530029,3248370000
2016-12-20,2266.500000,2272.560059,2266.139893,2270.760010,2270.760010,3298780000
2016-12-21,2270.540039,2271.229980,2265.149902,2265.179932,2265.179932,2852230000
2016-12-22,2262.929932,2263.179932,2256.080078,2260.959961,2260.959961,2876320000
2016-12-23,2260.250000,2263.790039,2258.840088,2263.790039,2263.790039,2020550000
2016-12-27,2266.229980,2273.820068,2266.149902,2268.879883,2268.879883,1987080000
2016-12-28,2270.229980,2271.310059,2249.110107,2249.919922,2249.919922,2392360000
2016-12-29,2249.500000,2254.510010,2244.560059,2249.260010,2249.260010,2336370000
2016-12-30,2251.610107,2253.580078,2233.620117,2238.830078,2238.830078,2670900000
2017-01-03,2251.570068,2263.879883,2245.129883,2257.830078,2257.830078,3770530000
2017-01-04,2261.600098,2272.820068,2261.600098,2270.750000,2270.750000,3764890000
2017-01-05,2268.179932,2271.500000,2260.449951,2269.000000,2269.000000,3761820000
2017-01-06,2271.139893,2282.100098,2264.060059,2276.979980,2276.979980,3339890000
2017-01-09,2273.590088,2275.489990,2268.899902,2268.899902,2268.899902,3217610000
2017-01-10,2269.719971,2279.270020,2265.270020,2268.899902,2268.899902,3638790000
2017-01-11,2268.600098,2275.320068,2260.830078,2275.320068,2275.320068,3620410000
2017-01-12,2271.139893,2271.780029,2254.250000,2270.439941,2270.439941,3462130000
2017-01-13,2272.739990,2278.679932,2271.510010,2274.639893,2274.639893,3081270000
2017-01-17,2269.139893,2272.080078,2262.810059,2267.889893,2267.889893,3584990000
2017-01-18,2269.139893,2272.010010,2263.350098,2271.889893,2271.889893,3315250000
2017-01-19,2271.899902,2274.330078,2258.409912,2263.689941,2263.689941,3165970000
2017-01-20,2269.959961,2276.959961,2265.010010,2271.310059,2271.310059,3524970000
2017-01-23,2267.780029,2271.780029,2257.020020,2265.199951,2265.199951,3152710000
2017-01-24,2267.879883,2284.629883,2266.679932,2280.070068,2280.070068,3810960000
2017-01-25,2288.879883,2299.550049,2288.879883,2298.370117,2298.370117,3846020000
2017-01-26,2298.629883,2300.989990,2294.080078,2296.679932,2296.679932,3610360000
2017-01-27,2299.020020,2299.020020,2291.620117,2294.689941,2294.689941,3135890000
2017-01-30,2286.010010,2286.010010,2268.040039,2280.899902,2280.899902,3591270000
2017-01-31,2274.020020,2279.090088,2267.209961,2278.870117,2278.870117,4087450000
2017-02-01,2285.590088,2289.139893,2272.439941,2279.550049,2279.550049,3916610000
2017-02-02,2276.689941,2283.969971,2271.649902,2280.850098,2280.850098,3807710000
2017-02-03,2288.540039,2298.310059,2287.879883,2297.419922,2297.419922,3597970000
2017-02-06,2294.280029,2296.179932,2288.570068,2292.560059,2292.560059,3109050000
2017-02-07,2295.870117,2299.399902,2290.159912,2293.080078,2293.080078,3448690000
2017-02-08,2289.550049,2295.909912,2285.379883,2294.669922,2294.669922,3609740000
2017-02-09,2296.699951,2311.080078,2296.610107,2307.870117,2307.870117,3677940000
2017-02-10,2312.270020,2319.229980,2311.100098,2316.100098,2316.100098,3475020000
2017-02-13,2321.719971,2331.580078,2321.419922,2328.250000,2328.250000,3349730000
2017-02-14,2326.120117,2337.580078,2322.169922,2337.580078,2337.580078,3520910000
2017-02-15,2335.580078,2351.300049,2334.810059,2349.250000,2349.250000,3775590000
2017-02-16,2349.639893,2351.310059,2338.870117,2347.219971,2347.219971,3672370000
2017-02-17,2343.010010,2351.159912,2339.580078,2351.159912,2351.159912,3513060000
2017-02-21,2354.909912,2366.709961,2354.909912,2365.379883,2365.379883,3579780000
2017-02-22,2361.110107,2365.129883,2358.340088,2362.820068,2362.820068,3468670000
2017-02-23,2367.500000,2368.260010,2355.090088,2363.810059,2363.810059,4015260000
2017-02-24,2355.729980,2367.340088,2352.870117,2367.340088,2367.340088,3831570000
2017-02-27,2365.229980,2371.540039,2361.870117,2369.750000,2369.750000,3582610000
2017-02-28,2366.080078,2367.790039,2358.959961,2363.639893,2363.639893,4210140000
2017-03-01,2380.129883,2400.979980,2380.129883,2395.959961,2395.959961,4345180000
2017-03-02,2394.750000,2394.750000,2380.169922,2381.919922,2381.919922,3821320000
2017-03-03,2380.919922,2383.889893,2375.389893,2383.120117,2383.120117,3555260000
2017-03-06,2375.229980,2378.800049,2367.979980,2375.310059,2375.310059,3232700000
2017-03-07,2370.739990,2375.120117,2365.510010,2368.389893,2368.389893,3518390000
2017-03-08,2369.810059,2373.090088,2361.010010,2362.979980,2362.979980,3812100000
2017-03-09,2363.489990,2369.080078,2354.540039,2364.870117,2364.870117,3716340000
2017-03-10,2372.520020,2376.860107,2363.040039,2372.600098,2372.600098,3432950000
2017-03-13,2371.560059,2374.419922,2368.520020,2373.469971,2373.469971,3133900000
2017-03-14,2368.550049,2368.550049,2358.179932,2365.449951,2365.449951,3172630000
2017-03-15,2370.340088,2390.010010,2368.939941,2385.260010,2385.260010,3906840000
2017-03-16,2387.709961,2388.100098,2377.179932,2381.379883,2381.379883,3365660000
2017-03-17,2383.709961,2385.709961,2377.639893,2378.250000,2378.250000,5178040000
2017-03-20,2378.239990,2379.550049,2369.659912,2373.469971,2373.469971,3054930000
2017-03-21,2379.320068,2381.929932,2341.899902,2344.020020,2344.020020,4265590000
2017-03-22,2343.000000,2351.810059,2336.449951,2348.449951,2348.449951,3572730000
2017-03-23,2345.969971,2358.919922,2342.129883,2345.959961,2345.959961,3260600000
2017-03-24,2350.419922,2356.219971,2335.739990,2343.979980,2343.979980,2975130000
2017-03-27,2329.110107,2344.899902,2322.250000,2341.590088,2341.590088,3240230000
2017-03-28,2339.790039,2363.780029,2337.629883,2358.570068,2358.570068,3367780000
2017-03-29,2356.540039,2363.360107,2352.939941,2361.129883,2361.129883,3106940000
2017-03-30,2361.310059,2370.419922,2358.580078,2368.060059,2368.060059,3158420000
2017-03-31,2364.820068,2370.350098,2362.600098,2362.719971,2362.719971,3354110000
2017-04-03,2362.340088,2365.870117,2344.729980,2358.840088,2358.840088,3416400000
2017-04-04,2354.760010,2360.530029,2350.719971,2360.159912,2360.159912,3206240000
2017-04-05,2366.590088,2378.360107,2350.520020,2352.949951,2352.949951,3770520000
2017-04-06,2353.790039,2364.159912,2348.899902,2357.489990,2357.489990,3201920000
2017-04-07,2356.590088,2363.760010,2350.739990,2355.540039,2355.540039,3053150000
2017-04-10,2357.159912,2366.370117,2351.500000,2357.159912,2357.159912,2785410000
2017-04-11,2353.919922,2355.219971,2337.250000,2353.780029,2353.780029,3117420000
2017-04-12,2352.149902,2352.719971,2341.179932,2344.929932,2344.929932,3196950000
2017-04-13,2341.979980,2348.260010,2328.949951,2328.949951,2328.949951,3143890000
2017-04-17,2332.620117,2349.139893,2332.510010,2349.010010,2349.010010,2824710000
2017-04-18,2342.530029,2348.350098,2334.540039,2342.189941,2342.189941,3269840000
2017-04-19,2346.790039,2352.629883,2335.050049,2338.169922,2338.169922,3519900000
2017-04-20,2342.689941,2361.370117,2340.909912,2355.840088,2355.840088,3647420000
2017-04-21,2354.739990,2356.179932,2344.510010,2348.689941,2348.689941,3503360000
2017-04-24,2370.330078,2376.979980,2369.189941,2374.149902,2374.149902,3690650000
2017-04-25,2381.510010,2392.479980,2381.149902,2388.610107,2388.610107,3995240000
2017-04-26,2388.979980,2398.159912,2386.780029,2387.449951,2387.449951,4105920000
2017-04-27,2389.699951,2392.100098,2382.679932,2388.770020,2388.770020,4098460000
2017-04-28,2393.679932,2393.679932,2382.360107,2384.199951,2384.199951,3718270000
2017-05-01,2388.500000,2394.489990,2384.830078,2388.330078,2388.330078,3199240000
2017-05-02,2391.050049,2392.929932,2385.820068,2391.169922,2391.169922,3813680000
2017-05-03,2386.500000,2389.820068,2379.750000,2388.129883,2388.129883,3893990000
2017-05-04,2389.790039,2391.429932,2380.350098,2389.520020,2389.520020,4362540000
2017-05-05,2392.370117,2399.290039,2389.379883,2399.290039,2399.290039,3540140000
2017-05-08,2399.939941,2401.360107,2393.919922,2399.379883,2399.379883,3429440000
2017-05-09,2401.580078,2403.870117,2392.439941,2396.919922,2396.919922,3653590000
2017-05-10,2396.790039,2399.739990,2392.790039,2399.629883,2399.629883,3643530000
2017-05-11,2394.840088,2395.719971,2381.739990,2394.439941,2394.439941,3727420000
2017-05-12,2392.439941,2392.439941,2387.189941,2390.899902,2390.899902,3305630000
2017-05-15,2393.979980,2404.050049,2393.939941,2402.320068,2402.320068,3473600000
2017-05-16,2404.550049,2405.770020,2396.050049,2400.669922,2400.669922,3420790000
2017-05-17,2382.949951,2384.870117,2356.209961,2357.030029,2357.030029,4163000000
2017-05-18,2354.689941,2375.739990,2352.719971,2365.719971,2365.719971,4319420000
2017-05-19,2371.370117,2389.060059,2370.429932,2381.729980,2381.729980,3825160000
2017-05-22,2387.209961,2395.459961,2386.919922,2394.020020,2394.020020,3172830000
2017-05-23,2397.040039,2400.850098,2393.879883,2398.419922,2398.419922,3213570000
2017-05-24,2401.409912,2405.580078,2397.989990,2404.389893,2404.389893,3389900000
2017-05-25,2409.540039,2418.709961,2408.010010,2415.070068,2415.070068,3535390000
2017-05-26,2414.500000,2416.679932,2412.199951,2415.820068,2415.820068,2805040000
2017-05-30,2411.669922,2415.260010,2409.429932,2412.909912,2412.909912,3203160000
2017-05-31,2415.629883,2415.989990,2403.590088,2411.800049,2411.800049,4516110000
2017-06-01,2415.649902,2430.060059,2413.540039,2430.060059,2430.060059,3857140000
2017-06-02,2431.280029,2440.229980,2427.709961,2439.070068,2439.070068,3461680000
2017-06-05,2437.830078,2439.550049,2434.320068,2436.100098,2436.100098,2912600000
2017-06-06,2431.919922,2436.209961,2428.120117,2429.330078,2429.330078,3357840000
2017-06-07,2432.030029,2435.280029,2424.750000,2433.139893,2433.139893,3572300000
2017-06-08,2434.270020,2439.270020,2427.939941,2433.790039,2433.790039,3728860000
2017-06-09,2436.389893,2446.199951,2415.699951,2431.770020,2431.770020,4027340000
2017-06-12,2425.879883,2430.379883,2419.969971,2429.389893,2429.389893,4027750000
2017-06-13,2434.149902,2441.489990,2431.280029,2440.350098,2440.350098,3275500000
2017-06-14,2443.750000,2443.750000,2428.340088,2437.919922,2437.919922,3555590000
2017-06-15,2424.139893,2433.949951,2418.530029,2432.459961,2432.459961,3353050000
2017-06-16,2431.239990,2433.149902,2422.879883,2433.149902,2433.149902,5284720000
2017-06-19,2442.550049,2453.820068,2441.790039,2453.459961,2453.459961,3264700000
2017-06-20,2450.659912,2450.659912,2436.600098,2437.030029,2437.030029,3416510000
2017-06-21,2439.310059,2442.229980,2430.739990,2435.610107,2435.610107,3594820000
2017-06-22,2437.399902,2441.620117,2433.270020,2434.500000,2434.500000,3468210000
2017-06-23,2434.649902,2441.399902,2431.110107,2438.300049,2438.300049,5278330000
2017-06-26,2443.320068,2450.419922,2437.030029,2439.070068,2439.070068,3238970000
2017-06-27,2436.340088,2440.149902,2419.379883,2419.379883,2419.379883,3563910000
2017-06-28,2428.699951,2442.969971,2428.020020,2440.689941,2440.689941,3500800000
2017-06-29,2442.379883,2442.729980,2405.699951,2419.699951,2419.699951,3900280000
2017-06-30,2429.199951,2432.709961,2421.649902,2423.409912,2423.409912,3361590000
2017-07-03,2431.389893,2439.169922,2428.689941,2429.010010,2429.010010,1962290000
2017-07-05,2430.780029,2434.899902,2422.050049,2432.540039,2432.540039,3367220000
2017-07-06,2423.439941,2424.280029,2407.699951,2409.750000,2409.750000,3364520000
2017-07-07,2413.520020,2426.919922,2413.520020,2425.179932,2425.179932,2901330000
2017-07-10,2424.510010,2432.000000,2422.270020,2427.429932,2427.429932,2999130000
2017-07-11,2427.350098,2429.300049,2412.790039,2425.530029,2425.530029,3106750000
2017-07-12,2435.750000,2445.760010,2435.750000,2443.250000,2443.250000,3171620000
2017-07-13,2444.989990,2449.320068,2441.689941,2447.830078,2447.830078,3067670000
2017-07-14,2449.159912,2463.540039,2446.689941,2459.270020,2459.270020,2736640000
2017-07-17,2459.500000,2462.820068,2457.159912,2459.139893,2459.139893,2793170000
2017-07-18,2455.879883,2460.919922,2450.340088,2460.610107,2460.610107,2962130000
2017-07-19,2463.850098,2473.830078,2463.850098,2473.830078,2473.830078,3059760000
2017-07-20,2475.560059,2477.620117,2468.429932,2473.449951,2473.449951,3182780000
2017-07-21,2467.399902,2472.540039,2465.060059,2472.540039,2472.540039,3059570000
2017-07-24,2472.040039,2473.100098,2466.320068,2469.909912,2469.909912,3010240000
2017-07-25,2477.879883,2481.239990,2474.909912,2477.129883,2477.129883,4108060000
2017-07-26,2479.969971,2481.689941,2474.939941,2477.830078,2477.830078,3557020000
2017-07-27,2482.760010,2484.040039,2459.929932,2475.419922,2475.419922,3995520000
2017-07-28,2469.120117,2473.530029,2464.659912,2472.100098,2472.100098,3294770000
2017-07-31,2475.939941,2477.959961,2468.530029,2470.300049,2470.300049,3469210000
2017-08-01,2477.100098,2478.510010,2471.139893,2476.350098,2476.350098,3460860000
2017-08-02,2480.379883,2480.379883,2466.479980,2477.570068,2477.570068,3478580000
2017-08-03,2476.030029,2476.030029,2468.850098,2472.159912,2472.159912,3645020000
2017-08-04,2476.879883,2480.000000,2472.080078,2476.830078,2476.830078,3235140000
2017-08-07,2477.139893,2480.949951,2475.879883,2480.909912,2480.909912,2931780000
2017-08-08,2478.350098,2490.870117,2470.320068,2474.919922,2474.919922,3344640000
2017-08-09,2465.350098,2474.409912,2462.080078,2474.020020,2474.020020,3308060000
2017-08-10,2465.379883,2465.379883,2437.750000,2438.209961,2438.209961,3621070000
2017-08-11,2441.040039,2448.090088,2437.850098,2441.320068,2441.320068,3159930000
2017-08-14,2454.959961,2468.219971,2454.959961,2465.840088,2465.840088,2822550000
2017-08-15,2468.659912,2468.899902,2461.610107,2464.610107,2464.610107,2913100000
2017-08-16,2468.629883,2474.929932,2463.860107,2468.110107,2468.110107,2953650000
2017-08-17,2462.949951,2465.020020,2430.010010,2430.010010,2430.010010,3142620000
2017-08-18,2427.639893,2440.270020,2420.689941,2425.550049,2425.550049,3415680000
2017-08-21,2425.500000,2430.580078,2417.350098,2428.370117,2428.370117,2788150000
2017-08-22,2433.750000,2454.770020,2433.669922,2452.510010,2452.510010,2777490000
2017-08-23,2444.879883,2448.909912,2441.419922,2444.040039,2444.040039,2785290000
2017-08-24,2447.909912,2450.389893,2436.189941,2438.969971,2438.969971,2846590000
2017-08-25,2444.719971,2453.959961,2442.219971,2443.050049,2443.050049,2588780000
2017-08-28,2447.350098,2449.120117,2439.030029,2444.239990,2444.239990,2677700000
2017-08-29,2431.939941,2449.189941,2428.199951,2446.300049,2446.300049,2737580000
2017-08-30,2446.060059,2460.310059,2443.770020,2457.590088,2457.590088,2633660000
2017-08-31,2462.649902,2475.010010,2462.649902,2471.649902,2471.649902,3348110000
2017-09-01,2474.419922,2480.379883,2473.850098,2476.550049,2476.550049,2710730000
2017-09-05,2470.350098,2471.969971,2446.550049,2457.850098,2457.850098,3490260000
2017-09-06,2463.830078,2469.639893,2459.199951,2465.540039,2465.540039,3374410000
2017-09-07,2468.060059,2468.620117,2460.290039,2465.100098,2465.100098,3353930000
2017-09-08,2462.250000,2467.110107,2459.399902,2461.429932,2461.429932,3302490000
2017-09-11,2474.520020,2488.949951,2474.520020,2488.110107,2488.110107,3291760000
2017-09-12,2491.939941,2496.770020,2490.370117,2496.479980,2496.479980,3230920000
2017-09-13,2493.889893,2498.370117,2492.139893,2498.370117,2498.370117,3368050000
2017-09-14,2494.560059,2498.429932,2491.350098,2495.620117,2495.620117,3414460000
2017-09-15,2495.669922,2500.229980,2493.159912,2500.229980,2500.229980,4853170000
2017-09-18,2502.510010,2508.320068,2499.919922,2503.870117,2503.870117,3194300000
2017-09-19,2506.290039,2507.840088,2503.189941,2506.649902,2506.649902,3249100000
2017-09-20,2506.840088,2508.850098,2496.669922,2508.239990,2508.239990,3530010000
2017-09-21,2507.159912,2507.159912,2499.000000,2500.600098,2500.600098,2930860000
2017-09-22,2497.260010,2503.469971,2496.540039,2502.219971,2502.219971,2865960000
2017-09-25,2499.389893,2502.540039,2488.030029,2496.659912,2496.659912,3297890000
2017-09-26,2501.040039,2503.510010,2495.120117,2496.840088,2496.840088,3043110000
2017-09-27,2503.300049,2511.750000,2495.909912,2507.040039,2507.040039,3456030000
2017-09-28,2503.409912,2510.810059,2502.929932,2510.060059,2510.060059,3168620000
2017-09-29,2509.959961,2519.439941,2507.989990,2519.360107,2519.360107,3211920000
2017-10-02,2521.199951,2529.229980,2520.399902,2529.120117,2529.120117,3199730000
2017-10-03,2530.340088,2535.129883,2528.850098,2534.580078,2534.580078,3068850000
2017-10-04,2533.479980,2540.530029,2531.800049,2537.739990,2537.739990,3017120000
2017-10-05,2540.860107,2552.510010,2540.020020,2552.070068,2552.070068,3045120000
2017-10-06,2547.439941,2549.409912,2543.790039,2549.330078,2549.330078,2884570000
2017-10-09,2551.389893,2551.820068,2541.600098,2544.729980,2544.729980,2483970000
2017-10-10,2549.989990,2555.229980,2544.860107,2550.639893,2550.639893,2960500000
2017-10-11,2550.620117,2555.239990,2547.949951,2555.239990,2555.239990,2976090000
2017-10-12,2552.879883,2555.330078,2548.310059,2550.929932,2550.929932,3151510000
2017-10-13,2555.659912,2557.649902,2552.090088,2553.169922,2553.169922,3149440000
2017-10-16,2555.570068,2559.469971,2552.639893,2557.639893,2557.639893,2916020000
2017-10-17,2557.169922,2559.709961,2554.689941,2559.360107,2559.360107,2889390000
2017-10-18,2562.870117,2564.110107,2559.669922,2561.260010,2561.260010,2998090000
2017-10-19,2553.389893,2562.360107,2547.919922,2562.100098,2562.100098,2990710000
2017-10-20,2567.560059,2575.439941,2567.560059,2575.209961,2575.209961,3384650000
2017-10-23,2578.080078,2578.290039,2564.330078,2564.979980,2564.979980,3211710000
2017-10-24,2568.659912,2572.179932,2565.580078,2569.129883,2569.129883,3427330000
2017-10-25,2566.520020,2567.399902,2544.000000,2557.149902,2557.149902,3874510000
2017-10-26,2560.080078,2567.070068,2559.800049,2560.399902,2560.399902,3869050000
2017-10-27,2570.260010,2582.979980,2565.939941,2581.070068,2581.070068,3887110000
2017-10-30,2577.750000,2580.030029,2568.250000,2572.830078,2572.830078,3658870000
2017-10-31,2575.989990,2578.290039,2572.149902,2575.260010,2575.260010,3827230000
2017-11-01,2583.209961,2588.399902,2574.919922,2579.360107,2579.360107,3813180000
2017-11-02,2579.459961,2581.110107,2566.169922,2579.850098,2579.850098,4048270000
2017-11-03,2581.929932,2588.419922,2576.770020,2587.840088,2587.840088,3567710000
2017-11-06,2587.469971,2593.379883,2585.659912,2591.129883,2591.129883,3539080000
2017-11-07,2592.110107,2597.020020,2584.350098,2590.639893,2590.639893,3809650000
2017-11-08,2588.709961,2595.469971,2585.020020,2594.379883,2594.379883,3899360000
2017-11-09,2584.000000,2586.500000,2566.330078,2584.620117,2584.620117,3831610000
2017-11-10,2580.179932,2583.810059,2575.570068,2582.300049,2582.300049,3486910000
2017-11-13,2576.530029,2587.659912,2574.479980,2584.840088,2584.840088,3402930000
2017-11-14,2577.750000,2579.659912,2566.560059,2578.870117,2578.870117,3641760000
2017-11-15,2569.449951,2572.840088,2557.449951,2564.620117,2564.620117,3558890000
2017-11-16,2572.949951,2590.090088,2572.949951,2585.639893,2585.639893,3312710000
2017-11-17,2582.939941,2583.959961,2577.620117,2578.850098,2578.850098,3300160000
2017-11-20,2579.489990,2584.639893,2578.239990,2582.139893,2582.139893,3003540000
2017-11-21,2589.169922,2601.189941,2589.169922,2599.030029,2599.030029,3332720000
2017-11-22,2600.310059,2600.939941,2595.229980,2597.080078,2597.080078,2762950000
2017-11-24,2600.419922,2604.209961,2600.419922,2602.419922,2602.419922,1349780000
2017-11-27,2602.659912,2606.409912,2598.870117,2601.419922,2601.419922,3006860000
2017-11-28,2605.939941,2627.689941,2605.439941,2627.040039,2627.040039,3488420000
2017-11-29,2627.820068,2634.889893,2620.320068,2626.070068,2626.070068,4078280000
2017-11-30,2633.929932,2657.739990,2633.929932,2647.580078,2647.580078,4938490000
2017-12-01,2645.100098,2650.620117,2605.520020,2642.219971,2642.219971,3942320000
2017-12-04,2657.189941,2665.189941,2639.030029,2639.439941,2639.439941,4023150000
2017-12-05,2639.780029,2648.719971,2627.729980,2629.570068,2629.570068,3539040000
2017-12-06,2626.239990,2634.409912,2624.750000,2629.270020,2629.270020,3229000000
2017-12-07,2628.379883,2640.989990,2626.530029,2636.979980,2636.979980,3292400000
2017-12-08,2646.209961,2651.649902,2644.100098,2651.500000,2651.500000,3106150000
2017-12-11,2652.189941,2660.330078,2651.469971,2659.989990,2659.989990,3091950000
2017-12-12,2661.729980,2669.719971,2659.780029,2664.110107,2664.110107,3555680000
2017-12-13,2667.590088,2671.879883,2662.850098,2662.850098,2662.850098,3542370000
2017-12-14,2665.870117,2668.090088,2652.010010,2652.010010,2652.010010,3430030000
2017-12-15,2660.629883,2679.629883,2659.139893,2675.810059,2675.810059,5723920000
2017-12-18,2685.919922,2694.969971,2685.919922,2690.159912,2690.159912,3724660000
2017-12-19,2692.709961,2694.439941,2680.739990,2681.469971,2681.469971,3368590000
2017-12-20,2688.179932,2691.010010,2676.110107,2679.250000,2679.250000,3241030000
2017-12-21,2683.020020,2692.639893,2682.399902,2684.570068,2684.570068,3273390000
2017-12-22,2684.219971,2685.350098,2678.129883,2683.340088,2683.340088,2399830000
2017-12-26,2679.090088,2682.739990,2677.959961,2680.500000,2680.500000,1968780000
2017-12-27,2682.100098,2685.639893,2678.909912,2682.620117,2682.620117,2202080000
2017-12-28,2686.100098,2687.659912,2682.689941,2687.540039,2687.540039,2153330000
2017-12-29,2689.149902,2692.120117,2673.610107,2673.610107,2673.610107,2443490000
2018-01-02,2683.729980,2695.889893,2682.360107,2695.810059,2695.810059,3367250000
2018-01-03,2697.850098,2714.370117,2697.770020,2713.060059,2713.060059,3538660000
2018-01-04,2719.310059,2729.290039,2719.070068,2723.989990,2723.989990,3695260000
2018-01-05,2731.330078,2743.449951,2727.919922,2743.149902,2743.149902,3236620000
2018-01-08,2742.669922,2748.510010,2737.600098,2747.709961,2747.709961,3242650000
2018-01-09,2751.149902,2759.139893,2747.860107,2751.290039,2751.290039,3453480000
2018-01-10,2745.550049,2750.800049,2736.060059,2748.229980,2748.229980,3576350000
2018-01-11,2752.969971,2767.560059,2752.780029,2767.560059,2767.560059,3641320000
2018-01-12,2770.179932,2787.850098,2769.639893,2786.239990,2786.239990,3573970000
2018-01-16,2798.959961,2807.540039,2768.639893,2776.419922,2776.419922,4325970000
2018-01-17,2784.989990,2807.040039,2778.379883,2802.560059,2802.560059,3778050000
2018-01-18,2802.399902,2805.830078,2792.560059,2798.030029,2798.030029,3681470000
2018-01-19,2802.600098,2810.330078,2798.080078,2810.300049,2810.300049,3639430000
2018-01-22,2809.159912,2833.030029,2808.120117,2832.969971,2832.969971,3471780000
2018-01-23,2835.050049,2842.239990,2830.590088,2839.129883,2839.129883,3519650000
2018-01-24,2845.419922,2852.969971,2824.810059,2837.540039,2837.540039,4014070000
2018-01-25,2846.239990,2848.560059,2830.939941,2839.250000,2839.250000,3835150000
2018-01-26,2847.479980,2872.870117,2846.179932,2872.870117,2872.870117,3443230000
2018-01-29,2867.229980,2870.620117,2851.479980,2853.530029,2853.530029,3573830000
2018-01-30,2832.739990,2837.750000,2818.270020,2822.429932,2822.429932,3990650000
2018-01-31,2832.409912,2839.260010,2813.040039,2823.810059,2823.810059,4261280000
2018-02-01,2816.449951,2835.959961,2812.699951,2821.979980,2821.979980,3938450000
2018-02-02,2808.919922,2808.919922,2759.969971,2762.129883,2762.129883,4301130000
2018-02-05,2741.060059,2763.389893,2638.169922,2648.939941,2648.939941,5283460000
2018-02-06,2614.780029,2701.040039,2593.070068,2695.139893,2695.139893,5891660000
2018-02-07,2690.949951,2727.669922,2681.330078,2681.659912,2681.659912,4626570000
2018-02-08,2685.010010,2685.270020,2580.560059,2581.000000,2581.000000,5305440000
2018-02-09,2601.780029,2638.669922,2532.689941,2619.550049,2619.550049,5680070000
2018-02-12,2636.750000,2672.610107,2622.449951,2656.000000,2656.000000,4055790000
2018-02-13,2646.270020,2668.840088,2637.080078,2662.939941,2662.939941,3472870000
2018-02-14,2651.209961,2702.100098,2648.870117,2698.629883,2698.629883,4003740000
2018-02-15,2713.459961,2731.510010,2689.820068,2731.199951,2731.199951,3684910000
2018-02-16,2727.139893,2754.419922,2725.110107,2732.219971,2732.219971,3637460000
2018-02-20,2722.989990,2737.600098,2706.760010,2716.260010,2716.260010,3627610000
2018-02-21,2720.530029,2747.750000,2701.290039,2701.330078,2701.330078,3779400000
2018-02-22,2710.419922,2731.260010,2697.770020,2703.959961,2703.959961,3701270000
2018-02-23,2715.800049,2747.760010,2713.739990,2747.300049,2747.300049,3189190000
2018-02-26,2757.370117,2780.639893,2753.780029,2779.600098,2779.600098,3424650000
2018-02-27,2780.449951,2789.149902,2744.219971,2744.280029,2744.280029,3745080000
2018-02-28,2753.780029,2761.520020,2713.540039,2713.830078,2713.830078,4230660000
2018-03-01,2715.219971,2730.889893,2659.649902,2677.669922,2677.669922,4503970000
2018-03-02,2658.889893,2696.250000,2647.320068,2691.250000,2691.250000,3882450000
2018-03-05,2681.060059,2728.090088,2675.750000,2720.939941,2720.939941,3710810000
2018-03-06,2730.179932,2732.080078,2711.260010,2728.120117,2728.120117,3370690000
2018-03-07,2710.179932,2730.600098,2701.739990,2726.800049,2726.800049,3393270000
2018-03-08,2732.750000,2740.449951,2722.649902,2738.969971,2738.969971,3212320000
2018-03-09,2752.909912,2786.570068,2751.540039,2786.570068,2786.570068,3364100000
2018-03-12,2790.540039,2796.979980,2779.260010,2783.020020,2783.020020,3185020000
2018-03-13,2792.310059,2801.899902,2758.679932,2765.310059,2765.310059,3301650000
2018-03-14,2774.060059,2777.110107,2744.379883,2749.479980,2749.479980,3391360000
2018-03-15,2754.270020,2763.030029,2741.469971,2747.330078,2747.330078,3500330000
2018-03-16,2750.570068,2761.850098,2749.969971,2752.010010,2752.010010,5372340000
2018-03-19,2741.379883,2741.379883,2694.590088,2712.919922,2712.919922,3302130000
2018-03-20,2715.050049,2724.219971,2710.050049,2716.939941,2716.939941,3261030000
2018-03-21,2714.989990,2739.139893,2709.790039,2711.929932,2711.929932,3415510000
2018-03-22,2691.360107,2695.679932,2641.590088,2643.689941,2643.689941,3739800000
2018-03-23,2646.709961,2657.669922,2585.889893,2588.260010,2588.260010,3815080000
2018-03-26,2619.350098,2661.360107,2601.810059,2658.550049,2658.550049,3511100000
2018-03-27,2667.570068,2674.780029,2596.120117,2612.620117,2612.620117,3706350000
2018-03-28,2611.300049,2632.649902,2593.060059,2605.000000,2605.000000,3864500000
2018-03-29,2614.409912,2659.070068,2609.719971,2640.870117,2640.870117,3565990000
2018-04-02,2633.449951,2638.300049,2553.800049,2581.879883,2581.879883,3598520000
2018-04-03,2592.169922,2619.139893,2575.489990,2614.449951,2614.449951,3392810000
2018-04-04,2584.040039,2649.860107,2573.610107,2644.689941,2644.689941,3350340000
2018-04-05,2657.360107,2672.080078,2649.580078,2662.840088,2662.840088,3178970000
2018-04-06,2645.820068,2656.879883,2586.270020,2604.469971,2604.469971,3299700000
2018-04-09,2617.179932,2653.550049,2610.790039,2613.159912,2613.159912,3062960000
2018-04-10,2638.409912,2665.449951,2635.780029,2656.870117,2656.870117,3543930000
2018-04-11,2643.889893,2661.429932,2639.250000,2642.189941,2642.189941,3020760000
2018-04-12,2653.830078,2674.719971,2653.830078,2663.989990,2663.989990,3021320000
2018-04-13,2676.899902,2680.260010,2645.050049,2656.300049,2656.300049,2960910000
2018-04-16,2670.100098,2686.489990,2665.159912,2677.840088,2677.840088,3019700000
2018-04-17,2692.739990,2713.340088,2692.050049,2706.389893,2706.389893,3234360000
2018-04-18,2710.110107,2717.489990,2703.629883,2708.639893,2708.639893,3383410000
2018-04-19,2701.159912,2702.840088,2681.899902,2693.129883,2693.129883,3349370000
2018-04-20,2692.560059,2693.939941,2660.610107,2670.139893,2670.139893,3388590000
2018-04-23,2675.399902,2682.860107,2657.989990,2670.290039,2670.290039,3017480000
2018-04-24,2680.800049,2683.550049,2617.320068,2634.560059,2634.560059,3706740000
2018-04-25,2634.919922,2645.300049,2612.669922,2639.399902,2639.399902,3499440000
2018-04-26,2651.649902,2676.479980,2647.159912,2666.939941,2666.939941,3665720000
2018-04-27,2675.469971,2677.350098,2659.010010,2669.909912,2669.909912,3219030000
2018-04-30,2682.510010,2682.870117,2648.040039,2648.050049,2648.050049,3734530000
2018-05-01,2642.959961,2655.270020,2625.409912,2654.800049,2654.800049,3559850000
2018-05-02,2654.239990,2660.870117,2631.699951,2635.669922,2635.669922,4010770000
2018-05-03,2628.080078,2637.139893,2594.620117,2629.729980,2629.729980,3851470000
2018-05-04,2621.449951,2670.929932,2615.320068,2663.419922,2663.419922,3327220000
2018-05-07,2680.340088,2683.350098,2664.699951,2672.629883,2672.629883,3237960000
2018-05-08,2670.260010,2676.340088,2655.199951,2671.919922,2671.919922,3717570000
2018-05-09,2678.120117,2701.270020,2674.139893,2697.790039,2697.790039,3909500000
2018-05-10,2705.020020,2726.110107,2704.540039,2723.070068,2723.070068,3333050000
2018-05-11,2722.699951,2732.860107,2717.449951,2727.719971,2727.719971,2862700000
2018-05-14,2738.469971,2742.100098,2725.469971,2730.129883,2730.129883,2972660000
2018-05-15,2718.590088,2718.590088,2701.909912,2711.449951,2711.449951,3290680000
2018-05-16,2712.620117,2727.760010,2712.169922,2722.459961,2722.459961,3202670000
2018-05-17,2719.709961,2731.959961,2711.360107,2720.129883,2720.129883,3475400000
2018-05-18,2717.350098,2719.500000,2709.179932,2712.969971,2712.969971,3368690000
2018-05-21,2735.389893,2739.189941,2725.699951,2733.010010,2733.010010,3019890000
2018-05-22,2738.340088,2742.239990,2721.879883,2724.439941,2724.439941,3366310000
2018-05-23,2713.979980,2733.330078,2709.540039,2733.290039,2733.290039,3326290000
2018-05-24,2730.939941,2731.969971,2707.379883,2727.760010,2727.760010,3256030000
2018-05-25,2723.600098,2727.360107,2714.989990,2721.330078,2721.330078,2995260000
2018-05-29,2705.110107,2710.669922,2676.810059,2689.860107,2689.860107,3736890000
2018-05-30,2702.429932,2729.340088,2702.429932,2724.010010,2724.010010,3561050000
2018-05-31,2720.979980,2722.500000,2700.679932,2705.270020,2705.270020,4235370000
2018-06-01,2718.699951,2736.929932,2718.699951,2734.620117,2734.620117,3684130000
2018-06-04,2741.669922,2749.159912,2740.540039,2746.870117,2746.870117,3376510000
2018-06-05,2748.459961,2752.610107,2739.510010,2748.800049,2748.800049,3517790000
2018-06-06,2753.250000,2772.389893,2748.459961,2772.350098,2772.350098,3651640000
2018-06-07,2774.840088,2779.899902,2760.159912,2770.370117,2770.370117,3711330000
2018-06-08,2765.840088,2779.389893,2763.590088,2779.030029,2779.030029,3123210000
2018-06-11,2780.179932,2790.209961,2780.169922,2782.000000,2782.000000,3232330000
2018-06-12,2785.600098,2789.800049,2778.780029,2786.850098,2786.850098,3401010000
2018-06-13,2787.939941,2791.469971,2774.649902,2775.629883,2775.629883,3779230000
2018-06-14,2783.209961,2789.060059,2776.520020,2782.489990,2782.489990,3526890000
2018-06-15,2777.780029,2782.810059,2761.729980,2779.659912,2779.659912,5428790000
2018-06-18,2765.790039,2774.989990,2757.120117,2773.750000,2773.750000,3287150000
2018-06-19,2752.010010,2765.050049,2743.189941,2762.590088,2762.590088,3661470000
2018-06-20,2769.729980,2774.860107,2763.909912,2767.320068,2767.320068,3327600000
2018-06-21,2769.280029,2769.280029,2744.389893,2749.760010,2749.760010,3300060000
2018-06-22,2760.790039,2764.169922,2752.679932,2754.879883,2754.879883,5450550000
2018-06-25,2742.939941,2742.939941,2698.669922,2717.070068,2717.070068,3655080000
2018-06-26,2722.120117,2732.909912,2715.600098,2723.060059,2723.060059,3555090000
2018-06-27,2728.449951,2746.090088,2699.379883,2699.629883,2699.629883,3776090000
2018-06-28,2698.689941,2724.340088,2691.989990,2716.310059,2716.310059,3428140000
2018-06-29,2727.129883,2743.260010,2718.030029,2718.370117,2718.370117,3565620000
2018-07-02,2704.949951,2727.260010,2698.949951,2726.709961,2726.709961,3073650000
2018-07-03,2733.270020,2736.580078,2711.159912,2713.219971,2713.219971,1911470000
2018-07-05,2724.189941,2737.830078,2716.020020,2736.610107,2736.610107,2953420000
2018-07-06,2737.679932,2764.409912,2733.520020,2759.820068,2759.820068,2554780000
2018-07-09,2775.620117,2784.649902,2770.729980,2784.169922,2784.169922,3050040000
2018-07-10,2788.560059,2795.580078,2786.239990,2793.840088,2793.840088,3063850000
2018-07-11,2779.820068,2785.909912,2770.770020,2774.020020,2774.020020,2964740000
2018-07-12,2783.139893,2799.219971,2781.530029,2798.290039,2798.290039,2821690000
2018-07-13,2796.929932,2804.530029,2791.689941,2801.310059,2801.310059,2614000000
2018-07-16,2797.360107,2801.189941,2793.389893,2798.429932,2798.429932,2812230000
2018-07-17,2789.340088,2814.189941,2789.239990,2809.550049,2809.550049,3050730000
2018-07-18,2811.350098,2816.760010,2805.889893,2815.620117,2815.620117,3089780000
2018-07-19,2809.370117,2812.050049,2799.770020,2804.489990,2804.489990,3266700000
2018-07-20,2804.550049,2809.699951,2800.010010,2801.830078,2801.830078,3230210000
2018-07-23,2799.169922,2808.610107,2795.139893,2806.979980,2806.979980,2907430000
2018-07-24,2820.679932,2829.989990,2811.120117,2820.399902,2820.399902,3417530000
2018-07-25,2817.729980,2848.030029,2817.729980,2846.070068,2846.070068,3553010000
2018-07-26,2835.489990,2845.570068,2835.260010,2837.439941,2837.439941,3653330000
2018-07-27,2842.350098,2843.169922,2808.340088,2818.820068,2818.820068,3415710000
2018-07-30,2819.000000,2821.739990,2798.110107,2802.600098,2802.600098,3245770000
2018-07-31,2809.729980,2824.459961,2808.060059,2816.290039,2816.290039,3892100000
2018-08-01,2821.169922,2825.830078,2805.850098,2813.360107,2813.360107,3496990000
2018-08-02,2800.479980,2829.909912,2796.340088,2827.219971,2827.219971,3467380000
2018-08-03,2829.620117,2840.379883,2827.370117,2840.350098,2840.350098,3030390000
2018-08-06,2840.290039,2853.290039,2835.979980,2850.399902,2850.399902,2874540000
2018-08-07,2855.919922,2863.429932,2855.919922,2858.449951,2858.449951,3162770000
2018-08-08,2856.790039,2862.439941,2853.090088,2857.699951,2857.699951,2972200000
2018-08-09,2857.189941,2862.479980,2851.979980,2853.580078,2853.580078,3047050000
2018-08-10,2838.899902,2842.199951,2825.810059,2833.280029,2833.280029,3256040000
2018-08-13,2835.459961,2843.399902,2819.879883,2821.929932,2821.929932,3158450000
2018-08-14,2827.879883,2843.110107,2826.580078,2839.959961,2839.959961,2976970000
2018-08-15,2827.949951,2827.949951,2802.489990,2818.370117,2818.370117,3645070000
2018-08-16,2831.439941,2850.489990,2831.439941,2840.689941,2840.689941,3219880000
2018-08-17,2838.320068,2855.629883,2833.729980,2850.129883,2850.129883,3024100000
2018-08-20,2853.929932,2859.760010,2850.620117,2857.050049,2857.050049,2748020000
2018-08-21,2861.510010,2873.229980,2861.320068,2862.959961,2862.959961,3147140000
2018-08-22,2860.989990,2867.540039,2856.050049,2861.820068,2861.820068,2689560000
2018-08-23,2860.290039,2868.780029,2854.030029,2856.979980,2856.979980,2713910000
2018-08-24,2862.350098,2876.159912,2862.350098,2874.689941,2874.689941,2596190000
2018-08-27,2884.689941,2898.250000,2884.689941,2896.739990,2896.739990,2854080000
2018-08-28,2901.449951,2903.770020,2893.500000,2897.520020,2897.520020,2683190000
2018-08-29,2900.620117,2916.500000,2898.399902,2914.040039,2914.040039,2791860000
2018-08-30,2908.939941,2912.459961,2895.219971,2901.129883,2901.129883,2802180000
2018-08-31,2898.370117,2906.320068,2891.729980,2901.520020,2901.520020,2880260000
2018-09-04,2896.959961,2900.179932,2885.129883,2896.719971,2896.719971,3077060000
2018-09-05,2891.590088,2894.209961,2876.919922,2888.600098,2888.600098,3241250000
2018-09-06,2888.639893,2892.050049,2867.290039,2878.050049,2878.050049,3139590000
2018-09-07,2868.260010,2883.810059,2864.120117,2871.679932,2871.679932,2946270000
2018-09-10,2881.389893,2886.929932,2875.939941,2877.129883,2877.129883,2731400000
2018-09-11,2871.570068,2892.520020,2866.780029,2887.889893,2887.889893,2899660000
2018-09-12,2888.290039,2894.649902,2879.199951,2888.919922,2888.919922,3264930000
2018-09-13,2896.850098,2906.760010,2896.389893,2904.179932,2904.179932,3254930000
2018-09-14,2906.379883,2908.300049,2895.770020,2904.979980,2904.979980,3149800000
2018-09-17,2903.830078,2904.649902,2886.159912,2888.800049,2888.800049,2947760000
2018-09-18,2890.739990,2911.169922,2890.429932,2904.310059,2904.310059,3074610000
2018-09-19,2906.600098,2912.360107,2903.820068,2907.949951,2907.949951,3280020000
2018-09-20,2919.729980,2934.800049,2919.729980,2930.750000,2930.750000,3337730000
2018-09-21,2936.760010,2940.909912,2927.110107,2929.669922,2929.669922,5607610000
2018-09-24,2921.830078,2923.790039,2912.629883,2919.370117,2919.370117,3372210000
2018-09-25,2921.750000,2923.949951,2913.699951,2915.560059,2915.560059,3285480000
2018-09-26,2916.979980,2931.149902,2903.280029,2905.969971,2905.969971,3388620000
2018-09-27,2911.649902,2927.219971,2909.270020,2914.000000,2914.000000,3060850000
2018-09-28,2910.030029,2920.530029,2907.500000,2913.979980,2913.979980,3432300000
2018-10-01,2926.290039,2937.060059,2917.909912,2924.590088,2924.590088,3364190000
2018-10-02,2923.800049,2931.419922,2919.370117,2923.429932,2923.429932,3401880000
2018-10-03,2931.689941,2939.860107,2921.360107,2925.510010,2925.510010,3598710000
2018-10-04,2919.350098,2919.780029,2883.919922,2901.610107,2901.610107,3496860000
2018-10-05,2902.540039,2909.639893,2869.290039,2885.570068,2885.570068,3328980000
2018-10-08,2877.530029,2889.449951,2862.080078,2884.429932,2884.429932,3330320000
2018-10-09,2882.510010,2894.830078,2874.270020,2880.340088,2880.340088,3520500000
2018-10-10,2873.899902,2874.020020,2784.860107,2785.679932,2785.679932,4501250000
2018-10-11,2776.870117,2795.139893,2710.510010,2728.370117,2728.370117,4890630000
2018-10-12,2770.540039,2775.770020,2729.439941,2767.129883,2767.129883,3966040000
2018-10-15,2763.830078,2775.989990,2749.030029,2750.790039,2750.790039,3300140000
2018-10-16,2767.050049,2813.459961,2766.909912,2809.919922,2809.919922,3428340000
2018-10-17,2811.669922,2816.939941,2781.810059,2809.209961,2809.209961,3321710000
2018-10-18,2802.000000,2806.040039,2755.179932,2768.780029,2768.780029,3616440000
2018-10-19,2775.659912,2797.770020,2760.270020,2767.780029,2767.780029,3566490000
2018-10-22,2773.939941,2778.939941,2749.219971,2755.879883,2755.879883,3307140000
2018-10-23,2721.030029,2753.590088,2691.429932,2740.689941,2740.689941,4348580000
2018-10-24,2737.870117,2742.590088,2651.889893,2656.100098,2656.100098,4709310000
2018-10-25,2674.879883,2722.699951,2667.840088,2705.570068,2705.570068,4634770000
2018-10-26,2667.860107,2692.379883,2628.159912,2658.689941,2658.689941,4803150000
2018-10-29,2682.649902,2706.850098,2603.540039,2641.250000,2641.250000,4673700000
2018-10-30,2640.679932,2685.429932,2635.340088,2682.629883,2682.629883,5106380000
2018-10-31,2705.600098,2736.689941,2705.600098,2711.739990,2711.739990,5112420000
2018-11-01,2717.580078,2741.669922,2708.850098,2740.370117,2740.370117,4708420000
2018-11-02,2745.449951,2756.550049,2700.439941,2723.060059,2723.060059,4237930000
2018-11-05,2726.370117,2744.270020,2717.939941,2738.310059,2738.310059,3623320000
2018-11-06,2738.399902,2756.820068,2737.080078,2755.449951,2755.449951,3510860000
2018-11-07,2774.129883,2815.149902,2774.129883,2813.889893,2813.889893,3914750000
2018-11-08,2806.379883,2814.750000,2794.989990,2806.830078,2806.830078,3630490000
2018-11-09,2794.100098,2794.100098,2764.239990,2781.010010,2781.010010,4019090000
2018-11-12,2773.929932,2775.989990,2722.000000,2726.219971,2726.219971,3670930000
2018-11-13,2730.050049,2754.600098,2714.979980,2722.179932,2722.179932,4091440000
2018-11-14,2737.899902,2746.800049,2685.750000,2701.580078,2701.580078,4402370000
2018-11-15,2693.520020,2735.379883,2670.750000,2730.199951,2730.199951,4179140000
2018-11-16,2718.540039,2746.750000,2712.159912,2736.270020,2736.270020,3975180000
2018-11-19,2730.739990,2733.159912,2681.090088,2690.729980,2690.729980,3772900000
2018-11-20,2654.600098,2669.439941,2631.520020,2641.889893,2641.889893,4357900000
2018-11-21,2657.739990,2670.729980,2649.820068,2649.929932,2649.929932,3233550000
2018-11-23,2633.360107,2647.550049,2631.090088,2632.560059,2632.560059,1651650000
2018-11-26,2649.969971,2674.350098,2649.969971,2673.449951,2673.449951,3443950000
2018-11-27,2663.750000,2682.530029,2655.889893,2682.169922,2682.169922,3485220000
2018-11-28,2691.449951,2744.000000,2684.379883,2743.790039,2743.790039,3951670000
2018-11-29,2736.969971,2753.750000,2722.939941,2737.800049,2737.800049,3560770000
2018-11-30,2737.760010,2760.879883,2732.760010,2760.169922,2760.169922,4658580000
2018-12-03,2790.500000,2800.179932,2773.379883,2790.370117,2790.370117,4186060000
2018-12-04,2782.429932,2785.929932,2697.179932,2700.060059,2700.060059,4499840000
2018-12-06,2663.510010,2696.149902,2621.530029,2695.949951,2695.949951,5141470000
2018-12-07,2691.260010,2708.540039,2623.139893,2633.080078,2633.080078,4216690000


================================================
FILE: ch_inference_for_props/figures/geomFitPValueForSP500/geomFitPValueForSP500.R
================================================
library(openintro)
data(COL)

myPDF('geomFitPValueForSP500.pdf', 6.6, 2.387,
      mar = c(2, 1, 1, 1),
      mgp = c(2.1, 0.5, 0))
ChiSquareTail(4.61,
              6,
              c(0, 25),
              col = COL[1])
arrows(15.1, 0.07,
       10.5, 0.05,
       length = 0.1,
       col = COL[1])
text(15.1, 0.07, 'Area representing\nthe p-value',
     pos = 4,
     col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/iPodChiSqTail/iPodChiSqTail.R
================================================
library(openintro)

x <- print(chisq.test(table(ask[2:3])))$statistic

myPDF('iPodChiSqTail.pdf', 5, 2.25,
    mar = c(2, 1, 1, 1),
    mgp = c(2.1, 0.7, 0))
ChiSquareTail(x, 2,
              c(0, 50),
              col = COL[1])
text(x, 0, "Tail area (1 / 500 million)\nis too small to see", pos = 3)
lines(c(x, 1000 * x), rep(0, 2), col = COL[1], lwd = 3)
dev.off()


================================================
FILE: ch_inference_for_props/figures/jurorHTPValueShown/jurorHTPValueShown.R
================================================
library(openintro)
data(COL)

myPDF('jurorHTPValueShown.pdf', 4.4, 1.87,
      mar = c(1.5, 1, 0.2, 1),
      mgp = c(2.1, 0.45, 0))
ChiSquareTail(5.89,
              3,
              c(0, 16),
              col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/mammograms/mammograms.R
================================================
require(openintro)
data(COL)

fn <- 'mammogramPValue.pdf'
myPDF(fn, 4, 1.2,
      mar = c(1.5, 0, 0.1, 0),
      mgp = c(3, 0.3, 0))
normTail(L = -0.17, U = 0.17,
        col = COL[1],
        axes = FALSE,
        xlim = c(-3.2, 3.2))
at <- c(-10, -2, 0, 2, 10)
labels <- c(0, -0.0014, 0, 0.0014, 0)
axis(1, at, labels, cex.axis = 0.9)
# lines(rep(0, 2), c(0, dnorm(0)), col = COL[4])
dev.off()


================================================
FILE: ch_inference_for_props/figures/paydayCC_norm_pvalue/paydayCC_norm_pvalue.R
================================================
require(openintro)

fn <- 'paydayCC_norm_pvalue.pdf'
myPDF(fn, 4, 1.5,
      mar = c(1.55, 0, 0.1, 0),
      mgp = c(3, 0.5, 0))
normTail(0.5, 0.017, L = 0.49, U = 0.51, col = COL[1])
dev.off()


================================================
FILE: ch_inference_for_props/figures/quadcopter/quadcopter_attribution.txt
================================================
https://secure.flickr.com/photos/sebilden/14642916088

Photographer: David J
License: CC BY 2.0


================================================
FILE: ch_intro_to_data/TeX/case_study_using_stents_to_prevent_strokes.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Migraine and acupuncture,
    Part I\label{migraine_and_acupuncture_intro}}
A migraine is a particularly painful type of headache,
which patients sometimes wish to treat with acupuncture.
To determine whether acupuncture relieves migraine 
pain, researchers conducted a randomized controlled study
where 89 females diagnosed with migraine headaches were
randomly assigned to one of two groups:
treatment or control.
43 patients in the treatment group received acupuncture 
that is specifically designed to treat migraines.
46 patients in the control group received placebo acupuncture
(needle insertion at non-acupoint locations).
24 hours after patients received acupuncture, they were asked 
if they were pain free.
Results are summarized in the contingency table
below.\footfullcite{Allais:2011}

\noindent\begin{minipage}[l]{0.4\textwidth}
\begin{tabular}{ll  cc c} 
			                         		&           & \multicolumn{2}{c}{\textit{Pain free}} \\
\cline{3-4}
			                        	 	&			& Yes 	& No 	                  & Total \\
\cline{2-5}
							& Treatment 	& 10	 	& 33		                  & 43 \\
\raisebox{1.5ex}[0pt]{\emph{Group}} & Control	 	& 2	 	& 44 	 	                  & 46 \\
\cline{2-5}
							& Total		& 12		& 77		                  & 89
\end{tabular}
\end{minipage}
\begin{minipage}[c]{0.05\textwidth}
\end{minipage}
\begin{minipage}[c]{0.27\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.25\textwidth}
{\footnotesize Figure from the original paper displaying the appropriate area 
(M) versus the inappropriate area (S) used in the treatment of migraine attacks.}
\end{minipage}
\begin{parts}
\item What percent of patients in the treatment group were pain free 24 hours 
after receiving acupuncture? 
\item What percent were pain free in the control group?
\item In which group did a higher percent of patients become pain free 24 hours 
after receiving acupuncture?
\item Your findings so far might suggest that acupuncture is an effective treatment 
for migraines for all people who suffer from migraines. However, this is not the 
only possible conclusion that can be drawn based on your findings so far. What is 
one other possible explanation for the observed difference between the percentages 
of patients that are pain free 24 hours after receiving acupuncture in the two groups?
\end{parts}
}{}

% 2

\eoce{\qt{Sinusitis and antibiotics,
    Part I\label{sinusitis_and_antibiotics_intro}} 
Researchers studying the effect of antibiotic treatment for acute sinusitis 
compared to symptomatic treatments randomly assigned 166 adults diagnosed 
with acute sinusitis to one of two groups: treatment or control. Study 
participants received either a 10-day course of amoxicillin (an antibiotic) 
or a placebo similar in appearance and taste. The placebo consisted of 
symptomatic treatments such as acetaminophen, nasal decongestants, etc.
At the end of the 10-day period, patients were asked if
they experienced improvement in symptoms.
The distribution of responses is summarized below. 
\footfullcite{Garbutt:2012}
\begin{center}
\begin{tabular}{ll  cc c} 
                                    			&			& \multicolumn{2}{c}{\textit{Self-reported improvement}} \\
                                    			&			& \multicolumn{2}{c}{\textit{in symptoms}} \\
\cline{3-4}
			                        		&			& Yes 	& No 	& Total \\
\cline{2-5}
							& Treatment 	& 66		& 19		& 85 \\
\raisebox{1.5ex}[0pt]{\emph{Group}}	& Control		& 65		& 16 		& 81 \\
\cline{2-5}
							& Total		& 131	& 35		& 166
\end{tabular}
\end{center}
\begin{parts}
\item What percent of patients in the treatment group experienced improvement 
in symptoms? 
\item What percent experienced improvement in symptoms in the 
control group?
\item In which group did a higher percentage of patients experience improvement
in symptoms?
\item
    Your findings so far might suggest a real difference
    in effectiveness of antibiotic and placebo treatments
    for improving symptoms of sinusitis.
    However, this is not the only possible conclusion that
    can be drawn based on your findings so far.
    What is one other possible explanation for the observed
    difference between the percentages of patients in the
    antibiotic and placebo treatment groups that experience
    improvement in symptoms of sinusitis?
\end{parts}
}{}


================================================
FILE: ch_intro_to_data/TeX/ch_intro_to_data.tex
================================================
\begin{chapterpage}{Introduction to data}
  \chaptertitle{Introduction to data}
  \label{introductionToData}
  \label{ch_intro_to_data}
  \chaptersection{basicExampleOfStentsAndStrokes}
  \chaptersection{dataBasics}
  \chaptersection{overviewOfDataCollectionPrinciples}
  % \chaptersection{section_obs_data_sampling}
  \chaptersection{experimentsSection}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_intro_to_data}

%\begin{tipBox}{\tipBoxTitle[Chapter Goal:]{Thinking about data}
%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.}
%\end{tipBox}

\chapterintro{Scientists seek to answer questions
  using rigorous methods and careful observations.
  These observations -- collected from the likes of field notes,
  surveys, and experiments -- form the backbone of a statistical
  investigation and are called \term{data}.
  Statistics is the study of how best to collect, analyze,
  and draw conclusions from data, %It is helpful to put statistics in the context of a general process of investigation:
%\begin{enumerate}
%\setlength{\itemsep}{0mm}
%\item Identify a question or problem.
%\item Collect relevant data on the topic.
%\item Analyze the data.
%\item Form a conclusion.
%%\item Make decisions based on the conclusion.
%\end{enumerate}
%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?
  and in this first chapter,
  we focus on both the properties of data
  and on the collection of data.}

%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.



\section{Case study: using stents to prevent strokes}
\label{basicExampleOfStentsAndStrokes}

\index{data!stroke|(}

Section~\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.

In this section we will consider an experiment that studies effectiveness of stents in treating patients at risk of stroke.
Stents 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:
\begin{quote}
Does the use of stents reduce the risk of stroke?
\end{quote}

The researchers who asked this question conducted an experiment with 451 at-risk patients. Each volunteer patient was randomly assigned to one of two groups:
\begin{itemize}
\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.
\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.
\end{itemize}
Researchers 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.

Researchers 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.

\begin{figure}[h]
\centering
\begin{tabular}{l ccc}
\hline
Patient	&	group	&	0-30 days 	&	0-365 days \\
\hline
1		&	treatment &	no event &	no event \\
2		&	treatment &	stroke & stroke \\
3		&	treatment &	no event & no event \\
$\vdots$	&	$\vdots$	  &	$\vdots$ \\
450	&	control &	no event &	no event \\
451	&	control &	no event &	no event \\
\hline
\end{tabular}
\caption{Results for five patients from the stent study.}
\label{stentStudyResultsDF}
% 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)))
\end{figure}

Considering 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.

\begin{figure}[h]
\centering
\begin{tabular}{l cc c cc}
& \multicolumn{2}{c}{0-30 days} &\hspace{5mm}\ & \multicolumn{2}{c}{0-365 days} \\
  \cline{2-3} \cline{5-6}
	& 	stroke 	& no event && 	stroke 	& no event \\
  \hline
treatment 	& 33		& 191	&&	45 	& 179 \\
control 		& 13		& 214	&& 	28	& 199 \\
  \hline
Total				& 46		& 405	&&	73	& 378 \\
  \hline
\end{tabular}
\caption{Descriptive statistics for the stent study.}
\label{stentStudyResults}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
Of 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
\end{nexercise}
\end{exercisewrap}\footnotetext{The proportion of the 224 patients who had a stroke within 365 days: $45/224 = 0.20$.}

We can compute summary statistics from the table.
A \term{summary statistic}%
\index{statistic|seealso{summary statistic}}
is a single number summarizing
a large amount of data.
For instance, the primary results of the study after 1~year
could be described by two summary statistics:
the proportion of people who had a stroke in the treatment
and control groups.
\begin{itemize}
\setlength{\itemsep}{0mm}
\item[] Proportion who had a stroke in the treatment (stent) group: $45/224 = 0.20 = 20\%$.
\item[] Proportion who had a stroke in the control group: $28/227 = 0.12 = 12\%$.
\end{itemize}
These 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?

This 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?

While 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.

\textbf{Be careful:}
Do not generalize the results of this study to all patients
and all stents.
This study looked at patients with very specific characteristics
who volunteered to be a part of this study and who may not be
representative of all stroke patients.
In addition, there are many types of stents and this study only
considered the self-expanding Wingspan stent (Boston Scientific).
However, this study does leave us with an important lesson:
we should keep our eyes open for surprises.

\index{data!stroke|)}


{\input{ch_intro_to_data/TeX/case_study_using_stents_to_prevent_strokes.tex}}



\section{Data basics}
\label{dataBasics}

Effective organization and description of data is a first
step in most analyses.
This section introduces the \emph{data matrix} for organizing
data as well as some terminology about different forms of data
that will be used throughout this book.

\subsection{Observations, variables, and data matrices}

\index{data!loan50|(}

Figure~\ref{loan50DF} displays rows 1, 2, 3, and 50 of a data set
for 50 randomly sampled loans offered through Lending Club,
which is a peer-to-peer lending company.
These observations will be referred to as the
\data{loan50} data set.

Each row in the table represents a single loan.
The formal name for a row is a \term{case}
or \term{observational unit}\index{unit of observation}.
The columns represent characteristics,
called \termsub{variables}{variable},
for each of the loans.
For 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.

\begin{exercisewrap}
\begin{nexercise}
What is the grade of the first loan in Figure~\ref{loan50DF}?
And what is the home ownership status of the borrower
for that first loan?
For these Guided Practice questions, you can check your answer
in the footnote.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The loan's grade is B,
  and the borrower rents their residence.}

In practice, it is especially important to ask clarifying
questions to ensure important aspects of the data are understood.
For instance, it is always important to be sure we know what
each variable means and the units of measurement.
Descriptions of the \data{loan50} variables are given
in Figure~\ref{loan50Variables}.

\begin{figure}[h]
\centering
{\small
\begin{tabular}{ccc ccc cc} %c}
  \hline
   & \var{loan\us{}amount}
   & \var{interest\us{}rate}
   & \var{term} & \var{grade} & \var{state}
   & \var{total\us{}income}
   & \var{homeownership} \\
  \hline
  1 & 22000 & 10.90 & 60.00 & B & NJ & 59000.00 & rent \\ 
  2 & 6000 & 9.92 & 36.00 & B & CA & 60000.00 & rent \\ 
  3 & 25000 & 26.30 & 36.00 & E & SC & 75000.00 & mortgage \\
  %1 & 7500 & 7.34 & 36 & A & MD & 70000 & rent \\
  %2 & 25000 & 9.43 & 60 & B & OH & 254000 & mortgage \\
  %3 & 14500 & 6.08 & 36 & A & MO & 80000 & mortgage \\
  $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$
      & $\vdots$ & $\vdots$ \\
  50 & 15000 & 6.08 & 36.00 & A & TX & 77500.00 & mortgage \\
  %50 & 3000 & 7.96 & 36 & A & CA & 34000 & rent \\
   \hline
\end{tabular}
}
\caption{Four rows from the \data{loan50} data matrix.}
\label{loan50DF}
\end{figure}
% Dropped: state, verified_income
% 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])

\begin{figure}[h]
\centering\small
\begin{tabular}{lp{10.5cm}}
\hline
{\bf variable} & {\bf description} \\
\hline
\var{loan\us{}amount} & Amount of the loan received,
    in US dollars.  \\
\var{interest\us{}rate} & Interest rate on the loan,
    in an annual percentage.  \\
\var{term} & The length of the loan, which is always set
    as a whole number of months. \\
\var{grade} & Loan grade, which takes values A through G
    and represents the quality of the loan and its likelihood
    of being repaid.  \\
\var{state} & US state where the borrower resides. \\
\var{total\us{}income} & Borrower's total income,
    including any second income, in US dollars.   \\
\var{homeownership} & Indicates whether the
    person owns, owns but has a mortgage, or rents.  \\
%\var{verified\us{}income} & Indicates whether the
%    income is verified, its source is verified but not the amount,
%    or it is not verified.   \\
\hline
\end{tabular}
\caption{Variables and their descriptions for the \data{loan50} data set.}
\label{loan50Variables}
\end{figure}

\index{data!loan50|)}

The data in Figure~\ref{loan50DF} represent a \term{data matrix},
which is a convenient and common way to organize data,
especially if collecting data in a spreadsheet.
Each row of a data matrix corresponds to a unique case
(observational unit),
and each column corresponds to a variable.
%A data matrix for the stroke study introduced in
%Section~\ref{basicExampleOfStentsAndStrokes} is shown
%in Figure~\vref{stentStudyResultsDF}, where the cases were
%patients and three variables were recorded for each
%patient.

\D{\newpage}

When recording data, use a data matrix unless you have
a very good reason to use a different structure.
This structure allows new cases to be added as rows
or new variables as new columns.

\begin{exercisewrap}
\begin{nexercise}
The grades for assignments, quizzes, and exams in a course are
often recorded in a gradebook that takes the form of a data matrix.
How might you organize grade data using a data
matrix?\footnotemark
\end{nexercise}
\end{exercisewrap}

\index{data!county|(}

\begin{exercisewrap}
\begin{nexercise}\label{desc_county_as_data_matrix}%
We consider data for 3,142 counties in the United States,
which includes each county's name,
the state where it resides, its population in 2017,
how its population changed from 2010 to 2017,
poverty rate,
and six additional characteristics.
How might these data be organized in
a data matrix?\footnotemark
\end{nexercise}
\end{exercisewrap}
\addtocounter{footnote}{-1}
\footnotetext{There are multiple strategies that can be followed.
  One common strategy is to have each student represented by a row,
  and then add a column for each assignment, quiz, or exam.
  Under this setup, it is easy to review a single line to understand
  a student's grade history.
  There should also be columns to include student information,
  such as one column to list student names.}
\addtocounter{footnote}{1}
\footnotetext{Each county may be viewed as a case,
  and there are eleven pieces of information recorded for
  each case.
  A table with 3,142 rows and 11 columns could hold these data,
  where each row represents a county and each column represents
  a particular piece of information.}

The data described in Guided
Practice~\ref{desc_county_as_data_matrix} represents the
\data{county} data set, which is shown as a data matrix
in Figure~\ref{countyDF}.
The variables are summarized in Figure~\ref{countyVariables}.

\begin{landscape}
\begin{figure}
\centering\small
\begin{tabular}{ccc ccc ccc ccc}
  \hline
 & \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} \\ 
  \hline
1 & Autauga  & Alabama &  55504 &  1.48 & 13.7 & 77.5 &  7.2 & 3.86 & yes & some\us{}college & 55317 \\ 
  2 & Baldwin  & Alabama & 212628 &  9.19 & 11.8 & 76.7 & 22.6 & 3.99 & yes & some\us{}college & 52562 \\ 
  3 & Barbour  & Alabama &  25270 & -6.22 & 27.2 & 68.0 & 11.1 & 5.90 & no  & hs\us{}diploma   & 33368 \\ 
  4 & Bibb     & Alabama &  22668 &  0.73 & 15.2 & 82.9 &  6.6 & 4.39 & yes & hs\us{}diploma   & 43404 \\ 
  5 & Blount   & Alabama &  58013 &  0.68 & 15.6 & 82.0 &  3.7 & 4.02 & yes & hs\us{}diploma   & 47412 \\ 
  6 & Bullock  & Alabama &  10309 & -2.28 & 28.5 & 76.9 &  9.9 & 4.93 & no  & hs\us{}diploma   & 29655 \\ 
  7 & Butler   & Alabama &  19825 & -2.69 & 24.4 & 69.0 & 13.7 & 5.49 & no  & hs\us{}diploma   & 36326 \\ 
  8 & Calhoun  & Alabama & 114728 & -1.51 & 18.6 & 70.7 & 14.3 & 4.93 & yes & some\us{}college & 43686 \\ 
  9 & Chambers & Alabama &  33713 & -1.20 & 18.8 & 71.4 &  8.7 & 4.08 & no  & hs\us{}diploma   & 37342 \\ 
  10 & Cherokee & Alabama &  25857 & -0.60 & 16.1 & 77.5 &  4.3 & 4.05 & no  & hs\us{}diploma   & 40041 \\ 
  $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ \\
  3142 & Weston   & Wyoming &   6927 & -2.93 & 14.4 & 77.9 &  6.5 & 3.98 & no  & some\us{}college & 59605 \\ 
   \hline
\end{tabular}
\caption{Eleven rows from the \data{county} data set.}
\label{countyDF}
% 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) })))
\end{figure}

\begin{figure}
\centering\small
\begin{tabular}{lp{11cm}}
\hline
{\bf variable} & {\bf description} \\
\hline
\var{name} &
    County name. \\
\var{state} &
    State where the county resides,
    or the District of Columbia. \\
\var{pop} &
    Population in 2017. \\
\var{pop\us{}change} &
    Percent change in the population from 2010 to 2017.
    For example, the value \resp{1.48} in the first row
    means the population for this county
    increased by 1.48\% from 2010 to 2017. \\
\var{poverty} &
    Percent of the population in poverty. \\
\var{homeownership}  &
    Percent of the population that lives in their own home
    or lives with the owner, e.g. children living with parents
    who own the home. \\
\var{multi\us{}unit}  &
    Percent of living units that are in multi-unit structures,
    e.g. apartments. \\
\var{unemp\us{}rate} &
    Unemployment rate as a percent. \\
\var{metro} &
    Whether the county contains a metropolitan area. \\
\var{median\us{}edu} & Median education level, which
    can take a value among
    \resp{below\us{}hs},
    \resp{hs\us{}diploma},
    \resp{some\us{}college},
    and \resp{bachelors}. \\
\var{median\us{}hh\us{}income} &
    Median household income for the county, where a household's
    income equals the total income of its occupants who are
    15~years or older. \\
%\var{per\us{}capita\us{}income} &
%    Per capita (per person) income for the county. \\
\hline
\end{tabular}
\centering
\caption{Variables and their descriptions for the \data{county} data set.}
\label{countyVariables}
\end{figure}
\end{landscape}

\subsection{Types of variables}
\label{variableTypes}

Examine the \var{unemp\us{}rate}, \var{pop}, \var{state},
and \var{median\us{}edu} variables in the \data{county}
data set. Each of these variables is inherently different from the
other three, yet some share certain characteristics.

First consider \var{unemp\us{}rate},
which is said to be a \term{numerical} variable since
it can take a wide range of numerical values,
and it is sensible to add, subtract, or take averages
with those values.
On the other hand, we would not classify a variable
reporting telephone area codes as numerical since the
average, sum, and difference of area codes doesn't have
any clear meaning.

The \var{pop} variable is also numerical, although it seems
to be a little different than \var{unemp\us{}rate}.
This variable of the population count can only take whole
non-negative numbers (\resp{0}, \resp{1}, \resp{2}, ...).
For~this reason, the population variable is said to be
\term{discrete} since it can only take numerical values
with jumps.
On the other hand, the unemployment rate variable is said
to be \term{continuous}.

The variable \var{state} can take up to 51 values after
accounting for Washington, DC: \resp{AL}, \resp{AK}, ...,
and \resp{WY}.
Because the responses themselves are categories,
\var{state} is called a \term{categorical} variable,
and the possible values are called the variable's \term{levels}.

Finally, consider the \var{median\us{}edu} variable,
which describes the median education level of county
residents and takes values
\resp{below\us{}hs}, \resp{hs\us{}diploma},
\resp{some\us{}college}, or \resp{bachelors}
in each county.
This variable seems to be a hybrid: it is a categorical variable
but the levels have a natural ordering.
A variable with these properties is called an \term{ordinal}
variable, while a regular categorical variable without this
type of special ordering is called a \term{nominal} variable.
To simplify analyses, any ordinal variable in this book will
be treated as a nominal (unordered) categorical variable.

\begin{figure}[h]
  \centering
  \Figure
    [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.]
    {0.57}{variables}
  \caption{Breakdown of variables into their respective types.}
  \label{variables}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Data were collected about students
    in a statistics course.
    Three variables were recorded for each student:
    number of siblings, student height, and whether
    the student had previously taken a statistics course.
    Classify each of the variables as continuous numerical,
    discrete numerical, or categorical.}
  The number of siblings and student height represent
  numerical variables.
  Because the number of siblings is a count, it is discrete.
  Height varies continuously, so it is a continuous numerical
  variable.
  The last variable classifies students into two categories
  -- those who have and those who have not taken a statistics
  course -- which makes this variable categorical.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}\index{data!stroke}%
An experiment is evaluating the effectiveness of a new drug
in treating migraines.
A \var{group} variable is used to indicate the experiment group
for each patient: treatment or control.
The \mbox{\var{num\us{}migraines}} variable represents the number
of migraines the patient experienced during a 3-month period.
\mbox{Classify} each variable as either numerical or
categorical.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{The
  \var{group} variable can take just one of two group names,
  making it categorical.
  The \var{num\us{}migraines} variable describes
  a count of the number of migraines, which is an outcome where
  basic arithmetic is sensible, which means this is numerical
  outcome; more specifically, since it represents a count,
  \var{num\us{}migraines} is a discrete numerical variable.}


\D{\newpage}

\subsection{Relationships between variables}
\label{variableRelations}

Many analyses are motivated by a researcher looking
for a relationship between two or more variables.
A social scientist may like to answer some of the
following questions:
\newcommand{\popchangevmedianhhincomequestion}[0]{
    % Note that this question is used to introduce the
    %explanatory / response variable topic.
    Does a higher than average increase in county population
    tend to correspond to counties with higher or lower median
    household incomes?}%
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item[(1)]\label{ownershipMultiUnitQuestion}
    If homeownership is lower than the national average
    in one county, will the percent of multi-unit structures
    in that county tend to be above or below the national average?
\item[(2)]\label{pop_change_v_median_hh_income_question}
    \popchangevmedianhhincomequestion{}
    % Do counties with a higher median household income
    % tend to be growing faster or slower than other counties?
\item[(3)]\label{isAverageIncomeAssociatedWithSmokingBans}
    How useful a predictor is median education level
    for the median household income for US counties?
\end{enumerate}

To answer these questions, data must be collected, such
as the \data{county} data set shown in Figure~\ref{countyDF}.
Examining summary statistics \index{summary statistic} could
provide insights for each of the three questions about counties.
Additionally, graphs can be used to visually explore data.

\indexthis{Scatterplots}{scatterplot} are one type of graph
used to study the relationship between two numerical variables.
Figure~\ref{multiunitsVsOwnership} compares the variables
\var{homeownership} and
\var{multi\us{}unit},
which is the percent of units in multi-unit structures
(e.g. apartments, condos).
Each point on the plot represents a single county.
For instance, the highlighted dot corresponds to
County~413 in the \data{county} data set:
Chattahoochee County, Georgia, which has 39.4\% of
units in multi-unit structures and a homeownership rate
of 31.3\%.
The scatterplot suggests a relationship between the
two variables: counties with a higher rate of multi-units
tend to have lower homeownership rates.
We might brainstorm as to why this relationship exists
and investigate each idea to determine which are the most
reasonable explanations.

\begin{figure}[h]
  \centering
  \Figure
    [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.\%).]
    {0.79}{multiunitsVsOwnership}
  \caption{A scatterplot of homeownership versus the percent
      of units that are in multi-unit structures for US counties.
      The highlighted dot represents Chattahoochee County, Georgia,
      which has a multi-unit rate of 39.4\% and a homeownership rate
      of 31.3\%.}
  \label{multiunitsVsOwnership}
\end{figure}

The multi-unit and homeownership rates are said to be
associated because the plot shows a discernible pattern.
When two variables show some connection with one another,
they are called \term{associated} variables.
Associated variables can also be called \term{dependent}
variables and vice-versa.

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
Examine the variables in the \data{loan50} data set,
which are described in Figure~\vref{loan50Variables}.
Create two questions about possible relationships
between variables in \data{loan50} that are of interest
to~you.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Two example questions:
  (1)~What is the relationship between loan amount and
      total income?
  (2)~If someone's income is above the average, will their
      interest rate tend to be above or below the average?}

\begin{examplewrap}
\begin{nexample}{This example examines the relationship
    between a county's population change
    from 2010 to 2017
    and median household income,
    which is visualized as a scatterplot in
    Figure~\ref{pop_change_v_med_income}.
    Are these variables associated?}
  The larger the median household income for a county,
  the higher the population growth observed for the county.
  While this trend isn't true for every county,
  the trend in the plot is evident.
  Since there is some relationship between the variables,
  they are associated.
\end{nexample}
\end{examplewrap}

%When two variables show some connection with one another,
%they are called \term{associated} variables.
%Associated variables can also be called \term{dependent}
%variables and vice-versa.
%When the variables increase together,
%as they do in Figure~\ref{loan_amount_vs_income},
%they are said to be \term{positively associated}.
%When the trend in the scatterplot goes down to the right,
%then they are described as \term{negatively correlated}.

%While we may find it interesting to consider the relationship
%between two variables such as those in the scatterplot,
%the relationship between those variables can be more complex.
%For example, interest rates on loans tend to be chosen based
%on the riskiness of the loan, i.e. how likely it is to be
%paid back, and that is likely to depend on a variety of
%details, such as what the loan is for, the person's
%creditworthiness, whether their income is verified, etc.
%We will begin exploring some of these more complex relationships
%in graphs in Chapter~\ref{ch_summarizing_data} and beyond.
%\Comment{Revise if we don't add these more rich plots...}

%\begin{example}{Figure~\ref{interest_rate_vs_loan_amount}
%    features a scatterplot of interest rate against loan amount.
%    Are these variables associated?}
%  There isn't an evident trend in the data,
%  so we would say these two variables are not associated.
%\end{example}

\begin{figure}
  \centering
  \Figure
    [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\%).]
    {0.9}{pop_change_v_med_income}
  \caption{A scatterplot showing
      \var{pop\us{}change}
      against \var{median\us{}hh\us{}income}.
      Owsley County of Kentucky, is highlighted,
      which lost 3.63\% of its population from 2010 to 2017
      and had median household income of \$22,736.}
  \label{pop_change_v_med_income}
\end{figure}

Because there is a downward trend in
Figure~\ref{multiunitsVsOwnership} --
counties with more units in multi-unit structures
are associated with lower homeownership --
these variables are said to be
\termsub{negatively associated}{negative association}.
A~\term{positive association} is shown in the relationship
between the
\var{median\us{}hh\us{}income}
and \var{pop\us{}change}
in Figure~\ref{pop_change_v_med_income},
where counties with higher median household income tend
to have higher rates of population growth.

If two variables are not associated,
then they are said to be \term{independent}.
That is, two variables are independent if there
is no evident relationship between the two.

\begin{onebox}{Associated or independent, not both}
A pair of variables are either related in some way (associated) or not (independent). No pair of variables is both associated and independent.
\end{onebox}


\D{\newpage}

\subsection{Explanatory and response variables}
\label{explanatoryAndResponse}

When we ask questions about the relationship
between two variables, we sometimes also want to determine
if the change in one variable causes a change in the other.
Consider the following rephrasing of an earlier question
about the \data{county} data set:
\begin{quote}\em
  If there is an increase in the median household income
  in a county, does this drive an increase in its population?
\end{quote}
In this question, we are asking whether one variable
affects another.
If this is our underlying belief,
then \emph{median household income} is the
\termsub{explanatory}{explanatory variable}
variable and the \emph{population change} is the
\termsub{response}{response variable} variable
in the hypothesized relationship.\footnote{Sometimes
  the explanatory variable is called the \term{independent}
  variable and the response variable is called the
  \term{dependent} variable.
  However, this becomes confusing since a \emph{pair}
  of variables might be independent or dependent,
  so we avoid this language.}

\index{data!county|)}

\begin{onebox}{Explanatory and response variables}
When we suspect one variable might causally affect another,
we label the first variable the explanatory variable and the
second the response variable.
\vspace{1mm}

\hspace{10mm}\Figure
    [Simple graphic shown the words "explanatory variable" pointing to "response variable", where the words "might affect" appear above the arrow.]
    {0.53}{expResp}

For many pairs of variables, there is no hypothesized
relationship, and these labels would not be applied to
either variable in such cases.
\end{onebox}

Bear in mind that the act of labeling the variables in this
way does nothing to guarantee that a causal relationship exists.
A formal evaluation to check whether one variable causes
a change in another requires an experiment.

%\begin{exercisewrap}
%\begin{nexercise}
%Consider the earlier question:
%\begin{quote}\em
%  If a county has a higher median household income,
%  does this drive an increase in its population?
%\end{quote}
%We could have just as easily reframed the causal relationship
%to be in the reverse direction:
%\begin{quote}\em
%  If a county more population growth, does this drive
%  it to have a higher median household income?
%\end{quote}
%What are the explanatory and response variables when framing
%the variable relationship in the second question?
%\end{nexercise}
%\end{exercisewrap}
%\footnotetext{In this framing, we have hypothesized
%  that population growth drives median household income.
%  That is, population growth is the explanatory variable,
%  and median household income is the response.
%  This exercise should emphasize that these variable labels
%  do not actually define whether one variable actually affects
%  the other.}


\subsection{Introducing observational studies and experiments}

\noindent%
There are two primary types of data collection:
observational studies and experiments.

Researchers perform an \term{observational study} when they
collect data in a way that does not directly interfere with
how the data arise.
For instance, researchers may collect information via surveys,
review medical or company records, or follow a \term{cohort}
of many similar individuals to form hypotheses about why certain
diseases might develop.
In each of these situations, researchers merely observe the
data that arise.
In general, observational studies can provide evidence of
a naturally occurring association between variables, but they
cannot by themselves show a causal connection.

When researchers want to investigate the possibility of
a causal connection, they conduct an \term{experiment}.
Usually there will be both an explanatory and a response
variable.
For instance, we may suspect administering a drug will reduce
mortality in heart attack patients over the following year.
To check if there really is a causal connection between the
explanatory variable and the response, researchers will collect
a sample of individuals and split them into groups.
The individuals in each group are \emph{assigned} a treatment.
When individuals are randomly assigned to a group,
the experiment is called a \term{randomized experiment}.
For example, each heart attack patient in the drug trial
could be randomly assigned,  perhaps by flipping a coin,
into one of two groups:
the first group receives a \term{placebo} (fake treatment)
and the second group receives the drug.
See the case study in
Section~\ref{basicExampleOfStentsAndStrokes} for another
example of an experiment, though that study did not employ
a~placebo.

\begin{onebox}{Association $\neq$ Causation}
  In general, association does not imply causation,
  and causation can only be inferred from a randomized experiment.
\end{onebox}


{\input{ch_intro_to_data/TeX/data_basics.tex}}




%%%%%
\section{Sampling principles and strategies}
\label{overviewOfDataCollectionPrinciples}
\label{section_obs_data_sampling}

\index{sample|(}
\index{population|(}

The first step in conducting research is to identify topics
or questions that are to be investigated.
A clearly laid out research question is helpful in identifying
what subjects or cases should be studied and what variables are
important.
It is also important to consider \emph{how} data are collected
so that they are reliable and help achieve the research goals.


\subsection{Populations and samples}
\label{populationsAndSamples}

\noindent%
Consider the following three research questions:
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item
    What is the average mercury content in swordfish
    in the Atlantic Ocean?
\item
    \label{timeToGraduationQuestionForUCLAStudents}%
    Over the last 5 years, what is the average time
    to complete a degree for Duke undergrads?
\item
    \label{identifyPopulationOfStentStudy}%
    Does a new drug reduce the number of deaths in patients
    with severe heart disease?
\end{enumerate}
Each 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.

\begin{exercisewrap}
\begin{nexercise}\label{identifyingThePopulationForTwoQuestionsInPopAndSampSubsection}%
For the second and third questions above,
identify the target population and what
represents an individual case.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{(\ref{timeToGraduationQuestionForUCLAStudents})
    The first question is only relevant
    to students who complete their degree;
    the average cannot be computed using a student
    who never finished her degree.
    Thus, only Duke undergrads who
    graduated in the last five years represent cases
    in the population under consideration.
    Each such student is an individual case.
    (\ref{identifyPopulationOfStentStudy})~A person with
    severe heart disease represents a case.
    The population includes all people with severe heart
    disease.}


\subsection{Anecdotal evidence}
\label{anecdotalEvidenceSubsection}

\index{bias|(}

\noindent%
Consider the following possible responses to the three research questions:
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item
    A man on the news got mercury poisoning from eating swordfish,
    so the average mercury concentration in swordfish must be
    dangerously high.
\item\label{iKnowThreeStudentsWhoTookMoreThan7YearsToGraduateAtDuke}
    I met two students who took more than 7 years to graduate
    from Duke, so it must take longer to graduate at Duke than
    at many other colleges.
\item\label{myFriendsDadDiedAfterSulphinpyrazon}
    My friend's dad had a heart attack and died after they gave
    him a new heart disease drug, so~the drug must not work.
\end{enumerate}
Each conclusion is based on data.
However, there are two problems.
First, the data only represent one or two cases.
Second, and more importantly, it is unclear whether these cases
are actually representative of the population.
Data collected in this haphazard fashion are called
\term{anecdotal evidence}.

\captionsetup{width=\textwidth-75mm}
\begin{figure}[h]
  \centering
  \hspace{8mm}\Figuress
    [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.]
    {55mm}{mnWinter}{mnWinter}\hspace{4mm}
  \begin{minipage}[b]{\textwidth-75mm}
    \caption[anecdotal evidence]{In February 2010,
        some media pundits cited one large snow storm
        as valid evidence against global warming.
        As comedian Jon Stewart pointed out,
        ``It's one storm, in one region, of one country.''
    \label{mnWinter}}
  \end{minipage}
\end{figure}
\captionsetup{width=\mycaptionwidth}

\begin{onebox}{Anecdotal evidence}
Be careful of data collected in a haphazard fashion.
Such evidence may be true and verifiable, but it may
only represent extraordinary cases.
\end{onebox}

\D{\newpage}

Anecdotal 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.

\subsection{Sampling from a population}

\index{sample!random sample|(}
\index{sample!bias|(}

We might try to estimate the time to graduation for Duke
undergraduates in the last 5 years by collecting a sample
of students.
All graduates in the last 5 years represent the
\emph{population}\index{population}, and graduates who are
selected for review are collectively called the
\emph{sample}\index{sample}.
In general, we always seek to \emph{randomly} select a sample
from a population.
The most basic type of random selection is equivalent to how
raffles are conducted.
For example, in selecting graduates, we could write each
graduate's name on a raffle ticket and draw 100 tickets.
The selected names would represent a random sample of 100 graduates.
We pick samples randomly to reduce the chance we introduce biases.

\begin{figure}[ht]
  \centering
  \Figures
    [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.]
    {0.5}{popToSample}{popToSampleGraduates}
  \caption{In this graphic, five graduates are randomly
      selected from the population to be included in the
      sample.}
  \label{popToSampleGraduates}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Suppose we ask a student who happens to be
    majoring in nutrition to select several graduates for
    the study.
    What kind of students do you think she might collect?
    Do you think her sample would be representative of all
    graduates?}
  Perhaps she would pick a disproportionate number of graduates
  from health-related fields.
  Or~perhaps her selection would be a good representation
  of the population.
  When selecting samples by hand, we run the risk of picking
  a \termsub{biased}{sample!bias} sample, even if their bias
  isn't intended.
\end{nexample}
\end{examplewrap}

\begin{figure}
  \centering
  \Figures
    [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.]
    {0.5}{popToSample}{popToSubSampleGraduates}
  \caption{Asked to pick a sample of graduates,
      a nutrition major might inadvertently pick a
      disproportionate number of graduates from
      health-related majors.}
  \label{popToSubSampleGraduates}
\end{figure}

\D{\newpage}

If someone was permitted to pick and choose exactly which
graduates were included in the sample, it is entirely possible
that the sample could be skewed to that person's interests,
which may be entirely unintentional.
This introduces \term{bias} into a sample.
Sampling randomly helps resolve this problem.
The most basic random sample is called a
\term{simple random sample}, and which is equivalent to using
a raffle to select cases.
This means that each case in the population has an equal chance
of being included and there is no implied connection between
the cases in the sample.

The act of taking a simple random sample helps minimize bias.
However, bias can crop up in other ways.
Even when people are picked at random, e.g. for surveys,
caution must be exercised if the
\term{non-response rate}
\index{sample!non-response rate|textbf} is high.
For instance, if only 30\% of the people randomly sampled
for a survey actually respond, then it is unclear whether
the results are \term{representative} of the entire population.
This \term{non-response bias}
\index{sample!non-response bias|textbf} can skew results.

\begin{figure}[h]
  \centering
  \Figures
    [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.]
    {0.5}{popToSample}{surveySample}
  \caption{Due to the possibility of non-response,
      surveys studies may only reach a certain group
      within the population.
      It is difficult, and often times impossible,
      to completely fix this problem.}
  \label{surveySample}
\end{figure}

Another common downfall is a
\term{convenience sample}\index{sample!convenience sample},
where individuals who are easily accessible are more likely
to be included in the sample.
For instance, if a political survey is done by stopping people
walking in the Bronx, this will not represent all of New York City.
It is often difficult to discern what sub-population a convenience
sample represents.

\begin{exercisewrap}
\begin{nexercise}
We 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{Answers will vary.
    From our own anecdotal experiences, we believe people
    tend to rant more about products that fell below
    expectations than rave about those that perform as
    expected.
    For this reason, we suspect there is a negative bias
    in product ratings on sites like Amazon.
    However, since our experiences may not be
    representative, we also keep an open mind.}

\index{sample!bias|)}
\index{sample!random sample|)}
\index{bias|)}
\index{population|)}
\index{sample|)}


\D{\newpage}

\subsection{Observational studies}

Data where no treatment has been explicitly applied
(or explicitly withheld) is called \term{observational data}.
For instance, the loan data and county data described in
Section~\ref{dataBasics}
are both examples of observational data.
%It is important to collect such data in
%a thoughtful and rigorous manner so that statistical
%analyses based on the data can have meaningful
%and generalizable results.
Making causal conclusions based on experiments is often reasonable.
However, making the same causal conclusions based on observational
data can be treacherous and is not recommended.
Thus, observational studies are generally only sufficient
to show associations or form hypotheses that we later check
using experiments.

\begin{exercisewrap}
\begin{nexercise}\label{sunscreenLurkingExample}%
Suppose 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{No.
    See the paragraph following the exercise for
    an explanation.}

Some 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.
\begin{center}
  \Figures
    [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.]
    {0.55}{variables}{sunCausesCancer}
\end{center}
% Some studies:
% http://www.sciencedirect.com/science/article/pii/S0140673698121682
% http://archderm.ama-assn.org/cgi/content/abstract/122/5/537
% Study with a similar scenario to that described here:
% http://onlinelibrary.wiley.com/doi/10.1002/ijc.22745/full

Sun 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.

%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.

\begin{exercisewrap}
\begin{nexercise}
Figure~\ref{multiunitsVsOwnership} shows a negative association
between the homeownership rate and the percentage of multi-unit
structures in a county.
However, it is unreasonable to conclude that there is a causal
relationship between the two variables.
Suggest a variable that might explain the negative
relationship.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Answers will vary.
    Population density may be important.
    If a county is very dense, then this may require
    a larger fraction of residents to live in
    multi-unit structures.
    Additionally, the high density may contribute
    to increases in property value, making
    homeownership infeasible for many residents.}

Observational studies come in two forms:
prospective and retrospective studies.
A \term{prospective study} identifies individuals
and collects information as events unfold.
For instance, medical researchers may identify and follow
a group of patients over many years to assess
the possible influences of behavior on cancer risk.
One example of such a study is The Nurses' Health Study,
started in 1976 and expanded in 1989.
This prospective study recruits registered nurses and then
collects data from them using questionnaires.
\termsub{Retrospective studies}{retrospective studies}
collect data after events have taken place,
e.g. researchers may review past events in medical records.
Some data sets may contain both prospectively- and
retrospectively-collected variables.


\subsection{Four sampling methods}
\label{fourSamplingMethods}
\label{threeSamplingMethods}

Almost 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.

\begin{figure}
  \centering
  \Figures
    [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.]
    {}{samplingMethodsFigure}{simple_stratified}
  \caption{
      Examples of simple random\index{sample!simple random sampling}
      and stratified sampling\index{sample!stratified sampling}.
      In the top panel, simple random sampling was used to randomly
      select the 18 cases.
      In the bottom panel, stratified sampling was used:
      cases were grouped into strata, then simple random sampling
      was employed within \mbox{each stratum}.}
  \label{simple_stratified}
\end{figure}

\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.

\termsub{Stratified sampling}{sample!stratified sampling}
is a divide-and-conquer sampling strategy.
The population is divided into groups called
\term{strata}\index{sample!strata|textbf}.
The strata are chosen so that similar cases are grouped
together, then a second sampling method, usually simple
random sampling, is employed within each stratum.
In~the baseball salary example, the teams could represent
the strata, since some teams have a lot more money
(up to 4~times as much!).
Then we might randomly sample 4 players from each team for
a total of 120 players.

Stratified 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.

\begin{examplewrap}
\begin{nexample}{Why would it be good for cases within
    each stratum to be very similar?}
  We might get a more stable estimate for the subpopulation
  in a stratum if the cases are very similar,
  leading to more precise estimates within each group.
  When we combine these estimates into a single estimate
  for the full population, that population estimate will
  tend to be more precise since each individual group
  estimate is itself more precise.
\end{nexample}
\end{examplewrap}

In 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.

\begin{figure}
  \centering
  \Figures
    [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.]
    {}{samplingMethodsFigure}{cluster_multistage}
  \caption{Examples of cluster\index{sample!cluster sampling}
  and multistage sampling\index{sample!multistage sampling}.
  In the top panel, cluster sampling was used:
  data were binned into nine clusters, three of these clusters
  were sampled, and all observations within these three cluster
  were included in the sample.
  In the bottom panel, multistage sampling was used,
  which differs from cluster sampling only in that we
  randomly select a subset of each cluster to be included
  in the sample rather than measuring every case in each
  sampled cluster.}
\label{cluster_multistage}
\end{figure}

Sometimes cluster or multistage sampling can be more economical
than the alternative sampling techniques.
Also, unlike stratified sampling, these approaches are most
helpful when there is a lot of case-to-case variability within
a cluster but the clusters themselves don't look very different
from one another.
For example, if neighborhoods represented clusters, then cluster
or multistage sampling work best when the neighborhoods are very
diverse.
A~downside of these methods is that more advanced techniques
are typically required to analyze the data, though the methods
in this book can be extended to handle such data.

\begin{examplewrap}
\begin{nexample}{Suppose we are interested in estimating
    the malaria rate in a densely tropical portion of rural
    Indonesia.
    We learn that there are 30 villages in that part of the
    Indonesian jungle, each more or less similar to the next.
    Our goal is to test 150 individuals for malaria.
    What sampling method should be employed?}
  A simple random sample would likely draw individuals from
  all 30 villages, which could make data collection extremely
  expensive.
  Stratified sampling would be a challenge since it is
  unclear how we would build strata of similar individuals.
  However, cluster sampling or multistage sampling seem like
  very good ideas.
  If we decided to use multistage sampling, we might randomly
  select half of the villages, then randomly select
  10 people from each.
  This would probably reduce our data collection costs
  substantially in comparison to a simple random sample,
  and the cluster sample would still give us reliable
  information, even if we would need to analyze the data
  with slightly more advanced methods than we discuss
  in this book.
\end{nexample}
\end{examplewrap}


{\input{ch_intro_to_data/TeX/sampling_principles_and_strategies.tex}}






%%%%%
\section{Experiments}
\label{experimentsSection}

%\sectionintro{
Studies 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.
%}\setstretch{1.0}


\subsection{Principles of experimental design}
\label{experimentalDesignPrinciples}

\noindent{}Randomized experiments are generally built on four principles.

\begin{description}
\item[Controlling.]
    Researchers assign treatments to cases, and they do their
    best to \term{control} any other differences in the
    groups.\footnote{This is a different concept than
      a \emph{control group}, which we discuss in
      the second principle and in
      Section~\ref{biasInHumanExperiments}.}
    For example, when patients take a drug in pill form,
    some patients take the pill with only a sip of water
    while others may have it with an entire glass of water.
    To control for the effect of water consumption,
    a doctor may ask all patients to drink a 12 ounce glass
    of water with the pill.
\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.
\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.

\begin{figure}
  \centering
  \Figure
    [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.]
    {0.82}{figureShowingBlocking}
  \caption{Blocking using a variable depicting patient risk.
      Patients are first divided into low-risk and high-risk
      blocks, then each block is evenly separated into the
      treatment groups using randomization.
      This strategy ensures an equal representation of patients
      in each treatment group from both the low-risk and high-risk
      categories.}
  \label{figureShowingBlocking}
\end{figure}

\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.
\end{description}

It is important to incorporate the first three experimental
design principles into any study, and this book describes
applicable methods for analyzing data from such experiments.
Blocking is a slightly more advanced technique, and statistical
methods in this book may be extended to analyze data collected
using blocking.

\subsection{Reducing bias in human experiments}
\label{biasInHumanExperiments}

Randomized experiments are the gold standard for data collection,
but they do not ensure an unbiased perspective into the cause and
effect relationship in all cases.
Human studies are perfect examples where bias can unintentionally
arise.
Here we reconsider a study where a new drug was used to treat
heart attack patients.
In particular, researchers wanted to know if the drug reduced
deaths in patients.

These 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.

Put 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.

Researchers aren't usually interested in the emotional effect,
which might bias the study.
To circumvent this problem, researchers do not want patients
to know which group they are in.
When researchers keep the patients uninformed about their
treatment, the study is said to be \term{blind}.
But there is one problem:
if a patient doesn't receive a treatment, she will know she
is in the control group.
The solution to this problem is to give fake treatments to
patients in the control group.
A fake treatment is called a \term{placebo}, and an effective
placebo is the key to making a study truly blind.
A classic example of a placebo is a sugar pill that is made
to look like the actual treatment pill.
Often times, a placebo results in a slight but real
improvement in patients.
This effect has been dubbed the \term{placebo~effect}.

The patients are not the only ones who should be blinded:
doctors and researchers can accidentally bias a study.
When a doctor knows a patient has been given the real treatment,
she might inadvertently give that patient more attention or care
than a patient that she knows is on the placebo.
To guard against this bias, which again has been found to have
a measurable effect in some instances, most modern studies employ
a \term{double-blind} setup where doctors or researchers who
interact with patients are, just like the patients,
unaware of who is or is not receiving the
treatment.\footnote{There are always some researchers involved
  in the study who do know which patients are receiving which
  treatment.
  However, they do not interact with the study's patients and
  do not tell the blinded health care professionals who is
  receiving which treatment.}

\begin{exercisewrap}
\begin{nexercise}
Look 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{}
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise}
\label{gp_sham_surgery}%
For the study in Section~\ref{basicExampleOfStentsAndStrokes},
could the researchers have employed a placebo?
If so, what would that placebo have looked like?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Ultimately, can we make patients think they
  got treated from a surgery?
  In fact, we can, and some experiments use
  what's called a \term{sham surgery}.
  In a sham surgery, the patient does undergo surgery,
  but the patient does not receive the full treatment,
  though they will still get a placebo effect.}

You may have many questions about the ethics of
sham surgeries to create a placebo after reading
Guided Practice~\ref{gp_sham_surgery}.
These questions may have even arisen in your mind when
in the general experiment context, where a possibly
helpful treatment was withheld from individuals in the
control group;
the main difference is that a sham surgery tends to
create additional risk, while withholding a treatment
only maintains a person's risk.

There are always multiple viewpoints of experiments
and placebos, and rarely is it obvious which is
ethically ``correct''.
For instance, is it ethical to use a sham surgery
when it creates a risk to the patient?
However, if we don't use sham surgeries,
we may promote the use of a costly treatment that
has no real effect;
if this happens, money and other resources will be diverted
away from other treatments that are known to be helpful.
Ultimately, this is a difficult situation where
we cannot perfectly protect both the patients
who have volunteered for the study and the patients who
may benefit (or not) from the treatment in the future.


{\input{ch_intro_to_data/TeX/experiments.tex}}


================================================
FILE: ch_intro_to_data/TeX/data_basics.tex
================================================
\exercisesheader{}

% 3

\eoce{\qt{Air pollution and birth outcomes, study components\label{study_components_airpoll}} 
Researchers collected data to examine the relationship between air pollutants 
and preterm births in Southern California. During the study air pollution levels 
were measured by air quality monitoring stations. Specifically, levels of carbon 
monoxide were recorded in parts per million, nitrogen dioxide and ozone in parts 
per hundred million, and coarse particulate matter (PM$_{10}$) in $\mu g/m^3$. 
Length of gestation data were collected on 143,196 births between the years 1989 
and 1993, and air pollution exposure during gestation was calculated for each 
birth. The analysis suggested that increased ambient PM$_{10}$ and, to a lesser 
degree, CO concentrations may be associated with the occurrence of preterm births.\footfullcite{Ritz+Yu+Chapa+Fruin:2000}
\begin{parts}
\item Identify the main research question of the study.
\item Who are the subjects in this study, and how many are included?
\item What are the variables in the study? Identify each variable as numerical or 
categorical. If numerical, state whether the variable is discrete or continuous.
If categorical, state whether the variable is ordinal.
\end{parts}
}{}

% 4

\eoce{\qt{Buteyko method, study components\label{study_components_buteyko}} 
The Buteyko method is a shallow breathing technique developed by Konstantin 
Buteyko, a Russian doctor, in 1952. Anecdotal evidence suggests that the Buteyko 
method can reduce asthma symptoms and improve quality of life. In a scientific 
study to determine the effectiveness of this method, researchers recruited 600 
asthma patients aged 18-69 who relied on medication for asthma treatment. These 
patients were randomly split into two research groups: one practiced the Buteyko 
method and the other did not. Patients were scored on quality of life, activity, 
asthma symptoms, and medication reduction on a scale from 0 to 10. On average, 
the participants in the Buteyko group experienced a significant reduction in 
asthma symptoms and an improvement in quality of life.\footfullcite{McDowan:2003} 
\begin{parts}
\item Identify the main research question of the study.
\item Who are the subjects in this study, and how many are included?
\item What are the variables in the study? Identify each variable as numerical or 
categorical. If numerical, state whether the variable is discrete or continuous.
If categorical, state whether the variable is ordinal.
\end{parts}
}{}

% 5

\eoce{\qt{Cheaters, study components\label{study_components_cheaters}} 
Researchers studying the relationship between honesty, age and self-control 
conducted an experiment on 160 children between the ages of 5 and 15. 
Participants reported their age, sex, and whether they were an only child 
or not. The researchers asked each child to toss a fair coin in private and 
to record the outcome (white or black) on a paper sheet, and said they 
would only reward children who report white.
The study's findings can be summarized as follows:
``Half the students were 
explicitly told not to cheat and the others were not given any explicit 
instructions.
In the no instruction group probability of cheating was found to 
be uniform across groups based on child's characteristics.
In the group that was 
explicitly told to not cheat, girls were less likely to cheat,
and while rate 
of cheating didn't vary by age for boys, it decreased with age
for girls.''\footfullcite{Bucciol:2011} 
\begin{parts}
\item Identify the main research question of the study.
\item Who are the subjects in this study, and how many are included?
\item
  How many variables were recorded for each subject in the study
  in order to conclude these findings? State the variables and their
  types.
\end{parts}
}{}

\D{\newpage}

% 6

\eoce{\qt{Stealers, study components\label{study_components_stealers}} 
In a study of the relationship between socio-economic class and unethical 
behavior, 129 University of California undergraduates at Berkeley were asked 
to identify themselves as having low or high social-class by comparing 
themselves to others with the most (least) money, most (least) education, and 
most (least) respected jobs. They were also presented with a jar of 
individually wrapped candies and informed that the candies were for children
in a nearby laboratory, but that they could take some if they wanted. After 
completing some unrelated tasks, participants reported the number of candies 
they had taken.\footfullcite{Piff:2012} 
\begin{parts}
\item Identify the main research question of the study.
\item Who are the subjects in this study, and how many are included?
\item The study found that students who were identified as upper-class took more 
candy than others. How many variables were recorded for each subject in the study 
in order to conclude these findings? State the variables and their types.
\end{parts}
}{}

% 7

\eoce{\qt{Migraine and acupuncture,
    Part II\label{migraine_and_acupuncture_exp_resp}}
Exercise~\ref{migraine_and_acupuncture_intro}
introduced a study exploring whether acupuncture had any
effect on migraines.
Researchers conducted a randomized controlled study
where patients were randomly assigned to one of two groups:
treatment or control.
The patients in the treatment group received acupuncture
that was specifically designed to treat migraines.
The patients in the control group received placebo acupuncture
(needle insertion at non-acupoint locations).
24 hours after patients received acupuncture, they were asked 
if they were pain free.
What are the explanatory and response variables in this study?
}{}

% 8

\eoce{\qt{Sinusitis and antibiotics,
    Part II\label{sinusitis_and_antibiotics_exp_resp}} 
Exercise~\ref{sinusitis_and_antibiotics_intro}
introduced a study exploring the effect of antibiotic treatment
for acute sinusitis.
Study participants either received either a 10-day course of
an antibiotic (treatment)
or a placebo similar in appearance and taste (control).
At the end of the 10-day period, patients were asked if
they experienced improvement in symptoms.
What are the explanatory and response variables in this study?
}{}

% 9

\eoce{\qt{Fisher's irises\label{fisher_irises}} Sir Ronald Aylmer Fisher was an 
English statistician, evolutionary biologist, and geneticist who worked on a 
data set that contained sepal length and width, and petal length and width from 
three species of iris flowers (\textit{setosa}, \textit{versicolor} and 
\textit{virginica}). There were 50 flowers from each species in the data set. 
\footfullcite{Fisher:1936} \\
\noindent\begin{minipage}[c]{0.48\textwidth}
\begin{parts}
\item How many cases were included in the data?
\item How many numerical variables are included in the data? Indicate what 
they are, and if they are continuous or discrete.
\item How many categorical variables are included in the data, and what are 
they? List the corresponding levels (categories).
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.01\textwidth}
\ 
\end{minipage}
\begin{minipage}[c]{0.2\textwidth}
\begin{center}
\Figures[Photo of a purple iris flower.]{}{eoce/fisher_irises}{irisversicolor}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.01\textwidth}
\ 
\end{minipage}
\begin{minipage}[c]{0.23\textwidth}
{\raggedright\footnotesize Photo by Ryan Claussen 
(\oiRedirect{textbook-flickr_ryan_claussen_iris_picture}{http://flic.kr/p/6QTcuX}) 
\oiRedirect{textbook-CC_BY_SA_2}{CC~BY-SA~2.0~license}}
\end{minipage}
}{}

% 10

\eoce{\qt{Smoking habits of UK residents\label{smoking_habits_UK_datamatrix}} A survey 
was conducted to study the smoking habits of UK residents. Below is a data 
matrix displaying a portion of the data collected in this survey. Note that 
``$\pounds$" stands for British Pounds Sterling, ``cig" stands for cigarettes, 
and ``N/A'' refers to a missing component of the data. \footfullcite{data:smoking}
\begin{center}
\scriptsize{
\begin{tabular}{rccccccc}
\hline
	& sex 	 & age 	& marital 	& grossIncome 					     & smoke & amtWeekends	& amtWeekdays \\ 
\hline
1 	& Female & 42 	& Single 	& Under $\pounds$2,600 			     & Yes 	 & 12 cig/day   & 12 cig/day \\ 
2 	& Male	 & 44	& Single 	& $\pounds$10,400 to $\pounds$15,600 & No	 & N/A 			& N/A \\ 
3 	& Male 	 & 53 	& Married   & Above $\pounds$36,400 		     & Yes 	 & 6 cig/day 	& 6 cig/day \\ 
\vdots & \vdots & \vdots & \vdots & \vdots 				             & \vdots & \vdots 	    & \vdots \\ 
1691 & Male  & 40   & Single 	& $\pounds$2,600 to $\pounds$5,200   & Yes 	 & 8 cig/day 	& 8 cig/day \\   
\hline
\end{tabular}
}
\end{center}
\begin{parts}
\item What does each row of the data matrix represent?
\item How many participants were included in the survey?
\item Indicate whether each variable in the study is numerical or categorical. If numerical, identify as 
continuous or discrete. If categorical, indicate if the variable is ordinal.
\end{parts}
}{}

\D{\newpage}

% 11

\eoce{\qt{US Airports\label{US Airports}}
The visualization below shows the
geographical distribution of airports in the contiguous United States
and Washington, DC.
This visualization was constructed based on a dataset where
each observation is an airport.\footfullcite{data:usairports}
\begin{center}
\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}
\end{center}
\begin{parts}
\item
    List the variables used in creating this visualization.
\item
    Indicate whether each variable in the study is numerical
    or categorical.
    If numerical, identify as continuous or discrete.
    If categorical, indicate if the variable is ordinal.
\end{parts}
}{}

% 12

\eoce{\qt{UN Votes\label{unvotes}}
The visualization below shows voting patterns in 
the United States, Canada, and Mexico in the United Nations General Assembly 
on a variety of issues. Specifically, for a given year between 1946 and 2015, 
it displays the percentage of roll calls in which the country voted yes for 
each issue. This visualization was constructed based on a dataset where each 
observation is a country/year pair.\footfullcite{data:unvotes}
\begin{center}
\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}
\end{center}
\begin{parts}
\item List the variables used in creating this visualization.
\item Indicate whether each variable in the study is numerical or categorical. 
If numerical, identify as continuous or discrete. If categorical, indicate if 
the variable is ordinal.
\end{parts}
}{}


================================================
FILE: ch_intro_to_data/TeX/experiments.tex
================================================
\exercisesheader{}

% 29

\eoce{\qt{Light and exam performance\label{light_exam_performance}} A study is designed to 
test the effect of light level on exam performance of students. The researcher believes 
that light levels might have different effects on males and females, so wants to make 
sure both are equally represented in each treatment. The treatments are fluorescent 
overhead lighting, yellow overhead lighting, no overhead lighting (only desk lamps). 
\begin{parts}
\item What is the response variable?
\item What is the explanatory variable? What are its levels?
\item What is the blocking variable? What are its levels?
\end{parts}
}{}

% 30

\eoce{\qt{Vitamin supplements\label{vitamin_supplement}}
To assess the effectiveness of taking large doses
of vitamin C in reducing the duration of the common cold, 
researchers recruited 400 healthy volunteers from staff
and students at a university.
A~quarter of the patients were assigned a placebo,
and the rest were evenly divided between 1g Vitamin C,
3g Vitamin C, or 3g Vitamin C plus additives to be
taken at onset of a cold for the following two days.
All tablets had identical appearance and packaging.
The nurses who handed the prescribed pills to the
patients knew which patient received which treatment,
but the researchers assessing the patients when they
were sick did not. 
No significant differences were observed in any measure
of cold duration or severity between the four groups,
and the placebo group had the shortest duration of 
symptoms.\footfullcite{Audera:2001}
\begin{parts}
\item Was this an experiment or an observational study? Why?
\item What are the explanatory and response variables in this study?
\item Were the patients blinded to their treatment?
\item Was this study double-blind?
\item Participants are ultimately able to choose whether or not to use the pills 
prescribed to them. We might expect that not all of them will adhere and take their 
pills. Does this introduce a confounding variable to the study? Explain your reasoning.
\end{parts}
}{}

% 31

\eoce{\qt{Light, noise, and exam performance\label{light_noise_exam_performance}} A study is 
designed to test the effect of light level and noise level on exam performance of 
students. The researcher believes that light and noise levels might have different 
effects on males and females, so wants to make sure both are equally represented in each 
treatment. The light treatments considered are fluorescent overhead lighting, yellow 
overhead lighting, no overhead lighting (only desk lamps). The noise treatments 
considered are no noise,  construction noise, and human chatter noise.
\begin{parts}
\item What type of study is this?
\item How many factors are considered in this study? Identify them, and describe their 
levels.
\item What is the role of the sex variable in this study?
\end{parts}
}{}

% 32

\eoce{\qt{Music and learning\label{music_learning}} You would like to conduct an experiment in 
class to see if students learn better if they study without any music, with music that 
has no lyrics (instrumental), or with music that has lyrics. Briefly outline a design for 
this study.
}{}

% 33

\eoce{\qt{Soda preference\label{soda_preference}} You would like to conduct an experiment in 
class to see if your classmates prefer the taste of regular Coke or Diet Coke. Briefly 
outline a design for this study.
}{}

% 34

\eoce{\qt{Exercise and mental health\label{exercise_mental_health}} A researcher is interested 
in the effects of exercise on mental health and he proposes the following study: Use 
stratified random sampling to ensure representative proportions of 18-30, 31-40 and 41-
55 year olds from the population. Next, randomly assign half the subjects from each age 
group to exercise twice a week, and instruct the rest not to exercise. Conduct a mental 
health exam at the beginning and at the end of the study, and compare the results.
\begin{parts}
\item What type of study is this? 
\item What are the treatment and control groups in this study?
\item Does this study make use of blocking? If so, what is the blocking variable?
\item Does this study make use of blinding?
\item Comment on whether or not the results of the study can be used to establish a 
causal relationship between exercise and mental health, and indicate whether or not the 
conclusions can be generalized to the population at large.
\item Suppose you are given the task of determining if this proposed study should get 
funding. Would you have any reservations about the study proposal?
\end{parts}
}{}


================================================
FILE: ch_intro_to_data/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 35

\eoce{% Replaces gpa_study_hours
\qt{Pet names\label{seattle_pet_names}}
The city of Seattle, WA has an open data portal that
includes pets registered in the city.
For each registered pet,
we have information on the pet's name and species.
The following visualization plots the proportion of dogs
with a given name versus the proportion of cats with the
same name.
The 20 most common cat and dog names are displayed.
The diagonal line on the plot is the $x = y$ line;
if a name appeared on this line, the name's popularity
would be exactly the same for dogs and cats.

\noindent\begin{minipage}[c]{0.4\textwidth}
\raggedright\begin{parts}
\item
    Are these data collected as part of an experiment
    or an observational study?
\item
    What is the most common dog name? What is the most
    common cat name?
\item
    What names are more common for cats than dogs?
\item
    Is the relationship between the two variables
    positive or negative? 
    What does this mean in context of the data?
\end{parts}\vspace{5mm}
\end{minipage}
\begin{minipage}[c]{0.05\textwidth}
\ 
\end{minipage}
\begin{minipage}[c]{0.53\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

% 36

\eoce{\qt{Stressed out, Part II\label{stressed_out_experiment}} In a study evaluating the 
relationship 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.
\begin{parts}
\item What type of study is this?
\item Can this study be used to conclude a causal relationship between increased stress 
and muscle cramps?
\end{parts}
}{}

% 37

\eoce{\qt{Chia seeds and weight loss\label{chia_weight_lostt}} Chia Pets -- those terra-cotta 
figurines that sprout fuzzy green hair -- made the chia plant a household name. But chia 
has gained an entirely new reputation as a diet supplement.  In one 2009 study, a team 
of researchers recruited 38 men and divided them randomly into two groups: treatment or 
control. They also recruited 38 women, and they randomly placed half of these 
participants into the treatment group and the other half into the control group. One 
group was given 25 grams of chia seeds twice a day, and the other was given a placebo. 
The subjects volunteered to be a part of the study. After 12 weeks, the scientists found 
no significant difference between the groups in appetite or weight loss. 
\footfullcite{Nieman:2009}
\begin{parts}
\item What type of study is this? 
\item What are the experimental and control treatments in this study?
\item Has blocking been used in this study? If so, what is the blocking variable?
\item Has blinding been used in this study?
\item Comment on whether or not we can make a causal statement, and indicate whether or 
not we can generalize the conclusion to the population at large.
\end{parts}
}{}

% 38

\eoce{\qt{City council survey\label{city_council_survey}}
A city council has requested a household survey be conducted
in a suburban area of their city.
The area is broken into many distinct and unique neighborhoods,
some including large homes, some with only apartments, and others
a diverse mixture of housing structures.
For each part below,
identify the sampling methods described,
and describe the statistical pros and cons of the method
in the city's context.
\begin{parts}
\item
    Randomly sample 200 households from the city.
\item
    Divide the city into 20 neighborhoods,
    and sample 10 households from each neighborhood.
\item
    Divide the city into 20 neighborhoods,
    randomly sample 3 neighborhoods,
    and then sample all households from those 3 neighborhoods.
\item
    Divide the city into 20 neighborhoods,
    randomly sample 8 neighborhoods,
    and then randomly sample 50 households
    from those neighborhoods.
\item
    Sample the 200 households closest to the city council offices.
\end{parts}
}{}

% 39

\eoce{\qt{Flawed reasoning\label{flawed_reasoning}} Identify the flaw(s) in reasoning 
in the following scenarios. Explain what the individuals in the study should 
have done differently if they wanted to make such strong conclusions.
\begin{parts}
\item Students at an elementary school are given a questionnaire that they 
are asked to return after their parents have completed it. One of the questions 
asked is, ``Do you find that your work schedule makes it difficult for you to 
spend time with your kids after school?" Of the parents who replied, 85\% said 
``no". Based on these results, the school officials conclude that a great 
majority of the parents have no difficulty spending time with their kids 
after school.
\item A survey is conducted on a simple random sample of 1,000 women who 
recently gave birth, asking them about whether or not they smoked during 
pregnancy. A follow-up survey asking if the children have respiratory problems 
is conducted 3 years later.
However, only 567 of these women are reached at the 
same address. The researcher reports that these 567 women are representative 
of all mothers.
\item An orthopedist administers a questionnaire to 30 of his patients who do 
not have any joint problems and finds that 20 of them regularly go running. 
He concludes that running decreases the risk of joint problems.
\end{parts}
}{}

% 40

\eoce{\qt{Income and education in US counties\label{income_education_county}} 
The scatterplot below shows the relationship between per capita income 
(in thousands of dollars) and percent of population with a bachelor's 
degree in 3,143 counties in the US in 2010.

\noindent\begin{minipage}[c]{0.44\textwidth}
\begin{parts}
\item What are the explanatory and response variables?
\item Describe the relationship between the two variables. Make sure to discuss 
unusual observations, if any.
\item Can we conclude that having a bachelor's degree increases one's income?
\end{parts}\vspace{8mm}
\end{minipage}
\begin{minipage}[c]{0.55\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

% 41

\eoce{\qt[?]{Eat better, feel better\label{eat_better_feel_better}}
In a public health 
study on the effects of consumption of fruits and vegetables on psychological 
well-being in young adults, participants were randomly assigned to three 
groups: (1) diet-as-usual, (2) an ecological momentary intervention involving 
text message reminders to increase their fruits and vegetable consumption plus 
a voucher to purchase them, or (3) a fruit and vegetable intervention in 
which participants were given two additional daily servings of fresh fruits and 
vegetables to consume on top of their normal diet. Participants were asked to 
take a nightly survey on their smartphones.
Participants were student volunteers at the University of 
Otago, New Zealand.
At the end of the 14-day study, only participants in the third
group showed improvements to their psychological well-being across
the 14-days relative to the other groups.\footfullcite{conner2017let}
\begin{parts}
\item
    What type of study is this?
\item
    Identify the explanatory and response variables.
\item
    Comment on whether the results of the study can be generalized to
    the population.
\item
    Comment on whether the results of the study can be used to establish
    causal relationships.
\item
    A newspaper article reporting on the study states,
    ``The results of this study provide proof that giving young adults
    fresh fruits and vegetables to eat can have psychological benefits,
    even over a brief period of time.''
    How would you suggest revising this statement so that it can be
    supported by the study?
\end{parts}
}{}

\D{\newpage}

% 42

\eoce{\qt{Screens, teens, and psychological well-being\label{screen_time_well_being}}
In a study of three nationally representative large-scale data sets from Ireland, 
the United States, and the United Kingdom (n = 17,247), teenagers between the 
ages of 12 to 15 were asked to keep a diary of their screen time and answer questions about how they felt or acted.
The answers to these questions 
were then used to compute a psychological well-being score.
Additional data were collected and included in the analysis,
such as each child's sex and age, and on the mother’s education,
ethnicity, psychological distress, and employment.
The study concluded that there is little clear-cut evidence
that screen time decreases adolescent
well-being.\footfullcite{orben2018screens}
\begin{parts}
\item
    What type of study is this?
\item
    Identify the explanatory variables.
\item
    Identify the response variable.
\item
    Comment on whether the results of the study can be generalized
    to the population, and why.
\item
    Comment on whether the results of the study can be used
    to establish causal relationships.
\end{parts}
}{}

% 43

\eoce{\qt{Stanford Open Policing\label{stanford_open_policing}}
The Stanford Open Policing project gathers, analyzes, and
releases records from traffic stops by law enforcement 
agencies across the United States.
Their goal is to help researchers, journalists, and policymakers
investigate and improve interactions between police and the
public.\footfullcite{pierson2017large}
The following is an excerpt from a summary table created based off of the data 
collected as part of this project.
\begin{center}
\begin{tabular}{lllrrr}
\hline
               &           & Driver's  & No. of stops & \multicolumn{2}{c}{\% of stopped}  \\
County         & State     & race      & per year     & cars searched & drivers arrested \\ 
\hline
Apaice County  & Arizona   & Black     & 266          & 0.08          & 0.02 \\ 
Apaice County  & Arizona   & Hispanic  & 1008         & 0.05          & 0.02 \\ 
Apaice County  & Arizona   & White     & 6322         & 0.02          & 0.01 \\ 
Cochise County & Arizona   & Black     & 1169         & 0.05          & 0.01 \\ 
Cochise County & Arizona   & Hispanic  & 9453         & 0.04          & 0.01 \\ 
Cochise County & Arizona   & White     & 10826        & 0.02          & 0.01 \\ 
$\cdots$       & $\cdots$  & $\cdots$  & $\cdots$     & $\cdots$      & $\cdots$ \\
Wood County    & Wisconsin & Black     & 16           & 0.24          & 0.10 \\ 
Wood County    & Wisconsin & Hispanic  & 27           & 0.04          & 0.03 \\ 
Wood County    & Wisconsin & White     & 1157         & 0.03          & 0.03 \\ 
\hline 
\end{tabular}
\end{center}
\begin{parts}
\item
    What variables were collected on each individual traffic stop
    in order to create to the summary table above?
\item
    State whether each variable is numerical or categorical.
    If numerical, state whether it is continuous or discrete.
    If categorical, state whether it is ordinal or not.
\item
    Suppose we wanted to evaluate whether vehicle search rates
    are different for drivers of different races.
    In this analysis, which variable would be the response
    variable and which variable would be the explanatory variable?
\end{parts}
}{}

% 44

\eoce{\qt{Space launches\label{space_launches}}
The following summary table shows the number of space
launches in the US by the type of launching agency and
the outcome of the launch (success or
failure).\footfullcite{data:spacelaunches}
\begin{center}
\begin{tabular}{l | rr | rr}
\hline
        & \multicolumn{2}{| c}{1957 - 1999} & \multicolumn{2}{| c}{2000 - 2018} \\
        & Failure & Success & Failure & Success \\ 
\hline
Private &      13 &     295 &      10 &     562 \\ 
State   &     281 &    3751 &      33 &     711 \\ 
Startup &       - &       - &       5 &      65 \\
\hline
\end{tabular}
\end{center}
\begin{parts}
\item
    What variables were collected on each launch in order
    to create to the summary table above?
\item
    State whether each variable is numerical or categorical.
    If numerical, state whether it is continuous or discrete.
    If categorical, state whether it is ordinal or not.
\item
    Suppose we wanted to study how the success rate of
    launches vary between launching agencies and over time.
    In this analysis, which variable would be the response
    variable and which variable would be the explanatory
    variable?
\end{parts}
}{}


================================================
FILE: ch_intro_to_data/TeX/sampling_principles_and_strategies.tex
================================================
\exercisesheader{}

% 13

\eoce{\qt{Air pollution and birth outcomes, scope of inference\label{scope_airpoll}} 
Exercise~\ref{study_components_airpoll} introduces a study where researchers 
collected data to examine the relationship between air pollutants and preterm 
births in Southern California. During the study air pollution levels were 
measured by air quality monitoring stations. Length of gestation data were 
collected on 143,196 births between the years 1989 and 1993, and air pollution 
exposure during gestation was calculated for each birth.
\begin{parts}
\item Identify the population of interest and the sample in this study.
\item Comment on whether or not the results of the study can be generalized to the 
population, and if the findings of the study can be used to establish causal relationships.
\end{parts}
}{}

% 14

\eoce{\qt{Cheaters, scope of inference\label{scope_cheaters}} 
Exercise~\ref{study_components_cheaters} introduces a study where researchers 
studying the relationship between honesty, age, and self-control conducted an 
experiment on 160 children between the ages of 5 and 15. The researchers asked 
each child to toss a fair coin in private and to record the outcome (white or black) 
on a paper sheet, and said they would only reward children who report white. 
Half the students were explicitly told not to cheat and the others were not given 
any explicit instructions. Differences were observed in the cheating rates in the
instruction and no instruction groups, as well as some differences across 
children's characteristics within each group.
\begin{parts}
\item Identify the population of interest and the sample in this study.
\item Comment on whether or not the results of the study can be generalized to the 
population, and if the findings of the study can be used to establish causal 
relationships.
\end{parts}
}{}

% 15

\eoce{\qt{Buteyko method, scope of inference\label{scope_buteyko}} 
Exercise~\ref{study_components_buteyko} introduces a study on using the Buteyko 
shallow breathing technique to reduce asthma symptoms and improve quality of life.
As part of this study 600 asthma patients aged 18-69 who relied on medication for 
asthma treatment were recruited and randomly assigned to two groups: one practiced 
the Buteyko method and the other did not. Those in the Buteyko group experienced,
on average, a significant reduction in asthma symptoms and an improvement in quality 
of life.
\begin{parts}
\item Identify the population of interest and the sample in this study.
\item Comment on whether or not the results of the study can be generalized to the 
population, and if the findings of the study can be used to establish causal 
relationships.
\end{parts}
}{}

% 16

\eoce{\qt{Stealers, scope of inference\label{scope_stealers}} 
Exercise~\ref{study_components_stealers} introduces a study on the relationship 
between socio-economic class and unethical behavior. As part of this study 129 
University of California Berkeley undergraduates were asked to identify themselves 
as having low or high social-class by comparing themselves to others with the most 
(least) money, most (least) education, and most (least) respected jobs. They were 
also presented  with a jar of individually wrapped candies and informed that the
candies were for children in a nearby laboratory, but that they could take some if 
they wanted. After completing some unrelated tasks, participants reported the 
number of candies they had taken. It was found that those who were identified as 
upper-class took more candy than others.
\begin{parts}
\item Identify the population of interest and the sample in this study.
\item Comment on whether or not the results of the study can be generalized to the 
population, and if the findings of the study can be used to establish causal 
relationships.
\end{parts}
}{}

% 17

\eoce{\qt{Relaxing after work\label{relax_after_work_definitions}} The General 
Social Survey asked the question, ``After an average work day, about how many 
hours do you have to relax or pursue activities that you enjoy?" to a random 
sample of 1,155 Americans. The average relaxing time was found to be 1.65 
hours. Determine which of the following is an observation, a variable, a 
sample statistic (value calculated based on the observed sample), or a 
population parameter.
\begin{parts}
\item An American in the sample.
\item Number of hours spent relaxing after an average work day.
\item 1.65.
\item Average number of hours all Americans spend relaxing after an average 
work day.
\end{parts}
}{}

\D{\newpage}

% 18

\eoce{\qt{Cats on YouTube\label{cats_on_youtube_definitions}} Suppose you want to 
estimate the percentage of videos on YouTube that are cat videos. It is 
impossible for you to watch all videos on YouTube so you use a random video 
picker to select 1000 videos for you. You find that 2\% of these videos are 
cat videos.
Determine which of the following is an observation, a variable, 
a sample statistic (value calculated based on the observed sample), 
or a population parameter.
\begin{parts}
\item Percentage of all videos on YouTube that are cat videos.
\item 2\%.
\item A video in your sample.
\item Whether or not a video is a cat video.
\end{parts}
}{}

% 19

\eoce{\qt{Course satisfaction across sections\label{course_satisfaction_sections}} 
A large college class has 160 students. All 160 students attend the lectures 
together, but the students are divided into 4 groups, each of 40 students, 
for lab sections administered by different teaching assistants. The professor 
wants to conduct a survey about how satisfied the students are with the course, 
and he believes that the lab section a student is in might affect the student's 
overall satisfaction with the course.
\begin{parts}
\item What type of study is this?
\item Suggest a sampling strategy for carrying out this study.
\end{parts}
}{}

% 20

\eoce{\qt{Housing proposal across dorms\label{housing_proposal_dorms}} On a large 
college campus first-year students and sophomores live in dorms located on 
the eastern part of the campus and juniors and seniors live in dorms located 
on the western part of the campus. Suppose you want to collect student opinions 
on a new housing structure the college administration is proposing and you want 
to make sure your survey equally represents opinions from students from all years.
\begin{parts}
\item What type of study is this?
\item Suggest a sampling strategy for carrying out this study.
\end{parts}
}{}

% 21

\eoce{\qt{Internet use and life expectancy\label{internet_life_expectancy}} The 
following scatterplot was created as part of a study evaluating the 
relationship between estimated life expectancy at birth (as of 2014) and 
percentage of internet users (as of 2009) in 208 countries for which such 
data were available.\footfullcite{data:ciaFactbook}

\noindent\begin{minipage}[c]{0.44\textwidth}
\begin{parts}
\item Describe the relationship between life expectancy and percentage of 
internet users.
\item What type of study is this?
\item State a possible confounding variable that might explain this relationship 
and describe its potential effect.
\end{parts} \vspace{15mm}
\end{minipage}
\begin{minipage}[r]{0.55\textwidth}
\hfill%
\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}
\end{minipage}
}{}

% 22

\eoce{\qt{Stressed out, Part I\label{stressed_out_observational}} A study that 
surveyed a random sample of otherwise healthy high school students found that 
they are more likely to get muscle cramps when they are stressed. The study 
also noted that students drink more coffee and sleep less when they are 
stressed.
\begin{parts}
\item What type of study is this?
\item Can this study be used to conclude a causal relationship between 
increased stress and muscle cramps?
\item State possible confounding variables that might explain the observed 
relationship between increased stress and muscle cramps. 
\end{parts}
}{}

% 23

\eoce{\qt{Evaluate sampling methods\label{evaluate_sampling_methods}} A university wants to 
determine what fraction of its undergraduate student body support a new \$25 annual fee 
to improve the student union. For each proposed method below, indicate whether 
the method is reasonable or not.
\begin{parts}
\item Survey a simple random sample of 500 students.
\item Stratify students by their field of study, then sample 10\% of students from  
each stratum.
\item Cluster students by their ages (e.g. 18 years old in one cluster, 19 years 
old in one cluster, etc.), then randomly sample three clusters and survey all 
students in those clusters.
\end{parts}
}{}

\D{\newpage}

% 24

\eoce{\qt{Random digit dialing\label{random_digit_dialing}} The Gallup Poll uses a 
procedure called random digit dialing, which creates phone numbers based on 
a list of all area codes in America in conjunction with the associated number 
of residential households in each area code. Give a possible reason the Gallup 
Poll chooses to use random digit dialing instead of picking phone numbers 
from the phone book.
}{}

% 25

\eoce{\qt{Haters are gonna hate, study confirms\label{scope_haters}}
A study published in the
\textit{Journal of Personality and Social Psychology}
asked a group of 200 randomly sampled men and
women to evaluate how they felt about various subjects,
such as camping, health care, architecture, taxidermy, 
crossword puzzles, and Japan in order to measure their
attitude towards mostly independent stimuli.
Then, they presented the participants with information
about a new product: a microwave oven. This microwave oven
does not exist, but the participants didn't know this,
and were given three positive and three negative fake reviews.
People who reacted positively to the subjects on the
dispositional attitude measurement also tended to react 
positively to the microwave oven, and those who reacted
negatively tended to react negatively to it.
Researchers concluded that ``some people tend to 
like things, whereas others tend to dislike things, and a more thorough 
understanding of this tendency will lead to a more thorough understanding of 
the psychology of attitudes." \footfullcite{Hepler:2013}
\begin{parts}
\item What are the cases?
\item What is (are) the response variable(s) in this study?
\item What is (are) the explanatory variable(s) in this study?
\item Does the study employ random sampling?
\item Is this an observational study or an experiment? Explain your reasoning.
\item Can we establish a causal link between the explanatory and response 
variables?
\item Can the results of the study be generalized to the population at large?
\end{parts}
}{}

% 26

\eoce{\qt{Family size\label{family_size}} Suppose we want to estimate household 
size, where a ``household" is defined as people living together in the 
same dwelling, and sharing living accommodations. If we select students 
at random at an elementary school and ask them what their family size is, 
will this be a good measure of household size? Or will our average be 
biased? If so, will it overestimate or underestimate the true value?
}{}

% 27

\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.
\begin{parts}
\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.
\item He gives out the survey only to his friends, making sure each one of them fills out the survey.
\item He posts a link to an online survey on Facebook and asks his friends to fill out the survey.
\item He randomly samples 5 classes and asks a random sample of students from those classes to fill out the survey.
\end{parts}
}{}

% 28

\eoce{\qt{Reading the paper\label{reading_paper}} Below are excerpts from two 
articles published in the \emph{NY Times}:
\begin{parts}
\item An article titled \emph{Risks: Smokers Found More Prone to Dementia} 
states the following: \footfullcite{news:smokingDementia}
\begin{adjustwidth}{1em}{1em}
{\footnotesize ``Researchers analyzed data from 23,123 health plan members who 
participated in a voluntary exam and health behavior survey from 1978 to 1985, 
when they were 50-60 years old. 23 years later, about 25\% of the group had 
dementia, including 1,136 with Alzheimer's disease and 416 with vascular 
dementia. After adjusting for other factors, the researchers concluded that 
pack-a-day smokers were 37\% more likely than nonsmokers to develop dementia, 
and the risks went up with increased smoking; 44\% for one to two packs a day; 
and twice the risk for more than two packs."}
\end{adjustwidth}
Based on this study, can we conclude that smoking causes dementia later in 
life? Explain your reasoning.
\item Another article titled \emph{The School Bully Is Sleepy} states the 
following: \footfullcite{news:bullySleep}
\begin{adjustwidth}{1em}{1em}
{\footnotesize ``The University of Michigan study, collected survey data from 
parents on each child's sleep habits and asked both parents and teachers to 
assess behavioral concerns. About a third of the students studied were 
identified by parents or teachers as having problems with disruptive behavior 
or bullying. The researchers found that children who had behavioral issues and 
those who were identified as bullies were twice as likely to have shown 
symptoms of sleep disorders."}
\end{adjustwidth}
A friend of yours who read the article says, ``The study shows that sleep 
disorders lead to bullying in school children." Is this statement justified? 
If not, how best can you describe the conclusion that can be drawn from this 
study?
\end{parts}
}{}


================================================
FILE: ch_intro_to_data/figures/county_fed_spendVsPoverty/county_fed_spendVsPoverty.R
================================================
library(openintro)
data(county)
data(COL)

myPDF("county_fed_spendVsPoverty.pdf", 6, 3.5,
      mar = c(3, 3.5, 0.5, 0.5),
      mgp = c(2.4, 0.5, 0))
plot(county$poverty, county$fed_spend, 
     pch = 20,
     cex = 0.7,
     col = COL[1, 3],
     ylim = c(0, 31.25),
     xlab = "",
     ylab = "Federal Spending Per Capita",
     axes = FALSE)
axis(1)
axis(2, at = seq(0, 30, 10))
box()
points(county$poverty, county$fed_spend, pch = ".")
mtext("Poverty Rate (Percent)", 1, 1.9)
t1 <- county$poverty[1088]
t2 <- county$fed_spend[1088]
lines(c(t1, t1), c(-10, t2),
      lty = 2,
      col = COL[4])
lines(c(-10, t1), c(t2, t2),
      lty = 2,
      col = COL[4])
points(t1, t2,
       col = COL[4])
text(43, 29,
     "32 counties with higher\nfederal spending are not shown",
     cex = 0.8)
dev.off()

county[1088, ]


================================================
FILE: ch_intro_to_data/figures/eoce/air_quality_durham/air_quality_durham.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
pm25_durham = read.csv("pm25_2011_durham.csv", 
                       na.strings = ".", stringsAsFactors = FALSE)

# calculate sample size ---------------------------------------------
n = pm25_durham %>%
  filter(!is.na(DAILY_AQI_VALUE)) %>%
  nrow() # n = 91

# histogram parameters ----------------------------------------------
histo = hist(pm25_durham$DAILY_AQI_VALUE, plot = FALSE)
breaks = histo$breaks
width = breaks[2] - breaks[1]
counts = histo$counts
rel_freqs = round(counts / n, 2)

five_perc = n * 0.05

# relative frequency histogram --------------------------------------
pdf("air_quality_durham_rel_freq_hist.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4))
axis(1)
axis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))
abline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4), add = TRUE)
dev.off()

# relative frequency histogram - solution ---------------------------
pdf("air_quality_durham_rel_freq_hist_soln.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4 + 1))
axis(1)
axis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))
abline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4), add = TRUE)
text(x = breaks[-1] - width/2, y = counts + 1, 
     labels = paste(rel_freqs),
     col = COL[4], cex = 1)
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/air_quality_durham/pm25_2011_durham.csv
================================================
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
1/3/11,37-063-0015,1,5.9,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
1/6/11,37-063-0015,1,10.4,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
1/9/11,37-063-0015,1,5.6,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
1/10/11,37-063-0015,1,6.2,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
1/18/11,37-063-0015,1,9.4,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
1/21/11,37-063-0015,1,5,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
1/24/11,37-063-0015,1,11.5,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
1/27/11,37-063-0015,1,9.8,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
1/30/11,37-063-0015,1,12.5,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
2/2/11,37-063-0015,1,5.5,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
2/5/11,37-063-0015,1,5.3,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
2/8/11,37-063-0015,1,5,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
2/11/11,37-063-0015,1,11.3,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
2/14/11,37-063-0015,1,5.9,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
2/17/11,37-063-0015,1,17.2,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
2/20/11,37-063-0015,1,5.3,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
2/23/11,37-063-0015,1,7.5,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
2/26/11,37-063-0015,1,7.6,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
3/1/11,37-063-0015,1,3.7,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
3/4/11,37-063-0015,1,8.9,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
3/7/11,37-063-0015,1,4.5,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
3/10/11,37-063-0015,1,2.7,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
3/13/11,37-063-0015,1,10.5,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
3/16/11,37-063-0015,1,6.1,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
3/19/11,37-063-0015,1,8.3,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
3/22/11,37-063-0015,1,13.8,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
3/25/11,37-063-0015,1,9.1,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
3/28/11,37-063-0015,1,10.6,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
3/31/11,37-063-0015,1,4.8,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
4/3/11,37-063-0015,1,6.1,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
4/6/11,37-063-0015,1,5.6,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
4/9/11,37-063-0015,1,9.1,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
4/12/11,37-063-0015,1,7.2,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
4/16/11,37-063-0015,1,6.6,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
4/18/11,37-063-0015,1,8.6,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
4/21/11,37-063-0015,1,8.6,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
4/24/11,37-063-0015,1,11,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
4/27/11,37-063-0015,1,5.6,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
4/30/11,37-063-0015,1,6.2,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
5/3/11,37-063-0015,1,8.5,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
5/6/11,37-063-0015,1,9.3,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
5/9/11,37-063-0015,1,8.8,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
5/11/11,37-063-0015,1,18.6,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
5/12/11,37-063-0015,1,20,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
5/15/11,37-063-0015,1,8,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
5/18/11,37-063-0015,1,6.3,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
5/21/11,37-063-0015,1,10.8,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
5/27/11,37-063-0015,1,6.8,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
5/30/11,37-063-0015,1,14.9,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
5/31/11,37-063-0015,1,22.5,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
6/2/11,37-063-0015,1,16.9,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
6/5/11,37-063-0015,1,16.8,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
6/8/11,37-063-0015,1,21.3,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
6/11/11,37-063-0015,1,14.2,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
6/14/11,37-063-0015,1,10.8,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
6/17/11,37-063-0015,1,12,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
6/20/11,37-063-0015,1,8.4,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
6/23/11,37-063-0015,1,4.3,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
6/26/11,37-063-0015,1,14.1,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
6/29/11,37-063-0015,1,8.4,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
7/2/11,37-063-0015,1,17.3,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
7/5/11,37-063-0015,1,10,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
7/8/11,37-063-0015,1,12.4,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
7/14/11,37-063-0015,1,9.2,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
7/17/11,37-063-0015,1,7.9,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
7/18/11,37-063-0015,1,9.6,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
7/21/11,37-063-0015,1,18,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
7/23/11,37-063-0015,1,17.2,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
7/26/11,37-063-0015,1,10,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
7/29/11,37-063-0015,1,14.3,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
8/1/11,37-063-0015,1,10.7,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
8/4/11,37-063-0015,1,16.2,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
8/7/11,37-063-0015,1,10.1,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
8/10/11,37-063-0015,1,8.8,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
8/13/11,37-063-0015,1,15.2,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
8/16/11,37-063-0015,1,10.1,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
8/19/11,37-063-0015,1,13.7,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
8/22/11,37-063-0015,1,8.4,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
8/25/11,37-063-0015,1,6.6,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
8/28/11,37-063-0015,1,15.2,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
8/31/11,37-063-0015,1,8.7,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
9/3/11,37-063-0015,1,15.8,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
9/6/11,37-063-0015,1,3.8,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
9/9/11,37-063-0015,1,10.7,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
9/12/11,37-063-0015,1,11.7,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
9/15/11,37-063-0015,1,13.2,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
9/18/11,37-063-0015,1,2.9,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
9/21/11,37-063-0015,1,4.6,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
9/24/11,37-063-0015,1,5.6,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
9/27/11,37-063-0015,1,8.2,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
9/30/11,37-063-0015,1,5.7,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
1/1/11,37-063-0015,3,16.7125,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
1/2/11,37-063-0015,3,3.754166667,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
1/3/11,37-063-0015,3,4.855555556,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
1/4/11,37-063-0015,3,8.6875,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
1/5/11,37-063-0015,3,10.18333333,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
1/6/11,37-063-0015,3,8.495833333,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
1/7/11,37-063-0015,3,5.991666667,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
1/8/11,37-063-0015,3,5.320833333,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
1/9/11,37-063-0015,3,6.9125,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
1/10/11,37-063-0015,3,6.604166667,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
1/11/11,37-063-0015,3,5.804166667,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
1/12/11,37-063-0015,3,7.808333333,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
1/13/11,37-063-0015,3,9.095833333,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
1/14/11,37-063-0015,3,10.45416667,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
1/15/11,37-063-0015,3,11.92916667,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
1/16/11,37-063-0015,3,14.01666667,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
1/17/11,37-063-0015,3,12.98333333,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
1/18/11,37-063-0015,3,8.579166667,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
1/19/11,37-063-0015,3,7.195833333,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
1/20/11,37-063-0015,3,6.9375,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
1/21/11,37-063-0015,3,4.9125,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
1/22/11,37-063-0015,3,7.183333333,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
1/23/11,37-063-0015,3,14.22916667,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
1/24/11,37-063-0015,3,10.61904762,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
1/25/11,37-063-0015,3,13.15833333,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
1/26/11,37-063-0015,3,3.95,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
1/27/11,37-063-0015,3,10.58333333,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
1/28/11,37-063-0015,3,12.18333333,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
1/29/11,37-063-0015,3,9.420833333,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
1/30/11,37-063-0015,3,14.25833333,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
1/31/11,37-063-0015,3,13.80833333,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
2/1/11,37-063-0015,3,10.25238095,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
2/2/11,37-063-0015,3,6.129166667,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
2/3/11,37-063-0015,3,6.7875,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
2/4/11,37-063-0015,3,7.604166667,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
2/5/11,37-063-0015,3,4.320833333,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
2/6/11,37-063-0015,3,8.225,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
2/7/11,37-063-0015,3,10.31666667,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
2/8/11,37-063-0015,3,6.833333333,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
2/9/11,37-063-0015,3,5.6125,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
2/10/11,37-063-0015,3,7.25,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
2/11/11,37-063-0015,3,11.30833333,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
2/12/11,37-063-0015,3,8.595833333,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
2/13/11,37-063-0015,3,5.2625,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
2/14/11,37-063-0015,3,7.25,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
2/15/11,37-063-0015,3,7.070833333,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
2/16/11,37-063-0015,3,11.10416667,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
2/17/11,37-063-0015,3,21.9125,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
2/18/11,37-063-0015,3,17.39166667,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
2/19/11,37-063-0015,3,2.683333333,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
2/20/11,37-063-0015,3,5.8875,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
2/21/11,37-063-0015,3,7.485714286,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
2/22/11,37-063-0015,3,8.186363636,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
2/23/11,37-063-0015,3,7.770833333,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
2/24/11,37-063-0015,3,10.55833333,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
2/25/11,37-063-0015,3,7.416666667,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
2/26/11,37-063-0015,3,8.770833333,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
2/27/11,37-063-0015,3,15.825,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
2/28/11,37-063-0015,3,10.32380952,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
3/1/11,37-063-0015,3,3.5125,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
3/2/11,37-063-0015,3,8.079166667,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
3/3/11,37-063-0015,3,4.595833333,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
3/4/11,37-063-0015,3,7.416666667,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
3/5/11,37-063-0015,3,5.041666667,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
3/6/11,37-063-0015,3,1.870833333,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
3/7/11,37-063-0015,3,4.6875,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
3/8/11,37-063-0015,3,4.470833333,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
3/9/11,37-063-0015,3,5.904166667,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
3/10/11,37-063-0015,3,2.3875,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
3/11/11,37-063-0015,3,4.395833333,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
3/12/11,37-063-0015,3,8.408333333,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
3/13/11,37-063-0015,3,11.71666667,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
3/14/11,37-063-0015,3,8.875,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
3/15/11,37-063-0015,3,8.416666667,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
3/16/11,37-063-0015,3,6.279166667,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
3/17/11,37-063-0015,3,5.491666667,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
3/18/11,37-063-0015,3,12.34166667,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
3/19/11,37-063-0015,3,7.575,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
3/20/11,37-063-0015,3,6.166666667,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
3/21/11,37-063-0015,3,9.225,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
3/22/11,37-063-0015,3,11.16363636,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
3/23/11,37-063-0015,3,9.745454545,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
3/24/11,37-063-0015,3,3.9625,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
3/25/11,37-063-0015,3,7.483333333,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
3/26/11,37-063-0015,3,6.354166667,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
3/27/11,37-063-0015,3,6.320833333,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
3/28/11,37-063-0015,3,9.5625,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
3/29/11,37-063-0015,3,11.50416667,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
3/30/11,37-063-0015,3,5.7,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
3/31/11,37-063-0015,3,2.891666667,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
4/1/11,37-063-0015,3,9.195833333,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
4/2/11,37-063-0015,3,7.733333333,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
4/3/11,37-063-0015,3,5.570833333,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
4/4/11,37-063-0015,3,7.454166667,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
4/5/11,37-063-0015,3,3.566666667,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
4/6/11,37-063-0015,3,5.520833333,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
4/7/11,37-063-0015,3,7.783333333,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
4/8/11,37-063-0015,3,16.52083333,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
4/9/11,37-063-0015,3,7.883333333,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
4/10/11,37-063-0015,3,5.645833333,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
4/11/11,37-063-0015,3,12.15833333,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
4/12/11,37-063-0015,3,6.129166667,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
4/13/11,37-063-0015,3,4.266666667,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
4/14/11,37-063-0015,3,9.8625,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
4/15/11,37-063-0015,3,8.891666667,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
4/16/11,37-063-0015,3,4.9875,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
4/17/11,37-063-0015,3,4.983333333,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
4/18/11,37-063-0015,3,9.775,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
4/19/11,37-063-0015,3,15.72916667,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
4/20/11,37-063-0015,3,11,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
4/21/11,37-063-0015,3,8.641666667,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
4/22/11,37-063-0015,3,5.8625,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
4/23/11,37-063-0015,3,8.85,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
4/24/11,37-063-0015,3,12.40833333,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
4/25/11,37-063-0015,3,10.1125,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
4/26/11,37-063-0015,3,4.220833333,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
4/27/11,37-063-0015,3,5.514285714,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
4/28/11,37-063-0015,3,6.6375,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
4/29/11,37-063-0015,3,5.904166667,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
4/30/11,37-063-0015,3,7.429166667,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
5/1/11,37-063-0015,3,9.325,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
5/2/11,37-063-0015,3,9.129166667,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
5/3/11,37-063-0015,3,8.104166667,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
5/4/11,37-063-0015,3,3.45,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
5/5/11,37-063-0015,3,5.541666667,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
5/6/11,37-063-0015,3,9.116666667,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
5/7/11,37-063-0015,3,8.679166667,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
5/8/11,37-063-0015,3,7.570833333,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
5/9/11,37-063-0015,3,8.645833333,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
5/10/11,37-063-0015,3,11.79166667,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
5/11/11,37-063-0015,3,16.47916667,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
5/12/11,37-063-0015,3,16.37083333,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
5/13/11,37-063-0015,3,11.47083333,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
5/14/11,37-063-0015,3,9.3875,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
5/15/11,37-063-0015,3,5.691666667,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
5/16/11,37-063-0015,3,4.429166667,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
5/17/11,37-063-0015,3,5.366666667,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
5/18/11,37-063-0015,3,5.170833333,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
5/21/11,37-063-0015,3,9.9375,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
5/22/11,37-063-0015,3,13.2625,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
5/23/11,37-063-0015,3,14.3875,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
5/24/11,37-063-0015,3,10.94166667,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
5/25/11,37-063-0015,3,8.961904762,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
5/26/11,37-063-0015,3,16.26666667,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
5/27/11,37-063-0015,3,3.995238095,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
5/28/11,37-063-0015,3,6.579166667,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
5/29/11,37-063-0015,3,11.26666667,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
5/30/11,37-063-0015,3,13.23333333,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
5/31/11,37-063-0015,3,19.67916667,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
6/1/11,37-063-0015,3,28.65,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
6/2/11,37-063-0015,3,15.675,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
6/3/11,37-063-0015,3,7.979166667,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
6/4/11,37-063-0015,3,14.50833333,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
6/7/11,37-063-0015,3,19.48333333,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
6/8/11,37-063-0015,3,23.2625,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
6/9/11,37-063-0015,3,23.37083333,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
6/10/11,37-063-0015,3,20.39166667,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
6/11/11,37-063-0015,3,14.49583333,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
6/12/11,37-063-0015,3,15.61666667,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
6/15/11,37-063-0015,3,10.1625,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
6/16/11,37-063-0015,3,14.79166667,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
6/17/11,37-063-0015,3,12.15,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
6/18/11,37-063-0015,3,12.39583333,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
6/19/11,37-063-0015,3,4.454545455,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
6/20/11,37-063-0015,3,9.2125,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
6/21/11,37-063-0015,3,42.44583333,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
6/22/11,37-063-0015,3,8.245833333,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
6/23/11,37-063-0015,3,4.825,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
6/24/11,37-063-0015,3,9.716666667,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
6/25/11,37-063-0015,3,11.20416667,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
6/26/11,37-063-0015,3,15.7125,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
6/27/11,37-063-0015,3,15.2,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
6/28/11,37-063-0015,3,9.85,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
6/29/11,37-063-0015,3,8.379166667,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
6/30/11,37-063-0015,3,12.5125,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
7/1/11,37-063-0015,3,16.475,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
7/2/11,37-063-0015,3,18.1875,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
7/3/11,37-063-0015,3,23.37916667,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
7/4/11,37-063-0015,3,19.64583333,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
7/5/11,37-063-0015,3,12.95833333,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
7/6/11,37-063-0015,3,19.87727273,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
7/7/11,37-063-0015,3,11.35833333,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
7/8/11,37-063-0015,3,11.95416667,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
7/9/11,37-063-0015,3,8.570833333,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
7/10/11,37-063-0015,3,17.77916667,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
7/11/11,37-063-0015,3,20.425,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
7/12/11,37-063-0015,3,18.9625,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
7/13/11,37-063-0015,3,18.22083333,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
7/14/11,37-063-0015,3,9.9,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
7/15/11,37-063-0015,3,5.266666667,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
7/16/11,37-063-0015,3,6.266666667,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
7/17/11,37-063-0015,3,7.05,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
7/18/11,37-063-0015,3,10.12916667,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
7/19/11,37-063-0015,3,21.9,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
7/20/11,37-063-0015,3,19.525,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
7/21/11,37-063-0015,3,18.91666667,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
7/22/11,37-063-0015,3,21.9375,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
7/23/11,37-063-0015,3,17.40416667,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
7/24/11,37-063-0015,3,13.30416667,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
7/25/11,37-063-0015,3,9.558333333,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
7/26/11,37-063-0015,3,10.93181818,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
7/27/11,37-063-0015,3,14.6,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
7/28/11,37-063-0015,3,18.75416667,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
7/29/11,37-063-0015,3,14.9,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
7/30/11,37-063-0015,3,19.44166667,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
7/31/11,37-063-0015,3,7.1375,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
8/1/11,37-063-0015,3,9.475,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
8/2/11,37-063-0015,3,15.2875,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
8/3/11,37-063-0015,3,19.225,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
8/4/11,37-063-0015,3,17.52083333,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
8/5/11,37-063-0015,3,15.625,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
8/6/11,37-063-0015,3,8.879166667,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
8/7/11,37-063-0015,3,13.99166667,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
8/8/11,37-063-0015,3,12.27727273,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
8/9/11,37-063-0015,3,9.370833333,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
8/10/11,37-063-0015,3,9.38,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
8/11/11,37-063-0015,3,12.19583333,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
8/12/11,37-063-0015,3,19.375,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
8/13/11,37-063-0015,3,15.075,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
8/14/11,37-063-0015,3,6.225,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
8/15/11,37-063-0015,3,7.8625,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
8/16/11,37-063-0015,3,12.025,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
8/17/11,37-063-0015,3,12.8,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
8/18/11,37-063-0015,3,13.99583333,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
8/19/11,37-063-0015,3,14.75454545,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
8/20/11,37-063-0015,3,12.20416667,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
8/21/11,37-063-0015,3,12.07083333,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
8/22/11,37-063-0015,3,8.283333333,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
8/23/11,37-063-0015,3,8.716666667,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
8/24/11,37-063-0015,3,9.663636364,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
8/25/11,37-063-0015,3,9.220833333,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
8/26/11,37-063-0015,3,8.695833333,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
8/27/11,37-063-0015,3,4.4375,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
8/28/11,37-063-0015,3,15.875,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
8/29/11,37-063-0015,3,13.91666667,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
8/30/11,37-063-0015,3,9.9625,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
8/31/11,37-063-0015,3,8.920833333,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
9/1/11,37-063-0015,3,11.32083333,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
9/2/11,37-063-0015,3,17.72083333,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
9/3/11,37-063-0015,3,16.54166667,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
9/4/11,37-063-0015,3,13.375,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
9/5/11,37-063-0015,3,11.91666667,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
9/6/11,37-063-0015,3,6.391666667,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
9/7/11,37-063-0015,3,5.941666667,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
9/8/11,37-063-0015,3,14.42916667,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
9/9/11,37-063-0015,3,14.83809524,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
9/10/11,37-063-0015,3,11.44166667,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
9/11/11,37-063-0015,3,9.333333333,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
9/12/11,37-063-0015,3,12.28333333,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
9/13/11,37-063-0015,3,14.55416667,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
9/14/11,37-063-0015,3,14.39166667,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
9/15/11,37-063-0015,3,13.4125,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
9/17/11,37-063-0015,3,5.391666667,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
9/18/11,37-063-0015,3,3.333333333,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
9/19/11,37-063-0015,3,5.35,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
9/20/11,37-063-0015,3,7.620833333,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
9/21/11,37-063-0015,3,4.880952381,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
9/22/11,37-063-0015,3,6.152380952,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
9/23/11,37-063-0015,3,5.1,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
9/24/11,37-063-0015,3,7.070833333,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
9/25/11,37-063-0015,3,3.683333333,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
9/26/11,37-063-0015,3,5.120833333,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
9/27/11,37-063-0015,3,9.870833333,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
9/28/11,37-063-0015,3,7.375,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
9/29/11,37-063-0015,3,8.533333333,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
9/30/11,37-063-0015,3,7.195833333,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
10/1/11,37-063-0015,3,2.145833333,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
10/2/11,37-063-0015,3,4.8875,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
10/3/11,37-063-0015,3,5.329166667,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
10/4/11,37-063-0015,3,6.033333333,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
10/5/11,37-063-0015,3,8.304166667,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
10/6/11,37-063-0015,3,9.7875,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
10/7/11,37-063-0015,3,7.325,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
10/8/11,37-063-0015,3,7.35,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
10/9/11,37-063-0015,3,5.775,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
10/10/11,37-063-0015,3,9.020833333,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
10/11/11,37-063-0015,3,10.58636364,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
10/12/11,37-063-0015,3,7.208333333,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
10/13/11,37-063-0015,3,6.2,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
10/14/11,37-063-0015,3,7.366666667,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
10/15/11,37-063-0015,3,7.15,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
10/16/11,37-063-0015,3,5.820833333,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
10/17/11,37-063-0015,3,11.775,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
10/18/11,37-063-0015,3,11.45238095,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
10/19/11,37-063-0015,3,1.5625,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
10/20/11,37-063-0015,3,4.6875,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
10/21/11,37-063-0015,3,6.641666667,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
10/22/11,37-063-0015,3,7.166666667,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
10/23/11,37-063-0015,3,9.904166667,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
10/24/11,37-063-0015,3,12.24583333,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
10/25/11,37-063-0015,3,10.27083333,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
10/26/11,37-063-0015,3,12.9625,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
10/27/11,37-063-0015,3,12.44166667,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
10/28/11,37-063-0015,3,1.645833333,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
10/29/11,37-063-0015,3,2.108333333,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
10/30/11,37-063-0015,3,9.079166667,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
10/31/11,37-063-0015,3,6.483333333,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
11/1/11,37-063-0015,3,7.7625,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
11/2/11,37-063-0015,3,9.508333333,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
11/3/11,37-063-0015,3,11.55416667,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
11/4/11,37-063-0015,3,8.425,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
11/5/11,37-063-0015,3,5.1625,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
11/6/11,37-063-0015,3,5.983333333,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
11/7/11,37-063-0015,3,6.841666667,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
11/8/11,37-063-0015,3,9.458333333,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
11/9/11,37-063-0015,3,8.616666667,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
11/10/11,37-063-0015,3,7.115,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
11/11/11,37-063-0015,3,6.475,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
11/12/11,37-063-0015,3,8.9125,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
11/13/11,37-063-0015,3,9.204166667,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
11/14/11,37-063-0015,3,9.370833333,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
11/15/11,37-063-0015,3,8.975,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
11/16/11,37-063-0015,3,10.07916667,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
11/17/11,37-063-0015,3,3.408333333,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
11/18/11,37-063-0015,3,5.879166667,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
11/19/11,37-063-0015,3,11.85,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
11/20/11,37-063-0015,3,13.17083333,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
11/21/11,37-063-0015,3,8.421052632,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
11/22/11,37-063-0015,3,14.00416667,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
11/23/11,37-063-0015,3,2.25,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
11/24/11,37-063-0015,3,6.575,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
11/25/11,37-063-0015,3,8.775,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
11/26/11,37-063-0015,3,9.8375,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
11/27/11,37-063-0015,3,6.395833333,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
11/28/11,37-063-0015,3,3.883333333,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
11/29/11,37-063-0015,3,2.175,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
11/30/11,37-063-0015,3,4.208333333,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
12/1/11,37-063-0015,3,6.15,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
12/2/11,37-063-0015,3,10.625,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
12/3/11,37-063-0015,3,9.533333333,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
12/4/11,37-063-0015,3,10.95416667,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
12/5/11,37-063-0015,3,9.2,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
12/6/11,37-063-0015,3,4.25,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
12/7/11,37-063-0015,3,1.9375,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
12/8/11,37-063-0015,3,5.558333333,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
12/9/11,37-063-0015,3,10.625,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
12/10/11,37-063-0015,3,9.554166667,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
12/11/11,37-063-0015,3,7.245833333,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
12/12/11,37-063-0015,3,8.633333333,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
12/13/11,37-063-0015,3,11.54583333,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
12/14/11,37-063-0015,3,10.37368421,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
12/15/11,37-063-0015,3,10.6125,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
12/16/11,37-063-0015,3,7.466666667,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
12/17/11,37-063-0015,3,7.541666667,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
12/18/11,37-063-0015,3,10.8375,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
12/19/11,37-063-0015,3,12.025,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
12/20/11,37-063-0015,3,15.22916667,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
12/21/11,37-063-0015,3,8.275,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
12/22/11,37-063-0015,3,7.366666667,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
12/23/11,37-063-0015,3,3.15,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
12/24/11,37-063-0015,3,7.929166667,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
12/25/11,37-063-0015,3,10.7875,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
12/26/11,37-063-0015,3,7.329166667,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
12/27/11,37-063-0015,3,4.120833333,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
12/28/11,37-063-0015,3,4.283333333,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
12/29/11,37-063-0015,3,8.4,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
12/30/11,37-063-0015,3,10.15833333,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
12/31/11,37-063-0015,3,8.616666667,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

================================================
FILE: ch_intro_to_data/figures/eoce/airports/airports.R
================================================
# load packages ----------------------------------------------------------------
library(tidyverse)
library(sf)
library(openintro)
library(nycflights13)
library(janitor)
library(measurements)

# data sources -----------------------------------------------------------------
# shapefile: https://catalog.data.gov/dataset/2013-cartographic-boundary-file-state-for-united-states-1-20000000
# Downloaded 2018-08-13

# set colors -------------------------------------------------------------------
lgray <- COL[7,4]
dgray <- COL[6,1]

# load spatial data ------------------------------------------------------------
# and filter out non-contigious states
usa_49 <- st_read("data/cb_2013_us_state_20m/cb_2013_us_state_20m.shp") %>%
  filter(!(NAME %in% c("Alaska", "Hawaii", "Puerto Rico")))

# load usairports data ------------------------------------------------------------
data(usairports, package = "openintro")

# clean airport data -----------------------------------------------------------
usairports <- usairports %>%
  filter(
    !str_detect(arp_latitude, "S"),
    !str_detect(state, "AK|HI|PR|MQ|GU|CQ|VI")
    ) %>%
  mutate(
    lat_dms = str_replace(arp_latitude, "N", "") %>%
      str_replace_all("-", " "),
    lon_dms = str_replace(arp_longitude, "W", "") %>%
      str_replace_all("-", " "),
    lat_dd = conv_unit(lat_dms, from = "deg_min_sec", to = "dec_deg") %>% as.numeric(),
    lon_dd = -1 * (conv_unit(lon_dms, from = "deg_min_sec", to = "dec_deg") %>% as.numeric())
  ) %>%
  filter(ownership %in% c("PR", "PU")) %>%   # only want public and private owned
  mutate(
    ownership = case_when(
      ownership == "PR" ~ "Privately owned",
      ownership == "PU" ~ "Publicly owned"
    ),
    use = case_when(
      use == "PR" ~ "Private use",
      use == "PU" ~ "Public use"
    ),
    region = case_when(
      region == "AAL" ~ "Alaska",
      region == "ACE" ~ "Central",
      region == "AEA" ~ "Eastern",
      region == "AGL" ~ "Great Lakes",
      region == "ANE" ~ "New England",
      region == "ANM" ~ "Northwest Mountain",
      region == "ASO" ~ "Southern",
      region == "ASW" ~ "Southwest",
      region == "AWP" ~ "Western-Pacific"
    )
  )
  
# plot -------------------------------------------------------------------------
ggplot(data = usa_49) +
  geom_sf(fill = lgray, color = dgray, size = 0.2) +
  geom_point(data = usairports, 
             aes(x = lon_dd, y = lat_dd, color = region),
             alpha = 0.3, show.legend = FALSE) +
  #scale_colour_manual(values = c(blue, green)) +
  coord_sf(xlim = c(-130, -60),
           ylim = c(20, 50)) +
  facet_grid(ownership ~ use) +
  labs(x = "", y = "", color = "Use") +
  theme_minimal()

# save plot --------------------------------------------------------------------
ggsave("airports.png", width = 7, height = 4)


================================================
FILE: ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/cb_2013_us_state_20m.prj
================================================
GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]

================================================
FILE: ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/cb_2013_us_state_20m.shp.iso.xml
================================================
<?xml version="1.0" encoding="ISO-8859-1"?>
<gmi:MI_Metadata xmlns:gmi="http://www.isotc211.org/2005/gmi">
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      <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">cb_2013_us_state_20m.shp.xml</gco:CharacterString>
   </gmd:fileIdentifier>
   <gmd:language xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">eng</gco:CharacterString>
   </gmd:language>
   <gmd:characterSet xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:MD_CharacterSetCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_CharacterSetCode"
                               codeListValue="8859part1"
                               codeSpace="006">8859part1</gmd:MD_CharacterSetCode>
   </gmd:characterSet>
   <!-- This part represents a link to the Series Collection File --><gmd:parentIdentifier xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <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>
   </gmd:parentIdentifier>
   <gmd:hierarchyLevel xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:MD_ScopeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_ScopeCode"
                        codeListValue="dataset">
dataset
</gmd:MD_ScopeCode>
   </gmd:hierarchyLevel>
   <gmd:contact xmlns:gmd="http://www.isotc211.org/2005/gmd"
                xmlns:xlink="http://www.w3.org/1999/xlink"
                xlink:href="https://www.ngdc.noaa.gov/docucomp/8dd6ee96-96e1-492c-be55-76cdde8f27f1"
                xlink:title="pointofContact - U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch"/>
   <gmd:dateStamp xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gco:Date xmlns:gco="http://www.isotc211.org/2005/gco">2014-04</gco:Date>
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   <gmd:metadataStandardName xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">ISO 19115 Geographic Information - Metadata </gco:CharacterString>
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   <gmd:metadataStandardVersion xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">2009-02-15 </gco:CharacterString>
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   <gmd:dataSetURI xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <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>
   </gmd:dataSetURI>
   <!-- This is the ptvctinf/sdtsterm/sdtstype from section 3 of the FGDC
                        Standard (Spatial Data Organization) -->
                <gmd:spatialRepresentationInfo xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:MD_VectorSpatialRepresentation>
         <gmd:geometricObjects>
            <gmd:MD_GeometricObjects>
               <gmd:geometricObjectType>
                  <gmd:MD_GeometricObjectTypeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_GeometricObjectTypeCode"
                                                  codeListValue="complex"
                                                  codeSpace="001">
                                                complex </gmd:MD_GeometricObjectTypeCode>
               </gmd:geometricObjectType>
               <gmd:geometricObjectCount>
                  <gco:Integer xmlns:gco="http://www.isotc211.org/2005/gco">52</gco:Integer>
               </gmd:geometricObjectCount>
            </gmd:MD_GeometricObjects>
         </gmd:geometricObjects>
      </gmd:MD_VectorSpatialRepresentation>
   </gmd:spatialRepresentationInfo>
   <!--This is the indirect spatial reference of section 3 and the Projection
               Information from Section 4 of the FGDC Standard-->
         
      <gmd:referenceSystemInfo xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:MD_ReferenceSystem>
         <gmd:referenceSystemIdentifier>
            <gmd:RS_Identifier>
               <gmd:code xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gmd:codeSpace>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">INCITS (formerly FIPS) codes.</gco:CharacterString>
               </gmd:codeSpace>
            </gmd:RS_Identifier>
         </gmd:referenceSystemIdentifier>
      </gmd:MD_ReferenceSystem>
   </gmd:referenceSystemInfo>
   <!-- This part represents Section 1 of the FGDC Metadata Standard -->
<gmd:identificationInfo xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:MD_DataIdentification>
         <gmd:citation>
            <gmd:CI_Citation>
               <gmd:title>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">2013 Cartographic Boundary File, State for United States, 1:20,000,000</gco:CharacterString>
               </gmd:title>
               <gmd:date>
                  <gmd:CI_Date>
<!-- This is the publication date -->
<gmd:date>
                        <gco:Date xmlns:gco="http://www.isotc211.org/2005/gco">201404</gco:Date>
                     </gmd:date>
                     <gmd:dateType>
                        <gmd:CI_DateTypeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_DateTypeCode"
                                             codeListValue="publication"
                                             codeSpace="002"> publication </gmd:CI_DateTypeCode>
                     </gmd:dateType>
                  </gmd:CI_Date>
               </gmd:date>
               <gmd:citedResponsibleParty xmlns:xlink="http://www.w3.org/1999/xlink"
                                          xlink:href="https://www.ngdc.noaa.gov/docucomp/ddd21bfb-2229-465b-95b2-bee36200b0e5"
                                          xlink:title="originator - U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch"/>
            </gmd:CI_Citation>
         </gmd:citation>
         <gmd:abstract>
            <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>
         </gmd:abstract>
         <gmd:purpose>
            <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>
         </gmd:purpose>
         <gmd:pointOfContact xmlns:xlink="http://www.w3.org/1999/xlink"
                             xlink:href="https://www.ngdc.noaa.gov/docucomp/09b8253a-e2dc-4a6b-a905-11f1e0e87b3b"
                             xlink:title="pointOfContact - U.S. Department of Commerce, U.S. Census Bureau, Geography Division"/>

         <gmd:resourceMaintenance>
            <gmd:MD_MaintenanceInformation>
               <gmd:maintenanceAndUpdateFrequency>
                  <gmd:MD_MaintenanceFrequencyCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_MaintenanceFrequencyCode"
                                                   codeListValue="notPlanned"
                                                   codeSpace="011">
                         notPlanned
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               </gmd:maintenanceAndUpdateFrequency>
            </gmd:MD_MaintenanceInformation>
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               <gmd:keyword>
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               <gmd:keyword>
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               <gmd:keyword>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Shapefile</gco:CharacterString>
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               <gmd:keyword>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">State</gco:CharacterString>
               </gmd:keyword>
               <gmd:type>
                  <gmd:MD_KeywordTypeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_KeywordTypeCode"
                                          codeListValue="theme"
                                          codeSpace="005"> theme </gmd:MD_KeywordTypeCode>
               </gmd:type>
               <gmd:thesaurusName>
                  <gmd:CI_Citation>
                     <gmd:title>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">None</gco:CharacterString>
                     </gmd:title>
                     <gmd:date xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
                  </gmd:CI_Citation>
               </gmd:thesaurusName>
            </gmd:MD_Keywords>
         </gmd:descriptiveKeywords>
         <gmd:descriptiveKeywords>
            <gmd:MD_Keywords>
               <gmd:keyword>
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               </gmd:keyword>
               <gmd:keyword>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">US
</gco:CharacterString>
               </gmd:keyword>
               <gmd:type>
                  <gmd:MD_KeywordTypeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_KeywordTypeCode"
                                          codeListValue="place"
                                          codeSpace="002"> place </gmd:MD_KeywordTypeCode>
               </gmd:type>
               <gmd:thesaurusName>
                  <gmd:CI_Citation>
                     <gmd:title>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">
INCITS 38:2009
  
 
</gco:CharacterString>
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                     <gmd:date xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
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               </gmd:thesaurusName>
            </gmd:MD_Keywords>
         </gmd:descriptiveKeywords>
         <gmd:resourceConstraints>
            <gmd:MD_LegalConstraints>
               <gmd:accessConstraints>
                  <gmd:MD_RestrictionCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_RestrictionCode"
                                          codeListValue="otherRestrictions"
                                          codeSpace="008 "> otherRestrictions </gmd:MD_RestrictionCode>
               </gmd:accessConstraints>
               <gmd:useConstraints>
                  <gmd:MD_RestrictionCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_RestrictionCode"
                                          codeListValue="otherRestrictions"
                                          codeSpace="008 "/>
               </gmd:useConstraints>
               <gmd:otherConstraints>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco"> Access Constraints: None</gco:CharacterString>
               </gmd:otherConstraints>
               <gmd:otherConstraints>
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These 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>
               </gmd:otherConstraints>
            </gmd:MD_LegalConstraints>
         </gmd:resourceConstraints>
            <!-- This is from the Direct Spatial Reference from Chapter 3 -->
            <gmd:spatialRepresentationType>
            <gmd:MD_SpatialRepresentationTypeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_SpatialRepresentationTypeCode"
                                                  codeListValue="vector">vector</gmd:MD_SpatialRepresentationTypeCode>
         </gmd:spatialRepresentationType>
         <gmd:language>
            <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">eng</gco:CharacterString>
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         <gmd:characterSet>
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         </gmd:topicCategory>
         <gmd:environmentDescription>
            <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>
         </gmd:environmentDescription>
         <gmd:extent>
            <gmd:EX_Extent id="boundingExtent">
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                     <gmd:westBoundLongitude>
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                     </gmd:eastBoundLongitude>
                     <gmd:southBoundLatitude>
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                     </gmd:southBoundLatitude>
                     <gmd:northBoundLatitude>
                        <gco:Decimal xmlns:gco="http://www.isotc211.org/2005/gco">71.342941</gco:Decimal>
                     </gmd:northBoundLatitude>
                  </gmd:EX_GeographicBoundingBox>
               </gmd:geographicElement>
               <gmd:temporalElement>
                  <gmd:EX_TemporalExtent id="boundingTemporalExtent">
                     <gmd:extent>
                        <gml:TimePeriod xmlns:gml="http://www.opengis.net/gml/3.2" gml:id="boundingTemporalExtentA">
                           <gml:description>publication date</gml:description>
                           <gml:beginPosition>2014-04</gml:beginPosition>
                           <gml:endPosition>2014-04</gml:endPosition>
                        </gml:TimePeriod>
                     </gmd:extent>
                  </gmd:EX_TemporalExtent>
               </gmd:temporalElement>
            </gmd:EX_Extent>
         </gmd:extent>
      </gmd:MD_DataIdentification>
   </gmd:identificationInfo>
   <!--This section provides the link for the file containing the Entity and
                        Attribute Information. -->
<gmd:contentInfo xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:MD_FeatureCatalogueDescription>
         <gmd:includedWithDataset>
            <gco:Boolean xmlns:gco="http://www.isotc211.org/2005/gco">true</gco:Boolean>
         </gmd:includedWithDataset>
         <gmd:featureCatalogueCitation>
            <gmd:CI_Citation>
               <gmd:title>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Feature Catalog for the State for United States 2013 Cartographic Boundary File000</gco:CharacterString>
               </gmd:title>
               <gmd:date>
                  <gmd:CI_Date>

                     <gmd:date xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="missing "/>
                     <gmd:dateType>
                        <gmd:CI_DateTypeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_DateTypeCode"
                                             codeListValue="publication"
                                             codeSpace="002"/>
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                  </gmd:CI_Date>
               </gmd:date>
               <gmd:citedResponsibleParty xmlns:xlink="http://www.w3.org/1999/xlink"
                                          xlink:href="https://www.ngdc.noaa.gov/docucomp/1df27e57-4768-42de-909b-52f530601fba"
                                          xlink:title="U.S Department of Commerce, U.S Census Bureau, Geography Division (distributor)"/>
               <gmd:otherCitationDetails>
                  <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>
               </gmd:otherCitationDetails>
            </gmd:CI_Citation>
         </gmd:featureCatalogueCitation>
      </gmd:MD_FeatureCatalogueDescription>
   </gmd:contentInfo>
   <gmd:distributionInfo xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:MD_Distribution>
         <gmd:distributionFormat>
            <gmd:MD_Format>
               <gmd:name>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">SHP (compressed)</gco:CharacterString>
               </gmd:name>
               <gmd:version xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gmd:fileDecompressionTechnique>
                  <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>
               </gmd:fileDecompressionTechnique>
            </gmd:MD_Format>
         </gmd:distributionFormat>
         <gmd:distributor>
            <gmd:MD_Distributor>
               <gmd:distributorContact xmlns:xlink="http://www.w3.org/1999/xlink"
                                       xlink:href="https://www.ngdc.noaa.gov/docucomp/1df27e57-4768-42de-909b-52f530601fba"
                                       xlink:title="U.S Department of Commerce, U.S Census Bureau, Geography Division (distributor)"/>
               <gmd:distributionOrderProcess>
                  <gmd:MD_StandardOrderProcess>
                     <gmd:fees>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">The online cartographic boundary files may be downloaded without charge.</gco:CharacterString>
                     </gmd:fees>
                  </gmd:MD_StandardOrderProcess>
               </gmd:distributionOrderProcess>
            </gmd:MD_Distributor>
         </gmd:distributor>
         <gmd:transferOptions>
            <gmd:MD_DigitalTransferOptions>
               <gmd:onLine>
                  <gmd:CI_OnlineResource>
                     <gmd:linkage>
                        <gmd:URL>http://www.census.gov/geo/maps-data/data/tiger.html</gmd:URL>
                     </gmd:linkage>
                  </gmd:CI_OnlineResource>
               </gmd:onLine>
            </gmd:MD_DigitalTransferOptions>
         </gmd:transferOptions>
      </gmd:MD_Distribution>
   </gmd:distributionInfo>
   <gmd:dataQualityInfo xmlns:gmd="http://www.isotc211.org/2005/gmd">
      <gmd:DQ_DataQuality>
         <gmd:scope>
            <gmd:DQ_Scope>
               <gmd:level>
                  <gmd:MD_ScopeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_ScopeCode"
                                    codeListValue="dataset"
                                    codeSpace="005"> dataset </gmd:MD_ScopeCode>
               </gmd:level>
            </gmd:DQ_Scope>
         </gmd:scope>
         <gmd:report>
            <gmd:DQ_AbsoluteExternalPositionalAccuracy>
               <gmd:nameOfMeasure>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Horizontal Positional
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               </gmd:nameOfMeasure>
               <gmd:measureDescription>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco"/>
               </gmd:measureDescription>
               <gmd:evaluationMethodDescription>
                  <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>
               </gmd:evaluationMethodDescription>
               <gmd:result>
                  <gmd:DQ_QuantitativeResult>
                     <gmd:valueUnit>
                        <gml:BaseUnit xmlns:gml="http://www.opengis.net/gml/3.2" gml:id="meters">
                           <gml:identifier codeSpace="meters ">
                                          meters</gml:identifier>
                           <gml:unitsSystem xmlns:xlink="http://www.w3.org/1999/xlink"
                                            xlink:href="http://www.bipm.org/en/si/ "/>
                        </gml:BaseUnit>
                     </gmd:valueUnit>
                     <gmd:value>
                        <gco:Record xmlns:gco="http://www.isotc211.org/2005/gco">Missing </gco:Record>
                     </gmd:value>
                  </gmd:DQ_QuantitativeResult>
               </gmd:result>
            </gmd:DQ_AbsoluteExternalPositionalAccuracy>
         </gmd:report>
         <gmd:report>
            <gmd:DQ_CompletenessCommission>
               <gmd:result xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
            </gmd:DQ_CompletenessCommission>
         </gmd:report>
         <gmd:report>
            <gmd:DQ_CompletenessOmission>
               <gmd:evaluationMethodDescription>
                  <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>
               </gmd:evaluationMethodDescription>
               <gmd:result xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
            </gmd:DQ_CompletenessOmission>
         </gmd:report>
         <gmd:report>
            <gmd:DQ_ConceptualConsistency>
               <gmd:measureDescription>
                  <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.  

For 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>
               </gmd:measureDescription>
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         </gmd:report>
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                     <gmd:description>
                        <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>
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                     <gmd:dateTime>
                        <gco:DateTime xmlns:gco="http://www.isotc211.org/2005/gco">2014-04-01T00:00:00</gco:DateTime>
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                              <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Geo-spatial Relational Database</gco:CharacterString>
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                                          <gco:Date xmlns:gco="http://www.isotc211.org/2005/gco">201404</gco:Date>
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                                       <gmd:dateType>
                                          <gmd:CI_DateTypeCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_DateTypeCode"
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   <gmd:metadataMaintenance xmlns:gmd="http://www.isotc211.org/2005/gmd">
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         </gmd:maintenanceAndUpdateFrequency>
         <gmd:maintenanceNote>
            <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">This was transformed from the Census Metadata Import
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         </gmd:maintenanceNote>
         <gmd:contact xmlns:xlink="http://www.w3.org/1999/xlink"
                      xlink:href="https://www.ngdc.noaa.gov/docucomp/e2a02c3c-01bf-42e7-bee9-0f64f2ef611c "
                      xlink:title="U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch  (custodian) "/>
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================================================
FILE: ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/cb_2013_us_state_20m.shp.xml
================================================
<?xml version="1.0" encoding="ISO-8859-1"?>
<metadata>
   <idinfo>
      <citation>
         <citeinfo>
            <origin>U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch</origin>
            <pubdate>201404</pubdate>
            <title>2013 Cartographic Boundary File, State for United States, 1:20,000,000</title>
            <geoform>vector digital data</geoform>
            <serinfo>
               <sername>Cartographic Boundary Files</sername>
               <issue>2013</issue>
            </serinfo>
            <onlink>http://www2.census.gov/geo/tiger/GENZ2013/STATE/cb_2013_us_state_20m.zip</onlink>
         </citeinfo>
      </citation>
      <descript>
         <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>
         <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>
      </descript>
      <timeperd>
         <timeinfo>
            <rngdates>
               <begdate>201404</begdate>
               <enddate>201404</enddate>
            </rngdates>
         </timeinfo>
         <current>publication date</current>
      </timeperd>
      <status>
         <progress>Complete</progress>
         <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>
      </status>
      <spdom>
         <bounding>
            <westbc>172.000000</westbc>
            <eastbc>-65.221527</eastbc>
            <northbc>71.342941</northbc>
            <southbc>-14.605210
</southbc>
         </bounding>
      </spdom>
      <keywords>
         <theme>
            <themekt>None</themekt>
            <themekey>2013</themekey>
            <themekey>Cartographic Boundary</themekey>
            <themekey>Generalized</themekey>
            <themekey>Shapefile</themekey>
            <themekey>State</themekey>
         </theme>
         <theme>
            <themekt>ISO 19115 Topic Categories</themekt>
            <themekey>Boundaries</themekey>
         </theme>
         <place>
            <placekt>INCITS 38:2009</placekt>
            <placekey>United States</placekey>
            <placekey>US</placekey>
         </place>
      </keywords>
      <accconst>None</accconst>
      <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.

These 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>
      <ptcontac>
         <cntinfo>
            <cntorgp>
               <cntorg>U.S. Department of Commerce, U.S. Census Bureau, Geography Division</cntorg>
            </cntorgp>
            <cntaddr>
               <addrtype>mailing</addrtype>
               <address>4600 Silver Hill Road</address>
               <city>Washington</city>
               <state>DC</state>
               <postal>20233-7400</postal>
               <country>United States</country>
            </cntaddr>
            <cntvoice>301.763.1128</cntvoice>
            <cntfax>301.763.4710</cntfax>
            <cntemail>geo.geography@census.gov</cntemail>
         </cntinfo>
      </ptcontac>
   </idinfo>
   <dataqual>
      <attracc>
         <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>
      </attracc>
      <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.  

For 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>
      <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>
      <posacc>
         <horizpa>
            <horizpar>Data are not accurate.  Data are generalized representations of geographic boundaries at 1:20,000,000.</horizpar>
         </horizpa>
      </posacc>
      <lineage>
         <srcinfo>
            <srccite>
               <citeinfo>
                  <origin>U.S. Department of Commerce, U.S. Census Bureau, Geography Division</origin>
                  <pubdate>unpublished material</pubdate>
                  <title>Census MAF/TIGER database</title>
               </citeinfo>
            </srccite>
            <typesrc>Geo-spatial Relational Database</typesrc>
            <srctime>
               <timeinfo>
                  <rngdates>
                     <begdate>20130101</begdate>
                     <enddate>20130101</enddate>
                  </rngdates>
               </timeinfo>
               <srccurr>The dates describe the effective date of 2013 cartographic boundaries.</srccurr>
            </srctime>
            <srccitea>MAF/TIGER</srccitea>
            <srccontr>All spatial and feature data</srccontr>
         </srcinfo>
         <procstep>
            <procdesc>Spatial data were extracted from the MAF/TIGER database and processed through a U.S. Census Bureau batch generalization system.</procdesc>
            <srcused>MAF/TIGER</srcused>
            <procdate>201404</procdate>
         </procstep>
      </lineage>
   </dataqual>
   <spdoinfo>
      <indspref>INCITS (formerly FIPS) codes.</indspref>
      <direct>Vector</direct>
      <ptvctinf>
         <sdtsterm>
            <sdtstype>G-polygon</sdtstype>
            <ptvctcnt>52</ptvctcnt>
         </sdtsterm>
      </ptvctinf>
   </spdoinfo>
   <spref>
      <horizsys>
         <geograph>
            <latres>0.000458</latres>
            <longres>0.000458</longres>
            <geogunit>Decimal degrees</geogunit>
         </geograph>
         <geodetic>
            <horizdn>North American Datum of 1983</horizdn>
            <ellips>Geodetic Reference System 80</ellips>
            <semiaxis>6378137.000000</semiaxis>
            <denflat>298.257222</denflat>
         </geodetic>
      </horizsys>
   </spref>
   <eainfo>
      <detailed>
         <enttyp>
            <enttypl>cb_2013_us_state_20m.shp</enttypl>
            <enttypd>Current Census State and Equivalent National entities</enttypd>
            <enttypds>U.S. Census Bureau</enttypds>
         </enttyp>
         <attr>
            <attrlabl>STATEFP</attrlabl>
            <attrdef>Current state Federal Information Processing Series (FIPS) code</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <codesetd>
                  <codesetn>National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</codesetn>
                  <codesets>U.S. Census Bureau</codesets>
               </codesetd>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>STATENS</attrlabl>
            <attrdef>Current state ANSI code</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <codesetd>
                  <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>
                  <codesets>U.S. Geological Survey (USGS)</codesets>
               </codesetd>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>AFFGEOID</attrlabl>
            <attrdef>American FactFinder summary level code + geovariant code + '00US' + GEOID</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <codesetd>
                  <codesetn>American FactFinder geographic identifier</codesetn>
                  <codesets>U.S. Census Bureau</codesets>
               </codesetd>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>GEOID</attrlabl>
            <attrdef>State identifier; state FIPS code</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <codesetd>
                  <codesetn>National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</codesetn>
                  <codesets>U.S. Census Bureau</codesets>
               </codesetd>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>STUSPS</attrlabl>
            <attrdef>Current United States Postal Service state abbreviation</attrdef>
            <attrdefs>U.S. Postal Service</attrdefs>
            <attrdomv>
               <codesetd>
                  <codesetn>Official USPS state abbreviations, as shown in Publication 65, National 5-Digit ZIP Code and Post Office Directory</codesetn>
                  <codesets>U.S. Postal Service</codesets>
               </codesetd>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>NAME</attrlabl>
            <attrdef>Current state name</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <codesetd>
                  <codesetn>National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</codesetn>
                  <codesets>U.S. Census Bureau</codesets>
               </codesetd>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>LSAD</attrlabl>
            <attrdef>Current legal/statistical area description code for state</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <edom>
                  <edomv>00</edomv>
                  <edomvd>Blank</edomvd>
                  <edomvds>U.S. Census Bureau</edomvds>
               </edom>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>ALAND</attrlabl>
            <attrdef>Current land area (square meters)</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <rdom>
                  <rdommin>0</rdommin>
                  <rdommax>9,999,999,999,999</rdommax>
                  <attrunit>square meters</attrunit>
               </rdom>
            </attrdomv>
         </attr>
         <attr>
            <attrlabl>AWATER</attrlabl>
            <attrdef>Current water area (square meters)</attrdef>
            <attrdefs>U.S. Census Bureau</attrdefs>
            <attrdomv>
               <rdom>
                  <rdommin>0</rdommin>
                  <rdommax>9,999,999,999,999</rdommax>
                  <attrunit>square meters</attrunit>
               </rdom>
            </attrdomv>
         </attr>
      </detailed>
   </eainfo>
   <distinfo>
      <distrib>
         <cntinfo>
            <cntorgp>
               <cntorg>U.S. Department of Commerce, U.S. Census Bureau, Geography Division</cntorg>
            </cntorgp>
            <cntaddr>
               <addrtype>mailing</addrtype>
               <address>4600 Silver Hill Road</address>
               <city>Washington</city>
               <state>DC</state>
               <postal>20233-7400</postal>
               <country>United States</country>
            </cntaddr>
            <cntvoice>301.763.1128</cntvoice>
            <cntemail>geo.geography@census.gov</cntemail>
         </cntinfo>
      </distrib>
      <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>
      <stdorder>
         <digform>
            <digtinfo>
               <formname>SHP (compressed)</formname>
               <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>
            </digtinfo>
            <digtopt>
               <onlinopt>
                  <computer>
                     <networka>
                        <networkr>http://www.census.gov/geo/maps-data/data/tiger.html</networkr>
                     </networka>
                  </computer>
               </onlinopt>
            </digtopt>
         </digform>
         <fees>The online cartographic boundary files may be downloaded without charge.</fees>
      </stdorder>
      <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>
   </distinfo>
   <metainfo>
      <metd>201404</metd>
      <metc>
         <cntinfo>
            <cntorgp>
               <cntorg>U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch</cntorg>
            </cntorgp>
            <cntaddr>
               <addrtype>mailing</addrtype>
               <address>4600 Silver Hill Road</address>
               <city>Washington</city>
               <state>DC</state>
               <postal>20233-7400</postal>
               <country>United States</country>
            </cntaddr>
            <cntvoice>301.763.1128</cntvoice>
            <cntfax>301.763.4710</cntfax>
            <cntemail>geo.geography@census.gov</cntemail>
         </cntinfo>
      </metc>
      <metstdn>Content Standard for Digital Geospatial Metadata</metstdn>
      <metstdv>FGDC-STD-001-1998</metstdv>
   </metainfo>
</metadata>

================================================
FILE: ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/state_20m.ea.iso.xml
================================================
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                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Current state ANSI code</gco:CharacterString>
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               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                        xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label>
                        <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>
                     </gfc:label>
                     <gfc:definitionReference>
                        <gfc:FC_DefinitionReference>
                           <gfc:definitionSource>
                              <gfc:FC_DefinitionSource>
                                 <gfc:source>
                                    <gmd:CI_Citation xmlns:gmd="http://www.isotc211.org/2005/gmd">
                                       <gmd:title xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="inapplicable"/>
                                       <gmd:date xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
                                       <gmd:citedResponsibleParty>
                                          <gmd:CI_ResponsibleParty>
                                             <gmd:organisationName>
                                                <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">U.S. Geological Survey (USGS)</gco:CharacterString>
                                             </gmd:organisationName>
                                             <gmd:role>
                                                <gmd:CI_RoleCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_RoleCode"
                                                                 codeListValue="resourceProvider"
                                                                 codeSpace="001">resourceProvider
                                                  </gmd:CI_RoleCode>
                                             </gmd:role>
                                          </gmd:CI_ResponsibleParty>
                                       </gmd:citedResponsibleParty>
                                    </gmd:CI_Citation>
                                 </gfc:source>
                              </gfc:FC_DefinitionSource>
                           </gfc:definitionSource>
                        </gfc:FC_DefinitionReference>
                     </gfc:definitionReference>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
         <gfc:carrierOfCharacteristics>
            <gfc:FC_FeatureAttribute>
               <gfc:memberName>
                  <gco:LocalName xmlns:gco="http://www.isotc211.org/2005/gco">AFFGEOID</gco:LocalName>
               </gfc:memberName>
               <gfc:definition>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">American FactFinder summary level code + geovariant code + '00US' + GEOID</gco:CharacterString>
               </gfc:definition>
               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                        xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">American FactFinder geographic identifier</gco:CharacterString>
                     </gfc:label>
                     <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                              xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
         <gfc:carrierOfCharacteristics>
            <gfc:FC_FeatureAttribute>
               <gfc:memberName>
                  <gco:LocalName xmlns:gco="http://www.isotc211.org/2005/gco">GEOID</gco:LocalName>
               </gfc:memberName>
               <gfc:definition>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">State identifier; state FIPS code</gco:CharacterString>
               </gfc:definition>
               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                        xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label>
                        <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>
                     </gfc:label>
                     <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                              xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
         <gfc:carrierOfCharacteristics>
            <gfc:FC_FeatureAttribute>
               <gfc:memberName>
                  <gco:LocalName xmlns:gco="http://www.isotc211.org/2005/gco">STUSPS</gco:LocalName>
               </gfc:memberName>
               <gfc:definition>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Current United States Postal Service state abbreviation</gco:CharacterString>
               </gfc:definition>
               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference>
                  <gfc:FC_DefinitionReference>
                     <gfc:definitionSource>
                        <gfc:FC_DefinitionSource>
                           <gfc:source>
                              <gmd:CI_Citation xmlns:gmd="http://www.isotc211.org/2005/gmd">
                                 <gmd:title xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="inapplicable"/>
                                 <gmd:date xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
                                 <gmd:citedResponsibleParty>
                                    <gmd:CI_ResponsibleParty>
                                       <gmd:organisationName>
                                          <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">U.S. Postal Service</gco:CharacterString>
                                       </gmd:organisationName>
                                       <gmd:role>
                                          <gmd:CI_RoleCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_RoleCode"
                                                           codeListValue="resourceProvider"
                                                           codeSpace="001">resourceProvider
                                                  </gmd:CI_RoleCode>
                                       </gmd:role>
                                    </gmd:CI_ResponsibleParty>
                                 </gmd:citedResponsibleParty>
                              </gmd:CI_Citation>
                           </gfc:source>
                        </gfc:FC_DefinitionSource>
                     </gfc:definitionSource>
                  </gfc:FC_DefinitionReference>
               </gfc:definitionReference>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label>
                        <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>
                     </gfc:label>
                     <gfc:definitionReference>
                        <gfc:FC_DefinitionReference>
                           <gfc:definitionSource>
                              <gfc:FC_DefinitionSource>
                                 <gfc:source>
                                    <gmd:CI_Citation xmlns:gmd="http://www.isotc211.org/2005/gmd">
                                       <gmd:title xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="inapplicable"/>
                                       <gmd:date xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
                                       <gmd:citedResponsibleParty>
                                          <gmd:CI_ResponsibleParty>
                                             <gmd:organisationName>
                                                <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">U.S. Postal Service</gco:CharacterString>
                                             </gmd:organisationName>
                                             <gmd:role>
                                                <gmd:CI_RoleCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_RoleCode"
                                                                 codeListValue="resourceProvider"
                                                                 codeSpace="001">resourceProvider
                                                  </gmd:CI_RoleCode>
                                             </gmd:role>
                                          </gmd:CI_ResponsibleParty>
                                       </gmd:citedResponsibleParty>
                                    </gmd:CI_Citation>
                                 </gfc:source>
                              </gfc:FC_DefinitionSource>
                           </gfc:definitionSource>
                        </gfc:FC_DefinitionReference>
                     </gfc:definitionReference>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
         <gfc:carrierOfCharacteristics>
            <gfc:FC_FeatureAttribute>
               <gfc:memberName>
                  <gco:LocalName xmlns:gco="http://www.isotc211.org/2005/gco">NAME</gco:LocalName>
               </gfc:memberName>
               <gfc:definition>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Current state name</gco:CharacterString>
               </gfc:definition>
               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                        xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label>
                        <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>
                     </gfc:label>
                     <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                              xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
         <gfc:carrierOfCharacteristics>
            <gfc:FC_FeatureAttribute>
               <gfc:memberName>
                  <gco:LocalName xmlns:gco="http://www.isotc211.org/2005/gco">LSAD</gco:LocalName>
               </gfc:memberName>
               <gfc:definition>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Current legal/statistical area description code for state</gco:CharacterString>
               </gfc:definition>
               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                        xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">00</gco:CharacterString>
                     </gfc:label>
                     <gfc:definition>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Blank</gco:CharacterString>
                     </gfc:definition>
                     <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                              xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
         <gfc:carrierOfCharacteristics>
            <gfc:FC_FeatureAttribute>
               <gfc:memberName>
                  <gco:LocalName xmlns:gco="http://www.isotc211.org/2005/gco">ALAND</gco:LocalName>
               </gfc:memberName>
               <gfc:definition>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Current land area (square meters)</gco:CharacterString>
               </gfc:definition>
               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                        xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
               <gfc:valueMeasurementUnit>
                  <gml:DerivedUnit xmlns:gml="http://www.opengis.net/gml/3.2" gml:id="areaInMetersSquaredforALAND">
                     <gml:identifier codeSpace="area"/>
                     <gml:derivationUnitTerm uom="m" exponent="2"/>
                  </gml:DerivedUnit>
               </gfc:valueMeasurementUnit>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="inapplicable"/>
                     <gfc:definition>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">
                                            Range Domain Minimum: 0
                                            Range Domain Maximum: 9,999,999,999,999</gco:CharacterString>
                     </gfc:definition>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
         <gfc:carrierOfCharacteristics>
            <gfc:FC_FeatureAttribute>
               <gfc:memberName>
                  <gco:LocalName xmlns:gco="http://www.isotc211.org/2005/gco">AWATER</gco:LocalName>
               </gfc:memberName>
               <gfc:definition>
                  <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">Current water area (square meters)</gco:CharacterString>
               </gfc:definition>
               <gfc:cardinality xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="unknown"/>
               <gfc:definitionReference xmlns:xlink="http://www.w3.org/1999/xlink" xlink:title="U.S. Census Bureau"
                                        xlink:href="http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4"/>
               <gfc:valueMeasurementUnit>
                  <gml:DerivedUnit xmlns:gml="http://www.opengis.net/gml/3.2" gml:id="areaInMetersSquaredforAWATER">
                     <gml:identifier codeSpace="area"/>
                     <gml:derivationUnitTerm uom="m" exponent="2"/>
                  </gml:DerivedUnit>
               </gfc:valueMeasurementUnit>
               <gfc:listedValue>
                  <gfc:FC_ListedValue>
                     <gfc:label xmlns:gco="http://www.isotc211.org/2005/gco" gco:nilReason="inapplicable"/>
                     <gfc:definition>
                        <gco:CharacterString xmlns:gco="http://www.isotc211.org/2005/gco">
                                            Range Domain Minimum: 0
                                            Range Domain Maximum: 9,999,999,999,999</gco:CharacterString>
                     </gfc:definition>
                  </gfc:FC_ListedValue>
               </gfc:listedValue>
            </gfc:FC_FeatureAttribute>
         </gfc:carrierOfCharacteristics>
      </gfc:FC_FeatureType>
   </gfc:featureType>
</gfc:FC_FeatureCatalogue>

================================================
FILE: ch_intro_to_data/figures/eoce/antibiotic_use_children/antibiotic_use_children.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# create data -------------------------------------------------------
conditions = c(rep("Prematurity", 33),
               rep("Neuromuscular", 10),
               rep("Cardiovascular", 16),
               rep("Genetic/metabolic", 6),
               rep("Respiratory", 13),
               rep("Trauma", 10),
               rep("Gastrointestinal", 2),
               rep("Immunocompromised", 2)
               )

# summary table -----------------------------------------------------
summary_table = sort(table(conditions))/sum(table(conditions))

# barplot -----------------------------------------------------------
pdf("antibiotic_use_children_bar.pdf", height = 3, width = 6)
par(mar = c(3.2, 10.5, 0, 0.5), las = 1, mgp = c(2, 0.45, 0),
    cex.lab = 1.25, cex.axis = 1.25)
barplot(summary_table, ylab = "", xlab = "Relative frequency", 
        col = COL[1], horiz = TRUE)
dev.off()

# pie chart ---------------------------------------------------------
pdf("antibiotic_use_children_pie.pdf", height = 3, width = 6)
par(mar=c(0, 2.8, 0, 6), las = 1)
pie(summary_table, 
    col = c(COL[1,1], COL[1,4], COL[2,1], COL[2,4], 
            COL[3,1], COL[3,4], COL[4,1], COL[4,4]), 
    cex = 1, clockwise = FALSE,
    labels = names(summary_table))
dev.off()


================================================
FILE: ch_intro_to_data/figures/eoce/association_plots/association_plots.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# set seed ----------------------------------------------------------
set.seed = 2306

# create x ----------------------------------------------------------
x = seq(0, 10, 0.1)

# create y_poslin: positive linear with x ---------------------------
y_poslin = x * runif(1, min = 0, max = 4) + 
  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - 
  runif(1, min = 0, max = 3)

# create y_neglin: negative linear with x ---------------------------
y_neglin = x * -runif(1, min = 0, max = 4) + 
  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - 
  runif(1, min = 0, max = 5)

# create y_poscur: curved positive with x ---------------------------
y_poscur = x^2 + rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4))

# create y_none: no association with x ------------------------------
y_none = x + rnorm(length(x), mean = 0, sd = runif(1, min = 30, max = 40))

# plot the associations --------------------------------------------- 

pdf("association_plots.pdf", 5.5, 4.3)
par(mar = c(2.5, 0.5, 0.5, 0.5), las = 1, mgp = c(1, 0.5, 0), 
    cex.lab = 1.75, pch = 20, mfrow = c(2,2), 
    yaxt = "n", xaxt = "n")
plot(y_poslin ~ x,  xlab = "(1)", ylab = "", col = COL[1])
plot(y_none ~ x, xlab = "(2)", ylab = "", col = COL[1])
plot(y_poscur ~ x, xlab = "(3)", ylab = "", col = COL[1])
plot(y_neglin ~ x, xlab = "(4)", ylab = "", col = COL[1])
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/cleveland_sacramento/cleveland_sacramento.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# take a sample -----------------------------------------------------
cle_sac = cle_sac[!is.na(cle_sac$personal_income),]

set.seed(8957)
sac = sample(cle_sac$personal_income[cle_sac$city == "Sacramento"], 17)
cle = sample(cle_sac$personal_income[cle_sac$city == "Cleveland"], 21)

# plot of total personal income in Cle and Sac ----------------------
pdf("cleveland_sacramento_hist.pdf", height = 5, width = 7)

par(mar = c(3.7, 2, 1,1), las = 1, mgp = c(2.5, 0.7, 0), 
    mfrow = c(2,1), cex.lab = 1.25)

histPlot(cle, xlim = c(0, 180000), ylim = c(0,10),
         ylab = "", xlab = "", col = COL[1], breaks = 8, axes = FALSE)
axis(1, at = seq(0,180000,45000))
axis(2, at = seq(0,10,5))
text(x = 120000, y = 8, labels = "Cleveland, OH", pos = 4, cex = 1.25)

histPlot(sac, xlim = c(0,180000), ylim = c(0,10), 
         ylab = "", xlab = "Total personal income", col = COL[1], breaks = 8,
         axes = FALSE)
axis(1, at = seq(0,180000,45000))
axis(2, at = seq(0,10,5))
text(x = 120000, y = 8, labels = "Sacramento, CA", pos = 4, cex = 1.25)

dev.off()

# summary stats -----------------------------------------------------
mean(cle, na.rm = TRUE)
sd(cle, na.rm = TRUE)
length(cle)

mean(sac, na.rm = TRUE)
sd(sac, na.rm = TRUE)
length(sac)


================================================
FILE: ch_intro_to_data/figures/eoce/county_commute_times/countyMap.R
================================================
library(maps)
countyMap <- function(values, FIPS,
                      col = c("red", "green", "blue"),
                      varTrans = I,
                      gtlt = "",
                      ...){
  if(missing(FIPS)){
    stop("Must provide the county FIPS")
  }
  
  # _____ Drop NAs _____ #
  FIPS   <- FIPS[!is.na(values)]
  values <- values[!is.na(values)]
  
  # _____ Scale Values _____ #
  MI  <- min(values)
  MA  <- max(values)
  Leg <- seq(MI, MA, length.out = 50)
  if(identical(varTrans, log)){
    VAL    <- varTrans(values+0.1)
    valCol <- varTrans(values+0.1)
    LegCol <- varTrans(Leg+0.1)
  } else {
    VAL    <- varTrans(values)
    valCol <- varTrans(values)
    LegCol <- varTrans(Leg)
  }
  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01
  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01
  
  val.000 <- 0.500*(1-valCol)
  val.114 <- 0.557*(1-valCol)
  val.200 <- 0.600*(1-valCol)
  val.298 <- 0.649*(1-valCol)
  val.318 <- 0.659*(1-valCol)
  val.337 <- 0.669*(1-valCol)
  val.447 <- 0.724*(1-valCol)
  val.608 <- 0.804*(1-valCol)
  val.741 <- 0.871*(1-valCol)
  val.863 <- 0.932*(1-valCol)
  val.941 <- 0.971*(1-valCol)
  val.957 <- 0.979*(1-valCol)
  
  Leg.000 <- 0.500*(1-LegCol)
  Leg.114 <- 0.557*(1-LegCol)
  Leg.200 <- 0.600*(1-LegCol)
  Leg.298 <- 0.649*(1-LegCol)
  Leg.318 <- 0.659*(1-LegCol)
  Leg.337 <- 0.669*(1-LegCol)
  Leg.447 <- 0.724*(1-LegCol)
  Leg.608 <- 0.804*(1-LegCol)
  Leg.741 <- 0.871*(1-LegCol)
  Leg.863 <- 0.932*(1-LegCol)
  Leg.941 <- 0.971*(1-LegCol)
  Leg.957 <- 0.979*(1-LegCol)
  
  if(col[1] == "red"){
    col <- rgb(val.941, val.318, val.200)
    COL <- rgb(Leg.941, Leg.318, Leg.200)
  } else if(col[1] == "green"){
    col <- rgb(val.298, val.447, val.114)
    COL <- rgb(Leg.298, Leg.447, Leg.114)
  } else if(col[1] == "bg"){
    col <- rgb(val.337, val.608, val.741)
    COL <- rgb(Leg.337, Leg.608, Leg.741)
  } else if(col[1] == "ye"){
    col <- rgb(val.957, val.863, val.000)
    COL <- rgb(Leg.957, Leg.863, Leg.000)
  } else {
    col <- rgb(val.06, val.06, val.10)
    COL <- rgb(Leg.06, Leg.06, Leg.10)
  }

  # _____ Remove These _____ #
  data(county.fips)
  col <- col[match(county.fips$fips, FIPS)]
  plot(0,0,type = "n", axes = FALSE, xlab = "", ylab = "")
  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))
  map("county", col = col, fill = TRUE, resolution = 0,
    lty = 0, projection = "polyconic", mar = rep(0.1,4), add = TRUE, ...)
  
  x1 <- 0.335
  x2 <- 0.365
  for(i in 1:50){
    y1 <- i/50 * 0.25 + 0.5
    y2 <- (i-1)/50 * 0.25 + 0.5
    rect(x1, y1, x2, y2, border = "#00000000", col = COL[i])
  }
  
  VR    <- range(VAL)
  VR[3] <- VR[2]
  VR[2] <- mean(VR[c(1,3)])
  
  VR1    <- c()
  VR1[1] <- values[which.min(abs(VAL - VR[1]))]
  VR1[2] <- values[which.min(abs(VAL - VR[2]))]
  VR1[2] <- values[which.min(abs(VAL - VR[3]))]
  
  VR    <- round(VR)
  if(gtlt %in% c(">", "><")){
    VR[3] <- paste(">", VR[3], sep = "")
  }
  if(gtlt %in% c("<", "><")){
    VR[1] <- paste("<", VR[1], sep = "")
  }
  text(0.365, 0.51, VR[1], pos = 4)
  text(0.365, 0.625, VR[2], pos = 4)
  text(0.365, 0.74, VR[3], pos = 4)
}







================================================
FILE: ch_intro_to_data/figures/eoce/county_commute_times/county_commute_times.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load mapproj package for map functions ----------------------------
library(mapproj)

# load data ---------------------------------------------------------
data(countyComplete)

# histogram of travel to work time ----------------------------------
pdf("county_commute_times_hist.pdf", 7.5, 4)

par(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(countyComplete$mean_work_travel, breaks = 40, 
         xlab = "Mean work travel (in min)", ylab = "", 
         col = COL[1], axes = FALSE)
axis(1)
axis(2, at = seq(0, 200, 100))

dev.off()

# source custom code for county maps --------------------------------
source("countyMap.R")

# map of travel to work time ----------------------------------------

pdf("county_commute_times_map.pdf", 7.5, 4)

val <- countyComplete$mean_work_travel
val[val >= 33] <- 33
countyMap(val, countyComplete$FIPS, "green", gtlt = ">")

dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/county_hispanic_pop/countyMap.R
================================================
library(maps)
countyMap <- function(values, FIPS,
                      col = c("red", "green", "blue"),
                      varTrans = I,
                      gtlt = "",
                      ...){
  if(missing(FIPS)){
    stop("Must provide the county FIPS")
  }
  
  # _____ Drop NAs _____ #
  FIPS   <- FIPS[!is.na(values)]
  values <- values[!is.na(values)]
  
  # _____ Scale Values _____ #
  MI  <- min(values)
  MA  <- max(values)
  Leg <- seq(MI, MA, length.out = 50)
  if(identical(varTrans, log)){
    VAL    <- varTrans(values+0.1)
    valCol <- varTrans(values+0.1)
    LegCol <- varTrans(Leg+0.1)
  } else {
    VAL    <- varTrans(values)
    valCol <- varTrans(values)
    LegCol <- varTrans(Leg)
  }
  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01
  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01
  
  val.000 <- 0.500*(1-valCol)
  val.114 <- 0.557*(1-valCol)
  val.200 <- 0.600*(1-valCol)
  val.298 <- 0.649*(1-valCol)
  val.318 <- 0.659*(1-valCol)
  val.337 <- 0.669*(1-valCol)
  val.447 <- 0.724*(1-valCol)
  val.608 <- 0.804*(1-valCol)
  val.741 <- 0.871*(1-valCol)
  val.863 <- 0.932*(1-valCol)
  val.941 <- 0.971*(1-valCol)
  val.957 <- 0.979*(1-valCol)
  
  Leg.000 <- 0.500*(1-LegCol)
  Leg.114 <- 0.557*(1-LegCol)
  Leg.200 <- 0.600*(1-LegCol)
  Leg.298 <- 0.649*(1-LegCol)
  Leg.318 <- 0.659*(1-LegCol)
  Leg.337 <- 0.669*(1-LegCol)
  Leg.447 <- 0.724*(1-LegCol)
  Leg.608 <- 0.804*(1-LegCol)
  Leg.741 <- 0.871*(1-LegCol)
  Leg.863 <- 0.932*(1-LegCol)
  Leg.941 <- 0.971*(1-LegCol)
  Leg.957 <- 0.979*(1-LegCol)
  
  if(col[1] == "red"){
    col <- rgb(val.941, val.318, val.200)
    COL <- rgb(Leg.941, Leg.318, Leg.200)
  } else if(col[1] == "green"){
    col <- rgb(val.298, val.447, val.114)
    COL <- rgb(Leg.298, Leg.447, Leg.114)
  } else if(col[1] == "bg"){
    col <- rgb(val.337, val.608, val.741)
    COL <- rgb(Leg.337, Leg.608, Leg.741)
  } else if(col[1] == "ye"){
    col <- rgb(val.957, val.863, val.000)
    COL <- rgb(Leg.957, Leg.863, Leg.000)
  } else {
    col <- rgb(val.06, val.06, val.10)
    COL <- rgb(Leg.06, Leg.06, Leg.10)
  }

  # _____ Remove These _____ #
  data(county.fips)
  col <- col[match(county.fips$fips, FIPS)]
  plot(0,0,type = "n", axes = FALSE, xlab = "", ylab = "")
  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))
  map("county", col = col, fill = TRUE, resolution = 0,
    lty = 0, projection = "polyconic", mar = rep(0.1,4), add = TRUE, ...)
  
  x1 <- 0.335
  x2 <- 0.365
  for(i in 1:50){
    y1 <- i/50 * 0.25 + 0.5
    y2 <- (i-1)/50 * 0.25 + 0.5
    rect(x1, y1, x2, y2, border = "#00000000", col = COL[i])
  }
  
  VR    <- range(VAL)
  VR[3] <- VR[2]
  VR[2] <- mean(VR[c(1,3)])
  
  VR1    <- c()
  VR1[1] <- values[which.min(abs(VAL - VR[1]))]
  VR1[2] <- values[which.min(abs(VAL - VR[2]))]
  VR1[2] <- values[which.min(abs(VAL - VR[3]))]
  
  VR    <- round(VR)
  if(gtlt %in% c(">", "><")){
    VR[3] <- paste(">", VR[3], sep = "")
  }
  if(gtlt %in% c("<", "><")){
    VR[1] <- paste("<", VR[1], sep = "")
  }
  text(0.365, 0.51, VR[1], pos = 4)
  text(0.365, 0.625, VR[2], pos = 4)
  text(0.365, 0.74, VR[3], pos = 4)
}







================================================
FILE: ch_intro_to_data/figures/eoce/county_hispanic_pop/county_hispanic_pop.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load mapproj package for map functions ----------------------------
library(mapproj)

# load data ---------------------------------------------------------
data(countyComplete)

# histogram of hispanic % -------------------------------------------
pdf("county_hispanic_pop_hist.pdf", 7.5, 4)

par(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(countyComplete$hispanic, breaks = 25, 
         xlab = "Hispanic %", ylab = "", 
         col = COL[1])

dev.off()

# log of histogram of hispanic % ------------------------------------
pdf("county_hispanic_pop_log_hist.pdf", 7.5, 4)

par(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(log(countyComplete$hispanic), breaks = 25, 
         xlab = "log(% Hispanic)", ylab = "", 
         col = COL[1])

dev.off()

# source custom code for county maps --------------------------------
source("countyMap.R")

# map of travel to work time ----------------------------------------

pdf("county_hispanic_pop_map.pdf", 7.5, 4)

val <- countyComplete$hispanic
val[val >= 40] <- 40
countyMap(val, countyComplete$FIPS, "bg", gtlt=">")

dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/county_income_education/county_income_education.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# number of observations --------------------------------------------
nrow(county_complete) # n = 3142

# scatterplot of income vs. % with bachelor's degree ----------------
pdf("county_income_education_scatterplot.pdf", 5, 4)
par(mar = c(4, 4.6, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.axis = 1.25, cex.lab = 1.4)
plot(county_complete$per_capita_income_2010 ~ county_complete$bachelors_2010, 
    xlab = "Percent with Bachelor's Degree", 
    ylab = "", 
    pch = 20, col = COL[1,3], axes = FALSE, 
    xlim = c(0,80), ylim = c(0, 65) * 1000)
AxisInDollars(2, at = seq(0, 70, 20) * 1000)
AxisInPercent(1, at = seq(0, 80, 20))
par(las = 0)
mtext("Per Capita Income", 2, 3.4, cex = 1.4)
box()
dev.off()


================================================
FILE: ch_intro_to_data/figures/eoce/dream_act_mosaic/dream_act_mosaic.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# create data -------------------------------------------------------

ideology = c(rep("Conservative", 372), rep("Moderate", 363), rep("Liberal", 175))
ideology = factor(ideology, levels = c("Conservative", "Moderate", "Liberal"))
dream = c(rep("Support", 186), rep("Not support", 151), rep("Not sure", 35), 
          rep("Support", 174), rep("Not support", 161), rep("Not sure", 28),
          rep("Support", 114), rep("Not support", 52), rep("Not sure", 9)
)
dream = factor(dream, levels = c("Support", "Not support", "Not sure"))


# mosaicplot --------------------------------------------------------

pdf("dream_act_mosaic.pdf", 7, 3)
par(mar=c(0.5,0,0.25,0.5), las=1, mgp=c(4,1,0))

mosaicplot(ideology ~ dream, main = "", cex.axis = 1.1, 
           xlab = "", ylab = "", color = COL[1])

dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/estimate_mean_median_simple/estimate_mean_median_simple.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------
set.seed(9823)
x <- 100 * rbeta(400, 12, 3)

# plot --------------------------------------------------------------
myPDF("estimate_mean_median_simple.pdf", 6, 2,
      mar = c(1.7, 2.2, 0.2, 0.4), cex = 1.1)
h <- hist(
    x,
    col = COL[1],
    xlab = "",
    ylab = "",
    main = "",
    axes = FALSE)
axis(1)
at <- pretty(par("yaxp")[1:2])
axis(2)
abline(h = at, col = COL[6, 2], lty = 2)
hist(x, col = COL[1, 2], add = TRUE)
dev.off()


================================================
FILE: ch_intro_to_data/figures/eoce/gpa_study_hours/gpa_study_hours.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
load("gpa_study_hours.rda")
# this dataset will also be available in the openintro package
# with the same name

# number of observations --------------------------------------------
nrow(survey) # n = 193

# scatterplot of gpa vs. study hours --------------------------------
pdf("gpa_study_hours_scatterplot.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
set.seed(193) # for jitter below
plot(jitter(gpa_study_hours$gpa) ~ gpa_study_hours$study_hours, 
     xlab="Study hours/week", ylab = "GPA", 
     pch=20, col = COL[1,2], cex.lab = 1.5, axes = FALSE,
     ylim = c(2.5, 4.4))
axis(1, at = seq(0, 70, 20), cex.axis = 1.5)
axis(2, at = c(2.5, 3, 3.5, 4), cex.axis = 1.5)
box()
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/gpa_study_hours/gpa_study_hours.csv
================================================
"gpa","study_hours"
4,10
3.8,25
3.93,45
3.4,10
3.2,4
3.52,10
3.68,24
3.4,40
3.7,10
3.75,10
3.3,30
3.425,7
3.795,15
3.83,60
3.3,10
3.75,10
3.15,6
3.7,20
3.8,8
3.63,30
3.9,35
3.294,12
3.7,6
3.4,20
4,10
3.4,14
3.7,10
3.8,10
3.4,30
3.4,20
3.4,7
3,20
3.6,16
3.567,14
3.3,21
3.4,21
3.6,11
3.67,10
3.82,10
2.9,15
3.9,10
3.4,10
3.6,20
3.1,10
3.4,10
3.8,12
3.7,25
3.7,20
3.8,25
3.92,15
3.8,10
3.868,40
3.35,15
3.85,10
3.55,10
3.7,25
3.65,25
3.125,36
4,30
3.25,14
3.86,2
3.5,10
3.45,5
3.6,4
3.866,20
3.82,12
3.2,15
3.5,3
3.8,10
3.8,15
3.7,25
3.75,15
3.3,10
3.875,15
3.7,7
3.5,14
3.2,7
3.566,40
3.5,6
4.3,10
3.6,10
3.2,20
3.825,20
3.85,69
3.75,8
4,10
3.4,3
3.9,8
3.825,15
3.7,45
3.8,10
2.91,18
3.559,10
3.69,10
3.3,35
3.75,10
3.9,8
3.65,15
3.5,30
3.6,35
3.675,20
3.9,12
3.6,35
3.675,8
3.7,30
3.66,10
3.733,14
3.7,28
2.6,7
4,20
3.2,15
3.16,24
3.5,20
3.65,20
3.9,20
3.785,25
3.1,15
3.15,16
3.61,10
3.3,35
3.7,15
3.7,20
3.75,40
3.4,4
3.6,12
3.5,49
3.8,20
3.7,30
3.84,12
3.41,8
3.825,60
2.95,6.5
3.925,20
3.3,18
3.3,10
3.6,40
4,21
3.3,12.5
3.89,12
3.2,20
3.97,10
3.3,10
3.86,20
3.76,20
3.5,10
3.6,30
3.55,15
3.97,20
3.925,15
3.68,14
3.25,5
3.56,5
2.85,8
3.6,8
3.45,14
3.5,15
3.15,20
3.35,14
3.5,14
3.79,25
3.022,30
3.46,20
3.55,30
3.97,20
3.925,7
3.2,8
3.4,20
3.9,14
3.6,20
3.83,60
3.8,15
4,20
3.5,15
3.3,8
4,15
3.1,10
3.5,7
3.62,20
3.6,10
3.8,28
3.2,12
3.925,5
3.84,30
3.1,5
4,6
3.35,30
3.925,15
3,9
3.6,24
3.7,12
3.84,15
3.8,10
3.1,15


================================================
FILE: ch_intro_to_data/figures/eoce/hist_box_match/hist_box_match.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# generate data -----------------------------------------------------
set.seed(7365)

sym = rnorm(1000, mean = 60, sd = 3)
uni = runif(1000, min = 0, max = 100)
rs = rgamma(1000, shape = 3, rate = 2)

# histograms and box plots ------------------------------------------
pdf("hist_box_match.pdf", width = 10, height = 3)
par(mar=c(4, 3.6, 0, 0), las = 1, mgp = c(2.7, 0.7, 0), 
    mfrow = c(1,6), 
    cex.lab = 1.5, cex.axis = 1.5)

histPlot(sym, xlab = "(a)", ylab = "", col = COL[1], axes = FALSE)
axis(1, seq(50,70,10))

histPlot(uni, xlab = "(b)", ylab = "", col = COL[1], axes = FALSE)
axis(1, seq(0,100,50))

histPlot(rs, xlab = "(c)", ylab = "", col = COL[1], axes = FALSE)
axis(1, seq(0,6,2))

boxPlot(rs, xlab = "(1)", ylab = "", col = COL[1,3])
boxPlot(sym, xlab = "(2)", ylab = "", col = COL[1,3])
boxPlot(uni, xlab = "(3)", ylab = "", col = COL[1,3])

dev.off()


================================================
FILE: ch_intro_to_data/figures/eoce/hist_vs_box/hist_vs_box.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# generate data -----------------------------------------------------
set.seed(12345)
bimod = c(rnorm(300, mean = 5, sd = 1), 
          rnorm(300, mean = 12, sd = 1), 
          runif(25, min = 13, max = 28))

# histogram and box plot --------------------------------------------
pdf("hist_vs_box.pdf", height = 2.2, width = 8)
par(mar = c(2, 2.8, 0.2, 0.5), las = 1, mgp = c(2.9, 0.7, 0),
    cex.axis = 1.5, cex.lab = 1.5)
layout(matrix(1:2, 1), 2:1)
histPlot(bimod, xlab = "", ylab = "", yaxt = "n", col = COL[1])
par(mar = c(2, 2.8, 0.2, 0))
boxPlot(bimod, col = COL[1,2], xlim = c(0.4, 1.6))
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/income_coffee_shop/income_coffee_shop.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# generate data -----------------------------------------------------
set.seed(956)

sal_symmetric = rnorm(40, mean = 65000, sd = 2000)
sal_skewed = c(sal_symmetric, 225000, 250000)

options(scipen=2)

# histograms --------------------------------------------------------
pdf("income_coffee_shop.pdf", 5.5, 4.3)
par(mar = c(3.6, 1, 0.5, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    mfrow = c(2,1), cex.lab = 1.5, cex.axis = 1.5)

histPlot(sal_symmetric, xlim = c(60000, 70000), 
         xlab = "(1)", ylim = c(0,12), col = COL[1], 
         axes = FALSE, ylab = "")
axis(1, at = seq(60000, 70000, 2500))
axis(2, at = seq(0,12,4), labels = NA)

histPlot(sal_skewed, xlab = "(2)", ylim = c(0,12), 
         breaks = seq(0, 260000, by = 1000), col = COL[1], 
         axes = FALSE, xlim = c(60000,260000), ylab = "")
axis(1, at = seq(60000, 260000, 50000))
axis(2, at = seq(0,12,4), labels = NA)

dev.off()

# summary stats -----------------------------------------------------
library(xtable)

summary_table = as.data.frame(cbind(summary(sal_symmetric), summary(sal_skewed)))
names(summary_table) = c("(1)","(2)")
summary_table = rbind(c(length(sal_symmetric), length(sal_skewed)), 
                      summary_table, c(sd(sal_symmetric), sd(sal_skewed)))
rownames(summary_table)[1] = "n"
rownames(summary_table)[dim(summary_table)[1]] = "SD"

xtable(summary_table, digits = 0)

================================================
FILE: ch_intro_to_data/figures/eoce/infant_mortality_rel_freq/infant_mortality.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(dplyr)

# load data ---------------------------------------------------------
load("factbook.rda")
# this dataset will also be available in the cia_factbook package
# with the same name

# calculate # of countries with life exp. & internet data -----------
cia_factbook %>%
  filter(!is.na(infant_mortality_rate)) %>%
  nrow() # n = 224

# histogram parameters ----------------------------------------------
histo = hist(cia_factbook$infant_mortality_rate, plot = FALSE)
breaks = histo$breaks
width = breaks[2] - breaks[1]
counts = histo$counts
n = sum(counts)
rel_freqs = round(counts / n, 2)

five_perc = n * 0.05

# rel. freq. histogram of infant mortality --------------------------
pdf("infant_mortality_rel_freq_hist.pdf", 5.5, 3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(cia_factbook$infant_mortality_rate, 
     main = "", xlab = "Infant mortality", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))
axis(1)
axis(2, at = seq(0, 8 * five_perc, 2 * five_perc),
     labels = seq(0, 0.4, 0.1))
axis(2, at = seq(five_perc, 7 * five_perc, 2 * five_perc),
     labels = rep("", 4), tcl = -0.25)
abline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])
hist(cia_factbook$infant_mortality_rate, 
     main = "", xlab = "", ylab = "",
     col = COL[1], axes = FALSE, add = TRUE)
dev.off()

# rel. freq. histogram of infant mortality  - solution --------------
summary(cia_factbook$infant_mortality_rate)

pdf("infant_mortality_rel_freq_hist_soln.pdf", height = 4.3, width = 8)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(cia_factbook$infant_mortality_rate, 
     main = "", xlab = "Infant mortality", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))
axis(1)
axis(2, at = seq(0, five_perc*8, five_perc), label = c(0, NA, 0.1, NA, 0.2, NA, 0.3, NA, 0.4))
abline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])
hist(cia_factbook$infant_mortality_rate, 
     main = "", xlab = "", ylab = "",
     col = COL[1], axes = FALSE, add = TRUE)
text(x = breaks[-1] - width/2, y = counts + 5, 
     labels = paste(rel_freqs),
     col = COL[4], cex = 1)
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/internet_life_expactancy/internet_life_expactancy.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
load("factbook.rda")
# this dataset will also be available in the cia_factbook package
# with the same name

# calculate % of internet users -------------------------------------
cia_factbook$internet_perc = cia_factbook$internet_users / cia_factbook$population * 100

# calculate # of countries with life exp. & internet data -----------
cia_factbook %>%
  filter(!is.na(internet_perc)) %>%
  filter(!is.na(life_exp_at_birth)) %>%
  nrow() # n = 208

# scatterplot of gpa vs. study hours --------------------------------
pdf("internet_life_expactancy.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
plot(cia_factbook$life_exp_at_birth ~ cia_factbook$internet_perc, 
     xlab = "% Internet users", ylab = "Life expectancy at birth", 
     pch = 20, col = COL[1,2], cex.lab = 1.5, cex.axis = 1.5, xlim = c(0,100))
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/internet_life_expectancy/internet_life_expectancy.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
load("factbook.rda")
# this dataset will also be available in the cia_factbook package
# with the same name

# calculate % of internet users -------------------------------------
cia_factbook$internet_perc = cia_factbook$internet_users / cia_factbook$population * 100

# calculate # of countries with life exp. & internet data -----------
cia_factbook %>%
  subset(!is.na(internet_perc)) %>%
  subset(!is.na(life_exp_at_birth)) %>%
  nrow() # n = 208

# scatterplot of gpa vs. study hours --------------------------------
pdf("internet_life_expectancy.pdf", 6, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
plot(cia_factbook$life_exp_at_birth ~ cia_factbook$internet_perc, 
     xlab = "Percent Internet Users",
     ylab = "Life Expectancy at Birth", 
     pch = 20, col = COL[1,2], cex.lab = 1.5, cex.axis = 1.5,
     xlim = c(0,100),
     axes = FALSE)
AxisInPercent(1, at = seq(0, 100, 20))
axis(2)
box()
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/mammal_life_spans/mammal_life_spans.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(mammals)

# calculate # of countries with life exp. & internet data -----------
nrow(mammals) # n = 62

# scatterplot of gpa vs. study hours --------------------------------
pdf("mammal_life_spans_scatterplot.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
plot(mammals$LifeSpan ~ mammals$Gestation, 
     xlab = "Gestation (days)", ylab = "Life Span (years)", 
     pch = 20, col = COL[1], axes = FALSE)
axis(1, at = seq(0, 600, 100), labels = c(0, NA, 200, NA, 400, NA, 600))
axis(2, at = seq(0, 100, 25))
box()
dev.off()


================================================
FILE: ch_intro_to_data/figures/eoce/marathon_winners/marathon_winners.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(marathon)

# histogram and box plot of marathon finishing times of winners -----
pdf("marathon_winners_hist_box.pdf", height = 2.2, width = 7)

par(mar = c(2, 2.8, 0.5, 5), las = 1, mgp = c(2.9, 0.7, 0),
    cex.axis = 1.5, cex.lab = 1.5)
layout(matrix(1:2, 1), 2:1)
histPlot(marathon$Time, col = COL[1], 
         xlab = "Marathon times", ylab = "", yaxt = "n", 
         axes = FALSE)
axis(1, at = seq(2, 3.2, 0.4))
axis(2, at = seq(0, 20, 10))

par(mar = c(2, 2.8, 0.5, 0))
boxPlot(marathon$Time, col = COL[1,2], ylim = c(2, 3.2),
        ylab = "Marathon times",
        axes = FALSE)
axis(2, at = seq(2, 3.2, 0.4))

dev.off()

# finishing times vs. gender ----------------------------------------
pdf("marathon_winners_gender_box.pdf", height = 1.5, width = 7)

par(mar = c(2, 5.1, 0, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    mfrow = c(1,1), cex.lab = 1.5, cex.axis = 1.5)
boxPlot(marathon$Time, horiz = TRUE, fact = marathon$Gender, 
        xlim = c(2,3.2), ylim = c(0.5, 2.5),
        axes = FALSE, col = COL[1,2])
axis(1, at = seq(2,3.2,0.4))
axis(2, at = c(1,2), labels = c("Women", "Men"))

dev.off()

# times series by gender --------------------------------------------
pdf("marathon_winners_time_series.pdf", height = 3, width = 9)

par(mar = c(2, 4, 0.5, 1.3), las = 1, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
plot(marathon$Time[marathon$Gender == "m"] ~ marathon$Year[marathon$Gender == "m"],
     xlab = "Year", ylab = "Marathon times", 
     pch = 19, col = COL[1], ylim = c(2, 3.2), axes = FALSE)

points(marathon$Time[marathon$Gender == "f"] ~ marathon$Year[marathon$Gender == "f"],
       xlab = "Year", pch = 4, lwd = 1.7, col = COL[2])
axis(1)
axis(2, at = seq(2, 3.2, 0.4))
legend("topright", inset = 0, pch = c(4, 19), col = c(COL[2], COL[1]), 
       legend = c("Women", "Men"))

dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/office_productivity/office_productivity.R
================================================
# set seed ------------------------------------------------
set.seed(2406)

# sketch --------------------------------------------------
pdf("office_productivity_sketch.pdf", 5.5, 3)
par(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), 
    mfrow = c(1,1), cex.lab = 1.5)
curve(rev(dgamma(x, 2.5,1/2)), 0, 14, 
      xlab = "stress", ylab = "productivity", lwd = 2, axes = FALSE)
box()
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/oscar_winners/oscar_winners.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(oscars)

# plot of oscar winner women and men ages ---------------------------
p <- oscars %>%
  ggplot(aes(x = age)) +
    geom_histogram(binwidth = 10, fill = COL[1,1], color = COL[5,1], size = 0.3) +
    facet_wrap(~fct_rev(award), ncol = 1) +
    theme_minimal() +
    theme(strip.text = element_text(hjust = 0)) +
    labs(x = "Age (in years)", y = "")

ggsave(p, file = "ch_intro_to_data/oscar_winners/figures/oscars_winners_hist.pdf",
       height = 6, width = 8)

# summary stats -----------------------------------------------------
oscars %>%
  group_by(award) %>%
  summarise(
    mean = mean(age),
    sd = sd(age),
    n = n()
  )


================================================
FILE: ch_intro_to_data/figures/eoce/raise_taxes_mosaic/raise_taxes_mosaic.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# create data -------------------------------------------------------
# based on http://www.publicpolicypolling.com/pdf/2015/PPP_Release_National_30215.pdf

n = 691

n_dem = round(n * 0.40)
n_rep = round(n * 0.34)
n_indep = 691 - (n_dem + n_rep)

party =  c(rep("Democrat", n_dem), rep("Republican", n_rep), rep("Indep / Other", n_indep))
party = factor(party, levels = c("Democrat", "Republican", "Indep / Other"))
taxes = c(rep("Raise taxes on the rich", round(n_dem * 0.91)), 
          rep("Raise taxes on the poor", round(n_dem * 0.04)), 
          rep("Not sure", round(n_dem * 0.05)),
          rep("Raise taxes on the rich", round(n_rep * 0.47)), 
          rep("Raise taxes on the poor", round(n_rep * 0.10)), 
          rep("Not sure", round(n_rep * 0.43)),
          rep("Raise taxes on the rich", round(n_indep * 0.49)), 
          rep("Raise taxes on the poor", round(n_indep * 0.11)), 
          rep("Not sure", round(n_indep * 0.40)) 
)
taxes = factor(taxes, levels = c("Raise taxes on the rich", "Raise taxes on the poor", "Not sure"))


# mosaicplot --------------------------------------------------------

pdf("raise_taxes_mosaic.pdf", 7, 3)
par(mar=c(0.5,0,0.2,0.5), las=1, mgp=c(4,1,0))

mosaicplot(party ~ taxes, main = "", cex.axis = 1.1, 
           xlab = "", ylab = "", color = COL[1])

dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/randomization_avandia/randomization_avandia.R
================================================
# load openintro package for colors -----------------------
library(openintro)

# create data ---------------------------------------------
gr <- c(rep("rosig", 67593), rep("piog",159978))
out <- c(rep(c("y", "n"), c(2593, 67593-2593)), 
         rep(c("y", "n"), c(5386, 159978-5386)))

set.seed(13)
N <- 10^2
rand_dist <- rep(NA, N)
for(i in 1:N){
  rand_group <- sample(gr)
  rand_dist[i] <- sum(out[rand_group == "rosig"] == "y")
}

# plot randomization distribution -----------------------------------
pdf("randomization_avandia.pdf", 6, 4)
par(mar = c(4,2.7,0,0), las = 1 , mgp = c(2.7, 0.9, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(rand_dist, main="", 
         xlab = "Simulated rosiglitazone cardiovascular events", ylab="", 
         col = COL[1], axes = FALSE)
axis(1, at = seq(2250, 2550, 100))
axis(2, at = (0:4)*N/20, labels = c(0, NA, 2, NA, 4)/20)
abline(h = 0)
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/randomization_heart_transplants/randomization_heart_transplants.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(heartTr)

# mosaic plot -------------------------------------------------------
pdf("randomization_heart_transplants_mosaic.pdf", 5.5, 4.3)
par(mar = c(0, 0, 0, 0), las = 1, mgp = c(2.7, 0.9, 0))
mosaicplot(transplant ~ survived, data = heartTr, 
           main = "", xlab = "", ylab = "", color = COL[1],
           cex.axis = 1.5)
dev.off()

# box plot ----------------------------------------------------------
pdf("randomization_heart_transplants_box.pdf", 5.5, 4.3)
par(mar = c(2, 4.8, 0, 0), las = 1, mgp = c(3.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
boxPlot(heartTr$survtime, fact = heartTr$transplant, 
        ylab = "Survival Time (days)", col = COL[1,2])
dev.off()

# randomization -----------------------------------------------------
load("inference.RData")

diffs = inference(heartTr$survived, heartTr$transplant, 
                  success = "dead", order = c("treatment","control"), 
                  est = "proportion", type = "ht", method = "simulation", 
                  nsim = 100, null = 0, alternative = "twosided", simdist = TRUE,
                  seed = 95632)

# plot randomization distribution -----------------------------------
pdf("randomization_heart_transplants_rando.pdf", height = 3, width = 7)

par(mar = c(3.6, 2.2, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)

values  <- table(diffs)
plot(diffs, type = "n", xlim = c(-0.25, 0.25), 
     xlab = "simulated differences in proportions", 
     ylab = "", axes = FALSE, ylim = c(0, max(values)))
axis(1, at = seq(-0.25, 0.25, 0.05), 
     labels = c(-0.25, NA,-0.15, NA,-0.05, NA, 0.05, NA, 0.15, NA, 0.25))
for(i in 1:length(diffs)){
  x   <- diffs[i]
  rec <- sum(diffs == x)
  points(rep(x, rec), 1:rec, pch = 20, cex = 0.8, col = COL[1])
}

dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/reproducing_bacteria/reproducing_bacteria.R
================================================
# set seed ------------------------------------------------
set.seed(2406)

# sketch --------------------------------------------------
pdf("reproducing_bacteria_sketch.pdf", 5.5, 3)
par(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), 
    mfrow = c(1,1), cex.lab = 1.5)
curve(-1*dexp(x, rate = 4), lwd = 2,
      xlab = "time", ylab = "number of bacteria cells", axes = FALSE)
box()
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/seattle_pet_names/seattle_pet_names.R
================================================
# load packages ----------------------------------------------------------------
library(tidyverse)
library(openintro)
library(ggimage)

# load data --------------------------------------------------------------------
data(seattlepets)

# create data for viz ----------------------------------------------------------
name_props <- seattlepets %>% 
  filter(
    !is.na(animals_name),
    species %in% c("Dog", "Cat")
    ) %>%
  group_by(species) %>% 
  count(animals_name, sort = TRUE) %>%
  mutate(prop = n / sum(n))

cat_name_props <- name_props %>%
  filter(species == "Cat") %>%
  rename(cat_prop = prop) %>%
  slice(1:30)

dog_name_props <- name_props %>%
  filter(species == "Dog") %>%
  rename(dog_prop = prop) %>%
  slice(1:30)

comb_name_props <- inner_join(cat_name_props, dog_name_props, by = "animals_name") %>%
  ungroup() %>%
  select(animals_name, cat_prop, dog_prop)

# create viz -------------------------------------------------------------------
p <- ggplot(comb_name_props, aes(x = cat_prop, y = dog_prop)) +
  geom_abline(intercept = 0, color = COL[7,10], alpha = 0.8, size = 1.5) +
  geom_text_repel(aes(label = animals_name), segment.color = COL[6,3], 
                   seed = 291252, max.iter = 10000) +
  geom_point(color = COL[1,3]) +
  theme_bw() +
  labs(x = "Proportion of cats", y = "Proportion of dogs") +
  xlim(0.002, 0.01) +
  ylim(0.002, 0.01) +
  ggimage::geom_emoji(image = "1f436", aes(x = 0.003, y = 0.009), size = 0.1) +
  ggimage::geom_emoji(image = "1f431", aes(x = 0.009, y = 0.003), size = 0.1)

ggsave(filename = "mine-new/ch_intro_to_data/seattle_pet_names/figures/seattle_pet_names.pdf", p, width = 5.5, height = 4.3)
  


================================================
FILE: ch_intro_to_data/figures/eoce/stats_scores_box/stats_scores_box.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# data --------------------------------------------------------------
stats_scores = c(79, 83, 57, 82, 94, 83, 72, 74, 73, 71, 66, 89, 78, 
                 81, 78, 81, 88, 69, 77, 79)

# summary -----------------------------------------------------------
summary(stats_scores)

# scatterplot of gpa vs. study hours --------------------------------
pdf("stats_scores_boxplot.pdf", 5.5, 2)
par(mar = c(3, 0.5, 0.5, 0.5), las = 1, mgp = c(1.75, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
boxplot(stats_scores, horizontal = TRUE, col = COL[1], xlab = "Scores")
dev.off()

================================================
FILE: ch_intro_to_data/figures/eoce/unvotes/unvotes.R
================================================
# load packages ----------------------------------------------------------------
library(tidyverse)
library(openintro)
library(unvotes)
library(lubridate)

# plot unvotes by issues -------------------------------------------------------

un_votes %>%
  mutate(country = ifelse(country == "United States of America", "US", country)) %>%
  filter(country %in% c("US", "Mexico", "Canada")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid") %>%
  mutate(
    issue = ifelse(issue == "Nuclear weapons and nuclear material", "Nuclear weapons and materials", issue),
    vote = fct_relevel(vote, "yes", "no", "abstain")
    ) %>%
  group_by(country, year = year(date), issue) %>%
  summarize(
    votes = n(),
    percent_yes = mean(vote == "yes")
  ) %>%
  filter(votes > 5) %>%  # only use records where there are more than 5 votes
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
    geom_point(alpha = 0.5) +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
      y = "% Yes",
      x = "Year",
      color = "Country"
    ) +
    theme_minimal() +
    scale_color_manual(values = c(COL[1,1], COL[2,1], COL[4,1]))

# save plot --------------------------------------------------------------------
ggsave(here::here("ch_intro_to_data/unvotes/figures/", "unvotes.png"), width = 7, height = 4)


================================================
FILE: ch_intro_to_data/figures/expResp/expResp.R
================================================

pdf("expResp.pdf", 3.82, 0.44)
par(mar = rep(0, 4))
plot(0:1, 0:1, type = 'n', axes = FALSE)
arrows(0.3, 0.4, 0.7, 0.4, length = 0.1)
text(0.5, 0.3, 'might affect', pos = 3, cex = 0.8)
text(0.15, 0.5, 'explanatory\nvariable')
text(0.85, 0.5, 'response\nvariable')
dev.off()


================================================
FILE: ch_intro_to_data/figures/figureShowingBlocking/figureShowingBlocking.R
================================================
library(openintro)
set.seed(2)
xlim     <- c(0, 1)
slimBox3 <- 0.03
data(COL)

myPDF("figureShowingBlocking.pdf",
      4,
      7,
      mar = rep(0, 4))
plot(c(0, 2.9),
     type = "n",
     axes = FALSE,
     xlab = "",
     ylab = "",
     xlim = c(-0.1, 1.1))
rect(0, 2.2, 1, 2.9)
text(0.5, 2.885, "Numbered patients",
     pos = 3, cex = 0.9)
rect(0, 1.2, 0.45, 1.9)
rect(0.55, 1.2, 1, 1.9)
arrows(0.56, 2.17, 0.75, 2.02, length = 0.1, lwd = 1.37)
arrows(0.44, 2.17, 0.25, 2.02, length = 0.1, lwd = 1.37)
text(0.5, 2.07, "create\nblocks", cex = 0.8)
text(0.2, 1.89, "Low-risk patients", pos = 3, cex = 0.7)
text(0.2+0.55, 1.89, "High-risk patients", pos = 3, cex = 0.7)
rect(0, 0.48, 1, 0.9, border = COL[5])
rect(0, 0.00, 1, 0.42, border = COL[5])
arrows(0.09, 1.16, y1 = 1, length = 0.1, lwd = 1.37)
text(0.1, 1.08, "randomly\nsplit in half", cex = 0.7, pos = 4)
arrows(0.12 + 0.55, 1.16, y1 = 1, length = 0.1, lwd = 1.37)
text(0.13 + 0.55, 1.08, "randomly\nsplit in half", cex = 0.7, pos = 4)

# _____ Inner Box _____ #
rect(0.02, 0.50, 0.41, 0.88, border = COL[5,4])
rect(0.02, 0.02, 0.41, 0.40, border = COL[5,4])
rect(0.57+slimBox3, 0.50, 0.98, 0.88, border = COL[5,4])
rect(0.57+slimBox3, 0.02, 0.98, 0.40, border = COL[5,4])

# _____ Labels _____ #
rect(-0.05, 0.39 + 0.47, 0.14, 0.45 + 0.47,
     col = "#FFFFFF",
     border = COL[5])
text(0.02, 0.424 + 0.47,
     "Control",
     cex = 0.6,
     col = COL[5])

rect(-0.05, 0.39, 0.14, 0.45,
     col = "#FFFFFF",
     border = COL[5])
text(0.04, 0.424,
     "Treatment",
     cex = 0.6,
     col = COL[5])

n   <- 6 * 9
pch <- c(1, 20)[sample(2, n, TRUE, c(0.8, 1.2))]
cex <- rnorm(n, 1, 0.001)
k   <- 0
for (x in seq(0.1, 0.9, len = 9)) {
  for (y in rev(seq(0.3, 0.8, len = 6))) {
    k <- k + 1
    col <- COL[ifelse(pch[k]==1, 4, 1)]
    points(x, y + 2,
           pch = pch[k],
           cex = cex[k],
           col = col)
    text(x, y + 1.98,
         k,
         cex = 0.45,
         pos = 3,
         col = col)
  }
}

trmt  <- rep(NA, n)

these <- which(pch == 1)
trmt[sample(these, length(these)/2)] <- "ctrl"
trmt[is.na(trmt) & pch == 1] <- "trmt"
k <- 0
x <- 0.078
y <- 1.83
for (i in these) {
  k <- k+1
  points(x, y,
         pch = pch[i],
         cex = cex[i],
         col = COL[4])
  text(x, y - 0.02,
       i,
       cex = 0.45,
       pos = 3,
       col = COL[4])
  if(y < 1.3){
    x <- x + 0.095
    y <- 1.83
  } else {
    y <- y - 0.11
  }
}
these <- which(pch != 1)
trmt[sample(these, length(these)/2)] <- "ctrl"
trmt[is.na(trmt) & pch != 1] <- "trmt"
k <- 0
x <- 0.615
y <- 1.82
for (i in these) {
  k <- k+1
  points(x, y,
         pch = pch[i],
         cex = cex[i],
         col = COL[1])
  text(x, y - 0.02,
       i,
       cex = 0.45,
       pos = 3,
       col = COL[1])
  if(y < 1.3){
    x <- x + 0.08
    y <- 1.83
  } else {
    y <- y - 0.095
  }
}

# _____ Low Risk _____ #
k <- rep(0, 4)
x <- c(0.10, 0.10, 0.665, 0.665)
y <- c(0.35, 0.83, 0.35, 0.83) - 0.03
for (i in 1:n) {
  j <- 1
  if (trmt[i] == "trmt") {
    j <- j + 1
  }
  if (pch[i] != 1) {
    j <- j + 2
  }
  k[j] <- k[j]+1
  col <- COL[ifelse(pch[i] == 1, 4, 1)]
  points(x[j], y[j],
         pch = pch[i],
         cex = cex[i],
         col = col)
  text(x[j], y[j] - 0.02,
       i,
       cex = 0.45,
       pos = 3,
       col = col)
  if (y[j] < 0.12 + 0.51 * (j %in% c(2, 4)) - 0.03) {
    x[j] <- x[j] + 0.11 - ifelse(j > 2, 0.025, 0)
    y[j] <- 0.35 + ifelse(j %in% c(2, 4), 0.48, 0) - 0.03
  } else {
    y[j] <- y[j] - 0.085
  }
}

dev.off()

================================================
FILE: ch_intro_to_data/figures/interest_rate_vs_income/interest_rate_vs_loan_amount.R
================================================
library(openintro)
data(loan50)
data(COL)

the.index <- 40

myPDF("interest_rate_vs_income.pdf", 6, 3.5,
      mar = c(3, 3.5, 0.5, 0.5),
      mgp = c(2.4, 0.5, 0))
x <- loan50$total_income
y <- loan50$interest_rate
plot(x, y, 
     pch = 20,
     cex = 1.5,
     col = COL[1, 3],
     xlim = c(0, max(x)),
     ylim = c(0, max(y)),
     xlab = "",
     ylab = "Interest Rate (%)",
     axes = FALSE)
AxisInDollars(1, pretty(c(0, x)))
AxisInPercent(2, pretty(c(0, y)))
box()
# points(x, y, pch = ".")
mtext("Total Income", 1, 1.9)
t1 <- x[the.index]
t2 <- y[the.index]
# lines(c(t1, t1), c(-1e4, t2), lty = 2, col = COL[4])
# lines(c(-1e4, t1), c(t2, t2), lty = 2, col = COL[4])
# points(t1, t2, col = COL[4])
dev.off()

summary(lm(y ~ x))

loan50[the.index, ]


================================================
FILE: ch_intro_to_data/figures/interest_rate_vs_loan_amount/interest_rate_vs_loan_amount.R
================================================
library(openintro)
data(loan50)
data(COL)

myPDF("interest_rate_vs_loan_amount.pdf", 6, 3.5,
      mar = c(3, 3.5, 0.5, 0.5),
      mgp = c(2.4, 0.5, 0))
x <- loan50$loan_amount
y <- loan50$interest_rate
plot(x, y, 
     pch = 20,
     cex = 1.5,
     col = COL[1, 3],
     xlim = c(0, max(x)),
     ylim = c(0, max(y)),
     xlab = "",
     ylab = "Interest Rate (%)",
     axes = FALSE)
AxisInDollars(1, pretty(c(0, x)))
AxisInPercent(2, pretty(c(0, y)))
box()
# points(x, y, pch = ".")
mtext("Loan Amount", 1, 1.9)
t1 <- x[35]
t2 <- y[35]
# lines(c(t1, t1), c(-1e4, t2), lty = 2, col = COL[4])
# lines(c(-1e4, t1), c(t2, t2), lty = 2, col = COL[4])
# points(t1, t2, col = COL[4])
dev.off()

loan50[35, ]


================================================
FILE: ch_intro_to_data/figures/interest_rate_vs_loan_income_ratio/interest_rate_vs_loan_income_ratio.R
================================================
library(openintro)
data(loan50)
data(COL)

myPDF("interest_rate_vs_loan_income_ratio.pdf", 6, 3.5,
      mar = c(3, 3.5, 0.5, 0.5),
      mgp = c(2.4, 0.5, 0))
x <- 100 * loan50$loan_amount / loan50$total_income
y <- loan50$interest_rate
plot(x, y, 
     pch = 20,
     cex = 1.5,
     col = COL[1, 3],
     xlim = c(0, max(x)),
     ylim = c(0, max(y)),
     xlab = "",
     ylab = "Interest Rate (%)",
     axes = FALSE)
AxisInPercent(1, pretty(c(0, x)))
AxisInPercent(2, pretty(c(0, y)))
box()
# points(x, y, pch = ".")
mtext("Loan Amount", 1, 1.9)
t1 <- x[35]
t2 <- y[35]
lines(c(t1, t1), c(-1e4, t2),
      lty = 2,
      col = COL[4])
lines(c(-1e4, t1), c(t2, t2),
      lty = 2,
      col = COL[4])
points(t1, t2,
       col = COL[4])
dev.off()

loan50[35, ]


================================================
FILE: ch_intro_to_data/figures/loan_amount_vs_income/loan_amount_vs_income.R
================================================
library(openintro)
data(loan50)
data(COL)

myPDF("loan_amount_vs_income.pdf", 6, 3.5,
      mar = c(3, 3.5, 0.5, 0.5),
      mgp = c(2.4, 0.5, 0))
x <- loan50$total_income
y <- loan50$loan_amount
plot(x, y, 
     pch = 20,
     cex = 1.5,
     col = COL[1, 3],
     xlim = c(0, max(x)),
     ylim = c(0, max(y)),
     xlab = "",
     ylab = "Loan Amount",
     axes = FALSE)
AxisInDollars(1, pretty(c(0, x)))
AxisInDollars(2, pretty(c(0, y)))
box()
# points(x, y, pch = ".")
mtext("Total Income", 1, 1.9)
t1 <- x[35]
t2 <- y[35]
lines(c(t1, t1), c(-1e4, t2),
      lty = 2,
      col = COL[4])
lines(c(-1e4, t1), c(t2, t2),
      lty = 2,
      col = COL[4])
points(t1, t2,
       col = COL[4])
dev.off()

loan50[35, ]


================================================
FILE: ch_intro_to_data/figures/mnWinter/ReadMe.txt
================================================

This photo was taken by David Diez. It is released under the same license as the textbook.


================================================
FILE: ch_intro_to_data/figures/multiunitsVsOwnership/multiunitsVsOwnership.R
================================================
library(openintro)
data(COL)

w3  <- 1 == 0
ind <- 413

if(w3){
  myPNG("MHP.png", 1200, 800,
        mar = c(3, 3.5, 0.5, 0.5),
        mgp = c(2.4, 0.5, 0),
        cex = 2)
} else {
  myPDF("multiunitsVsOwnership.pdf", 6, 3.5,
        mar = c(3, 3.8, 0.5, 0.5),
        mgp = c(2.7, 0.4, 0))
}
pch    <- 1
cex    <- sqrt(county$pop2017 / 1e6)
cex[is.na(cex)] <- 0.1
colPop <- fadeColor(ifelse(cex > 0.35, COL[4], COL[1]),
                    substr(gray(0.6 + cex * 0.1), 2, 3))
colSm  <- colPop
cexF   <- 2
gp1 <- cex < 0.32
if(!w3){
  cex <- 0.7
  gp1 <- rep(TRUE, nrow(county))
  pch <- 20
  colSm  <- COL[1, 3]
  colPop <- COL[1, 3]
  cexF   <- 1
}
x <- county$multi_unit
y <- county$homeownership
plot(x[gp1], y[gp1],
     pch = pch,
     col = colSm,
     xlab = "",
     ylab = "Homeownership Rate",
     axes = FALSE,
     cex = ifelse(gp1 & cex < 0.32, 0.32, cex)[gp1],
     xlim = c(0, 100), # range(x, na.rm = TRUE),
     ylim = range(y, na.rm = TRUE))
at  =  seq(0, 100, 20)
axis(1, at, paste0(at, "%"))
axis(2, at, paste0(at, "%"))
abline(h = at, v = at, col = COL[7, 2])
box()
points(x[gp1],
       y[gp1],
       pch = '.')
points(x[!gp1], y[!gp1],
       pch = pch,
       col = colPop,
       cex = ifelse(cex < 0.32, 0.32, cex)[!gp1])
points(x[!gp1],
       y[!gp1],
       pch = '.')
t1 <- x[ind]
t2 <- y[ind]
lines(c(t1, t1), c(-1e5, t2),
      lty = 2,
      col = COL[4])
lines(c(-1e5, t1), c(t2, t2),
      lty = 2,
      col = COL[4])
points(t1, t2,
       col = COL[4])
mtext("Percent of Units in Multi-Unit Structures",
      1,
      1.9,
      cex = ifelse(w3, 2, 1))

if(w3){
  usr <- par("usr")
  szs <- c(0.1, 0.4, 2, 5)
  cex <- sqrt(szs) # *c(1.2, 1.1, 1, 1)
  szs <- format(szs)
  szs[1] <- paste("<", szs[1])
  text(102, 95-5, "Population Size", pos = 2)
  colPop <- rgb(ifelse(cex > 0.35, 1, 0),
                0.15 * cex, 0.05 * cex,
                0.6 + cex * 0.1)
  for(i in 1:4){
    points(82, 89 - 5 * i,
           cex = cex[i],
           col = colPop[i])
    txt <- paste(szs[i], "million")
    text(101, 89 - 5 * i, txt, pos = 2)
  }
  rect(78, 63, 120, 120)
  text(25, 10,
       "Counties with >100,000 people are colored red")
}

dev.off()

county[ind, ]


================================================
FILE: ch_intro_to_data/figures/popToSample/popToSampleGraduates.R
================================================
library(openintro)
data(COL)

set.seed(52)
myPDF("popToSampleGraduates.pdf",
      4,
      2.1,
      mar = rep(0, 4))

plot(c(0, 2),
     c(0, 1.1),
     type = 'n',
     axes = FALSE)
temp <- seq(0, 2 * pi, 2 * pi / 100)
x <- 0.5 + 0.5 * cos(temp)
y <- 0.5 + 0.5 * sin(temp)
lines(x, y)

s <- matrix(runif(700), ncol = 2)
S <- matrix(NA, 350, 2)
j <- 0
for (i in 1:nrow(s)) {
  if(sum((s[i, ] - 0.5)^2) < 0.23){
    j <- j + 1
    S[j, ] <- s[i, ]
  }
}
points(S, col = COL[1, 3], pch = 20)
text(0.5, 1, 'all graduates', pos = 3)

set.seed(50)
N <- sample(j, 25)
lines((x - 0.5) / 2 + 1.5, (y - 0.5) / 2 +  0.5, pch = 20)

SS <- (S[N, ] - 0.5) / 2 + 0.5
these <- c(2, 5, 11, 10, 12)
points(SS[these, 1] + 1,
       SS[these, 2],
       col = COL[4, 2],
       pch = 20,
       cex = 1.5)
text(1.5, 0.75, 'sample', pos = 3)

for (i in these) {
  arrows(S[N[i], 1], S[N[i], 2],
         SS[i, 1] + 1 - 0.03, SS[i, 2],
         length = 0.08, col = COL[5], lwd = 1.5)
}
dev.off()


================================================
FILE: ch_intro_to_data/figures/popToSample/popToSubSampleGraduates.R
================================================
library(openintro)
data(COL)

set.seed(52)
myPDF("popToSubSampleGraduates.pdf",
      4,
      2.1,
      mar = rep(0, 4))

plot(c(0, 2),
     c(0, 1.1),
     type = 'n',
     axes = FALSE)
temp <- seq(0, 2 * pi, 2 * pi / 100)
x <- 0.5 + 0.5 * cos(temp)
y <- 0.5 + 0.5 * sin(temp)
lines(x, y)

s <- matrix(runif(700), ncol = 2)
S <- matrix(NA, 350, 2)
j <- 0
sub <- rep(FALSE, 1000)
for (i in 1:nrow(s)) {
  if(sum((s[i,] - 0.5)^2) < 0.23){
    j <- j+1
    S[j,] <- s[i,]
  }
  if(sum((s[i, ] - c(0.05, 0.18) - 0.5)^2) < 0.07){
    sub[j] <- TRUE
  }
}
points(S, col = COL[1, 4 - 2 * sub], pch = 20)
text(0.5, 1, 'all graduates', pos = 3)
lines((x - 0.5) * 2 * sqrt(0.07) + 0.55,
      (y - 0.5) * 2 * sqrt(0.07) + 0.68)

set.seed(7)
N <- sample((1:j)[sub], 25)
lines((x - 0.5) / 2 + 1.5,
      (y - 0.5) / 2 + 0.5,
      pch = 20)

SS <- (S[N, ] - 0.5) / 2 + 0.5
these <- c(2, 5, 7, 12, 15)
points(SS[these, 1] + 1,
       SS[these, 2],
       col = COL[4, 2],
       pch = 20,
       cex = 1.5)
text(1.5, 0.75, 'sample', pos = 3)

for (i in these)  {
  arrows(S[N[i], 1], S[N[i], 2],
         SS[i, 1] + 1 - 0.03, SS[i, 2],
         length = 0.08,
         col = COL[5],
         lwd = 1.5)
}
rect(0.143, 0.2, 0.952, 0.301,
     border = "#00000000",
     col = "#FFFFFF88")
rect(0.236, 0.301, 0.858, 0.403,
     border = "#00000000",
     col = "#FFFFFF88")
text(0.55, 0.5 + 0.18 - sqrt(0.07),
     'graduates from\nhealth-related fields',
     pos = 1)
dev.off()


================================================
FILE: ch_intro_to_data/figures/popToSample/surveySample.R
================================================
library(openintro)
data(COL)

set.seed(52)
myPDF("surveySample.pdf",
      4,
      2.1,
      mar = rep(0, 4))

plot(c(0, 2),
     c(0, 1.1),
     type='n',
     axes=FALSE)
temp <- seq(0, 2 * pi, 2 * pi / 100)
x <- 0.5 + 0.5 * cos(temp)
y <- 0.5 + 0.5 * sin(temp)
lines(x, y)

s <- matrix(runif(700), ncol = 2)
S <- matrix(NA, 350, 2)
j <- 0
sub <- rep(FALSE, 1000)
for (i in 1:nrow(s)) {
  if (sum((s[i,] - 0.5)^2) < 0.23) {
    j <- j + 1
    S[j, ] <- s[i, ]
  }
  if (sum((s[i, ] - c(-0.15, 0.05) - 0.5)^2) < 0.115) {
    sub[j] <- TRUE
  }
}
points(S, col = COL[1, 4 - 2 * sub], pch = 20)
text(0.5, 1, 'population of interest', pos = 3)
lines((x - 0.5) * 2 * sqrt(0.115) + 0.35,
      (y - 0.5) * 2 * sqrt(0.115) + 0.55)

set.seed(7)
N <- sample((1:j)[sub], 25)
lines((x - 0.5) / 2 + 1.5,
      (y - 0.5) / 2 + 0.5,
      pch=20)

SS <- (S[N, ] - 0.5) / 2 + 0.5
these <- c(2, 5, 6, 7, 15)
points(SS[these, 1] + 1,
       SS[these, 2],
       col = COL[4, 2],
       pch = 20,
       cex = 1.5)
text(1.5, 0.75, 'sample', pos=3)

for(i in these){
	arrows(S[N[i], 1],
	       S[N[i], 2],
	       SS[i, 1] + 1 - 0.03,
	       SS[i, 2],
	       length=0.08,
	       col=COL[5],
	       lwd=1.5)
}
rect(0.145, 0.195, 0.775, 0.11,
     border="#00000000",
     col="#FFFFFF88")
rect(0.31, 0.018, 0.605, 0.11,
     border="#00000000",
     col="#FFFFFF88")
text(0.46, 0.5 + 0.06 - sqrt(0.115),
     'population actually\nsampled',
     pos=1,
     cex=0.8)
dev.off()


================================================
FILE: ch_intro_to_data/figures/pop_change_v_med_income/pop_change_v_med_income.R
================================================
library(openintro)
data(county)
data(COL)

ind <- 1088

myPDF("pop_change_v_med_income.pdf", 7, 3.5,
      mar = c(3, 5.1, 0.5, 1),
      mgp = c(2.4, 0.5, 0))
x <- county$median_hh_income
y <- county$pop_change
ylim <- c(-15, 25) # range(y, na.rm = TRUE)
plot(x, y,
     pch = 20,
     cex = 0.7,
     type = "n",
     xlim = c(0, max(x, na.rm = TRUE)),
     ylim = ylim,
     xlab = "",
     ylab = "",
     axes = FALSE)
AxisInDollars(1, pretty(c(0, x)))
AxisInPercent(2, pretty(ylim))
abline(h = pretty(ylim), v = pretty(c(0, x)), col = COL[7, 2])
box()
points(x, y, pch = 20, cex = 0.7, col = COL[1, 3])
points(x, y, pch = ".")
mtext("Median Household Income", 1, 1.9)
par(las = 0)
mtext("Population Change\nover 7 Years", 2, 3)
t1 <- x[ind]
t2 <- y[ind]
lines(c(t1, t1), c(-1e5, t2),
      lty = 2,
      col = COL[4])
lines(c(-1e5, t1), c(t2, t2),
      lty = 2,
      col = COL[4])
points(t1, t2,
       col = COL[4])
dev.off()

county[ind, ]


================================================
FILE: ch_intro_to_data/figures/pop_change_v_per_capita_income/pop_change_v_per_capita_income.R
================================================
library(openintro)
data(county)
data(COL)

ind <- 1088

myPDF("pop_change_v_per_capita_income.pdf", 6, 3.5,
      mar = c(3, 5.1, 0.5, 1),
      mgp = c(2.4, 0.5, 0))
x <- county$per_capita_income
y <- county$pop_change
ylim <- c(-15, 25) # range(y, na.rm = TRUE)
plot(x, y,
     pch = 20,
     cex = 0.7,
     type = "n",
     xlim = c(0, max(x, na.rm = TRUE)),
     ylim = ylim,
     xlab = "",
     ylab = "",
     axes = FALSE)
AxisInDollars(1, pretty(c(0, x)))
AxisInPercent(2, pretty(ylim))
abline(h = pretty(ylim), v = pretty(c(0, x)), col = COL[7, 2])
box()
points(x, y, pch = 20, cex = 0.7, col = COL[1, 3])
points(x, y, pch = ".")
mtext("Per Capita Income", 1, 1.9)
par(las = 0)
mtext("Population Change\nover 7 Years (Percent)", 2, 3)
t1 <- x[ind]
t2 <- y[ind]
lines(c(t1, t1), c(-1e5, t2),
      lty = 2,
      col = COL[4])
lines(c(-1e5, t1), c(t2, t2),
      lty = 2,
      col = COL[4])
points(t1, t2,
       col = COL[4])
dev.off()

county[ind, ]


================================================
FILE: ch_intro_to_data/figures/samplingMethodsFigure/SamplingMethodsFunctions.R
================================================
# _____ Simple Random _____ #
BuildSRS <- function() {
  plot(0, xlim = c(0,2), ylim = 0:1, type = 'n', axes = FALSE)
  box()
  x   <- runif(N, 0, 2)
  y   <- runif(N)
  inc <- n
  points(x, y, col = col, pch = pch)

  these <- sample(N, n)
  points(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)
  points(x[these], y[these], cex = 1.4, col = colSamp)
}


# _____ Systematic Sample _____ #
BuildSystematic <- function() {
  plot(0, xlim = c(0, 2), ylim = 0:1, type = 'n', axes = FALSE)
  box()
  nx  <- 17
  ny  <- (nx + 1) / 2
  x   <- rep(seq(0.02, 1.98, length.out = nx), ny)
  y   <- rep(seq(0.05, 0.95, length.out = ny), rep(nx, ny))
  points(x, y, col = col, pch = pch)
  these <- 1:(nx * ny)
  these <- these[(these + 3) %% 7 == 0]
  points(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)
  points(x[these], y[these], cex = 1.4, col = colSamp)
}


# _____ Stratified _____ #
BuildStratified <- function() {
  PCH <- rep(c(1, 3, 20)[3], 3)
  plot(0, xlim = c(0,2), ylim = 0:1 + 0.01,
       type = 'n', axes = FALSE)
  box()
  X    <- c(0.18, 0.3, 0.68, 1.18, 1.4, 1.74)
  Y    <- c(0.2, 0.78, 0.44, 0.7, 0.25, 0.65)
  locs <- c(1, 4, 5, 3, 6, 2)
  gps  <- list()
  N    <- 1.1*c(15, 12, 35, 22, 13, 28)
  R    <- sqrt(N/500)
  p    <- matrix(c(12, 2, NA,
  				 1,  2, NA,
  				 4,  30, NA,
  				 19, 1, NA,
  				 11, 0, NA,
  				 3, 24, NA), 3)
  p     <- round(p * 1.1)
  p[3,] <- N - p[1,] - p[2,]
  above <- c(1, 1, 1, 1, -1, 1)
  for(i in 1:6){
  	hold <- seq(0, 2 * pi, len = 99)
  	x    <- X[i] + (R[i]+0.01)*cos(hold)
  	y    <- Y[i] + (R[i]+0.01)*sin(hold)
  	polygon(x, y, border = COL[5,4])
  	x    <- rep(NA, N[i])
  	y    <- rep(NA, N[i])
  	for(j in 1:N[i]){
  		inside <- FALSE
  		while(!inside){
  			xx <- runif(1, -R[i], R[i])
  			yy <- runif(1, -R[i], R[i])
  			if(sqrt(xx^2 + yy^2) < R[i]){
  				inside <- TRUE
  				x[j]   <- xx
  				y[j]   <- yy
  			}
  		}
  	}
  	type <- sample(1, N[i], TRUE)
  	pch  <- PCH[type]
  	col  <- COL[type]
  	x    <- X[i]+x
  	y    <- Y[i]+y
  	points(x, y, pch = pch, col = col)
  	these  <- sample(N[i], 3)
  	points(x[these], y[these],
  	       pch = 20, cex = 0.8, col = colSamp)
  	points(x[these], y[these], cex = 1.4, col = colSamp)
  }
  text(X, Y + above * (R),
       paste("Stratum", 1:6),
       pos = 2 + above,
       cex = 1.1)
}


# _____ Cluster _____ #
BuildCluster <- function() {
  PCH <- rep(c(1, 3, 20)[3], 3)
  plot(0, xlim = c(0, 2), ylim  =  c(0.01, 1.04), type = 'n', axes = FALSE)
  box()
  X    <- c(0.17, 0.19, 0.52, 0.85, 1, 1.22, 1.49, 1.79, 1.85)
  Y    <- c(0.3, 0.75, 0.5, 0.26, 0.73, 0.38, 0.67, 0.3, 0.8)
  locs <- c(1, 4, 5, 3, 6, 2)
  gps  <- list()
  N    <- c(18, 12, 11, 13, 16, 14, 15, 16, 12)
  R    <- sqrt(N/500)
  p    <- matrix(c(6,  8, NA,
  				 4,  4, NA,
  				 4,  4, NA,
  				 5,  4, NA,
  				 8,  5, NA,
  				 4,  5, NA,
  				 5,  9, NA,
  				 6,  5, NA,
  				 4,  5, NA), 3)
  p[3,] <- N - p[1,] - p[2,]
  above <- c(-1, 1, 1, 1, 1, -1, 1, 1, 1)
  for(i in 1:length(X)){
  	hold <- seq(0, 2 * pi, len = 99)
  	x    <- X[i] + (R[i] + 0.02) * cos(hold)
  	y    <- Y[i] + (R[i] + 0.02) * sin(hold)
  	polygon(x, y, border = COL[5,4])
  	if(i %in% c(3, 4, 8)){
  		polygon(x, y, border = COL[4], lty = 2, lwd = 1.5)
  	}
  	x    <- rep(NA, N[i])
  	y    <- rep(NA, N[i])
  	for(j in 1:N[i]){
  		inside <- FALSE
  		while(!inside){
  			xx <- runif(1, -R[i], R[i])
  			yy <- runif(1, -R[i], R[i])
  			if(sqrt(xx^2 + yy^2) < R[i]){
  				inside <- TRUE
  				x[j]   <- xx
  				y[j]   <- yy
  			}
  		}
  	}
  	type <- sample(1, N[i], TRUE)
  	pch  <- PCH[type]
  	col  <- COL[type]
  	x    <- X[i]+x
  	y    <- Y[i]+y
  	points(x, y, pch = pch, col = col)
  	these  <- sample(N[i], N[i])
  	if(i %in% c(3, 4, 8)){
  	points(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)
  	points(x[these], y[these], cex = 1.4, col = colSamp)
  		#points(x[these], y[these], pch = 19, col = colSamp)
  	}
  }
  text(X, Y + above * (R + 0.01),
       paste("Cluster", 1:length(X)),
       pos = 2 + above,
       cex = 1.1)
}


  
# _____ Multistage Sampling _____ #
BuildMultistage <- function() {
  PCH <- rep(c(1, 3, 20)[3], 3)
  plot(0, xlim = c(0, 2), ylim = 0:1 + 0.035,
       type = 'n', axes = FALSE)
  box()
  X    <- c(0.17, 0.19, 0.52, 0.85, 1, 1.22, 1.49, 1.79, 1.85)
  Y    <- c(0.3, 0.75, 0.5, 0.26, 0.73, 0.38, 0.67, 0.3, 0.8)
  locs <- c(1, 4, 5, 3, 6, 2)
  gps  <- list()
  N    <- c(18, 12, 11, 13, 16, 14, 15, 16, 12)
  R    <- sqrt(N/500)
  p    <- matrix(c(6,  8, NA,
  				 4,  4, NA,
  				 4,  4, NA,
  				 5,  4, NA,
  				 8,  5, NA,
  				 4,  5, NA,
  				 5,  9, NA,
  				 6,  5, NA,
  				 4,  5, NA), 3)
  p[3,] <- N - p[1,] - p[2,]
  above <- c(-1, 1, 1, 1, 1, -1, 1, 1, 1)
  for(i in 1:length(X)){
  	hold <- seq(0, 2*pi, len = 99)
  	x    <- X[i] + (R[i]+0.02)*cos(hold)
  	y    <- Y[i] + (R[i]+0.02)*sin(hold)
  	polygon(x, y, border = COL[5,4])
  	if(i %in% c(3, 4, 8)){
  		polygon(x, y, border = COL[4], lty = 2, lwd = 1.5)
  	}
  	x    <- rep(NA, N[i])
  	y    <- rep(NA, N[i])
  	for(j in 1:N[i]){
  		inside <- FALSE
  		while(!inside){
  			xx <- runif(1, -R[i], R[i])
  			yy <- runif(1, -R[i], R[i])
  			if(sqrt(xx^2 + yy^2) < R[i]){
  				inside <- TRUE
  				x[j]   <- xx
  				y[j]   <- yy
  			}
  		}
  	}
  	type <- sample(1, N[i], TRUE)
  	pch  <- PCH[type]
  	col  <- COL[type]
  	x    <- X[i]+x
  	y    <- Y[i]+y
  	points(x, y, pch = pch, col = col)
  	these  <- sample(N[i], 6)
  	if(i %in% c(3, 4, 8)){
  	points(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)
  	points(x[these], y[these], cex = 1.4, col = colSamp)
  		#points(x[these], y[these], pch = 19, col = colSamp)
  	}
  }
  text(X, Y + above * (R + 0.01),
       paste("Cluster", 1:length(X)),
       pos = 2 + above, cex = 1.1)
}


================================================
FILE: ch_intro_to_data/figures/samplingMethodsFigure/samplingMethodsFigure.R
================================================
library(openintro)
data(COL)
set.seed(3)
N   <- 108
n   <- 18
colSamp <- COL[4]
PCH <- rep(c(1, 3, 20)[3], 3)

col <- rep(COL[1], N)
pch <- PCH[match(col, COL)]

myPDF("samplingMethodsFigure.pdf", 5.9, 9, mar=rep(0.5,4), mfrow=c(3,1))

#=====> SRS <=====#
plot(0, xlim=c(0,2), ylim=0:1, type='n', axes=FALSE)
box()
x   <- runif(N, 0, 2)
y   <- runif(N)
inc <- n
points(x, y, col=col, pch=pch)

these <- sample(N, n)
points(x[these], y[these], pch=20, cex=0.8, col=colSamp)
points(x[these], y[these], cex=1.4, col=colSamp)


#=====> Stratified <=====#
PCH <- rep(c(1, 3, 20)[3], 3)
plot(0, xlim=c(0,2), ylim=0:1, type='n', axes=FALSE)
box()
X    <- c(0.18, 0.3, 0.68, 1.18, 1.4, 1.74)
Y    <- c(0.2, 0.78, 0.44, 0.7, 0.25, 0.65)
locs <- c(1, 4, 5, 3, 6, 2)
gps  <- list()
N    <- 1.1*c(15, 12, 35, 22, 13, 28)
R    <- sqrt(N/500)
p    <- matrix(c(12, 2, NA,
				 1,  2, NA,
				 4,  30, NA,
				 19, 1, NA,
				 11, 0, NA,
				 3, 24, NA), 3)
p     <- round(p*1.1)
p[3,] <- N - p[1,] - p[2,]
above <- c(1, 1, 1, 1, -1, 1)
for(i in 1:6){
	hold <- seq(0, 2*pi, len=99)
	x    <- X[i] + (R[i]+0.01)*cos(hold)
	y    <- Y[i] + (R[i]+0.01)*sin(hold)
	polygon(x, y, border=COL[5,4])
	x    <- rep(NA, N[i])
	y    <- rep(NA, N[i])
	for(j in 1:N[i]){
		inside <- FALSE
		while(!inside){
			xx <- runif(1, -R[i], R[i])
			yy <- runif(1, -R[i], R[i])
			if(sqrt(xx^2 + yy^2) < R[i]){
				inside <- TRUE
				x[j]   <- xx
				y[j]   <- yy
			}
		}
	}
	type <- sample(1, N[i], TRUE)
	pch  <- PCH[type]
	col  <- COL[type]
	x    <- X[i]+x
	y    <- Y[i]+y
	points(x, y, pch=pch, col=col)
	these  <- sample(N[i], 3)
	points(x[these], y[these], pch=20, cex=0.8, col=colSamp)
	points(x[these], y[these], cex=1.4, col=colSamp)
}
text(X, Y+above*(R+0.01), paste("Stratum", 1:6), pos=2+above, cex=1.1)

#=====> Cluster <=====#
PCH <- rep(c(1, 3, 20)[3], 3)
plot(0, xlim=c(0,2), ylim=0:1, type='n', axes=FALSE)
box()
X    <- c(0.17, 0.19, 0.52, 0.85, 1, 1.22, 1.49, 1.79, 1.85)
Y    <- c(0.3, 0.75, 0.5, 0.26, 0.73, 0.38, 0.67, 0.3, 0.8)
locs <- c(1, 4, 5, 3, 6, 2)
gps  <- list()
N    <- c(18, 12, 11, 13, 16, 14, 15, 16, 12)
R    <- sqrt(N/500)
p    <- matrix(c(6,  8, NA,
				 4,  4, NA,
				 4,  4, NA,
				 5,  4, NA,
				 8,  5, NA,
				 4,  5, NA,
				 5,  9, NA,
				 6,  5, NA,
				 4,  5, NA), 3)
p[3,] <- N - p[1,] - p[2,]
above <- c(-1, 1, 1, 1, 1, -1, 1, 1, 1)
for(i in 1:length(X)){
	hold <- seq(0, 2*pi, len=99)
	x    <- X[i] + (R[i]+0.02)*cos(hold)
	y    <- Y[i] + (R[i]+0.02)*sin(hold)
	polygon(x, y, border=COL[5,4])
	if(i %in% c(3, 4, 8)){
		polygon(x, y, border=COL[4], lty=2, lwd=1.5)
	}
	x    <- rep(NA, N[i])
	y    <- rep(NA, N[i])
	for(j in 1:N[i]){
		inside <- FALSE
		while(!inside){
			xx <- runif(1, -R[i], R[i])
			yy <- runif(1, -R[i], R[i])
			if(sqrt(xx^2 + yy^2) < R[i]){
				inside <- TRUE
				x[j]   <- xx
				y[j]   <- yy
			}
		}
	}
	type <- sample(1, N[i], TRUE)
	pch  <- PCH[type]
	col  <- COL[type]
	x    <- X[i]+x
	y    <- Y[i]+y
	points(x, y, pch=pch, col=col)
	these  <- sample(N[i], 6)
	if(i %in% c(3, 4, 8)){
	points(x[these], y[these], pch=20, cex=0.8, col=colSamp)
	points(x[these], y[these], cex=1.4, col=colSamp)
		#points(x[these], y[these], pch=19, col=colSamp)
	}
}
text(X, Y+above*(R+0.01), paste("Cluster", 1:length(X)), pos=2+above, cex=1.1)

dev.off()

================================================
FILE: ch_intro_to_data/figures/samplingMethodsFigure/samplingMethodsFigures.R
================================================
library(openintro)
source("SamplingMethodsFunctions.R")
data(COL)
set.seed(4)
N <- 108
n <- 18
colSamp <- COL[4]
PCH <- rep(c(1, 3, 20)[3], 3)

col <- rep(COL[1], N)
pch <- PCH[match(col, COL)]

# BuildSystematic()


set.seed(4)
myPDF("simple_stratified.pdf", 7.4, 7.5,
      mar = rep(0.5,4), mfrow = c(2,1))
BuildSRS()
BuildStratified()
dev.off()


set.seed(4)
myPDF("cluster_multistage.pdf", 7.4, 7.5,
      mar = rep(0.5,4), mfrow = c(2,1))
BuildCluster()
BuildMultistage()
dev.off()


================================================
FILE: ch_intro_to_data/figures/variables/sunCausesCancer.R
================================================
library(openintro)
data(COL)

myPDF("sunCausesCancer.pdf", 4.7, 1.2,
      mar = rep(0, 4))
plot(c(-0.05, 1.2),
     c(0.39, 1),
     type = 'n',
     axes = FALSE)
text(0.59, 0.89, 'sun exposure')
rect(0.4, 0.8, 0.78, 1)
text(0.3, 0.49, 'use sunscreen')
rect(0.1, 0.4, 0.48, 0.6)
arrows(0.49, 0.78, 0.38, 0.62,
       length = 0.08, lwd = 1.5)
text(0.87, 0.5, 'skin cancer')
rect(0.71,0.4, 1.01, 0.6)
arrows(0.67, 0.78, 0.8, 0.62,
       length = 0.08, lwd = 1.5)

arrows(0.5, 0.5, 0.69, 0.5,
       length = 0.08, col = COL[6,2])
text(0.595, 0.565, "?",
     cex = 1.5, col = COL[4])
dev.off()


================================================
FILE: ch_intro_to_data/figures/variables/variables.R
================================================
library(openintro)
data(COL)

myPDF('variables.pdf', 4.2, 1.5, mar = rep(0,4))
plot(c(-0.15, 1.3),
     c(0, 1),
     type = 'n',
     axes = FALSE)

text(0.6, 0.9, 'all variables')
rect(0.4, 0.8, 0.8, 1)

text(0.25, 0.5, 'numerical')
rect(0.1, 0.4, 0.4, 0.6)
arrows(0.45, 0.78, 0.34, 0.62, length = 0.08)

text(0.9, 0.5, 'categorical')
rect(0.73, 0.4, 1.07, 0.6)
arrows(0.76, 0.78, 0.85, 0.62, length = 0.08)

text(0, 0.1, 'continuous')
rect(-0.17, 0, 0.17, 0.2)
arrows(0.13, 0.38, 0.05, 0.22, length = 0.08)

text(0.39, 0.1, 'discrete')
rect(0.25, 0, 0.53, 0.2)
arrows(0.35, 0.38, 0.4, 0.22, length = 0.08)

text(0.77, 0.14, 'nominal', col = COL[6], cex = 0.7)
text(0.77, 0.05, '(unordered categorical)',
     col = COL[6],
     cex = 0.5)
rect(0.6, 0, 0.94, 0.2, border = COL[6])
arrows(0.82, 0.38, 0.77, 0.22, length = 0.08, col = COL[6])

text(1.14, 0.14, 'ordinal', col = COL[6], cex = 0.7)
text(1.14, 0.05, '(ordered categorical)', col = COL[6], cex = 0.5)
rect(0.98, 0, 1.3, 0.2, border = COL[6])
arrows(1.03, 0.38, 1.11, 0.22, length = 0.08, col = COL[6])

dev.off()


================================================
FILE: ch_probability/TeX/ch_probability.tex
================================================
\begin{chapterpage}{Probability}
  \chaptertitle{Probability}
  \label{probability}
  \label{ch_probability}
  \chaptersection{basicsOfProbability}
  \chaptersection{conditionalProbabilitySection}
  \chaptersection{smallPop}
  \chaptersection{randomVariablesSection}
  \chaptersection{contDist}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_probability}

\index{probability|(}

\chapterintro{Probability forms the foundation of statistics,
  and you're probably \mbox{already}
  aware of many of the ideas presented in this chapter.
  However, formalization of probability concepts is likely
  new for most readers. \\

  \noindent%
  While this chapter provides a theoretical foundation
  for the ideas in later chapters and provides a path
  to a deeper understanding,
  mastery of the concepts introduced in this chapter
  is not required for applying the
  methods introduced in the rest of this book.}

%  This chapter provides a theoretical foundation for
%  the ideas introduced in later chapters.
%  However, this chapter is not strictly required to
%  understand or apply the methods introduced in the
%  rest of this book.}




\section{Defining probability}
\label{basicsOfProbability}

Statistics is based on probability,
and while probability is not required for the applied
techniques in this book, it may help you gain a deeper
understanding of the methods and set a better foundation
for future courses.


\subsection{Introductory examples}

Before we get into technical ideas, let's walk through
some basic examples that may feel more familiar.

\begin{examplewrap}
\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}
If 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, $1/6$.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{What is the chance of getting a \resp{1} or \resp{2} in the next roll?}\label{probOf1Or2}
\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 $2/6 = 1/3$.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\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}
100\%. The outcome must be one of these numbers.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{What is the chance of not rolling a \resp{2}?}\label{probNot2}
Since 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 $5/6$.

Alternatively, 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 $5/6$.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\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}
If $16.\bar{6}$\% 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 $1/36$.
\end{nexample}
\end{examplewrap}


\D{\newpage}

\subsection{Probability}

\index{random process|(}

We 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}.
\begin{center}
\begin{tabular}{lll}
Roll a die &$\rightarrow$ & \resp{1}, \resp{2}, \resp{3}, \resp{4}, \resp{5}, or \resp{6} \\
Flip a coin &$\rightarrow$ & \resp{H} or \resp{T} \\
\end{tabular}
\end{center}
Rolling a die or flipping a coin is a seemingly random process and each gives rise to an outcome. 

\begin{onebox}{Probability}
The \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.
\end{onebox}

Probability 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\%.

Probability 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}. 

\begin{figure}[h]
\centering
\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}
\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.}
\label{dieProp}
\end{figure}

\begin{onebox}{Law of Large Numbers}
As more observations are collected, the proportion $\hat{p}_n$ of occurrences with a particular outcome converges to the probability $p$ of that outcome.
\end{onebox}

Occasionally 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.

Above we write $p$ as the probability of rolling a \resp{1}. We can also write this probability as
\begin{align*}
P(\text{rolling a \resp{1}})
\end{align*}
As 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}$)$. 

\begin{exercisewrap}
\begin{nexercise} \label{randomProcessExercise}
Random 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
\end{nexercise}
\end{exercisewrap}
\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}.}

What 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. 

%\begin{tipBox}{\tipBoxTitle{Modeling a process as random}
%It can be helpful to model a process as random even if it is not truly random.}
%\end{tipBox}

\index{random process|)}

\subsection{Disjoint or mutually exclusive outcomes}

\index{disjoint|(}
\index{mutually exclusive|(}

Two 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.

Calculating 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:
\begin{align*}
P(\text{\resp{1} or \resp{2}})
  = P(\text{\resp{1}})+P(\text{\resp{2}})
  = 1/6 + 1/6
  = 1/3
\end{align*}
What 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:
\begin{align*}
&P(\text{\resp{1} or \resp{2} or \resp{3} or \resp{4}
    or \resp{5} or \resp{6}}) \\
  &\quad = P(\text{\resp{1}})+P(\text{\resp{2}})
      + P(\text{\resp{3}})+P(\text{\resp{4}})
      + P(\text{\resp{5}})+P(\text{\resp{6}}) \\
  &\quad = 1/6 + 1/6 + 1/6 + 1/6 + 1/6 + 1/6
  = 1
\end{align*}
The \term{Addition Rule} guarantees the accuracy of this approach when the outcomes are disjoint. 

\begin{onebox}{Addition Rule of disjoint outcomes}
  If $A_1$ and $A_2$ represent two disjoint outcomes,
  then the probability that one of them occurs is given by
  \begin{align*}
  P(A_1\text{ or } A_2) = P(A_1) + P(A_2)
  \end{align*}
  If there are many disjoint outcomes $A_1$, ..., $A_k$,
  then the probability that one of these outcomes will occur is
  \begin{align*}
  P(A_1) + P(A_2) + \cdots + P(A_k)
  \end{align*}
\end{onebox}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
We 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
\end{nexercise}
\end{exercisewrap}
\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}$) = \frac{1}{6} + \frac{1}{6} + \frac{1}{6} = \frac{3}{6} = \frac{1}{2}$}

\index{data!loans|(}
\begin{exercisewrap}
\begin{nexercise}
In the \data{loans} data set in Chapter~\ref{ch_summarizing_data},
the \var{homeownership} variable described whether the borrower
rents, has a mortgage, or owns her property.
Of the 10,000 borrowers, 3858 rented, 4789 had a mortgage,
and 1353 owned their home.\footnotemark{}
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item
    Are the outcomes \resp{rent}, \resp{mortgage},
    and \resp{own} disjoint?
\item
    Determine the proportion of loans with value \resp{mortgage}
    and \resp{own} separately.
\item
    Use the Addition Rule for disjoint outcomes to compute
    the probability a randomly selected loan from the data set
    is for someone who has a mortgage or owns her
    home.
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~Yes. Each loan is categorized in only one
level of \var{homeownership}.
(b)~Mortgage: $\frac{4789}{10000} = 0.479$.
Own: $\frac{1353}{10000} = 0.135$.
(c)~$P($\resp{mortgage} or \resp{own}$) = P($\resp{mortgage}$) + P($\resp{own}$) = 0.479 + 0.135 = 0.614$.}
\index{data!loans|)}

\index{event|(}

Data 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}.

\begin{figure}[hhh]
  \centering
  \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}
  \caption{Three events, $A$, $B$, and $D$, consist of
      outcomes from rolling a die.
      $A$ and $B$ are disjoint since they do not have
      any outcomes in common.}
  \label{disjointSets}
\end{figure}

The 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:
\begin{align*}
P(A\text{ or }B) = P(A) + P(B) = 1/3 + 1/3 = 2/3
\end{align*}

\begin{exercisewrap}
\begin{nexercise}
(a) Verify the probability of event $A$, $P(A)$,
is $1/3$ using the Addition Rule.
(b)~Do the same for event $B$.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~$P(A) = P($\resp{1} or \resp{2}$)
    = P($\resp{1}$) + P($\resp{2}$)
    = \frac{1}{6} + \frac{1}{6}
    = \frac{2}{6}
    = \frac{1}{3}$.
    (b)~Similarly, $P(B) = 1/3$.}

\begin{exercisewrap}
\begin{nexercise} \label{exerExaminingDisjointSetsABD}
(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
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise}
In 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{Since $B$ and $D$ are disjoint events, use the Addition Rule: $P(B$ or $D) = P(B) + P(D) = \frac{1}{3} + \frac{1}{3} = \frac{2}{3}$.}

\index{event|)}
\index{disjoint|)}
\index{mutually exclusive|)}


\D{\newpage}

\subsection{Probabilities when events are not disjoint}

Let'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.}

\begin{figure}[h]
\centering
\begin{tabular}{lll lll lll lll l}
\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$\clubsuit$}  \\
\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$\diamondsuit$} \\
\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$\heartsuit$} \\
\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$\spadesuit$}
\end{tabular}
\caption{Representations of the 52 unique cards in a deck.}
\label{deckOfCards}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
(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
\end{nexercise}
\end{exercisewrap}
\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$) = \frac{12}{52} = \frac{3}{13} = 0.231$.}

\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. $10/52 = 0.1923$).

\begin{figure}[h]
\centering
\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}
\caption{A Venn diagram for diamonds and face cards.}
\label{cardsDiamondFaceVenn}
\end{figure}

Let $A$ 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:
\begin{align*}
P(A) + P(B)
  = P({\color{redcards}\diamondsuit}) + P(\text{face card})
  = 13/52 + 12/52
\end{align*}

\D{\newpage}

\noindent%
However, the three cards that are in both events were counted twice, once in each probability. We must correct this double counting:
\begin{align*}
P(A\text{ or } B)
  &= P({\color{redcards}\diamondsuit}\text{ or face card}) \\
  &= P({\color{redcards}\diamondsuit}) + P(\text{face card})
      - P({\color{redcards}\diamondsuit}\text{ and face card}) \\
  &= 13/52 + 12/52 - 3/52 \\
  &= 22/52 = 11/26
\end{align*}
This equation is an example of the \term{General Addition Rule}. 

\begin{onebox}{General Addition Rule}
  If $A$ and $B$ are any two events, disjoint or not, then
  the probability that at least one of them will occur is
  \begin{align*}
  P(A\text{ or }B) = P(A) + P(B) - P(A\text{ and }B)
  \end{align*}
  where $P(A$ and $B)$ is the probability that both events occur.
\end{onebox}

\begin{tipBox}{\tipBoxTitle{``or'' is inclusive}
When 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.}
\end{tipBox}

\begin{exercisewrap}
\begin{nexercise}
(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
\end{nexercise}
\end{exercisewrap}
\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.}

\index{data!loans|(}

\begin{exercisewrap}
\begin{nexercise}\label{emailSpamNumberVennExer}
% library(openintro); d <- loans_full_schema; table(d[,c("application_type", "homeownership")]); table(d[,c("application_type")]); table(d[,c("homeownership")])
In the \data{loans} data set describing 10,000 loans,
1495 loans were from joint applications
(e.g. a couple applied together),
4789 applicants had a mortgage,
and 950 had both of these characteristics.
Create a Venn diagram for this setup.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{%
  \begin{minipage}[t]{0.65\textwidth}
  Both the counts and corresponding {\color{oiB}probabilities}
  (e.g. $3839/10000 = 0.384$) are shown.
  Notice that the number of loans represented in the left
  circle corresponds to $3839 + 950 = 4789$, and the number
  represented in the right circle is $950 + 545 = 1495$.
  \end{minipage}\ %
  \begin{minipage}[c]{0.3\textwidth}
  \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}
  \end{minipage}}

\begin{exercisewrap}
\begin{nexercise}
(a)~Use your Venn diagram from Guided Practice~\ref{emailSpamNumberVennExer} to determine the
probability a randomly drawn loan from the \data{loans}
data set is from a joint application where the couple had
a mortgage.
(b)~What is the probability that the loan had either of
these attributes?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{%
  (a)~The solution is represented by the intersection of
  the two circles: 0.095.
  (b)~This is the sum of the three disjoint probabilities shown
  in the circles: $0.384 + 0.095 + 0.055 = 0.534$
  (off by 0.001 due to a rounding error).}

\index{data!loans|)}


\D{\newpage}

\subsection{Probability distributions}

A \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. 

\begin{figure}[h] \small
\centering
\begin{tabular}{l ccc ccc ccc cc}
  \hline
  \ \vspace{-3mm} \\
Dice sum\vspace{0.3mm} & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12  \\
Probability & $\frac{1}{36}$ & $\frac{2}{36}$ & $\frac{3}{36}$ & $\frac{4}{36}$ & $\frac{5}{36}$ & $\frac{6}{36}$ & $\frac{5}{36}$ & $\frac{4}{36}$ & $\frac{3}{36}$ & $\frac{2}{36}$ & $\frac{1}{36}$\vspace{1mm} \\
   \hline
\end{tabular}
\caption{Probability distribution for the sum of two dice.}
\label{diceProb}
\end{figure}

\begin{onebox}{Rules for probability distributions}
A probability distribution is a list of the possible outcomes with corresponding probabilities that satisfies three rules: \vspace{-2mm}
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item The outcomes listed must be disjoint.
\item Each probability must be between 0 and 1.
\item The probabilities must total 1. \vspace{1mm}
\end{enumerate}
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}\label{usHouseholdIncomeDistsExercise}
Figure~\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
\end{nexercise}
\end{exercisewrap}
\footnotetext{The probabilities of (a) do not sum to~1.
    The second probability in (b) is negative.
    This leaves~(c), which sure enough satisfies the
    requirements of a distribution.
    One of the three was said to be the actual
    distribution of US household incomes,
    so it must be~(c).}

\begin{figure}[h]
\centering
\begin{tabular}{r | cc cc}
  \hline
Income Range & \$0-25k & \$25k-50k & \$50k-100k & \$100k+ \\
  \hline
(a)\hspace{0.2mm}	 & 0.18 & 0.39 & 0.33 & 0.16 \\
(b)				 & 0.38 & -0.27 & 0.52 & 0.37 \\
(c)\hspace{0.2mm}	 & 0.28 & 0.27 & 0.29 & 0.16 \\
  \hline
\end{tabular}
\caption{Proposed distributions of US household incomes (Guided Practice~\ref{usHouseholdIncomeDistsExercise}).}
\label{usHouseholdIncomeDists}
\end{figure}

Chapter~\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}.}
The probability distribution for the sum of two dice is shown in Figure~\ref{diceProb} and plotted in Figure~\ref{diceSumDist}.

\begin{figure}[h]
  \centering
  \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}
  \caption{The probability distribution of US household income.}
  \label{usHouseholdIncomeDistBar}
\end{figure}

\begin{figure}
  \centering
  \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}
  \caption{The probability distribution of the sum of two dice.}
  \label{diceSumDist}
\end{figure}

In 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}.

\subsection{Complement of an event}

Rolling 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.

Let $D=\{$\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 $S$. 

\begin{figure}[hht]
  \centering
  \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}
  \caption{Event $D=\{$\resp{2}, \resp{3}$\}$ and its complement,
      $D^c = \{$\resp{1}, \resp{4}, \resp{5}, \resp{6}$\}$.
      $S$~represents the sample space, which is the set of
      all possible outcomes.}
  \label{complementOfD}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
(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
\end{nexercise}
\end{exercisewrap}
\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, $P(D) + P(D^c) = 1$.}

\begin{exercisewrap}
\begin{nexercise}
Events $A=\{$\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
\end{nexercise}
\end{exercisewrap}
\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 $P(B) + P(B^c) = 1$.}

\D{\newpage}

A 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
\begin{align*}
P(A\text{ or }A^c) = 1
\end{align*}
That 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):
\begin{align*}
P(A\text{ or }A^c) = P(A) + P(A^c)
\end{align*}
Combining the last two equations yields a very useful
relationship between the probability of an event and
its complement.

\begin{onebox}{Complement}
  The complement of event $A$ is denoted $A^c$, and $A^c$
  represents all outcomes not in~$A$. $A$ and $A^c$ are
  mathematically related:
  \begin{align*}
  P(A) + P(A^c) = 1, \quad\text{i.e.}\quad P(A) = 1-P(A^c)
  \end{align*}
\end{onebox}

In 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.

\begin{exercisewrap}
\begin{nexercise}
Let $A$ 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
\end{nexercise}
\end{exercisewrap}
\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}$) = 1 - 1/36 = 35/36$.}

\begin{exercisewrap}
\begin{nexercise}
Find the following probabilities for rolling two dice:\footnotemark
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item The sum of the dice is \emph{not} \resp{6}. 
\item The sum is at least \resp{4}.
    That is, determine the probability of the event
    $B = \{$\resp{4}, \resp{5}, ..., \resp{12}$\}$.
\item The sum is no more than \resp{10}.
    That is, determine the probability of the event
    $D=\{$\resp{2}, \resp{3}, ..., \resp{10}$\}$.
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~First find $P($\resp{6}$)=5/36$, then use the complement: $P($not \resp{6}$) = 1 - P($\resp{6}$) = 31/36$.

(b)~First find  the complement, which requires much less effort: $P($\resp{2} or \resp{3}$)=1/36+2/36=1/12$. Then calculate $P(B) = 1-P(B^c) = 1-1/12 = 11/12$.

(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 $P(D) = 1 - P(D^c) = 11/12$.}


\subsection{Independence}
\label{probabilityIndependence}

Just 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.

Example~\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. 

\begin{figure}[hht]
\centering
\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}
\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}.}
\label{indepForRollingTwo1s}
\end{figure}

\begin{examplewrap}
\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}
The 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:
{\begin{align*}
P(white=\text{\small\resp{1} and } red=\text{\small\resp{1} and } blue=\text{\small\resp{1}})
	&= P(white=\text{\small\resp{1}})\times P(red=\text{\small\resp{1}})\times P(blue=\text{\small\resp{1}}) \\
	&= (1/6)\times (1/6)\times (1/6)
	= 1/216
\end{align*}} \vspace{-7mm}
\end{nexample}
\end{examplewrap}

Example~\ref{threeDice} illustrates what is called the Multiplication Rule for independent processes. 

\begin{onebox}{Multiplication Rule for independent processes}
  \index{Multiplication Rule|textbf}%
  If $A$ and $B$ represent events from two different and
  independent processes, then the probability that both $A$
  and $B$ occur can be calculated as the product of their
  separate probabilities:
  \begin{align*}
  P(A \text{ and }B) = P(A) \times  P(B)
  \end{align*}
  Similarly, if there are $k$ events $A_1$, ..., $A_k$
  from $k$ independent processes, then the probability
  they all occur is
  \begin{align*}
  P(A_1) \times  P(A_2)\times  \cdots \times  P(A_k)
  \end{align*}\vspace{-6mm}
\end{onebox}

\begin{exercisewrap}
\begin{nexercise} \label{ex2Handedness}
About 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
\end{nexercise}
\end{exercisewrap}
\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: $0.09\times 0.09 = 0.0081$.

(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 $0.91\times 0.91 = 0.8281$.}

\begin{exercisewrap}
\begin{nexercise}
\label{ex5Handedness}%
Suppose 5 people are selected at random.\footnotemark\vspace{-1.5mm}
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item[(a)] What is the probability that all are right-handed?
\item[(b)] What is the probability that all are left-handed?
\item[(c)] What is the probability that not all of the people are right-handed?
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\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:
\begin{align*}
P(\text{all five are \resp{RH}})
&= P(\text{first = \resp{RH}, second = \resp{RH}, ..., fifth = \resp{RH}}) \\
&= P(\text{first = \resp{RH}})\times P(\text{second = \resp{RH}})\times  \dots \times P(\text{fifth = \resp{RH}}) \\
&= 0.91\times 0.91\times 0.91\times 0.91\times 0.91 = 0.624
\end{align*}

(b)~Using the same reasoning as in~(a), $0.09\times 0.09\times 0.09\times 0.09\times 0.09 = 0.0000059$

(c)~Use the complement, $P($all five are \resp{RH}$)$, to answer this question:
\begin{align*}
P(\text{not all \resp{RH}})
	= 1 - P(\text{all \resp{RH}})
	= 1 - 0.624 = 0.376
\end{align*}}

Suppose the variables \var{handedness} and
\var{sex} are independent,
i.e. knowing someone's \var{sex} provides no useful
information about their \var{handedness} and vice-versa.
Then we can compute whether a randomly selected person is
right-handed and female\footnote{The actual proportion of
  the U.S. population that is \resp{female} is about 50\%,
  and so we use 0.5 for the probability of sampling a woman.
  However, this probability does differ in other countries.}
using the Multiplication Rule:
\begin{align*}
P(\text{right-handed and female})
    &= P(\text{right-handed}) \times  P(\text{female}) \\
    &= 0.91 \times  0.50 = 0.455
\end{align*}


\begin{exercisewrap}
\begin{nexercise}
Three people are selected at random.\footnotemark \vspace{-1.5mm}
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item[(a)] What is the probability that the first person is male and right-handed?
\item[(b)] What is the probability that the first two people are male and right-handed?.
\item[(c)] What is the probability that the third person is female and left-handed?
\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?
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\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.}

Sometimes 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
$P(A \text{ and }B) = P(A) \times  P(B)$.

\begin{examplewrap}
\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?}
The 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 $1/52$.
We check whether $P(A \text{ and }B) = P(A) \times  P(B)$
is satisfied:
\begin{align*}
P({\color{redcards}\heartsuit})\times P(\text{ace}) = \frac{1}{4}\times \frac{1}{13} = \frac{1}{52} 
					= P({\color{redcards}\heartsuit}\text{ and ace})
\end{align*}
Because the equation holds, the event that the card is a heart and the event that the card is an ace are independent events.
\end{nexample}
\end{examplewrap}


{\input{ch_probability/TeX/defining_probability.tex}}




%_________________
\section{Conditional probability}
\label{conditionalProbabilitySection}

There can be rich relationships between two or more
variables that are useful to understand.
For example a car insurance company will consider
information about a person's driving history to assess
the risk that they will be responsible for an accident.
These types of relationships are the realm of conditional
probabilities.


\subsection{Exploring probabilities with a contingency table}

\index{data!photo\_classify|(}

\newcommand{\fashN}{1822}
% In order of ML, then Human
\newcommand{\fashYY}{197}
\newcommand{\fashYN}{22}
\newcommand{\fashYA}{219}
\newcommand{\fashNY}{112}
\newcommand{\fashNN}{1491}
\newcommand{\fashNA}{1603}
\newcommand{\fashAY}{309}
\newcommand{\fashAN}{1513}
\newcommand{\fashAA}{\fashN{}}
%\newcommand{\fashPYY}{}
%\newcommand{\fashPYN}{}
%\newcommand{\fashPNY}{}
%\newcommand{\fashPNN}{}
%\newcommand{\fashPYA}{0.12}
%\newcommand{\fashPNA}{0.88}
%\newcommand{\fashPAY}{}
%\newcommand{\fashPAN}{}
%\newcommand{\fashPYCY}{}
%\newcommand{\fashPYCN}{}
%\newcommand{\fashPNCY}{}
%\newcommand{\fashPNCN}{}
\newcommand{\fashCYPY}{0.96}
\newcommand{\fashCYPN}{0.04}
\newcommand{\fashCNPY}{0.07}
\newcommand{\fashCNPN}{0.93}

The \data{photo\us{}classify} data set represents
a classifier a sample of \fashN{} photos from a photo sharing website.
Data scientists have been working to improve a classifier for
whether the photo is about fashion or not, and these 1822 photos
represent a test for their classifier.
Each photo gets two classifications:
the first is called \var{mach\us{}learn} and gives
a classification from a machine
learning~(ML)\index{machine learning (ML)} system of
either \resp{pred\us{}fashion} or \resp{pred\us{}not}.
Each of these \fashN{} photos have also been classified carefully
by a team of people, which we take to be the source of truth;
this variable is called \var{truth} and takes values
\resp{fashion} and \resp{not}.
Figure~\ref{contTableOfFashionPhotos} summarizes the results.

\begin{figure}[ht]
\centering
\begin{tabular}{ll ccc rr}
&& \multicolumn{2}{c}{\var{truth}} & \hspace{1cm} &  \\
\cline{3-4}
&& \resp{fashion} & \resp{not} & Total  \\
\cline{2-5}
& \resp{pred\us{}fashion} &
    \fashYY{} & \fashYN{} & \fashYA{} \\
\raisebox{1.5ex}[0pt]{\var{mach\us{}learn}}
    & \resp{pred\us{}not} \hspace{0.5cm} &
    \fashNY{} & \fashNN{} & \fashNA{}   \\
\cline{2-5}
& Total & \fashAY{} & \fashAN{} & \fashN{} \\
\end{tabular}
\caption{Contingency table summarizing the
    \data{photo\us{}classify} data set.}
\label{contTableOfFashionPhotos}
\end{figure}
% library(openintro); table(photo_classify)

\begin{figure}[ht]
  \centering
  \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}
  \caption{A Venn diagram using boxes for the
      \data{photo\us{}classify} data set.}
  \label{photoClassifyVenn}
\end{figure}

\begin{examplewrap}
\begin{nexample}{If a photo is actually about fashion,
    what is the chance the ML classifier correctly identified
    the photo as being about fashion?}
  We can estimate this probability using the data.
  Of the \fashAY{} fashion photos,
  the ML algorithm correctly classified \fashYY{} of the photos:
\begin{align*}
P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}
    given \var{truth} is \resp{fashion}})
  = \frac{\fashYY{}}{\fashAY{}}
  = 0.638
\end{align*}
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{We sample a photo from the data set
    and learn the ML algorithm predicted this photo
    was not about fashion.
    What is the probability that it was incorrect and
    the photo is about fashion?}
  If the ML classifier suggests a photo is not about fashion,
  then it comes from the second row in the data set.
  Of~these \fashNA{} photos, \fashNY{} were actually
  about fashion:
\begin{align*}
P(\text{\var{truth} is \resp{fashion}
    given \var{mach\us{}learn} is \resp{pred\us{}not}})
  = \frac{\fashNY{}}{\fashNA{}}
  = 0.070
\end{align*}
\end{nexample}
\end{examplewrap}

\subsection{Marginal and joint probabilities}
\label{marginalAndJointProbabilities}

\index{marginal probability|(}
\index{joint probability|(}

Figure~\ref{contTableOfFashionPhotos} includes row and
column totals for each variable separately in the
\data{photo\us{}classify} data set.
These totals represent
\termsub{marginal probabilities}{marginal probability}
for the sample, which are the probabilities based on a
single variable without regard to any other variables.
For instance, a probability based solely on the
\var{mach\us{}learn} variable is a marginal probability:
\begin{align*}
P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}})
    = \frac{\fashYA{}}{\fashN{}}
    = 0.12
\end{align*}
A probability of outcomes for two or more variables
or processes is called a
\termsub{joint \mbox{probability}}{joint probability}:
\begin{align*}
P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}
    and \var{truth} is \resp{fashion}})
  = \frac{\fashYY{}}{\fashN{}}
  = 0.11
\end{align*}
It is common to substitute a comma for ``and'' in a joint
probability, although using either the word ``and'' or a
comma is acceptable:
\begin{center}
$P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion},
    \var{truth} is \resp{fashion}})$ \\[2mm]
means the same thing as \\[2mm]
$P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}
    and \var{truth} is \resp{fashion}})$
\end{center}

\begin{onebox}{Marginal and joint probabilities}
  If a probability is based on a single variable,
  it is a \emph{\hiddenterm{marginal probability}}.
  The probability of outcomes for two or more variables
  or processes is called a \emph{\hiddenterm{joint probability}}.
\end{onebox}

We use \term{table proportions} to summarize joint probabilities
for the \data{photo\us{}classify} sample.
These proportions are computed by dividing each count in
Figure~\ref{contTableOfFashionPhotos} by the table's total,
\fashN{}, to obtain the proportions in
Figure~\ref{photoClassifyProbTable}.
The joint probability distribution of the \var{mach\us{}learn}
and \var{truth} variables is shown in
Figure~\ref{photoClassifyDistribution}.

\begin{figure}[h]
\centering
\begin{tabular}{l rr r}
\hline
& \var{truth}: \resp{fashion} &
    \var{truth}: \resp{not} & Total  \\
\hline
\var{mach\us{}learn}: \resp{pred\us{}fashion} \hspace{0.5cm}
    & 0.1081 & 0.0121 & 0.1202 \\
\var{mach\us{}learn}: \resp{pred\us{}not}
    & 0.0615 & 0.8183 & 0.8798  \\
\hline
Total & 0.1696 & 0.8304 & 1.00 \\
\hline
\end{tabular}
\caption{Probability table summarizing the
    \var{photo\us{}classify} data set.}
\label{photoClassifyProbTable}
\end{figure}

\begin{figure}[h]
\centering
\begin{tabular}{l c}
  \hline
Joint outcome & Probability \\
  \hline
\var{mach\us{}learn} is \resp{pred\us{}fashion}
    and \var{truth} is \resp{fashion} & 0.1081 \\
\var{mach\us{}learn} is \resp{pred\us{}fashion}
    and \var{truth} is \resp{not} & 0.0121 \\
\var{mach\us{}learn} is \resp{pred\us{}not}
    and \var{truth} is \resp{fashion} & 0.0615 \\
\var{mach\us{}learn} is \resp{pred\us{}not}
    and \var{truth} is \resp{not} & 0.8183 \\
   \hline
Total & 1.0000 \\
\hline
\end{tabular}
\caption{Joint probability distribution for the \data{photo\us{}classify} data set.}
\label{photoClassifyDistribution}
\end{figure}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
Verify Figure~\ref{photoClassifyDistribution} represents
a probability distribution: events are disjoint,
all probabilities are non-negative, and the probabilities
sum to~1.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Each of the four outcome combination are disjoint,
   all probabilities are indeed non-negative, and the sum of
   the probabilities is $0.1081 + 0.0121 + 0.0615 + 0.8183 = 1.00$.}

We can compute marginal probabilities using joint probabilities
in simple cases.
For example, the probability a randomly selected photo from the
data set is about fashion is found by summing the outcomes where
\var{truth} takes value \resp{fashion}:%
\index{marginal probability|)}\index{joint probability|)}
\newcommand{\ultruthfashion}[0]
    {\underline{\var{truth} is \resp{fashion}}}%
\begin{align*}
P(\text{\ultruthfashion{}})
  &= P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}
        and \ultruthfashion{}}) \\
    & \qquad + P(\text{\var{mach\us{}learn} is \resp{pred\us{}not}
        and \ultruthfashion{}}) \\
  &= 0.1081 + 0.0615 \\
  &= 0.1696
\end{align*}


\subsection{Defining conditional probability}

\index{conditional probability|(}

The ML classifier predicts whether a photo is about fashion,
even if it is not perfect.
We would like to better understand how to use information
from a variable like \var{mach\us{}learn} to improve our
probability estimation of a second variable, which in this
example is \var{truth}.

The probability that a random photo from the data set is about
fashion is about 0.17.
If we knew the machine learning classifier predicted the
photo was about fashion, could we get a better estimate of the
probability the photo is actually about fashion?
Absolutely.
To do so, we limit our view to only those \fashYA{} cases
where the ML classifier predicted that the photo was about
fashion and look at the fraction where the photo was actually
about fashion:
\begin{align*}
P(\text{\var{truth} is \resp{fashion} given
    \var{mach\us{}learn} is \resp{pred\us{}fashion}})
  = \frac{\fashYY{}}{\fashYA{}}
  = 0.900
\end{align*}
We call this a \term{conditional probability} because
we computed the probability under a condition:
the ML classifier prediction said the photo was about fashion.

There are two parts to a conditional probability,
the \term{outcome of interest} and the \term{condition}.
It is useful to think of the condition as information we know
to be true, and this information usually can be described as
a known outcome or~event.
We generally separate the text inside our probability notation
into the outcome of interest and the condition with a
vertical bar:
\begin{align*}
&& P(\text{\var{truth} is \resp{fashion} given
    \var{mach\us{}learn} is \resp{pred\us{}fashion}}) \\
&& \quad = P(\text{\var{truth} is \resp{fashion}\ }|
    \text{\ \var{mach\us{}learn} is \resp{pred\us{}fashion}})
  = \frac{\fashYY{}}{\fashYA{}}
  = 0.900
\end{align*}
The vertical bar ``$|$'' is read as \emph{given}.

\D{\newpage}

In the last equation, we computed the probability a photo
was about fashion based on the condition that the ML algorithm
predicted it was about fashion as a fraction:
\begin{align*}
& P(\text{\var{truth} is \resp{fashion}\ }|
    \text{\ \var{mach\us{}learn} is \resp{pred\us{}fashion}}) \\
  &\quad = \frac{\text{\# cases where \var{truth} is \resp{fashion}
       and \var{mach\us{}learn} is \resp{pred\us{}fashion}}}
     {\text{\# cases where \var{mach\us{}learn} is \resp{pred\us{}fashion}}} \\
  &\quad = \frac{\fashYY{}}{\fashYA{}}
      = 0.900
\end{align*}
We considered only those cases that met the condition,
\var{mach\us{}learn} is \resp{pred\us{}fashion}, and then
we computed the ratio of those cases that satisfied our
outcome of interest, photo was actually about fashion.

Frequently, marginal and joint probabilities are provided
instead of count data.
For example, disease rates are commonly listed in percentages
rather than in a count format.
We would like to be able to compute conditional probabilities
even when no counts are available, and we use the last equation
as a template to understand this technique.

We considered only those cases that satisfied the condition,
where the ML algorithm predicted fashion.
Of these cases, the conditional probability was the
fraction representing the outcome of interest, that the
photo was about fashion.
Suppose we were provided only the information in
Figure~\ref{photoClassifyProbTable}, i.e. only probability data.
Then if we took a sample of 1000 photos, we would anticipate
about 12.0\% or $0.120\times 1000 = 120$ would be predicted to be
about fashion (\var{mach\us{}learn} is \resp{pred\us{}fashion}).
Similarly, we would expect about 10.8\% or
$0.108\times 1000 = 108$ to meet both the information criteria
and represent our outcome of interest.
Then the conditional probability can be computed as
\begin{align*}
&P(\text{\var{truth} is \resp{fashion}}\ |\ 
    \text{\var{mach\us{}learn} is \resp{pred\us{}fashion}}) \\
  &= \frac{\text{\# (\var{truth} is \resp{fashion}
      and \var{mach\us{}learn} is \resp{pred\us{}fashion})}}
    {\text{\# (\var{mach\us{}learn} is \resp{pred\us{}fashion})}} \\
  &= \frac{108}{120}
		= \frac{0.108}{0.120}
		= 0.90
\end{align*}
Here we are examining exactly the fraction of two probabilities,
0.108 and 0.120, which we can write as
\begin{align*}
P(\text{\var{truth} is \resp{fashion} and
    \var{mach\us{}learn} is \resp{pred\us{}fashion}})
\quad\text{and}\quad
P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}}).
\end{align*}
The fraction of these probabilities is an example of the
general formula for conditional probability.

\begin{onebox}{Conditional probability}
  The conditional probability of outcome $A$
  given condition $B$ is computed as the following:
  \begin{align*}
  P(A | B) = \frac{P(A\text{ and }B)}{P(B)}
  \end{align*}
\end{onebox}

%\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
\label{fashionProbOfMLNotGivenTruthNot}%
(a) Write out the following statement in conditional
probability notation:
``\emph{The probability that the ML prediction was correct,
if the photo was about fashion}''.
Here the condition is now based on the photo's
\var{truth} status, not the ML algorithm. \\[1mm]
(b)~Determine the probability from part (a).
Table~\vref{photoClassifyProbTable} may be helpful.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a) If the photo is about fashion and the
  ML algorithm prediction was correct, then the ML algorithm
  my have a value of \resp{pred\us{}fashion}:
  \begin{align*}
  P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}}\ |
      \ \text{\var{truth} is \resp{fashion}})
  \end{align*}
  (b)~The equation for conditional probability indicates we
  should first find \\
  $P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}
    and \var{truth} is \resp{fashion}}) = 0.1081$
  and $P(\text{\var{truth} is \resp{fashion}}) = 0.1696$. \\
  Then the ratio represents the conditional probability:
  $0.1081 / 0.1696 = 0.6374$.}

\begin{exercisewrap}
\begin{nexercise}
\label{whyCondProbSumTo1}%
(a)~Determine the probability that the algorithm is incorrect
if it is known the photo is about fashion. \\[1mm]
(b)~Using the answers from part~(a) and
Guided Practice~\ref{fashionProbOfMLNotGivenTruthNot}(b),
compute
\begin{align*}
&P(\text{\var{mach\us{}learn} is \resp{pred\us{}fashion}}
    \ |\ \text{\var{truth} is \resp{fashion}}) \\
&\qquad  +\ P(\text{\var{mach\us{}learn} is \resp{pred\us{}not}}
    \ |\ \text{\var{truth} is \resp{fashion}})
\end{align*}
(c)~Provide an intuitive argument to explain why the sum
in~(b) is~1.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~This probability is
  $\frac{P(\text{\var{mach\us{}learn} is \resp{pred\us{}not},
      \var{truth} is \resp{fashion}})}
    {P(\text{\var{truth} is \resp{fashion}})}
  = \frac{0.0615}{0.1696} = 0.3626$.
  (b)~The total equals~1.
  (c)~Under the condition the photo is about fashion,
      the ML algorithm must have either predicted it was
      about fashion or predicted it was not about fashion.
      The complement still works for conditional probabilities,
      provided the probabilities are conditioned on the same
      information.}

\index{conditional probability|)}
\index{data!photo\_classify|)}


\subsection{Smallpox in Boston, 1721}

\index{data!smallpox|(}

The \data{smallpox} data set provides a sample of 6,224 individuals from the year 1721 who were exposed to smallpox in Boston.
Doctors at the time believed that inoculation, which involves exposing a person to the disease in a controlled form, could reduce the likelihood of death.

Each 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}.

\begin{figure}[h]
\centering
\begin{tabular}{ll rr r}
& & \multicolumn{2}{c}{inoculated} & \\
\cline{3-4}
& & \resp{yes} & \resp{no} & Total  \\
\cline{2-5}
		& \resp{lived}     & 238 & 5136 & 5374 \\
\raisebox{1.5ex}[0pt]{\var{result}} &  \resp{died} \hspace{0.5cm} & 6 & 844 & 850  \\
\cline{2-5}
	& Total & 244 & 5980 & 6224 \\
\end{tabular}
\caption{Contingency table for the \data{smallpox} data set.}
\label{smallpoxContingencyTable}
\end{figure}

\begin{figure}[h]
\centering
\begin{tabular}{ll rr r}
& & \multicolumn{2}{c}{inoculated} & \\
\cline{3-4}
& & \resp{yes} & \resp{no} & Total  \\
   \cline{2-5}
 & \resp{lived}     & 0.0382 & 0.8252 & 0.8634 \\
\raisebox{1.5ex}[0pt]{\var{result}} & \resp{died} \hspace{0.5cm} & 0.0010 & 0.1356  & 0.1366  \\
   \cline{2-5}
& Total & 0.0392 & 0.9608 & 1.0000 \\
\end{tabular}
\caption{Table proportions for the \data{smallpox} data, computed by dividing each count by the table total, 6224.}
\label{smallpoxProbabilityTable}
\end{figure}

%\D{\newpage}

\begin{exercisewrap}
\begin{nexercise} \label{probDiedIfNotInoculated}
Write out, in formal notation, the probability a randomly selected person who was not inoculated died from smallpox, and find this \mbox{probability.}\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$P($\var{result} = \resp{died} $|$ \var{inoculated} = \resp{no}$) = \frac{P(\text{\var{result} = \resp{died} and \var{inoculated} = \resp{no}})}{P(\text{\var{inoculated} = \resp{no}})} = \frac{0.1356}{0.9608} = 0.1411$.}

\begin{exercisewrap}
\begin{nexercise}
Determine the probability that an inoculated person died from smallpox. How does this result compare with the result of Guided Practice~\ref{probDiedIfNotInoculated}?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise}\label{SmallpoxInoculationObsExpExercise}
The 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
\end{nexercise}
\end{exercisewrap}
\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).}


\subsection{General multiplication rule}

Section~\ref{probabilityIndependence} introduced the Multiplication Rule for independent processes. Here we provide the \term{General Multiplication Rule} for events that might not be independent.

\begin{onebox}{General Multiplication Rule}
If $A$ and $B$ represent two outcomes or events, then \vspace{-1.5mm}
\begin{align*}
P(A\text{ and }B) = P(A | B)\times P(B)
\end{align*} \vspace{-6.5mm} \par
It is useful to think of $A$ as the outcome of interest and $B$ as the condition.
\end{onebox}

\noindent%
This General Multiplication Rule is simply a rearrangement of the conditional probability equation.

%\D{\newpage}

\begin{examplewrap}
\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?}
We will compute our answer using the General Multiplication Rule and then verify it using Figure~\ref{smallpoxProbabilityTable}. We want to determine
\begin{align*}
P(\text{\var{result}
    = \resp{lived} and \var{inoculated} = \resp{no}})
\end{align*}
and we are given that
\begin{align*}
P(\text{\var{result}
    = \resp{lived} }|\text{ \var{inoculated} = \resp{no}})
    &= 0.8588 %\\
&&P(\text{\var{inoculated} = \resp{no}})
    = 0.9608
\end{align*}
Among the 96.08\% of people who were not inoculated, 85.88\% survived:
\begin{align*}
P(\text{\var{result} = \resp{lived}
        and \var{inoculated} = \resp{no}})
    = 0.8588 \times 0.9608
    = 0.8251
\end{align*}
This 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).
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Use $P($\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
\end{nexercise}
\end{exercisewrap}
\footnotetext{The answer is 0.0382, which can be verified using Figure~\ref{smallpoxProbabilityTable}.}

%\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
If 97.54\% of the inoculated people lived,
what proportion of inoculated people must have died?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{onebox}{Sum of conditional probabilities}
Let $A_1$, ..., $A_k$ 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}
\begin{align*}
P(A_1|B) + \cdots + P(A_k|B) = 1
\end{align*}%
\vspace{-5.5mm} \par
The rule for complements also holds when an event and its complement are conditioned on the same information: \vspace{-1.5mm}
\begin{align*}
P(A | B) = 1 - P(A^c | B)
\end{align*}
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
Based on the probabilities computed above, does it appear that inoculation is effective at reducing the risk of death from smallpox?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.)}


%\D{\newpage}

\subsection{Independence considerations in conditional probability}

If 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.

\begin{exercisewrap}
\begin{nexercise} \label{condProbOfRollingA1AfterOne1}
Let $X$ and $Y$ represent the outcomes of rolling two dice.\footnotemark
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}
\item What is the probability that the first die, $X$, is \resp{1}?
\item What is the probability that both $X$ and $Y$ are \resp{1}?
\item Use the formula for conditional probability to compute $P(Y =$ \resp{1}$\ |\ X = $ \resp{1}$)$.
\item What is $P(Y=1)$? Is this different from the answer from part (c)? Explain.
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\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.}

We can show in Guided Practice~\ref{condProbOfRollingA1AfterOne1}(c) that the conditioning information has no influence by using the Multiplication Rule for independence processes:
\begin{align*}
P(Y=\text{\resp{1}}\ |\ X=\text{\resp{1}})
    &= \frac{P(Y=\text{\resp{1} and }X=\text{\resp{1}})}
      {P(X=\text{\resp{1}})} \\
    &= \frac{P(Y=\text{\resp{1}}) \times
        \color{oiGB}P(X=\text{\resp{1}})}
      {\color{oiGB}P(X=\text{\resp{1}})} \\
    &= P(Y=\text{\resp{1}}) \\
\end{align*}

\begin{exercisewrap}
\begin{nexercise}
Ron 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
\end{nexercise}
\end{exercisewrap}
\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}.}


\D{\newpage}

\subsection{Tree diagrams}

\index{data!smallpox|)}
\index{tree diagram|(}

\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.

The \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}.

\begin{figure}[ht]
\centering
\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}
\caption{A tree diagram of the \data{smallpox} data set.}
\label{smallpoxTreeDiagram}
\end{figure}

Tree 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:
\begin{align*}
& P(\text{\var{inoculated} = \resp{yes}
    and \var{result} = \resp{lived}}) \\
  &\quad = P(\text{\var{inoculated} = \resp{yes}})\times
      P(\text{\var{result} = \resp{lived}}|
          \text{\var{inoculated} = \resp{yes}}) \\
  &\quad = 0.0392\times 0.9754=0.0382
\end{align*}

\begin{examplewrap}
\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}
The 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:
\begin{align*}
P(\text{\var{midterm} = \resp{A} and \var{final} = \resp{A}})
  \qquad\text{and}\qquad
  P(\text{\var{final} = \resp{A}})
\end{align*}
However, 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:
\begin{center}
\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}
\end{center}
When 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.

With the tree diagram constructed, we may compute the required probabilities:
\begin{align*}
&P(\text{\var{midterm} = \resp{A} and \var{final} = \resp{A}}) = 0.0611 \\
&P(\text{\underline{\color{black}\var{final} = \resp{A}}})  \\
& \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}}}) \\
& \quad= 0.0957 + 0.0611  = 0.1568
\end{align*}
The 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:
\begin{align*}
P(\text{\var{midterm} = \resp{A}} | \text{\var{final} = \resp{A}})
  &= \frac{P(\text{\var{midterm} = \resp{A}
      and \var{final} = \resp{A}})}
    {P(\text{\var{final} = \resp{A}})} \\
  &= \frac{0.0611}{0.1568} = 0.3897
\end{align*}
The probability the student also earned an A on the midterm is about 0.39.
\end{nexample}
\end{examplewrap}

%\begin{figure}[ht]
%  \centering
%  \Figure{0.9}{testTree}
%  \caption{A tree diagram describing the \var{midterm}
%      and \var{final} variables.}
%  \label{testTree}
%\end{figure}

\begin{exercisewrap}
\begin{nexercise}
After 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{%\begin{minipage}[t]{0.27\linewidth}
(a) The tree diagram is shown to the right. \\
(b)~Identify which two joint probabilities represent
    students who passed, and add them:
    $P($passed$) = 0.7566+0.1254= 0.8820$. \\
(c)~$P($construct tree diagram $|$ passed$)
   = \frac{0.7566}{0.8820} = 0.8578$. \\ %\vspace{15mm} \\
%\end{minipage}
%\begin{minipage}[c]{0.7\linewidth}
\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}
%\end{minipage}}


\subsection{Bayes' Theorem}
\label{bayesTheoremSubsection}

\index{Bayes' Theorem|(}

In many instances, we are given a conditional probability of the form
\begin{align*}
P(\text{statement about variable 1 } | \text{ statement about variable 2})
\end{align*}
but we would really like to know the inverted conditional probability:
\begin{align*}
P(\text{statement about variable 2 } | \text{ statement about variable 1})
\end{align*}
Tree 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.

We first take a critical look at an example of inverting conditional probabilities where we still apply a tree diagram.

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{In Canada, about 0.35\% of women over 40
    will develop breast cancer in any given year.
    A common screening test for cancer is the mammogram,
    but this test is not perfect.
    In about 11\% of patients with breast cancer, the test
    gives a \term{false negative}:
    it indicates a woman does not have breast cancer when
    she does have breast cancer.
    Similarly, the test gives a \term{false positive}
    in 7\% of patients who do not have breast cancer:
    it indicates these patients have breast cancer when
    they actually do not.
    If we tested a random woman over 40 for breast cancer
    using a mammogram and the test came back positive
    -- that is, the test suggested the patient has cancer --
    what is the probability that the patient actually has
    breast cancer?}

\label{probBreastCancerGivenPositiveTestExample}

Notice 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:
\begin{align*}
P(\text{has BC } | \text{ mammogram$^+$}) = \frac{P(\text{has BC and mammogram$^+$})}{P(\text{mammogram$^+$})}
\end{align*}
where ``has BC'' is an abbreviation for the patient having
breast cancer and ``mammogram$^+$'' means the mammogram screening
was positive.
We can construct a tree diagram for these probabilities:
\begin{center}
\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}
\end{center}
The probability the patient has breast cancer
and the mammogram is positive is
\begin{align*}
P(\text{has BC and mammogram$^+$}) &= P(\text{mammogram$^+$ } | \text{ has BC})P(\text{has BC}) \\
	&= 0.89\times 0.0035 = 0.00312
\end{align*}
The probability of a positive test result is the sum of the two corresponding scenarios:
\begin{align*}
P(\text{\underline{\color{black}mammogram$^+$}})
  &= P(\text{\underline{\color{black}mammogram$^+$} and has BC}) \\
  &\qquad\qquad
      + P(\text{\underline{\color{black}mammogram$^+$} and no BC})\\
  &= P(\text{has BC})P(\text{mammogram$^+$ } | \text{ has BC}) \\
  &\qquad\qquad
      + P(\text{no BC})P(\text{mammogram$^+$ } | \text{ no BC}) \\
  &= 0.0035\times 0.89 + 0.9965\times 0.07 = 0.07288
\end{align*}
Then if the mammogram screening is positive for a patient, the probability the patient has breast cancer is
\begin{align*}
P(\text{has BC } | \text{ mammogram$^+$})
	&= \frac{P(\text{has BC and mammogram$^+$})}{P(\text{mammogram$^+$})}\\
	&= \frac{0.00312}{0.07288} \approx 0.0428
\end{align*}
That is, even if a patient has a positive mammogram screening, there is still only a~4\%~chance that she has breast cancer.
\end{nexample}
\end{examplewrap}

%\begin{figure}[h]
%  \centering
%  \Figure{0.75}{BreastCancerTreeDiagram}
%  \caption{Tree diagram for
%      Example~\ref{probBreastCancerGivenPositiveTestExample}.}%,
%      %computing the probability a random patient who tests
%      %positive on a mammogram actually has breast cancer.}
%\label{BreastCancerTreeDiagram}
%\end{figure}

\D{\newpage}

Example~\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.

Consider again the last equation of Example~\ref{probBreastCancerGivenPositiveTestExample}.
Using the tree diagram, we can see that the numerator (the top of the fraction) is equal to the following product:
\begin{align*}
P(\text{has BC and mammogram$^+$}) = P(\text{mammogram$^+$ } | \text{ has BC})P(\text{has BC})
\end{align*}
The denominator -- the probability the screening was positive -- is equal to the sum of probabilities for each positive screening scenario:
\begin{align*}
P(\text{\underline{\color{black}mammogram$^+$}})
	&= P(\text{\underline{\color{black}mammogram$^+$} and no BC})
		+ P(\text{\underline{\color{black}mammogram$^+$} and has BC})
\end{align*}
In 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.
\begin{align*}
P(\text{mammogram$^+$})
	&= P(\text{mammogram$^+$ and no BC}) + P(\text{mammogram$^+$ and has BC}) \\
	&= P(\text{mammogram$^+$ } | \text{ no BC})P(\text{no BC}) \\
			   &\qquad\qquad + P(\text{mammogram$^+$ } | \text{ has BC})P(\text{has BC})
\end{align*}
We can see an application of Bayes' Theorem by substituting the resulting probability expressions into the numerator and denominator of the original conditional probability.
\begin{align*}
& P(\text{has BC } | \text{ mammogram$^+$})  \\
& \qquad= \frac{P(\text{mammogram$^+$ } | \text{ has BC})P(\text{has BC})}
	{P(\text{mammogram$^+$ } | \text{ no BC})P(\text{no BC}) + P(\text{mammogram$^+$ } | \text{ has BC})P(\text{has BC})}
\end{align*}

\begin{onebox}{Bayes' Theorem: inverting probabilities}
Consider the following conditional probability for variable 1 and variable 2:\vspace{-1.5mm}
\begin{align*}
P(\text{outcome $A_1$ of variable 1 } | \text{ outcome $B$ of variable 2})
\end{align*}
Bayes' Theorem states that this conditional probability can be identified as the following fraction:\vspace{-1.5mm}
\begin{align*}
\frac{P(B | A_1) P(A_1)}
	{P(B | A_1) P(A_1) + P(B | A_2) P(A_2) + \cdots + P(B | A_k) P(A_k)}
\end{align*}
where $A_2$, $A_3$, ..., and $A_k$ represent all other possible outcomes of the first variable.\index{Bayes' Theorem|textbf}
\end{onebox}

Bayes' Theorem is a generalization of what we have done
using tree diagrams.
The numerator identifies the probability of getting both
$A_1$ and~$B$.
The denominator is the marginal probability of getting~$B$.
This bottom component of the fraction appears long and
complicated 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.

\noindent%
To apply Bayes' Theorem correctly, there are two preparatory steps:
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item[(1)] First identify the marginal probabilities of each possible outcome of the first variable: $P(A_1)$, $P(A_2)$, ..., $P(A_k)$.
\item[(2)] Then identify the probability of the outcome $B$, conditioned on each possible scenario for the first variable: $P(B | A_1)$, $P(B | A_2)$, ..., $P(B | A_k)$.
\end{enumerate}
Once each of these probabilities are identified, they can be applied directly within the formula.
Bayes' Theorem tends to be a good option when there are so many scenarios that drawing a tree diagram would be complex.

\begin{exercisewrap}
\begin{nexercise} \label{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsASportingEvent}
Jose 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{\begin{minipage}[t]{0.27\linewidth}
The 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} \\\ 
\end{minipage}
\begin{minipage}[c]{0.65\linewidth}
\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}
\end{minipage}}

\begin{examplewrap}
\begin{nexample}{Here we solve the same problem presented in Guided Practice~\ref{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsASportingEvent}, except this time we use Bayes' Theorem.}
The 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
\begin{align*}
&P(A_1) = 0.2 &&P(A_2) = 0.35 &&P(A_3) = 0.45 \\
&P(B | A_1) = 0.7 &&P(B | A_2) = 0.25 &&P(B | A_3) = 0.05
\end{align*}
Bayes' Theorem can be used to compute the probability of a sporting event ($A_1$) under the condition that the parking lot is full ($B$):
\begin{align*}
P(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)} \\
		&= \frac{(0.7)(0.2)}{(0.7)(0.2) + (0.25)(0.35) + (0.05)(0.45)} \\
		&= 0.56 
\end{align*}
Based on the information that the garage is full, there is a 56\% probability that a sporting event is being held on campus that evening.
\end{nexample}
\end{examplewrap}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise} \label{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsAnAcademicEvent}
Use 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{Short answer:
\begin{align*}
P(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)} \\
		&= \frac{(0.25)(0.35)}{(0.7)(0.2) + (0.25)(0.35) + (0.05)(0.45)} \\
		&= 0.35
\end{align*}}

\begin{exercisewrap}
\begin{nexercise} \label{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsNoEvent}
In 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{Each probability is conditioned on the same information that the garage is full, so the complement may be used: $1.00 - 0.56 - 0.35 = 0.09$.}

The 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.

\index{Bayes' Theorem|)}
\index{tree diagram|)}
\index{conditional probability|)}
\index{probability|)}


{\input{ch_probability/TeX/conditional_probability.tex}}





%_________________
\section{Sampling from a small population}
\label{smallPop}

\noindent%
When we sample observations from a population,
usually we're only sampling a small fraction of
the possible individuals or cases.
However, sometimes our sample size is large enough
or the population is small enough that we
sample more than 10\% of a population\footnote{The 10\%
  guideline is a rule of thumb cutoff for when these
  considerations become more important.}
\emph{without
replacement} (meaning we do not have a chance of
sampling the same cases twice).
Sampling such a notable fraction of a population
can be important for how we analyze the
sample.

\begin{examplewrap}
\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?}
If there are 15 people to ask and none are skipping class, then the probability is $1/15$, or about $0.067$.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\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}
For 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
\begin{align*}
&P(\text{not picked in 3 questions}) \\
&\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}.}) \\
&\quad = \frac{14}{15}\times\frac{13}{14}\times\frac{12}{13} = \frac{12}{15} = 0.80
\end{align*}
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
What rule permitted us to multiply the probabilities in Example~\ref{3woRep}?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{The three probabilities we computed were actually one marginal probability, $P($\var{Q1}$ = $\resp{not\us{}picked}$)$, and two conditional probabilities:
\begin{align*}
&P(\text{\var{Q2}} =  \text{\resp{not\us{}picked} }|
    \text{ \var{Q1}} = \text{\resp{not\us{}picked}}) \\
&P(\text{\var{Q3}} =  \text{\resp{not\us{}picked} }|
    \text{ \var{Q1}} = \text{\resp{not\us{}picked}, }
    \text{\var{Q2}} = \text{\resp{not\us{}picked}})
\end{align*}
Using the General Multiplication Rule, the product of these three probabilities is the probability of not being picked in 3 questions.}

\D{\newpage}

\begin{examplewrap}
\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}
Each 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.
\begin{align*}
&P(\text{not picked in 3 questions}) \\
&\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}.}) \\
&\quad = \frac{14}{15}\times\frac{14}{15}\times\frac{14}{15} = 0.813
\end{align*}
You 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.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Under the setup of Example~\ref{3wRep}, what is the probability of being picked to answer all three questions?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$P($being picked to answer all three questions$) = \left(\frac{1}{15}\right)^3 = 0.00030$.}

If 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. 

\begin{exercisewrap}
\begin{nexercise} \label{raffleOf30TicketsWWOReplacement}
Your 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
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise} \label{followUpToRaffleOf30TicketsWWOReplacement}
Compare your answers in Guided Practice~\ref{raffleOf30TicketsWWOReplacement}. How much influence does the sampling method have on your chances of winning a prize?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}

Had 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.


{\input{ch_probability/TeX/sampling_from_a_small_population.tex}}





%_________________
\section{Random variables}
\label{randomVariablesSection}

\index{random variable|(}

\noindent%
It's often useful to model a process using what's
called a \term{random variable}.
Such a model allows us to apply a mathematical
framework and statistical principles for
better understanding and predicting outcomes
in the real world.

\begin{examplewrap}
\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}
Around 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.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Would you be surprised if the bookstore sold slightly more or less than 105 books?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{examplewrap}
\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}
About 55 students will just buy a textbook, providing revenue of
\begin{align*}
\$137 \times  55 = \$7,535
\end{align*}
The roughly 25 students who buy both the textbook and the study guide would pay a total of
\begin{align*}
(\$137 + \$33) \times  25 = \$170 \times  25 = \$4,250
\end{align*}
Thus, 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.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
  \caption{Probability distribution for the bookstore's
      revenue from one student.
      The triangle represents
      the average revenue per student.}
  \label{bookCostDist}
\end{figure}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{What is the average revenue per student for this course?}\label{revFromStudent}
The expected total revenue is \$11,785, and there are 100 students. Therefore the expected revenue per student is $\$11,785/100 =  \$117.85$.
\end{nexample}
\end{examplewrap}

\subsection{Expectation}

\index{expectation|(}

We 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 $X$.

\begin{onebox}{Random variable}
A random process or variable with a numerical outcome.
\end{onebox}

The 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}.

\begin{figure}[h]
\centering
\begin{tabular}{l ccc r}
\hline
$i$	  & 1 & 2 & 3  & Total\\
\hline
$x_i$ & \$0 & \$137 & \$170 & --\\
$P(X=x_i)$ & 0.20 & 0.55 & 0.25 & 1.00 \\
\hline
\end{tabular}
\caption{The probability distribution for the random variable $X$, representing the bookstore's revenue from a single student.}
\label{statSpendDist}
\end{figure}

We computed the average outcome of $X$ as \$117.85 in Example~\ref{revFromStudent}.
We call this average the \term{expected value} of $X$, denoted by $E(X)$\index{EX@$E(X)$}.
The expected value of a random variable is computed by adding each outcome weighted by its probability:
\begin{align*}
E(X) &= 0 \times  P(X=0) + 137 \times  P(X=137) + 170 \times  P(X=170) \\
	&= 0 \times  0.20 + 137 \times  0.55 + 170 \times  0.25 = 117.85
\end{align*}

\begin{onebox}{Expected value of a Discrete Random Variable}
If $X$ 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:
\begin{align*}
E(X)
  &= x_1 \times P(X = x_1) + \cdots + x_k\times P(X = x_k) \\
  &= \sum_{i = 1}^{k} x_i P(X = x_i)
\end{align*}
The Greek letter $\mu$\index{Greek!mu@mu ($\mu$)}
may be used in place of the notation $E(X)$.
\end{onebox}

\D{\newpage}

The 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 $\mu=117.85$.

It 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.}

In 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.

\begin{figure}
\centering
\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}
\caption{A weight system representing the probability distribution for $X$. The string holds the distribution at the mean to keep the system balanced.}
\label{bookWts}
\end{figure}

\begin{figure}
\centering
\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}
\caption{A continuous distribution can also be balanced at its mean.}
\label{contBalance}
\end{figure}

\index{expectation|)}


\D{\newpage}

\subsection{Variability in random variables}

Suppose 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. 

The \indexthis{variance}{variance} and \indexthis{standard deviation}{standard deviation} can be used to describe the variability of a random variable. Section~\ref{variability}
introduced 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}.

\begin{onebox}{General variance formula}
If $X$ 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
\begin{align*}
\sigma^2 &= (x_1-\mu)^2\times P(X=x_1) + \cdots \\
	& \qquad\quad\cdots+ (x_k-\mu)^2\times P(X=x_k) \\
	&= \sum_{j=1}^{k} (x_j - \mu)^2 P(X=x_j)
\end{align*}
The standard deviation of $X$, labeled
$\sigma$\index{Greek!sigma@sigma ($\sigma$)},
is the square root of the variance.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{Compute the expected value, variance, and standard deviation of $X$, the revenue of a single statistics student for the bookstore.}
It is useful to construct a table that holds computations for each outcome separately, then add up the results.
\begin{center}
\begin{tabular}{l rrr r}
\hline
$i$ & 1 & 2 & 3 & Total \\
\hline
$x_i$ & \$0 & \$137 & \$170 &  \\
$P(X=x_i)$ & 0.20 & 0.55 & 0.25 &  \\
$x_i \times  P(X=x_i)$ & 0 & 75.35 & 42.50 & 117.85 \\
\hline
\end{tabular}
\end{center}
Thus, the expected value is $\mu=117.85$, which we computed earlier. The variance can be constructed by extending this table:
\begin{center}
\begin{tabular}{l rrr r}
\hline
$i$ & 1 & 2 & 3 & Total \\
\hline
$x_i$ & \$0 & \$137 & \$170 &  \\
$P(X=x_i)$ & 0.20 & 0.55 & 0.25 &  \\
$x_i \times  P(X=x_i)$ & 0 & 75.35 & 42.50 & 117.85 \\
$x_i - \mu$ & -117.85 & 19.15 & 52.15 &  \\
$(x_i-\mu)^2$ & 13888.62 &  366.72 & 2719.62 &  \\
$(x_i-\mu)^2\times P(X=x_i)$ & 2777.7 & 201.7 & 679.9 & 3659.3 \\
\hline
\end{tabular}
\end{center}
The variance of $X$ is $\sigma^2 = 3659.3$, which means the standard deviation is $\sigma = \sqrt{3659.3} = \$60.49$.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
The 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
\begin{enumerate}
\item[(a)] What proportion of students don't buy either book? Assume no students buy the supplement without the textbook.
\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.
\item[(c)] Compute the expected revenue from a single chemistry student. 
\item[(d)] Find the standard deviation to describe the variability associated with the revenue from a single student.
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\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: $\sigma = \sqrt{Var(Y)} = \$69.28$.
\begin{center}
\begin{tabular}{rrrrr}
  \hline
$i$ (scenario) & 1 (\resp{noBook}) & 2 (\resp{textbook}) & 3 (\resp{both}) & Total \\
  \hline
$y_i$ & 0.00 & 159.00 & 200.00 &  \\
$P(Y=y_i)$ & 0.15 & 0.25 & 0.60 & \\
$y_i\times P(Y=y_i)$ & 0.00 & 39.75 & 120.00 & $E(Y) = 159.75$\\
$y_i-E(Y)$ & -159.75 & -0.75 & 40.25 & \\
$(y_i-E(Y))^2$ & 25520.06 & 0.56 & 1620.06 & \\
$(y_i-E(Y))^2\times P(Y)$ & 3828.0 & 0.1 & 972.0 & $Var(Y) \approx 4800$ \\
   \hline
\end{tabular}
\end{center}}

\subsection{Linear combinations of random variables}

So 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.

\begin{examplewrap}
\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 $W$.}
His total weekly travel time is the sum of the five daily values:
\begin{align*}
W = X_1 + X_2 + X_3 + X_4 + X_5
\end{align*}
Breaking the weekly travel time $W$ into pieces provides a framework for understanding each source of randomness and is useful for modeling $W$.
\end{nexample}
\end{examplewrap}

\D{\newpage}

\begin{examplewrap}
\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?}
We 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:
\begin{align*}
E(W) &= E(X_1 + X_2 + X_3 + X_4 + X_5) \\
	&= E(X_1) + E(X_2) + E(X_3) + E(X_4) + E(X_5) \\
	&= 18 + 18 + 18 + 18 + 18 = 90\text{ minutes}
\end{align*}
The 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.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
\label{elenaIsSellingATVAndBuyingAToasterOvenAtAnAuction}%
Elena 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{She will make $X$ dollars on the TV but spend $Y$ dollars on the toaster oven: $X-Y$.}

\begin{exercisewrap}
\begin{nexercise}
Based 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{$E(X-Y) = E(X) - E(Y) = 175 - 23 = \$152$. She should expect to make about \$152.}

\begin{exercisewrap}
\begin{nexercise} \label{explainWhyThereIsUncertaintyInTheSum}
Would you be surprised if John's weekly commute wasn't exactly 90 minutes or if Elena didn't make exactly \$152? Explain.\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}

Two 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}.

A \term{linear combination} of two random variables $X$ and $Y$ is a fancy phrase to describe a combination
\begin{align*}
aX + bY
\end{align*}
where $a$ 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:
\begin{align*}
1X_1 + 1 X_2 + 1 X_3 + 1 X_4 + 1 X_5
\end{align*}
For Elena's net gain or loss, the $X$ random variable had
a coefficient of +1 and the $Y$ random variable had
a coefficient of~-1.

\D{\newpage}

When 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.}

\begin{onebox}{Linear combinations of random variables and the average result}
If $X$ and $Y$ are random variables, then a linear combination of the random variables is given by
\begin{align*}
aX + bY
\end{align*}
where $a$ 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:
\begin{align*}
a\times E(X) + b\times E(Y)
\end{align*}
Recall that the expected value is the same as the mean, e.g. $E(X) = \mu_X$.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{Leonard has invested \$6000 in Caterpillar Inc
    (stock ticker: CAT) and \$2000 in Exxon Mobil Corp (XOM).
    If $X$ represents the change in Caterpillar's stock next month
    and $Y$ represents the change in Exxon Mobil's stock
    next month, write an equation that describes how much
    money will be made or lost in Leonard's stocks for the
    month.}
  For simplicity, we will suppose $X$ and $Y$ are not
  in percents but are in decimal form
  (e.g. if Caterpillar's stock increases 1\%, then $X=0.01$;
  or if it loses 1\%, then $X=-0.01$).
  Then we can write an equation for Leonard's gain as
  \begin{align*}
  \$6000\times X + \$2000\times Y
  \end{align*}
  If we plug in the change in the stock value for $X$ and $Y$,
  this equation gives the change in value of Leonard's stock
  portfolio for the month. A positive value represents a gain,
  and a negative value represents a loss.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}\label{expectedChangeInLeonardsStockPortfolio}
Caterpillar stock has recently been rising
at 2.0\% and Exxon Mobil's at 0.2\% per month, respectively.
Compute the expected change in Leonard's stock portfolio
for next month.\footnotemark
\end{nexercise}
\end{exercisewrap}
% library(openintro); d <- stocks_18; cols <- 2:4; apply(d[, cols], 2, mean); apply(d[, cols], 2, sd)
\footnotetext{%
  $E(\$6000\times X + \$2000\times Y) =
    \$6000\times 0.020 + \$2000\times 0.002 = \$124$.}

\begin{exercisewrap}
\begin{nexercise}
You should have found that Leonard expects a positive gain
in Guided Practice~\ref{expectedChangeInLeonardsStockPortfolio}.
However, would you be surprised if he actually had
a loss this month?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{No.
  While stocks tend to rise over time, they are often
  volatile in the short term.}


\D{\newpage}

\subsection{Variability in linear combinations of random variables}
\label{var_lin_combo_of_RVs}

Quantifying the average outcome from a linear combination
of random variables is helpful, but it is also important
to have some sense of the uncertainty associated with
the total outcome of that combination of random variables.
The expected net gain or loss of Leonard's stock portfolio
was considered in Guided Practice~\ref{expectedChangeInLeonardsStockPortfolio}.
However, there was no quantitative discussion of the
volatility of this portfolio.
For instance, while the average monthly gain might be
about \$124 according to the data, that gain is not guaranteed.
Figure~\ref{changeInLeonardsStockPortfolioFor36Months}
shows the monthly changes in a portfolio like Leonard's during
a three year period.
The gains and losses vary widely, and quantifying these
fluctuations is important when investing in stocks.

\begin{figure}[ht]
\centering
\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}
\caption{The change in a portfolio like Leonard's for 36 months,
    where \$6000 is in Caterpillar's stock and \$2000 is in
    Exxon Mobil's.}
\label{changeInLeonardsStockPortfolioFor36Months}
\end{figure}

Just as we have done in many previous cases,
we use the variance and standard deviation to describe
the uncertainty associated with Leonard's monthly returns.
To do so, the variances of each stock's monthly return
will be useful, and these are shown in
Figure~\ref{sumStatOfCATXOM}.
The stocks' returns are nearly independent.

\begin{figure}
\centering
\begin{tabular}{lrrr}
\hline
    & Mean ($\bar{x}$) & Standard deviation ($s$) &
	    Variance ($s^2$) \\
\hline
CAT & 0.0204  & 0.0757  & 0.0057 \\
XOM & 0.0025  & 0.0455  & 0.0021 \\
\hline
\end{tabular}
\caption{The mean, standard deviation, and variance of the
    CAT and XOM stocks.
    These statistics were estimated from historical
    stock data, so notation used for sample statistics
    has been used.}
\label{sumStatOfCATXOM}
\end{figure}

Here we use an equation from probability theory to
describe the uncertainty of Leonard's monthly returns;
we leave the proof of this method to a dedicated
probability course.
The variance of a linear combination of random variables
can be computed by plugging in the variances of the
individual random variables and squaring the coefficients
of the random variables:
\begin{align*}
Var(aX + bY) = a^2\times Var(X) + b^2\times Var(Y)
\end{align*}
It is important to note that this equality assumes the
random variables are independent;
%\Comment{new description here about if independence is broken}
if independence doesn't hold, then a modification to
this equation would be required that we leave as a topic
for a future course to cover.
This equation can be used to compute the variance of
Leonard's monthly return:
\begin{align*}
Var(6000\times X + 2000\times Y)
	&= 6000^2\times Var(X) + 2000^2\times Var(Y) \\
	&= 36,000,000\times 0.0057 + 4,000,000\times 0.0021 \\
	&\approx 213,600
% sum(c(36e6, 4e6) * c(0.0057, 0.0021))
\end{align*}
The standard deviation is computed as the square root
of the variance: $\sqrt{213,600} = \$463$.
While an average monthly return of \$124 on an
\$8000 investment is nothing to scoff at,
the monthly returns are so volatile that Leonard should
not expect this income to be very stable.

\begin{onebox}{Variability of linear combinations of random variables}
The 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:
\begin{align*}
Var(aX + bY) = a^2\times Var(X) + b^2\times Var(Y)
\end{align*}
This 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.
\end{onebox}

\begin{examplewrap}
\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}
The expression for John's commute time was
\begin{align*}
X_1 + X_2 + X_3 + X_4 + X_5
\end{align*}
Each 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
\begin{align*}
&\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 \\
&\text{standard deviation } = \sqrt{\text{variance}} = \sqrt{80} = 8.94
\end{align*}
The standard deviation for John's weekly work commute time is about 9 minutes.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
The 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
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise}\label{elenaIsSellingATVAndBuyingAToasterOvenAtAnAuctionVariability}
Consider 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
\end{nexercise}
\end{exercisewrap}
\footnotetext{The equation for Elena can be written as
\begin{align*}
(1)\times X + (-1)\times Y
\end{align*}
The variances of $X$ and $Y$ are 625 and 64. We square the coefficients and plug in the variances:
\begin{align*}
(1)^2\times Var(X) + (-1)^2\times Var(Y) = 1\times 625 + 1\times 64 = 689
\end{align*}
The variance of the linear combination is 689, and the standard deviation is the square root of 689: about \$26.25.}

Consider 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.

\index{random variable|)}


{\input{ch_probability/TeX/random_variables.tex}}





%_________________
\section{Continuous distributions}
\label{contDist}

\noindent%
So far in this chapter we've discussed cases
where the outcome of a variable is discrete.
In this section, we consider a context where
the outcome is a continuous numerical variable.

\index{data!US adult heights|(}
\index{hollow histogram|(}
\begin{examplewrap}
\begin{nexample}{Figure~\ref{fdicHistograms} shows a few
    different hollow histograms for the heights of US adults.
    How does changing the number of bins allow you to make
    different interpretations of the data?}
  \label{usHeights}%
  Adding more bins provides greater detail.
  This sample is extremely large, which is why much smaller
  bins still work well.
  Usually we do not use so many bins with smaller sample
  sizes since small counts per bin mean the bin heights
  are very volatile.
\end{nexample}
\end{examplewrap}

\begin{figure}[ht]
  \centering
  \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}
  \caption{Four hollow histograms of US adults heights
      with varying bin widths.}
  \label{fdicHistograms}
\end{figure}

\begin{examplewrap}
\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}
We 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:
\begin{align*}
\frac{195307 + 156239}{\text{3,000,000}} = 0.1172
\end{align*}
This fraction is the same as the proportion of the histogram's area that falls in the range \resp{180} to \resp{185} cm.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \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}
  \caption{A histogram with bin sizes of 2.5 cm.
      The shaded region represents individuals with
      heights between \resp{180} and \resp{185} cm.}
  \label{usHeightsHist180185}
\end{figure}


\D{\newpage}

\subsection{From histograms to continuous distributions}

Examine 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.

This smooth curve represents a
\termsub{probability density function}
    {probability!density function}
(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. 

\begin{figure}[tbh]
\centering
\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}
\caption{The continuous probability distribution of heights for US adults.}
\label{fdicHeightContDist}
\end{figure}

\index{hollow histogram|)}


\D{\newpage}

\subsection{Probabilities from continuous distributions}

We computed the proportion of individuals with heights \resp{180} to \resp{185} cm in Example~\ref{contDistProb} as a fraction:
\begin{align*}
\frac{\text{number of people between \resp{180} and \resp{185}}}{\text{total sample size}}
\end{align*}
We 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):
\begin{align*}
P(\text{\var{height} between \resp{180} and \resp{185}})
	= \text{area between \resp{180} and \resp{185}}
	= 0.1157
\end{align*}
The 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. 

\begin{figure}[h]
  \centering
  \Figure[A density curve for heights is shown, with the region between 180 and 185 centimeters being shaded.]{0.7}{fdicHeightContDistFilled}
  \caption{Density for heights in the US adult population
      with the area between 180 and 185 cm shaded.
      Compare this plot with Figure~\ref{usHeightsHist180185}.}
  \label{fdicHeightContDistFilled}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
Three 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}
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item[(a)] What is the probability that all three are between \resp{180} and \resp{185} cm tall?
\item[(b)] What is the probability that none are between \resp{180} and \resp{185} cm?
\end{enumerate}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Brief answers:
  (a) $0.1157 \times 0.1157 \times 0.1157 = 0.0015$.
  (b) $(1-0.1157)^3 = 0.692$}

\begin{examplewrap}
\begin{nexample}{What is the probability that a randomly selected person is \textbf{exactly} \resp{180}~cm? Assume you can measure perfectly.}
\label{probabilityOfExactly180cm}
This 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.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Suppose 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
\end{nexercise}
\end{exercisewrap}
\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}.}

\index{data!US adult heights|)}


{\input{ch_probability/TeX/continuous_distributions.tex}}


================================================
FILE: ch_probability/TeX/conditional_probability.tex
================================================
\exercisesheader{}

% 13

\eoce{\qt{Joint and conditional probabilities\label{joint_cond}} P(A) = 0.3, 
P(B) = 0.7
\begin{parts}
\item Can you compute P(A and B) if you only know P(A) and P(B)?
\item Assuming that events A and B arise from independent random processes,
\begin{subparts}
\item what is P(A and B)?
\item what is P(A or B)?
\item what is P(A$|$B)?
\end{subparts}
\item If we are given that P(A and B) = 0.1, are the random variables giving rise 
to events A and B independent?
\item If we are given that P(A and B) = 0.1, what is P(A$|$B)?
\end{parts}
}{}

% 14

\eoce{\qt{PB \& J\label{pbj}} Suppose 80\% of people like peanut butter, 89\% 
like jelly, and 78\% like both. Given that a randomly sampled person likes peanut 
butter, what's the probability that he also likes jelly?
}{}

% 15

\eoce{\qt{Global warming\label{global_warming}} A Pew Research poll asked 
1,306 Americans ``From what you've read and heard, is there solid evidence that 
the average temperature on earth has been getting warmer over the past few 
decades, or not?". The table below shows the distribution of responses by party 
and ideology, where the counts have been replaced with relative frequencies.
\footfullcite{globalWarming}
\begin{center}
\begin{tabular}{ll  ccc c} 
                    &                           & \multicolumn{3}{c}{\textit{Response}} \\
\cline{3-5}
                    &                           & Earth is  & Not       & Don't Know    &   \\
                    &                           & warming   & warming   & Refuse        & Total\\
\cline{2-6}
                    & Conservative Republican   & 0.11      & 0.20      & 0.02      & 0.33  \\
\textit{Party and}  & Mod/Lib Republican        & 0.06      & 0.06      & 0.01      & 0.13 \\
\textit{Ideology}   & Mod/Cons Democrat         & 0.25      & 0.07      & 0.02      & 0.34 \\
                    & Liberal Democrat          & 0.18      & 0.01      & 0.01      & 0.20\\
\cline{2-6}
                    &Total                      & 0.60      & 0.34      & 0.06      & 1.00
\end{tabular}
\end{center}
\begin{parts}
\item Are believing that the earth is warming and being a liberal Democrat mutually 
exclusive?
\item What is the probability that a randomly chosen respondent believes the 
earth is warming or is a liberal Democrat?
\item What is the probability that a randomly chosen respondent believes the 
earth is warming given that he is a liberal Democrat?
\item What is the probability that a randomly chosen respondent believes the 
earth is warming given that he is a conservative Republican?
\item Does it appear that whether or not a respondent believes the earth is 
warming is independent of their party and ideology? Explain your reasoning.
\item What is the probability that a randomly chosen respondent is a 
moderate/liberal Republican given that he does not believe that the earth is 
warming? 
\end{parts}
}{}

\D{\newpage}

% 16

\eoce{\qt{Health coverage, relative frequencies\label{health_coverage_rel_freqs}} 
The Behavioral Risk Factor Surveillance System (BRFSS) is an annual telephone 
survey designed to identify risk factors in the adult population and report 
emerging health trends. The following table displays the distribution of health 
status of respondents to this survey (excellent, very good, good, fair, poor) 
and whether or not they have health insurance.
\begin{center}
\begin{tabular}{rrrrrrrr}
& &  \multicolumn{5}{c}{\textit{Health Status}} &  \\ 
\cline{3-7}
                    &       & Excellent & Very good & Good      & Fair      & Poor      & Total \\ 
\cline{2-8}
\textit{Health}     & No    & 0.0230    & 0.0364    & 0.0427    & 0.0192    & 0.0050    & 0.1262 \\ 
\textit{Coverage}   & Yes   & 0.2099    & 0.3123    & 0.2410    & 0.0817    & 0.0289    & 0.8738 \\ 
\cline{2-8}
                    & Total & 0.2329    & 0.3486    & 0.2838    & 0.1009    & 0.0338    & 1.0000
\end{tabular}
\end{center}
\begin{parts}
\item Are being in excellent health and having health coverage mutually 
exclusive?
\item What is the probability that a randomly chosen individual has excellent 
health?
\item What is the probability that a randomly chosen individual has excellent 
health given that he has health coverage?
\item What is the probability that a randomly chosen individual has excellent 
health given that he doesn't have health coverage?
\item Do having excellent health and having health coverage appear to be 
independent?
\end{parts}
}{}

% 17

\eoce{\qt{Burger preferences\label{burger_preferences}} A 2010 SurveyUSA poll 
asked 500 Los Angeles residents, ``What is the best hamburger place in Southern 
California? Five Guys Burgers? In-N-Out Burger? Fat Burger? Tommy's Hamburgers? 
Umami Burger? Or somewhere else?'' The distribution of responses by gender is 
shown below. \footfullcite{burgers}
\begin{center}
\begin{tabular}{l p{4cm} r r r }
                    &                       & \multicolumn{2}{c}{\textit{Gender}} \\
\cline{3-4}
                    &                       & Male  & Female    & Total \\
\cline{2-5}
                    & Five Guys Burgers     & 5     & 6         & 11 \\
                    & In-N-Out Burger       & 162   & 181       & 343 \\
\textit{Best}       & Fat Burger            & 10    & 12        & 22 \\
\textit{hamburger}  & Tommy's Hamburgers    & 27    & 27        & 54 \\ 
\textit{place}      & Umami Burger          & 5     & 1         & 6 \\
                    & Other                 & 26    & 20        & 46 \\
                    & Not Sure              & 13    & 5         & 18 \\
\cline{2-5}  
                    & Total                 & 248   & 252       & 500
\end{tabular}
\end{center}
\begin{parts}
\item Are being female and liking Five Guys Burgers mutually exclusive?
\item What is the probability that a randomly chosen male likes In-N-Out the best?
\item What is the probability that a randomly chosen female likes In-N-Out the 
best?
\item What is the probability that a man and a woman who are dating both like 
In-N-Out the best? Note any assumption you make and evaluate whether you think 
that assumption is reasonable.
\item What is the probability that a randomly chosen person likes Umami best or 
that person is female?
\end{parts}
}{}

\D{\newpage}

% 18

\eoce{\qt{Assortative mating\label{assortative_mating}} Assortative mating is a 
nonrandom mating pattern where individuals with similar genotypes and/or 
phenotypes mate with one another more frequently than what would be expected 
under a random mating pattern. Researchers studying this topic collected data on 
eye colors of 204 Scandinavian men and their female partners. The table below 
summarizes the results.\footfullcite{Laeng:2007}
\begin{center}
\begin{tabular}{ll  ccc c} 
                                        &           & \multicolumn{3}{c}{\textit{Partner (female)}} \\
\cline{3-5}
                                        &           & Blue  & Brown     & Green     & Total \\
\cline{2-6}
                                        & Blue      & 78    & 23        & 13        & 114 \\
\multirow{2}{*}{\textit{Self (male)}}   & Brown     & 19    & 23        & 12        & 54 \\
                                        & Green     & 11    & 9         & 16        & 36 \\
\cline{2-6}  
                                        & Total     & 108   & 55        & 41        & 204
\end{tabular}
\end{center}
\begin{parts}
\item What is the probability that a randomly chosen male respondent or his 
partner has blue eyes?
\item What is the probability that a randomly chosen male respondent with blue 
eyes has a partner with blue eyes? 
\item What is the probability that a randomly chosen male respondent with brown 
eyes has a partner with blue eyes? What about the probability of a randomly 
chosen male respondent with green eyes having a partner with blue eyes?
\item Does it appear that the eye colors of male respondents and their partners 
are independent? Explain your reasoning.
\end{parts}
}{}

% 19

\eoce{\qt{Drawing box plots\label{tree_drawing_box_plots}} After an introductory 
statistics course, 80\% of students can successfully construct box plots. Of 
those who can construct box plots, 86\% passed, while only 65\% of those students 
who could not construct box plots passed.
\begin{parts}
\item Construct a tree diagram of this scenario.
\item Calculate the probability that a student is able to construct a box plot 
if it is known that he passed.
\end{parts}
}{}

% 20

\eoce{\qt{Predisposition for thrombosis\label{tree_thrombosis}} A genetic test is 
used to determine if people have a predisposition for \textit{thrombosis}, which 
is the formation of a blood clot inside a blood vessel that obstructs the flow of 
blood through the circulatory system. It is believed that 3\% of people actually 
have this predisposition. The genetic test is 99\% accurate if a person actually 
has the predisposition, meaning that the probability of a positive test result 
when a person actually has the predisposition is 0.99. The test is 98\% accurate 
if a person does not have the predisposition. What is the probability that a 
randomly selected person who tests positive for the predisposition by the test 
actually has the predisposition?
}{}

% 21

\eoce{\qt{It's never lupus\label{tree_lupus}} Lupus is a medical phenomenon where 
antibodies that are supposed to attack foreign cells to prevent infections 
instead see plasma proteins as foreign bodies, leading to a high risk of blood 
clotting. It is believed that 2\% of the population suffer from this disease. The 
test is 98\% accurate if a person actually has the disease. The test is 74\% 
accurate if a person does not have the disease. There is a line from the Fox 
television show \emph{House} that is often used after a patient tests positive 
for lupus: ``It's never lupus." Do you think there is truth to this statement? 
Use appropriate probabilities to support your answer.
}{}

% 22

\eoce{\qt{Exit poll\label{tree_exit_poll}} Edison Research gathered exit poll 
results from several sources for the Wisconsin recall election of Scott Walker. 
They found that 53\% of the respondents voted in favor of Scott Walker. 
Additionally, they estimated that of those who did vote in favor for Scott 
Walker, 37\% had a college degree, while 44\% of those who voted against Scott 
Walker had a college degree. Suppose we randomly sampled a person who 
participated in the exit poll and found that he had a college degree. What is the 
probability that he voted in favor of Scott Walker?
\footfullcite{data:scott}
}{}


================================================
FILE: ch_probability/TeX/continuous_distributions.tex
================================================
\exercisesheader{}

% 37

\eoce{\qt{Cat weights\label{cat_weights}} The histogram shown below represents 
the weights (in kg) of 47 female and 97 male cats. \footfullcite{cats} \\
\begin{minipage}[c]{0.47\textwidth}
\begin{parts}
\item What fraction of these cats weigh less than 2.5 kg?
\item What fraction of these cats weigh between 2.5 and 2.75 kg?
\item What fraction of these cats weigh between 2.75 and 3.5 kg?
\end{parts} \vspace{27mm}
\end{minipage}
\begin{minipage}[c]{0.05\textwidth}
$\:$ 
\end{minipage}
\begin{minipage}[c]{0.48\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

% 38

\eoce{\qt{Income and gender\label{income_gender}} The relative frequency table 
below displays the distribution of annual total personal income (in 2009 
inflation-adjusted dollars) for a representative sample of 96,420,486 Americans. 
These data come from the American Community Survey for 2005-2009. This sample is 
comprised of 59\% males and 41\% females. \footfullcite{acsIncome2005-2009} \\

\noindent\begin{minipage}[c]{0.60\textwidth}
\begin{parts}
\item Describe the distribution of total personal income.
\item What is the probability that a randomly chosen US resident makes less than 
\$50,000 per year?
\item What is the probability that a randomly chosen US resident makes less than 
\$50,000 per year and is female? Note any assumptions you make.
\item The same data source indicates that 71.8\% of females make less than 
\$50,000 per year. Use this value to determine whether or not the assumption you 
made in part (c) is valid.
\end{parts} 
\end{minipage}
\begin{minipage}[c]{0.4\textwidth}
{\small
\begin{center}
\begin{tabular}{lr}
  \hline
\textit{Income}         & \textit{Total} \\
  \hline
\$1 to \$9,999 or loss  & 2.2\% \\
\$10,000 to \$14,999    & 4.7\% \\
\$15,000 to \$24,999    & 15.8\% \\
\$25,000 to \$34,999    & 18.3\% \\
\$35,000 to \$49,999    & 21.2\% \\
\$50,000 to \$64,999    & 13.9\% \\
\$65,000 to \$74,999    & 5.8\% \\
\$75,000 to \$99,999    & 8.4\% \\
\$100,000 or more       & 9.7\% \\
   \hline
\end{tabular}
\end{center}
}
\end{minipage}
}{}


================================================
FILE: ch_probability/TeX/defining_probability.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{True or false\label{tf_prob_definitions}} Determine if the statements 
below are true or false, and explain your reasoning.
\begin{parts}
\item If a fair coin is tossed many times and the last eight tosses are all heads, 
then the chance that the next toss will be heads is somewhat less than 50\%.
\item Drawing a face card (jack, queen, or king) and drawing a red card from a 
full deck of playing cards are mutually exclusive events.
\item Drawing a face card and drawing an ace from a full deck of playing cards 
are mutually exclusive events.
\end{parts}
}{}

% 2

\eoce{\qt{Roulette wheel\label{roulette_wheel}} The game of roulette involves 
spinning a wheel with 38 slots: 18 red, 18 black, and 2 green. A ball is spun 
onto the wheel and will eventually land in a slot, where each slot has an equal 
chance of capturing the ball.

\noindent%
\begin{minipage}[c]{0.65\textwidth}
\raggedright\begin{parts}
\item You watch a roulette wheel spin 3 consecutive times and the ball lands on a 
red slot each time. What is the probability that the ball will land on a red slot 
on the next spin?
\item You watch a roulette wheel spin 300 consecutive times and the ball lands on 
a red slot each time. What is the probability that the ball will land on a red 
slot on the next spin?
\item Are you equally confident of your answers to parts~(a) and~(b)? Why or why 
not?
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.05\textwidth}
\ 
\end{minipage}
\begin{minipage}[c]{0.28\textwidth}
\begin{center}
\Figures[A photo of a roulette wheel.]{}{eoce/roulette_wheel}{roulette_wheel.jpg} \\
{\footnotesize Photo by H\r{a}kan Dahlstr\"{o}m \\
  (\oiRedirect{textbook-flickr_hakan_dahlstrom_roulette_wheel}{http://flic.kr/p/93fEzp}) \\
  \oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}}
\end{center}
\end{minipage}
}{}

% 3

\eoce{\qt{Four games, one winner\label{four_games_one_winner}} Below are four 
versions of the same game. Your archnemesis gets to pick the version of the game, 
and then you get to choose how many times to flip a coin: 10 times or 100 times. 
Identify how many coin flips you should choose for each version of the game. It 
costs \$1 to play each game. Explain your reasoning.
\begin{parts}
\item If the proportion of heads is larger than 0.60, you win \$1.
\item If the proportion of heads is larger than 0.40, you win \$1.
\item If the proportion of heads is between 0.40 and 0.60, you win \$1.
\item If the proportion of heads is smaller than 0.30, you win \$1.
\end{parts}
}{}

% 4

\eoce{\qt{Backgammon\label{backgammon}} Backgammon is a board game for two 
players in which the playing pieces are moved according to the roll of two dice. 
Players win by removing all of their pieces from the board, so it is usually good 
to roll high numbers. You are playing backgammon with a friend and you roll two 
6s in your first roll and two 6s in your second roll. Your friend rolls two 3s in 
his first roll and again in his second row. Your friend claims that you are 
cheating, because rolling double 6s twice in a row is very unlikely. Using 
probability, show that your rolls were just as likely as~his.
}{}

% 5

\eoce{\qt{Coin flips\label{coin_flips}} If you flip a fair coin 10 times, what is 
the probability of
\begin{parts}
\item getting all tails? 
\item getting all heads? 
\item getting at least one tails? 
\end{parts}
}{}

% 6

\eoce{\qt{Dice rolls\label{dice_rolls}} If you roll a pair of fair dice, what is 
the probability of
\begin{parts}
\item getting a sum of 1?
\item getting a sum of 5?
\item getting a sum of 12?
\end{parts}
}{}

\D{\newpage}

% 7

\eoce{\qt{Swing voters\label{swing_voters}} A Pew Research survey asked 2,373 
randomly sampled registered voters their political affiliation (Republican, 
Democrat, or Independent) and whether or not they identify as swing voters. 35\% 
of respondents identified as Independent, 23\% identified as swing voters, and 
11\% identified as both.\footfullcite{indepSwing}
\begin{parts}
\item Are being Independent and being a swing voter disjoint, i.e. mutually 
exclusive?
\item Draw a Venn diagram summarizing the variables and their associated 
probabilities.
\item What percent of voters are Independent but not swing voters?
\item What percent of voters are Independent or swing voters?
\item What percent of voters are neither Independent nor swing voters?
\item Is the event that someone is a swing voter independent of the event that 
someone is a political Independent?
\end{parts}
}{}

% 8

\eoce{\qt{Poverty and language\label{poverty_language}} The American Community 
Survey is an ongoing survey that provides data every year to give communities the 
current information they need to plan investments and services. The 2010 American 
Community Survey estimates that 14.6\% of Americans live below the poverty line, 
20.7\% speak a language other than English (foreign language) at home, and 4.2\% 
fall into both categories. \footfullcite{poorLang}
\begin{parts}
\item Are living below the poverty line and speaking a foreign language at home 
disjoint?
\item Draw a Venn diagram summarizing the variables and their associated 
probabilities.
\item What percent of Americans live below the poverty line and only speak 
English at home?
\item What percent of Americans live below the poverty line or speak a foreign 
language at home?
\item What percent of Americans live above the poverty line and only speak 
English at home? 
\item Is the event that someone lives below the poverty line independent of the 
event that the person speaks a foreign language at home?
\end{parts}
}{}

% 9

\eoce{\qt{Disjoint vs. independent\label{disjoint_indep}} In parts~(a) and~(b), 
identify whether the events are disjoint, independent, or neither (events cannot 
be both disjoint and independent).
\begin{parts}
\item You and a randomly selected student from your class both earn A's in this 
course. 
\item You and your class study partner both earn A's in this course.
\item If two events can occur at the same time, must they be dependent?
\end{parts}
}{}

% 10

\eoce{\qt{Guessing on an exam\label{guessing_on_exam}} In a multiple choice exam, 
there are 5 questions and 4 choices for each question (a, b, c, d). Nancy has not 
studied for the exam at all and decides to randomly guess the answers. What is 
the probability that:
\begin{parts}
\item the first question she gets right is the $5^{th}$ question?
\item she gets all of the questions right?
\item she gets at least one question right?
\end{parts}
}{}

\D{\newpage}

% 11

\eoce{\qt{Educational attainment of couples\label{edu_attain_couples}} The table 
below shows the distribution of education level attained by US residents by 
gender based on data collected in the 2010 American Community Survey.
\footfullcite{eduSex}
\begin{center}
\begin{tabular}{l p{7cm} c c }
&                                       & \multicolumn{2}{c}{\textit{Gender}} \\
\cline{3-4}
&                                                   & Male  & Female \\
\cline{2-4}
& Less than 9th grade                               & 0.07  & 0.13 \\
& 9th to 12th grade, no diploma                     & 0.10  & 0.09 \\
\textit{Highest}    & HS graduate (or equivalent)   & 0.30  & 0.20 \\
\textit{education}  & Some college, no degree       & 0.22  & 0.24 \\ 
\textit{attained}   & Associate's degree            & 0.06  & 0.08 \\
& Bachelor's degree                                 & 0.16  & 0.17 \\
& Graduate or professional degree                   & 0.09  & 0.09 \\
\cline{2-4} 
& Total                                             & 1.00  & 1.00
\end{tabular}
\end{center}
\begin{parts}
\item What is the probability that a randomly chosen man has at least a 
Bachelor's degree?
\item What is the probability that a randomly chosen woman has at least a 
Bachelor's degree?
\item What is the probability that a man and a woman getting married both have at 
least a Bachelor's degree? Note any assumptions you must make to answer this 
question.
\item If you made an assumption in part~(c), do you think it was reasonable? If 
you didn't make an assumption, double check your earlier answer and then return 
to this part.
\end{parts}
}{}

% 12

\eoce{\qt{School absences\label{school_absences}} Data collected at elementary 
schools in DeKalb County, GA suggest that each year roughly 25\% of students miss 
exactly one day of school, 15\% miss 2 days, and 28\% miss 3 or more days due to 
sickness. \footfullcite{Mizan:2011}
\begin{parts}
\item What is the probability that a student chosen at random doesn't miss any 
days of school due to sickness this year?
\item What is the probability that a student chosen at random misses no more than 
one day?
\item What is the probability that a student chosen at random misses at least one 
day?
\item If a parent has two kids at a DeKalb County elementary school, what is the 
probability that neither kid will miss any school? Note any assumption you must 
make to answer this question.
\item If a parent has two kids at a DeKalb County elementary school, what is the 
probability that both kids will miss some school, i.e. at least one day? Note any 
assumption you make.
\item If you made an assumption in part~(d) or~(e), do you think it was 
reasonable? If you didn't make any assumptions, double check your earlier answers.
\end{parts}
}{}


================================================
FILE: ch_probability/TeX/random_variables.tex
================================================
\exercisesheader{}

% 29

\eoce{\qt{College smokers\label{college_smokers}} At a university, 13\% of 
students smoke.
\begin{parts}
\item Calculate the expected number of smokers in a random sample of 100 students 
from this university.
\item The university gym opens at 9 am on Saturday mornings. One Saturday morning 
at 8:55 am there are 27 students outside the gym waiting for it to open. Should 
you use the same approach from part (a) to calculate the expected number of 
smokers among these 27 students?
\end{parts}
}{}

% 30

\eoce{\qt{Ace of clubs wins\label{ace_of_clubs}} Consider the following card game 
with a well-shuffled deck of cards. If you draw a red card, you win nothing. If 
you get a spade, you win \$5. For any club, you win \$10 plus an extra \$20 for 
the ace of clubs.
\begin{parts}
\item Create a probability model for the amount you win at this game. Also, find 
the expected winnings for a single game and the standard deviation of the 
winnings.
\item What is the maximum amount you would be willing to pay to play this game? 
Explain your reasoning.
\end{parts}
}{}

% 31

\eoce{\qt{Hearts win\label{hearts}} In a new card game, you start
with a well-shuffled full deck and draw 3 cards without replacement.
If you draw 3 hearts, 
you win \$50. If you draw 3 black cards, you win \$25. For any other draws, you 
win nothing.
\begin{parts}
\item Create a probability model for the amount you win at this game, and find 
the expected winnings. Also compute the standard deviation of this distribution.
\item If the game costs \$5 to play, what would be the expected value and 
standard deviation of the net profit (or loss)? \textit{(Hint: 
profit = winnings $-$ cost; $X-5$)}
\item If the game costs \$5 to play, should you play this game? Explain.
\end{parts}
}{}

% 32

\eoce{\qtq{Is it worth it\label{worth_it}} Andy is always looking for ways to 
make money fast. Lately, he has been trying to make money by gambling. Here is 
the game he is considering playing: The game costs \$2 to play. He draws a card 
from a deck. If he gets a number card (2-10), he wins nothing. For any face card (
jack, queen or king), he wins \$3. For any ace, he wins \$5, and he wins an 
\textit{extra} \$20 if he draws the ace of clubs.
\begin{parts}
\item Create a probability model and find Andy's expected profit per game.
\item Would you recommend this game to Andy as a good way to make money? Explain.
\end{parts}
}{}

% 33

\eoce{\qt{Portfolio return\label{portfolio_return}} A portfolio's value increases 
by 18\% during a financial boom and by 9\% during normal times. It decreases by 
12\% during a recession. What is the expected return on this portfolio if each 
scenario is equally likely?
}{}

% 34

\eoce{\qt{Baggage fees\label{baggage_fees}} An airline charges the following 
baggage fees: \$25 for the first bag and \$35 for the second. Suppose 54\% of 
passengers have no checked luggage, 34\% have one piece of checked luggage and 
12\% have two pieces. We suppose a negligible portion of people check more than 
two bags.
\begin{parts}
\item Build a probability model, compute the average revenue per passenger, and 
compute the corresponding standard deviation.
\item About how much revenue should the airline expect for a flight of 120 
passengers? With what standard deviation? Note any assumptions you make and if 
you think they are justified.
\end{parts}
}{}

% 35

\eoce{\qt{American roulette\label{roulette_american}} The game of American 
roulette involves spinning a wheel with 38 slots: 18 red, 18 black, and 2 green. 
A ball is spun onto the wheel and will eventually land in a slot, where each slot 
has an equal chance of capturing the ball. Gamblers can place bets on red or 
black. If the ball lands on their color, they double their money. If it lands on 
another color, they lose their money. Suppose you bet \$1 on red. What's the 
expected value and standard deviation of your winnings?
}{}

% 36

\eoce{\qt{European roulette\label{roulette_european}} The game of European 
roulette involves spinning a wheel with 37 slots: 18 red, 18 black, and 1 green. 
A ball is spun onto the wheel and will eventually land in a slot, where each slot 
has an equal chance of capturing the ball. Gamblers can place bets on red or 
black. If the ball lands on their color, they double their money. If it lands on 
another color, they lose their money.
\begin{parts}
\item Suppose you play roulette and bet \$3 on a single round. What is the 
expected value and standard deviation of your total winnings?
\item Suppose you bet \$1 in three different rounds. What is the expected value 
and standard deviation of your total winnings?
\item How do your answers to parts (a) and (b) compare? What does this say about 
the riskiness of the two games?
\end{parts}
}{}


================================================
FILE: ch_probability/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 39

\eoce{\qt{Grade distributions\label{grade_dists}} Each row in the table below is 
a proposed grade distribution for a class. Identify each as a valid or invalid 
probability distribution, and explain your reasoning.
\begin{center}
\begin{tabular}{l  ccccc} 
    & \multicolumn{5}{c}{\textit{Grades}} \\
\cline{2-6}
    & A     & B     & C     & D     & F  \\
\cline{2-6}
(a) & 0.3   & 0.3   & 0.3   & 0.2   & 0.1\\
(b) & 0     & 0     & 1     & 0     & 0 \\
(c) & 0.3   & 0.3   & 0.3   & 0     & 0 \\
(d) & 0.3   & 0.5   & 0.2   & 0.1   & -0.1 \\
(e) & 0.2   & 0.4   & 0.2   & 0.1   & 0.1 \\
(f) & 0     & -0.1  & 1.1   & 0     & 0 \\
\end{tabular}
\end{center}
}{}

% 40

\eoce{\qt{Health coverage, frequencies\label{health_coverage_freqs}} The 
Behavioral Risk Factor Surveillance System (BRFSS) is an annual telephone survey 
designed to identify risk factors in the adult population and report emerging 
health trends. The following table summarizes two variables for the respondents: 
health status and health coverage, which describes whether each respondent had 
health insurance. \footfullcite{data:BRFSS2010}
\begin{center}
\begin{tabular}{rrrrrrrr}
                    &       & \multicolumn{5}{c}{\textit{Health Status}} &  \\ 
\cline{3-7}
                    &       & Excellent & Very good & Good  & Fair  & Poor  & Total\\ 
\cline{2-8}
\textit{Health}     & No    & 459       & 727       & 854   & 385   & 99    & 2,524 \\ 
\textit{Coverage}   & Yes   & 4,198     & 6,245     & 4,821 & 1,634 & 578   & 17,476 \\ 
\cline{2-8}
                    & Total & 4,657     & 6,972     & 5,675 & 2,019 & 677   & 20,000
\end{tabular}
\end{center}
\begin{parts}
\item If we draw one individual at random, what is the probability that the 
respondent has excellent health and doesn't have health coverage?
\item If we draw one individual at random, what is the probability that the 
respondent has excellent health or doesn't have health coverage?
\end{parts}
}{}

% 41

\eoce{\qt{HIV in Swaziland\label{tree_hiv_swaziland}} Swaziland has the highest 
HIV prevalence in the world: 25.9\% of this country's population is infected with 
HIV.\footfullcite{ciaFactBookHIV:2012} The ELISA test is one of the first and 
most accurate tests for HIV. For those who carry HIV, the ELISA test is 99.7\% 
accurate. For those who do not carry HIV, the test is 92.6\% accurate. If an 
individual from Swaziland has tested positive, what is the probability that he 
carries HIV?
}{}

% 42

\eoce{\qt{Twins\label{tree_twins}} About 30\% of human twins are identical, and 
the rest are fraternal. Identical twins are necessarily the same sex -- half are 
males and the other half are females. One-quarter of fraternal twins are both 
male, one-quarter both female, and one-half are mixes: one male, one female. You 
have just become a parent of twins and are told they are both girls. Given this 
information, what is the probability that they are identical?
}{}

% 43

\eoce{\qt{Cost of breakfast\label{cost_of_breakfast}} Sally gets a cup of coffee 
and a muffin every day for breakfast from one of the many coffee shops in her 
neighborhood. She picks a coffee shop each morning at random and independently of 
previous days. The average price of a cup of coffee is \$1.40 with a standard 
deviation of 30\textcent{} (\$0.30), the average price of a muffin is \$2.50 with a 
standard deviation of 15\textcent{}, and the two prices are independent of each 
other.
\begin{parts}
\item What is the mean and standard deviation of the amount she spends on 
breakfast daily?
\item What is the mean and standard deviation of the amount she spends on 
breakfast weekly (7~days)?
\end{parts}
}{}

\D{\newpage}

% 44

\eoce{\qt{Scooping ice cream\label{scoop_ice_cream}} Ice cream usually comes in 1.5 
quart boxes (48 fluid ounces), and ice cream scoops hold about 2 ounces. 
However, there is some variability in the amount of ice cream in a box as well as 
the amount of ice cream scooped out. We represent the amount of ice cream in the 
box as $X$ and the amount scooped out as $Y$. Suppose these random variables have 
the following means, standard deviations, and variances:
\begin{center}
\begin{tabular}{l ccc}
\hline
    & mean & SD & variance \\
\hline
$X$ & 48       & 1      & 1     \\
$Y$ & 2    & 0.25   & 0.0625    \\
\hline
\end{tabular}
\end{center}
\begin{parts}
\item An entire box of ice cream, plus 3 scoops from a second box is served at a 
party. How much ice cream do you expect to have been served at this party? What 
is the standard deviation of the amount of ice cream served?
\item How much ice cream would you expect to be left in the box after scooping 
out one scoop of ice cream? That is, find the expected value of $X-Y$. What is 
the standard deviation of the amount left in the box?
\item Using the context of this exercise, explain why we add variances when we 
subtract one random variable from another.
\end{parts}
}{}

% 45

\eoce{\qt{Variance of a mean, Part I\label{var_of_mean_1}}
Suppose we have independent observations $X_1$ and $X_2$ from
a distribution with mean $\mu$ and standard deviation $\sigma$.
What is the variance of the mean of the two values:
$\frac{X_1 + X_2}{2}$?
}{}

% 46

\eoce{\qt{Variance of a mean, Part II\label{var_of_mean_2}}
Suppose we have 3 independent observations
$X_1$, $X_2$, $X_3$ from
a distribution with mean $\mu$ and standard deviation $\sigma$.
What is the variance of the mean of these 3 values:
$\frac{X_1 + X_2 + X_3}{3}$?
}{}

% 47

\eoce{\qt{Variance of a mean, Part III\label{var_of_mean_3}}
Suppose we have $n$ independent observations
$X_1$, $X_2$, ..., $X_n$ from
a distribution with mean $\mu$ and standard deviation $\sigma$.
What is the variance of the mean of these $n$ values:
$\frac{X_1 + X_2 + \dots + X_n}{n}$?
}{}


================================================
FILE: ch_probability/TeX/sampling_from_a_small_population.tex
================================================
\exercisesheader{}

% 23

\eoce{\qt{Marbles in an urn\label{marbles_in_urn}} Imagine you have an urn 
containing 5 red, 3 blue, and 2 orange marbles in it. 
\begin{parts}
\item What is the probability that the first marble you draw is blue?
\item Suppose you drew a blue marble in the first draw. If drawing with 
replacement, what is the probability of drawing a blue marble in the second draw?
\item Suppose you instead drew an orange marble in the first draw. If drawing 
with replacement, what is the probability of drawing a blue marble in the second 
draw?
\item If drawing with replacement, what is the probability of drawing two blue 
marbles in a row?
\item When drawing with replacement, are the draws independent? Explain.
\end{parts}
}{}

% 24

\eoce{\qt{Socks in a drawer\label{socks_in_drawer}} In your sock drawer you have 
4 blue, 5 gray, and 3 black socks. Half asleep one morning you grab 2 socks at 
random and put them on. Find the probability you end up wearing
\begin{parts}
\item 2 blue socks
\item no gray socks
\item at least 1 black sock
\item a green sock
\item matching socks
\end{parts}
}{}

% 25

\eoce{\qt{Chips in a bag\label{chips_in_bag}} Imagine you have a bag 
containing 5 red, 3 blue, and 2 orange chips.
\begin{parts}
\item Suppose you draw a chip and it is blue. If drawing without replacement, 
what is the probability the next is also blue?
\item Suppose you draw a chip and it is orange, and then you draw a second chip 
without replacement. What is the probability this second chip is blue?
\item If drawing without replacement, what is the probability of drawing two blue 
chips in a row?
\item When drawing without replacement, are the draws independent? Explain.
\end{parts}
}{}

% 26

\eoce{\qt{Books on a bookshelf\label{books_on_shelf}} The table below shows the 
distribution of books on a bookcase based on whether they are nonfiction or 
fiction and hardcover or paperback.
\begin{center}
\begin{tabular}{ll  cc c} 
                                &           & \multicolumn{2}{c}{\textit{Format}} \\
\cline{3-4}
                                &           & Hardcover     & Paperback     & Total \\
\cline{2-5}
\multirow{2}{*}{\textit{Type}}  & Fiction   & 13            & 59            & 72 \\
                                & Nonfiction& 15            & 8             & 23 \\
\cline{2-5} 
                                & Total     & 28            & 67            & 95 \\
\cline{2-5}
\end{tabular}
\end{center}
\begin{parts}
\item Find the probability of drawing a hardcover book first then a paperback 
fiction book second when drawing without replacement.
\item Determine the probability of drawing a fiction book first and then a 
hardcover book second, when drawing without replacement.
\item Calculate the probability of the scenario in part~(b), except this time 
complete the calculations under the scenario where the first book is placed back 
on the bookcase before randomly drawing the second book.
\item The final answers to parts~(b) and~(c) are very similar. Explain why this 
is the case.
\end{parts}
}{}

% 27

\eoce{\qt{Student outfits\label{student_outfits}} In a classroom with 24 
students, 7 students are wearing jeans, 4 are wearing shorts, 8 are wearing 
skirts, and the rest are wearing leggings. If we randomly select 3 students 
without replacement, what is the probability that one of the selected students is 
wearing leggings and the other two are wearing jeans? Note that these are 
mutually exclusive clothing options.
}{}

% 28

\eoce{\qt{The birthday problem\label{birthday_problem}} Suppose we pick three 
people at random. For each of the following questions, ignore the special case 
where someone might be born on February 29th, and assume that births are evenly 
distributed throughout the year.
\begin{parts}
\item What is the probability that the first two people share a birthday? 
\item What is the probability that at least two people share a birthday?
\end{parts}
}{}


================================================
FILE: ch_probability/figures/BreastCancerTreeDiagram/BreastCancerTreeDiagram.R
================================================
library(openintro)

myPDF("BreastCancerTreeDiagram.pdf", 7.5, 2.5)
treeDiag(c('Truth', 'Mammogram'),
         c(0.0035, 0.9965),
         list(c(0.89, 0.11),
              c(0.07, 0.93)),
         textwd = 0.2,
         solwd = 0.35,
         cex.main = 1.4,
         c('cancer', 'no cancer'),
         c('positive','negative'),
         digits = 5,
         col.main = COL[1],
         showWork = TRUE)
dev.off()


================================================
FILE: ch_probability/figures/BreastCancerTreeDiagram/Mammogram Research.txt
================================================

Two studies in Canada
http://www.breastcancer.org/symptoms/testing/new_research/20090831b.jsp
- Mammograms were 89% effective in detecting breast cancer
- 7.4% of screenings using mammogram alone resulte in false positive
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1173421/
- About 0.35% of women have breast cancer

		Mammogram
	Cancer	+, 0.89
	Y, 0.0035
		-, 0.11
1.00
		+, 0.07
	N, 0.9965
		-, 0.93

treeDiag(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)

		Cancer
	Mamm.	Y, 0.04
	+, 0.07
		N, 0.96
1.00
		Y, 0.001
	-, 0.93
		N, 0.999







Wikipedia (no source)
1000 -> 70 called back for diagnostic session -> 10 referred for biopsy -> 3.5 have cancer


http://www.ucsf.edu/news/2011/10/10778/high-rate-false-positives-annual-mammogram
Over 1 decade, age 50 and up
61% of population has false positive



http://ww5.komen.org/BreastCancer/AccuracyofMammograms.html
			1.00
Mammogram	+, ?	
	





http://www.acponline.org/pressroom/mammo_study.htm




http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1173421/






================================================
FILE: ch_probability/figures/bookCostDist/bookCostDist.R
================================================
library(openintro)
data(COL)

make.bar <- function(at,
                     height,
                     thickness = NA,
                     col = NA) {
  if(is.na(thickness)){
    R <- range(at)
    minDiff <- min(diff(at))
    thickness <- min(minDiff, diff(R) / 12)
  }
  x1 <- at - thickness / 2
  x2 <- at + thickness / 2
  if(is.na(col)) {
    col <- 'grey'
  }
  for (i in 1:length(at)) {
    rect(x1[i], 0,
         x2[i], height[i],
         col = col)
  }
}

probDist <- function(x,
                     prob,
                     labels1 = NA,
                     labels2 = NA,
                     thickness = NA,
                     col = NA,
                     ylim = NULL,
                     ...) {
  R <- range(x)
  R <- R + c(-1, 1) * diff(R)/20
  Ry <- c(0, range(prob)[2])
  if(!is.null(ylim)[1]){
    Ry <- ylim
  }
  plot(x, prob,
       type = 'n',
       axes = FALSE,
       xlim = R,
       ylim = Ry,
       ...)
  if (is.na(labels1)[1]) {
    labels1 <- x
  }
  if (is.na(labels2)[1]) {
    labels2 <- TRUE
  }
  axis(1, at = x, labels = paste0("$", labels1))
  make.bar(x, prob, thickness = thickness, col = col)
}

myPDF('bookCostDist.pdf', 5, 2.3)
at <- c(0, 137, 170)
prob <- c(0.2, .55, .25)

par(mar = c(2.9, 4, 0.1, 0.5),
    mgp = c(1.7, 0.7, 0))
probDist(at, prob,
         xlab = 'Cost',
         ylab = '',
         ylim = c(-0.02, 0.55),
         col = COL[1])
axis(2, at = seq(0, 0.4, 0.2))
lines(c(-10, 180), c(0,0))
polygon(117.85 + c(-17, 17, 0),
        c(-0.08, -0.08, 0),
        col = COL[4])
par(las = 0)
mtext('Probability', side = 2, line = 2.8)
dev.off()


================================================
FILE: ch_probability/figures/bookWts/bookWts.R
================================================
library(openintro)
data(COL)

at <- c(0, 137, 170)
wt <- c(0.2, 0.55, 0.25)

createWtSystem <- function(at, wt, size = 1, label = TRUE){
  R <- range(at)
  r <- diff(R)
  W <- range(wt)
  M <- weighted.mean(at, wt)
  par(mar = rep(0, 4))
  plot(R + c(-1, 1) * r / 12,
       0:1,
       type = 'n')

  # make hanger
  x <- c(M, M)
  y <- c(0.7, 1.0)
  lines(x, y)

  # make the board
  rect(R[1],0.685,R[2],0.7)

  # add weights
  for(i in 1:length(at)) {
    createWt(at[i],wt[i], size)
  }

  # label
  if(label){
    text(at, rep(0.74, length(at)), at)
    text(M, 0.64, M)
  }
}

createWt <- function(at, wt, size = 1){
  # hook
  x <- rep(at, 2)
  y <- c(0.64, 0.6925)
  lines(x, y)

  # the weight
  x <- x + c(-1, 1) * size
  y <- c(0.64, 0.64 - wt)
  rect(x[1], y[1],
       x[2], y[2],
       col = COL[1])
}

myPDF('bookWts.pdf', 5.5, 3)
createWtSystem(at, wt, 5, TRUE)
dev.off()


================================================
FILE: ch_probability/figures/cardsDiamondFaceVenn/cardsDiamondFaceVenn.R
================================================
library(openintro)
data(COL)

myPDF('cardsDiamondFaceVenn.pdf', 1.2 * 4.2, 1.2 * 1.7,
      mar = c(0.2, 0.2, 0.2, 0.2))
plot(c(0.2, 2.5),
     c(-0.13, 1.15),
     type = 'n',
     axes = FALSE)

z <- seq(0,2 * pi, len = 99)
x2 <- cos(z) / 2 + 1.3
y2 <- sin(z) / 3 + 0.5
polygon(c(x2, x2[1]), c(y2, y2[1]), col = COL[3,3])

x1 <- cos(z) / 2 + 0.7
y1 <- sin(z) / 3 + 0.5
polygon(c(x1, x1[1]),c(y1, y1[1]), col = COL[1,3])

text(c(0.55, 1, 1.45),
     rep(0.57, 3),
     c(10, 3, 9),
     cex = c(1.3, 1.2, 1.3))
text(c(0.55, 1, 1.45),
     c(0.41, 0.43, 0.41),
     c('0.1923', '0.0577', '0.1731'),
	 cex = c(1, 0.9, 1))
# text(0.5, -0.25, 'Other cards: 30', cex = 0.8)
# text(0.98, -0.26, '(0.5769)', cex = 0.8)
text(2.25, 0.55, cex = 0.8,
     paste("There are also", "30 cards that are",
           "neither diamonds", "nor face cards", sep = "\n"))
# text(2.25, 0.28, '(0.5769)', cex = 0.8)
Braces(0.7, 0.92, 3 * pi / 2, 0.98, 0.12)
text(0.7, 1.09, 'Diamonds, 0.2500')
Braces(1.3, 0.08, pi / 2, 0.98, 0.12)
text(1.3, -0.08, 'Face cards, 0.2308')
dev.off()


================================================
FILE: ch_probability/figures/changeInLeonardsStockPortfolioFor36Months/changeinleonardsstockportfoliofor36months.R
================================================
library(openintro)

t <- c("cat", "xom")
s <- stocks_18[t]
apply(s, 2, mean)
apply(s, 2, sd)
apply(s, 2, var)
cor(s)
summary(lm(s))
ret <- 6000 * s$cat + 2000 * s$xom
# baselines <- c(cat = 65.39, goog = 742.60, xom = 72.33)
# dates <- stocks_18$date


myPDF("changeInLeonardsStockPortfolioFor36Months.pdf", 5, 2.15,
      mar = c(3.5, 0.5, 0.5, 0.5),
      mgp = c(2.3, 0.6, 0))
boxPlot(ret,
        main = "",
        xlab = "Monthly Returns Over 3 Years",
        ylab = "",
        horiz = TRUE,
        axes = FALSE,
        ylim = c(0.6, 1.4))
points(ret,
       rep(0.9, 36),
       col = COL[1, 3],
       pch = 19)
buildAxis(1, ret, 2, 4)
dev.off()


================================================
FILE: ch_probability/figures/complementOfD/complementOfD.R
================================================
library(openintro)
data(COL)

pdf('complementOfD.pdf', 4, 1.05)
par(mar = rep(0, 4))
plot(c(-0.05, 1), c(0.18, 0.92), type = 'n', axes = FALSE)

for(i in c(1,4,5,6)){
  text(i / 7, 0.5, i)
}
for(i in 2:3){
  text(i / 7, 0.55, i)
}
theta <- seq(0,2 * pi,length.out = 100)

# _____ D _____ #
lines(1 / 7 * cos(theta) + 2.5 / 7,
      1 / 9 * sin(theta) + 0.55,
      lty = 3,
      col = COL[4],
      lwd = 2.425)
text(2.5 / 7, 0.75, 'D', col = COL[4])

# _____ D^c _____ #
x <- 1 / 20 * cos(seq(0.5, 3 * pi / 2, length.out = 20)) + 1 / 7
y <- 1 / 5 * sin(seq(0.2, 3 * pi / 2, length.out = 20)) + 0.5
x <- c(x, 1 / 20 * cos(seq(-pi / 2, pi / 2, length.out = 20)) + 6 / 7)
y <- c(y, 0.175 * sin(seq(-pi / 2, pi / 2, length.out = 20)) + 0.47)
x <- c(x, 1 / 20 * cos(seq(pi / 2, pi, length.out = 10)) + 4 / 7)
y <- c(y, 1 / 5 * sin(seq(pi / 2, pi-0.5, length.out = 10)) + .45)
x <- c(x, seq(1 / 2, 3 / 14, length.out = 10))
y <- c(y, seq(-0.35, 0.35, length.out = 10)^2 + 0.33)
x <- c(x, x[1])
y <- c(y, y[1])
lines(x, y, lty = 2, col = COL[2])
text(5 / 7, 0.75, expression(D^C), col = COL[2])

# _____ S _____ #
x <- 1 / 10 * cos(seq(pi / 2, 3 * pi / 2, length.out = 20)) + 1 / 9
y <- 1 / 3 * sin(seq(pi / 2, 3 * pi / 2, length.out = 20)) + 0.55
x <- c(x, 1 / 10 * cos(seq(-pi / 2, pi / 2, length.out = 20)) + 8 / 9)
y <- c(y, 1 / 3 * sin(seq(-pi / 2, pi / 2, length.out = 20)) + 0.55)
#x <- c(x, 1 / 20 * cos(seq(pi / 2, pi, length.out = 10)) + 4 / 7)
#y <- c(y, 1 / 5 * sin(seq(pi / 2, pi-0.5, length.out = 10)) + .45)
#x <- c(x, seq(1 / 2, 3 / 14, length.out = 10))
#y <- c(y, seq(-0.35, 0.35, length.out = 10)^2 + 0.33)
x <- c(x, x[1])
y <- c(y, y[1])
lines(x, y, lty = 1, col = COL[1])
text(0, 0.55, expression(S), col = COL[1], pos = 2, cex = 1.3)

dev.off()


================================================
FILE: ch_probability/figures/contBalance/contBalance.R
================================================
library(openintro)
data(COL)

x <- seq(0, 22, 0.01)
y <- dchisq(x, 5)
M <- weighted.mean(x, y)

pdf('contBalance.pdf', 4, 2.2)
par(mar = c(1.65, 0, 0, 0), mgp = c(5, 0.5, 0))
plot(x, y + 0.035,
     type = 'l',
     ylim = range(c(0.025, y + 0.035)),
     axes = FALSE)
axis(1, at = c(-100, M, 100), labels = c('', expression(mu), ''))
lines(c(0, 22), rep(0.035, 2))
polygon(x, y + 0.035, col = COL[1])
polygon(c(M - 20, M + 20, M),
        c(-0.2, -0.2, 0.035),
        col = COL[4])
dev.off()


================================================
FILE: ch_probability/figures/diceSumDist/diceSumDist.R
================================================
library(openintro)
data(COL)

probDist <- function(x,
                     prob,
                     labels1 = NA,
                     labels2 = NA,
                     thickness = NA,
                     col = NA,
                     ylim = NULL,
                     ...) {
  R <- range(x)
  R <- R + c(-1,1)*(R[2]-R[1])/20
  Ry <- c(0, range(prob)[2])
  if (!is.null(ylim)[1]) {
    Ry <- ylim
  }
  plot(x, prob, type = 'n', axes = F, xlim = R, ylim = Ry, ...)
  if(is.na(labels1)[1]) labels1 <- x
  if(is.na(labels2)[1]) labels2 <- TRUE
  axis(1, at = x, labels = labels1)
  make.bar(x, prob, thickness = thickness, col = col)
}

make.bar <- function(at,
                     height,
                     thickness = NA,
                     col = NA) {
  if (is.na(thickness)) {
    R <- range(at)
    minDiff <- min(diff(at))
    thickness <- min(c(minDiff), (R[2]-R[1])/12)
  }
  x1 <- at - thickness/2
  x2 <- at + thickness/2
  if (is.na(col)) {
    col <- 'grey'
  }
  for (i in 1:length(at)) {
    rect(x1[i], 0, x2[i], height[i], col = col)
  }
}

at = 2:12
prob = c(1:6, 5:1)/36

myPDF('diceSumDist.pdf', 5.5, 3,
      mar = c(3.3, 4.5, 0.8, 1),
      mgp = c(2, 0.55, 0))
probDist(at, prob,
         xlab = 'Dice Sum',
         ylab = '',
         thickness = 0.5,
         col = COL[1])
abline(h = 0)
axis(2)
mtext('Probability', side = 2, 3.3, las = 0)
dev.off()


================================================
FILE: ch_probability/figures/dieProp/dieProp.R
================================================
library(openintro)
data(COL)

# _____ Simulate _____ #
set.seed(51)
n <- 10^5
x <- sample(0:1, n, TRUE, p = c(5 / 6, 1 / 6))
y <- cumsum(x) / 1:n
X <- c(1:100, seq(102, 500, 2),
	seq(510, 1500, 10), seq(1550, 10000, 50),
	seq(10100, 25000, 100), seq(25250, 100000, 250))
Y <- y[X]

# _____ Plotting _____ #
myPDF('dieProp.pdf', 6.5, 3,
      mar = c(3.8, 3.8, 0.5, 1))
plot(X, Y,
     log = 'x',
     type = 'l',
     xlab = '',
     ylab = '',
     axes = FALSE,
     col = COL[1],
     lwd = 2)
mtext('n (number of rolls)', side = 1, line = 2.5)
abline(h = 1 / 6, lty = 2)
at <- 10^(0:5)
labels <- c('1', '10', '100', '1,000', '10,000', '100,000')
axis(1, at, labels)
axis(2, at = seq(0, 0.3, 0.1))
axis(2, at = seq(0.05, 0.3, 0.1), labels = rep(NA, 3), tcl = -0.15)
at <- 1 / 6
labels <- expression(paste(hat(p)[n]))
axis(2, at, labels,
     line = 2.3,
     tick = FALSE,
     cex.axis = 1.1)
dev.off()


================================================
FILE: ch_probability/figures/disjointSets/disjointSets.R
================================================
library(openintro)
data(COL)

pdf('disjointSets.pdf', 3.35, 0.8)
par(mar = rep(0, 4))
plot(c(0.05, 0.95),
     c(0.13, 0.82),
     type = 'n',
     axes = FALSE)

for(i in 1:6){
  text(i / 7, 0.5, i)
}
theta <- seq(0, 2 * pi, length.out = 100)

# _____ A _____ #
lines(1 / 7 * cos(theta) + 1.5 / 7,
      1 / 6 * sin(theta) + 0.5,
      col = COL[1])
text(1.5 / 7, 0.75, 'A', col = COL[1])

# _____ B _____ #
x <- 1 / 15 * cos(seq(3 * pi / 2, 3 * pi-0.3, length.out = 40)) + 6 / 7
y <- 1 / 6 * sin(seq(3 * pi / 2, 3 * pi, length.out = 40)) + 0.5
x <- c(x, seq(11 / 14, 9 / 14, length.out = 10))
y <- c(y, seq(-0.3, 0.3, length.out = 10)^2 + 0.4)
x <- c(x, 1 / 15 * cos(seq(0.3, 3 * pi / 2, length.out = 40)) + 4 / 7)
y <- c(y, 1 / 6 * sin(seq(0, 3 * pi / 2, length.out = 40)) + 0.5)
x <- c(x, x[1])
y <- c(y, y[1])
lines(x, y, lty = 2, col = COL[2])
text(5 / 7, 0.2, 'B', col = COL[2])

# _____ D _____ #
lines(1 / 7 * cos(theta) + 2.5 / 7,
      1 / 6 * sin(theta) + 0.5,
      lty = 3,
      col = COL[4],
      lwd = 2.425)
text(2.5 / 7, 0.75, 'D', col = COL[4])

dev.off()


================================================
FILE: ch_probability/figures/eoce/cat_weights/cat_weights.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load MASS for data ------------------------------------------------
library(MASS)
data(cats)

# histogram of weights ----------------------------------------------
pdf("cat_weights.pdf", 5.5, 4.3)
par(mar=c(3.7, 2.2, 0.5, 0.5), las=1, mgp=c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
hist(cats$Bwt, breaks = seq(2, 4, 0.25), ylim = c(0, 35), 
     xlab = "Body weight", col = COL[1], main = "", 
     axes = FALSE)
axis(1)
axis(2, at = seq(0,40,10))
dev.off()

================================================
FILE: ch_probability/figures/eoce/poverty_language/poverty_language.R
================================================
# load openintro package for colors ---------------------------------
library("openintro")

# draw venn diagram -------------------------------------------------
venn.plot <- draw.pairwise.venn(
  area1 = 146,
  area2 = 207,
  cross.area = 42,
  category = c("below PL", "speak FL"),
  fill = c(COL[1,3], COL[2,3]),
  lty = "blank",
  cex = 2,
  cat.cex = 2,
  cat.pos = c(20, -30),
  cat.dist = 0.09,
  cat.just = list(c(-1, -1), c(1, 1)),
  ext.pos = 30,
  ext.dist = -0.05,
  ext.length = 0.85,
  ext.line.lwd = 2,
  ext.line.lty = "dashed"
);
grid.draw(venn.plot)

tiff(filename = "poverty_language.tiff", compression = "lzw");
grid.draw(venn.plot);
dev.off();

================================================
FILE: ch_probability/figures/eoce/swing_voters/swing_voters.R
================================================
# load openintro package for colors ---------------------------------
library("openintro")

# draw venn diagram -------------------------------------------------
venn.plot <- draw.pairwise.venn(
  area1 = 35,
  area2 = 23,
  cross.area = 11,
  category = c("Independent", "Swing"),
  fill = c(COL[1,3], COL[2,3]),
  lty = "blank",
  cex = 2,
  cat.cex = 2,
  cat.pos = c(310, 105),
  cat.dist = 0.09,
  cat.just = list(c(-1, -1), c(1, 1)),
  ext.pos = 30,
  ext.dist = -0.05,
  ext.length = 0.85,
  ext.line.lwd = 2,
  ext.line.lty = "dashed"
);
grid.draw(venn.plot)

tiff(filename = "swing_voters.tiff", compression = "lzw");
grid.draw(venn.plot);
dev.off();

================================================
FILE: ch_probability/figures/eoce/tree_drawing_box_plots/tree_drawing_box_plots.R
================================================
# load openintro for treeDiag function ------------------------------
library(openintro)

# tree --------------------------------------------------------------
pdf("tree_drawing_box_plots.pdf", width = 6, height = 2.5)
treeDiag(c("\nCan construct\nbox plots?", "Passed?"), 
         c(0.80, 0.20), list(c(0.86, 0.14), c(0.65, 0.35)), 
         c("yes", "no"), textwd = 0.19, solwd = 0.25, showWork = TRUE,
         col.main = COL[1])
dev.off()

================================================
FILE: ch_probability/figures/eoce/tree_exit_poll/tree_exit_poll.R
================================================
# load openintro for treeDiag function ------------------------------
library(openintro)

# tree --------------------------------------------------------------
pdf("tree_exit_poll.pdf", width = 6, height = 3)
treeDiag(c("Support Walker", "College degree"), 
         c(0.53, 0.47), p2=list(c(0.37, 0.63), c(0.44, 0.56)), 
         cex.main=1.1, col.main = COL[1])
dev.off()

================================================
FILE: ch_probability/figures/eoce/tree_hiv_swaziland/tree_hiv_swaziland.R
================================================
# load openintro for treeDiag function ------------------------------
library(openintro)

# tree --------------------------------------------------------------
pdf("tree_hiv_swaziland.pdf", width = 7, height = 2.5)
treeDiag(c("HIV?", "Result"), 
         c(0.259, 1-0.259), list(c(0.997, 0.003), c(1-0.926, 0.926)), 
         c("yes","no"), c("positive","negative"), 
         textwd=0.19, solwd=0.25, showWork=TRUE,
         col.main = COL[1])
dev.off()

================================================
FILE: ch_probability/figures/eoce/tree_lupus/tree_lupus.R
================================================
# load openintro for treeDiag function ------------------------------
library(openintro)

# tree --------------------------------------------------------------
pdf("tree_lupus.pdf", width = 6, height = 3)
treeDiag(c("Lupus?", "Result"), 
         c(0.02, 0.98), list(c(0.98, 0.02), c(0.26, 0.74)), 
         c("yes","no"), c("positive","negative"), 
         textwd=0.19, solwd=0.25, showWork=TRUE,
         col.main = COL[1])
dev.off()

================================================
FILE: ch_probability/figures/eoce/tree_thrombosis/tree_thrombosis.R
================================================
# load openintro for treeDiag function ------------------------------
library(openintro)

# tree --------------------------------------------------------------
pdf("tree_thrombosis.pdf", width = 6, height = 2.5)
treeDiag(c("Predisposition?", "Result"), 
         c(0.03, 0.97), list(c(0.99, 0.01), c(0.02, 0.98)), c("yes","no"), 
         c("positive","negative"), textwd=0.19, solwd=0.25, showWork=TRUE,
         col.main = COL[1])
dev.off()

================================================
FILE: ch_probability/figures/eoce/tree_twins/tree_twins.R
================================================
# load openintro for treeDiag function ------------------------------
library(openintro)

# tree --------------------------------------------------------------
pdf("tree_twins.pdf", width = 10, height = 3.5)
treeDiag(main = c("Type of twins","Gender"), 
         p1 = c(0.3, 0.7), p2 = list(c(0.5,0.5,0), c(0.25,0.25,0.5)), 
         out1 = c("identical","fraternal"), 
         out2 = c("males","females","male&female"), 
         showWork = TRUE, textwd=0.19, solwd=0.25,
         col.main = COL[1])
dev.off()

================================================
FILE: ch_probability/figures/fdicHeightContDist/fdicHeightContDist.R
================================================
library(openintro)
data(COL)

# _____ Load Data Set From fdicHistograms _____ #
load("../fdicHistograms/fdicHistograms.rda")

BR <- list()
MIDS <- br[-1]-0.25
BR[[1]] <- seq(110, 210, 10)
BR[[2]] <- seq(115, 210, 2.5)
COUNTS <- list()
for (i in 1:2) {
  COUNTS[[i]] <- rep(0, length(BR[[i]])-1)
  for (j in 1:(length(BR[[i]]) - 1)) {
    these <- apply(cbind(MIDS < BR[[i]][j + 1],
                         MIDS >= BR[[i]][j]),
                   1,
                   all)
    if (any(these)) {
      COUNTS[[i]][j] <- sum(counts[these])
    }
  }
}

histTemp <- function(
    BR, COUNTS, col = fadeColor(COL[1], "10"),
    border = COL[1, 4], probability = TRUE,
    xlab = '', ylab = NULL, xlim = NULL, ylim = NULL,
    ...) {
  br <- BR
  h  <- COUNTS
  if (probability) {
    h <- h/sum(h)/diff(br)
  }
  if (is.null(ylab)) {
    ylab <- 'frequency'
    if (probability) {
      ylab <- 'probability'
    }
  }
  if (is.null(xlim)[1]) {
    xR <- range(br)
    xlim <- xR + c(-0.05, 0.05)*diff(xR)
  }
  if (is.null(ylim)[1]) {
    ylim <- range(c(0,h))
  }
  plot(-1, -1,
       xlab = xlab,
       ylab = ylab,
       xlim = xlim,
       ylim = ylim,
       type = 'n',
       ...)
  abline(h = 0)
  lines(c(br[1], br[1]), c(0, h[1]), col = border)
  for (i in 1:length(h)) {
    if (i > 1) {
      if (h[i] > h[i - 1]) {
        lines(rep(br[i], 2), h[c(i - 1, i)], col = border)
      }
    }
    lines(br[i + 0:1], rep(h[i], 2), col = border)
    lines(rep(br[i + 1], 2), c(0, h[i]), col = border)
    rect(br[i], 0, br[i + 1], h[i], col = col, border = border)
  }
}


pdf('fdicHeightContDist.pdf', 6.67, 3.22)
par(mfrow = c(1, 1),
    mar = c(3, 1, 0.1, 1),
    mgp = c(1.8, 0.7, 0))
histTemp(BR[[2]],
         COUNTS[[2]],
         xlab = 'height (cm)',
         axes = FALSE,
         xlim = c(125, 210),
         col = fadeColor(COL[1], "10"),
         border = COL[1,4])
axis(1)
lines(dens$x, dens$y,
      col = COL[1],
      lwd = 2)
dev.off()


================================================
FILE: ch_probability/figures/fdicHeightContDistFilled/fdicHeightContDistFilled.R
================================================
library(openintro)
data(COL)

# _____ Load Data Set From fdicHistograms _____ #
load("../fdicHistograms/fdicHistograms.rda")

BR <- list()
MIDS <- br[-1] - 0.25
BR[[1]] <- seq(110, 210, 10)
BR[[2]] <- seq(115, 210, 2.5)
COUNTS <- list()
for (i in 1:2) {
  COUNTS[[i]] <- rep(0, length(BR[[i]]) - 1)
  for (j in 1:(length(BR[[i]]) - 1)) {
    these <- apply(cbind(MIDS < BR[[i]][j + 1],
                         MIDS >= BR[[i]][j]),
                   1,
                   all)
    if (any(these)) {
      COUNTS[[i]][j] <- sum(counts[these])
    }
  }
}

BR <- list()
MIDS <- br[-1] - 0.25
BR[[1]] <- seq(110, 210, 10)
BR[[2]] <- seq(115, 210, 2.5)
COUNTS <- list()
for (i in 1:2) {
  COUNTS[[i]] <- rep(0, length(BR[[i]]) - 1)
  for (j in 1:(length(BR[[i]]) - 1)) {
    these <- apply(cbind(MIDS < BR[[i]][j + 1],
                         MIDS >= BR[[i]][j]),
                   1,
                   all)
    if (any(these)) {
      COUNTS[[i]][j] <- sum(counts[these])
    }
  }
}


histTemp <- function(
    BR, COUNTS, col = fadeColor(COL[1], "10"),
    border = COL[1, 4], probability = TRUE,
    xlab = '', ylab = NULL,
    xlim = NULL, ylim = NULL,
    ...) {
  br <- BR
  h  <- COUNTS
  if (probability) {
    h <- h/sum(h)/diff(br)
  }
  if (is.null(ylab)) {
    ylab <- 'frequency'
    if (probability) {
      ylab <- 'probability'
    }
  }
  if (is.null(xlim)[1]) {
    xR <- range(br)
    xlim <- xR + c(-0.05, 0.05)*diff(xR)
  }
  if (is.null(ylim)[1]) {
    ylim <- range(c(0,h))
  }
  plot(-1, -1,
       xlab = xlab,
       ylab = ylab,
       xlim = xlim,
       ylim = ylim,
       type = 'n',
       ...)
  abline(h = 0)
  lines(c(br[1],br[1]), c(0,h[1]), col = border)
  for (i in 1:length(h)) {
    if (i > 1) {
      if (h[i] > h[i-1]) {
        lines(rep(br[i],2), h[c(i-1,i)], col = border)
      }
    }
    lines(br[i + 0:1],
          rep(h[i], 2),
          col = border)
    lines(rep(br[i + 1], 2),
          c(0, h[i]),
          col = border)
    rect(br[i], 0,
         br[i + 1], h[i],
         col = col,
         border = border)
  }
}

pdf('fdicHeightContDistFilled.pdf', 5.7, 2.75)
par(mfrow = c(1, 1),
    mar = c(3, 1, 0.1, 1),
    mgp = c(1.8, 0.7, 0))
histTemp(BR[[2]],
         COUNTS[[2]],
         col = fadeColor(COL[1], "10"),
         border = COL[1,4],
         xlim = c(125, 210),
         axes = FALSE,
         xlab = 'height (cm)',
         ylab = '',
         probability = TRUE)
axis(1)
lines(dens$x, dens$y, col = COL[1], lwd = 2)
these <- dens$x > 180 & dens$x < 185
polygon(c(dens$x[these][1], dens$x[these], rev(dens$x[these])[1]),
        c(0, dens$y[these], 0),
        col = COL[1],
        border = COL[1])
sum(dens$y[these] * diff(dens$x[1:2]))
dev.off()


================================================
FILE: ch_probability/figures/fdicHistograms/fdicHistograms.R
================================================
library(openintro)
data(COL)
load("fdicHistograms.rda")

MIDS <- br[-1] - diff(br[1:2]) / 2
BR <- list()
BR[[1]] <- seq(110, 210, 10)
BR[[2]] <- seq(115, 210, 5)
BR[[3]] <- seq(110, 210, 2)
BR[[4]] <- seq(110, 210, 1)
COUNTS <- list()
for (i in 1:4) {
  COUNTS[[i]] <- rep(0, length(BR[[i]])-1)
  for (j in 1:(length(BR[[i]])-1)) {
    these <- apply(cbind(MIDS < BR[[i]][j+1],
                         MIDS >= BR[[i]][j]),
                   1,
                   all)
    if (any(these)) {
      COUNTS[[i]][j] <- sum(counts[these])
    }
  }
}

histTemp <- function(
    BR, COUNTS, col = fadeColor(COL[1], "10"),
    border = COL[1,4], probability = FALSE,
    xlab = '', ylab = NULL,
    xlim = NULL, ylim = NULL,
    ...) {
  br <- BR
  h  <- COUNTS
  if (probability) {
    h <- h / sum(h) / diff(br)
  }
  if (is.null(ylab)) {
    ylab <- 'frequency'
    if (probability) {
      ylab <- 'probability'
    }
  }
  if (is.null(xlim)[1]) {
    xR <- range(br)
    xlim <- xR + c(-0.05, 0.05) * diff(xR)
  }
  if (is.null(ylim)[1]) {
    ylim <- range(c(0, h))
  }
  plot(-1, -1,
       xlab = xlab,
       ylab = ylab,
       xlim = xlim,
       ylim = ylim,
       type = 'n',
       ...)
  abline(h = 0)
  lines(c(br[1], br[1]), c(0, h[1]), col = border)
  for (i in 1:length(h)) {
    if (i > 1) {
      if (h[i] > h[i-1]) {
        lines(rep(br[i], 2), h[c(i - 1, i)], col = border)
      }
    }
    lines(br[i + 0:1], rep(h[i], 2), col = border)
    lines(rep(br[i + 1], 2), c(0, h[i]), col = border)
    rect(br[i], 0,
         br[i + 1], h[i],
         col = col,
         border = '#00000000')
  }
}



myPDF('fdicHistograms.pdf', 6.2, 3.3,
      mfrow = c(2, 2),
      mar = c(2.7, 1, 1, 1),
      mgp = c(1.6, 0.4, 0))
for (i in 1:4) {
  histTemp(BR[[i]],
           COUNTS[[i]],
           xlim = c(125, 210),
           axes = FALSE,
           xlab = 'height (cm)')
  lines(BR[[i]],
        c(COUNTS[[i]], 0),
        type = 's',
        col = COL[1],
        lwd = 2)
  axis(1, cex.axis = 0.9)
}
dev.off()


================================================
FILE: ch_probability/figures/indepForRollingTwo1s/indepForRollingTwo1s.R
================================================
library(openintro)
data(COL)

pdf('indepForRollingTwo1s.pdf', 4.5, 2.7)
par(mar = rep(0, 4))
plot(0:1, c(0, 1.1), type = 'n', axes = FALSE)
x <- cos(seq(0, 2 * pi, length.out = 99))
y <- sin(seq(0, 2 * pi, length.out = 99))
#lines(x / 2 + 0.5, y / 2 + 0.5)
text(0, 1.05, pos = 4, 'All rolls')
rect(0, 0, 1, 1)
rect(1/6, 0, 2/6, 1,
     lty = 2,
     border = COL[1],
     col = COL[1,3])
text(2/6, 0.7,
     '1/6th of the first\nrolls are a 1.',
     pos = 4,
     col = COL[1])
rect(1/6, 1/6, 2/6, 2/6,
     lty = 3,
     border = "#00000000",
     col = COL[2])
the.text <- paste("1/6th of those times where",
                  "the first roll is a 1 the",
                  "second roll is also a 1.",
                  sep  =  "\n")
text(2 / 6, 3 / 12 - 0.02,
    the.text,
    pos = 4,
    col = COL[2])
dev.off()


================================================
FILE: ch_probability/figures/loans_app_type_home_venn/loans_app_type_home_venn.R
================================================
library(openintro)

d <- loans_full_schema
table(d[,c("application_type", "homeownership")])
table(d[,c("application_type")])
table(d[,c("homeownership")])

myPDF('loans_app_type_home_venn.pdf', 5, 1.5,
      mar = c(0.1, 1.5, 0.1, 0.1))
plot(c(-0.2, 2.2),
     c(0, 1),
     type = 'n',
     ylab = "",
     axes = FALSE)
box()

z <- seq(0, 2 * pi, len = 99)
x1 <- cos(z) * 1.04 + 0.8
y1 <- sin(z) / 3 + 0.5
lines(c(x1, x1[1]), c(y1, y1[1]))

x2 <- cos(z) / 1.8 + 1.65
y2 <- sin(z) / 3 + 0.5
lines(c(x2, x2[1]),c(y2, y2[1]))

text(0.6, 0.9, 'applicant had a mortgage')
text(1.9, 0.9, 'joint application')
text(c(0.6, 1.46, 2),
     c(0.6, 0.58, 0.57),
     c(3839, 950, 545),
     cex = c(1.7, 1.2, 1.25))
text(c(0.6, 1.46, 2),
     c(0.4, 0.44, 0.43),
     format(c('0.384', '0.095', '0.055')),
     cex = c(1.3, 0.95, 1),
     col = COL[1])
text(0.77, 0.07, 'Other loans: 10000 - 3839 - 950 - 545 = 4666')
text(1.9, 0.06, '(0.467)', col = COL[1])

dev.off()
# table(email[,c("joint application", "number")])


================================================
FILE: ch_probability/figures/photoClassifyVenn/photoClassifyVenn.R
================================================
library(openintro)
data(COL)

# Proportions not exactly right. Adjusted slightly for aesthetics.

pdf('photoClassifyVenn.pdf', 4.5, 2.4)
par(mar = rep(0, 4))
plot(0:1, 0:1, type = 'n', axes = FALSE)
rect(0, 0, 1, 1, lwd=2)
rect(0.10, 0.35,
     0.75, 0.58,
     border = COL[4, 2],
     col = paste0(COL[4], "25"),
     lty = 3,
     lwd = 2.512)
text(0.33, 0.28, 'ML Predicts Fashion', col=COL[4,2])
rect(0.18, 0.34,
     0.77, 0.69,
     border = COL[1],
     col = COL[1, 4],
     lty = 2,
     lwd = 2)
text(0.54, 0.68, 'Fashion Photos', col = COL[1], pos = 3)
text(0.45, 0.45, 0.11, col = COL[5]) # intersection
text(0.14, 0.49, 0.01, col = COL[4], cex = 0.9)
text(0.6, 0.63, 0.06, col = COL[1])
text(0.8, 0.11, 'Neither: 0.82', col = COL[6]) # outersection
dev.off()


================================================
FILE: ch_probability/figures/smallpoxTreeDiagram/smallpoxTreeDiagram.R
================================================
library(openintro)

myPDF("smallpoxTreeDiagram.pdf", 7, 3.5)
treeDiag(c('Inoculated', 'Result'),
         c(0.0392, 0.9608),
         list(c(0.9754, 0.0246),
              c(0.8589, 0.1411)),
         textwd = 0.2,
         solwd = 0.35,
         cex.main = 1.4,
         c('yes', 'no'),
         c('lived', 'died'),
         digits = 5,
         col.main = COL[1],
         showWork = TRUE)
dev.off()


================================================
FILE: ch_probability/figures/testTree/testTree.R
================================================
library(openintro)

myPDF('testTree.pdf', 6.5, 3.4)
treeDiag(c('Midterm', 'Final'),
         c(0.13, 0.87),
         list(c(0.47, 0.53),
              c(0.11, 0.89)),
         textwd = 0.2,
         solwd = 0.35,
         cex.main = 1.4,
         c('A', 'other'),
         c('A', 'other'),
         digits = 5,
         col.main = COL[1],
         showWork = TRUE)
dev.off()


================================================
FILE: ch_probability/figures/treeDiagramAndPass/treeDiagramAndPass.R
================================================
library(openintro)


myPDF('treeDiagramAndPass.pdf', 6, 2.7)
treeDiag(c('\nAble to construct\ntree diagrams', 'Pass class'),
         c(0.78, 0.22),
         list(c(0.97, 0.03),
              c(0.57, 0.43)),
         textwd = 0.2,
         solwd = 0.35,
         cex.main = 1.4,
         c('yes', 'no'),
         c('pass', 'fail'),
         digits = 5,
         col.main = COL[1],
         showWork = TRUE)
dev.off()


================================================
FILE: ch_probability/figures/treeDiagramGarage/treeDiagramGarage.R
================================================
library(openintro)


myPDF('treeDiagramGarage.pdf', 7, 3.5)
treeDiag(c('Event', 'Garage full'),
         c(0.35, 0.20, 0.45),
         list(c(0.25, 0.75),
              c(0.7, 0.3),
              c(0.05, 0.95)),
         textwd = 0.22,
         solwd = 0.35,
         cex.main = 1.4,
         c('Academic', 'Sporting', 'None'),
         c('Full', 'Spaces Available'),
         digits = 5,
         col.main = COL[1],
         showWork = TRUE)
dev.off()


================================================
FILE: ch_probability/figures/usHeightsHist180185/usHeightsHist180185.R
================================================
library(openintro)
data(COL)

# _____ Load Data Set From fdicHistograms _____ #
load("../fdicHistograms/fdicHistograms.rda")

BR      <- list()
MIDS    <- br[-1] - 0.25
BR[[1]] <- seq(110, 210, 10)
BR[[2]] <- seq(115, 210, 2.5)
COUNTS  <- list()
for (i in 1:2) {
  COUNTS[[i]] <- rep(0, length(BR[[i]])-1)
  for (j in 1:(length(BR[[i]])-1)) {
    these <- apply(cbind(MIDS < BR[[i]][j + 1],
                         MIDS >= BR[[i]][j]),
                   1,
                   all)
    if (any(these)) {
      COUNTS[[i]][j] <- sum(counts[these])
    }
  }
}

histTemp <- function(
    BR, COUNTS, col = fadeColor(COL[1], "10"),
    border = COL[1,4], probability = FALSE,
    xlab = '', ylab = NULL,
    xlim = NULL, ylim = NULL,
    ...) {
  br <- BR
  h  <- COUNTS
  if (probability) {
    h <- h / sum(h) / diff(br)
  }
  if (is.null(ylab)) {
    ylab <- 'frequency'
    if (probability) {
      ylab <- 'probability'
    }
  }
  if (is.null(xlim)[1]) {
    xR <- range(br)
    xlim <- xR + c(-0.05, 0.05) * diff(xR)
  }
  if (is.null(ylim)[1]) {
    ylim <- range(c(0,h))
  }
  plot(-1, -1,
       xlab = xlab,
       ylab = ylab,
       xlim = xlim,
       ylim = ylim,
       type = 'n',
       ...)
  abline(h = 0)
  lines(c(br[1], br[1]), c(0, h[1]), col = border)
  for (i in 1:length(h)) {
    if (i > 1) {
      if (h[i] > h[i - 1]) {
        lines(rep(br[i], 2), h[c(i - 1, i)], col = border)
      }
    }
    lines(br[i + 0:1], rep(h[i], 2), col = border)
    lines(rep(br[i + 1], 2), c(0, h[i]), col = border)
    rect(br[i], 0, br[i + 1], h[i],
         col = col,
         border = '#00000000')
  }
}


myPDF('usHeightsHist180185.pdf', 6.9, 3.1625,
      mar = c(3, 1, 0.1, 1),
      mgp = c(1.8, 0.7, 0))
histTemp(BR[[2]],
         COUNTS[[2]],
         xlim = c(125, 210),
         axes = FALSE,
         xlab = 'height (cm)',
         probability = FALSE)
lines(BR[[i]],
      c(COUNTS[[i]], 0),
      type = 's',
      col = COL[1],
      lwd = 2)
axis(1)
rect(BR[[2]][27], 0,
     BR[[2]][28], COUNTS[[2]][27],
     col = COL[1],
     border = COL[1])
rect(BR[[2]][28], 0,
     BR[[2]][29], COUNTS[[2]][28],
     col = COL[1],
     border = COL[1])
dev.off()


================================================
FILE: ch_probability/figures/usHouseholdIncomeDistBar/usHouseholdIncomeDistBar.R
================================================
library(openintro)
data(COL)

myPDF('usHouseholdIncomeDistBar.pdf', 5.2, 3,
      mar = c(3.4, 4.2, 0.8, 1))
p <- c(0.28, 0.27, 0.29, 0.16)
names(p) <- c('$0-25k', '$25k-50k', '$50k-100k', '$100k+')
barplot(p, xlab = '', col = COL[1])
par(las = 0)
mtext('US Household Incomes', side = 1, line = 2.3)
mtext('Probability', side = 2, line = 3)
abline(h = 0)
dev.off()


================================================
FILE: ch_regr_mult_and_log/TeX/ch_regr_mult_and_log.tex
================================================
\begin{chapterpage}{Multiple and logistic regression}
  \chaptertitle{Multiple and logistic \titlebreak{} regression}
  \label{multipleRegressionAndANOVA}
  \label{multipleAndLogisticRegression}
  \label{ch_regr_mult_and_log}
  \chaptersection{introductionToMultipleRegression}
  \chaptersection{model_selection_section}
  \chaptersection{multipleRegressionModelAssumptions}
  \chaptersection{mario_kart_case_study}
  \chaptersection{logisticRegression}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_regr_mult_and_log}

\chapterintro{The principles of simple linear regression
  lay the foundation for more sophisticated regression
  models used in a wide range of challenging settings.
  In Chapter~\ref{multipleAndLogisticRegression},
  we explore multiple regression, which introduces the
  possibility of more than one predictor in a linear model,
  and logistic regression,
  a technique for predicting categorical
  outcomes with two levels.}




\section{Introduction to multiple regression}
\label{introductionToMultipleRegression}

\index{multiple regression|seealso{regression}}
\index{regression!multiple|(}
\index{regression|(}

Multiple 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.

\index{data!loans|(}

\newcommand{\loNcomma}{10,000}
\newcommand{\loN}{10000}

We will consider data about loans from the peer-to-peer lender,
Lending Club, which is a data set we first encountered in
Chapters~\ref{ch_intro_to_data}
and~\ref{ch_summarizing_data}.
The loan data includes terms of the loan as well as
information about the borrower.
The outcome variable we would like to better understand
is the interest rate assigned to the loan.
For instance, all other characteristics held constant,
does it matter how much debt someone already has?
Does it matter if their income has been verified?
Multiple regression will help us answer these and other questions.

The data set \data{loans} includes results from \loNcomma{} loans,
and we'll be looking at a subset of the available variables,
some of which will be new from those we saw in earlier chapters.
The first six observations in the data set are shown in
Figure~\ref{loansDataMatrix},
and descriptions for each variable are shown in
Figure~\ref{loansVariables}.
Notice that the past bankruptcy variable (\var{bankruptcy})
is an indicator variable\index{indicator variable},
where it takes the value 1 if the borrower had a past
bankruptcy in their record and 0 if not.
Using an indicator variable in place of a category name
allows for these variables to be directly used in regression.
Two of the other variables are
categorical\index{categorical variable}
(\var{income\us{}ver} and \var{issued}), each of which
can take one of a few different non-numerical values;
we'll discuss how these are handled in the model in
Section~\ref{ind_and_cat_vars_as_predictors}.

\begin{figure}[h]
\centering\footnotesize
\begin{tabular}{r ccc ccc cc}
  \hline
   & interest\us{}rate & income\us{}ver
       & debt\us{}to\us{}income & credit\us{}util
       & bankruptcy & term
       & issued & credit\us{}checks \\ 
  \hline
  1 & 14.07 & verified & 18.01 & 0.55 & 0 & 60 & Mar2018 & 6 \\ 
  2 & 12.61 & not & 5.04 & 0.15 & 1 & 36 & Feb2018 & 1 \\ 
  3 & 17.09 & source\_only & 21.15 & 0.66 & 0 & 36 & Feb2018 & 4 \\ 
  4 & 6.72 & not & 10.16 & 0.20 & 0 & 36 & Jan2018 & 0 \\ 
  5 & 14.07 & verified & 57.96 & 0.75 & 0 & 36 & Mar2018 & 7 \\ 
  6 & 6.72 & not & 6.46 & 0.09 & 0 & 36 & Jan2018 & 6 \\
  $\vdots$ & $\vdots$ & $\vdots$ &
      $\vdots$ & $\vdots$ & $\vdots$ &
      $\vdots$ & $\vdots$ & $\vdots$ \\
   \hline
\end{tabular}
\caption{First six rows from the \data{loans} data set.}
\label{loansDataMatrix}
\end{figure}
%library(openintro)  # Run some example code from loans_full_schema
%library(xtable); xtable(rbind.data.frame(head(d[, c("interest_rate", co)], 6))) #, tail(d[, c("interest_rate", co)], 2)))

\begin{figure}[h]
\centering\small
\begin{tabular}{lp{11.5cm}}
\hline
{\bf variable} & {\bf description} \\
\hline
\var{interest\us{}rate} &
    Interest rate for the loan. \\
\var{income\us{}ver} &
    Categorical variable describing whether the borrower's
    income source and amount have been verified,
    with levels \resp{verified}, \resp{source\us{}only},
    and \resp{not}. \\
\var{debt\us{}to\us{}income} &
    Debt-to-income ratio, which is the percentage of total debt
    of the borrower divided by their total income. \\
\var{credit\us{}util} &
    Of all the credit available to the borrower,
    what fraction are they utilizing.
    For example, the credit utilization on a credit card would
    be the card's balance divided by the card's credit limit. \\
\var{bankruptcy} &
    An indicator variable for whether the borrower has a past
    bankruptcy in her record. This variable takes a value of
    \resp{1} if the answer is ``yes''
    and \resp{0} if the answer is ``no''. \\
\var{term} &
    The length of the loan, in months. \\
\var{issued} &
    The month and year the loan was issued,
    which for these loans is always during the first
    quarter of 2018. \\
\var{credit\us{}checks} &
    Number of credit checks in the last 12 months.
    For example, when filing an application for a credit card,
    it is common for the company receiving the application
    to run a credit check. \\
\hline
\end{tabular}
\caption{Variables and their descriptions for the
    \data{loans} data set.}
\label{loansVariables}
\end{figure}


\newpage

\subsection{Indicator and categorical variables as predictors}
\label{ind_and_cat_vars_as_predictors}

\newcommand{\pastbankrACoef}{0.74}
\newcommand{\pastbankrACoefSE}{0.15}

Let's start by fitting a linear regression model for
interest rate with a single predictor indicating whether
or not a person has a bankruptcy in their record:
\begin{align*}
\widehat{rate} &= 12.33 + \pastbankrACoef{} \times bankruptcy
\end{align*}
Results of this model are shown in
Figure~\ref{intRateVsPastBankrModel}.
%and a scatterplot for price
%versus game condition is shown in
%Figure~\ref{intRateVsPastBankrScatter}.

\begin{figure}[h]
\centering
\begin{tabular}{l rrr r}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  (Intercept) & 12.3380 & 0.0533 & 231.49 & $<$0.0001 \\ 
  bankruptcy & 0.7368 & 0.1529 & 4.82 & $<$0.0001 \\ 
  \hline
  &&&\multicolumn{2}{r}{$df=9998$}
\end{tabular}
\caption{Summary of a linear model for predicting
    interest rate based on whether the borrower has
    a bankruptcy in their record.}
\label{intRateVsPastBankrModel}
\end{figure}

%\begin{figure}[h]
%  \centering
%  \Figures{0.45}{loansSingles}{intRateVsPastBankrScatter}
%  \caption{Scatterplot of interest rate against
%      the past bankruptcy indicator variable.
%      The least squares line is also shown,
%      representing a relatively small difference
%      between the two bankruptcy groups.}
%  \label{intRateVsPastBankrScatter}
%\end{figure}

%\begin{exercisewrap}
%\begin{nexercise}
%Examine Figure~\ref{intRateVsPastBankrScatter}.
%Are the conditions for a linear model reasonable?\footnotemark
%\end{nexercise}
%\end{exercisewrap}
%\footnotetext{Yes. Constant variability, nearly normal residuals, and linearity all appear reasonable.}

\begin{examplewrap}
\begin{nexample}{Interpret the coefficient for the
     past bankruptcy variable in the model.
     Is this coefficient significantly different from 0?}
  The \var{bankruptcy} variable takes one of two values:
  1 when the borrower has a bankruptcy
  in their history and 0 otherwise.
  A slope of \pastbankrACoef{} means that the model predicts a
  \pastbankrACoef{}\% higher
  interest rate for those borrowers with a bankruptcy in
  their record.
  (See Section~\ref{categoricalPredictorsWithTwoLevels}
  for a review of the interpretation for two-level
  categorical predictor variables.)
  Examining the regression output in
  Figure~\ref{intRateVsPastBankrModel},
  we can see that the p-value for \var{bankruptcy}
  is very close to zero, indicating there is strong evidence
  the coefficient is different from zero when using this
  simple one-predictor model.
\end{nexample}
\end{examplewrap}

Suppose we had fit a model using a 3-level categorical variable,
such as \var{income\us{}ver}.
The output from software is shown in
Figure~\ref{intRateVsVerIncomeModel}.
This regression output provides multiple
rows for the \var{income\us{}ver} variable.
Each row represents the relative difference for
each level of \var{income\us{}ver}.
However, we are missing one of the levels:
\resp{not} (for \emph{not verified}).
The missing level is called the \term{reference level},
and it represents the default level that
other levels are measured against.
%This will make more sense after we write out the equation.

\begin{figure}[h]
\centering
\begin{tabular}{l rrr r}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  (Intercept) &
      11.0995 & 0.0809 & 137.18 & $<$0.0001 \\
  income\us{}ver\lmlevel{source\us{}only} &
      1.4160 & 0.1107 & 12.79 & $<$0.0001 \\ 
  income\us{}ver\lmlevel{verified} &
      3.2543 & 0.1297 & 25.09 & $<$0.0001 \\ 
  \hline
  &&&\multicolumn{2}{r}{$df=9998$}
\end{tabular}
\caption{Summary of a linear model for predicting
    interest rate based on whether the borrower's
    income source and amount has been verified.
    This predictor has three levels, which results
    in 2 rows in the regression output.}
\label{intRateVsVerIncomeModel}
\end{figure}

\begin{examplewrap}
\begin{nexample}{How would we write an equation for
    this regression model?}
  \label{verIncomeEquationExample}%
  The equation for the regression model may be written as
  a model with two predictors:
  \begin{align*}
  \widehat{rate} = 11.10 +
      1.42 \times
          \indfunc{income\us{}ver}{source\us{}only} +
      3.25 \times
          \indfunc{income\us{}ver}{verified}
  \end{align*}
  We use the notation $\indfunc{variable}{level}$
  to represent indicator variables\index{indicator variable}
  for when the categorical variable takes a particular value.
  For example, $\indfunc{income\us{}ver}{source\us{}only}$
  would take a value of 1 if \var{income\us{}ver} was
  \resp{source\us{}only} for a loan,
  and it would take a value of 0 otherwise.
  Likewise, $\indfunc{income\us{}ver}{verified}$ would take
  a value of 1 if \var{income\us{}ver} took a value
  of \resp{verified} and 0 if it took any other value.
  % In Example~\ref{}, we'll run through a few examples
  % of how we can use the equation for the model.
\end{nexample}
\end{examplewrap}

The notation used in Example~\ref{verIncomeEquationExample}
may feel a bit confusing.
Let's figure out how to use the equation for each level
of the \var{income\us{}ver} variable.

\begin{examplewrap}
\begin{nexample}{Using the model from
    Example~\ref{verIncomeEquationExample},
    compute the average interest rate for borrowers
    whose income source and amount are both unverified.}
  When \var{income\us{}ver} takes a value of \resp{not},
  then both indicator functions in the equation from
  Example~\ref{verIncomeEquationExample}
  are set to zero:
  \begin{align*}
  \widehat{rate} &= 11.10 +
      1.42 \times 0 +
      3.25 \times 0 \\
    &= 11.10
  \end{align*}
  The average interest rate for these borrowers is 11.1\%.
  Because the \resp{not} level does not have its own
  coefficient and it is the reference value,
  the indicators for the other levels for this variable
  all drop out.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Using the model from
    Example~\ref{verIncomeEquationExample},
    compute the average interest rate for borrowers
    whose income source is verified but the amount is not.}
  When \var{income\us{}ver} takes a value of
  \resp{source\us{}only},
  then the corresponding variable takes a value of 1
  while the other ($\indfunc{income\us{}ver}{verified}$) is 0:
  \begin{align*}
  \widehat{rate} &= 11.10 +
      1.42 \times 1 +
      3.25 \times 0 \\
    &= 12.52
  \end{align*}
  The average interest rate for these borrowers is 12.52\%.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Compute the average interest rate for borrowers
whose income source and amount are both verified.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{When \var{income\us{}ver} takes a value of
  \resp{verified},
  then the corresponding variable takes a value of 1
  while the other ($\indfunc{income\us{}ver}{source\us{}only}$)
  is~0:
  \begin{align*}
  \widehat{rate} &= 11.10 +
      1.42 \times 0 +
      3.25 \times 1 \\
    &= 14.35
  \end{align*}
  The average interest rate for these borrowers is 14.35\%.}

\begin{onebox}{Predictors with several categories}
When fitting a regression model with a categorical variable
that has $k$ levels where $k > 2$, software will provide
a coefficient for $k - 1$ of those levels.
For the last level that does not receive a coefficient,
this is the \term{reference level}, and the coefficients
listed for the other levels are all considered relative
to this reference level.
\end{onebox}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
Interpret the coefficients in the \var{income\us{}ver}
model.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Each of the coefficients gives the
  incremental interest rate for the corresponding level
  relative to the \resp{not} level, which is the reference
  level.
  For example, for a borrower whose income source and
  amount have been verified, the model predicts that
  they will have a 3.25\% higher interest rate than
  a borrower who has not had their income source or
  amount verified.}

The higher interest rate for borrowers who have verified
their income source or amount is surprising.
Intuitively, we'd think that a loan would look \emph{less}
risky if the borrower's income has been verified.
However, note that the situation may be more complex,
and there may be confounding variables
that we didn't account for.
For example, perhaps lender require borrowers with
poor credit to verify their income.
That is, verifying income in our data set might be
a signal of some concerns about the borrower
rather than a reassurance that the borrower will pay
back the loan.
For this reason, the borrower could be deemed higher
risk, resulting in a higher interest rate.
(What other confounding variables might explain this
counter-intuitive relationship suggested by the model?)

\begin{exercisewrap}
\begin{nexercise}
How much larger of an interest rate would we expect for
a borrower who has verified their income source and amount
vs a borrower whose income source has only been
verified?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Relative to the \resp{not} category,
  the \resp{verified} category has an interest rate of
  3.25\% higher, while the \resp{source\us{}only}
  category is only 1.42\% higher.
  Thus, \resp{verified} borrowers will tend to get
  an interest rate about $3.25\% - 1.42\% = 1.83\%$
  higher than \resp{source\us{}only} borrowers.}


\subsection{Including and assessing many variables in a model}
\label{includingAndAssessingManyVariablesInAModel}

The world is complex, and it can be helpful to
consider many factors at once in statistical modeling.
For example, we might like to use the full context of
borrower to predict the interest rate they receive
rather than using a single variable.
This is the strategy used in
\termsub{multiple regression}{regression!multiple}.
While we remain cautious about making any causal
interpretations using multiple regression
on observational data,
such models are a common first step in gaining insights
or providing some evidence of a causal connection.

We want to construct a model that accounts not only
for any past bankruptcy or whether the borrower had
their income source or amount verified,
but simultaneously accounts for all the variables
in the data set:
\var{income\us{}ver},
\var{debt\us{}to\us{}income},
\var{credit\us{}util},
\var{bankruptcy},
\var{term},
\var{issued},
and \var{credit\us{}checks}.
\begin{align*}
\widehat{\var{rate}}
	&= \beta_0 +
	    \beta_1\times \indfunc{income\us{}ver}{source\us{}only} +
	    \beta_2\times \indfunc{income\us{}ver}{verified} +
		\beta_3\times \var{debt\us{}to\us{}income} \\
	&\qquad\  +
	    \beta_4 \times \var{credit\us{}util} +
	    \beta_5 \times \var{bankruptcy} +
		\beta_6 \times \var{term} \\
	&\qquad\  +
	    \beta_7 \times \indfunc{issued}{Jan2018} +
	    \beta_8 \times \indfunc{issued}{Mar2018} +
		\beta_9 \times \var{credit\us{}checks}
\end{align*}
This equation represents a holistic approach for modeling
all of the variables simultaneously.
Notice that there are two coefficients for \var{income\us{}ver}
and also two coefficients for \var{issued}, since both are
3-level categorical variables.

%\Comment{Work on this paragraph.}
%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.


We estimate the parameters
$\beta_0$, $\beta_1$, $\beta_2$, ..., $\beta_9$
in the same way as we did in the case of a single predictor.
We select $b_0$, $b_1$, $b_2$, ..., $b_9$ that minimize the
sum of the squared residuals:
\begin{align}\label{sumOfSqResInMultRegr}
SSE = e_1^2 + e_2^2 + \dots + e_{\loN}^2
	= \sum_{i=1}^{\loN} e_i^2
	 = \sum_{i=1}^{\loN} \left(y_i - \hat{y}_i\right)^2
\end{align}
where $y_i$ and $\hat{y}_i$ represent the observed
interest rates and their estimated values according to
the model, respectively.
\loNcomma{} residuals are calculated, one for each observation.
We typically use a computer to minimize the sum of squares
and compute point estimates, as shown in the sample output
in Figure~\ref{loansFullModelOutput}.
Using this output, we identify the point estimates $b_i$ of
each $\beta_i$, just as we did in the one-predictor case.

\newcommand{\pastbankrFullCoef}{0.39}
\newcommand{\pastbankrFullCoefSE}{0.13}

\begin{figure}[ht]
\centering
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.8mm} & & & & \\
  (Intercept) & 1.9251 & 0.2102 & 9.16 & $<$0.0001 \\ 
  income\us{}ver\lmlevel{source\us{}only} &
      0.9750 & 0.0991 & 9.83 & $<$0.0001 \\ 
  income\us{}ver\lmlevel{verified} &
      2.5374 & 0.1172 & 21.65 & $<$0.0001 \\ 
  debt\us{}to\us{}income & 0.0211 & 0.0029 & 7.18 & $<$0.0001 \\ 
  credit\us{}util & 4.8959 & 0.1619 & 30.24 & $<$0.0001 \\ 
  bankruptcy & 0.3864 & 0.1324 & 2.92 & 0.0035 \\ 
  term & 0.1537 & 0.0039 & 38.96 & $<$0.0001 \\ 
  issued\lmlevel{Jan2018} & 0.0276 & 0.1081 & 0.26 & 0.7981 \\ 
  issued\lmlevel{Mar2018} & -0.0397 & 0.1065 & -0.37 & 0.7093 \\ 
  credit\us{}checks & 0.2282 & 0.0182 & 12.51 & $<$0.0001 \\ 
   \hline
   &&&\multicolumn{2}{r}{$df=9990$}
\end{tabular}
\caption{Output for the regression model, where
    \var{interest\us{}rate} is the outcome and
    the variables listed are the predictors.}
\label{loansFullModelOutput}
\end{figure}

\begin{onebox}{Multiple regression model}
  A multiple regression model is a linear model
  with many predictors.
  In general, we write the model as
  \begin{align*}
  \hat{y} =
      \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \cdots + \beta_k x_k
  \end{align*}
  when there are $k$ predictors.
  We always estimate the $\beta_i$ parameters using
  statistical software.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{Write out the regression model using
    the point estimates from
    Figure~\ref{loansFullModelOutput}.
    How many predictors are there in this model?}
  \label{loansFullModelEqWCoef}%
  The fitted model for the interest rate is given by:
  {\small\begin{align*}
  \widehat{\var{rate}}
	&= 1.925 +
	    0.975 \times \indfunc{income\us{}ver}{source\us{}only} +
	    2.537 \times \indfunc{income\us{}ver}{verified} +
		0.021 \times \var{debt\us{}to\us{}income} \\
	&\qquad\  +
	    4.896 \times \var{credit\us{}util} +
	    0.386 \times \var{bankruptcy} +
		0.154 \times \var{term} \\
	&\qquad\  +
	    0.028 \times \indfunc{issued}{Jan2018}
	    -0.040 \times \indfunc{issued}{Mar2018} +
		0.228 \times \var{credit\us{}checks}
  \end{align*}}%
  If we count up the number of predictor coefficients,
  we get the \emph{effective} number of predictors
  in the model:~$k = 9$.
  Notice that the \var{issued} categorical predictor
  counts as two, once for the two levels shown in the model.
  In general, a categorical predictor with $p$ different
  levels will be represented by $p - 1$ terms in a multiple
  regression model.
\end{nexample}
\end{examplewrap}


\begin{exercisewrap}
\begin{nexercise}
What does $\beta_4$, the coefficient of variable
\var{credit\us{}util}, represent?
What is the point estimate of~$\beta_4$?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$\beta_4$ represents the change in
   interest rate we would expect if someone's credit
   utilization was 0 and went to 1,
   all other factors held even.
   The point estimate is $b_4 = 4.90\%$.}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{Compute the residual of the first observation
    in Figure~\ref{loansDataMatrix} on
    page~\pageref{loansDataMatrix} using the equation identified
    in Guided Practice~\ref{loansFullModelEqWCoef}.}
  To compute the residual, we first need the predicted value,
  which we compute by plugging values into the equation from
  Example~\ref{loansFullModelEqWCoef}.
  For example, $\indfunc{income\us{}ver}{source\us{}only}$
  takes a value of 0,
  $\indfunc{income\us{}ver}{verified}$ takes a value of 1
  (since the borrower's income source and amount were verified),
  \var{debt\us{}to\us{}income} was 18.01, and so on.
  This leads to a prediction of $\widehat{rate}_1 = 18.09$.
  The observed interest rate was 14.07\%, which leads to
  a residual of $e_1 = 14.07 - 18.09 = -4.02$.
\end{nexample}
\end{examplewrap}
% sum(model.matrix(m)[1, ] * round(m$coef, 3))

\begin{examplewrap}
\begin{nexample}{We estimated a coefficient for
    \var{bankruptcy} in
    Section~\ref{ind_and_cat_vars_as_predictors}
    of $b_4 = \pastbankrACoef{}$ with a standard error
    of $SE_{b_1} = \pastbankrACoefSE{}$ when using simple
    linear regression.
    Why is there a difference between that estimate
    and the estimated coefficient of \pastbankrFullCoef{}
    in the multiple regression setting?}
  \label{pastBankrCoefDiffExplained}%
  If we examined the data carefully, we would see that
  some predictors are correlated.
  For instance, when we estimated the connection of the
  outcome \var{interest\us{}rate} and predictor
  \var{bankruptcy} using simple linear regression,
  we were unable to control for other variables like
  whether the borrower had her income verified,
  the borrower's debt-to-income ratio, and other variables.
  That original model was constructed in a vacuum and did
  not consider the full context.
  When we include all of the variables,
  underlying and unintentional
  bias that was missed by these other variables is reduced
  or eliminated.
  Of course, bias can still exist from other confounding
  variables.
\end{nexample}
\end{examplewrap}

Example~\ref{pastBankrCoefDiffExplained} describes a common
issue in multiple regression: correlation among predictor
variables.
We say the two predictor variables are \term{collinear}
(pronounced as \emph{co-linear}) when they are correlated,
and this collinearity complicates model estimation.
While it is impossible to prevent collinearity from arising
in observational data, experiments are usually designed to
prevent predictors from being collinear.

\begin{exercisewrap}
\begin{nexercise}
The estimated value of the intercept is 1.925, and one might
be tempted to make some interpretation of this coefficient,
such as, it is the model's predicted price when each of the
variables take value zero: income source is not verified,
the borrower has no debt (debt-to-income and credit
utilization are zero), and so on.
Is this reasonable?
Is there any value gained by making this
interpretation?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Many of the variables do take a value 0
  for at least one data point, and for those variables,
  it is reasonable.
  However, one variable never takes a value of zero:
  \var{term}, which describes the length of the loan,
  in months.
  If \var{term} is set to zero, then the loan
  must be paid back immediately; the borrower
  must give the money back as soon as she receives it,
  which means it is not a real loan.
  Ultimately, the interpretation of the intercept in
  this setting is not insightful.}


\D{\newpage}

\subsection[Adjusted $R^2$ as a better tool
    for multiple regression]
    {Adjusted $\pmb{R^2}$ as a better tool
        for multiple regression}

\index{adjusted r squared@adjusted $R^2$ ($R_{adj}^2$)|(}

We first used $R^2$ in Section~\ref{fittingALineByLSR}
to determine the amount of variability in the response
that was explained by the model:
\begin{align*}
R^2 =
    1 - \frac{\text{variability in residuals}}
        {\text{variability in the outcome}}
	= 1 - \frac{Var(e_i)}{Var(y_i)}
\end{align*}
where $e_i$ represents the residuals of the model and
$y_i$ the outcomes.
This equation remains valid in the multiple regression
framework, but a small enhancement can make it even
more informative when comparing models.

\begin{exercisewrap}
\begin{nexercise}
\label{computeUnadjR2ForFullLoansModel}%
The variance of the residuals for the model given in
Guided Practice~\ref{loansFullModelEqWCoef}
is 18.53, and the variance of the total price in all
the auctions is 25.01.
Calculate $R^2$ for this model.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$R^2 = 1 - \frac{18.53}{25.01} = 0.2591$.}

This strategy for estimating $R^2$ is acceptable when there
is just a single variable.
However, it becomes less helpful when there are many
variables.
The regular $R^2$ is a biased estimate of the amount of
variability explained by the model
when applied to a new sample of data.
To get a better estimate, we use the adjusted $R^2$.

\begin{onebox}{Adjusted $\pmb{R^2}$ as a tool for
    model assessment}
  The \termsub{adjusted $\pmb{R^2}$}
      {adjusted r squared@adjusted $R^2$ ($R_{adj}^2$)}
  is computed as
  \begin{align*}
  R_{adj}^{2}
    = 1 - \frac{s_{\text{residuals}}^2 / (n-k-1)}
        {s_{\text{outcome}}^2 / (n-1)}
    = 1 - \frac{s_{\text{residuals}}^2}{s_{\text{outcome}}^2}
        \times \frac{n-1}{n-k-1}
  \end{align*}
  where $n$ is the number of cases used to fit the model
  and $k$ is the number of predictor variables in the model.
  Remember that a categorical predictor with $p$ levels will
  contribute $p - 1$ to the number of variables in the model.
\end{onebox}

Because $k$ is never negative, the adjusted $R^2$ will be
smaller -- often times just a little smaller -- than the
unadjusted $R^2$.
The reasoning behind the adjusted $R^2$ lies in the
\termsub{degrees of freedom}{degrees of freedom (df)!regression}
associated with each variance,
which is equal to $n - k - 1$ for the multiple regression
context.
If we were to make predictions for \emph{new data}
using our current model, we would find that the unadjusted
$R^2$ would tend to be slightly overly optimistic, while
the adjusted $R^2$ formula helps correct this bias.

\begin{exercisewrap}
\begin{nexercise}
There were $n=10000$ auctions in the \data{loans} data set
and $k=9$ predictor variables in the model.
Use $n$, $k$, and the variances from
Guided Practice~\ref{computeUnadjR2ForFullLoansModel}
to calculate $R_{adj}^2$ for the interest rate
model.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{$R_{adj}^2
    = 1 - \frac{18.53}{25.01}\times \frac{10000-1}{1000-9-1}
    = 0.2584$.
  While the difference is very small, it will be important
  when we fine tune the model in the next section.}

\begin{exercisewrap}
\begin{nexercise}
Suppose you added another predictor to the model, but the
variance of the errors $Var(e_i)$ didn't go down.
What would happen to the~$R^2$?
What would happen to the
adjusted~$R^2$?\hspace{0.7mm}\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{The unadjusted $R^2$ would stay the same
    and the adjusted $R^2$ would go down.}

Adjusted $R^2$ could have been used in
Chapter~\ref{linRegrForTwoVar}.
However, when there is only $k = 1$ predictors,
adjusted $R^2$ is very close to regular $R^2$,
so this nuance isn't typically important when
the model has only one predictor.

\index{adjusted r squared@adjusted $R^2$ ($R_{adj}^2$)|)}


{\input{ch_regr_mult_and_log/TeX/introduction_to_multiple_regression.tex}}






%__________________
\section{Model selection}
\label{model_selection_section}
\label{modelSelection}

\index{model selection|(}

The best model is not always the most complicated.
Sometimes including variables that are not evidently
important can actually reduce the accuracy of predictions.
In this section, we discuss model selection strategies,
which will help us eliminate variables from the model that
are found to be less important.
It's common (and hip, at least in the statistical world)
to refer to models that have undergone such variable pruning
as \term{parsimonious}.

In practice, the model that includes all available explanatory
variables is often referred to as the \term{full model}.
The full model may not be the best model, and if it isn't,
we want to identify a smaller model that is preferable.


\subsection{Identifying variables in the model that may
    not be helpful}

Adjusted $R^2$ describes the strength of a model fit,
and it is a useful tool for evaluating which predictors
are adding value to the model, where \emph{adding value}
means they are (likely) improving the accuracy in
predicting future outcomes.

Let's consider two models, which are shown in
Tables~\ref{loansFullModelModelSelectionSection}
and~\ref{loansModelAllButIssued}.
The first table summarizes the full model since it includes
all predictors, while the second does not include the
\var{issued} variable.

\begin{figure}[ht]
\centering
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.8mm} & & & & \\
  (Intercept) & 1.9251 & 0.2102 & 9.16 & $<$0.0001 \\ 
  income\us{}ver\lmlevel{source\us{}only} &
      0.9750 & 0.0991 & 9.83 & $<$0.0001 \\ 
  income\us{}ver\lmlevel{verified} &
      2.5374 & 0.1172 & 21.65 & $<$0.0001 \\ 
  debt\us{}to\us{}income & 0.0211 & 0.0029 & 7.18 & $<$0.0001 \\ 
  credit\us{}util & 4.8959 & 0.1619 & 30.24 & $<$0.0001 \\ 
  bankruptcy & 0.3864 & 0.1324 & 2.92 & 0.0035 \\ 
  term & 0.1537 & 0.0039 & 38.96 & $<$0.0001 \\ 
  issued\lmlevel{Jan2018} & 0.0276 & 0.1081 & 0.26 & 0.7981 \\ 
  issued\lmlevel{Mar2018} & -0.0397 & 0.1065 & -0.37 & 0.7093 \\ 
  credit\us{}checks & 0.2282 & 0.0182 & 12.51 & $<$0.0001 \\ 
  \hline
  \multicolumn{3}{l}{$R_{adj}^2 = 0.25843$}&
      \multicolumn{2}{r}{$df=9990$}
\end{tabular}
\caption{The fit for the full regression model,
    including the adjusted $R^2$.}
\label{loansFullModelModelSelectionSection}
\end{figure}

\begin{figure}[ht]
\centering
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.8mm} & & & & \\
  (Intercept) & 1.9213 & 0.1982 & 9.69 & $<$0.0001 \\ 
  income\us{}ver\lmlevel{source\us{}only} &
      0.9740 & 0.0991 & 9.83 & $<$0.0001 \\ 
  income\us{}ver\lmlevel{verified} &
      2.5355 & 0.1172 & 21.64 & $<$0.0001 \\ 
  debt\us{}to\us{}income & 0.0211 & 0.0029 & 7.19 & $<$0.0001 \\ 
  credit\us{}util & 4.8958 & 0.1619 & 30.25 & $<$0.0001 \\ 
  bankruptcy & 0.3869 & 0.1324 & 2.92 & 0.0035 \\ 
  term & 0.1537 & 0.0039 & 38.97 & $<$0.0001 \\ 
  credit\us{}checks & 0.2283 & 0.0182 & 12.51 & $<$0.0001 \\ 
  \hline
  \vspace{-3.6mm} & & & & \\
  \multicolumn{3}{l}{$R_{adj}^2 = 0.25854$}&
      \multicolumn{2}{r}{$df=9992$}
\end{tabular}
\caption{The fit for the regression model after dropping
   the \var{issued} variable.} %, which represented 3 categories
   % and 2 degrees of freedom.}
\label{loansModelAllButIssued}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Which of the two models is better?}
  We compare the adjusted $R^2$ of each model to determine
  which to choose.
  Since the first model has an $R^2_{adj}$ smaller than
  the $R^2_{adj}$ of the second model, we prefer the second
  model to the first.
\end{nexample}
\end{examplewrap}

Will the model without \var{issued} be better than the
model with \var{issued}?
We~cannot know for sure, but based on the adjusted $R^2$,
this is our best assessment.


\subsection{Two model selection strategies}

Two common strategies for adding or removing variables
in a multiple regression model are called
\emph{backward elimination} and \emph{forward selection}.
These techniques are often referred to as \term{stepwise}
model selection strategies, because they add or delete
one variable at a time as they ``step'' through the
candidate predictors.

\termsub{Backward elimination}{backward elimination}
starts with the model that includes all potential
predictor variables.
Variables are eliminated one-at-a-time from the model
until we cannot improve the adjusted $R^2$.
The strategy within each elimination step is to eliminate
the variable that leads to the largest improvement in
adjusted $R^2$.

\begin{examplewrap}
\begin{nexample}{Results corresponding to the \emph{full model}
    for the \data{loans} data are shown in
    Figure~\ref{loansFullModelModelSelectionSection}.
    How should we proceed under the backward elimination
    strategy?}
  \label{loansBackwardElimEx}%
  Our baseline adjusted $R^2$ from the full model is
  $R^2_{adj} = 0.25843$, and we need to determine whether
  dropping a predictor will improve the adjusted $R^2$.
  To check, we fit models that each drop a different
  predictor, and we record the adjusted $R^2$:
  \begin{center}
  \begin{tabular}{lllll}
  Exclude ... &
      \var{income\us{}ver} &
      \var{debt\us{}to\us{}income} &
      \var{credit\us{}util} &
      \var{bankruptcy} \\
  &
      $R^2_{adj} = 0.22380$ &
      $R^2_{adj} = 0.25468$ &
      $R^2_{adj} = 0.19063$ &
      $R^2_{adj} = 0.25787$ \\
  \\
  &
      \var{term} &
      \var{issued} &
      \var{credit\us{}checks} \\
  &
      $R^2_{adj} = 0.14581$ &
      $R^2_{adj} = 0.25854$ &
      $R^2_{adj} = 0.24689$ \\
  \end{tabular}
  \end{center}
  The model without \var{issued} has the highest adjusted $R^2$
  of 0.25854, higher than the adjusted $R^2$ for the full model.
  Because eliminating \var{issued} leads to a model with
  a higher adjusted $R^2$, we drop \var{issued} from the model.

  Since we eliminated a predictor from the model in the first step,
  we see whether we should eliminate any additional predictors.
  Our baseline adjusted $R^2$ is now $R^2_{adj} = 0.25854$.
  We now fit new models, which consider eliminating each of the
  remaining predictors in addition to \var{issued}:
  \begin{center}
  \begin{tabular}{llll}
  Exclude \var{issued} and ... &
      \var{income\us{}ver} &
      \var{debt\us{}to\us{}income} &
      \var{credit\us{}util} \\
  &
      $R^2_{adj} = 0.22395$ &
      $R^2_{adj} = 0.25479$ &
      $R^2_{adj} = 0.19074$ \\
  \\
  &
      \var{bankruptcy} &
      \var{term} &
      \var{credit\us{}checks} \\
  &
      $R^2_{adj} = 0.25798$ &
      $R^2_{adj} = 0.14592$ &
      $R^2_{adj} = 0.24701$ \\
  \end{tabular}
  \end{center}
  None 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 all predictors except \var{issued},
  which we can summarize using the coefficients from
  Figure~\ref{loansModelAllButIssued}:
  \begin{align*}
  \widehat{rate} &= \ 1.921
      + 0.974 \times \indfunc{income\us{}ver}{source\us{}only}
      + 2.535 \times \indfunc{income\us{}ver}{verified} \\
    &\qquad
      + 0.021 \times \var{debt\us{}to\us{}income}
      + 4.896 \times \var{credit\us{}util}
      + 0.387 \times \var{bankruptcy} \\
    &\qquad
      + 0.154 \times \var{term}
      + 0.228 \times \var{credit\us{}check}
  \end{align*}
\end{nexample}
\end{examplewrap}

The \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 $R^2$).

\begin{examplewrap}
\begin{nexample}{Construct a model for the \data{loans} data
    set using the forward selection strategy.}
  \label{loansForwardElimEx}%
  We start with the model that includes no variables.
  Then we fit each of the possible models with just one
  variable.
  That is, we fit the model including just \var{income\us{}ver},
  then the model including just \var{debt\us{}to\us{}income},
  then a model with just \var{credit\us{}util}, and so on.
  Then we examine the adjusted $R^2$ for each of these models:
  \begin{center}
  \begin{tabular}{lllll}
  Add ... &
      \var{income\us{}ver} &
      \var{debt\us{}to\us{}income} &
      \var{credit\us{}util} &
      \var{bankruptcy} \\
  &
      $R^2_{adj} = 0.05926$ &
      $R^2_{adj} = 0.01946$ &
      $R^2_{adj} = 0.06452$ &
      $R^2_{adj} = 0.00222$ \\
  \\
  &
      \var{term} &
      \var{issued} &
      \var{credit\us{}checks} \\
  &
      $R^2_{adj} = 0.12855$ &
      $R^2_{adj} = 0.00018$ &
      $R^2_{adj} = 0.01711$ \\
  \end{tabular}
  \end{center}
  % for (i in 1:7) { m <- lm(F(co, i), data = d);
  %   cat(i, " ", co[i], " ", AdjR2(m), "\n") }
  In this first step, we compare the adjusted $R^2$ against
  a baseline model that has no predictors.
  The no-predictors model always has $R_{adj}^2 = 0$.
  The model with one predictor that has the largest
  adjusted $R^2$ is the model with the \var{term} predictor,
  and because this adjusted $R^2$ is larger than the
  adjusted $R^2$ from the model with no predictors
  ($R_{adj}^2 = 0$), we will add this variable to our model.

  We repeat the process again, this time considering
  2-predictor models where one of the predictors is
  \var{term} and with a new baseline of $R^2_{adj} = 0.12855$:
  \begin{center}
  \begin{tabular}{llll}
  Add \var{term} and ... &
      \var{income\us{}ver} &
      \var{debt\us{}to\us{}income} &
      \var{credit\us{}util} \\
  &
      $R^2_{adj} = 0.16851$ &
      $R^2_{adj} = 0.14368$ &
      $R^2_{adj} = 0.20046$ \\
  \\
  &
      \var{bankruptcy} &
      \var{issued} &
      \var{credit\us{}checks} \\
  &
      $R^2_{adj} = 0.13070$ &
      $R^2_{adj} = 0.12840$ &
      $R^2_{adj} = 0.14294$ \\
  \end{tabular}
  \end{center}
  The best second predictor, \var{credit\us{}util},
  has a higher adjusted $R^2$ (0.20046) than the
  baseline (0.12855), so we also add \var{credit\us{}util}
  to the model.

  Since we have again added a variable to the model,
  we continue and see whether it would be beneficial
  to add a third variable:
  \begin{center}
  \begin{tabular}{llll}
  Add \var{term}, \var{credit\us{}util}, and ... &
      \var{income\us{}ver} &
      \var{debt\us{}to\us{}income} \\
  &
      $R^2_{adj} = 0.24183$ &
      $R^2_{adj} = 0.20810$ \\
  \\
  &
      \var{bankruptcy} &
      \var{issued} &
      \var{credit\us{}checks} \\
  &
      $R^2_{adj} = 0.20169$ &
      $R^2_{adj} = 0.20031$ &
      $R^2_{adj} = 0.21629$ \\
  \end{tabular}
  \end{center}
  The model adding \var{income\us{}ver} improved adjusted $R^2$
  (0.24183 to 0.20046), so we add \var{income\us{}ver} to the
  model.

  We continue on in this way,
  next adding \var{debt\us{}to\us{}income},
  then \var{credit\us{}checks},
  and \var{bankruptcy}.
  At this point, we come again to the \var{issued} variable:
  adding this variable leads to $R_{adj}^2 = 0.25843$,
  while keeping all the other variables but excluding \var{issued}
  leads to a higher $R_{adj}^2 = 0.25854$.
  This means we do not add \var{issued}.
  In this example, we have arrived at the same model that we
  identified from backward elimination.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Model selection strategies}
  Backward elimination begins with the model
  having the largest number of predictors
  and eliminates variables one-by-one until we are satisfied
  that all remaining variables are important to the model.
  Forward selection starts with no variables included in
  the model, then it adds in variables according to their
  importance until no other important variables are found.
\end{onebox}

Backward elimination and forward selection sometimes
arrive at different final models.
If trying both techniques and this happens, it's common
to choose the model with the larger $R_{adj}^2$.


\subsection{The p-value approach,
    an alternative to adjusted $\pmb{R^2}$}

\noindent%
The p-value may be used as an alternative to $R_{adj}^2$
for model selection:
\begin{description}
\item[Backward elimination with the p-value approach.]
    In backward elimination, we would identify the predictor
    corresponding to the largest p-value.
    If the p-value is above the significance level,
    usually $\alpha = 0.05$, then we would drop that variable,
    refit the model, and repeat the process.
    If the largest p-value is less than $\alpha = 0.05$,
    then we would not eliminate any predictors and the current
    model would be our best-fitting model.
\item[Forward selection with the p-value approach.]
    In forward selection with p-values, we reverse the process.
    We begin with a model that has no predictors, then we fit
    a model for each possible predictor, identifying the model
    where the corresponding predictor's p-value is smallest.
    If that p-value is smaller than $\alpha = 0.05$, we add
    it to the model and repeat the process, considering whether
    to add more variables one-at-a-time.
    When none of the remaining predictors can be added to the
    model and have a p-value less than 0.05,
    then we stop adding variables and the current model would
    be our best-fitting model.
\end{description}

\begin{exercisewrap}
\begin{nexercise}
Examine Figure~\ref{loansModelAllButIssued} on
page~\pageref{loansModelAllButIssued}, which considers the
model including all variables except the variable for the month
the loan was issued.
If we were using the p-value approach with backward elimination
and we were considering this model, which of these variables
would be up for elimination?
Would we drop that variable, or would we keep it in the
model?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{The \var{bankruptcy} predictor is up for
  elimination since it has the largest p-value.
  However, since that p-value is smaller than 0.05,
  we would still keep it in the model.}

While the adjusted $R^2$ and p-value approaches are similar,
they sometimes lead to different models, with the $R_{adj}^2$
approach tending to include more predictors in the final model.

\begin{onebox}{Adjusted $\pmb{R^2}$ vs p-value approach}
  When the sole goal is to improve prediction accuracy,
  use $R_{adj}^2$.
  This is commonly the case in machine learning
  applications.\vspace{3mm}

  When we care about understanding which variables are
  statistically significant predictors of the response,
  or if there is interest in producing a simpler model
  at the potential cost of a little prediction accuracy,
  then the p-value approach is preferred.
\end{onebox}

Regardless of whether you use $R_{adj}^2$ or the p-value approach,
or if you use the backward elimination of forward selection
strategy, our job is not done after variable selection.
We must still verify the model conditions are reasonable.

\index{model selection|)}


{\input{ch_regr_mult_and_log/TeX/model_selection.tex}}







%%%%%
\section{Checking model conditions using graphs}
\label{multipleRegressionModelAssumptions}

\index{regression!model assumptions|(}
\index{regression!model conditions|(}
\index{regression!technical conditions|(}
\index{regression!conditions|(}

\noindent%
Multiple regression methods using the model
\begin{align*}
\hat{y} &= \beta_0 + \beta_1x_1 + \beta_2x_2 + \cdots + \beta_kx_k
\end{align*}
generally depend on the following four conditions:
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item the residuals of the model are nearly normal
    (less important for larger data sets),
\item the variability of the residuals is nearly constant,
\item the residuals are independent, and
\item each variable is linearly related to the outcome.
\end{enumerate}


\subsection{Diagnostic plots}
\label{diagnostic_plots_subsection}

\termsub{Diagnostic plots}{diagnostic plots} can be used
to check each of these conditions.
We will consider the
model from the Lending Club loans data, and check whether
there are any notable concerns:
\begin{align*}
\widehat{rate} &= \ 1.921
    + 0.974 \times \indfunc{income\us{}ver}{source\us{}only}
    + 2.535 \times \indfunc{income\us{}ver}{verified} \\
  &\qquad
    + 0.021 \times \var{debt\us{}to\us{}income}
    + 4.896 \times \var{credit\us{}util}
    + 0.387 \times \var{bankruptcy} \\
  &\qquad
    + 0.154 \times \var{term}
    + 0.228 \times \var{credit\us{}check}
\end{align*}

\begin{description}
\item[Check for outliers.]
    In theory, the distribution of the residuals should
    be nearly normal;
    in practice, normality can be relaxed for most applications.
    Instead, we examine a histogram of the residuals
    to check if there are any outliers:
    Figure~\ref{loansDiagNormalHistogram}
    is a histogram of these outliers.
    Since this is a very large data set,
    only particularly extreme observations would be a concern
    in this particular case.
    There are no extreme observations that might cause a~concern.

    If we intended to construct what are called
    \termsub{prediction intervals}{prediction interval}
    for future observations,
    we would be more strict and
    require the residuals to be nearly normal.
    Prediction intervals are further discussed in
    an online extra on the OpenIntro website:\vspace{-2mm}
    \begin{center}
      \oiRedirect{stat_extra_linear_regression_supp}
          {www.openintro.org/d?id=stat\us{}extra\us{}linear\us{}regression\us{}supp}
    \end{center}

\begin{figure}[h]
  \centering
  \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.]
      {0.75}
      {loansDiagnostics}
      {loansDiagNormalHistogram}
  \caption{A histogram of the residuals.}
  \label{loansDiagNormalHistogram}
\end{figure}

\item[Absolute values of residuals against fitted values.]
    A plot of the absolute value of the residuals against
    their corresponding fitted values ($\hat{y}_i$) is shown
    in Figure~\ref{loansDiagEvsAbsF}.
    This plot is helpful to check the condition that the
    variance of the residuals is approximately constant,
    and a smoothed line has been added to represent the
    approximate trend in this plot.
    There is more evident variability for fitted values that are
    larger, which we'll discuss further.

\begin{figure}[h]
  \centering
  \Figures{0.7}
      {loansDiagnostics}
      {loansDiagEvsAbsF}
  \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.}
  \label{loansDiagEvsAbsF}
\end{figure}

\item[Residuals in order of their data collection.]
    This type of plot can be helpful when observations were
    collected in a sequence.
    Such a plot is helpful in identifying any connection
    between cases that are close to one another.
    The loans in this data set were issued over a 3 month period,
    and the month the loan was issued was not found to be important,
    suggesting this is not a concern for this data set.
    In cases where a data set does show some pattern
    for this check, \term{time series} methods may be useful.

\item[Residuals against each predictor variable.]
    We consider a plot of the residuals against each of
    the predictors in Figure~\ref{loansDiagEvsVariables}.
    For those instances where there are only 2-3 groups,
    box plots are shown.
    For the numerical outcomes, a smoothed line has been
    fit to the data to make it easier to review.
    Ultimately, we are looking for any notable change
    in variability between groups or pattern in the data.

    Here are the things of importance from these plots:
    \begin{itemize}
    \item
        There is some minor differences in variability
        between the verified income groups.
    \item
        There is a very clear pattern for the
        debt-to-income variable.
        What also stands out is that this variable
        is very strongly right skewed:
        there are few observations with very high
        debt-to-income ratios.
    \item
        The downward curve on the right side of the
        credit utilization and credit check plots suggests
        some minor misfitting for those larger values.
    \end{itemize}

\begin{figure}
  \centering
  \Figures{}{loansDiagnostics}{loansDiagEvsVariables_1}
  \Figures{}{loansDiagnostics}{loansDiagEvsVariables_2}
  \Figures{}{loansDiagnostics}{loansDiagEvsVariables_3}
  \caption{Diagnostic plots for residuals against each of the
      predictors.
      For the box plots, we're looking for notable differences
      in variability.
      For numerical predictors, we also check for trends
      or other structure in the data.}
  \label{loansDiagEvsVariables}
\end{figure}

\end{description}

Having reviewed the diagnostic plots, there are two options.
The first option is to, if we're not concerned about the issues
observed, use this as the final model;
if going this route, it is important to still note any
abnormalities observed in the diagnostics.
The second option is to try to improve the model,
which is what we'll try to do with this particular model fit.



\D{\newpage}

\subsection{Options for improving the model fit}

There are several options for improvement of a model,
including transforming variables,
seeking out additional variables to fill model gaps,
or using more advanced methods that would account for
challenges around inconsistent variability or nonlinear
relationships between predictors and the outcome.

The main concern for the initial model is that
there is a notable nonlinear relationship
between the debt-to-income variable observed in
Figure~\ref{loansDiagEvsVariables}.
To resolve this issue, we're going to consider
a couple strategies for adjusting the relationship
between the predictor variable and the outcome.

Let's start by taking a look at a histogram of
\var{debt\us{}to\us{}income} in
Figure~\ref{loansDebtToIncomeHist}.
The variable is extremely skewed,
and upper values will have a lot of leverage
on the fit.
Below are several options:
\begin{itemize}
\item log transformation ($\log{x}$),
    \index{transformation!log}
\item square root transformation ($\sqrt{x}$),
    \index{transformation!square root}
\item inverse transformation ($1 / x$),
    \index{transformation!inverse}
\item truncation (cap the max value possible)
    \index{truncation}\index{transformation!truncation}
\end{itemize}
If we inspected the data more closely, we'd observe
some instances where the variable takes a value of~0,
and since $\log(0)$ and $1 / x$ are undefined when $x = 0$,
we'll exclude these transformations from further
consideration.\footnote{There are ways to make them work,
   but we'll leave those options to a later course.}
A square root transformation is valid for all values
the variable takes, and truncating some of the larger
observations is also a valid approach.
We'll consider both of these approaches.

\begin{figure}[h]
  \centering
  \Figures{0.62}{loansDiagnostics}{loansDebtToIncomeHist}
  \caption{Histogram of \var{debt\us{}to\us{}income},
      where extreme skew is evident.}
  \label{loansDebtToIncomeHist}
\end{figure}

To try transforming the variable, we make two new variables
representing the transformed versions:
\begin{description}
\item[Square root.]
    We create a new variable,
    \var{sqrt\us{}debt\us{}to\us{}income},
    where all the values are simply the square roots of the
    values in \var{debt\us{}to\us{}income},
    and then refit the model as before.
    The result is shown in the left panel of
    Figure~\ref{loansDiagEvsTransformDebtToIncome}.
    The square root pulled in the higher values
    a bit, but the fit still doesn't look great
    since the smoothed line is still wavy.
\item[Truncate at 50.]
    We create a new variable,
    \var{debt\us{}to\us{}income\us{}50},
    where any values in \var{debt\us{}to\us{}income}
    that are greater than 50 are shrunk to exactly 50.
    Refitting the model once more,
    the diagnostic plot for this new variable is shown
    in the right panel of
    Figure~\ref{loansDiagEvsTransformDebtToIncome}.
    Here the fit looks much more reasonable,
    so this appears to be a reasonable approach.
    %If we inspected the data, we'd also observe that
    %the debt-to-income ratio tends to be large when
    %income is very small, so these values may also
    %have been a bit inflated if someone was between jobs.
\end{description}
The downside of using transformations is that it reduces
the ease of interpreting the results.
Fortunately, since the truncation transformation only affects
a relatively small number of cases, the interpretation
isn't dramatically impacted.

\begin{figure}[h]
  \centering
  \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.]
      {0.9}
      {loansDiagnostics}
      {loansDiagEvsTransformDebtToIncome}
  \caption{Histogram of \var{debt\us{}to\us{}income},
      where extreme skew is evident.}
  \label{loansDiagEvsTransformDebtToIncome}
\end{figure}

\D{\newpage}

As a next step, we'd evaluate the new model using
the truncated version of \var{debt\us{}to\us{}income},
we would complete all the same procedures as before.
The other two issues noted while inspecting diagnostics
in Section~\ref{diagnostic_plots_subsection} are still
present in the updated model.
If we choose to report this model, we would want to also
discuss these shortcomings to be transparent in our work.
Depending on what the model will be used for, we could either
try to bring those under control, or we could stop since
those issues aren't severe.
Had the non-constant variance been a little more dramatic,
it would be a higher priority.
Ultimately we decided that the model was reasonable,
and we report its final form here:
\begin{align*}
\widehat{rate} &= \ 1.562
    + 1.002 \times \indfunc{income\us{}ver}{source\us{}only}
    + 2.436 \times \indfunc{income\us{}ver}{verified} \\
  &\qquad
    + 0.048 \times \var{debt\us{}to\us{}income\us{}50}
    + 4.694 \times \var{credit\us{}util}
    + 0.394 \times \var{bankruptcy} \\
  &\qquad
    + 0.153 \times \var{term}
    + 0.223 \times \var{credit\us{}check}
\end{align*}
A sharp eye would notice that the coefficient for
\var{debt\us{}to\us{}income\us{}50} is more than twice
as large as what the coefficient had been for the
\var{debt\us{}to\us{}income} variable in the earlier model.
This suggests those larger values not only were points
with high leverage, but they were influential points that
were dramatically impacting the coefficient.

\begin{onebox}{``All models are wrong,
    but some are useful''~~~-George E.P. Box}
  The truth is that no model is perfect.
  However, even imperfect models can be useful.
  Reporting a flawed model can be reasonable so long
  as we are clear and report the model's shortcomings.
\end{onebox}

Don't report results when conditions are grossly violated.
While there is a little leeway in model conditions,
don't go too far.
If model conditions are very clearly violated,
consider a new model, even if it means learning more
statistical methods or hiring someone who can help.
To help you get started, we've developed a couple additional
sections that you may find on OpenIntro's website.
These sections provide a light introduction to what are
called \termsub{interaction terms}{interaction term}
\index{regression!interaction term|textbf}
and to fitting
\termsub{nonlinear curves}{nonlinear curve}%
\index{regression!nonlinear curve|textbf}
to data, respectively:
\begin{center}
\oiRedirect{stat_extra_interaction_effects}
    {www.openintro.org/d?file=stat\_extra\_interaction\_effects}
  \\[3mm]
\oiRedirect{stat_extra_nonlinear_relationships}
    {www.openintro.org/d?file=stat\_extra\_nonlinear\_relationships}
\end{center}

\index{regression!conditions|)}
\index{regression!technical conditions|)}
\index{regression!model conditions|)}
\index{regression!model assumptions|)}
\index{data!mario\_kart|)}
\index{regression!multiple|)}


{\input{ch_regr_mult_and_log/TeX/checking_model_assumptions_using_graphs.tex}}






%_____________________
\section{Multiple regression case study: Mario Kart}
\label{mario_kart_case_study}

\noindent%
We'll consider Ebay auctions of a video game called
\emph{Mario Kart} for the Nintendo Wii.
The outcome variable of interest is the total price of
an auction, which is the highest bid plus the shipping cost.
We will try to determine how total price is related to each
characteristic in an auction while simultaneously controlling
for other variables.
For instance, all other characteristics held constant,
are longer auctions associated with higher or lower prices?
And, on average, how much more do buyers tend to pay for
additional Wii wheels
(plastic steering wheels that attach to the Wii controller)
in auctions?
Multiple regression will help us answer these and other questions.

\newcommand{\mknum}{141}


\subsection{Data set and the full model}

The \data{mariokart} data set includes results
from \mknum{}~auctions.
Four observations from this data set are shown in
Figure~\ref{marioKartDataMatrix},
and descriptions for each variable are shown in
Figure~\ref{marioKartVariables}. 
Notice that the condition and stock photo variables
are indicator variables\index{indicator variable},
similar to \var{bankruptcy} in the \data{loan} data set.
%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.

\begin{figure}[ht]
\centering
\begin{tabular}{rrrrlr}
  \hline
  & price & cond\us{}new & stock\us{}photo & duration & wheels \\ 
  \hline
  1 & 51.55 &   1 & 1 & 3 &   1 \\ 
  2 & 37.04 &  0 &  1 & 7 &   1 \\ 
  $\vdots$ &$\vdots$ &$\vdots$ &$\vdots$ &$\vdots$ &$\vdots$ \\
  140 & 38.76 &  0 &  0 & 7 &   0 \\ 
  141 & 54.51 &  1 &  1 & 1 &   2 \\ 
  \hline
\end{tabular}
\caption{Four observations from the \data{mariokart}
    data set.}
\label{marioKartDataMatrix}
\end{figure}
%library(openintro); data(marioKart); d <- marioKart[marioKart$totalPr < 100,]; row.names(d) <- NULL; d

\begin{figure}[h]
\centering\small
\begin{tabular}{lp{9.5cm}}
\hline
{\bf variable} & {\bf description} \\
\hline
\var{price} &
  Final auction price plus shipping costs, in US dollars. \\
\var{cond\us{}new} &
  Indicator variable for if the game is new (\resp{1}) or used (\resp{0}). \\
\var{stock\us{}photo} &
  Indicator variable for if the auction's main photo
  is a stock photo. \\
\var{duration} &
  The length of the auction, in days, taking values from 1 to 10. \\
\var{wheels} &
  The number of Wii wheels included with the auction.
  A \emph{Wii wheel} is an optional steering wheel accessory
  that holds the Wii controller. \\
\hline
\end{tabular}
\caption{Variables and their descriptions for the
    \data{mariokart} data set.}
\label{marioKartVariables}
\end{figure}

% 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))

%\begin{figure}[h]
%  \centering
%  \Figure{0.5}{marioKartSingle}
%  \caption{Scatterplot of the total auction price against the
%      game's condition.
%      The least squares line is also shown.}
%  \label{marioKartSingle}
%\end{figure}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
\label{condNewVarForMarioKartOnly}
We fit a linear regression model with
the game's condition as a predictor of auction price.
Results of this model are summarized below:
\begin{center}
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
 & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.8mm} & & & & \\
(Intercept) & 42.8711 & 0.8140 & 52.67 & $<$0.0001 \\ 
  cond\us{}new & 10.8996 & 1.2583 & 8.66 & $<$0.0001 \\ 
   \hline
   &&&\multicolumn{2}{r}{$df=139$}
\end{tabular}
\end{center}
Write down the equation for the model,
note whether the slope is statistically different from zero,
and interpret the coefficient.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The equation for the line may be written as
  \begin{align*}
  \widehat{price}
      &= 42.87 + 10.90\times cond\us{}new
  \end{align*}
  Examining the regression output in
  Guided Practice~\ref{condNewVarForMarioKartOnly},
  we can see that the p-value for \var{cond\us{}new}
  is very close to zero, indicating there is strong evidence
  that the coefficient is different from zero when using this
  simple one-variable model.

  The \var{cond\us{}new} is a two-level
  categorical variable that takes value 1 when the game is new
  and value 0 when the game is used.
  This means the 10.90 model coefficient predicts an extra
  \$10.90 for those games that are new versus those that are used.}

Sometimes 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.

We want to construct a model that accounts for not only the game
condition, as in Guided Practice~\ref{condNewVarForMarioKartOnly},
but simultaneously accounts for three other variables:
\begin{align*}
\widehat{\var{price}}
	&= \beta_0 + \beta_1\times \var{cond\us{}new} +
		\beta_2\times \var{stock\us{}photo} \\
	&\qquad\  + \beta_3 \times  \var{duration} +
		\beta_4 \times  \var{wheels}
\end{align*}
Figure~\ref{MarioKartFullModelOutput} summarizes the full model.
Using this output, we identify the point estimates of each
coefficient.

\begin{figure}[ht]
\centering
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
 & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.8mm} & & & & \\
(Intercept) & 36.2110 & 1.5140 & 23.92 & $<$0.0001 \\ 
  cond\us{}new & 5.1306 & 1.0511 & 4.88 & $<$0.0001 \\ 
  stock\us{}photo & 1.0803 & 1.0568 & 1.02 & 0.3085 \\ 
  duration & -0.0268 & 0.1904 & -0.14 & 0.8882 \\ 
  wheels & 7.2852 & 0.5547 & 13.13 & $<$0.0001 \\ 
   \hline
   &&&\multicolumn{2}{r}{$df=136$}
\end{tabular}
\caption{Output for the regression model where \var{price} is the outcome and \var{cond\us{}new}, \var{stock\us{}photo}, \var{duration}, and \var{wheels} are the predictors.}
\label{MarioKartFullModelOutput}
\end{figure}
%library(openintro); library(xtable); data(marioKart); d <- marioKart[marioKart$totalPr < 100,]; d$cond <- relevel(d$cond, "used"); g <-lm(totalPr ~ cond + stockPhoto + duration + wheels, d)

\begin{exercisewrap}
\begin{nexercise}
\label{eqForMultRegrOfTotalPrForAllPredWithCoef}%
Write out the model's equation using the point estimates from
Figure~\ref{MarioKartFullModelOutput}.
How many predictors are there in this model?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{$\widehat{price}
    = 36.21
        + 5.13 \times \var{cond\us{}new}
        + 1.08 \times \var{stock\us{}photo}
        - 0.03 \times \var{duration}
        + 7.29 \times \var{wheels}$,
  with the $k=4$ predictors.}

\begin{exercisewrap}
\begin{nexercise}
What does $\beta_4$, the coefficient of variable
$x_4$ (Wii wheels), represent?
What is the point estimate of $\beta_4$?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{It is the average difference in auction price
  for each additional Wii wheel included when holding the
  other variables constant.
  The point estimate is $b_4 = 7.29$.}

\begin{exercisewrap}
\begin{nexercise}
\label{computeMultipleRegressionResidualForMarioKart}%
Compute the residual of the first observation in
Figure~\ref{marioKartDataMatrix} using the equation identified
in Guided Practice~\ref{eqForMultRegrOfTotalPrForAllPredWithCoef}.%
\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{$e_i = y_i - \hat{y_i} = 51.55 - 49.62 = 1.93$,
  where 49.62 was computed using the variables values from the
  observation and the equation identified in
  Guided Practice~\ref{eqForMultRegrOfTotalPrForAllPredWithCoef}.}

\begin{examplewrap}
\begin{nexample}{We estimated a coefficient for
    \var{cond\us{}new} in
    Section~\ref{condNewVarForMarioKartOnly}
    of $b_1 = 10.90$ with a standard error of $SE_{b_1} = 1.26$
    when using simple linear regression.
    Why might there be a difference between that estimate
    and the one in the multiple regression setting?}
  \label{colinearityOfCondNewAndStockPhoto}%
  If we examined the data carefully, we would see that
  there is collinearity\index{collinear} among some predictors.
  For instance, when we estimated the connection of the outcome
  \var{price} and predictor \var{cond\us{}new} using simple linear
  regression, we were unable to control for other variables like
  the number of Wii wheels included in the auction.
  That model was biased by the confounding variable \var{wheels}.
  When we use both variables, this particular underlying and
  unintentional bias is reduced or eliminated (though bias
  from other confounding variables may still remain).
\end{nexample}
\end{examplewrap}


\subsection{Model selection}

\noindent%
Let's revisit the model for the Mario Kart auction and complete
model selection using backwards selection.
Recall that the full model took the following form:
\begin{align*}
\widehat{price} = 36.21
    + 5.13 \times \var{cond\us{}new}
    + 1.08 \times \var{stock\us{}photo}
    - 0.03 \times \var{duration}
    + 7.29 \times \var{wheels}
\end{align*}


\begin{examplewrap}
\begin{nexample}{Results corresponding to the full model
    for the \data{mariokart} data were shown
    in Figure~\vref{MarioKartFullModelOutput}.
    For this model, we consider what would happen if dropping
    each of the variables in the model:
    \begin{center}
    \begin{tabular}{lllll}
    Exclude ... &
      \var{cond\us{}new} &
      \var{stock\us{}photo} &
      \var{duration} &
      \var{wheels} \\
    &
      $R^2_{adj} = 0.6626$ &
      $R^2_{adj} = 0.7107$ &
      $R^2_{adj} = 0.7128$ &
      $R^2_{adj} = 0.3487$ \\
    \end{tabular}
    \end{center}
    For the full model, $R_{adj}^2 = 0.7108$.
    How should we proceed under the backward elimination strategy?}
  \label{backwardEliminationExampleWMarioKartData}%
  The third model without \var{duration} has the highest
  $R_{adj}^2$ of 0.7128, so we compare it to
  $R_{adj}^2$ for the full model.
  Because eliminating \var{duration} leads to a model with
  a higher $R_{adj}^2$, we drop \var{duration} from the model.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
In Example~\ref{backwardEliminationExampleWMarioKartData},
we eliminated the \var{duration} variable,
which resulted in a model with $R_{adj}^2 = 0.7128$.
Let's look at if we would eliminate another variable from the
model using backwards elimination:
\begin{center}
\begin{tabular}{llll}
Exclude \var{duration} and ... &
	\var{cond\us{}new} &
	\var{stock\us{}photo} &
	\var{wheels} \\
&
	$R^2_{adj} = 0.6587$ &
	$R^2_{adj} = 0.7124$ &
	$R^2_{adj} = 0.3414$ \\
\end{tabular}
\end{center}
Should we eliminate any additional variable, and if so,
which variable should we eliminate?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Removing any of the three remaining variables
  would lead to a decrease in $R_{adj}^2$, so we should not
  remove any additional variables from the model after we
  removed \var{duration}.}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
\label{totPrPredictionUsedStockPhotoTwoWheels}%
After eliminating the auction's duration from the model,
we are left with the following reduced model:
\begin{align*}
\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}}
\end{align*}
How much would you predict for the total price for
the Mario Kart game if it was used, used a stock photo,
and included two wheels and put up for auction during
the time period that the Mario Kart data were
collected?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We would plug in \resp{0} for \var{cond\us{}new}
  \resp{1} for \var{stock\us{}photo},
  and \resp{2} for \var{wheels} into the equation,
  which would return \$51.77, which is the total price
  we would expect for the auction.}

\begin{exercisewrap}
\begin{nexercise}
Would you be surprised if the seller from
Guided Practice~\ref{totPrPredictionUsedStockPhotoTwoWheels}
didn't get the exact price predicted?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{No.
  The model provides the \emph{average} auction price
  we would expect, and the price for one auction to the next
  will continue to vary a bit
  (but less than what our prediction would be without the model).}

%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}:
%\begin{align*}
%\hat{y} \ &= \ b_0 + b_1x_1 + b_2x_2 + b_4x_4 \\
%\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}}
%\end{align*}


\subsection{Checking model conditions using graphs}

\noindent%
Let's take a closer look at the diagnostics for the Mario Kart
model to check if the model we have identified is reasonable.

\begin{description}
\item[Check for outliers.]
    A histogram of the residuals is shown in
    Figure~\ref{mkDiagResHist}.
    With a data set well over a hundred, we're primarily
    looking for major outliers.
    While one minor outlier appears on the upper end,
    it is not a concern for this large of a data set.

\begin{figure}[h]
  \centering
  \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.]
      {0.6}
      {marioKartDiagnostics}
      {mkDiagResHist}
  \caption{Histogram of the residuals.
      No clear outliers are evident.}
  \label{mkDiagResHist}
\end{figure}

\item[Absolute values of residuals against fitted values.]
    A plot of the absolute value of the residuals against
    their corresponding fitted values ($\hat{y}_i$) is shown
    in Figure~\ref{mkDiagnosticEvsAbsF}.
    We don't see any obvious deviations from constant variance
    in this example.

\begin{figure}
  \centering
  \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.]
      {0.6}{marioKartDiagnostics}{mkDiagnosticEvsAbsF}
  \caption{Absolute value of the residuals against
      the fitted values.
      No patterns are evident.}
  \label{mkDiagnosticEvsAbsF}
\end{figure}

\item[Residuals in order of their data collection.]
    A plot of the residuals in the order their corresponding
    auctions were observed is shown in
    Figure~\ref{mkDiagnosticInOrder}.
    Here we see no structure that indicates a problem.

\begin{figure}[h]
  \centering
  \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.]
      {0.55}{marioKartDiagnostics}{mkDiagnosticInOrder}
  \caption{Residuals in the order that their
      corresponding observations were collected.
      There are no evident patterns.}
  \label{mkDiagnosticInOrder}
\end{figure}

\item[Residuals against each predictor variable.]
    We consider a plot of the residuals against the
    \var{cond\us{}new} variable, the residuals against
    the \var{stock\us{}photo} variable,
    and the residuals against the \var{wheels} variable.
    These plots are shown in Figure~\ref{mkDiagnosticEvsVariables}.
    For the two-level condition variable, we are guaranteed not
    to see any remaining trend, and instead we are checking that
    the variability doesn't fluctuate across groups,
    which it does not.
    However, looking at the stock photo variable,
    we find that there is some difference in the variability
    of the residuals in the two groups.
    Additionally, when we consider the residuals against the
    \var{wheels} variable, we see some possible structure.
    There appears to be curvature in the residuals,
    indicating the relationship is probably not linear.

\begin{figure}
  \centering
  \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.]
      {0.9}{marioKartDiagnostics}{mkDiagnosticEvsVariables}
  \caption{For the condition and stock photo variables,
      we check
      for differences in the distribution shape
      or variability of
      the residuals.
      In the case of the stock photos variable,
      we see a little
      less variability in the unique photo group
      than the stock
      photo group.
      For numerical predictors, we also check for
      trends or other structure.
      We see some slight bowing in the residuals against the
      \var{wheels} variable in the bottom plot.}
  \label{mkDiagnosticEvsVariables}
\end{figure}

\end{description}

As with the \data{loans} analysis, we would summarize
diagnostics when reporting the model results.
In the case of this auction data,
we would report that there appears to be non-constant variance
in the stock photo variable and that there may be a nonlinear
relationship between the total price and the number of wheels
included for an auction.
This information would be important to buyers and sellers who
may review the analysis, and omitting this information could be
a setback to the very people who the model might assist. \\

\noindent%
\textbf{Note: there are no exercises for this section.}




%__________________
\section{Introduction to logistic regression}
\label{logisticRegression}

\index{logistic regression|seealso{regression}}
\index{regression!logistic|(}

\noindent%
In this section we introduce
\termsub{logistic regression}{regression!logistic}
as a tool for building models when there is a categorical
response variable with two levels, e.g. yes and no.
Logistic regression is a type of
\term{generalized linear model} (\term{GLM})
for response variables
where regular multiple regression does not work very well.
In particular, the response variable in these settings often
takes a form where residuals look completely different from
the normal distribution.

GLMs can be thought of as a two-stage modeling approach.
We first model the response variable using a probability
distribution, such as the binomial or Poisson distribution.
Second, we model the parameter of the distribution using
a collection of predictors and a special form of multiple
regression.
Ultimately, the application of a GLM will feel very similar
to multiple regression, even if some of the details are
different.

%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. 

\subsection{Resume data}

\index{data!resume|(}

\newcommand{\resN}{4870}
\newcommand{\resCallbackProp}{0.0805}
\newcommand{\resCallbackPerc}{8.05\%}
\newcommand{\resNumPred}{8}
\newcommand{\resUniqueNames}{36}
\newcommand{\resHonorsInt}{-2.4998}
\newcommand{\resHonorsCoef}{0.8668}
\newcommand{\resHonorsIntPlusCoef}{-1.6330}
\newcommand{\resHonorsCoefSE}{0.1776}
\newcommand{\resHonorsCoefZ}{4.88}
\newcommand{\resHonorsProb}{0.163}
\newcommand{\resHonorsPerc}{16.3\%}
\newcommand{\resHonorsNotProb}{0.076}
\newcommand{\resHonorsNotPerc}{7.6\%}

We will consider experiment data from a study that sought
to understand the effect of race and sex on job application
callback rates;
details of the study and a link to the data set may be
found in Appendix~\ref{ch_regr_mult_and_log_data}.
To evaluate which factors were important,
job postings were identified in Boston and Chicago
for the study,
and researchers created many fake resumes to send off
to these jobs to see which would elicit a callback.
The researchers enumerated important characteristics,
such as years of
experience and education details, and they used these
characteristics to randomly generate the resumes.
Finally, they randomly assigned a name to each resume,
where the name would imply the applicant's sex and race.

The first names that were used and randomly assigned
in this experiment were selected so that they
would predominantly be recognized as belonging
to Black or White individuals;
other races were not considered in this study.
While no name would definitively be inferred as pertaining
to a Black individual or to a White individual,
the researchers conducted a survey to check for
racial association of the names;
names that did not pass this survey check were excluded
from usage in the experiment.
You can find the full set of names that did pass the
survey test and were ultimately used in the study in
Figure~\ref{resumeFirstName}.
For example, Lakisha was a name that their survey indicated
would be interpreted as a Black woman, while Greg was a name
that would generally be interpreted to be associated with
a White male.

\begin{figure}[h]
\centering\small
\begin{tabular}{lll c lll c lll}
  \cline{1-3} \cline{5-7} \cline{9-11}
  first\us{}name & race & sex
      & \ \hspace{2mm}\ &
      first\us{}name & race & sex
      & \ \hspace{2mm}\ &
      first\us{}name & race & sex
      \\
  \cline{1-3} \cline{5-7} \cline{9-11}
  Aisha & black & female &&
      Hakim & black & male &&
      Laurie & white & female \\
  Allison & white & female &&
      Jamal & black & male &&
      Leroy & black & male \\
  Anne & white & female &&
      Jay & white & male &&
      Matthew & white & male \\
  Brad & white & male &&
      Jermaine & black & male &&
      Meredith & white & female \\
  Brendan & white & male &&
      Jill & white & female &&
      Neil & white & male \\
  Brett & white & male &&
      Kareem & black & male &&
      Rasheed & black & male \\
  Carrie & white & female &&
      Keisha & black & female &&
      Sarah & white & female \\
  Darnell & black & male &&
      Kenya & black & female &&
      Tamika & black & female \\
  Ebony & black & female &&
      Kristen & white & female &&
      Tanisha & black & female \\
  Emily & white & female &&
      Lakisha & black & female &&
      Todd & white & male \\
  Geoffrey & white & male &&
      Latonya & black & female &&
      Tremayne & black & male \\
  Greg & white & male &&
      Latoya & black & female &&
      Tyrone & black & male \\
  \cline{1-3} \cline{5-7} \cline{9-11}
\end{tabular}
\caption{List of all \resUniqueNames{} unique names along
    with the commonly inferred race and sex associated
    with these names.}
\label{resumeFirstName}
\end{figure}
% 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.)

The response variable of interest is whether or not there
was a callback from the employer for the applicant,
and there were \resNumPred{} attributes that
were randomly assigned that we'll consider,
with special interest in the race and sex variables.
Race and sex are \term{protected classes} in the
United States, meaning they are not legally permitted
factors for hiring or employment decisions.
The full set of attributes considered is provided in
Figure~\ref{resumeVariables}.

\D{\newpage}

\begin{figure}[h]
\centering\small
\begin{tabular}{lp{112mm}}
\hline
{\bf variable} & {\bf description} \\
\hline
\var{callback} &
    Specifies whether the employer called the applicant
    following submission of the application for the job. \\
%\var{first\us{}name} &
%    First name of the applicant that is listed on the resume. \\
\var{job\us{}city} &
    City where the job was located: Boston or Chicago.\\
%\var{job\us{}industry} &
%    The job industry, e.g. manufacturing or transportation,
%    for the job listing. \\
%\var{job\us{}type} &
%    The type of job, e.g. supervisor or sales representative,
%    for the job listing. \\
%\var{job\us{}req} &
%    An indicator for if there were any job requirements listed
%    in the job listing. \\
\var{college\us{}degree} &
    An indicator for whether the resume listed a college degree. \\
\var{years\us{}experience} &
    Number of years of experience listed on the resume. \\
\var{honors} &
    Indicator for the resume listing some sort of honors,
    e.g.~employee of the month. \\
\var{military} &
    Indicator for if the resume listed any military experience. \\
\var{email\us{}address} &
    Indicator for if the resume listed an email address for
    the applicant. \\
\var{race} &
    Race of the applicant, implied by their first name
    listed on the resume. \\
\var{sex} &
    Sex of the applicant (limited to only \resp{male}
    and \resp{female} in this study),
    implied by the first name listed on the resume. \\
\hline
\end{tabular}
\caption{Descriptions for the \var{callback} variable
    along with \resNumPred{} other variables
    in the \data{resume} data set.
    Many of the variables are
    indicator\index{indicator variable} variables,
    meaning they take the value 1 if the specified
    characteristic is present and 0 otherwise.}
\label{resumeVariables}
\end{figure}

All of the attributes listed on each resume were
randomly assigned.
This means that no attributes that might be favorable
or detrimental to employment would favor one demographic
over another on these resumes.
Importantly, due to the experimental nature of this study,
we can infer causation between these variables and the
callback rate, if the variable is statistically significant.
Our analysis will allow us to compare the practical
importance of each of the variables relative to each other.




\subsection{Modeling the probability of an event}
\label{modelingTheProbabilityOfAnEvent}

Logistic regression is a generalized linear model where
the outcome is a two-level categorical variable.
The outcome, $Y_i$, takes the value 1
(in our application, this represents a callback
for the resume)
with probability $p_i$
and the value 0 with probability $1 - p_i$.
Because each observation has a slightly different
context, e.g. different education level or a different
number of years of experience, the probability $p_i$
will differ for each observation.
Ultimately, it is this probability that we model
in relation to the predictor variables:
we will examine which resume characteristics
correspond to higher or lower callback rates.

\begin{onebox}{Notation for a logistic regression model}
The outcome variable for a GLM is denoted by $Y_i$,
where the index $i$ is used to represent observation $i$.
In the resume application, $Y_i$ will be used to represent
whether resume $i$ received a callback ($Y_i=1$)
or not ($Y_i=0$). \vspace{3mm}

The predictor variables are represented as follows:
$x_{1,i}$ is the value of variable 1 for observation $i$,
$x_{2,i}$ is the value of variable 2 for observation $i$,
and so on.
\end{onebox}

The logistic regression model relates the probability
a resume would receive a callback ($p_i$) to the predictors
$x_{1,i}$, $x_{2,i}$, ..., $x_{k,i}$
through a framework much like that of multiple regression:
\begin{align}
transformation(p_{i})
  = \beta_0 +
      \beta_1x_{1,i} +
      \beta_2 x_{2,i} +
      \cdots +
      \beta_k x_{k,i}
\label{linkTransformationEquation}
\end{align}
We want to choose a transformation in the equation
that makes practical and mathematical sense.
For example, we want a transformation that makes
the range of possibilities on the left hand side
of the equation equal to the range of possibilities
for the right hand side;
if there was no transformation for this equation,
the left hand side could only take values between 0 and 1,
but the right hand side could take values outside of this
range.
A common transformation for $p_i$ is the \term{logit transformation}, which may be written as
\begin{align*}
logit(p_i) = \log_{e}\left( \frac{p_i}{1-p_i} \right)
\end{align*}
The logit transformation is shown in
Figure~\ref{logitTransformationFigureHoriz}.
Below, we rewrite the equation relating $Y_i$ to its
predictors using the logit transformation of $p_i$:
\begin{align*}
\log_{e}\left( \frac{p_i}{1-p_i} \right)
  = \beta_0 +
      \beta_1 x_{1,i} +
      \beta_2 x_{2,i} +
      \cdots +
      \beta_k x_{k,i}
\end{align*}
In our resume example, there are \resNumPred{} predictor
variables, so $k = \resNumPred{}$.
While the precise choice of a logit function isn't intuitive,
it is based on theory that underpins generalized linear models,
which is beyond the scope of this book.
Fortunately, once we fit a model using software,
it will start to feel like we're back in the
multiple regression context, even if the
interpretation of the coefficients is more complex.

\begin{figure}
  \centering
  \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).]
      {}{logitTransformationFigureHoriz}
  \caption{Values of $p_i$ against values of $logit(p_i)$.}
  \label{logitTransformationFigureHoriz}
\end{figure}

\begin{examplewrap}
\begin{nexample}{We start by fitting a model with a single
    predictor: \var{honors}.
    This variable indicates whether the applicant had any
    type of honors listed on their resume,
    such as employee of the month.
    The following logistic regression model was fit using
    statistical software:
    \begin{align*}
    \log_e \left( \frac{p_i}{1-p_i} \right)
      = \resHonorsInt{} +
          \resHonorsCoef{} \times\text{\var{honors}}
    \end{align*}
    %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)
    (a) If a resume is randomly selected from the study
    and it does not have any honors listed,
    what is the probability resulted in a callback?
    
    (b) What would the probability be if the resume did
    list some honors?}
    \label{logisticExampleWithHonors}%
  (a) If a randomly chosen resume from those sent out is considered,
  and it does not list honors, then \var{honors} takes
  value~0 and the right side of the model equation equals
  \resHonorsInt{}.
  Solving for $p_i$:
  $\frac{e^{\resHonorsInt{}}}{1 + e^{\resHonorsInt{}}}
      = \resHonorsNotProb{}$.
  Just as we labeled a fitted value of $y_i$ with a ``hat''
  in single-variable and multiple regression, we do the same
  for this probability: $\hat{p}_i = \resHonorsNotProb{}$.

  (b) If the resume had listed some honors,
  then the right side of the model equation is
  $\resHonorsInt{} + \resHonorsCoef{} \times 1
      = \resHonorsIntPlusCoef{}$,
  which corresponds to a probability
  $\hat{p}_i = \resHonorsProb{}$.

  Notice that we could examine \resHonorsInt{} and
  \resHonorsIntPlusCoef{} in
  Figure~\ref{logitTransformationFigureHoriz}
  to estimate the probability before formally calculating
  the value.
\end{nexample}
\end{examplewrap}

\D{\newpage}

To convert from values on the logistic regression scale
(e.g. \resHonorsInt{} and \resHonorsIntPlusCoef{} in
Example~\ref{logisticExampleWithHonors}),
use the following formula, which is the result
of solving for $p_i$ in the regression model:
\newcommand{\exponentialToSolveForPi}
    {e^{\beta_0 + \beta_1 x_{1,i}+\cdots+\beta_k x_{k,i}}}%
\begin{align*}
p_i = \frac{\exponentialToSolveForPi{}}
    {\ 1\ \ +\ \ \exponentialToSolveForPi{}\ }
\end{align*}
As with most applied data problems,
we substitute the point estimates for the parameters
(the $\beta_i$) so that we can make use of this formula.
In Example~\ref{logisticExampleWithHonors},
the probabilities were calculated as
\begin{align*}
&\frac{\ e^{\resHonorsInt{}}\ }
    {\ 1\ +\ e^{\resHonorsInt{}}\ }
  = \resHonorsNotProb{} &&
\frac{\ e^{\resHonorsInt{} + \resHonorsCoef{}}\ }
    {\ 1\ +\ e^{\resHonorsInt{} + \resHonorsCoef{}}\ }
  = \resHonorsProb{}
\end{align*}
While knowing whether a resume listed honors provides
some signal when predicting whether or not the employer
would call, we would like to account for many different
variables at once to understand how each of the different
resume characteristics affected the chance of a callback.


\subsection{Building the logistic model with many variables}

We used statistical software to fit the logistic regression
model with all \resNumPred{} predictors described in
Figure~\ref{resumeVariables}.
Like multiple regression, the result may be presented
in a summary table, which is shown in
Figure~\ref{resumeLogisticModelResults}.
The structure of this table is almost identical to that
of multiple regression;
the only notable difference is that the p-values are
calculated using the normal distribution rather than
the $t$-distribution.

\begin{figure}[ht]
\centering
\begin{tabular}{l rrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & z value & Pr($>$$|$z$|$) \\
  \hline
  \vspace{-3.8mm} & & & & \\
  (Intercept) & -2.6632 & 0.1820 & -14.64 & $<$0.0001 \\
  job\us{}city\lmlevel{Chicago} &
      -0.4403 & 0.1142 & -3.85 & 0.0001 \\
  college\us{}degree & -0.0666 & 0.1211 & -0.55 & 0.5821 \\
  years\us{}experience & 0.0200 & 0.0102 & 1.96 & 0.0503 \\
  honors & 0.7694 & 0.1858 & 4.14 & $<$0.0001 \\
  military & -0.3422 & 0.2157 & -1.59 & 0.1127 \\
  email\us{}address & 0.2183 & 0.1133 & 1.93 & 0.0541 \\
  race\lmlevel{white} & 0.4424 & 0.1080 & 4.10 & $<$0.0001 \\
  sex\lmlevel{male} & -0.1818 & 0.1376 & -1.32 & 0.1863 \\
  \hline
\end{tabular}
\caption{Summary table for the full logistic regression model
    for the resume callback example.}
\label{resumeLogisticModelResults}
\end{figure}
% library(openintro); library(dplyr); a <- resume; d <- data.frame(callback = a$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"))
% job_industry = a$job_industry, job_type = a$job_type, 
% m <- glm(callback ~ job_city + college_degree + years_experience + honors + military + email_address + race + gender, data = d, family = binomial); summary(m); xtable(m)
\newcommand{\resRaceWhiteCoef}{0.4424}

Just like multiple regression, we could trim some variables
from the model.
Here we'll use a statistic called
\term{Akaike information criterion (AIC)},
which is an analog to how we used adjusted R-squared
in multiple regression,
and we look for models with a lower AIC
through a backward elimination strategy.
After using this criteria, the \var{college\us{}degree}
variable is eliminated, giving the smaller model summarized
in Figure~\ref{resumeLogisticReducedModel},
which is what we'll rely on for the remainder
of this section.
%\Comment{Do we want to discuss that one variable dropping out more?}

\begin{figure}[ht]
\centering
\begin{tabular}{l rrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & z value & Pr($>$$|$z$|$) \\ 
  \hline
  \vspace{-3.8mm} & & & & \\
  (Intercept) & -2.7162 & 0.1551 & -17.51 & $<$0.0001 \\ 
  job\us{}city\lmlevel{Chicago} &
      -0.4364 & 0.1141 & -3.83 & 0.0001 \\ 
  years\us{}experience & 0.0206 & 0.0102 & 2.02 & 0.0430 \\ 
  honors & 0.7634 & 0.1852 & 4.12 & $<$0.0001 \\ 
  military & -0.3443 & 0.2157 & -1.60 & 0.1105 \\ 
  email\us{}address & 0.2221 & 0.1130 & 1.97 & 0.0494 \\ 
  race\lmlevel{white} & 0.4429 & 0.1080 & 4.10 & $<$0.0001 \\ 
  sex\lmlevel{male} & -0.1959 & 0.1352 & -1.45 & 0.1473 \\ 
\hline
\end{tabular}
\caption{Summary table for the logistic regression model
    for the resume callback example, where variable selection
    has been performed using AIC.}
\label{resumeLogisticReducedModel}
\end{figure}
% # Run code for table above first
% % m. <- step(m); summary(m.); xtable(m.)
\newcommand{\resRaceWhiteCoefReduced}{0.4429}

\begin{examplewrap}
\begin{nexample}{The \var{race} variable had taken
    only two levels: \resp{black} and \resp{white}.
    Based on the model results, was race a meaningful
    factor for if a prospective employer would
    call back?}
  We see that the p-value for this coefficient is very
  small (very nearly zero), which implies that race
  played a statistically significant role in whether
  a candidate received a callback.
  Additionally, we see that the coefficient shown
  corresponds to the level of \resp{white},
  and it is positive.
  This positive coefficient reflects a positive gain
  in callback rate for resumes where the candidate's
  first name implied they were White.  
  The data provide very strong evidence of racism
  by prospective employers that favors resumes where the
  first name is typically interpreted to be White.
\end{nexample}
\end{examplewrap}

%We, the authors, found this conclusion saddening,
%though not surprising.
%It is also important to consider that this data only
%highlights one stage of racial bias in employment --
%when someone is trying to get hired --
%and it does not consider racial bias during employment.
%It does not scratch the surface of racial bias
%for individuals who are hired.

%\begin{examplewrap}
%\begin{nexample}{Compare the coefficient of t.
%    Why are the two estimated coefficients different?}
%  We earlier discussed how the implied race on the resume
%  was randomized and this variable is independent of
%  other predictors.
%  This means that the estimated effect will be virtually
%  unchanged even after we add or remove other variables
%  from the model.
%  This property is the product of thoughtful experiment
%  design by this study's researchers.
%\end{nexample}
%\end{examplewrap}

The coefficient of $\indfunc{race}{white}$ in the full model in
Figure~\ref{resumeLogisticModelResults},
is nearly identical to the model shown in
Figure~\ref{resumeLogisticReducedModel}.
The predictors in this experiment were thoughtfully
laid out so that the coefficient estimates would typically
not be much influenced by which other predictors were 
in the model,
which aligned with the motivation of the study to tease
out which effects were important to getting a callback.
In most observational data,
it's common for point estimates to change a little,
and sometimes a lot, depending on which other
variables are included in the model.
%Collinearity can also occur in experiments,
%but in this case the experiment was designed in such a way
%that collinearity it was not an issue.
%This might happen if predictor variables are correlated,
%where the inclusion of one of the variables can influence.
%\Comment{Revisit the end of this paragraph,
%  e.g. if removing the Ebay auction example.}
%We previously saw this in the Ebay auction example when
%we compared the coefficient of \var{cond\us{}new} in a
%single-variable model and the corresponding coefficient
%in the multiple regression model when including three
%additional variables (see
%Sections~\ref{ind_and_cat_vars_as_predictors}
%and~\ref{includingAndAssessingManyVariablesInAModel}).

\begin{examplewrap}
\begin{nexample}{Use the model summarized in
    Figure~\ref{resumeLogisticReducedModel}
    to estimate the probability
    of receiving a callback for a job in Chicago
    where the candidate lists 14 years experience,
    no honors,
    no military experience,
    includes an email address,
    and has a first name that implies they are a White male.}
  \label{exampleForResumeAndWhiteQuantified}%
  We can start by writing out the equation using the
  coefficients from the model, then we can
  add in the corresponding values of each variable for this
  individual:
  \begin{align*}
  &log_e \left(\frac{p}{1 - p}\right) \\
    &\quad= - 2.7162
        - 0.4364 \times \indfunc{job\us{}city}{Chicago}
        + 0.0206 \times \var{years\us{}experience}
        + 0.7634 \times \var{honors} \\
      &\quad\qquad
          - 0.3443 \times \var{military}
          + 0.2221 \times \var{email}
          + 0.4429 \times \indfunc{race}{white}
          - 0.1959 \times \indfunc{sex}{male} \\
    &\quad= - 2.7162
        - 0.4364 \times 1
        + 0.0206 \times 14
        + 0.7634 \times 0 \\
      &\quad\qquad
          - 0.3443 \times 0
          + 0.2221 \times 1
          + 0.4429 \times 1
          - 0.1959 \times 1 \\
    &\quad= - 2.3955
  \end{align*}
  We can now back-solve for $p$:
  the chance such an individual will receive
  a callback is about~8.35\%.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Compute the probability of a callback
    for an individual with a name commonly inferred
    to be from a Black male but who otherwise
    has the same characteristics as the one described
    in Example~\ref{exampleForResumeAndWhiteQuantified}.}
  \index{exampleForResumeAndBlackQuantified}%
  We can complete the same steps for an individual
  with the same characteristics who is Black,
  where the only difference in the calculation is that
  the indicator variable
  $\indfunc{race}{white}$ will take a value of \resp{0}.
  Doing so yields a probability of 0.0553.
  Let's compare the results with those of
  Example~\ref{exampleForResumeAndWhiteQuantified}.

  In practical terms, an individual perceived
  as White based on their first name would need to
  apply to $\frac{1}{0.0835} \approx 12$ jobs on average
  to receive a callback,
  while an individual perceived as Black
  based on their first name
  would need
  to apply to $\frac{1}{0.0553} \approx 18$ jobs on average
  to receive a callback.
  That is, applicants who are perceived as
  Black need to apply to 50\% more employers
  to receive a callback than someone who is perceived
  as White based on their first name for jobs like
  those in the study.
\end{nexample}
\end{examplewrap}

What we've quantified in this section is alarming and disturbing.
However, one aspect that makes this racism so difficult to
address is that the experiment, as well-designed as it is,
cannot send us much signal about which employers are
discriminating.
It is only possible to say that discrimination is happening,
even if we cannot say which particular callbacks
-- or non-callbacks -- represent discrimination.
Finding strong evidence of racism for individual cases is
a persistent challenge in enforcing anti-discrimination laws.
%For observational data on racial discrimination,
%there are even more challenges:
%some variables may be correlated with race
%or there may be potential confounding variables that
%cannot reasonably be modeled,
%making the challenges even more profound in reliably
%identifying racism.



\subsection{Diagnostics for the callback rate model}
\label{logistic_regr_diagnostics_subsection}

\begin{onebox}{Logistic regression conditions}
There are two key conditions for fitting a logistic regression model:\vspace{-1mm}
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item
    Each outcome $Y_i$ is independent of the other outcomes.
\item
    Each predictor $x_i$ is linearly related to logit$(p_i)$
    if all other predictors are held constant.
\end{enumerate}
\end{onebox}

The first logistic regression model condition
-- independence of the outcomes --
is reasonable for the experiment since characteristics
of resumes were randomly assigned to the resumes that
were sent out.
%This is further discussed in Appendix~\ref{}.

The second condition of the logistic regression model is
not easily checked without a fairly sizable amount of data.
Luckily, we have \resN{} resume submissions in the data set!
Let's first visualize these data by plotting the true
classification of the resumes against the model's fitted
probabilities, as shown in
Figure~\ref{logisticModelPredict}.
%The vast majority of emails (spam or not) still have fitted probabilities below 0.5.

\begin{figure}[h]
  \centering
  \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.]
      {0.95}{logisticModel}{logisticModelPredict}
  \caption{The predicted probability that each of the
      \resN{} resumes results in a callback.
      \hiddenterm{Noise}
      (small, random vertical shifts) have been added
      to each point so points with nearly identical
      values aren't plotted exactly on top of one another.}
  \label{logisticModelPredict}
\end{figure}

\D{\newpage}

%The probabilities predicted by the model fall between
%4.3\% and 29.9\%.
We'd like to assess the quality of the model.
For example, we might ask:
if we look at resumes that we modeled as having
a 10\% chance of getting a callback, do we find
about 10\% of them actually receive a callback?
We can check this for groups of the data by constructing
a plot as follows:
\begin{enumerate}
\item
    Bucket the data into groups based on their
    predicted probabilities.
\item
    Compute the average predicted probability for each group.
\item
    Compute the observed probability for each group,
    along with a 95\% confidence interval.
\item
    Plot the observed probabilities
    (with 95\% confidence intervals)
    against the average predicted probabilities for each group.
\end{enumerate}
The points plotted should fall close to the line $y = x$,
since the predicted probabilities should be similar to the
observed probabilities.
We can use the confidence intervals to roughly gauge whether
anything might be amiss.
Such a plot is shown in Figure~\ref{logisticModelBucketDiag}.

%To help us out, we'll borrow an advanced statistical
%method called \term{natural splines} that estimates
%the local probability over the region 0.04 to 0.30,
%which is the range of the predicted probabilities.
%All you need to know about natural splines to understand
%what we are doing is that they are used to fit flexible
%lines rather than straight lines.
%
%The curve fit using natural splines is shown in
%Figure~\ref{logisticModelSpline} as a solid black line.
%If the logistic model fits well, the curve should closely
%follow the dashed $y = x$ line.
%We have added shading to represent the confidence bound for
%the curved line to clarify what fluctuations might plausibly
%be due to chance.
%The dashed line generally stays within the error bound
%of the solid curve, suggesting the fit is reasonable.

\begin{figure}
  \centering
  \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.]
      {0.95}{logisticModel}{logisticModelBucketDiag}
  \caption{The dashed line is within the confidence bound
       of the 95\% confidence intervals of each of the buckets,
       suggesting the logistic fit is reasonable.}
%  \caption{The dashed line is within the confidence bound
%       of the smoothed line, suggesting the logistic fit is
%       reasonable.}
  \label{logisticModelBucketDiag}
  %\label{logisticModelSpline}
\end{figure}

Additional diagnostics may be created that are similar to those
featured in Section~\ref{multipleRegressionModelAssumptions}.
For instance, we could compute residuals as
the observed outcome minus the expected outcome
($e_i = Y_i - \hat{p}_i$),
and then we could create plots of these residuals
against each predictor.
We might also create a plot like that in
Figure~\ref{logisticModelBucketDiag}
to better understand the deviations.
%We might also create a smoothed average like that in
%Figure~\ref{logisticModelSpline} to better understand
%deviations.

\index{data!resume|)}
\index{regression!logistic|)}
\index{regression|)}


\D{\newpage}

\subsection{Exploring discrimination between groups
    of different sizes}
    % An exercise in critical thinking around a hypothetical setting

\index{discrimination|(}

%Discrimination is an incredibly important and complex societal issue, and this study only examined discrimination in a single aspect
Any form of discrimination is concerning,
and this is why we decided it was so important to discuss
this topic using data.
The resume study also only examined discrimination in a
single aspect: whether a prospective employer would
call a candidate who submitted their resume.
There was a 50\% higher barrier for resumes simply when
the candidate had a first name that was perceived to be
from a Black individual.
It's unlikely that discrimination would stop there.

%Of course, discrimination can happen to anyone.
%Yet, discrimination against dominant groups is
%considered to be much less impactful than
%the discrimination experienced by oppressed groups.
%\emph{Why?}

\begin{examplewrap}
\begin{nexample}{Let's consider a sex-imbalanced
    company that consists of 20\% women
    and 80\% men,\footnotemark{}
    and we'll suppose that the
    company is very large, consisting of perhaps
    20,000 employees.
    Suppose when someone goes up for promotion at this
    company, 5~of their colleagues are randomly chosen
    to provide feedback on their work.
    \exspace{}

    Now let's imagine that 10\% of the people in the
    company are prejudiced against the other sex.
    That~is, 10\% of men are prejudiced against women,
    and similarly, 10\% of women are prejudiced against men.
    \exspace{}
    
    Who is discriminated against more at the company,
    men or women?}
  \label{sex_imbalance_leads_to_discrimination}%
  Let's suppose we took 100 men who have gone up for
  promotion in the past few years.
  For these men, $5 \times 100 = 500$ random colleagues
  will be tapped for their feedback, of which
  about 20\% will be women (100 women).
  Of these 100 women, 10 are expected to be biased
  against the man they are reviewing.
  Then, of the 500 colleagues reviewing them,
  men will experience
  discrimination by about 2\% of their colleagues when
  they go up for promotion.

  Let's do a similar calculation for 100 women
  who have gone up for promotion in the last few years.
  They will also have 500 random colleagues providing
  feedback, of which about 400 (80\%) will be men.
  Of these 400 men, about 40 (10\%) hold a bias against
  women.
  Of the 500 colleagues providing feedback on the
  promotion packet for these women, 8\% of the
  colleagues hold a bias against the women.
\end{nexample}
\end{examplewrap}
\footnotetext{A more thoughtful example would include
    non-binary individuals.}

Example~\ref{sex_imbalance_leads_to_discrimination}
highlights something profound:
even in a hypothetical setting where each demographic
has the same degree of prejudice
against the other demographic, the smaller group
experiences the negative effects more frequently.
Additionally, if we would complete a handful of examples
like the one above with different numbers,
we'd learn that the greater the imbalance
in the population groups, the more the smaller group
is disproportionately impacted.\footnote{%
  If a proportion $p$ of a company are
  women and the rest of the company consists of men,
  then under the hypothetical situation
  the ratio of rates of discrimination against women
  vs men would be given by $\frac{1 - p}{p}$;
  this ratio is always greater than 1 when $p < 0.5$.}%
%That is, this mathematical property may lead
%to more discrimination against a minority group,
%and the degree of that discrimination
%will be larger the greater the imbalance in the
%population under the scenario described.

Of course, there are other considerable real-world omissions
from the hypothetical example.
For example, studies have found instances where people from an
oppressed group also discriminate against others within their
own oppressed group.
As another example,
there are also instances where a majority group
can be oppressed, with apartheid in South Africa being one
such historic example.
%\footnote{Two examples of majority groups
%  being oppressed include Black slaves in some regions
%  of southern states of early America,
%  and apartheid in South Africa.}
Ultimately, discrimination is complex,
and there are many factors at play beyond
the mathematics property we observed in
Example~\ref{sex_imbalance_leads_to_discrimination}.
% That is, the mathematical property we've discussed
%  here is far from the only factor in discrimination
%  and oppression, yet it can be an important one
%  in some settings.}
%For one study on this topic, see
%\begin{quote}\em
%Milkman KL, Akinola M, Chugh D. 2015.
%What Happens Before?
%A Field Experiment Exploring How Pay and
%Representation Differentially Shape Bias
%on the Pathway Into Organizations.
%Journal of Applied Psychology, 100:6, p1678-1712.
%\end{quote}
%The paper's abstract summarizes the findings,
%and substantial detail of the analysis is provided
%within the paper.
%We've also made the data set available,
%which is noted in Appendix~\ref{ch_regr_mult_and_log_data}
%so that you may also explore it directly.
%\Comment{If we do not obtain the data from this study,
%  then need to delete the last sentence.}
%That is, discrimination isn't generally symmetric,
%which makes this topic all the more complex.
%For example, a study published in 2015 performed an
%experiment similar to the job discrimination experiment
%we analyzed earlier, but in this case an email was sent
%to each of 6,500 faculty members at top US universities.
%The emails sent were from fictional prospective students
%seeking to discuss research opportunities prior to applying
%to a doctoral program.
%The emails were identical, except for the name of the
%fictional student sending the message was randomly assigned,
%and each name used was chosen to suggest a specific race
%and sex.
%Generally, White males were more likely to receive replies.
%What was most profound was that female faculty members
%were also more likely to reply to male students than their
%female students.
%Similarly, faculty members who were from oppressed groups
%favored white 
%assistants than for male research assistants,
%even though there was no difference in the fabricated
%resumes;
%this study was performed by surveying thousands of faculty
%members, so while no faculty member could individually be
%identified as being sexist, it was conclusive that the
%females were being discriminated against in aggregate.

%The 8\%-to-2\% is a direct result of the 80\%-to-20\% ratio
%in Example~\ref{sex_imbalance_leads_to_discrimination}.
%More generally, if 

%No discrimination has a place in our society,
%be it discrimination against a minority group
%or a majority group.
%Yet we cannot deny the mathematics behind
%discrimination: minority groups may be more
%prone to the negative impacts from discrimination
%than majority groups.

%Discrimination is a complex topic and discussed
%thoughtfully by many others.
%For further reading,
%please consider the following excellent resources:
%\Comment{Need to identify appropriate resources.
%  Suggestions welcome!}
%\begin{itemize}
%\item
%     \Add{https://www.theatlantic.com/education/archive/2017/08/myth-of-reverse-racism/535689/}
%\item
%    \Comment{Resource \#1}
%\item
%    \Comment{Resource \#2}
%\item
%    \Comment{Resource \#3}
%\end{itemize}

We close this book on this serious topic,
and we hope it inspires you to think about
the power of reasoning with data.
Whether it is with a formal statistical model
or by using critical thinking skills to structure
a problem, we hope the ideas you have learned will
help you do more and do better in life.

\index{discrimination|)}


{\input{ch_regr_mult_and_log/TeX/introduction_to_logistic_regression.tex}}


================================================
FILE: ch_regr_mult_and_log/TeX/checking_model_assumptions_using_graphs.tex
================================================
\exercisesheader{}

% 13

\eoce{\qt{Baby weights, Part VI\label{baby_weights_conds}} 
Exercise~\ref{baby_weights_mlr} presents a regression model for predicting the 
average birth weight of babies based on length of gestation, parity, height, 
weight, and smoking status of the mother. Determine if the model assumptions are 
met using the plots below. If not, describe how to proceed with the analysis.
\begin{center}
\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}
\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}
\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}
\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}
\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}
\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}
\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}
\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}
\end{center}
}{}

\D{\newpage}

% 14

\eoce{\qt{Movie returns, Part I\label{movie_returns_altogether}}
A FiveThirtyEight.com article reports that
``Horror movies get nowhere near as much draw at the box 
office as the big-time summer blockbusters or
action/adventure movies ... but there’s a huge incentive
for studios to continue pushing them out.
The return-on-investment potential for horror movies
is absurd."
To investigate how the return-on-investment compares
between genres and how this relationship has changed over
time, an introductory statistics student fit a model
predicting the ratio of gross revenue of movies from
genre and release year for 1,070 movies released between
2000 and 2018.
Using the plots given below, determine if this regression
model is appropriate for these
data.\footfullcite{webpage:horrormovies}
\begin{center}
\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}
\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]
\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}
\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]
\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}
\end{center}
}{}


================================================
FILE: ch_regr_mult_and_log/TeX/introduction_to_logistic_regression.tex
================================================
\exercisesheader{}

% 15

\eoce{\qt{Possum classification, Part I\label{possum_classification_model_select}} 
The common brushtail possum of the Australia region is a bit cuter than its 
distant cousin, the American opossum (see Figure~\vref{brushtail_possum}). We 
consider 104 brushtail possums from two regions in Australia, where the possums 
may be considered a random sample from the population. The first region is 
Victoria, which is in the eastern half of Australia and traverses the southern 
coast. The second region consists of New South Wales and Queensland, which make 
up eastern and northeastern Australia.
We use logistic regression to differentiate between possums in these two 
regions. The outcome variable, called \var{population}, takes value 1 when a 
possum is from Victoria and 0 when it is from New South Wales or Queensland. We 
consider five predictors: \var{sex\_\hspace{0.3mm}male} (an indicator for a 
possum being male), \var{head\_\hspace{0.3mm}length}, \var{skull\_\hspace{0.3mm}
width}, \var{total\_\hspace{0.3mm}length}, and \var{tail\_\hspace{0.3mm}length}. 
Each variable is summarized in a histogram. The full logistic regression model 
and a reduced model after variable selection are summarized in the table.
\begin{center}
\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}
\end{center}
\begin{center}\footnotesize
\begin{tabular}{r rrrr r rrrr}
                            & \multicolumn{4}{c}{\emph{Full Model}} &
                            & \multicolumn{4}{c}{\emph{Reduced Model}}  \\
  \cline{2-5}\cline{7-10}
\vspace{-3.1mm} \\
                            & Estimate & SE & Z & Pr($>$$|$Z$|$) &
                            & Estimate & SE & Z & Pr($>$$|$Z$|$) \\ 
  \hline
\vspace{-3.1mm} \\
(Intercept)                 & 39.2349 & 11.5368 & 3.40  & 0.0007 &
                            & 33.5095 & 9.9053  & 3.38  & 0.0007 \\ 
sex\_\hspace{0.3mm}male     & -1.2376 & 0.6662  & -1.86 & 0.0632 &
                            & -1.4207 & 0.6457  & -2.20 & 0.0278 \\ 
head\_\hspace{0.3mm}length  & -0.1601 & 0.1386  & -1.16 & 0.2480 \\ 
skull\_\hspace{0.3mm}width  & -0.2012 & 0.1327  & -1.52 & 0.1294 &
                            & -0.2787 & 0.1226  & -2.27 & 0.0231 \\ 
total\_\hspace{0.3mm}length & 0.6488  & 0.1531  & 4.24  & 0.0000 &
                            & 0.5687  & 0.1322  & 4.30  & 0.0000 \\ 
tail\_\hspace{0.3mm}length  & -1.8708 & 0.3741  & -5.00 & 0.0000 &
                            & -1.8057 & 0.3599  & -5.02 & 0.0000 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item Examine each of the predictors. Are there any outliers that are likely to 
have a very large influence on the logistic regression model?
\item The summary table for the full model indicates that at least one variable 
should be eliminated when using the p-value approach for variable selection: 
\var{head\_\hspace{0.3mm}length}. The second component of the table summarizes 
the reduced model following variable selection. Explain why the remaining estimates 
change between the two models.
\end{parts}
}{}

\D{\newpage}

% 16

\eoce{\qt{Challenger disaster, Part I\label{challenger_disaster_model_select}} 
On January 28, 1986, a routine launch was anticipated for the Challenger space 
shuttle. Seventy-three seconds into the flight, disaster happened: the shuttle 
broke apart, killing all seven crew members on board. An investigation into the 
cause of the disaster focused on a critical seal called an O-ring, and it is 
believed that damage to these O-rings during a shuttle launch may be related to 
the ambient temperature during the launch. The table below summarizes 
observational data on O-rings for 23 shuttle missions, where the mission order 
is based on the temperature at the time of the launch. \emph{Temp} gives the 
temperature in Fahrenheit, \emph{Damaged} represents the number of damaged O-
rings, and \emph{Undamaged} represents the number of O-rings that were not 
damaged.
\begin{center}
\begin{tabular}{l rrrrr rrrrr rrrrr rrrrr rrr}
\hline
\vspace{-3.1mm} \\
Shuttle Mission   & 1  & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 \\
\hline
\vspace{-3.1mm} \\
Temperature       & 53 & 57 & 58 & 63 & 66 & 67 & 67 & 67 & 68 & 69 & 70 & 70  \\
Damaged           & 5  & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\
Undamaged         & 1  & 5 & 5 & 5 & 6 & 6 & 6 & 6 & 6 & 6 & 5 & 6 \\
\hline
\\ 
\cline{1-12}
\vspace{-3.1mm} \\
Shuttle Mission   & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 & 21 & 22 & 23 \\
\cline{1-12}
\vspace{-3.1mm} \\
Temperature       & 70 & 70 & 72 & 73 & 75 & 75 & 76 & 76 & 78 & 79 & 81 \\
Damaged           & 1  & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\
Undamaged         & 5  & 6 & 6 & 6 & 6 & 5 & 6 & 6 & 6 & 6 & 6 \\
\cline{1-12}
\end{tabular}
\end{center}
\begin{parts}
\item Each column of the table above represents a different shuttle mission. 
Examine these data and describe what you observe with respect to the 
relationship between temperatures and damaged O-rings.
\item Failures have been coded as 1 for a damaged O-ring and 0 for an undamaged 
O-ring, and a logistic regression model was fit to these data. A summary of this 
model is given below. Describe the key components of this summary table in words.
\begin{center}
\begin{tabular}{rrrrr}
  \hline
            & Estimate & Std. Error & z value   & Pr($>$$|$z$|$) \\ 
  \hline
(Intercept) & 11.6630  & 3.2963     & 3.54      & 0.0004 \\ 
Temperature & -0.2162  & 0.0532     & -4.07     & 0.0000 \\ 
  \hline
\end{tabular}
\end{center}
\item Write out the logistic model using the point estimates of the model 
parameters.
\item Based on the model, do you think concerns regarding O-rings are justified? 
Explain.
\end{parts}
}{}

% 17

\eoce{\qt{Possum classification, Part II\label{possum_classification_predict}} 
A logistic regression model was proposed for classifying common brushtail 
possums into their two regions in 
Exercise~\ref{possum_classification_model_select}. The outcome variable took 
value 1 if the possum was from Victoria and 0 otherwise.
\begin{center}
\begin{tabular}{r rrrr}
  \hline
\vspace{-3.1mm} \\
                            & Estimate  & SE      & Z     & Pr($>$$|$Z$|$) \\ 
  \hline
\vspace{-3.1mm} \\
(Intercept)                 & 33.5095   & 9.9053  & 3.38  & 0.0007 \\ 
sex\_\hspace{0.3mm}male     & -1.4207   & 0.6457  & -2.20 & 0.0278 \\ 
skull\_\hspace{0.3mm}width  & -0.2787   & 0.1226  & -2.27 & 0.0231 \\ 
total\_\hspace{0.3mm}length & 0.5687    & 0.1322  & 4.30  & 0.0000 \\ 
tail\_\hspace{0.3mm}length  & -1.8057   & 0.3599  & -5.02 & 0.0000 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item Write out the form of the model. Also identify which of the variables are 
positively associated when controlling for other variables.
\item Suppose we see a brushtail possum at a zoo in the US, and a sign says the 
possum had been captured in the wild in Australia, but it doesn't say which part 
of Australia. However, the sign does indicate that the possum is male, its skull 
is about 63 mm wide, its tail is 37 cm long, and its total length is 83 cm. What 
is the reduced model's computed probability that this possum is from Victoria? 
How confident are you in the model's accuracy of this probability calculation?
%logitp <- 33.5095 - 1.4207 - 0.2787*63 + 0.5687*83 - 1.8057*37; exp(logitp)/(1+exp(logitp))
\end{parts}
}{}

\D{\newpage}

% 18

\eoce{\qt{Challenger disaster, Part II\label{challenger_disaster_predict}} 
Exercise~\ref{challenger_disaster_model_select} introduced us to O-rings that 
were identified as a plausible explanation for the breakup of the Challenger 
space shuttle 73 seconds into takeoff in 1986. The investigation found that the 
ambient temperature at the time of the shuttle launch was closely related to the 
damage of O-rings, which are a critical component of the shuttle. See this 
earlier exercise if you would like to browse the original data.
\begin{center}
\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} 
\end{center}
\begin{parts}
\item The data provided in the previous exercise are shown in the plot. The logistic 
model fit to these data may be written as
\begin{align*}
\log\left( \frac{\hat{p}}{1 - \hat{p}} \right) = 11.6630 - 0.2162\times Temperature
\end{align*}
where $\hat{p}$ is the model-estimated probability that an O-ring will become 
damaged. Use the model to calculate the probability that an O-ring will become 
damaged at each of the following ambient temperatures: 51, 53, and 55 degrees 
Fahrenheit. The model-estimated probabilities for several additional ambient 
temperatures are provided below, where subscripts indicate the temperature:
\begin{align*}
&\hat{p}_{57} = 0.341
	&& \hat{p}_{59} = 0.251
	&& \hat{p}_{61} = 0.179
	&& \hat{p}_{63} = 0.124 \\
&\hat{p}_{65} = 0.084
	&& \hat{p}_{67} = 0.056
	&& \hat{p}_{69} = 0.037
	&& \hat{p}_{71} = 0.024
\end{align*}
\item Add the model-estimated probabilities from part~(a) on the plot, then 
connect these dots using a smooth curve to represent the model-estimated 
probabilities.
\item Describe any concerns you may have regarding applying logistic regression 
in this application, and note any assumptions that are required to accept the 
model's validity.
\end{parts}
}{}


================================================
FILE: ch_regr_mult_and_log/TeX/introduction_to_multiple_regression.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Baby weights, Part I\label{baby_weights_smoke}} The Child Health 
and Development Studies investigate a range of topics. One study 
considered all pregnancies between 1960 and 1967 among women in the 
Kaiser Foundation Health Plan in the San Francisco East Bay area. Here, 
we study the relationship between smoking and weight of the baby. The 
variable \texttt{smoke} is coded 1 if the mother is a smoker, and 0 if 
not. The summary table below shows the results of a linear regression 
model for predicting the average birth weight of babies, measured in 
ounces, based on the smoking status of the mother. 
\footfullcite{data:babies}
\begin{center}
\begin{tabular}{rrrrr}
  \hline
            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept) & 123.05    & 0.65        & 189.60    & 0.0000 \\ 
smoke       & -8.94     & 1.03        & -8.65     & 0.0000 \\ 
  \hline
\end{tabular}
\end{center}
The variability within the smokers and non-smokers are about equal and the 
distributions are symmetric. With these conditions satisfied, it is reasonable 
to apply the model. (Note that we don't need to check linearity since the 
predictor has only two levels.)
\begin{parts}
\item Write the equation of the regression model.
\item Interpret the slope in this context, and calculate the predicted birth 
weight of babies born to smoker and non-smoker mothers.
\item Is there a statistically significant relationship between the average birth 
weight and smoking?
\end{parts}
}{}

% 2

\eoce{\qt{Baby weights, Part II\label{baby_weights_parity}} 
Exercise~\ref{baby_weights_smoke} introduces a data set
on birth weight of babies.
Another variable we consider is \texttt{parity},
which is 1 if the child is the first born,
and 0 otherwise.
The summary table below shows the results of
a linear regression model for predicting the
average birth weight of babies, measured in ounces,
from \texttt{parity}. 
\begin{center}
\begin{tabular}{rrrrr}
  \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept) & 120.07    & 0.60        & 199.94    & 0.0000 \\ 
parity      	& -1.93     	  & 1.19        & -1.62       & 0.1052 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item Write the equation of the regression model.
\item Interpret the slope in this context, and calculate the predicted birth 
weight of first borns and others.
\item Is there a statistically significant relationship between the average 
birth weight and parity?
\end{parts}
}{}

\D{\newpage}

% 3

\eoce{\qt{Baby weights, Part III\label{baby_weights_mlr}} We considered the 
variables \texttt{smoke} and \texttt{parity}, one at a time, in modeling birth 
weights of babies in Exercises~\ref{baby_weights_smoke} and~\ref{baby_weights_parity}. 
A more realistic approach to modeling infant 
weights is to consider all possibly related variables at once. Other variables 
of interest include length of pregnancy in days (\texttt{gestation}), mother's 
age in years (\texttt{age}), mother's height in inches (\texttt{height}), and 
mother's pregnancy weight in pounds (\texttt{weight}). Below are three 
observations from this data set. 
\begin{center}
\begin{tabular}{r c c c c c c c}
  \hline
      & bwt & gestation & parity  & age   & height  & weight  & smoke \\ 
  \hline
1     & 120 & 284       & 0       & 27    &  62     & 100     &   0 \\ 
2     & 113 & 282       & 0       & 33    &  64     & 135     &   0 \\ 
$\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ &  $\vdots$ & $\vdots$ & $\vdots$ &   $\vdots$ \\ 
1236  & 117 & 297       & 0       & 38    &  65     & 129     &   0 \\ 
   \hline
\end{tabular}
\end{center}
The summary table below shows the results of a regression model for predicting 
the average birth weight of babies based on all of the variables included in 
the data set.
\begin{center}
\begin{tabular}{rrrrr}
  \hline
            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept) & -80.41    & 14.35       & -5.60     & 0.0000 \\ 
gestation   & 0.44      & 0.03        & 15.26     & 0.0000 \\ 
parity      & -3.33     & 1.13        & -2.95     & 0.0033 \\ 
age         & -0.01     & 0.09        & -0.10     & 0.9170 \\ 
height      & 1.15      & 0.21        & 5.63      & 0.0000 \\ 
weight      & 0.05      & 0.03        & 1.99      & 0.0471 \\ 
smoke       & -8.40     & 0.95        & -8.81     & 0.0000 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item Write the equation of the regression model that includes all of the 
variables.
\item Interpret the slopes of \texttt{gestation} and \texttt{age} in this 
context.
\item The coefficient for \texttt{parity} is different than in the linear 
model shown in Exercise~\ref{baby_weights_parity}. Why might there be a difference?
\item Calculate the residual for the first observation in the data set.
\item The variance of the residuals is 249.28, and the variance of the birth 
weights of all babies in the data set is 332.57. Calculate the $R^2$ and the 
adjusted $R^2$. Note that there are 1,236 observations in the data set.
\end{parts}
}{}

\D{\newpage}

% 4

\eoce{\qt{Absenteeism, Part I\label{absent_from_school_mlr}} Researchers interested in the 
relationship between absenteeism from school and certain demographic 
characteristics of children collected data from 146 randomly sampled students 
in rural New South Wales, Australia, in a particular school year. Below are 
three observations from 
this data set. 
\begin{center}
\begin{tabular}{r c c c c}
  \hline
 	  & eth 	& sex 	& lrn 	& days \\   
  \hline
1 	& 0 		& 1 		& 1 		&   2 \\ 
2 	& 0 		& 1 		& 1 		&  11 \\ 
$\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ \\ 
146 & 1 		& 0 		& 0 		&  37 \\ 
  \hline
\end{tabular}
\end{center}
The summary table below shows the results of a linear regression model for 
predicting the average number of days absent based on ethnic background 
(\texttt{eth}: 0 - aboriginal, 1 - not aboriginal), sex (\texttt{sex}: 0 - 
female, 1 - male), and learner status (\texttt{lrn}: 0 - average learner, 1 - 
slow learner). \footfullcite{data:quine}
\begin{center}
\begin{tabular}{rrrrr}
  \hline
            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept) & 18.93     & 2.57        & 7.37      & 0.0000 \\ 
eth         & -9.11     & 2.60        & -3.51     & 0.0000 \\ 
sex         & 3.10      & 2.64        & 1.18      & 0.2411 \\ 
lrn         & 2.15      & 2.65        & 0.81      & 0.4177 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item Write the equation of the regression model.
\item Interpret each one of the slopes in this context.
\item Calculate the residual for the first observation in the data set: a 
student who is aboriginal, male, a slow learner, and missed 2 days of school.
\item The variance of the residuals is 240.57, and the variance of the number of 
absent days for all students in the data set is 264.17. Calculate the $R^2$ and 
the adjusted $R^2$. Note that there are 146 observations in the data set.
\end{parts}
}{}

% 5

\eoce{\qt{GPA\label{gpa}} A survey of 55 Duke University students asked about their 
GPA, number of hours they study at night, number of nights they go out, and 
their gender. Summary output of the regression model is shown below. Note that 
male is coded as 1. 
\begin{center}
\begin{tabular}{rrrrr}
  \hline
            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept) & 3.45      & 0.35        & 9.85      & 0.00 \\ 
studyweek   & 0.00      & 0.00        & 0.27      & 0.79 \\ 
sleepnight  & 0.01      & 0.05        & 0.11      & 0.91 \\ 
outnight    & 0.05      & 0.05        & 1.01      & 0.32 \\ 
gender      & -0.08     & 0.12        & -0.68     & 0.50 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item Calculate a 95\% confidence interval for the coefficient of gender in the 
model, and interpret it in the context of the data.
\item Would you expect a 95\% confidence interval for the slope of the remaining 
variables to include 0? Explain
\end{parts}
}{}

% 6

\eoce{\qt{Cherry trees\label{cherry_trees}} Timber yield is approximately equal to the 
volume of a tree, however, this value is difficult to measure without first 
cutting the tree down. Instead, other variables, such as height and diameter, 
may be used to predict a tree's volume and yield. Researchers wanting to 
understand the relationship between these variables for black cherry trees 
collected data from 31 such trees in the Allegheny National Forest, 
Pennsylvania. Height is measured in feet, diameter in inches (at 54 inches above 
ground), and volume in cubic feet.\footfullcite{Hand:1994}
\begin{table}[ht]
\begin{center}
\begin{tabular}{rrrrr}
  \hline
            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept) & -57.99    & 8.64        & -6.71     & 0.00 \\ 
height      & 0.34      & 0.13        & 2.61      & 0.01 \\ 
diameter    & 4.71      & 0.26        & 17.82     & 0.00 \\ 
  \hline
\end{tabular}
\end{center}
\end{table}
\begin{parts}
\item Calculate a 95\% confidence interval for the coefficient of height, and 
interpret it in the context of the data.
\item One tree in this sample is 79 feet tall, has a diameter of 11.3 inches, 
and is 24.2 cubic feet in volume. Determine if the model overestimates or 
underestimates the volume of this tree, and by how much.
\end{parts}
}{}


================================================
FILE: ch_regr_mult_and_log/TeX/model_selection.tex
================================================
\exercisesheader{}

% 7

\eoce{\qt{Baby weights, Part IV\label{baby_weights_model_select_backward}} 
Exercise~\ref{baby_weights_mlr} considers a model that predicts a newborn's 
weight using several predictors (gestation length, parity, age of mother, height 
of mother, weight of mother, smoking status of mother). The table below shows 
the adjusted R-squared for the full model as well as adjusted R-squared values 
for all models we evaluate in the first step of the backwards elimination 
process. 
\begin{center}
\begin{tabular}{rlr}
  \hline
  & Model               & Adjusted $R^2$ \\ 
  \hline
1 & Full model          & 0.2541 \\ 
2 & No gestation        & 0.1031 \\ 
3 & No parity           & 0.2492 \\ 
4 & No age              & 0.2547 \\ 
5 & No height           & 0.2311 \\ 
6 & No weight           & 0.2536 \\ 
7 & No smoking status   & 0.2072 \\ 
  \hline
\end{tabular}
\end{center}
Which, if any, variable should be removed from the model first?
}{}

% 8

\eoce{\qt{Absenteeism, Part II\label{absent_from_school_model_select_backward}} 
Exercise~\ref{absent_from_school_mlr} considers a model that predicts the number 
of days absent using three predictors: ethnic background (\var{eth}), 
gender (\var{sex}), and learner status (\var{lrn}). The table below shows the 
adjusted R-squared for the model as well as adjusted R-squared values for all 
models we evaluate in the first step of the backwards elimination process. 
\begin{center}
\begin{tabular}{rlr}
  \hline
  & Model               & Adjusted $R^2$ \\ 
  \hline
1 & Full model          & 0.0701 \\ 
2 & No ethnicity        & -0.0033 \\ 
3 & No sex              & 0.0676 \\ 
4 & No learner status   & 0.0723 \\ 
  \hline
\end{tabular}
\end{center}
Which, if any, variable should be removed from the model first?
}{}

% 9

\eoce{\qt{Baby weights, Part V\label{baby_weights_model_select_forward}} 
Exercise~\ref{baby_weights_mlr} provides regression output for the full 
model (including all explanatory variables available in the data set) for 
predicting birth weight of babies. In this exercise we consider a
forward-selection algorithm and add variables to the model
one-at-a-time. The table 
below shows the p-value and adjusted $R^2$ of each model where we include only 
the corresponding predictor. Based on this table, which variable should be added 
to the model first?\vspace{0.5mm}
\begin{center}
\begin{tabular}{l c c c c c c}
\hline
variable    & gestation	            & parity  & age	    
                & height          
                    & weight              
                        & smoke \\
\hline
p-value	    & $2.2 \times 10^{-16}$	& 0.1052	& 0.2375	
                & $2.97 \times 10^{-12}$
                    & $8.2 \times 10^{-8}$
                        & $2.2 \times 10^{-16}$ \\
$R_{adj}^2$	& 0.1657				        & 0.0013	& 0.0003	
                & 0.0386				
                    & 0.0229				
                        & 0.0569 \\
\hline
\end{tabular}
\end{center}
}{}

% 10

\eoce{\qt{Absenteeism, Part III\label{absent_from_school_model_select_forward}} 
Exercise~\ref{absent_from_school_mlr} provides regression output for the full 
model, including all explanatory variables available in the data set, for 
predicting the number of days absent from school. In this exercise we consider a 
forward-selection algorithm and add variables to the model one-at-a-time. The 
table below shows the p-value and adjusted $R^2$ of each model where we include 
only the corresponding predictor. Based on this table, which variable should be 
added to the model first?\vspace{0.5mm}
\begin{center}
\begin{tabular}{l c c c}
  \hline
variable    & ethnicity  & sex	   & learner status	 \\
  \hline
p-value		& 0.0007     & 0.3142  & 0.5870	 \\
$R_{adj}^2$	& 0.0714     & 0.0001  & 0 \\
  \hline
\end{tabular}
\end{center}
}{}

% 11

\eoce{\qt{Movie lovers, Part I\label{movie_lovers_pval_select}} Suppose a social 
scientist is interested in studying what makes audiences love or hate a movie. 
She collects a random sample of movies (genre, length, cast, director, budget, 
etc.) as well as a measure of the success of the movie (score on a film review 
aggregator website). If as part of her research she is interested in finding out
which variables are significant predictors of movie success, what type of model 
selection method should she use?
}{}

% 12

\eoce{\qt{Movie lovers, Part II\label{movie_lovers_adjrsq_select}} Suppose an online 
media streaming company is interested in building a movie recommendation system. 
The website maintains data on the movies in their database (genre, length, cast, 
director, budget, etc.) and additionally collects data from their subscribers (
demographic information, previously watched movies, how they rated previously 
watched movies, etc.). The recommendation system will be deemed successful if 
subscribers actually watch, and rate highly, the movies recommended to them. 
Should the company use the adjusted $R^2$ or the p-value approach in selecting
variables for their recommendation system?
}{}


================================================
FILE: ch_regr_mult_and_log/TeX/mult_regr_case_study.tex
================================================



%_______________
\subsection*{Exercises}

There are no exercises for this section.

================================================
FILE: ch_regr_mult_and_log/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 19

\eoce{\qt{Multiple regression fact checking\label{mult_regr_facts}}
Determine which of the following statements are
true and false.
For each statement that is false, explain why it is false.
\begin{parts}
\item
    If predictors are collinear, then removing
    one variable will have no influence on the
    point estimate of another variable's coefficient.
\item
    Suppose a numerical variable $x$ has a coefficient of
    $b_1 = 2.5$ in the multiple regression model.
    Suppose also that the first observation has $x_1 = 7.2$,
    the second observation has a value of $x_1 = 8.2$,
    and these two observations have the same values
    for all other predictors.
    Then the predicted value of the second observation
    will be 2.5 higher than the prediction of the first
    observation based on the multiple regression model.
\item
    If a regression model's first variable has
    a coefficient of $b_1 = 5.7$, then if we are
    able to influence the data so that an observation
    will have its $x_1$ be 1 larger than it would
    otherwise, the value $y_1$ for this observation
    would increase by 5.7.
\item
    Suppose we fit a multiple regression model
    based on a data set of 472 observations.
    We also notice that the distribution of the
    residuals includes some skew but does not
    include any particularly extreme outliers.
    Because the residuals are not nearly normal,
    we should not use this model and require
    more advanced methods to model these data.
\end{parts}
}{}

% 20

\eoce{\qt{Logistic regression fact checking\label{log_regr_facts}}
Determine which of the following statements are
true and false.
For each statement that is false, explain why it is false.
\begin{parts}
\item
    Suppose we consider the first two observations
    based on a logistic regression model,
    where the first variable in observation~1
    takes a value of $x_1 = 6$ and observation~2
    has $x_1 = 4$.
%    Each observation has all the same values for the
%    other variables used in the model.
    Suppose we realized we made an error for these
    two observations, and the first observation
    was actually $x_1 = 7$ (instead of~6)
    and the second observation actually had
    $x_1 = 5$ (instead of~4).
    Then the predicted probability from the
    logistic regression model would increase
    the same amount for each observation after
    we correct these variables.
\item
    When using a logistic regression model,
    it is impossible for the model to predict
    a probability that is negative or a probability
    that is greater than 1.
\item
    Because logistic regression predicts probabilities
    of outcomes, observations used to build a logistic
    regression model need not be independent.
\item
    When fitting logistic regression,
    we typically complete model selection using
    adjusted $R^2$.
\end{parts}
}{}

% 21

\eoce{\qt{Spam filtering, Part I\label{spam_filtering_model_sel}}
Spam filters are built on principles similar to those
used in logistic regression.
We fit a probability that each message is spam
or not spam.
We have several email variables for this problem:
\resp{to\us{}multiple},
\resp{cc},
\resp{attach},
\resp{dollar},
\resp{winner},
\resp{inherit},
\resp{password},
\resp{format},
\resp{re\us{}subj},
\resp{exclaim\us{}subj}, and
\resp{sent\us{}email}.
We won't describe what each variable means
here for the sake of brevity, but each is
either a numerical or indicator variable.
\begin{parts}
\item
    For variable selection,
    we fit the full model, which includes all
    variables, and then we also fit each model
    where we've dropped exactly one of the variables.
    In each of these reduced models, the AIC value
    for the model is reported below.
    Based on these results, which variable,
    if any, should we drop as part of model
    selection?
    Explain.
    \begin{center}
    \begin{tabular}{lc}
      \hline
      Variable Dropped & AIC \\ 
      \hline
      None Dropped & 1863.50 \\ 
      \resp{to\us{}multiple} & 2023.50 \\ 
      \resp{cc} & 1863.18 \\ 
      \resp{attach} & 1871.89 \\ 
      \resp{dollar} & 1879.70 \\ 
      \resp{winner} & 1885.03 \\ 
      \resp{inherit} & 1865.55 \\ 
      \resp{password} & 1879.31 \\ 
      \resp{format} & 2008.85 \\ 
      \resp{re\us{}subj} & 1904.60 \\ 
      \resp{exclaim\us{}subj} & 1862.76 \\ 
      \resp{sent\us{}email} & 1958.18 \\ 
      \hline
    \end{tabular}
    \end{center}
    \textbf{See the next page for part~(b).}

\D{\newpage}

\item
    Consider the following model selection stage.
    Here again we've computed the AIC
    for each leave-one-variable-out model.
    Based on the results, which variable,
    if any, should we drop as part of model
    selection?
    Explain.
    \begin{center}
    \begin{tabular}{lc}
      \hline
      Variable Dropped & AIC \\ 
      \hline
      None Dropped & 1862.41 \\ 
      \resp{to\us{}multiple} & 2019.55 \\ 
      \resp{attach} & 1871.17 \\ 
      \resp{dollar} & 1877.73 \\ 
      \resp{winner} & 1884.95 \\ 
      \resp{inherit} & 1864.52 \\ 
      \resp{password} & 1878.19 \\ 
      \resp{format} & 2007.45 \\ 
      \resp{re\us{}subj} & 1902.94 \\ 
      \resp{sent\us{}email} & 1957.56 \\ 
      \hline
    \end{tabular}
    \end{center}
\end{parts}
}{}

% 22

\eoce{\qt{Movie returns, Part II\label{movie_returns_by_genre}}
The student from
Exercise~\ref{movie_returns_altogether} analyzed
return-on-investment (ROI) for movies based on
release year and genre of movies.
The plots below show the predicted ROI vs. actual
ROI for each of the genres separately.
Do these figures support the comment in the
FiveThirtyEight.com article that states,
``The return-on-investment potential for horror movies
is absurd.''
Note that the x-axis range varies for each plot.
\begin{center}
\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}
\end{center}
}{}

% 23

\eoce{\qt{Spam filtering, Part II\label{spam_filtering_predict}}
In Exercise~\ref{spam_filtering_model_sel},
we encountered a data set where we applied
logistic regression to aid in spam classification
for individual emails.
In this exercise, we've taken a small set of these
variables and fit a formal model with the following
output:
\begin{center}
\begin{tabular}{rrrrr}
  \hline
  & Estimate & Std. Error & z value & Pr($>$$|$z$|$) \\ 
  \hline
  (Intercept) & -0.8124 & 0.0870 & -9.34 & 0.0000 \\ 
  to\us{}multiple & -2.6351 & 0.3036 & -8.68 & 0.0000 \\ 
  winner & 1.6272 & 0.3185 & 5.11 & 0.0000 \\ 
  format & -1.5881 & 0.1196 & -13.28 & 0.0000 \\ 
  re\us{}subj & -3.0467 & 0.3625 & -8.40 & 0.0000 \\ 
  \hline
\end{tabular}
\end{center}
\begin{parts}
\item
    Write down the model using the coefficients
    from the model fit.

\item
    Suppose we have an observation where
    $\var{to\us{}multiple} = 0$,
    $\var{winner} = 1$,
    $\var{format} = 0$, and
    $\var{re\us{}subj} = 0$.
    What is the predicted probability that this message
    is spam?

\item
    Put yourself in the shoes of a data scientist
    working on a spam filter.
    For a given message, how high must the probability
    a message is spam be before you think it would be
    reasonable to put it in a \emph{spambox}
    (which the user is unlikely to check)?
    What tradeoffs might you consider?
    Any ideas about how you might make your spam-filtering
    system even better from the perspective of someone
    using your email service?
\end{parts}
}{}


================================================
FILE: ch_regr_mult_and_log/figures/eoce/absent_from_school_mlr/absent_from_school_mlr.R
================================================
# load packages -----------------------------------------------------
library(xtable)
library(MASS)

# load data ---------------------------------------------------------
data(quine)

# convert categorical variables to 0/1 ------------------------------

quine$Eth <- as.character(quine$Eth)
quine$Eth[quine$Eth == "A"] <- 0
quine$Eth[quine$Eth == "N"] <- 1
quine$Eth <- as.factor(quine$Eth)

quine$Sex <- as.character(quine$Sex)
quine$Sex[quine$Sex == "F"] <- 0
quine$Sex[quine$Sex == "M"] <- 1
quine$Sex <- as.factor(quine$Sex)

quine$Lrn <- as.character(quine$Lrn)
quine$Lrn[quine$Lrn == "AL"] <- 0
quine$Lrn[quine$Lrn == "SL"] <- 1
quine$Lrn <- as.factor(quine$Lrn)

# print out dataset -------------------------------------------------

quine_sub <- quine[c(1,2,nrow(quine)), ]
xtable(quine_sub[ ,c(1, 2, 4, 5)])

# mlr for absent days  ----------------------------------------------

mlr_absent_full <- lm(Days ~ Eth + Sex + Lrn, data = quine)

xtable(summary(mlr_absent_full), digits = 2)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/absent_from_school_model_select_backward/absent_from_school_model_select_backward.R
================================================
# load packages -----------------------------------------------------
library(xtable)
library(MASS)

# load data ---------------------------------------------------------
data(quine)

# convert categorical variables to 0/1 ------------------------------

quine$Eth <- as.character(quine$Eth)
quine$Eth[quine$Eth == "A"] <- 0
quine$Eth[quine$Eth == "N"] <- 1
quine$Eth <- as.factor(quine$Eth)

quine$Sex <- as.character(quine$Sex)
quine$Sex[quine$Sex == "F"] <- 0
quine$Sex[quine$Sex == "M"] <- 1
quine$Sex <- as.factor(quine$Sex)

quine$Lrn <- as.character(quine$Lrn)
quine$Lrn[quine$Lrn == "AL"] <- 0
quine$Lrn[quine$Lrn == "SL"] <- 1
quine$Lrn <- as.factor(quine$Lrn)

# mlr for absent days  ----------------------------------------------

mlr_absent_full <- lm(Days ~ Eth + Sex + Lrn, data = quine)

round(summary(mlr_absent_full)$adj.r.squared, 4)

# no Ethnicity ------------------------------------------------------

mlr_absent_no_eth <- lm(Days ~ Sex + Lrn, data = quine)

round(summary(mlr_absent_no_eth)$adj.r.squared, 4)

# no Sex ------------------------------------------------------------

mlr_absent_no_sex <- lm(Days ~ Eth + Lrn, data = quine)

round(summary(mlr_absent_no_sex)$adj.r.squared, 4)

# no Lrn ------------------------------------------------------------

mlr_absent_no_lrn <- lm(Days ~ Eth + Sex, data = quine)

round(summary(mlr_absent_no_lrn)$adj.r.squared, 4)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/absent_from_school_model_select_forward/absent_from_school_model_select_forward.R
================================================
# load packages -----------------------------------------------------
library(xtable)
library(MASS)

# load data ---------------------------------------------------------
data(quine)

# convert categorical variables to 0/1 ------------------------------

quine$Eth <- as.character(quine$Eth)
quine$Eth[quine$Eth == "A"] <- 0
quine$Eth[quine$Eth == "N"] <- 1
quine$Eth <- as.factor(quine$Eth)

quine$Sex <- as.character(quine$Sex)
quine$Sex[quine$Sex == "F"] <- 0
quine$Sex[quine$Sex == "M"] <- 1
quine$Sex <- as.factor(quine$Sex)

quine$Lrn <- as.character(quine$Lrn)
quine$Lrn[quine$Lrn == "AL"] <- 0
quine$Lrn[quine$Lrn == "SL"] <- 1
quine$Lrn <- as.factor(quine$Lrn)

# add Ethnicity -----------------------------------------------------

mlr_absent_eth <- lm(Days ~ Eth, data = quine)

round(summary(mlr_absent_eth)$coefficients[2,4], 4)

round(summary(mlr_absent_eth)$adj.r.squared, 4)

# add Sex -----------------------------------------------------------

mlr_absent_sex <- lm(Days ~ Sex, data = quine)

round(summary(mlr_absent_sex)$coefficients[2,4], 4)

round(summary(mlr_absent_sex)$adj.r.squared, 4)

# add Lrn -----------------------------------------------------------

mlr_absent_lrn <- lm(Days ~ Lrn, data = quine)

round(summary(mlr_absent_lrn)$coefficients[2,4], 4)

round(summary(mlr_absent_lrn)$adj.r.squared, 4)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_conds/babies.csv
================================================
case,bwt,gestation,parity,age,height,weight,smoke
1,120,284,0,27,62,100,0
2,113,282,0,33,64,135,0
3,128,279,0,28,64,115,1
4,123,NA,0,36,69,190,0
5,108,282,0,23,67,125,1
6,136,286,0,25,62,93,0
7,138,244,0,33,62,178,0
8,132,245,0,23,65,140,0
9,120,289,0,25,62,125,0
10,143,299,0,30,66,136,1
11,140,351,0,27,68,120,0
12,144,282,0,32,64,124,1
13,141,279,0,23,63,128,1
14,110,281,0,36,61,99,1
15,114,273,0,30,63,154,0
16,115,285,0,38,63,130,0
17,92,255,0,25,65,125,1
18,115,261,0,33,60,125,1
19,144,261,0,33,68,170,0
20,119,288,0,43,66,142,1
21,105,270,0,22,56,93,0
22,115,274,0,27,67,175,1
23,137,287,0,25,66,145,0
24,122,276,0,30,68,182,0
25,131,294,0,23,65,122,0
26,103,261,0,27,65,112,1
27,146,280,0,26,58,106,0
28,114,266,0,20,65,175,1
29,125,292,0,32,65,125,0
30,114,274,0,28,66,132,1
31,122,270,0,26,61,105,0
32,93,278,0,34,61,146,0
33,130,268,0,30,66,123,0
34,119,275,0,23,60,105,0
35,113,281,0,24,65,120,0
36,134,283,0,22,67,130,0
37,107,279,0,24,63,115,0
38,134,288,0,23,63,92,1
39,122,267,0,27,65,101,1
40,128,282,0,31,65,NA,0
41,129,293,0,30,61,160,0
42,110,278,0,23,63,177,0
43,138,302,0,26,NA,NA,1
44,111,270,0,27,61,119,0
45,87,248,0,37,65,130,1
46,143,274,0,27,63,110,1
47,155,294,0,32,66,150,0
48,110,272,0,25,60,90,0
49,122,275,0,26,66,147,0
50,145,291,0,26,63,119,1
51,115,258,0,26,62,130,0
52,108,283,0,31,65,148,1
53,102,282,0,28,61,110,0
54,143,286,0,31,64,126,0
55,146,267,0,30,67,132,0
56,124,275,0,22,60,130,0
57,124,278,0,26,70,145,1
58,145,257,0,33,65,140,0
59,106,273,0,28,60,116,0
60,75,232,0,33,61,110,0
61,107,273,0,24,61,96,0
62,124,288,0,22,67,118,0
63,122,280,0,23,65,125,1
64,101,245,0,23,63,130,1
65,128,283,0,28,63,125,1
66,104,282,0,36,65,115,1
67,97,246,0,37,63,150,0
68,137,274,0,26,69,137,1
69,103,273,0,31,63,170,1
70,142,276,0,38,63,170,0
71,130,289,0,27,66,130,0
72,156,292,0,26,63,118,0
73,133,284,0,25,66,125,1
74,120,274,0,24,62,120,0
75,91,270,0,24,60,149,1
76,127,274,0,21,62,110,0
77,153,286,0,26,63,107,1
78,121,276,0,39,63,130,0
79,120,277,0,27,63,126,0
80,99,272,0,27,62,103,1
81,149,293,0,35,65,116,0
82,129,280,0,23,64,104,0
83,139,292,0,25,68,135,0
84,114,274,0,33,67,148,1
85,138,287,0,30,66,145,0
86,129,274,0,29,71,NA,1
87,138,294,0,32,65,117,0
88,131,296,0,37,63,143,0
89,125,305,0,22,70,196,1
90,114,NA,0,24,67,113,1
91,128,281,0,33,59,117,0
92,134,268,0,28,62,112,0
93,114,271,0,27,60,104,0
94,92,NA,0,31,67,130,0
95,85,278,0,23,61,103,1
96,135,282,0,22,64,100,0
97,87,255,0,28,61,100,1
98,125,302,0,37,62,162,0
99,128,NA,0,35,62,110,0
100,105,254,0,29,64,137,0
101,120,279,0,27,60,121,1
102,119,274,0,33,64,120,0
103,116,286,0,24,61,NA,0
104,107,280,0,36,65,117,1
105,119,273,0,24,61,108,1
106,133,279,0,37,66,140,0
107,155,287,0,33,66,143,0
108,126,273,0,22,65,150,0
109,129,303,0,27,64,125,0
110,137,274,0,29,65,154,0
111,103,269,0,26,65,NA,1
112,125,302,0,28,65,125,0
113,91,255,0,19,67,136,1
114,134,293,0,21,65,NA,0
115,95,279,0,22,66,145,1
116,118,276,0,29,64,114,0
117,141,278,0,33,66,109,1
118,131,283,0,25,67,215,0
119,121,264,0,32,66,145,0
120,100,243,0,39,65,170,1
121,131,288,0,24,61,103,0
122,118,284,0,26,66,133,0
123,152,288,0,35,67,130,0
124,121,284,0,34,69,155,0
125,117,276,0,31,69,150,0
126,115,283,0,25,61,150,1
127,112,277,0,23,65,110,0
128,94,267,0,30,62,120,1
129,109,272,0,35,66,154,0
130,132,225,0,28,67,148,0
131,117,278,0,25,62,103,0
132,101,266,0,20,67,110,1
133,112,294,0,25,64,125,1
134,128,283,0,24,60,100,0
135,128,279,0,25,66,147,1
136,117,258,0,31,64,120,0
137,134,278,0,24,69,135,0
138,127,284,0,28,65,145,0
139,93,269,0,21,65,104,1
140,122,275,0,27,65,165,0
141,100,265,0,39,62,107,1
142,147,293,0,32,65,123,0
143,120,299,0,25,65,110,0
144,144,277,0,30,63,127,0
145,105,268,0,32,61,115,1
146,136,276,0,23,66,155,0
147,102,262,0,24,63,125,0
148,160,300,0,29,71,175,1
149,113,275,0,24,68,140,1
150,126,282,0,38,66,250,0
151,126,271,0,29,68,148,0
152,115,278,0,29,61,128,0
153,127,336,0,29,NA,NA,0
154,119,284,0,20,66,132,0
155,129,NA,0,23,NA,NA,1
156,123,318,0,21,64,152,0
157,118,282,0,22,68,135,1
158,133,287,0,24,60,104,1
159,105,281,0,39,61,NA,0
160,134,290,0,22,60,121,0
161,144,288,0,21,67,111,0
162,111,273,0,43,62,138,0
163,125,262,0,36,66,190,0
164,135,296,0,30,63,123,0
165,134,289,0,22,63,125,0
166,116,289,0,22,65,160,1
167,129,291,0,29,69,123,0
168,113,301,0,26,67,105,1
169,131,295,0,23,65,123,1
170,126,293,0,29,59,110,NA
171,121,272,0,22,62,109,0
172,121,271,0,25,68,118,1
173,138,287,0,24,65,115,0
174,136,278,0,23,61,105,0
175,120,279,0,30,66,131,0
176,122,278,0,31,72,155,1
177,134,267,0,30,66,170,0
178,101,280,0,25,65,123,1
179,112,288,0,32,62,125,0
180,132,290,0,25,64,120,0
181,136,285,0,23,62,175,0
182,113,277,0,23,65,192,1
183,96,271,0,23,64,116,0
184,124,277,0,29,63,220,0
185,113,306,0,21,62,150,0
186,131,286,0,34,NA,NA,1
187,137,258,0,25,63,117,0
188,133,268,0,24,61,93,0
189,107,244,0,20,58,97,0
190,96,265,0,28,59,135,1
191,142,278,0,35,66,136,1
192,136,275,0,22,63,110,0
193,75,239,0,26,63,124,1
194,125,302,0,32,61,NA,1
195,104,295,0,26,65,155,1
196,130,274,0,30,63,150,0
197,90,290,0,22,63,168,0
198,118,276,0,22,66,147,1
199,123,320,0,22,66,117,0
200,137,291,0,34,61,110,0
201,101,268,0,19,63,140,0
202,142,275,0,25,64,132,0
203,98,282,0,20,63,97,1
204,124,283,0,23,63,112,0
205,151,310,0,21,65,NA,0
206,109,281,0,23,61,105,0
207,150,285,0,22,61,110,1
208,119,282,0,26,68,150,1
209,131,280,0,38,65,125,0
210,101,272,0,29,63,150,1
211,113,246,0,19,62,138,1
212,127,270,0,25,62,150,0
213,97,260,0,23,61,99,1
214,117,282,0,28,64,115,0
215,150,290,0,21,65,125,0
216,85,234,0,33,67,130,0
217,128,288,0,27,70,145,0
218,105,233,0,34,61,130,0
219,90,269,0,26,67,125,NA
220,115,274,0,22,65,130,1
221,107,290,0,28,62,135,0
222,121,275,0,24,63,121,1
223,119,286,0,20,64,180,0
224,117,275,0,20,64,145,1
225,134,264,0,26,68,136,0
226,117,288,0,35,65,142,0
227,115,268,0,28,66,128,0
228,110,254,0,23,63,120,1
229,130,282,0,21,62,106,1
230,140,274,0,23,63,106,1
231,111,284,0,22,NA,NA,1
232,93,249,0,33,66,117,0
233,154,292,0,42,65,116,1
234,125,290,0,19,64,127,0
235,93,318,0,31,66,135,0
236,122,277,0,33,63,135,1
237,129,267,0,22,63,160,0
238,126,276,0,23,63,120,0
239,85,274,0,24,68,155,0
240,173,293,0,30,63,110,0
241,144,329,0,22,65,190,1
242,114,278,0,25,65,140,1
243,111,NA,0,27,63,105,1
244,154,287,0,27,65,125,1
245,150,274,0,25,67,117,1
246,111,278,0,21,62,125,0
247,126,277,0,32,66,128,0
248,122,261,0,28,65,124,0
249,141,282,0,24,68,169,0
250,142,274,0,24,63,125,0
251,99,262,0,38,59,110,1
252,113,286,0,23,63,105,0
253,149,282,0,21,61,110,0
254,117,328,0,29,65,125,1
255,130,274,0,26,64,185,NA
256,106,275,0,31,65,142,NA
257,128,290,0,22,64,118,0
258,125,286,0,21,64,139,0
259,114,290,0,30,66,160,0
260,130,285,0,23,63,128,1
261,116,148,0,28,66,135,0
262,81,256,0,30,64,148,1
263,124,287,0,27,62,105,1
264,125,292,0,22,65,122,0
265,110,262,0,25,66,140,0
266,125,279,0,23,63,104,1
267,138,294,0,40,64,125,0
268,142,284,0,39,66,132,0
269,115,278,0,23,60,102,1
270,102,280,0,38,67,140,0
271,140,294,0,25,61,103,0
272,133,276,1,22,63,119,0
273,127,290,0,35,66,165,0
274,104,274,1,20,62,115,1
275,119,275,0,42,67,156,1
276,152,301,0,29,65,150,0
277,123,284,1,20,65,120,1
278,143,273,0,19,66,135,0
279,131,308,0,40,65,160,0
280,141,319,1,20,67,140,1
281,129,277,0,30,66,142,1
282,113,282,1,36,59,140,0
283,119,292,0,33,62,118,1
284,109,295,1,23,63,103,1
285,104,280,1,27,68,146,1
286,131,282,1,21,66,126,0
287,110,293,1,28,64,135,1
288,148,279,0,27,71,189,0
289,137,283,1,20,65,157,0
290,117,283,0,27,63,108,0
291,115,302,1,22,67,135,0
292,98,280,0,35,64,122,1
293,136,303,1,20,68,148,1
294,121,276,1,23,71,152,1
295,132,285,1,25,63,140,0
296,91,264,0,36,60,100,1
297,119,294,0,34,59,105,0
298,85,273,0,26,60,105,1
299,106,271,1,26,61,110,1
300,132,284,0,29,64,122,0
301,80,266,1,25,62,125,0
302,109,286,0,24,64,125,1
303,111,306,0,27,61,102,0
304,143,292,1,21,65,125,0
305,136,290,0,26,66,135,0
306,110,285,1,19,64,130,0
307,98,257,0,29,66,130,1
308,108,305,1,24,65,112,0
309,101,295,0,18,62,145,1
310,71,281,0,32,60,117,1
311,124,292,0,29,68,176,1
312,93,256,0,34,66,NA,1
313,106,276,0,30,66,130,0
314,101,278,0,25,62,112,1
315,100,277,0,31,62,100,1
316,104,269,0,35,63,110,1
317,117,270,0,24,67,135,1
318,117,267,0,29,65,120,1
319,149,279,0,25,67,135,0
320,135,284,0,25,66,123,0
321,110,283,1,21,66,129,0
322,121,276,0,31,67,130,0
323,142,285,1,24,66,136,0
324,104,260,0,33,64,145,0
325,138,296,0,34,66,120,0
326,112,278,1,21,63,120,0
327,117,293,0,39,60,120,1
328,109,282,0,25,62,106,1
329,131,266,1,28,67,135,0
330,120,273,0,29,64,130,1
331,116,270,0,29,63,132,0
332,140,290,0,23,65,110,0
333,103,273,1,22,64,110,1
334,120,279,1,23,67,135,0
335,139,260,1,32,64,127,0
336,123,254,0,26,62,130,1
337,104,280,1,23,64,107,1
338,131,283,0,31,NA,NA,0
339,111,270,0,22,59,103,0
340,122,277,0,32,63,157,1
341,116,271,1,30,67,144,1
342,129,277,0,27,68,130,1
343,133,292,0,30,65,112,1
344,110,277,0,25,61,130,0
345,105,276,0,22,67,130,0
346,93,246,0,37,65,130,0
347,122,281,0,42,63,103,1
348,133,293,0,23,64,110,1
349,130,296,1,22,66,117,1
350,104,307,0,24,59,122,0
351,106,278,0,31,65,110,1
352,120,281,0,33,63,113,0
353,121,284,0,27,63,NA,1
354,118,276,1,18,63,128,0
355,140,290,1,19,67,132,1
356,114,268,0,22,64,104,0
357,116,280,0,40,62,159,0
358,129,284,0,24,64,115,0
359,120,286,0,22,62,115,1
360,127,281,0,24,63,112,1
361,107,278,1,27,NA,135,0
362,71,234,0,32,64,110,1
363,88,274,0,30,66,130,0
364,107,300,0,19,NA,NA,1
365,122,286,0,23,64,145,0
366,106,302,1,19,66,147,0
367,135,285,0,30,66,130,0
368,107,290,0,26,63,112,0
369,129,294,0,32,62,170,1
370,126,274,0,39,62,122,0
371,116,293,1,26,64,125,0
372,124,294,0,26,62,122,0
373,123,281,0,23,68,136,0
374,145,315,0,39,67,143,1
375,102,278,0,27,67,135,1
376,129,293,0,30,65,130,1
377,98,276,1,22,61,121,0
378,110,272,0,28,60,108,0
379,135,282,0,24,67,128,1
380,101,278,1,20,62,105,0
381,96,266,0,26,65,125,0
382,104,276,1,18,60,109,1
383,100,249,0,24,67,100,0
384,154,292,0,40,66,145,0
385,127,293,0,31,67,137,0
386,126,288,0,31,62,150,0
387,126,282,1,23,66,115,1
388,127,279,0,26,67,155,1
389,98,275,0,25,65,112,1
390,127,288,1,21,66,130,0
391,129,299,0,22,68,145,0
392,131,292,1,22,64,124,1
393,132,289,1,19,66,145,0
394,127,280,0,27,62,118,0
395,99,313,1,34,59,100,1
396,115,290,0,30,64,140,1
397,145,290,1,24,67,125,0
398,102,249,1,23,67,134,1
399,136,299,0,29,64,115,0
400,121,286,1,NA,NA,NA,0
401,121,282,0,22,66,133,0
402,120,286,0,25,62,105,0
403,118,261,0,26,60,104,0
404,127,304,1,26,62,105,0
405,132,281,1,24,63,117,0
406,102,258,1,22,65,135,0
407,143,279,0,39,65,129,1
408,118,277,0,25,62,120,0
409,102,286,1,22,64,140,0
410,163,280,0,35,69,139,0
411,132,294,0,32,64,116,0
412,116,276,0,33,61,180,0
413,138,288,1,19,66,124,0
414,139,279,0,20,64,143,0
415,132,298,1,23,61,137,0
416,87,282,0,27,63,104,1
417,131,297,0,30,67,132,0
418,130,282,0,26,67,147,1
419,123,290,0,28,66,107,1
420,115,276,1,18,63,110,0
421,116,272,0,27,64,130,1
422,119,286,1,20,67,130,0
423,125,279,1,19,67,135,0
424,144,282,0,33,66,155,1
425,123,269,0,26,67,132,0
426,120,276,0,23,66,114,0
427,140,251,0,28,63,210,0
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429,116,272,0,NA,63,138,1
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903,112,292,1,28,62,110,1
904,115,281,0,28,61,128,1
905,72,271,0,39,61,136,0
906,122,281,1,24,65,137,1
907,116,291,0,26,66,153,0
908,127,272,0,20,64,130,1
909,90,266,0,23,61,99,1
910,99,273,1,27,59,115,0
911,144,307,1,26,66,125,0
912,138,280,1,30,65,175,0
913,58,245,0,34,64,156,1
914,109,265,1,24,63,107,1
915,110,277,1,19,62,160,0
916,129,278,0,27,63,128,0
917,150,284,0,40,67,130,0
918,128,279,0,27,66,135,0
919,142,284,1,31,66,137,1
920,115,268,1,31,64,125,0
921,108,274,0,28,66,175,NA
922,108,283,0,35,62,108,0
923,139,281,0,27,63,137,0
924,115,275,0,25,61,155,1
925,136,288,0,23,62,217,0
926,163,289,1,25,64,126,1
927,131,285,0,26,64,130,0
928,77,238,0,38,67,135,1
929,124,283,1,33,67,156,1
930,104,270,1,26,62,115,0
931,102,267,1,24,61,109,1
932,94,268,0,30,62,105,1
933,158,295,1,37,70,137,0
934,112,275,1,21,68,143,1
935,119,286,0,26,64,123,1
936,97,279,0,29,68,178,1
937,99,252,0,21,64,120,0
938,115,264,1,23,67,134,1
939,139,284,0,37,61,121,0
940,144,304,1,27,58,102,1
941,99,270,0,22,63,115,1
942,105,280,1,22,63,116,0
943,89,275,0,34,66,170,0
944,129,270,0,43,67,160,0
945,119,270,1,20,64,109,0
946,114,291,0,35,60,112,0
947,106,289,0,28,67,120,1
948,122,292,1,34,65,133,0
949,136,261,0,24,65,110,0
950,121,286,1,22,69,130,1
951,112,282,0,26,65,122,0
952,112,266,0,26,64,122,0
953,123,314,0,22,61,121,1
954,139,286,0,33,65,125,1
955,125,290,0,36,59,105,0
956,105,295,1,20,64,112,1
957,130,276,0,41,68,130,0
958,146,294,0,22,66,145,1
959,133,290,0,21,64,145,0
960,147,296,1,19,67,124,0
961,109,269,0,23,63,113,0
962,122,286,0,23,64,120,1
963,135,260,0,43,65,135,0
964,107,NA,0,19,60,118,0
965,117,272,0,32,66,118,0
966,138,284,0,30,66,133,1
967,120,283,0,28,64,122,1
968,119,273,0,35,65,125,1
969,118,278,1,19,62,126,0
970,105,330,0,23,64,112,1
971,113,306,1,21,65,137,0
972,136,NA,0,36,66,135,0
973,148,291,1,21,63,115,0
974,140,281,1,22,69,135,0
975,134,287,1,33,67,131,0
976,120,280,0,31,61,111,0
977,123,296,1,26,64,110,1
978,102,275,0,43,64,160,0
979,55,204,0,35,65,140,0
980,103,276,1,19,63,149,1
981,123,283,0,21,65,110,0
982,105,270,1,27,65,134,1
983,138,289,0,33,65,155,0
984,128,281,0,28,63,150,0
985,139,285,0,30,65,129,1
986,104,288,1,27,61,122,1
987,159,296,1,27,64,112,0
988,118,276,0,29,62,130,1
989,99,285,0,25,69,128,1
990,144,281,0,20,63,120,0
991,121,270,0,25,62,108,1
992,117,265,1,24,66,98,0
993,119,293,1,23,65,127,0
994,105,281,1,19,61,130,0
995,125,283,0,37,63,145,1
996,119,259,0,37,62,130,0
997,101,273,0,39,60,113,0
998,105,277,1,25,64,156,0
999,110,281,0,27,60,110,0
1000,100,270,1,21,65,132,1
1001,98,284,0,29,68,140,0
1002,127,276,0,37,64,159,0
1003,117,324,0,22,62,164,1
1004,122,278,0,37,68,114,0
1005,122,273,1,23,64,130,1
1006,118,281,1,36,66,140,1
1007,137,303,1,23,66,127,1
1008,120,275,0,32,63,115,1
1009,143,285,0,27,68,185,0
1010,108,270,0,29,67,124,1
1011,131,284,1,19,61,114,1
1012,110,277,0,36,61,116,0
1013,105,276,0,20,62,112,1
1014,133,274,0,30,63,NA,0
1015,125,255,0,23,63,133,0
1016,78,258,1,24,66,115,1
1017,114,289,0,36,60,115,0
1018,111,278,0,29,65,145,1
1019,103,250,0,40,59,140,0
1020,114,276,0,26,62,127,0
1021,75,247,0,36,64,120,1
1022,169,296,0,33,67,185,0
1023,94,271,0,36,61,130,1
1024,150,287,0,36,62,135,0
1025,144,248,0,30,70,145,0
1026,144,291,0,28,67,130,0
1027,143,313,0,20,68,150,0
1028,145,304,1,25,63,109,1
1029,121,285,0,34,64,110,0
1030,105,256,0,31,66,142,0
1031,134,286,0,25,64,125,0
1032,129,294,1,21,65,132,0
1033,114,276,0,24,63,110,0
1034,97,265,0,30,61,110,0
1035,160,292,0,28,64,120,0
1036,65,237,0,31,67,130,0
1037,145,288,0,28,64,116,0
1038,95,273,0,23,60,90,0
1039,139,293,1,21,69,130,0
1040,123,288,0,27,63,125,0
1041,109,283,0,23,65,112,1
1042,110,268,0,34,64,127,0
1043,122,296,1,24,65,132,0
1044,115,307,0,34,65,128,1
1045,117,323,0,26,62,NA,0
1046,108,279,1,19,64,115,0
1047,120,287,0,23,67,116,1
1048,131,269,0,36,68,145,0
1049,136,283,1,24,63,119,0
1050,125,290,0,32,63,135,0
1051,96,285,1,20,66,117,1
1052,102,282,1,29,65,125,1
1053,102,288,1,18,65,117,0
1054,112,277,1,22,67,120,0
1055,135,272,0,30,65,130,0
1056,91,266,0,23,60,120,1
1057,129,276,0,31,63,125,0
1058,155,290,0,26,66,129,1
1059,109,274,0,33,69,144,1
1060,80,262,1,31,61,100,1
1061,125,273,0,30,64,145,0
1062,94,284,0,24,63,104,1
1063,148,281,0,27,63,110,1
1064,73,277,0,29,65,145,0
1065,123,267,1,19,66,132,1
1066,65,232,0,24,66,125,1
1067,118,279,1,21,64,108,0
1068,102,283,0,39,60,119,0
1069,120,280,0,24,61,118,0
1070,108,270,1,21,65,130,1
1071,122,280,1,45,62,128,0
1072,103,268,0,32,62,97,1
1073,105,312,0,41,61,115,1
1074,126,273,1,25,68,135,0
1075,145,316,0,22,67,142,0
1076,139,293,0,34,66,131,0
1077,124,290,0,26,65,165,0
1078,121,282,0,30,65,122,0
1079,126,299,1,21,60,114,0
1080,119,286,1,33,67,137,0
1081,114,277,1,19,63,107,0
1082,118,272,0,23,64,113,0
1083,127,295,0,36,65,145,0
1084,117,290,1,22,67,110,0
1085,137,277,0,41,65,126,0
1086,133,292,0,29,65,135,0
1087,100,264,0,28,60,111,1
1088,107,273,1,26,65,135,0
1089,115,276,1,20,62,105,1
1090,91,292,1,26,61,113,1
1091,112,287,0,27,64,110,1
1092,125,289,1,31,61,120,0
1093,157,291,0,33,65,121,0
1094,108,256,1,26,67,130,0
1095,130,279,0,31,62,122,0
1096,135,289,0,25,64,127,0
1097,123,277,0,24,66,122,0
1098,100,281,0,24,61,115,0
1099,124,277,1,23,64,104,0
1100,174,284,0,39,65,163,0
1101,129,278,0,26,67,146,0
1102,119,275,0,27,59,113,1
1103,126,272,1,35,61,120,1
1104,128,267,0,37,61,142,0
1105,116,282,1,19,64,124,0
1106,100,285,0,18,68,127,1
1107,96,285,0,37,66,135,1
1108,131,279,1,20,68,122,1
1109,110,292,0,35,62,127,0
1110,108,278,0,28,63,125,1
1111,129,275,0,24,65,135,0
1112,141,285,0,23,67,150,0
1113,110,276,0,31,70,155,0
1114,118,273,0,21,63,120,0
1115,111,267,1,24,60,115,0
1116,160,297,0,20,68,136,0
1117,120,280,0,30,60,115,0
1118,121,281,0,29,63,108,0
1119,113,282,0,30,64,118,1
1120,117,270,0,23,58,115,0
1121,158,267,0,35,64,125,0
1122,128,277,0,39,61,120,0
1123,158,289,0,30,66,140,0
1124,133,289,0,22,65,123,1
1125,163,298,0,37,61,98,0
1126,128,282,1,19,66,118,0
1127,126,271,1,21,60,105,0
1128,127,283,0,42,62,154,1
1129,134,287,0,40,63,118,0
1130,140,274,0,41,63,122,0
1131,102,285,0,29,63,117,1
1132,100,252,0,24,61,150,0
1133,120,295,0,29,59,100,1
1134,98,279,1,18,65,115,1
1135,130,246,0,19,62,118,0
1136,104,280,0,41,63,118,1
1137,122,285,0,31,62,102,1
1138,137,276,1,25,64,127,0
1139,114,285,1,20,61,104,0
1140,63,236,1,24,58,99,0
1141,98,318,0,23,63,107,0
1142,99,268,0,32,63,124,1
1143,89,238,1,26,64,136,0
1144,117,283,0,22,65,142,1
1145,143,281,0,29,67,132,0
1146,106,279,0,29,63,125,1
1147,99,246,0,35,62,106,0
1148,156,300,0,27,65,120,1
1149,72,266,1,25,66,200,1
1150,75,266,0,37,61,113,1
1151,97,285,0,35,61,112,1
1152,106,264,0,41,64,114,0
1153,91,225,0,18,68,117,1
1154,117,269,1,28,61,99,0
1155,117,284,0,25,66,177,1
1156,112,291,0,23,66,145,0
1157,112,270,0,29,61,124,0
1158,141,293,0,28,61,125,0
1159,131,259,0,19,63,134,0
1160,130,290,0,19,65,123,1
1161,132,270,0,26,67,140,0
1162,114,265,0,23,67,130,1
1163,160,291,0,34,64,110,1
1164,106,283,0,24,63,119,0
1165,84,260,1,20,64,104,1
1166,112,268,1,25,59,103,0
1167,139,311,0,37,66,135,0
1168,104,267,0,30,63,180,0
1169,130,294,0,32,63,110,1
1170,71,254,0,19,61,145,1
1171,82,270,0,21,65,150,1
1172,119,280,1,21,64,128,0
1173,123,353,0,26,63,115,0
1174,115,278,0,27,59,95,0
1175,124,289,1,21,67,145,1
1176,138,292,0,25,65,130,1
1177,88,276,0,25,63,103,1
1178,146,305,0,23,NA,NA,0
1179,128,241,1,17,64,126,0
1180,82,274,0,31,64,101,1
1181,100,274,0,24,63,113,0
1182,114,271,0,32,61,130,0
1183,97,269,0,20,65,137,1
1184,126,298,0,24,61,112,0
1185,122,275,1,20,65,127,0
1186,152,295,0,39,62,140,0
1187,116,274,0,21,62,110,1
1188,132,302,0,36,63,145,1
1189,84,260,1,37,66,140,0
1190,119,277,1,18,61,89,1
1191,104,275,0,24,NA,NA,0
1192,106,312,0,24,62,135,1
1193,124,NA,1,39,65,228,0
1194,139,291,0,24,65,160,0
1195,103,273,0,36,65,158,1
1196,112,299,0,24,67,145,1
1197,96,276,0,33,64,127,1
1198,102,281,1,19,67,135,1
1199,120,300,0,34,63,150,1
1200,102,338,0,19,64,170,0
1201,97,255,1,22,63,107,1
1202,113,285,0,22,70,145,0
1203,130,297,0,32,58,130,0
1204,97,260,1,25,63,115,1
1205,116,273,0,31,61,120,0
1206,114,266,0,29,64,113,0
1207,127,242,0,17,61,135,1
1208,87,247,1,18,66,125,1
1209,141,281,0,29,54,156,1
1210,144,283,1,25,66,140,0
1211,116,273,0,33,66,130,1
1212,75,265,0,21,65,103,1
1213,138,286,1,28,68,120,0
1214,99,271,0,39,69,151,0
1215,118,293,0,21,63,103,0
1216,152,267,0,28,NA,119,1
1217,97,266,0,24,62,109,0
1218,146,319,0,28,66,145,0
1219,81,285,0,19,63,150,1
1220,110,321,0,28,66,180,0
1221,135,284,1,19,60,95,0
1222,114,290,1,21,65,120,1
1223,124,288,1,21,64,116,1
1224,115,262,1,23,64,136,1
1225,143,281,0,28,65,135,1
1226,113,287,1,29,70,145,1
1227,109,244,1,21,63,102,1
1228,103,278,0,30,60,87,1
1229,118,276,0,34,64,116,0
1230,127,290,0,27,65,121,0
1231,132,270,0,27,65,126,0
1232,113,275,1,27,60,100,0
1233,128,265,0,24,67,120,0
1234,130,291,0,30,65,150,1
1235,125,281,1,21,65,110,0
1236,117,297,0,38,65,129,0

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

babies <- read.csv("babies.csv")

# mlr for birth weight ----------------------------------------------

m_bwt_mlr <- lm(bwt ~ gestation + parity + age + 
                  height + weight + smoke , data = babies)

# complete cases data for plotting residuals plots ------------------

babies_cc <- na.omit(babies)

# normal prob plot for residuals ------------------------------------

pdf("baby_weights_conds_normal_qq.pdf", 5.5, 4.3)

par(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

qqnorm(m_bwt_mlr$residuals, 
       ylab = "Residuals", main = "",
       pch = 19, col = COL[1,2],
       ylim = c(-60,60), axes = FALSE)
axis(1)
axis(2, seq(-40, 40, 40))
box()

qqline(m_bwt_mlr$residuals, col = COL[1])

dev.off()

# histogram for residuals ------------------------------------

pdf("baby_weights_conds_normal_hist.pdf", 5.5, 4.3)
par(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(m_bwt_mlr$residuals,
    xlab = "Residuals",
    ylab = "",
    col = COL[1])
box()
dev.off()

# absolute values of residuals against fitted -----------------------

pdf("baby_weights_conds_abs_res_fitted.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_bwt_mlr$residuals ~ m_bwt_mlr$fitted.values, 
     ylab = "Residuals", xlab = "Fitted values", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, seq(80, 160, 40))
axis(2, seq(-40, 40, 40))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals in order of their data collection -----------------------

pdf("baby_weights_conds_res_order.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.9, 0.5, 1), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_bwt_mlr$residuals ~ c(1:length(m_bwt_mlr$residuals)), 
     ylab = "Residuals", xlab = "Order of collection",
     pch = 19, col = COL[1,2], axes = FALSE)
axis(1, seq(0,1200,400))
axis(2, seq(-40,40,40))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals vs. gestation -------------------------------------------

pdf("baby_weights_conds_res_gestation.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_bwt_mlr$residuals ~ babies_cc$gestation, 
     ylab = "Residuals", xlab = "Length of gestation", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, seq(150, 350, 50))
axis(2, seq(-40, 40, 40))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals vs. parity -------------------------------------------

pdf("baby_weights_conds_res_parity.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_bwt_mlr$residuals ~ babies_cc$parity, 
     ylab = "Residuals", xlab = "Parity", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, seq(0, 1, 1))
axis(2, seq(-40, 40, 40))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals vs. height -------------------------------------------

pdf("baby_weights_conds_res_height.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_bwt_mlr$residuals ~ babies_cc$height, 
     ylab = "Residuals", xlab = "Height of mother", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, at = seq(55, 70, 5))
axis(2, at = seq(-40, 40, 40))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals vs. weight -------------------------------------------

pdf("baby_weights_conds_res_weight.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_bwt_mlr$residuals ~ babies_cc$weight, 
     ylab = "Residuals", xlab = "Weight of mother", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, at = seq(100, 250, 50))
axis(2, at = seq(-40, 40, 40))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals vs. smoke -------------------------------------------

pdf("baby_weights_conds_res_smoke.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_bwt_mlr$residuals ~ babies_cc$smoke, 
     ylab = "Residuals", xlab = "Smoke", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, at = seq(0, 1, 1))
axis(2, at = seq(-40, 40, 40))
box()

abline(h = 0, lty = 2)

dev.off()

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_mlr/babies.csv
================================================
case,bwt,gestation,parity,age,height,weight,smoke
1,120,284,0,27,62,100,0
2,113,282,0,33,64,135,0
3,128,279,0,28,64,115,1
4,123,NA,0,36,69,190,0
5,108,282,0,23,67,125,1
6,136,286,0,25,62,93,0
7,138,244,0,33,62,178,0
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958,146,294,0,22,66,145,1
959,133,290,0,21,64,145,0
960,147,296,1,19,67,124,0
961,109,269,0,23,63,113,0
962,122,286,0,23,64,120,1
963,135,260,0,43,65,135,0
964,107,NA,0,19,60,118,0
965,117,272,0,32,66,118,0
966,138,284,0,30,66,133,1
967,120,283,0,28,64,122,1
968,119,273,0,35,65,125,1
969,118,278,1,19,62,126,0
970,105,330,0,23,64,112,1
971,113,306,1,21,65,137,0
972,136,NA,0,36,66,135,0
973,148,291,1,21,63,115,0
974,140,281,1,22,69,135,0
975,134,287,1,33,67,131,0
976,120,280,0,31,61,111,0
977,123,296,1,26,64,110,1
978,102,275,0,43,64,160,0
979,55,204,0,35,65,140,0
980,103,276,1,19,63,149,1
981,123,283,0,21,65,110,0
982,105,270,1,27,65,134,1
983,138,289,0,33,65,155,0
984,128,281,0,28,63,150,0
985,139,285,0,30,65,129,1
986,104,288,1,27,61,122,1
987,159,296,1,27,64,112,0
988,118,276,0,29,62,130,1
989,99,285,0,25,69,128,1
990,144,281,0,20,63,120,0
991,121,270,0,25,62,108,1
992,117,265,1,24,66,98,0
993,119,293,1,23,65,127,0
994,105,281,1,19,61,130,0
995,125,283,0,37,63,145,1
996,119,259,0,37,62,130,0
997,101,273,0,39,60,113,0
998,105,277,1,25,64,156,0
999,110,281,0,27,60,110,0
1000,100,270,1,21,65,132,1
1001,98,284,0,29,68,140,0
1002,127,276,0,37,64,159,0
1003,117,324,0,22,62,164,1
1004,122,278,0,37,68,114,0
1005,122,273,1,23,64,130,1
1006,118,281,1,36,66,140,1
1007,137,303,1,23,66,127,1
1008,120,275,0,32,63,115,1
1009,143,285,0,27,68,185,0
1010,108,270,0,29,67,124,1
1011,131,284,1,19,61,114,1
1012,110,277,0,36,61,116,0
1013,105,276,0,20,62,112,1
1014,133,274,0,30,63,NA,0
1015,125,255,0,23,63,133,0
1016,78,258,1,24,66,115,1
1017,114,289,0,36,60,115,0
1018,111,278,0,29,65,145,1
1019,103,250,0,40,59,140,0
1020,114,276,0,26,62,127,0
1021,75,247,0,36,64,120,1
1022,169,296,0,33,67,185,0
1023,94,271,0,36,61,130,1
1024,150,287,0,36,62,135,0
1025,144,248,0,30,70,145,0
1026,144,291,0,28,67,130,0
1027,143,313,0,20,68,150,0
1028,145,304,1,25,63,109,1
1029,121,285,0,34,64,110,0
1030,105,256,0,31,66,142,0
1031,134,286,0,25,64,125,0
1032,129,294,1,21,65,132,0
1033,114,276,0,24,63,110,0
1034,97,265,0,30,61,110,0
1035,160,292,0,28,64,120,0
1036,65,237,0,31,67,130,0
1037,145,288,0,28,64,116,0
1038,95,273,0,23,60,90,0
1039,139,293,1,21,69,130,0
1040,123,288,0,27,63,125,0
1041,109,283,0,23,65,112,1
1042,110,268,0,34,64,127,0
1043,122,296,1,24,65,132,0
1044,115,307,0,34,65,128,1
1045,117,323,0,26,62,NA,0
1046,108,279,1,19,64,115,0
1047,120,287,0,23,67,116,1
1048,131,269,0,36,68,145,0
1049,136,283,1,24,63,119,0
1050,125,290,0,32,63,135,0
1051,96,285,1,20,66,117,1
1052,102,282,1,29,65,125,1
1053,102,288,1,18,65,117,0
1054,112,277,1,22,67,120,0
1055,135,272,0,30,65,130,0
1056,91,266,0,23,60,120,1
1057,129,276,0,31,63,125,0
1058,155,290,0,26,66,129,1
1059,109,274,0,33,69,144,1
1060,80,262,1,31,61,100,1
1061,125,273,0,30,64,145,0
1062,94,284,0,24,63,104,1
1063,148,281,0,27,63,110,1
1064,73,277,0,29,65,145,0
1065,123,267,1,19,66,132,1
1066,65,232,0,24,66,125,1
1067,118,279,1,21,64,108,0
1068,102,283,0,39,60,119,0
1069,120,280,0,24,61,118,0
1070,108,270,1,21,65,130,1
1071,122,280,1,45,62,128,0
1072,103,268,0,32,62,97,1
1073,105,312,0,41,61,115,1
1074,126,273,1,25,68,135,0
1075,145,316,0,22,67,142,0
1076,139,293,0,34,66,131,0
1077,124,290,0,26,65,165,0
1078,121,282,0,30,65,122,0
1079,126,299,1,21,60,114,0
1080,119,286,1,33,67,137,0
1081,114,277,1,19,63,107,0
1082,118,272,0,23,64,113,0
1083,127,295,0,36,65,145,0
1084,117,290,1,22,67,110,0
1085,137,277,0,41,65,126,0
1086,133,292,0,29,65,135,0
1087,100,264,0,28,60,111,1
1088,107,273,1,26,65,135,0
1089,115,276,1,20,62,105,1
1090,91,292,1,26,61,113,1
1091,112,287,0,27,64,110,1
1092,125,289,1,31,61,120,0
1093,157,291,0,33,65,121,0
1094,108,256,1,26,67,130,0
1095,130,279,0,31,62,122,0
1096,135,289,0,25,64,127,0
1097,123,277,0,24,66,122,0
1098,100,281,0,24,61,115,0
1099,124,277,1,23,64,104,0
1100,174,284,0,39,65,163,0
1101,129,278,0,26,67,146,0
1102,119,275,0,27,59,113,1
1103,126,272,1,35,61,120,1
1104,128,267,0,37,61,142,0
1105,116,282,1,19,64,124,0
1106,100,285,0,18,68,127,1
1107,96,285,0,37,66,135,1
1108,131,279,1,20,68,122,1
1109,110,292,0,35,62,127,0
1110,108,278,0,28,63,125,1
1111,129,275,0,24,65,135,0
1112,141,285,0,23,67,150,0
1113,110,276,0,31,70,155,0
1114,118,273,0,21,63,120,0
1115,111,267,1,24,60,115,0
1116,160,297,0,20,68,136,0
1117,120,280,0,30,60,115,0
1118,121,281,0,29,63,108,0
1119,113,282,0,30,64,118,1
1120,117,270,0,23,58,115,0
1121,158,267,0,35,64,125,0
1122,128,277,0,39,61,120,0
1123,158,289,0,30,66,140,0
1124,133,289,0,22,65,123,1
1125,163,298,0,37,61,98,0
1126,128,282,1,19,66,118,0
1127,126,271,1,21,60,105,0
1128,127,283,0,42,62,154,1
1129,134,287,0,40,63,118,0
1130,140,274,0,41,63,122,0
1131,102,285,0,29,63,117,1
1132,100,252,0,24,61,150,0
1133,120,295,0,29,59,100,1
1134,98,279,1,18,65,115,1
1135,130,246,0,19,62,118,0
1136,104,280,0,41,63,118,1
1137,122,285,0,31,62,102,1
1138,137,276,1,25,64,127,0
1139,114,285,1,20,61,104,0
1140,63,236,1,24,58,99,0
1141,98,318,0,23,63,107,0
1142,99,268,0,32,63,124,1
1143,89,238,1,26,64,136,0
1144,117,283,0,22,65,142,1
1145,143,281,0,29,67,132,0
1146,106,279,0,29,63,125,1
1147,99,246,0,35,62,106,0
1148,156,300,0,27,65,120,1
1149,72,266,1,25,66,200,1
1150,75,266,0,37,61,113,1
1151,97,285,0,35,61,112,1
1152,106,264,0,41,64,114,0
1153,91,225,0,18,68,117,1
1154,117,269,1,28,61,99,0
1155,117,284,0,25,66,177,1
1156,112,291,0,23,66,145,0
1157,112,270,0,29,61,124,0
1158,141,293,0,28,61,125,0
1159,131,259,0,19,63,134,0
1160,130,290,0,19,65,123,1
1161,132,270,0,26,67,140,0
1162,114,265,0,23,67,130,1
1163,160,291,0,34,64,110,1
1164,106,283,0,24,63,119,0
1165,84,260,1,20,64,104,1
1166,112,268,1,25,59,103,0
1167,139,311,0,37,66,135,0
1168,104,267,0,30,63,180,0
1169,130,294,0,32,63,110,1
1170,71,254,0,19,61,145,1
1171,82,270,0,21,65,150,1
1172,119,280,1,21,64,128,0
1173,123,353,0,26,63,115,0
1174,115,278,0,27,59,95,0
1175,124,289,1,21,67,145,1
1176,138,292,0,25,65,130,1
1177,88,276,0,25,63,103,1
1178,146,305,0,23,NA,NA,0
1179,128,241,1,17,64,126,0
1180,82,274,0,31,64,101,1
1181,100,274,0,24,63,113,0
1182,114,271,0,32,61,130,0
1183,97,269,0,20,65,137,1
1184,126,298,0,24,61,112,0
1185,122,275,1,20,65,127,0
1186,152,295,0,39,62,140,0
1187,116,274,0,21,62,110,1
1188,132,302,0,36,63,145,1
1189,84,260,1,37,66,140,0
1190,119,277,1,18,61,89,1
1191,104,275,0,24,NA,NA,0
1192,106,312,0,24,62,135,1
1193,124,NA,1,39,65,228,0
1194,139,291,0,24,65,160,0
1195,103,273,0,36,65,158,1
1196,112,299,0,24,67,145,1
1197,96,276,0,33,64,127,1
1198,102,281,1,19,67,135,1
1199,120,300,0,34,63,150,1
1200,102,338,0,19,64,170,0
1201,97,255,1,22,63,107,1
1202,113,285,0,22,70,145,0
1203,130,297,0,32,58,130,0
1204,97,260,1,25,63,115,1
1205,116,273,0,31,61,120,0
1206,114,266,0,29,64,113,0
1207,127,242,0,17,61,135,1
1208,87,247,1,18,66,125,1
1209,141,281,0,29,54,156,1
1210,144,283,1,25,66,140,0
1211,116,273,0,33,66,130,1
1212,75,265,0,21,65,103,1
1213,138,286,1,28,68,120,0
1214,99,271,0,39,69,151,0
1215,118,293,0,21,63,103,0
1216,152,267,0,28,NA,119,1
1217,97,266,0,24,62,109,0
1218,146,319,0,28,66,145,0
1219,81,285,0,19,63,150,1
1220,110,321,0,28,66,180,0
1221,135,284,1,19,60,95,0
1222,114,290,1,21,65,120,1
1223,124,288,1,21,64,116,1
1224,115,262,1,23,64,136,1
1225,143,281,0,28,65,135,1
1226,113,287,1,29,70,145,1
1227,109,244,1,21,63,102,1
1228,103,278,0,30,60,87,1
1229,118,276,0,34,64,116,0
1230,127,290,0,27,65,121,0
1231,132,270,0,27,65,126,0
1232,113,275,1,27,60,100,0
1233,128,265,0,24,67,120,0
1234,130,291,0,30,65,150,1
1235,125,281,1,21,65,110,0
1236,117,297,0,38,65,129,0

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_mlr/baby_weights_mlr.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

babies <- read.csv("babies.csv")

# print out dataset -------------------------------------------------

babies_sub <- babies[c(1,2,nrow(babies)), ]
xtable(babies_sub)

# mlr for birth weight ----------------------------------------------

m_bwt_mlr <- lm(bwt ~ gestation + parity + age + 
                     height + weight + smoke , data = babies)

xtable(summary(m_bwt_mlr), digits = 2)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_backward/babies.csv
================================================
case,bwt,gestation,parity,age,height,weight,smoke
1,120,284,0,27,62,100,0
2,113,282,0,33,64,135,0
3,128,279,0,28,64,115,1
4,123,NA,0,36,69,190,0
5,108,282,0,23,67,125,1
6,136,286,0,25,62,93,0
7,138,244,0,33,62,178,0
8,132,245,0,23,65,140,0
9,120,289,0,25,62,125,0
10,143,299,0,30,66,136,1
11,140,351,0,27,68,120,0
12,144,282,0,32,64,124,1
13,141,279,0,23,63,128,1
14,110,281,0,36,61,99,1
15,114,273,0,30,63,154,0
16,115,285,0,38,63,130,0
17,92,255,0,25,65,125,1
18,115,261,0,33,60,125,1
19,144,261,0,33,68,170,0
20,119,288,0,43,66,142,1
21,105,270,0,22,56,93,0
22,115,274,0,27,67,175,1
23,137,287,0,25,66,145,0
24,122,276,0,30,68,182,0
25,131,294,0,23,65,122,0
26,103,261,0,27,65,112,1
27,146,280,0,26,58,106,0
28,114,266,0,20,65,175,1
29,125,292,0,32,65,125,0
30,114,274,0,28,66,132,1
31,122,270,0,26,61,105,0
32,93,278,0,34,61,146,0
33,130,268,0,30,66,123,0
34,119,275,0,23,60,105,0
35,113,281,0,24,65,120,0
36,134,283,0,22,67,130,0
37,107,279,0,24,63,115,0
38,134,288,0,23,63,92,1
39,122,267,0,27,65,101,1
40,128,282,0,31,65,NA,0
41,129,293,0,30,61,160,0
42,110,278,0,23,63,177,0
43,138,302,0,26,NA,NA,1
44,111,270,0,27,61,119,0
45,87,248,0,37,65,130,1
46,143,274,0,27,63,110,1
47,155,294,0,32,66,150,0
48,110,272,0,25,60,90,0
49,122,275,0,26,66,147,0
50,145,291,0,26,63,119,1
51,115,258,0,26,62,130,0
52,108,283,0,31,65,148,1
53,102,282,0,28,61,110,0
54,143,286,0,31,64,126,0
55,146,267,0,30,67,132,0
56,124,275,0,22,60,130,0
57,124,278,0,26,70,145,1
58,145,257,0,33,65,140,0
59,106,273,0,28,60,116,0
60,75,232,0,33,61,110,0
61,107,273,0,24,61,96,0
62,124,288,0,22,67,118,0
63,122,280,0,23,65,125,1
64,101,245,0,23,63,130,1
65,128,283,0,28,63,125,1
66,104,282,0,36,65,115,1
67,97,246,0,37,63,150,0
68,137,274,0,26,69,137,1
69,103,273,0,31,63,170,1
70,142,276,0,38,63,170,0
71,130,289,0,27,66,130,0
72,156,292,0,26,63,118,0
73,133,284,0,25,66,125,1
74,120,274,0,24,62,120,0
75,91,270,0,24,60,149,1
76,127,274,0,21,62,110,0
77,153,286,0,26,63,107,1
78,121,276,0,39,63,130,0
79,120,277,0,27,63,126,0
80,99,272,0,27,62,103,1
81,149,293,0,35,65,116,0
82,129,280,0,23,64,104,0
83,139,292,0,25,68,135,0
84,114,274,0,33,67,148,1
85,138,287,0,30,66,145,0
86,129,274,0,29,71,NA,1
87,138,294,0,32,65,117,0
88,131,296,0,37,63,143,0
89,125,305,0,22,70,196,1
90,114,NA,0,24,67,113,1
91,128,281,0,33,59,117,0
92,134,268,0,28,62,112,0
93,114,271,0,27,60,104,0
94,92,NA,0,31,67,130,0
95,85,278,0,23,61,103,1
96,135,282,0,22,64,100,0
97,87,255,0,28,61,100,1
98,125,302,0,37,62,162,0
99,128,NA,0,35,62,110,0
100,105,254,0,29,64,137,0
101,120,279,0,27,60,121,1
102,119,274,0,33,64,120,0
103,116,286,0,24,61,NA,0
104,107,280,0,36,65,117,1
105,119,273,0,24,61,108,1
106,133,279,0,37,66,140,0
107,155,287,0,33,66,143,0
108,126,273,0,22,65,150,0
109,129,303,0,27,64,125,0
110,137,274,0,29,65,154,0
111,103,269,0,26,65,NA,1
112,125,302,0,28,65,125,0
113,91,255,0,19,67,136,1
114,134,293,0,21,65,NA,0
115,95,279,0,22,66,145,1
116,118,276,0,29,64,114,0
117,141,278,0,33,66,109,1
118,131,283,0,25,67,215,0
119,121,264,0,32,66,145,0
120,100,243,0,39,65,170,1
121,131,288,0,24,61,103,0
122,118,284,0,26,66,133,0
123,152,288,0,35,67,130,0
124,121,284,0,34,69,155,0
125,117,276,0,31,69,150,0
126,115,283,0,25,61,150,1
127,112,277,0,23,65,110,0
128,94,267,0,30,62,120,1
129,109,272,0,35,66,154,0
130,132,225,0,28,67,148,0
131,117,278,0,25,62,103,0
132,101,266,0,20,67,110,1
133,112,294,0,25,64,125,1
134,128,283,0,24,60,100,0
135,128,279,0,25,66,147,1
136,117,258,0,31,64,120,0
137,134,278,0,24,69,135,0
138,127,284,0,28,65,145,0
139,93,269,0,21,65,104,1
140,122,275,0,27,65,165,0
141,100,265,0,39,62,107,1
142,147,293,0,32,65,123,0
143,120,299,0,25,65,110,0
144,144,277,0,30,63,127,0
145,105,268,0,32,61,115,1
146,136,276,0,23,66,155,0
147,102,262,0,24,63,125,0
148,160,300,0,29,71,175,1
149,113,275,0,24,68,140,1
150,126,282,0,38,66,250,0
151,126,271,0,29,68,148,0
152,115,278,0,29,61,128,0
153,127,336,0,29,NA,NA,0
154,119,284,0,20,66,132,0
155,129,NA,0,23,NA,NA,1
156,123,318,0,21,64,152,0
157,118,282,0,22,68,135,1
158,133,287,0,24,60,104,1
159,105,281,0,39,61,NA,0
160,134,290,0,22,60,121,0
161,144,288,0,21,67,111,0
162,111,273,0,43,62,138,0
163,125,262,0,36,66,190,0
164,135,296,0,30,63,123,0
165,134,289,0,22,63,125,0
166,116,289,0,22,65,160,1
167,129,291,0,29,69,123,0
168,113,301,0,26,67,105,1
169,131,295,0,23,65,123,1
170,126,293,0,29,59,110,NA
171,121,272,0,22,62,109,0
172,121,271,0,25,68,118,1
173,138,287,0,24,65,115,0
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1117,120,280,0,30,60,115,0
1118,121,281,0,29,63,108,0
1119,113,282,0,30,64,118,1
1120,117,270,0,23,58,115,0
1121,158,267,0,35,64,125,0
1122,128,277,0,39,61,120,0
1123,158,289,0,30,66,140,0
1124,133,289,0,22,65,123,1
1125,163,298,0,37,61,98,0
1126,128,282,1,19,66,118,0
1127,126,271,1,21,60,105,0
1128,127,283,0,42,62,154,1
1129,134,287,0,40,63,118,0
1130,140,274,0,41,63,122,0
1131,102,285,0,29,63,117,1
1132,100,252,0,24,61,150,0
1133,120,295,0,29,59,100,1
1134,98,279,1,18,65,115,1
1135,130,246,0,19,62,118,0
1136,104,280,0,41,63,118,1
1137,122,285,0,31,62,102,1
1138,137,276,1,25,64,127,0
1139,114,285,1,20,61,104,0
1140,63,236,1,24,58,99,0
1141,98,318,0,23,63,107,0
1142,99,268,0,32,63,124,1
1143,89,238,1,26,64,136,0
1144,117,283,0,22,65,142,1
1145,143,281,0,29,67,132,0
1146,106,279,0,29,63,125,1
1147,99,246,0,35,62,106,0
1148,156,300,0,27,65,120,1
1149,72,266,1,25,66,200,1
1150,75,266,0,37,61,113,1
1151,97,285,0,35,61,112,1
1152,106,264,0,41,64,114,0
1153,91,225,0,18,68,117,1
1154,117,269,1,28,61,99,0
1155,117,284,0,25,66,177,1
1156,112,291,0,23,66,145,0
1157,112,270,0,29,61,124,0
1158,141,293,0,28,61,125,0
1159,131,259,0,19,63,134,0
1160,130,290,0,19,65,123,1
1161,132,270,0,26,67,140,0
1162,114,265,0,23,67,130,1
1163,160,291,0,34,64,110,1
1164,106,283,0,24,63,119,0
1165,84,260,1,20,64,104,1
1166,112,268,1,25,59,103,0
1167,139,311,0,37,66,135,0
1168,104,267,0,30,63,180,0
1169,130,294,0,32,63,110,1
1170,71,254,0,19,61,145,1
1171,82,270,0,21,65,150,1
1172,119,280,1,21,64,128,0
1173,123,353,0,26,63,115,0
1174,115,278,0,27,59,95,0
1175,124,289,1,21,67,145,1
1176,138,292,0,25,65,130,1
1177,88,276,0,25,63,103,1
1178,146,305,0,23,NA,NA,0
1179,128,241,1,17,64,126,0
1180,82,274,0,31,64,101,1
1181,100,274,0,24,63,113,0
1182,114,271,0,32,61,130,0
1183,97,269,0,20,65,137,1
1184,126,298,0,24,61,112,0
1185,122,275,1,20,65,127,0
1186,152,295,0,39,62,140,0
1187,116,274,0,21,62,110,1
1188,132,302,0,36,63,145,1
1189,84,260,1,37,66,140,0
1190,119,277,1,18,61,89,1
1191,104,275,0,24,NA,NA,0
1192,106,312,0,24,62,135,1
1193,124,NA,1,39,65,228,0
1194,139,291,0,24,65,160,0
1195,103,273,0,36,65,158,1
1196,112,299,0,24,67,145,1
1197,96,276,0,33,64,127,1
1198,102,281,1,19,67,135,1
1199,120,300,0,34,63,150,1
1200,102,338,0,19,64,170,0
1201,97,255,1,22,63,107,1
1202,113,285,0,22,70,145,0
1203,130,297,0,32,58,130,0
1204,97,260,1,25,63,115,1
1205,116,273,0,31,61,120,0
1206,114,266,0,29,64,113,0
1207,127,242,0,17,61,135,1
1208,87,247,1,18,66,125,1
1209,141,281,0,29,54,156,1
1210,144,283,1,25,66,140,0
1211,116,273,0,33,66,130,1
1212,75,265,0,21,65,103,1
1213,138,286,1,28,68,120,0
1214,99,271,0,39,69,151,0
1215,118,293,0,21,63,103,0
1216,152,267,0,28,NA,119,1
1217,97,266,0,24,62,109,0
1218,146,319,0,28,66,145,0
1219,81,285,0,19,63,150,1
1220,110,321,0,28,66,180,0
1221,135,284,1,19,60,95,0
1222,114,290,1,21,65,120,1
1223,124,288,1,21,64,116,1
1224,115,262,1,23,64,136,1
1225,143,281,0,28,65,135,1
1226,113,287,1,29,70,145,1
1227,109,244,1,21,63,102,1
1228,103,278,0,30,60,87,1
1229,118,276,0,34,64,116,0
1230,127,290,0,27,65,121,0
1231,132,270,0,27,65,126,0
1232,113,275,1,27,60,100,0
1233,128,265,0,24,67,120,0
1234,130,291,0,30,65,150,1
1235,125,281,1,21,65,110,0
1236,117,297,0,38,65,129,0

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_backward/baby_weights_model_select_backward.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

babies <- read.csv("babies.csv")

# mlr for birth weight ----------------------------------------------

m_bwt_mlr <- lm(bwt ~ gestation + parity + age + 
                     height + weight + smoke , data = babies)

round(summary(m_bwt_mlr)$adj.r.squared, 4)

# no gestation ------------------------------------------------------

m_bwt_mlr_no_gestation <- lm(bwt ~ parity + age + 
                               height + weight + smoke , data = babies)

round(summary(m_bwt_mlr_no_gestation)$adj.r.squared, 4)

# no parity ---------------------------------------------------------

m_bwt_mlr_no_parity <- lm(bwt ~ gestation + age + 
                            height + weight + smoke , data = babies)

round(summary(m_bwt_mlr_no_parity)$adj.r.squared, 4)

# no age ------------------------------------------------------------

m_bwt_mlr_no_age <- lm(bwt ~ gestation + parity + 
                         height + weight + smoke , data = babies)

round(summary(m_bwt_mlr_no_age)$adj.r.squared, 4)

# no height ---------------------------------------------------------

m_bwt_mlr_no_height <- lm(bwt ~ gestation + parity + 
                         age + weight + smoke , data = babies)

round(summary(m_bwt_mlr_no_height)$adj.r.squared, 4)

# no weight ---------------------------------------------------------

m_bwt_mlr_no_weight <- lm(bwt ~ gestation + parity + 
                            age + height + smoke , data = babies)

round(summary(m_bwt_mlr_no_weight)$adj.r.squared, 4)

# no smoking --------------------------------------------------------

m_bwt_mlr_no_smoking <- lm(bwt ~ gestation + parity + 
                            age + height + weight , data = babies)

round(summary(m_bwt_mlr_no_smoking)$adj.r.squared, 4)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_forward/babies.csv
================================================
case,bwt,gestation,parity,age,height,weight,smoke
1,120,284,0,27,62,100,0
2,113,282,0,33,64,135,0
3,128,279,0,28,64,115,1
4,123,NA,0,36,69,190,0
5,108,282,0,23,67,125,1
6,136,286,0,25,62,93,0
7,138,244,0,33,62,178,0
8,132,245,0,23,65,140,0
9,120,289,0,25,62,125,0
10,143,299,0,30,66,136,1
11,140,351,0,27,68,120,0
12,144,282,0,32,64,124,1
13,141,279,0,23,63,128,1
14,110,281,0,36,61,99,1
15,114,273,0,30,63,154,0
16,115,285,0,38,63,130,0
17,92,255,0,25,65,125,1
18,115,261,0,33,60,125,1
19,144,261,0,33,68,170,0
20,119,288,0,43,66,142,1
21,105,270,0,22,56,93,0
22,115,274,0,27,67,175,1
23,137,287,0,25,66,145,0
24,122,276,0,30,68,182,0
25,131,294,0,23,65,122,0
26,103,261,0,27,65,112,1
27,146,280,0,26,58,106,0
28,114,266,0,20,65,175,1
29,125,292,0,32,65,125,0
30,114,274,0,28,66,132,1
31,122,270,0,26,61,105,0
32,93,278,0,34,61,146,0
33,130,268,0,30,66,123,0
34,119,275,0,23,60,105,0
35,113,281,0,24,65,120,0
36,134,283,0,22,67,130,0
37,107,279,0,24,63,115,0
38,134,288,0,23,63,92,1
39,122,267,0,27,65,101,1
40,128,282,0,31,65,NA,0
41,129,293,0,30,61,160,0
42,110,278,0,23,63,177,0
43,138,302,0,26,NA,NA,1
44,111,270,0,27,61,119,0
45,87,248,0,37,65,130,1
46,143,274,0,27,63,110,1
47,155,294,0,32,66,150,0
48,110,272,0,25,60,90,0
49,122,275,0,26,66,147,0
50,145,291,0,26,63,119,1
51,115,258,0,26,62,130,0
52,108,283,0,31,65,148,1
53,102,282,0,28,61,110,0
54,143,286,0,31,64,126,0
55,146,267,0,30,67,132,0
56,124,275,0,22,60,130,0
57,124,278,0,26,70,145,1
58,145,257,0,33,65,140,0
59,106,273,0,28,60,116,0
60,75,232,0,33,61,110,0
61,107,273,0,24,61,96,0
62,124,288,0,22,67,118,0
63,122,280,0,23,65,125,1
64,101,245,0,23,63,130,1
65,128,283,0,28,63,125,1
66,104,282,0,36,65,115,1
67,97,246,0,37,63,150,0
68,137,274,0,26,69,137,1
69,103,273,0,31,63,170,1
70,142,276,0,38,63,170,0
71,130,289,0,27,66,130,0
72,156,292,0,26,63,118,0
73,133,284,0,25,66,125,1
74,120,274,0,24,62,120,0
75,91,270,0,24,60,149,1
76,127,274,0,21,62,110,0
77,153,286,0,26,63,107,1
78,121,276,0,39,63,130,0
79,120,277,0,27,63,126,0
80,99,272,0,27,62,103,1
81,149,293,0,35,65,116,0
82,129,280,0,23,64,104,0
83,139,292,0,25,68,135,0
84,114,274,0,33,67,148,1
85,138,287,0,30,66,145,0
86,129,274,0,29,71,NA,1
87,138,294,0,32,65,117,0
88,131,296,0,37,63,143,0
89,125,305,0,22,70,196,1
90,114,NA,0,24,67,113,1
91,128,281,0,33,59,117,0
92,134,268,0,28,62,112,0
93,114,271,0,27,60,104,0
94,92,NA,0,31,67,130,0
95,85,278,0,23,61,103,1
96,135,282,0,22,64,100,0
97,87,255,0,28,61,100,1
98,125,302,0,37,62,162,0
99,128,NA,0,35,62,110,0
100,105,254,0,29,64,137,0
101,120,279,0,27,60,121,1
102,119,274,0,33,64,120,0
103,116,286,0,24,61,NA,0
104,107,280,0,36,65,117,1
105,119,273,0,24,61,108,1
106,133,279,0,37,66,140,0
107,155,287,0,33,66,143,0
108,126,273,0,22,65,150,0
109,129,303,0,27,64,125,0
110,137,274,0,29,65,154,0
111,103,269,0,26,65,NA,1
112,125,302,0,28,65,125,0
113,91,255,0,19,67,136,1
114,134,293,0,21,65,NA,0
115,95,279,0,22,66,145,1
116,118,276,0,29,64,114,0
117,141,278,0,33,66,109,1
118,131,283,0,25,67,215,0
119,121,264,0,32,66,145,0
120,100,243,0,39,65,170,1
121,131,288,0,24,61,103,0
122,118,284,0,26,66,133,0
123,152,288,0,35,67,130,0
124,121,284,0,34,69,155,0
125,117,276,0,31,69,150,0
126,115,283,0,25,61,150,1
127,112,277,0,23,65,110,0
128,94,267,0,30,62,120,1
129,109,272,0,35,66,154,0
130,132,225,0,28,67,148,0
131,117,278,0,25,62,103,0
132,101,266,0,20,67,110,1
133,112,294,0,25,64,125,1
134,128,283,0,24,60,100,0
135,128,279,0,25,66,147,1
136,117,258,0,31,64,120,0
137,134,278,0,24,69,135,0
138,127,284,0,28,65,145,0
139,93,269,0,21,65,104,1
140,122,275,0,27,65,165,0
141,100,265,0,39,62,107,1
142,147,293,0,32,65,123,0
143,120,299,0,25,65,110,0
144,144,277,0,30,63,127,0
145,105,268,0,32,61,115,1
146,136,276,0,23,66,155,0
147,102,262,0,24,63,125,0
148,160,300,0,29,71,175,1
149,113,275,0,24,68,140,1
150,126,282,0,38,66,250,0
151,126,271,0,29,68,148,0
152,115,278,0,29,61,128,0
153,127,336,0,29,NA,NA,0
154,119,284,0,20,66,132,0
155,129,NA,0,23,NA,NA,1
156,123,318,0,21,64,152,0
157,118,282,0,22,68,135,1
158,133,287,0,24,60,104,1
159,105,281,0,39,61,NA,0
160,134,290,0,22,60,121,0
161,144,288,0,21,67,111,0
162,111,273,0,43,62,138,0
163,125,262,0,36,66,190,0
164,135,296,0,30,63,123,0
165,134,289,0,22,63,125,0
166,116,289,0,22,65,160,1
167,129,291,0,29,69,123,0
168,113,301,0,26,67,105,1
169,131,295,0,23,65,123,1
170,126,293,0,29,59,110,NA
171,121,272,0,22,62,109,0
172,121,271,0,25,68,118,1
173,138,287,0,24,65,115,0
174,136,278,0,23,61,105,0
175,120,279,0,30,66,131,0
176,122,278,0,31,72,155,1
177,134,267,0,30,66,170,0
178,101,280,0,25,65,123,1
179,112,288,0,32,62,125,0
180,132,290,0,25,64,120,0
181,136,285,0,23,62,175,0
182,113,277,0,23,65,192,1
183,96,271,0,23,64,116,0
184,124,277,0,29,63,220,0
185,113,306,0,21,62,150,0
186,131,286,0,34,NA,NA,1
187,137,258,0,25,63,117,0
188,133,268,0,24,61,93,0
189,107,244,0,20,58,97,0
190,96,265,0,28,59,135,1
191,142,278,0,35,66,136,1
192,136,275,0,22,63,110,0
193,75,239,0,26,63,124,1
194,125,302,0,32,61,NA,1
195,104,295,0,26,65,155,1
196,130,274,0,30,63,150,0
197,90,290,0,22,63,168,0
198,118,276,0,22,66,147,1
199,123,320,0,22,66,117,0
200,137,291,0,34,61,110,0
201,101,268,0,19,63,140,0
202,142,275,0,25,64,132,0
203,98,282,0,20,63,97,1
204,124,283,0,23,63,112,0
205,151,310,0,21,65,NA,0
206,109,281,0,23,61,105,0
207,150,285,0,22,61,110,1
208,119,282,0,26,68,150,1
209,131,280,0,38,65,125,0
210,101,272,0,29,63,150,1
211,113,246,0,19,62,138,1
212,127,270,0,25,62,150,0
213,97,260,0,23,61,99,1
214,117,282,0,28,64,115,0
215,150,290,0,21,65,125,0
216,85,234,0,33,67,130,0
217,128,288,0,27,70,145,0
218,105,233,0,34,61,130,0
219,90,269,0,26,67,125,NA
220,115,274,0,22,65,130,1
221,107,290,0,28,62,135,0
222,121,275,0,24,63,121,1
223,119,286,0,20,64,180,0
224,117,275,0,20,64,145,1
225,134,264,0,26,68,136,0
226,117,288,0,35,65,142,0
227,115,268,0,28,66,128,0
228,110,254,0,23,63,120,1
229,130,282,0,21,62,106,1
230,140,274,0,23,63,106,1
231,111,284,0,22,NA,NA,1
232,93,249,0,33,66,117,0
233,154,292,0,42,65,116,1
234,125,290,0,19,64,127,0
235,93,318,0,31,66,135,0
236,122,277,0,33,63,135,1
237,129,267,0,22,63,160,0
238,126,276,0,23,63,120,0
239,85,274,0,24,68,155,0
240,173,293,0,30,63,110,0
241,144,329,0,22,65,190,1
242,114,278,0,25,65,140,1
243,111,NA,0,27,63,105,1
244,154,287,0,27,65,125,1
245,150,274,0,25,67,117,1
246,111,278,0,21,62,125,0
247,126,277,0,32,66,128,0
248,122,261,0,28,65,124,0
249,141,282,0,24,68,169,0
250,142,274,0,24,63,125,0
251,99,262,0,38,59,110,1
252,113,286,0,23,63,105,0
253,149,282,0,21,61,110,0
254,117,328,0,29,65,125,1
255,130,274,0,26,64,185,NA
256,106,275,0,31,65,142,NA
257,128,290,0,22,64,118,0
258,125,286,0,21,64,139,0
259,114,290,0,30,66,160,0
260,130,285,0,23,63,128,1
261,116,148,0,28,66,135,0
262,81,256,0,30,64,148,1
263,124,287,0,27,62,105,1
264,125,292,0,22,65,122,0
265,110,262,0,25,66,140,0
266,125,279,0,23,63,104,1
267,138,294,0,40,64,125,0
268,142,284,0,39,66,132,0
269,115,278,0,23,60,102,1
270,102,280,0,38,67,140,0
271,140,294,0,25,61,103,0
272,133,276,1,22,63,119,0
273,127,290,0,35,66,165,0
274,104,274,1,20,62,115,1
275,119,275,0,42,67,156,1
276,152,301,0,29,65,150,0
277,123,284,1,20,65,120,1
278,143,273,0,19,66,135,0
279,131,308,0,40,65,160,0
280,141,319,1,20,67,140,1
281,129,277,0,30,66,142,1
282,113,282,1,36,59,140,0
283,119,292,0,33,62,118,1
284,109,295,1,23,63,103,1
285,104,280,1,27,68,146,1
286,131,282,1,21,66,126,0
287,110,293,1,28,64,135,1
288,148,279,0,27,71,189,0
289,137,283,1,20,65,157,0
290,117,283,0,27,63,108,0
291,115,302,1,22,67,135,0
292,98,280,0,35,64,122,1
293,136,303,1,20,68,148,1
294,121,276,1,23,71,152,1
295,132,285,1,25,63,140,0
296,91,264,0,36,60,100,1
297,119,294,0,34,59,105,0
298,85,273,0,26,60,105,1
299,106,271,1,26,61,110,1
300,132,284,0,29,64,122,0
301,80,266,1,25,62,125,0
302,109,286,0,24,64,125,1
303,111,306,0,27,61,102,0
304,143,292,1,21,65,125,0
305,136,290,0,26,66,135,0
306,110,285,1,19,64,130,0
307,98,257,0,29,66,130,1
308,108,305,1,24,65,112,0
309,101,295,0,18,62,145,1
310,71,281,0,32,60,117,1
311,124,292,0,29,68,176,1
312,93,256,0,34,66,NA,1
313,106,276,0,30,66,130,0
314,101,278,0,25,62,112,1
315,100,277,0,31,62,100,1
316,104,269,0,35,63,110,1
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851,112,267,1,22,62,138,0
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857,126,282,0,33,62,117,0
858,109,291,0,39,64,107,0
859,136,291,0,41,66,191,0
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861,103,267,1,21,66,150,1
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873,164,286,1,32,66,143,0
874,133,297,0,36,61,125,0
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901,111,281,1,27,64,112,0
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909,90,266,0,23,61,99,1
910,99,273,1,27,59,115,0
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912,138,280,1,30,65,175,0
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918,128,279,0,27,66,135,0
919,142,284,1,31,66,137,1
920,115,268,1,31,64,125,0
921,108,274,0,28,66,175,NA
922,108,283,0,35,62,108,0
923,139,281,0,27,63,137,0
924,115,275,0,25,61,155,1
925,136,288,0,23,62,217,0
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993,119,293,1,23,65,127,0
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995,125,283,0,37,63,145,1
996,119,259,0,37,62,130,0
997,101,273,0,39,60,113,0
998,105,277,1,25,64,156,0
999,110,281,0,27,60,110,0
1000,100,270,1,21,65,132,1
1001,98,284,0,29,68,140,0
1002,127,276,0,37,64,159,0
1003,117,324,0,22,62,164,1
1004,122,278,0,37,68,114,0
1005,122,273,1,23,64,130,1
1006,118,281,1,36,66,140,1
1007,137,303,1,23,66,127,1
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1012,110,277,0,36,61,116,0
1013,105,276,0,20,62,112,1
1014,133,274,0,30,63,NA,0
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1022,169,296,0,33,67,185,0
1023,94,271,0,36,61,130,1
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1027,143,313,0,20,68,150,0
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1103,126,272,1,35,61,120,1
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1123,158,289,0,30,66,140,0
1124,133,289,0,22,65,123,1
1125,163,298,0,37,61,98,0
1126,128,282,1,19,66,118,0
1127,126,271,1,21,60,105,0
1128,127,283,0,42,62,154,1
1129,134,287,0,40,63,118,0
1130,140,274,0,41,63,122,0
1131,102,285,0,29,63,117,1
1132,100,252,0,24,61,150,0
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1136,104,280,0,41,63,118,1
1137,122,285,0,31,62,102,1
1138,137,276,1,25,64,127,0
1139,114,285,1,20,61,104,0
1140,63,236,1,24,58,99,0
1141,98,318,0,23,63,107,0
1142,99,268,0,32,63,124,1
1143,89,238,1,26,64,136,0
1144,117,283,0,22,65,142,1
1145,143,281,0,29,67,132,0
1146,106,279,0,29,63,125,1
1147,99,246,0,35,62,106,0
1148,156,300,0,27,65,120,1
1149,72,266,1,25,66,200,1
1150,75,266,0,37,61,113,1
1151,97,285,0,35,61,112,1
1152,106,264,0,41,64,114,0
1153,91,225,0,18,68,117,1
1154,117,269,1,28,61,99,0
1155,117,284,0,25,66,177,1
1156,112,291,0,23,66,145,0
1157,112,270,0,29,61,124,0
1158,141,293,0,28,61,125,0
1159,131,259,0,19,63,134,0
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1163,160,291,0,34,64,110,1
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1175,124,289,1,21,67,145,1
1176,138,292,0,25,65,130,1
1177,88,276,0,25,63,103,1
1178,146,305,0,23,NA,NA,0
1179,128,241,1,17,64,126,0
1180,82,274,0,31,64,101,1
1181,100,274,0,24,63,113,0
1182,114,271,0,32,61,130,0
1183,97,269,0,20,65,137,1
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1185,122,275,1,20,65,127,0
1186,152,295,0,39,62,140,0
1187,116,274,0,21,62,110,1
1188,132,302,0,36,63,145,1
1189,84,260,1,37,66,140,0
1190,119,277,1,18,61,89,1
1191,104,275,0,24,NA,NA,0
1192,106,312,0,24,62,135,1
1193,124,NA,1,39,65,228,0
1194,139,291,0,24,65,160,0
1195,103,273,0,36,65,158,1
1196,112,299,0,24,67,145,1
1197,96,276,0,33,64,127,1
1198,102,281,1,19,67,135,1
1199,120,300,0,34,63,150,1
1200,102,338,0,19,64,170,0
1201,97,255,1,22,63,107,1
1202,113,285,0,22,70,145,0
1203,130,297,0,32,58,130,0
1204,97,260,1,25,63,115,1
1205,116,273,0,31,61,120,0
1206,114,266,0,29,64,113,0
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1208,87,247,1,18,66,125,1
1209,141,281,0,29,54,156,1
1210,144,283,1,25,66,140,0
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1212,75,265,0,21,65,103,1
1213,138,286,1,28,68,120,0
1214,99,271,0,39,69,151,0
1215,118,293,0,21,63,103,0
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1217,97,266,0,24,62,109,0
1218,146,319,0,28,66,145,0
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1226,113,287,1,29,70,145,1
1227,109,244,1,21,63,102,1
1228,103,278,0,30,60,87,1
1229,118,276,0,34,64,116,0
1230,127,290,0,27,65,121,0
1231,132,270,0,27,65,126,0
1232,113,275,1,27,60,100,0
1233,128,265,0,24,67,120,0
1234,130,291,0,30,65,150,1
1235,125,281,1,21,65,110,0
1236,117,297,0,38,65,129,0

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_forward/baby_weights_model_select_backward.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

babies <- read.csv("babies.csv")

# birth weight vs. gestation ----------------------------------------

m_bwt_gestation <- lm(bwt ~ gestation, data = babies)

round(summary(m_bwt_gestation)$coefficients[2,4], 4) # p-val
round(summary(m_bwt_gestation)$adj.r.squared, 4) # adj r-sq

# birth weight vs. parity ----------------------------------------

m_bwt_parity <- lm(bwt ~ parity, data = babies)

round(summary(m_bwt_parity)$coefficients[2,4], 4) # p-val
round(summary(m_bwt_parity)$adj.r.squared, 4) # adj r-sq

# birth weight vs. age --------------------------------------------

m_bwt_age <- lm(bwt ~ age, data = babies)

round(summary(m_bwt_age)$coefficients[2,4], 4) # p-val
round(summary(m_bwt_age)$adj.r.squared, 4) # adj r-sq

# birth weight vs. height ------------------------------------------

m_bwt_height <- lm(bwt ~ height, data = babies)

round(summary(m_bwt_height)$coefficients[2,4], 4) # p-val
round(summary(m_bwt_height)$adj.r.squared, 4) # adj r-sq

# birth weight vs. weight ------------------------------------------

m_bwt_weight <- lm(bwt ~ weight, data = babies)

round(summary(m_bwt_weight)$coefficients[2,4], 4) # p-val
round(summary(m_bwt_weight)$adj.r.squared, 4) # adj r-sq

# birth weight vs. smoke ------------------------------------------

m_bwt_smoke <- lm(bwt ~ smoke, data = babies)

round(summary(m_bwt_smoke)$coefficients[2,4], 4) # p-val
round(summary(m_bwt_smoke)$adj.r.squared, 4) # adj r-sq

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_parity/babies.csv
================================================
case,bwt,gestation,parity,age,height,weight,smoke
1,120,284,0,27,62,100,0
2,113,282,0,33,64,135,0
3,128,279,0,28,64,115,1
4,123,NA,0,36,69,190,0
5,108,282,0,23,67,125,1
6,136,286,0,25,62,93,0
7,138,244,0,33,62,178,0
8,132,245,0,23,65,140,0
9,120,289,0,25,62,125,0
10,143,299,0,30,66,136,1
11,140,351,0,27,68,120,0
12,144,282,0,32,64,124,1
13,141,279,0,23,63,128,1
14,110,281,0,36,61,99,1
15,114,273,0,30,63,154,0
16,115,285,0,38,63,130,0
17,92,255,0,25,65,125,1
18,115,261,0,33,60,125,1
19,144,261,0,33,68,170,0
20,119,288,0,43,66,142,1
21,105,270,0,22,56,93,0
22,115,274,0,27,67,175,1
23,137,287,0,25,66,145,0
24,122,276,0,30,68,182,0
25,131,294,0,23,65,122,0
26,103,261,0,27,65,112,1
27,146,280,0,26,58,106,0
28,114,266,0,20,65,175,1
29,125,292,0,32,65,125,0
30,114,274,0,28,66,132,1
31,122,270,0,26,61,105,0
32,93,278,0,34,61,146,0
33,130,268,0,30,66,123,0
34,119,275,0,23,60,105,0
35,113,281,0,24,65,120,0
36,134,283,0,22,67,130,0
37,107,279,0,24,63,115,0
38,134,288,0,23,63,92,1
39,122,267,0,27,65,101,1
40,128,282,0,31,65,NA,0
41,129,293,0,30,61,160,0
42,110,278,0,23,63,177,0
43,138,302,0,26,NA,NA,1
44,111,270,0,27,61,119,0
45,87,248,0,37,65,130,1
46,143,274,0,27,63,110,1
47,155,294,0,32,66,150,0
48,110,272,0,25,60,90,0
49,122,275,0,26,66,147,0
50,145,291,0,26,63,119,1
51,115,258,0,26,62,130,0
52,108,283,0,31,65,148,1
53,102,282,0,28,61,110,0
54,143,286,0,31,64,126,0
55,146,267,0,30,67,132,0
56,124,275,0,22,60,130,0
57,124,278,0,26,70,145,1
58,145,257,0,33,65,140,0
59,106,273,0,28,60,116,0
60,75,232,0,33,61,110,0
61,107,273,0,24,61,96,0
62,124,288,0,22,67,118,0
63,122,280,0,23,65,125,1
64,101,245,0,23,63,130,1
65,128,283,0,28,63,125,1
66,104,282,0,36,65,115,1
67,97,246,0,37,63,150,0
68,137,274,0,26,69,137,1
69,103,273,0,31,63,170,1
70,142,276,0,38,63,170,0
71,130,289,0,27,66,130,0
72,156,292,0,26,63,118,0
73,133,284,0,25,66,125,1
74,120,274,0,24,62,120,0
75,91,270,0,24,60,149,1
76,127,274,0,21,62,110,0
77,153,286,0,26,63,107,1
78,121,276,0,39,63,130,0
79,120,277,0,27,63,126,0
80,99,272,0,27,62,103,1
81,149,293,0,35,65,116,0
82,129,280,0,23,64,104,0
83,139,292,0,25,68,135,0
84,114,274,0,33,67,148,1
85,138,287,0,30,66,145,0
86,129,274,0,29,71,NA,1
87,138,294,0,32,65,117,0
88,131,296,0,37,63,143,0
89,125,305,0,22,70,196,1
90,114,NA,0,24,67,113,1
91,128,281,0,33,59,117,0
92,134,268,0,28,62,112,0
93,114,271,0,27,60,104,0
94,92,NA,0,31,67,130,0
95,85,278,0,23,61,103,1
96,135,282,0,22,64,100,0
97,87,255,0,28,61,100,1
98,125,302,0,37,62,162,0
99,128,NA,0,35,62,110,0
100,105,254,0,29,64,137,0
101,120,279,0,27,60,121,1
102,119,274,0,33,64,120,0
103,116,286,0,24,61,NA,0
104,107,280,0,36,65,117,1
105,119,273,0,24,61,108,1
106,133,279,0,37,66,140,0
107,155,287,0,33,66,143,0
108,126,273,0,22,65,150,0
109,129,303,0,27,64,125,0
110,137,274,0,29,65,154,0
111,103,269,0,26,65,NA,1
112,125,302,0,28,65,125,0
113,91,255,0,19,67,136,1
114,134,293,0,21,65,NA,0
115,95,279,0,22,66,145,1
116,118,276,0,29,64,114,0
117,141,278,0,33,66,109,1
118,131,283,0,25,67,215,0
119,121,264,0,32,66,145,0
120,100,243,0,39,65,170,1
121,131,288,0,24,61,103,0
122,118,284,0,26,66,133,0
123,152,288,0,35,67,130,0
124,121,284,0,34,69,155,0
125,117,276,0,31,69,150,0
126,115,283,0,25,61,150,1
127,112,277,0,23,65,110,0
128,94,267,0,30,62,120,1
129,109,272,0,35,66,154,0
130,132,225,0,28,67,148,0
131,117,278,0,25,62,103,0
132,101,266,0,20,67,110,1
133,112,294,0,25,64,125,1
134,128,283,0,24,60,100,0
135,128,279,0,25,66,147,1
136,117,258,0,31,64,120,0
137,134,278,0,24,69,135,0
138,127,284,0,28,65,145,0
139,93,269,0,21,65,104,1
140,122,275,0,27,65,165,0
141,100,265,0,39,62,107,1
142,147,293,0,32,65,123,0
143,120,299,0,25,65,110,0
144,144,277,0,30,63,127,0
145,105,268,0,32,61,115,1
146,136,276,0,23,66,155,0
147,102,262,0,24,63,125,0
148,160,300,0,29,71,175,1
149,113,275,0,24,68,140,1
150,126,282,0,38,66,250,0
151,126,271,0,29,68,148,0
152,115,278,0,29,61,128,0
153,127,336,0,29,NA,NA,0
154,119,284,0,20,66,132,0
155,129,NA,0,23,NA,NA,1
156,123,318,0,21,64,152,0
157,118,282,0,22,68,135,1
158,133,287,0,24,60,104,1
159,105,281,0,39,61,NA,0
160,134,290,0,22,60,121,0
161,144,288,0,21,67,111,0
162,111,273,0,43,62,138,0
163,125,262,0,36,66,190,0
164,135,296,0,30,63,123,0
165,134,289,0,22,63,125,0
166,116,289,0,22,65,160,1
167,129,291,0,29,69,123,0
168,113,301,0,26,67,105,1
169,131,295,0,23,65,123,1
170,126,293,0,29,59,110,NA
171,121,272,0,22,62,109,0
172,121,271,0,25,68,118,1
173,138,287,0,24,65,115,0
174,136,278,0,23,61,105,0
175,120,279,0,30,66,131,0
176,122,278,0,31,72,155,1
177,134,267,0,30,66,170,0
178,101,280,0,25,65,123,1
179,112,288,0,32,62,125,0
180,132,290,0,25,64,120,0
181,136,285,0,23,62,175,0
182,113,277,0,23,65,192,1
183,96,271,0,23,64,116,0
184,124,277,0,29,63,220,0
185,113,306,0,21,62,150,0
186,131,286,0,34,NA,NA,1
187,137,258,0,25,63,117,0
188,133,268,0,24,61,93,0
189,107,244,0,20,58,97,0
190,96,265,0,28,59,135,1
191,142,278,0,35,66,136,1
192,136,275,0,22,63,110,0
193,75,239,0,26,63,124,1
194,125,302,0,32,61,NA,1
195,104,295,0,26,65,155,1
196,130,274,0,30,63,150,0
197,90,290,0,22,63,168,0
198,118,276,0,22,66,147,1
199,123,320,0,22,66,117,0
200,137,291,0,34,61,110,0
201,101,268,0,19,63,140,0
202,142,275,0,25,64,132,0
203,98,282,0,20,63,97,1
204,124,283,0,23,63,112,0
205,151,310,0,21,65,NA,0
206,109,281,0,23,61,105,0
207,150,285,0,22,61,110,1
208,119,282,0,26,68,150,1
209,131,280,0,38,65,125,0
210,101,272,0,29,63,150,1
211,113,246,0,19,62,138,1
212,127,270,0,25,62,150,0
213,97,260,0,23,61,99,1
214,117,282,0,28,64,115,0
215,150,290,0,21,65,125,0
216,85,234,0,33,67,130,0
217,128,288,0,27,70,145,0
218,105,233,0,34,61,130,0
219,90,269,0,26,67,125,NA
220,115,274,0,22,65,130,1
221,107,290,0,28,62,135,0
222,121,275,0,24,63,121,1
223,119,286,0,20,64,180,0
224,117,275,0,20,64,145,1
225,134,264,0,26,68,136,0
226,117,288,0,35,65,142,0
227,115,268,0,28,66,128,0
228,110,254,0,23,63,120,1
229,130,282,0,21,62,106,1
230,140,274,0,23,63,106,1
231,111,284,0,22,NA,NA,1
232,93,249,0,33,66,117,0
233,154,292,0,42,65,116,1
234,125,290,0,19,64,127,0
235,93,318,0,31,66,135,0
236,122,277,0,33,63,135,1
237,129,267,0,22,63,160,0
238,126,276,0,23,63,120,0
239,85,274,0,24,68,155,0
240,173,293,0,30,63,110,0
241,144,329,0,22,65,190,1
242,114,278,0,25,65,140,1
243,111,NA,0,27,63,105,1
244,154,287,0,27,65,125,1
245,150,274,0,25,67,117,1
246,111,278,0,21,62,125,0
247,126,277,0,32,66,128,0
248,122,261,0,28,65,124,0
249,141,282,0,24,68,169,0
250,142,274,0,24,63,125,0
251,99,262,0,38,59,110,1
252,113,286,0,23,63,105,0
253,149,282,0,21,61,110,0
254,117,328,0,29,65,125,1
255,130,274,0,26,64,185,NA
256,106,275,0,31,65,142,NA
257,128,290,0,22,64,118,0
258,125,286,0,21,64,139,0
259,114,290,0,30,66,160,0
260,130,285,0,23,63,128,1
261,116,148,0,28,66,135,0
262,81,256,0,30,64,148,1
263,124,287,0,27,62,105,1
264,125,292,0,22,65,122,0
265,110,262,0,25,66,140,0
266,125,279,0,23,63,104,1
267,138,294,0,40,64,125,0
268,142,284,0,39,66,132,0
269,115,278,0,23,60,102,1
270,102,280,0,38,67,140,0
271,140,294,0,25,61,103,0
272,133,276,1,22,63,119,0
273,127,290,0,35,66,165,0
274,104,274,1,20,62,115,1
275,119,275,0,42,67,156,1
276,152,301,0,29,65,150,0
277,123,284,1,20,65,120,1
278,143,273,0,19,66,135,0
279,131,308,0,40,65,160,0
280,141,319,1,20,67,140,1
281,129,277,0,30,66,142,1
282,113,282,1,36,59,140,0
283,119,292,0,33,62,118,1
284,109,295,1,23,63,103,1
285,104,280,1,27,68,146,1
286,131,282,1,21,66,126,0
287,110,293,1,28,64,135,1
288,148,279,0,27,71,189,0
289,137,283,1,20,65,157,0
290,117,283,0,27,63,108,0
291,115,302,1,22,67,135,0
292,98,280,0,35,64,122,1
293,136,303,1,20,68,148,1
294,121,276,1,23,71,152,1
295,132,285,1,25,63,140,0
296,91,264,0,36,60,100,1
297,119,294,0,34,59,105,0
298,85,273,0,26,60,105,1
299,106,271,1,26,61,110,1
300,132,284,0,29,64,122,0
301,80,266,1,25,62,125,0
302,109,286,0,24,64,125,1
303,111,306,0,27,61,102,0
304,143,292,1,21,65,125,0
305,136,290,0,26,66,135,0
306,110,285,1,19,64,130,0
307,98,257,0,29,66,130,1
308,108,305,1,24,65,112,0
309,101,295,0,18,62,145,1
310,71,281,0,32,60,117,1
311,124,292,0,29,68,176,1
312,93,256,0,34,66,NA,1
313,106,276,0,30,66,130,0
314,101,278,0,25,62,112,1
315,100,277,0,31,62,100,1
316,104,269,0,35,63,110,1
317,117,270,0,24,67,135,1
318,117,267,0,29,65,120,1
319,149,279,0,25,67,135,0
320,135,284,0,25,66,123,0
321,110,283,1,21,66,129,0
322,121,276,0,31,67,130,0
323,142,285,1,24,66,136,0
324,104,260,0,33,64,145,0
325,138,296,0,34,66,120,0
326,112,278,1,21,63,120,0
327,117,293,0,39,60,120,1
328,109,282,0,25,62,106,1
329,131,266,1,28,67,135,0
330,120,273,0,29,64,130,1
331,116,270,0,29,63,132,0
332,140,290,0,23,65,110,0
333,103,273,1,22,64,110,1
334,120,279,1,23,67,135,0
335,139,260,1,32,64,127,0
336,123,254,0,26,62,130,1
337,104,280,1,23,64,107,1
338,131,283,0,31,NA,NA,0
339,111,270,0,22,59,103,0
340,122,277,0,32,63,157,1
341,116,271,1,30,67,144,1
342,129,277,0,27,68,130,1
343,133,292,0,30,65,112,1
344,110,277,0,25,61,130,0
345,105,276,0,22,67,130,0
346,93,246,0,37,65,130,0
347,122,281,0,42,63,103,1
348,133,293,0,23,64,110,1
349,130,296,1,22,66,117,1
350,104,307,0,24,59,122,0
351,106,278,0,31,65,110,1
352,120,281,0,33,63,113,0
353,121,284,0,27,63,NA,1
354,118,276,1,18,63,128,0
355,140,290,1,19,67,132,1
356,114,268,0,22,64,104,0
357,116,280,0,40,62,159,0
358,129,284,0,24,64,115,0
359,120,286,0,22,62,115,1
360,127,281,0,24,63,112,1
361,107,278,1,27,NA,135,0
362,71,234,0,32,64,110,1
363,88,274,0,30,66,130,0
364,107,300,0,19,NA,NA,1
365,122,286,0,23,64,145,0
366,106,302,1,19,66,147,0
367,135,285,0,30,66,130,0
368,107,290,0,26,63,112,0
369,129,294,0,32,62,170,1
370,126,274,0,39,62,122,0
371,116,293,1,26,64,125,0
372,124,294,0,26,62,122,0
373,123,281,0,23,68,136,0
374,145,315,0,39,67,143,1
375,102,278,0,27,67,135,1
376,129,293,0,30,65,130,1
377,98,276,1,22,61,121,0
378,110,272,0,28,60,108,0
379,135,282,0,24,67,128,1
380,101,278,1,20,62,105,0
381,96,266,0,26,65,125,0
382,104,276,1,18,60,109,1
383,100,249,0,24,67,100,0
384,154,292,0,40,66,145,0
385,127,293,0,31,67,137,0
386,126,288,0,31,62,150,0
387,126,282,1,23,66,115,1
388,127,279,0,26,67,155,1
389,98,275,0,25,65,112,1
390,127,288,1,21,66,130,0
391,129,299,0,22,68,145,0
392,131,292,1,22,64,124,1
393,132,289,1,19,66,145,0
394,127,280,0,27,62,118,0
395,99,313,1,34,59,100,1
396,115,290,0,30,64,140,1
397,145,290,1,24,67,125,0
398,102,249,1,23,67,134,1
399,136,299,0,29,64,115,0
400,121,286,1,NA,NA,NA,0
401,121,282,0,22,66,133,0
402,120,286,0,25,62,105,0
403,118,261,0,26,60,104,0
404,127,304,1,26,62,105,0
405,132,281,1,24,63,117,0
406,102,258,1,22,65,135,0
407,143,279,0,39,65,129,1
408,118,277,0,25,62,120,0
409,102,286,1,22,64,140,0
410,163,280,0,35,69,139,0
411,132,294,0,32,64,116,0
412,116,276,0,33,61,180,0
413,138,288,1,19,66,124,0
414,139,279,0,20,64,143,0
415,132,298,1,23,61,137,0
416,87,282,0,27,63,104,1
417,131,297,0,30,67,132,0
418,130,282,0,26,67,147,1
419,123,290,0,28,66,107,1
420,115,276,1,18,63,110,0
421,116,272,0,27,64,130,1
422,119,286,1,20,67,130,0
423,125,279,1,19,67,135,0
424,144,282,0,33,66,155,1
425,123,269,0,26,67,132,0
426,120,276,0,23,66,114,0
427,140,251,0,28,63,210,0
428,120,271,1,17,64,142,1
429,116,272,0,NA,63,138,1
430,120,289,1,31,59,102,0
431,146,280,0,23,61,145,0
432,112,283,1,21,62,102,1
433,115,269,0,30,62,115,NA
434,132,278,0,20,64,150,1
435,146,263,0,39,53,110,1
436,122,275,0,30,68,140,0
437,128,292,0,32,66,130,0
438,119,277,0,24,63,120,1
439,135,278,0,27,66,148,0
440,116,315,0,26,NA,NA,0
441,129,235,0,24,66,135,0
442,116,293,1,28,62,108,0
443,100,275,0,27,64,111,1
444,118,280,0,27,NA,NA,1
445,138,257,0,38,67,138,0
446,123,282,0,22,65,130,0
447,113,288,1,21,61,120,0
448,129,280,1,24,65,140,1
449,122,280,0,24,67,127,1
450,132,281,1,21,67,140,0
451,120,269,1,40,63,130,0
452,114,283,1,20,65,115,0
453,130,280,0,29,66,135,0
454,117,286,0,32,66,127,1
455,142,285,0,33,63,124,0
456,144,273,0,27,62,118,1
457,127,262,1,32,64,125,0
458,115,270,0,25,67,165,1
459,85,258,0,41,67,137,0
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932,94,268,0,30,62,105,1
933,158,295,1,37,70,137,0
934,112,275,1,21,68,143,1
935,119,286,0,26,64,123,1
936,97,279,0,29,68,178,1
937,99,252,0,21,64,120,0
938,115,264,1,23,67,134,1
939,139,284,0,37,61,121,0
940,144,304,1,27,58,102,1
941,99,270,0,22,63,115,1
942,105,280,1,22,63,116,0
943,89,275,0,34,66,170,0
944,129,270,0,43,67,160,0
945,119,270,1,20,64,109,0
946,114,291,0,35,60,112,0
947,106,289,0,28,67,120,1
948,122,292,1,34,65,133,0
949,136,261,0,24,65,110,0
950,121,286,1,22,69,130,1
951,112,282,0,26,65,122,0
952,112,266,0,26,64,122,0
953,123,314,0,22,61,121,1
954,139,286,0,33,65,125,1
955,125,290,0,36,59,105,0
956,105,295,1,20,64,112,1
957,130,276,0,41,68,130,0
958,146,294,0,22,66,145,1
959,133,290,0,21,64,145,0
960,147,296,1,19,67,124,0
961,109,269,0,23,63,113,0
962,122,286,0,23,64,120,1
963,135,260,0,43,65,135,0
964,107,NA,0,19,60,118,0
965,117,272,0,32,66,118,0
966,138,284,0,30,66,133,1
967,120,283,0,28,64,122,1
968,119,273,0,35,65,125,1
969,118,278,1,19,62,126,0
970,105,330,0,23,64,112,1
971,113,306,1,21,65,137,0
972,136,NA,0,36,66,135,0
973,148,291,1,21,63,115,0
974,140,281,1,22,69,135,0
975,134,287,1,33,67,131,0
976,120,280,0,31,61,111,0
977,123,296,1,26,64,110,1
978,102,275,0,43,64,160,0
979,55,204,0,35,65,140,0
980,103,276,1,19,63,149,1
981,123,283,0,21,65,110,0
982,105,270,1,27,65,134,1
983,138,289,0,33,65,155,0
984,128,281,0,28,63,150,0
985,139,285,0,30,65,129,1
986,104,288,1,27,61,122,1
987,159,296,1,27,64,112,0
988,118,276,0,29,62,130,1
989,99,285,0,25,69,128,1
990,144,281,0,20,63,120,0
991,121,270,0,25,62,108,1
992,117,265,1,24,66,98,0
993,119,293,1,23,65,127,0
994,105,281,1,19,61,130,0
995,125,283,0,37,63,145,1
996,119,259,0,37,62,130,0
997,101,273,0,39,60,113,0
998,105,277,1,25,64,156,0
999,110,281,0,27,60,110,0
1000,100,270,1,21,65,132,1
1001,98,284,0,29,68,140,0
1002,127,276,0,37,64,159,0
1003,117,324,0,22,62,164,1
1004,122,278,0,37,68,114,0
1005,122,273,1,23,64,130,1
1006,118,281,1,36,66,140,1
1007,137,303,1,23,66,127,1
1008,120,275,0,32,63,115,1
1009,143,285,0,27,68,185,0
1010,108,270,0,29,67,124,1
1011,131,284,1,19,61,114,1
1012,110,277,0,36,61,116,0
1013,105,276,0,20,62,112,1
1014,133,274,0,30,63,NA,0
1015,125,255,0,23,63,133,0
1016,78,258,1,24,66,115,1
1017,114,289,0,36,60,115,0
1018,111,278,0,29,65,145,1
1019,103,250,0,40,59,140,0
1020,114,276,0,26,62,127,0
1021,75,247,0,36,64,120,1
1022,169,296,0,33,67,185,0
1023,94,271,0,36,61,130,1
1024,150,287,0,36,62,135,0
1025,144,248,0,30,70,145,0
1026,144,291,0,28,67,130,0
1027,143,313,0,20,68,150,0
1028,145,304,1,25,63,109,1
1029,121,285,0,34,64,110,0
1030,105,256,0,31,66,142,0
1031,134,286,0,25,64,125,0
1032,129,294,1,21,65,132,0
1033,114,276,0,24,63,110,0
1034,97,265,0,30,61,110,0
1035,160,292,0,28,64,120,0
1036,65,237,0,31,67,130,0
1037,145,288,0,28,64,116,0
1038,95,273,0,23,60,90,0
1039,139,293,1,21,69,130,0
1040,123,288,0,27,63,125,0
1041,109,283,0,23,65,112,1
1042,110,268,0,34,64,127,0
1043,122,296,1,24,65,132,0
1044,115,307,0,34,65,128,1
1045,117,323,0,26,62,NA,0
1046,108,279,1,19,64,115,0
1047,120,287,0,23,67,116,1
1048,131,269,0,36,68,145,0
1049,136,283,1,24,63,119,0
1050,125,290,0,32,63,135,0
1051,96,285,1,20,66,117,1
1052,102,282,1,29,65,125,1
1053,102,288,1,18,65,117,0
1054,112,277,1,22,67,120,0
1055,135,272,0,30,65,130,0
1056,91,266,0,23,60,120,1
1057,129,276,0,31,63,125,0
1058,155,290,0,26,66,129,1
1059,109,274,0,33,69,144,1
1060,80,262,1,31,61,100,1
1061,125,273,0,30,64,145,0
1062,94,284,0,24,63,104,1
1063,148,281,0,27,63,110,1
1064,73,277,0,29,65,145,0
1065,123,267,1,19,66,132,1
1066,65,232,0,24,66,125,1
1067,118,279,1,21,64,108,0
1068,102,283,0,39,60,119,0
1069,120,280,0,24,61,118,0
1070,108,270,1,21,65,130,1
1071,122,280,1,45,62,128,0
1072,103,268,0,32,62,97,1
1073,105,312,0,41,61,115,1
1074,126,273,1,25,68,135,0
1075,145,316,0,22,67,142,0
1076,139,293,0,34,66,131,0
1077,124,290,0,26,65,165,0
1078,121,282,0,30,65,122,0
1079,126,299,1,21,60,114,0
1080,119,286,1,33,67,137,0
1081,114,277,1,19,63,107,0
1082,118,272,0,23,64,113,0
1083,127,295,0,36,65,145,0
1084,117,290,1,22,67,110,0
1085,137,277,0,41,65,126,0
1086,133,292,0,29,65,135,0
1087,100,264,0,28,60,111,1
1088,107,273,1,26,65,135,0
1089,115,276,1,20,62,105,1
1090,91,292,1,26,61,113,1
1091,112,287,0,27,64,110,1
1092,125,289,1,31,61,120,0
1093,157,291,0,33,65,121,0
1094,108,256,1,26,67,130,0
1095,130,279,0,31,62,122,0
1096,135,289,0,25,64,127,0
1097,123,277,0,24,66,122,0
1098,100,281,0,24,61,115,0
1099,124,277,1,23,64,104,0
1100,174,284,0,39,65,163,0
1101,129,278,0,26,67,146,0
1102,119,275,0,27,59,113,1
1103,126,272,1,35,61,120,1
1104,128,267,0,37,61,142,0
1105,116,282,1,19,64,124,0
1106,100,285,0,18,68,127,1
1107,96,285,0,37,66,135,1
1108,131,279,1,20,68,122,1
1109,110,292,0,35,62,127,0
1110,108,278,0,28,63,125,1
1111,129,275,0,24,65,135,0
1112,141,285,0,23,67,150,0
1113,110,276,0,31,70,155,0
1114,118,273,0,21,63,120,0
1115,111,267,1,24,60,115,0
1116,160,297,0,20,68,136,0
1117,120,280,0,30,60,115,0
1118,121,281,0,29,63,108,0
1119,113,282,0,30,64,118,1
1120,117,270,0,23,58,115,0
1121,158,267,0,35,64,125,0
1122,128,277,0,39,61,120,0
1123,158,289,0,30,66,140,0
1124,133,289,0,22,65,123,1
1125,163,298,0,37,61,98,0
1126,128,282,1,19,66,118,0
1127,126,271,1,21,60,105,0
1128,127,283,0,42,62,154,1
1129,134,287,0,40,63,118,0
1130,140,274,0,41,63,122,0
1131,102,285,0,29,63,117,1
1132,100,252,0,24,61,150,0
1133,120,295,0,29,59,100,1
1134,98,279,1,18,65,115,1
1135,130,246,0,19,62,118,0
1136,104,280,0,41,63,118,1
1137,122,285,0,31,62,102,1
1138,137,276,1,25,64,127,0
1139,114,285,1,20,61,104,0
1140,63,236,1,24,58,99,0
1141,98,318,0,23,63,107,0
1142,99,268,0,32,63,124,1
1143,89,238,1,26,64,136,0
1144,117,283,0,22,65,142,1
1145,143,281,0,29,67,132,0
1146,106,279,0,29,63,125,1
1147,99,246,0,35,62,106,0
1148,156,300,0,27,65,120,1
1149,72,266,1,25,66,200,1
1150,75,266,0,37,61,113,1
1151,97,285,0,35,61,112,1
1152,106,264,0,41,64,114,0
1153,91,225,0,18,68,117,1
1154,117,269,1,28,61,99,0
1155,117,284,0,25,66,177,1
1156,112,291,0,23,66,145,0
1157,112,270,0,29,61,124,0
1158,141,293,0,28,61,125,0
1159,131,259,0,19,63,134,0
1160,130,290,0,19,65,123,1
1161,132,270,0,26,67,140,0
1162,114,265,0,23,67,130,1
1163,160,291,0,34,64,110,1
1164,106,283,0,24,63,119,0
1165,84,260,1,20,64,104,1
1166,112,268,1,25,59,103,0
1167,139,311,0,37,66,135,0
1168,104,267,0,30,63,180,0
1169,130,294,0,32,63,110,1
1170,71,254,0,19,61,145,1
1171,82,270,0,21,65,150,1
1172,119,280,1,21,64,128,0
1173,123,353,0,26,63,115,0
1174,115,278,0,27,59,95,0
1175,124,289,1,21,67,145,1
1176,138,292,0,25,65,130,1
1177,88,276,0,25,63,103,1
1178,146,305,0,23,NA,NA,0
1179,128,241,1,17,64,126,0
1180,82,274,0,31,64,101,1
1181,100,274,0,24,63,113,0
1182,114,271,0,32,61,130,0
1183,97,269,0,20,65,137,1
1184,126,298,0,24,61,112,0
1185,122,275,1,20,65,127,0
1186,152,295,0,39,62,140,0
1187,116,274,0,21,62,110,1
1188,132,302,0,36,63,145,1
1189,84,260,1,37,66,140,0
1190,119,277,1,18,61,89,1
1191,104,275,0,24,NA,NA,0
1192,106,312,0,24,62,135,1
1193,124,NA,1,39,65,228,0
1194,139,291,0,24,65,160,0
1195,103,273,0,36,65,158,1
1196,112,299,0,24,67,145,1
1197,96,276,0,33,64,127,1
1198,102,281,1,19,67,135,1
1199,120,300,0,34,63,150,1
1200,102,338,0,19,64,170,0
1201,97,255,1,22,63,107,1
1202,113,285,0,22,70,145,0
1203,130,297,0,32,58,130,0
1204,97,260,1,25,63,115,1
1205,116,273,0,31,61,120,0
1206,114,266,0,29,64,113,0
1207,127,242,0,17,61,135,1
1208,87,247,1,18,66,125,1
1209,141,281,0,29,54,156,1
1210,144,283,1,25,66,140,0
1211,116,273,0,33,66,130,1
1212,75,265,0,21,65,103,1
1213,138,286,1,28,68,120,0
1214,99,271,0,39,69,151,0
1215,118,293,0,21,63,103,0
1216,152,267,0,28,NA,119,1
1217,97,266,0,24,62,109,0
1218,146,319,0,28,66,145,0
1219,81,285,0,19,63,150,1
1220,110,321,0,28,66,180,0
1221,135,284,1,19,60,95,0
1222,114,290,1,21,65,120,1
1223,124,288,1,21,64,116,1
1224,115,262,1,23,64,136,1
1225,143,281,0,28,65,135,1
1226,113,287,1,29,70,145,1
1227,109,244,1,21,63,102,1
1228,103,278,0,30,60,87,1
1229,118,276,0,34,64,116,0
1230,127,290,0,27,65,121,0
1231,132,270,0,27,65,126,0
1232,113,275,1,27,60,100,0
1233,128,265,0,24,67,120,0
1234,130,291,0,30,65,150,1
1235,125,281,1,21,65,110,0
1236,117,297,0,38,65,129,0

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_parity/baby_weights_parity.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

babies <- read.csv("babies.csv")

# model birth weight vs. parity -------------------------------------

m_bwt_parity <- lm(bwt ~ as.factor(parity), data = babies)

xtable(summary(m_bwt_parity), digits = 2)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_smoke/babies.csv
================================================
case,bwt,gestation,parity,age,height,weight,smoke
1,120,284,0,27,62,100,0
2,113,282,0,33,64,135,0
3,128,279,0,28,64,115,1
4,123,NA,0,36,69,190,0
5,108,282,0,23,67,125,1
6,136,286,0,25,62,93,0
7,138,244,0,33,62,178,0
8,132,245,0,23,65,140,0
9,120,289,0,25,62,125,0
10,143,299,0,30,66,136,1
11,140,351,0,27,68,120,0
12,144,282,0,32,64,124,1
13,141,279,0,23,63,128,1
14,110,281,0,36,61,99,1
15,114,273,0,30,63,154,0
16,115,285,0,38,63,130,0
17,92,255,0,25,65,125,1
18,115,261,0,33,60,125,1
19,144,261,0,33,68,170,0
20,119,288,0,43,66,142,1
21,105,270,0,22,56,93,0
22,115,274,0,27,67,175,1
23,137,287,0,25,66,145,0
24,122,276,0,30,68,182,0
25,131,294,0,23,65,122,0
26,103,261,0,27,65,112,1
27,146,280,0,26,58,106,0
28,114,266,0,20,65,175,1
29,125,292,0,32,65,125,0
30,114,274,0,28,66,132,1
31,122,270,0,26,61,105,0
32,93,278,0,34,61,146,0
33,130,268,0,30,66,123,0
34,119,275,0,23,60,105,0
35,113,281,0,24,65,120,0
36,134,283,0,22,67,130,0
37,107,279,0,24,63,115,0
38,134,288,0,23,63,92,1
39,122,267,0,27,65,101,1
40,128,282,0,31,65,NA,0
41,129,293,0,30,61,160,0
42,110,278,0,23,63,177,0
43,138,302,0,26,NA,NA,1
44,111,270,0,27,61,119,0
45,87,248,0,37,65,130,1
46,143,274,0,27,63,110,1
47,155,294,0,32,66,150,0
48,110,272,0,25,60,90,0
49,122,275,0,26,66,147,0
50,145,291,0,26,63,119,1
51,115,258,0,26,62,130,0
52,108,283,0,31,65,148,1
53,102,282,0,28,61,110,0
54,143,286,0,31,64,126,0
55,146,267,0,30,67,132,0
56,124,275,0,22,60,130,0
57,124,278,0,26,70,145,1
58,145,257,0,33,65,140,0
59,106,273,0,28,60,116,0
60,75,232,0,33,61,110,0
61,107,273,0,24,61,96,0
62,124,288,0,22,67,118,0
63,122,280,0,23,65,125,1
64,101,245,0,23,63,130,1
65,128,283,0,28,63,125,1
66,104,282,0,36,65,115,1
67,97,246,0,37,63,150,0
68,137,274,0,26,69,137,1
69,103,273,0,31,63,170,1
70,142,276,0,38,63,170,0
71,130,289,0,27,66,130,0
72,156,292,0,26,63,118,0
73,133,284,0,25,66,125,1
74,120,274,0,24,62,120,0
75,91,270,0,24,60,149,1
76,127,274,0,21,62,110,0
77,153,286,0,26,63,107,1
78,121,276,0,39,63,130,0
79,120,277,0,27,63,126,0
80,99,272,0,27,62,103,1
81,149,293,0,35,65,116,0
82,129,280,0,23,64,104,0
83,139,292,0,25,68,135,0
84,114,274,0,33,67,148,1
85,138,287,0,30,66,145,0
86,129,274,0,29,71,NA,1
87,138,294,0,32,65,117,0
88,131,296,0,37,63,143,0
89,125,305,0,22,70,196,1
90,114,NA,0,24,67,113,1
91,128,281,0,33,59,117,0
92,134,268,0,28,62,112,0
93,114,271,0,27,60,104,0
94,92,NA,0,31,67,130,0
95,85,278,0,23,61,103,1
96,135,282,0,22,64,100,0
97,87,255,0,28,61,100,1
98,125,302,0,37,62,162,0
99,128,NA,0,35,62,110,0
100,105,254,0,29,64,137,0
101,120,279,0,27,60,121,1
102,119,274,0,33,64,120,0
103,116,286,0,24,61,NA,0
104,107,280,0,36,65,117,1
105,119,273,0,24,61,108,1
106,133,279,0,37,66,140,0
107,155,287,0,33,66,143,0
108,126,273,0,22,65,150,0
109,129,303,0,27,64,125,0
110,137,274,0,29,65,154,0
111,103,269,0,26,65,NA,1
112,125,302,0,28,65,125,0
113,91,255,0,19,67,136,1
114,134,293,0,21,65,NA,0
115,95,279,0,22,66,145,1
116,118,276,0,29,64,114,0
117,141,278,0,33,66,109,1
118,131,283,0,25,67,215,0
119,121,264,0,32,66,145,0
120,100,243,0,39,65,170,1
121,131,288,0,24,61,103,0
122,118,284,0,26,66,133,0
123,152,288,0,35,67,130,0
124,121,284,0,34,69,155,0
125,117,276,0,31,69,150,0
126,115,283,0,25,61,150,1
127,112,277,0,23,65,110,0
128,94,267,0,30,62,120,1
129,109,272,0,35,66,154,0
130,132,225,0,28,67,148,0
131,117,278,0,25,62,103,0
132,101,266,0,20,67,110,1
133,112,294,0,25,64,125,1
134,128,283,0,24,60,100,0
135,128,279,0,25,66,147,1
136,117,258,0,31,64,120,0
137,134,278,0,24,69,135,0
138,127,284,0,28,65,145,0
139,93,269,0,21,65,104,1
140,122,275,0,27,65,165,0
141,100,265,0,39,62,107,1
142,147,293,0,32,65,123,0
143,120,299,0,25,65,110,0
144,144,277,0,30,63,127,0
145,105,268,0,32,61,115,1
146,136,276,0,23,66,155,0
147,102,262,0,24,63,125,0
148,160,300,0,29,71,175,1
149,113,275,0,24,68,140,1
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1095,130,279,0,31,62,122,0
1096,135,289,0,25,64,127,0
1097,123,277,0,24,66,122,0
1098,100,281,0,24,61,115,0
1099,124,277,1,23,64,104,0
1100,174,284,0,39,65,163,0
1101,129,278,0,26,67,146,0
1102,119,275,0,27,59,113,1
1103,126,272,1,35,61,120,1
1104,128,267,0,37,61,142,0
1105,116,282,1,19,64,124,0
1106,100,285,0,18,68,127,1
1107,96,285,0,37,66,135,1
1108,131,279,1,20,68,122,1
1109,110,292,0,35,62,127,0
1110,108,278,0,28,63,125,1
1111,129,275,0,24,65,135,0
1112,141,285,0,23,67,150,0
1113,110,276,0,31,70,155,0
1114,118,273,0,21,63,120,0
1115,111,267,1,24,60,115,0
1116,160,297,0,20,68,136,0
1117,120,280,0,30,60,115,0
1118,121,281,0,29,63,108,0
1119,113,282,0,30,64,118,1
1120,117,270,0,23,58,115,0
1121,158,267,0,35,64,125,0
1122,128,277,0,39,61,120,0
1123,158,289,0,30,66,140,0
1124,133,289,0,22,65,123,1
1125,163,298,0,37,61,98,0
1126,128,282,1,19,66,118,0
1127,126,271,1,21,60,105,0
1128,127,283,0,42,62,154,1
1129,134,287,0,40,63,118,0
1130,140,274,0,41,63,122,0
1131,102,285,0,29,63,117,1
1132,100,252,0,24,61,150,0
1133,120,295,0,29,59,100,1
1134,98,279,1,18,65,115,1
1135,130,246,0,19,62,118,0
1136,104,280,0,41,63,118,1
1137,122,285,0,31,62,102,1
1138,137,276,1,25,64,127,0
1139,114,285,1,20,61,104,0
1140,63,236,1,24,58,99,0
1141,98,318,0,23,63,107,0
1142,99,268,0,32,63,124,1
1143,89,238,1,26,64,136,0
1144,117,283,0,22,65,142,1
1145,143,281,0,29,67,132,0
1146,106,279,0,29,63,125,1
1147,99,246,0,35,62,106,0
1148,156,300,0,27,65,120,1
1149,72,266,1,25,66,200,1
1150,75,266,0,37,61,113,1
1151,97,285,0,35,61,112,1
1152,106,264,0,41,64,114,0
1153,91,225,0,18,68,117,1
1154,117,269,1,28,61,99,0
1155,117,284,0,25,66,177,1
1156,112,291,0,23,66,145,0
1157,112,270,0,29,61,124,0
1158,141,293,0,28,61,125,0
1159,131,259,0,19,63,134,0
1160,130,290,0,19,65,123,1
1161,132,270,0,26,67,140,0
1162,114,265,0,23,67,130,1
1163,160,291,0,34,64,110,1
1164,106,283,0,24,63,119,0
1165,84,260,1,20,64,104,1
1166,112,268,1,25,59,103,0
1167,139,311,0,37,66,135,0
1168,104,267,0,30,63,180,0
1169,130,294,0,32,63,110,1
1170,71,254,0,19,61,145,1
1171,82,270,0,21,65,150,1
1172,119,280,1,21,64,128,0
1173,123,353,0,26,63,115,0
1174,115,278,0,27,59,95,0
1175,124,289,1,21,67,145,1
1176,138,292,0,25,65,130,1
1177,88,276,0,25,63,103,1
1178,146,305,0,23,NA,NA,0
1179,128,241,1,17,64,126,0
1180,82,274,0,31,64,101,1
1181,100,274,0,24,63,113,0
1182,114,271,0,32,61,130,0
1183,97,269,0,20,65,137,1
1184,126,298,0,24,61,112,0
1185,122,275,1,20,65,127,0
1186,152,295,0,39,62,140,0
1187,116,274,0,21,62,110,1
1188,132,302,0,36,63,145,1
1189,84,260,1,37,66,140,0
1190,119,277,1,18,61,89,1
1191,104,275,0,24,NA,NA,0
1192,106,312,0,24,62,135,1
1193,124,NA,1,39,65,228,0
1194,139,291,0,24,65,160,0
1195,103,273,0,36,65,158,1
1196,112,299,0,24,67,145,1
1197,96,276,0,33,64,127,1
1198,102,281,1,19,67,135,1
1199,120,300,0,34,63,150,1
1200,102,338,0,19,64,170,0
1201,97,255,1,22,63,107,1
1202,113,285,0,22,70,145,0
1203,130,297,0,32,58,130,0
1204,97,260,1,25,63,115,1
1205,116,273,0,31,61,120,0
1206,114,266,0,29,64,113,0
1207,127,242,0,17,61,135,1
1208,87,247,1,18,66,125,1
1209,141,281,0,29,54,156,1
1210,144,283,1,25,66,140,0
1211,116,273,0,33,66,130,1
1212,75,265,0,21,65,103,1
1213,138,286,1,28,68,120,0
1214,99,271,0,39,69,151,0
1215,118,293,0,21,63,103,0
1216,152,267,0,28,NA,119,1
1217,97,266,0,24,62,109,0
1218,146,319,0,28,66,145,0
1219,81,285,0,19,63,150,1
1220,110,321,0,28,66,180,0
1221,135,284,1,19,60,95,0
1222,114,290,1,21,65,120,1
1223,124,288,1,21,64,116,1
1224,115,262,1,23,64,136,1
1225,143,281,0,28,65,135,1
1226,113,287,1,29,70,145,1
1227,109,244,1,21,63,102,1
1228,103,278,0,30,60,87,1
1229,118,276,0,34,64,116,0
1230,127,290,0,27,65,121,0
1231,132,270,0,27,65,126,0
1232,113,275,1,27,60,100,0
1233,128,265,0,24,67,120,0
1234,130,291,0,30,65,150,1
1235,125,281,1,21,65,110,0
1236,117,297,0,38,65,129,0

================================================
FILE: ch_regr_mult_and_log/figures/eoce/baby_weights_smoke/baby_weights_smoke.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

babies <- read.csv("babies.csv")

# model birth weight vs. smoking ------------------------------------

m_bwt_smoke <- lm(bwt ~ as.factor(smoke), data = babies)

xtable(summary(m_bwt_smoke), digits = 2)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/challenger_disaster_predict/challenger_disaster_predict.R
================================================
# load packages -----------------------------------------------------

library(openintro)

# load data ---------------------------------------------------------

load("orings.rda")
set.seed(17)

# plot probability of damage vs. temperature ------------------------

myPDF("challenger_disaster_damage_temp.pdf", 4.5, 2.7, 
      mar = c(3.2, 3.7, 0.8, 0.8), mgp = c(2.5, 0.55, 0))

these <- orings[,1] %in% c(67, 70, 76)
plot(orings[,1] + 
       c(rep(0, 5), c(-0.1, 0, 0.1), 0, 0, -0.07, -0.07, 0.07, 0.07, 
         rep(0, 4), -0.07, 0.07, 0, 0, 0), 
     orings[,2]/6, 
     xlab = "", ylab = "Probability of damage", 
     xlim = c(50, 82), ylim = c(0,1), 
     col = COL[1,2], pch = 19)
mtext("Temperature (Fahrenheit)", 1, 2)

dev.off()

# probability calculations ------------------------------------------

temperature <- c(51, 53, 55)
logitp <- 11.6630 - 0.2162 * temperature
p <- exp(logitp) / (1+exp(logitp))

# plot of predicted probabilities -----------------------------------

myPDF("challenger_disaster_pred_damage_temp.pdf", 4.5, 2.7, 
      mar=c(3.2, 3.7, 0.8, 0.8), mgp = c(2.5, 0.55, 0))

these <- orings[,1] %in% c(67, 70, 76)
plot(orings[,1] + 
       c(rep(0, 5), c(-0.1, 0, 0.1), 0, 0, -0.07, -0.07, 0.07, 0.07, 
         rep(0, 4), -0.07, 0.07, 0, 0, 0), 
     orings[,2]/6, 
     xlab = "", ylab = "Probability of damage", 
     xlim = c(50, 82), ylim = c(0,1), 
     col = COL[1,2], pch = 19)
mtext("Temperature (Fahrenheit)", 1, 2)
temperature <- seq(51, 75, 2)
logitp      <- 11.6630 - 0.2162*temperature
p           <- exp(logitp)/(1+exp(logitp))
points(temperature, p, col=COL[4], cex=0.7)
temperature <- seq(25, 100, 0.2)
logitp      <- 11.6630 - 0.2162 * temperature
p           <- exp(logitp) / (1+exp(logitp))
lines(temperature, p, col = COL[4])

dev.off()

================================================
FILE: ch_regr_mult_and_log/figures/eoce/gpa/gpa.R
================================================
# load packages -----------------------------------------------------
library(xtable)

# load data ---------------------------------------------------------

gpa_survey <- read.csv("gpa_survey.csv")

# gpa mlr -----------------------------------------------------------

m_gpa <- lm(gpa ~ studyweek + sleepnight + outnight + gender,
            data = gpa_survey)

xtable(summary(m_gpa), digits = 2)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/gpa/gpa_survey.csv
================================================
gpa,studyweek,sleepnight,outnight,gender
3.89,50,6,3,female
3.9,15,6,1,female
3.75,15,7,1,female
3.6,10,6,4,male
4,25,7,3,female
3.15,20,7,3,male
3.25,15,6,1,female
3.925,10,8,3,female
3.428,12,8,2,female
3.8,2,8,4,male
3.9,10,8,1,female
2.9,30,6,2,female
3.925,30,7,2,female
3.65,21,9,3,female
3.75,10,8.5,3.5,female
4.67,14,6.5,3,male
3.1,12,7.5,3.5,male
3.8,12,8,1,female
3.4,4,9,3,female
3.575,45,6.5,1.5,female
3.85,6,7,2.5,female
3.4,10,7,3,female
3.5,12,8,2,male
3.6,13,6,3.5,female
3.825,35,8,4,female
3.925,10,8,3,female
4,40,8,3,female
3.425,14,9,3,female
3.75,30,6,0,female
3.15,8,6,0,female
3.4,8,6.5,2,female
3.7,20,7,1,female
3.36,40,7,1,female
3.7,15,7,1.5,male
3.7,25,5,1,female
3.6,10,7,2,female
3.825,18,7,1.5,female
3.2,15,6,1,female
3.5,30,8,3,male
3.5,11,7,1.5,female
3,28,6,1.5,female
3.98,4,7,1.5,female
3.7,4,5,1,male
3.81,25,7.5,2.5,female
4,42,5,1,female
3.1,3,7,2,male
3.4,42,9,2,male
3.5,25,8,2,male
3.65,20,6,2,female
3.7,7,8,2,female
3.1,6,8,1,female
4,20,7,3,female
3.35,45,6,2,female
3.541,30,7.5,1.5,female
2.9,20,6,3,female

================================================
FILE: ch_regr_mult_and_log/figures/eoce/gpa_iq_conds/gpa_iq.csv
================================================
obs,gpa,iq,gender,concept
1,7.94,111,2,67
2,8.292,107,2,43
3,4.643,100,2,52
4,7.47,107,2,66
5,8.882,114,1,58
6,7.585,115,2,51
7,7.65,111,2,71
8,2.412,97,2,51
9,6,100,1,49
10,8.833,112,2,51
11,7.47,104,1,35
12,5.528,89,1,54
13,7.167,104,2,54
14,7.571,102,1,64
15,4.7,91,1,56
16,8.167,114,1,69
17,7.822,114,1,55
18,7.598,103,1,65
19,4,106,2,40
20,6.231,105,1,66
21,7.643,113,2,55
22,1.76,109,2,20
24,6.419,108,1,56
26,9.648,113,2,68
27,10.7,130,1,69
28,10.58,128,2,70
29,9.429,128,2,80
30,8,118,2,53
31,9.585,113,2,65
32,9.571,120,1,67
33,8.998,132,1,62
34,8.333,111,1,39
35,8.175,124,2,71
36,8,127,2,59
37,9.333,128,1,60
38,9.5,136,2,64
39,9.167,106,2,71
40,10.14,118,1,72
41,9.999,119,1,54
43,10.76,123,2,64
44,9.763,124,2,58
45,9.41,126,2,70
46,9.167,116,2,72
47,9.348,127,2,70
48,8.167,119,2,47
50,3.647,97,2,52
51,3.408,86,1,46
52,3.936,102,2,66
53,7.167,110,2,67
54,7.647,120,2,63
55,0.53,103,2,53
56,6.173,115,2,67
57,7.295,93,2,61
58,7.295,72,1,54
59,8.938,111,1,60
60,7.882,103,1,60
61,8.353,123,2,63
62,5.062,79,2,30
63,8.175,119,2,54
64,8.235,110,2,66
65,7.588,110,2,44
68,7.647,107,2,49
69,5.237,74,1,44
71,7.825,105,2,67
72,7.333,112,1,64
74,9.167,105,2,73
76,7.996,110,2,59
77,8.714,107,1,37
78,7.833,103,1,63
79,4.885,77,2,36
80,7.998,98,1,64
83,3.82,90,2,42
84,5.936,96,1,28
85,9,112,1,60
86,9.5,112,1,70
87,6.057,114,2,51
88,6.057,93,1,21
89,6.938,106,2,56

================================================
FILE: ch_regr_mult_and_log/figures/eoce/gpa_iq_conds/gpa_iq_conds.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

gpa_iq_data <- read.csv("gpa_iq.csv")

# mlr for birth weight ----------------------------------------------

m_gpa <- lm(gpa ~ iq + gender, data = gpa_iq_data)

# normal prob plot for residuals ------------------------------------

pdf("gpa_iq_conds_normal_qq.pdf", 5.5, 4.3)
par(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)
qqnorm(m_gpa$residuals, 
       ylab = "Residuals", main = "",
       pch = 19, col = COL[1,2])
qqline(m_gpa$residuals, col = COL[1])
dev.off()

# Histogram for residuals ------------------------------------

pdf("gpa_iq_conds_normal_hist.pdf", 5.5, 4.3)
par(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(m_gpa$residuals, 
       xlab = "Residuals", ylab = "",
       col = COL[1])
dev.off()

# absolute values of residuals against fitted -----------------------

pdf("gpa_iq_conds_abs_res_fitted.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_gpa$residuals ~ m_gpa$fitted.values, 
     ylab = "Residuals", xlab = "Fitted values", 
     pch = 19, col = COL[1,2])
     
abline(h = 0, lty = 2)

dev.off()

# residuals in order of their data collection -----------------------

pdf("gpa_iq_conds_res_order.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.9, 0.5, 1), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_gpa$residuals ~ c(1:length(m_gpa$residuals)), 
     ylab = "Residuals", xlab = "Order of collection",
     pch = 19, col = COL[1,2],
     axes = FALSE)
axis(1, at = seq(0, 80, 40))
axis(2, at = seq(-6, 2, 4))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals vs. iq -------------------------------------------

pdf("gpa_iq_conds_res_iq.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_gpa$residuals ~ gpa_iq_data$iq, 
     ylab = "Residuals", xlab = "IQ", 
     pch = 19, col = COL[1,2],
     axes = FALSE)
axis(1, at = seq(75, 125, 25))
axis(2, at = seq(-6, 2, 4))
box()

abline(h = 0, lty = 2)

dev.off()

# residuals vs. gender -------------------------------------------

pdf("gpa_iq_conds_res_gender.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_gpa$residuals ~ gpa_iq_data$gender, 
     ylab = "Residuals", xlab = "Gender", 
     pch = 19, col = COL[1,2],
     axes = FALSE)
axis(1, at = c(1, 2), labels = c(0, 1))
axis(2, at = seq(-6, 2, 4))
box()

abline(h = 0, lty = 2)

dev.off()


================================================
FILE: ch_regr_mult_and_log/figures/eoce/log_regr_ex/log_regr_ex.R
================================================
library(openintro)
library(xtable)
d <- email
names(d)

table(d$sent_email, d$spam)
SGlm <- function(form, data = d) {
  m <- glm(
      form,
      data = d,
      family = binomial)
  summary(m)
}

vars <- c(
    "to_multiple", "cc", "attach", "dollar",
    "winner", "inherit", "password", "format",
    "re_subj", "exclaim_subj", "sent_email")
form <- spam ~ 1
for (v in vars) {
  form <- update(form, paste(". ~ . +", v))
}
m <- glm(
    form,
    data = d,
    family = binomial)
summary(m)

# form <- update(form, . ~ . - exclaim_subj - cc)

aic <- c("Drop None" = SGlm(form))
vars. <- names(unlist(sapply(vars, grep, x = as.character(form)[3], fixed = TRUE)))
for (v in vars.) {
  m. <- update(form, paste(". ~ . -", v))
  aic[v] <- SGlm(m.)$aic
}
which.min(aic)
# xtable(data.frame(cbind(aic, aic[1] - aic)))
xtable(data.frame(aic))




================================================
FILE: ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/horror_movies_conds.R
================================================
# load packages ----------------------------------------------------------------
library(tidyverse)
library(lubridate)
library(openintro)
library(broom)

# load data --------------------------------------------------------------------
movie_profit <- read_csv("mine-new/ch_regr_mult_and_log/horror_movies/figures/movie_profit.csv") %>%
  select(-X1)


# fix dates --------------------------------------------------------------------
movie_profit <- movie_profit %>%
  mutate(
    release_date = mdy(release_date),
    release_year = year(release_date),
    oct_release = ifelse(month(release_date) == 10, "yes", "no"),
    dom_gross_to_prod = domestic_gross / production_budget,
    ww_gross_to_prod = worldwide_gross / production_budget
    ) 

# subset for movies after 2000 -------------------------------------------------
movie_profit_2000 <- movie_profit %>%
  filter(
    release_year >= 2010,
    release_year < 2019
    )

# mlr --------------------------------------------------------------------------

m <- lm(ww_gross_to_prod ~ release_year + genre, data = movie_profit_2000)
m_aug <- augment(m)

# histogram of residuals -------------------------------------------------------

pdf("horror_movies_conds_hist_res.pdf", 5.5, 4.3)

par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
histPlot(m_aug$.resid, breaks = seq(-25, 100, 5), 
         col = COL[1], 
         axes = FALSE, xlab="Residuals", ylab="")
axis(1)
axis(2, at = seq(0, 600, 200))

dev.off()

# residuals against fitted -----------------------------------------------------

cols <- c(
  "Action" = COL[1,1],
  "Adventure" = COL[2,1],
  "Comedy" = COL[3,1],
  "Drama" = COL[4,1],
  "Horror" = COL[5,1]
)

ggplot(m_aug, aes(y = .resid, x = .fitted, color = genre)) + 
  geom_point(alpha = 0.7) +
  theme_minimal() + 
  labs(x = "Fitted values", y = "Residuals", color = "Genre") +
  scale_color_manual(values = cols) +
  geom_hline(yintercept = 0, linetype = "dashed", size = 0.2)

ggsave(filename = "horror_movies_conds_res_genre_fitted.pdf",
       width = 5.5, height = 4.3)

# residuals in order of their data collection -----------------------

pdf("horror_movies_conds_res_order.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.9, 0.5, 1), las = 1, mgp = c(2.7,0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_aug$.resid ~ c(1:length(m$residuals)), 
     ylab = "Residuals", xlab = "Order of collection",
     pch = 19, col = COL[1,2],
     axes = FALSE)
axis(1, at = seq(0, 1000, 200))
axis(2, at = seq(-20, 80, 20))

abline(h = 0, lty = 2)

dev.off()

# residuals vs. release year ---------------------------------------------------

pdf("horror_movies_conds_res_year.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_aug$.resid ~ m_aug$release_year, 
     ylab = "Residuals", xlab = "Release year", 
     pch = 19, col = COL[1,2],
     axes = FALSE)
axis(1, at = seq(2010, 2018, 1))
axis(2, at = seq(-20, 80, 20))

abline(h = 0, lty = 2)

dev.off()

# residuals vs. genre -------------------------------------------

ggplot(m_aug, aes(y = .resid, x = genre, color = genre)) +
  geom_jitter(alpha = 0.7) + 
  guides(color = FALSE) +
  scale_color_manual(values = cols) +
  theme_minimal() + 
  labs(x = "Genre", y = "Residuals") +
  geom_hline(yintercept = 0, linetype = "dashed", size = 0.2)

ggsave(filename = "horror_movies_conds_res_genre.pdf",
       width = 5.5, height = 4.3)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/movie_profit.csv
================================================
"","release_date","movie","production_budget","domestic_gross","worldwide_gross","distributor","mpaa_rating","genre"
"1","6/22/2007","Evan Almighty",1.75e+08,100289690,174131329,"Universal","PG","Comedy"
"2","7/28/1995","Waterworld",1.75e+08,88246220,264246220,"Universal","PG-13","Action"
"3","5/12/2017","King Arthur: Legend of the Sword",1.75e+08,39175066,139950708,"Warner Bros.","PG-13","Adventure"
"4","12/25/2013","47 Ronin",1.75e+08,38362475,151716815,"Universal","PG-13","Action"
"5","6/22/2018","Jurassic World: Fallen Kingdom",1.7e+08,416769345,1304866322,"Universal","PG-13","Action"
"6","8/1/2014","Guardians of the Galaxy",1.7e+08,333172112,771051335,"Walt Disney","PG-13","Action"
"7","5/7/2010","Iron Man 2",1.7e+08,312433331,621156389,"Paramount Pictures","PG-13","Action"
"8","4/4/2014","Captain America: The Winter Soldier",1.7e+08,259746958,714401889,"Walt Disney","PG-13","Action"
"9","7/11/2014","Dawn of the Planet of the Apes",1.7e+08,208545589,710644566,"20th Century Fox","PG-13","Adventure"
"10","11/10/2004","The Polar Express",1.7e+08,186493587,310634169,"Warner Bros.","G","Adventure"
"11","6/1/2012","Snow White and the Huntsman",1.7e+08,155136755,401021746,"Universal","PG-13","Adventure"
"12","7/1/2003","Terminator 3: Rise of the Machines",1.7e+08,150358296,433058296,"Warner Bros.","R","Action"
"13","5/7/2004","Van Helsing",1.7e+08,120150546,300150546,"Universal","PG-13","Action"
"14","5/22/2015","Tomorrowland",1.7e+08,93436322,207283457,"Walt Disney","PG","Adventure"
"15","5/27/2016","Alice Through the Looking Glass",1.7e+08,77042381,276934087,"Walt Disney","PG","Adventure"
"16","5/21/2010","Shrek Forever After",1.65e+08,238736787,756244673,"Paramount Pictures","PG","Adventure"
"17","11/4/2016","Doctor Strange",1.65e+08,232641920,676486457,"Walt Disney","PG-13","Action"
"18","11/7/2014","Big Hero 6",1.65e+08,222527828,652127828,"Walt Disney","PG","Adventure"
"19","3/26/2010","How to Train Your Dragon",1.65e+08,217581232,494870992,"Paramount Pictures","PG","Adventure"
"20","11/2/2012","Wreck-It Ralph",1.65e+08,189412677,496511521,"Walt Disney","PG","Adventure"
"21","11/5/2014","Interstellar",1.65e+08,188017894,667752422,"Paramount Pictures","PG-13","Adventure"
"22","6/24/2016","Independence Day: Resurgence",1.65e+08,103144286,384413934,"20th Century Fox","PG-13","Action"
"23","7/29/2011","Cowboys and Aliens",1.63e+08,100368560,176038324,"Universal","PG-13","Action"
"24","5/17/2007","Shrek the Third",1.6e+08,322719944,807330936,"Paramount Pictures","PG","Adventure"
"25","5/24/2013","Fast and Furious 6",1.6e+08,238679850,789300444,"Universal","PG-13","Action"
"26","6/3/2011","X-Men: First Class",1.6e+08,146408305,355408305,"20th Century Fox","PG-13","Action"
"27","12/25/2008","The Curious Case of Benjamin Button",1.6e+08,127509326,329631958,"Paramount Pictures","PG-13","Drama"
"28","7/14/2010","The Sorcerer's Apprentice",1.6e+08,63150991,217986320,"Walt Disney","PG","Adventure"
"29","5/12/2006","Poseidon",1.6e+08,60674817,181674817,"Warner Bros.","PG-13","Adventure"
"30","6/10/2016","Warcraft",1.6e+08,47225655,425547111,"Universal","PG-13","Action"
"31","12/21/2018","Aquaman",1.6e+08,0,0,"Warner Bros.","PG-13","Action"
"32","9/30/2016","Deepwater Horizon",1.56e+08,61433527,122631306,"Lionsgate","PG-13","Drama"
"33","7/1/2015","Terminator: Genisys",1.55e+08,89760956,432150894,"Paramount Pictures","PG-13","Action"
"34","3/23/2018","Pacific Rim: Uprising",1.55e+08,59185715,290241338,"Universal","PG-13","Action"
"35","11/24/2004","Alexander",1.55e+08,34297191,167297191,"Warner Bros.","R","Action"
"36","7/14/2017","War for the Planet of the Apes",1.52e+08,146880162,489592267,"20th Century Fox","PG-13","Action"
"37","5/25/2001","Pearl Harbor",151500000,198539855,449239855,"Walt Disney","PG-13","Action"
"38","7/2/2007","Transformers",1.51e+08,319246193,708272592,"Paramount Pictures","PG-13","Action"
"39","6/2/2017","Wonder Woman",1.5e+08,412563408,821133378,"Warner Bros.","PG-13","Action"
"40","3/4/2016","Zootopia",1.5e+08,341268248,1019706594,"Walt Disney","PG","Adventure"
"41","11/18/2005","Harry Potter and the Goblet of Fire",1.5e+08,290013036,896911078,"Warner Bros.","PG-13","Adventure"
"42","5/15/2003","The Matrix Reloaded",1.5e+08,281553689,738576929,"Warner Bros.","R","Action"
"43","12/14/2007","I am Legend",1.5e+08,256393010,585532684,"Warner Bros.","PG-13","Horror"
"44","7/1/2008","Hancock",1.5e+08,227946274,624234272,"Sony Pictures","PG-13","Action"
"45","7/15/2005","Charlie and the Chocolate Factory",1.5e+08,206459076,475825484,"Warner Bros.","PG","Adventure"
"46","6/29/2007","Ratatouille",1.5e+08,206445654,626549695,"Walt Disney","G","Adventure"
"47","11/8/2013","Thor: The Dark World",1.5e+08,206362140,644602516,"Walt Disney","PG-13","Action"
"48","6/15/2005","Batman Begins",1.5e+08,205343774,359142722,"Warner Bros.","PG-13","Action"
"49","7/31/2015","Mission: Impossible—Rogue Nation",1.5e+08,195042377,689388363,"Paramount Pictures","PG-13","Action"
"50","7/21/2017","Dunkirk",1.5e+08,190068280,499900860,"Warner Bros.","PG-13","Action"
"51","5/6/2011","Thor",1.5e+08,181030624,449326618,"Paramount Pictures","PG-13","Action"
"52","11/7/2008","Madagascar: Escape 2 Africa",1.5e+08,180174880,599680774,"Paramount Pictures","PG","Adventure"
"53","5/1/2009","X-Men Origins: Wolverine",1.5e+08,179883157,374825760,"20th Century Fox","PG-13","Action"
"54","5/26/2011","Kung Fu Panda 2",1.5e+08,165249063,664837547,"Paramount Pictures","PG","Adventure"
"55","5/15/2015","Mad Max: Fury Road",1.5e+08,153636354,370651733,"Warner Bros.","R","Action"
"56","8/10/2018","The Meg",1.5e+08,142700791,527100791,"Warner Bros.","PG-13","Action"
"57","11/5/2003","The Matrix Revolutions",1.5e+08,139270910,427300260,"Warner Bros.","R","Action"
"58","3/29/2018","Ready Player One",1.5e+08,137018455,578621729,"Warner Bros.","PG-13","Adventure"
"59","5/5/2006","Mission: Impossible III",1.5e+08,133501348,397501348,"Paramount Pictures","PG-13","Action"
"60","5/14/2004","Troy",1.5e+08,133298577,484161265,"Warner Bros.","R","Action"
"61","7/1/2010","The Last Airbender",1.5e+08,131772187,319713881,"Paramount Pictures","PG","Adventure"
"62","11/2/2007","Bee Movie",1.5e+08,126631277,287594577,"Paramount Pictures","PG","Adventure"
"63","7/24/2009","G-Force",1.5e+08,119436770,292817841,"Walt Disney","PG","Adventure"
"64","11/21/2008","Bolt",1.5e+08,114053579,328015029,"Walt Disney","PG","Adventure"
"65","3/30/2012","Wrath of the Titans",1.5e+08,83670083,305270083,"Warner Bros.","PG-13","Adventure"
"66","11/16/2007","Beowulf",1.5e+08,82280579,195080579,"Paramount Pictures","PG-13","Adventure"
"67","2/12/2010","The Wolfman",1.5e+08,62189884,142634358,"Universal","R","Horror"
"68","2/17/2017","The Great Wall",1.5e+08,45157105,334550106,"Universal","PG-13","Action"
"69","10/9/2015","Pan",1.5e+08,35088320,151543635,"Warner Bros.","PG","Adventure"
"70","3/11/2011","Mars Needs Moms",1.5e+08,21392758,39549758,"Walt Disney","PG","Adventure"
"71","11/3/2006","Flushed Away",1.49e+08,64665672,179357126,"Paramount Pictures","PG","Adventure"
"72","6/8/2012","Madagascar 3: Europe's Most Wanted",1.45e+08,216391482,746921271,"Paramount Pictures","PG","Adventure"
"73","6/13/2014","How to Train Your Dragon 2",1.45e+08,177002924,614586270,"20th Century Fox","PG","Adventure"
"74","6/16/1999","Tarzan",1.45e+08,171091819,448191819,"Walt Disney","G","Adventure"
"75","3/7/2014","Mr. Peabody & Sherman",1.45e+08,111506430,269806430,"20th Century Fox","PG","Adventure"
"76","11/21/2012","Rise of the Guardians",1.45e+08,103412758,306900902,"Paramount Pictures","PG","Adventure"
"77","11/22/2002","Die Another Day",1.42e+08,160942139,431942139,"MGM","PG-13","Action"
"78","5/8/2009","Star Trek",1.4e+08,257730019,385680446,"Paramount Pictures","PG-13","Adventure"
"79","7/1/1998","Armageddon",1.4e+08,201578182,554600000,"Walt Disney","PG-13","Adventure"
"80","7/3/2002","Men in Black 2",1.4e+08,190418803,441767803,"Sony Pictures","PG-13","Action"
"81","7/22/2011","Captain America: The First Avenger",1.4e+08,176654505,370569776,"Paramount Pictures","PG-13","Action"
"82","1/29/2016","Kung Fu Panda 3",1.4e+08,143528619,518418751,"20th Century Fox","PG","Adventure"
"83","7/10/1998","Lethal Weapon 4",1.4e+08,130444603,285400000,"Warner Bros.","R","Action"
"84","3/27/2013","G.I. Joe: Retaliation",1.4e+08,122523060,375740705,"Paramount Pictures","PG-13","Action"
"85","12/5/2003","The Last Samurai",1.4e+08,111110575,456810575,"Warner Bros.","R","Action"
"86","12/21/2005","Fun With Dick And Jane",1.4e+08,110550000,203018919,"Sony Pictures","PG-13","Comedy"
"87","12/12/2014","Exodus: Gods and Kings",1.4e+08,65014513,268314513,"20th Century Fox","PG-13","Drama"
"88","7/1/2016","The BFG",1.4e+08,55483770,199676255,"Walt Disney","PG","Adventure"
"89","2/26/2016","Gods of Egypt",1.4e+08,31153464,138587563,"Lionsgate","PG-13","Adventure"
"90","5/3/2002","Spider-Man",1.39e+08,403706375,821706375,"Sony Pictures","PG-13","Adventure"
"91","3/6/2009","Watchmen",1.38e+08,107509799,186976250,"Warner Bros.","R","Action"
"92","7/29/2005","Stealth",1.38e+08,32116746,76416746,"Sony Pictures","PG-13","Action"
"93","6/13/2008","The Incredible Hulk",137500000,134806913,265573859,"Universal","PG-13","Adventure"
"94","6/20/2003","Hulk",1.37e+08,132177234,245075434,"Universal","PG-13","Action"
"95","7/11/2001","Final Fantasy: The Spirits Within",1.37e+08,32131830,85131830,"Sony Pictures","PG-13","Adventure"
"96","3/22/2013","The Croods",1.35e+08,187168425,573068425,"20th Century Fox","PG","Adventure"
"97","12/25/2015","The Revenant",1.35e+08,183637894,532950503,"20th Century Fox","R","Adventure"
"98","11/19/1999","The World is Not Enough",1.35e+08,126930660,361730660,"MGM","PG-13","Action"
"99","3/4/2011","Rango",1.35e+08,123477607,245724600,"Paramount Pictures","PG","Adventure"
"100","7/17/2013","Turbo",1.35e+08,83028130,286896578,"20th Century Fox","PG","Adventure"
"101","11/18/2011","Happy Feet Two",1.35e+08,64006466,157956466,"Warner Bros.","PG","Adventure"
"102","7/28/2006","Miami Vice",1.35e+08,63478838,163818556,"Universal","R","Action"
"103","6/29/2005","War of the Worlds",1.32e+08,234280354,606836535,"Paramount Pictures","PG-13","Action"
"104","11/26/2014","Penguins of Madagascar",1.32e+08,83350911,367650911,"20th Century Fox","PG","Adventure"
"105","11/22/2013","The Hunger Games: Catching Fire",1.3e+08,424668047,864868047,"Lionsgate","PG-13","Adventure"
"106","7/6/2018","Ant-Man and the Wasp",1.3e+08,216565229,617176819,"Walt Disney","PG-13","Action"
"107","6/6/2008","Kung Fu Panda",1.3e+08,215434591,631910531,"Paramount Pictures","PG","Adventure"
"108","7/17/2015","Ant-Man",1.3e+08,180202163,518860086,"Walt Disney","PG-13","Action"
"109","3/27/2015","Home",1.3e+08,177397510,386031994,"20th Century Fox","PG","Adventure"
"110","10/28/2011","Puss in Boots",1.3e+08,149260504,554987477,"Paramount Pictures","PG","Adventure"
"111","11/5/2010","Megamind",1.3e+08,148415853,321887208,"Paramount Pictures","PG","Adventure"
"112","7/18/2003","Bad Boys II",1.3e+08,138540870,273271982,"Sony Pictures","R","Action"
"113","4/11/2014","Rio 2",1.3e+08,131538435,492846291,"20th Century Fox","G","Adventure"
"114","3/28/2014","Noah",1.3e+08,101200044,352831065,"Paramount Pictures","PG-13","Drama"
"115","12/21/2011","The Adventures of Tintin",1.3e+08,77591831,373993951,"Paramount Pictures","PG","Adventure"
"116","5/31/2013","After Earth",1.3e+08,60522097,251499665,"Sony Pictures","PG-13","Action"
"117","11/26/2008","Australia",1.3e+08,49554002,215080810,"20th Century Fox","PG-13","Drama"
"118","7/19/2013","R.I.P.D.",1.3e+08,33618855,79076678,"Universal","PG-13","Action"
"119","5/19/2000","Dinosaur",127500000,137748063,356148063,"Walt Disney","PG","Adventure"
"120","3/3/2017","Logan",1.27e+08,226277068,615476965,"20th Century Fox","R","Action"
"121","5/2/2003","X-Men 2",1.25e+08,214949694,406875536,"20th Century Fox","PG-13","Action"
"122","4/29/2011","Fast Five",1.25e+08,210031325,630163454,"Universal","PG-13","Action"
"123","12/16/2011","Sherlock Holmes: A Game of Shadows",1.25e+08,186848418,535663443,"Warner Bros.","PG-13","Action"
"124","5/28/2004","The Day After Tomorrow",1.25e+08,186740799,556319450,"20th Century Fox","PG-13","Adventure"
"125","3/31/2017","The Boss Baby",1.25e+08,175003033,510888357,"20th Century Fox","PG","Adventure"
"126","4/1/2010","Clash of the Titans",1.25e+08,163214888,493214888,"Warner Bros.","PG-13","Action"
"127","11/4/2016","Trolls",1.25e+08,153707064,344527425,"20th Century Fox","PG","Adventure"
"128","5/19/1998","Godzilla",1.25e+08,136314294,3.76e+08,"Sony Pictures","PG-13","Action"
"129","6/8/2012","Prometheus",1.25e+08,126477084,402448265,"20th Century Fox","R","Adventure"
"130","6/20/1997","Batman & Robin",1.25e+08,107325195,238317814,"Warner Bros.","PG-13","Action"
"131","7/13/2018","Skyscraper",1.25e+08,67796355,304034615,"Universal","PG","Action"
"132","12/21/2016","Assassin’s Creed",1.25e+08,54647948,240497948,"20th Century Fox","PG-13","Action"
"133","1/13/2017","Monster Trucks",1.25e+08,33370166,61642798,"Paramount Pictures","PG-13","Adventure"
"134","8/27/1999","The 13th Warrior",1.25e+08,32698899,61698899,"Walt Disney","R","Action"
"135","11/17/2000","How the Grinch Stole Christmas",1.23e+08,260044825,345141403,"Universal","PG","Adventure"
"136","5/24/2000","Mission: Impossible 2",1.2e+08,215409889,549588516,"Paramount Pictures","PG-13","Action"
"137","6/30/2000","The Perfect Storm",1.2e+08,182618434,328711434,"Warner Bros.","PG-13","Drama"
"138","7/29/2016","Jason Bourne",1.2e+08,162192920,416197059,"Universal","PG-13","Action"
"139","11/21/2012","Life of Pi",1.2e+08,124987022,607258634,"20th Century Fox","PG","Drama"
"140","2/16/2007","Ghost Rider",1.2e+08,115802596,229545589,"Sony Pictures","PG-13","Action"
"141","6/27/2003","Charlie's Angels: Full Throttle",1.2e+08,100814328,227200000,"Sony Pictures","PG-13","Action"
"142","4/13/2018","Rampage",1.2e+08,99345950,424745950,"Warner Bros.","PG-13","Action"
"143","8/9/2013","Elysium",1.2e+08,93050117,286192091,"Sony Pictures","R","Action"
"144","3/24/2017","Power Rangers",1.2e+08,85364450,142545357,"Lionsgate","PG-13","Action"
"145","7/19/2002","Stuart Little 2",1.2e+08,64956806,1.66e+08,"Sony Pictures","PG","Adventure"
"146","6/11/2004","The Chronicles of Riddick",1.2e+08,57712751,107212751,"Universal","PG-13","Adventure"
"147","5/9/2008","Speed Racer",1.2e+08,43945766,93394462,"Warner Bros.","PG","Action"
"148","7/22/2005","The Island",1.2e+08,35818913,163018913,"Dreamworks SKG","PG-13","Action"
"149","6/23/2010","Knight and Day",1.17e+08,76423035,258751370,"20th Century Fox","PG-13","Action"
"150","5/19/1999","Star Wars Ep. I: The Phantom Menace",1.15e+08,474544677,1027044677,"20th Century Fox","PG","Adventure"
"151","11/2/2001","Monsters, Inc.",1.15e+08,289423425,559757719,"Walt Disney","G","Adventure"
"152","7/26/2013","The Wolverine",1.15e+08,132556852,416456852,"20th Century Fox","PG-13","Action"
"153","2/7/1997","Dante's Peak",1.15e+08,67163857,178200000,"Universal","PG-13","Drama"
"154","4/22/2016","The Huntsman: Winter’s War",1.15e+08,48003015,165149302,"Universal","PG-13","Action"
"155","6/14/2002","Windtalkers",1.15e+08,40914068,77628265,"MGM","R","Action"
"156","12/25/2010","Gulliver's Travels",1.12e+08,42779261,232017848,"20th Century Fox","PG","Adventure"
"157","12/15/2017","Ferdinand",1.11e+08,84410380,289867087,"20th Century Fox","PG","Adventure"
"158","5/18/2018","Deadpool 2",1.1e+08,318491426,733809601,"20th Century Fox","R","Action"
"159","12/22/2006","Night at the Museum",1.1e+08,250863268,579446407,"20th Century Fox","PG","Adventure"
"160","6/10/2005","Mr. and Mrs. Smith",1.1e+08,186336279,486124090,"20th Century Fox","PG-13","Action"
"161","5/29/2015","San Andreas",1.1e+08,155190832,457199280,"Warner Bros.","PG-13","Adventure"
"162","7/29/2011","The Smurfs",1.1e+08,142614158,563749323,"Sony Pictures","PG","Adventure"
"163","6/27/2007","Live Free or Die Hard",1.1e+08,134529403,382288147,"20th Century Fox","PG-13","Action"
"164","3/20/2015","The Divergent Series: Insurgent",1.1e+08,130179072,295075882,"Lionsgate","PG-13","Action"
"165","12/10/2004","Ocean's Twelve",1.1e+08,125531634,362989076,"Warner Bros.","PG-13","Adventure"
"166","12/19/1997","Tomorrow Never Dies",1.1e+08,125304276,339504276,"MGM","PG-13","Action"
"167","6/28/2000","The Patriot",1.1e+08,113330342,215300000,"Sony Pictures","R","Drama"
"168","3/7/2014","300: Rise of an Empire",1.1e+08,106580051,330780051,"Warner Bros.","R","Action"
"169","1/14/2011","The Green Hornet",1.1e+08,98780042,229155503,"Sony Pictures","PG-13","Action"
"170","10/7/2011","Real Steel",1.1e+08,85463309,263880341,"Walt Disney","PG-13","Action"
"171","6/11/2010","The A-Team",1.1e+08,77222099,177241171,"20th Century Fox","PG-13","Action"
"172","7/31/2013","The Smurfs 2",1.1e+08,71017784,348547523,"Sony Pictures","PG","Adventure"
"173","3/18/2016","The Divergent Series: Allegiant",1.1e+08,66184051,171871661,"Lionsgate","PG-13","Action"
"174","6/12/2009","The Taking of Pelham 123",1.1e+08,65452312,152364370,"Sony Pictures","R","Action"
"175","11/1/2013","Ender's Game",1.1e+08,61737191,127983283,"Lionsgate","PG-13","Adventure"
"176","4/2/2004","Home on the Range",1.1e+08,50026353,76482461,"Walt Disney","PG","Adventure"
"177","6/13/1997","Speed 2: Cruise Control",1.1e+08,48097081,150468000,"20th Century Fox","PG-13","Action"
"178","5/6/2005","Kingdom of Heaven",1.1e+08,47398413,218853353,"20th Century Fox","R","Adventure"
"179","3/31/2017","Ghost in the Shell",1.1e+08,40563557,167918847,"Paramount Pictures","PG-13","Action"
"180","11/21/2003","The Cat in the Hat",1.09e+08,101018283,133818283,"Universal","PG","Adventure"
"181","12/25/2001","Ali",1.09e+08,58183966,87683966,"Sony Pictures","R","Drama"
"182","11/23/2016","Allied",1.06e+08,40098064,119285656,"Paramount Pictures","R","Drama"
"183","7/16/2004","I, Robot",1.05e+08,144801023,348629585,"20th Century Fox","PG-13","Action"
"184","12/17/1999","Stuart Little",1.05e+08,140015224,298815224,"Sony Pictures","PG","Adventure"
"185","11/25/2009","The Princess and the Frog",1.05e+08,104400899,270997378,"Walt Disney","G","Adventure"
"186","3/7/2008","10,000 B.C.",1.05e+08,94784201,269065678,"Warner Bros.","PG-13","Adventure"
"187","7/22/2016","Ice Age: Collision Course",1.05e+08,64063008,403092412,"20th Century Fox","PG","Adventure"
"188","9/22/2017","Kingsman: The Golden Circle",1.04e+08,100234838,408822328,"20th Century Fox","R","Action"
"189","6/9/2000","Gone in 60 Seconds",103300000,101643008,232643008,"Walt Disney","PG-13","Action"
"190","5/23/2013","The Hangover 3",1.03e+08,112200072,362000072,"Warner Bros.","R","Comedy"
"191","3/9/2018","A Wrinkle in Time",1.03e+08,100478608,133401882,"Walt Disney","PG","Adventure"
"192","7/1/2009","Public Enemies",102500000,97104620,212282709,"Universal","R","Drama"
"193","11/17/2006","Casino Royale",1.02e+08,167365000,594420283,"Sony Pictures","PG-13","Action"
"194","6/21/2002","Minority Report",1.02e+08,132024714,358824714,"20th Century Fox","PG-13","Action"
"195","10/26/2012","Cloud Atlas",1.02e+08,27108272,130673154,"Warner Bros.","R","Drama"
"196","7/2/1991","Terminator 2: Judgment Day",1e+08,203464105,515419827,"Sony Pictures","R","Action"
"197","6/16/1995","Batman Forever",1e+08,184031112,336529144,"Warner Bros.","PG-13","Action"
"198","7/27/2001","Planet of the Apes",1e+08,180011740,362211740,"20th Century Fox","PG-13","Adventure"
"199","11/19/2004","National Treasure",1e+08,173005002,331323410,"Walt Disney","PG","Adventure"
"200","10/5/2018","Venom",1e+08,171125095,461825095,"Sony Pictures","PG-13","Action"
"201","12/22/2010","Little Fockers",1e+08,148438600,310650574,"Universal","PG-13","Comedy"
"202","7/15/1994","True Lies",1e+08,146282411,365300000,"20th Century Fox","R","Action"
"203","11/2/2007","American Gangster",1e+08,130164645,267985456,"Universal","R","Drama"
"204","9/18/2009","Cloudy with a Chance of Meatballs",1e+08,124870275,236827677,"Sony Pictures","PG","Adventure"
"205","8/6/2010","The Other Guys",1e+08,119219978,170936470,"Sony Pictures","PG-13","Comedy"
"206","5/24/2013","Epic",1e+08,107518682,262794441,"20th Century Fox","PG","Adventure"
"207","6/21/1996","Eraser",1e+08,101295562,234400000,"Warner Bros.","R","Action"
"208","6/21/1996","The Hunchback of Notre Dame",1e+08,100138851,325500000,"Walt Disney","G","Adventure"
"209","12/15/2000","The Emperor's New Groove",1e+08,89296573,169296573,"Walt Disney","G","Adventure"
"210","8/17/2012","The Expendables 2",1e+08,85028192,311979256,"Lionsgate","R","Action"
"211","10/16/2009","Where the Wild Things Are",1e+08,77233467,99123656,"Warner Bros.","PG","Adventure"
"212","12/15/2006","Eragon",1e+08,75030163,249488115,"20th Century Fox","PG","Adventure"
"213","7/25/2014","Hercules",1e+08,72688614,243388614,"Paramount Pictures","PG-13","Action"
"214","11/24/1999","End of Days",1e+08,66889043,212026975,"Universal","R","Action"
"215","6/11/2004","The Stepford Wives",1e+08,59475623,96221971,"Paramount Pictures","PG-13","Comedy"
"216","6/8/2007","Surf's Up",1e+08,58867694,145395745,"Sony Pictures","PG","Adventure"
"217","12/8/2006","Blood Diamond",1e+08,57377916,171377916,"Warner Bros.","R","Action"
"218","11/7/1997","Starship Troopers",1e+08,54768952,121100000,"Sony Pictures","R","Action"
"219","6/5/2009","Land of the Lost",1e+08,49438370,69548641,"Universal","PG-13","Comedy"
"220","7/23/2004","Catwoman",1e+08,40202379,82145379,"Warner Bros.","PG-13","Action"
"221","8/15/2014","The Expendables 3",1e+08,39322544,209461378,"Lionsgate","PG-13","Action"
"222","11/27/2002","Treasure Planet",1e+08,38120554,91800000,"Walt Disney","PG","Adventure"
"223","3/12/2010","Green Zone",1e+08,35497337,97523020,"Universal","R","Drama"
"224","10/20/2017","Geostorm",1e+08,33700160,220800160,"Warner Bros.","PG-13","Action"
"225","12/11/2015","In the Heart of the Sea",1e+08,25020758,89693309,"Warner Bros.","PG-13","Adventure"
"226","2/18/2005","Son of the Mask",1e+08,17018422,59918422,"New Line","PG","Adventure"
"227","8/16/2002","The Adventures of Pluto Nash",1e+08,4411102,7094995,"Warner Bros.","PG-13","Comedy"
"228","1/20/2012","Jin líng shí san chai",1e+08,311434,98227017,"Wrekin Hill Enterta…","R","Drama"
"229","3/15/2019","Wonder Park",1e+08,0,0,"Paramount Pictures","PG","Adventure"
"230","11/6/2015","The Peanuts Movie",9.9e+07,130178411,250091610,"20th Century Fox","G","Adventure"
"231","5/4/2001","The Mummy Returns",9.8e+07,202007640,435040395,"Universal","PG-13","Adventure"
"232","12/20/2002","Gangs of New York",9.7e+07,77730500,183124621,"Miramax","R","Drama"
"233","5/19/2017","Alien: Covenant",9.7e+07,74262031,238521247,"20th Century Fox","R","Horror"
"234","3/13/2015","Cinderella",9.5e+07,201151353,534551353,"Walt Disney","PG","Drama"
"235","7/13/2012","Ice Age: Continental Drift",9.5e+07,161321843,879765137,"20th Century Fox","PG","Adventure"
"236","12/28/2001","Black Hawk Down",9.5e+07,108638745,159691085,"Sony Pictures","R","Action"
"237","5/27/2010","Sex and the City 2",9.5e+07,95347692,294680778,"Warner Bros.","R","Comedy"
"238","8/10/2012","The Campaign",9.5e+07,86907746,104907746,"Warner Bros.","R","Comedy"
"239","11/12/2010","Unstoppable",9.5e+07,81562942,165720921,"20th Century Fox","PG-13","Action"
"240","5/9/1997","The Fifth Element",9.5e+07,63570862,263898761,"Sony Pictures","PG-13","Action"
"241","3/31/2000","The Road to El Dorado",9.5e+07,50802661,65700000,"Dreamworks SKG","PG","Adventure"
"242","12/11/2009","The Lovely Bones",9.5e+07,44114232,94894448,"Paramount Pictures","PG-13","Drama"
"243","2/6/2015","Seventh Son",9.5e+07,17725785,109485785,"Universal","PG-13","Adventure"
"244","5/30/2003","Finding Nemo",9.4e+07,380529370,936429370,"Walt Disney","G","Adventure"
"245","6/15/2001","Lara Croft: Tomb Raider",9.4e+07,131144183,273330185,"Paramount Pictures","PG-13","Adventure"
"246","2/13/2015","Kingsman: The Secret Service",9.4e+07,128261724,404561724,"20th Century Fox","R","Action"
"247","7/18/2001","Jurassic Park III",9.3e+07,181166115,365900000,"Universal","PG-13","Action"
"248","8/5/2011","Rise of the Planet of the Apes",9.3e+07,176760185,482860185,"20th Century Fox","PG-13","Adventure"
"249","2/14/2008","The Spiderwick Chronicles",92500000,71195053,162839667,"Paramount Pictures","PG","Adventure"
"250","11/5/2004","The Incredibles",9.2e+07,261441092,614726752,"Walt Disney","PG","Adventure"
"251","2/14/2013","A Good Day to Die Hard",9.2e+07,67349198,304249198,"20th Century Fox","R","Action"
"252","12/22/1995","Cutthroat Island",9.2e+07,10017322,18517322,"MGM","PG-13","Adventure"
"253","12/25/2013","The Secret Life of Walter Mitty",9.1e+07,58236838,187861183,"20th Century Fox","PG","Adventure"
"254","12/20/2017","Jumanji: Welcome to the Jungle",9e+07,404508916,961758540,"Sony Pictures","PG-13","Adventure"
"255","7/1/1997","Men in Black",9e+07,250690539,587790539,"Sony Pictures","PG-13","Adventure"
"256","11/19/1999","Toy Story 2",9e+07,245852179,511358276,"Walt Disney","G","Adventure"
"257","8/3/2001","Rush Hour 2",9e+07,226164286,347425832,"New Line","PG-13","Action"
"258","12/25/2009","Sherlock Holmes",9e+07,209028679,498438212,"Warner Bros.","PG-13","Adventure"
"259","7/1/2009","Ice Age: Dawn of the Dinosaurs",9e+07,196573705,859701857,"20th Century Fox","PG","Adventure"
"260","4/15/2011","Rio",9e+07,143619809,487519809,"20th Century Fox","G","Adventure"
"261","10/6/2006","The Departed",9e+07,132384315,289660619,"Warner Bros.","R","Drama"
"262","11/3/2000","Charlie's Angels",9e+07,125305545,259736090,"Sony Pictures","PG-13","Action"
"263","6/19/1998","Mulan",9e+07,120620254,303500000,"Walt Disney","G","Adventure"
"264","8/13/2008","Tropic Thunder",9e+07,110515313,191145256,"Paramount Pictures","R","Comedy"
"265","7/11/1997","Contact",9e+07,100920329,165900000,"Warner Bros.","PG","Drama"
"266","6/6/2008","You Don't Mess With the Zohan",9e+07,100018837,202910991,"Sony Pictures","PG-13","Comedy"
"267","5/19/1995","Die Hard: With a Vengeance",9e+07,100012499,366101666,"20th Century Fox","R","Action"
"268","6/8/2001","Atlantis: The Lost Empire",9e+07,84052762,186049020,"Walt Disney","PG","Adventure"
"269","7/24/2015","Pixels",9e+07,78765986,244041804,"Sony Pictures","PG-13","Adventure"
"270","6/29/2001","Artificial Intelligence: AI",9e+07,78616689,235900000,"Warner Bros.","PG-13","Drama"
"271","11/26/2003","The Haunted Mansion",9e+07,75817994,155750628,"Walt Disney","PG","Adventure"
"272","8/4/2000","Hollow Man",9e+07,73209340,191200000,"Sony Pictures","R","Horror"
"273","8/7/2013","Percy Jackson: Sea of Monsters",9e+07,68559554,200859554,"20th Century Fox","PG","Adventure"
"274","11/21/2001","Spy Game",9e+07,62362560,143049560,"Universal","R","Action"
"275","4/4/1997","The Saint",9e+07,61363304,169400000,"Paramount Pictures","PG-13","Action"
"276","3/10/2000","Mission to Mars",9e+07,60874615,1.06e+08,"Walt Disney","PG","Adventure"
"277","12/17/1999","Bicentennial Man",9e+07,58220776,87420776,"Walt Disney","PG","Drama"
"278","3/16/2018","Tomb Raider",9e+07,57421715,272648985,"Warner Bros.","PG-13","Action"
"279","7/7/2004","King Arthur",9e+07,51877963,203877963,"Walt Disney","PG-13","Adventure"
"280","4/25/1997","Volcano",9e+07,47546796,120100000,"20th Century Fox","PG-13","Action"
"281","7/19/2002","K-19: The Widowmaker",9e+07,35168966,65716126,"Paramount Pictures","PG-13","Action"
"282","4/21/2017","The Promise",9e+07,8224288,10551417,"Open Road","PG-13","Drama"
"283","5/10/1996","Twister",8.8e+07,241688385,495700000,"Warner Bros.","PG-13","Action"
"284","6/3/2005","Cinderella Man",8.8e+07,61649911,105021488,"Universal","PG-13","Drama"
"285","9/14/2018","The Predator",8.8e+07,50787159,127987159,"20th Century Fox","R","Action"
"286","7/8/2005","Fantastic Four",87500000,154696080,333132750,"20th Century Fox","PG-13","Action"
"287","2/9/2001","Hannibal",8.7e+07,165092266,350100280,"MGM","R","Horror"
"288","7/25/2003","Seabiscuit",8.6e+07,120277854,148715342,"Universal","PG-13","Drama"
"289","12/22/2000","Cast Away",8.5e+07,233632142,427230516,"20th Century Fox","PG-13","Drama"
"290","11/17/2006","Happy Feet",8.5e+07,198000317,385000317,"Warner Bros.","PG","Adventure"
"291","7/25/1997","Air Force One",8.5e+07,172956409,315268353,"Sony Pictures","R","Action"
"292","4/3/2009","Fast & Furious",8.5e+07,155064265,363064265,"Universal","PG-13","Action"
"293","3/14/2008","Horton Hears a Who",8.5e+07,154529439,299477886,"20th Century Fox","G","Adventure"
"294","3/21/2014","Divergent",8.5e+07,150947895,276014965,"Lionsgate","PG-13","Adventure"
"295","9/28/2012","Hotel Transylvania",8.5e+07,148313048,378505812,"Sony Pictures","PG","Adventure"
"296","7/20/2007","I Now Pronounce You Chuck and Larry",8.5e+07,119725280,185708462,"Universal","PG-13","Comedy"
"297","6/8/2007","Ocean's Thirteen",8.5e+07,117144465,311744465,"Warner Bros.","PG-13","Adventure"
"298","11/20/1998","Enemy of the State",8.5e+07,111549836,250649836,"Walt Disney","R","Action"
"299","9/29/2006","Open Season",8.5e+07,85105259,191932158,"Sony Pictures","PG","Adventure"
"300","11/4/2011","Tower Heist",8.5e+07,78046570,150422946,"Universal","PG-13","Comedy"
"301","11/22/2000","102 Dalmatians",8.5e+07,66941559,66941559,"Walt Disney","G","Adventure"
"302","3/30/2012","Mirror Mirror",8.5e+07,64935167,173613482,"Relativity","PG","Adventure"
"303","12/9/2005","Memoirs of a Geisha",8.5e+07,57010853,161510853,"Sony Pictures","PG-13","Drama"
"304","3/16/2001","Enemy at the Gates",8.5e+07,51396781,96971293,"Paramount Pictures","R","Drama"
"305","6/18/1993","Last Action Hero",8.5e+07,50016394,137298489,"Sony Pictures","PG-13","Action"
"306","9/26/2003","The Rundown",8.5e+07,47641743,80831893,"Universal","PG-13","Action"
"307","11/23/2011","Arthur Christmas",8.5e+07,46462469,149717124,"Sony Pictures","PG","Adventure"
"308","1/20/2017","xXx: Return of Xander Cage",8.5e+07,44898413,345044476,"Paramount Pictures","PG-13","Action"
"309","11/13/1998","Meet Joe Black",8.5e+07,44650003,44650003,"Universal","PG-13","Drama"
"310","2/8/2002","Collateral Damage",8.5e+07,40048332,78353508,"Warner Bros.","R","Action"
"311","3/15/2002","Showtime",8.5e+07,37948765,78948765,"Warner Bros.","PG-13","Comedy"
"312","6/30/1995","Judge Dredd",8.5e+07,34687912,113487912,"Walt Disney","R","Action"
"313","8/13/2010","Scott Pilgrim vs. The World",8.5e+07,31611316,48056764,"Universal","PG-13","Comedy"
"314","3/28/2003","The Core",8.5e+07,31111260,74132631,"Paramount Pictures","PG-13","Action"
"315","5/9/1997","Father's Day",8.5e+07,28681080,35681080,"Warner Bros.","PG-13","Comedy"
"316","6/14/2002","Scooby-Doo",8.4e+07,153294164,276294164,"Warner Bros.","PG","Adventure"
"317","7/28/2000","Nutty Professor II: The Klumps",8.4e+07,123307945,166307945,"Universal","PG-13","Comedy"
"318","7/19/2013","RED 2",8.4e+07,53262560,141507355,"Lionsgate","PG-13","Action"
"319","6/23/2006","Click",82500000,137355633,237685089,"Sony Pictures","PG-13","Comedy"
"320","12/15/2006","Charlotte's Web",82500000,82985708,143985708,"Paramount Pictures","G","Drama"
"321","2/14/2008","Jumper",82500000,80172128,222640812,"20th Century Fox","PG-13","Adventure"
"322","7/11/2008","Hellboy II: The Golden Army",82500000,75986503,160388063,"Universal","PG-13","Action"
"323","5/27/2005","The Longest Yard",8.2e+07,158119460,191558505,"Paramount Pictures","PG-13","Comedy"
"324","8/13/2010","The Expendables",8.2e+07,103068524,268268174,"Lionsgate","R","Action"
"325","11/17/2000","The 6th Day",8.2e+07,34543701,96024898,"Sony Pictures","PG-13","Action"
"326","5/23/2003","Bruce Almighty",8.1e+07,242704995,484468608,"Universal","PG-13","Comedy"
"327","5/26/2011","The Hangover Part II",8e+07,254464305,586464305,"Warner Bros.","R","Comedy"
"328","5/21/1996","Mission: Impossible",8e+07,180981886,457697994,"Paramount Pictures","PG-13","Action"
"329","2/10/2017","The Lego Batman Movie",8e+07,175750384,310692896,"Warner Bros.","PG","Adventure"
"330","9/25/2015","Hotel Transylvania 2",8e+07,169700110,469500298,"Sony Pictures","PG","Adventure"
"331","6/18/1992","Batman Returns",8e+07,162833635,266824291,"Warner Bros.","PG-13","Action"
"332","5/7/1999","The Mummy",8e+07,155385488,416385488,"Universal","PG-13","Adventure"
"333","5/19/2006","Over the Hedge",8e+07,155019340,343397247,"Paramount Pictures","PG","Adventure"
"334","6/21/2002","Lilo & Stitch",8e+07,145771527,245800000,"Walt Disney","PG","Adventure"
"335","5/8/1998","Deep Impact",8e+07,140464664,349464664,"Paramount Pictures","PG-13","Adventure"
"336","7/12/2013","Grown Ups 2",8e+07,133668525,247023808,"Sony Pictures","PG-13","Comedy"
"337","6/20/2008","Get Smart",8e+07,130319208,226739416,"Warner Bros.","PG-13","Comedy"
"338","3/11/2005","Robots",8e+07,128200012,260700012,"20th Century Fox","PG","Adventure"
"339","11/26/2008","Four Christmases",8e+07,120146040,168311558,"Warner Bros.","PG-13","Comedy"
"340","6/27/1997","Face/Off",8e+07,112276146,241200000,"Paramount Pictures","R","Action"
"341","12/25/2008","Bedtime Stories",8e+07,110101975,221468935,"Walt Disney","PG","Adventure"
"342","7/12/2002","Road to Perdition",8e+07,104054514,183354514,"Dreamworks SKG","R","Drama"
"343","2/14/2003","Daredevil",8e+07,102543518,182782518,"20th Century Fox","PG-13","Action"
"344","6/6/1997","Con Air",8e+07,101117573,224117573,"Walt Disney","R","Action"
"345","12/17/2010","Yogi Bear",8e+07,100246011,204774690,"Warner Bros.","PG","Adventure"
"346","12/25/2003","Cold Mountain",8e+07,95632614,165173909,"Miramax","R","Drama"
"347","1/15/2010","The Book of Eli",8e+07,94835059,158750817,"Warner Bros.","R","Action"
"348","11/26/1997","Flubber",8e+07,92993801,177993801,"Walt Disney","PG","Comedy"
"349","7/23/1999","The Haunting",8e+07,91188905,180188905,"Dreamworks SKG","PG-13","Horror"
"350","11/15/1996","Space Jam",8e+07,90463534,250200000,"Warner Bros.","PG","Adventure"
"351","10/17/2014","Fury",8e+07,85817906,210315681,"Sony Pictures","R","Drama"
"352","2/10/2006","The Pink Panther",8e+07,82226474,158926474,"Sony Pictures","PG","Adventure"
"353","12/12/2008","The Day the Earth Stood Still",8e+07,79366978,233066978,"20th Century Fox","PG-13","Adventure"
"354","5/24/2002","Spirit: Stallion of the Cimarron",8e+07,73215310,106515310,"Dreamworks SKG","G","Adventure"
"355","6/8/2001","Swordfish",8e+07,69772969,147080413,"Warner Bros.","R","Action"
"356","4/3/1998","Lost In Space",8e+07,69117629,136047317,"New Line","PG-13","Adventure"
"357","9/28/2018","Smallfoot",8e+07,66361035,137161035,"Warner Bros.","PG","Adventure"
"358","6/24/2005","Bewitched",8e+07,63313159,131159306,"Sony Pictures","PG-13","Comedy"
"359","3/8/2002","The Time Machine",8e+07,56684819,98983590,"Dreamworks SKG","PG-13","Adventure"
"360","10/2/1998","What Dreams May Come",8e+07,55485043,71485043,"Polygram","PG-13","Drama"
"361","12/25/1998","Mighty Joe Young",8e+07,50632037,50632037,"Walt Disney","PG","Adventure"
"362","10/28/2005","The Legend of Zorro",8e+07,45575336,141475336,"Sony Pictures","PG","Adventure"
"363","11/10/2000","Little Nicky",8e+07,39442871,58270391,"New Line","PG-13","Comedy"
"364","9/25/2009","Surrogates",8e+07,38577772,119668350,"Walt Disney","PG-13","Action"
"365","6/8/2001","Evolution",8e+07,38311134,98341932,"Dreamworks SKG","PG-13","Comedy"
"366","8/26/2005","The Brothers Grimm",8e+07,37899638,105299638,"Miramax/Dimension","PG-13","Adventure"
"367","12/13/1996","Mars Attacks!",8e+07,37771017,101371017,"Warner Bros.","PG-13","Comedy"
"368","4/14/2006","The Wild",8e+07,37384046,99010667,"Walt Disney","G","Adventure"
"369","12/20/2013","Walking with Dinosaurs",8e+07,36076121,123386322,"20th Century Fox","PG","Adventure"
"370","12/22/2000","Thirteen Days",8e+07,34566746,66554547,"New Line","PG-13","Drama"
"371","12/6/1996","Daylight",8e+07,32908290,158908290,"Universal","PG-13","Action"
"372","10/23/2015","The Last Witch Hunter",8e+07,27367660,131437876,"Lionsgate","PG-13","Action"
"373","2/21/2014","Pompeii",8e+07,23169033,108469033,"Sony Pictures","PG-13","Drama"
"374","11/14/2003","Looney Tunes: Back in Action",8e+07,20950820,54540662,"Warner Bros.","PG","Adventure"
"375","11/26/2003","Timeline",8e+07,19480739,26703184,"Paramount Pictures","PG-13","Adventure"
"376","11/25/1998","Babe: Pig in the City",8e+07,18319860,69131860,"Universal","G","Adventure"
"377","12/25/1997","The Postman",8e+07,17650704,20841123,"Warner Bros.","R","Action"
"378","11/10/2000","Red Planet",8e+07,17480890,33463969,"Warner Bros.","PG-13","Action"
"379","1/12/2007","Arthur et les Minimoys",8e+07,15132763,113325743,"Weinstein Co.","PG","Adventure"
"380","9/2/2005","A Sound of Thunder",8e+07,1900451,6300451,"Warner Bros.","PG-13","Action"
"381","6/15/1994","The Lion King",79300000,421785283,986332275,"Walt Disney","G","Adventure"
"382","2/10/2012","Journey 2: The Mysterious Island",7.9e+07,103860290,318146162,"Warner Bros.","PG","Adventure"
"383","11/11/2011","Jack and Jill",7.9e+07,74158157,150519217,"Sony Pictures","PG","Comedy"
"384","12/21/2001","A Beautiful Mind",7.8e+07,170708996,317668058,"Universal","PG-13","Drama"
"385","9/27/2013","Cloudy with a Chance of Meatballs 2",7.8e+07,119793567,274392649,"Sony Pictures","PG","Adventure"
"386","8/20/2004","Exorcist: The Beginning",7.8e+07,41814863,43957541,"Warner Bros.","R","Horror"
"387","2/12/2016","The Little Prince",77500000,1311213,102029819,"Entertainment One","PG","Adventure"
"388","7/3/2013","Despicable Me 2",7.6e+07,368065385,975216835,"Universal","PG","Adventure"
"389","6/6/2003","2 Fast 2 Furious",7.6e+07,127120058,236410607,"Universal","PG-13","Action"
"390","7/8/2016","The Secret Life of Pets",7.5e+07,368384330,886767422,"Universal","PG","Adventure"
"391","7/2/1996","Independence Day",7.5e+07,306169255,817400878,"20th Century Fox","PG-13","Adventure"
"392","12/21/2016","Sing",7.5e+07,270329045,634547945,"Universal","PG","Adventure"
"393","6/30/2017","Despicable Me 3",7.5e+07,264624300,1034520868,"Universal","PG","Adventure"
"394","5/22/1997","The Lost World: Jurassic Park",7.5e+07,229086679,618638999,"Universal","PG-13","Action"
"395","3/31/2006","Ice Age: The Meltdown",7.5e+07,195330621,651899282,"20th Century Fox","PG","Adventure"
"396","5/27/2005","Madagascar",7.5e+07,193595521,556559566,"Dreamworks SKG","PG","Adventure"
"397","6/25/2010","Grown Ups",7.5e+07,162001186,272223430,"Sony Pictures","PG-13","Comedy"
"398","10/1/2004","Shark Tale",7.5e+07,161412000,371917043,"Dreamworks SKG","PG","Adventure"
"399","7/14/2000","X-Men",7.5e+07,157299717,296872367,"20th Century Fox","PG-13","Action"
"400","6/27/2008","Wanted",7.5e+07,134508551,342416460,"Universal","R","Action"
"401","6/7/1996","The Rock",7.5e+07,134069511,336069511,"Walt Disney","R","Action"
"402","8/3/2018","Christopher Robin",7.5e+07,98677443,186977443,"Walt Disney","PG","Adventure"
"403","7/23/1999","Inspector Gadget",7.5e+07,97387965,97387965,"Walt Disney","PG","Adventure"
"404","11/11/2011","Immortals",7.5e+07,83504017,211562435,"Relativity","R","Action"
"405","6/18/2004","The Terminal",7.5e+07,77073959,218673959,"Dreamworks SKG","PG-13","Drama"
"406","2/18/2005","Constantine",7.5e+07,75976178,221594911,"Warner Bros.","R","Action"
"407","7/21/2006","Monster House",7.5e+07,73661010,141267370,"Sony Pictures","PG","Adventure"
"408","12/8/2000","Vertical Limit",7.5e+07,68473360,213500000,"Sony Pictures","PG-13","Action"
"409","12/21/2007","Charlie Wilson's War",7.5e+07,66661095,119512771,"Universal","R","Drama"
"410","3/4/2005","Be Cool",7.5e+07,55849401,94944017,"MGM","PG-13","Comedy"
"411","12/23/2005","Munich",7.5e+07,47379090,131492772,"Universal","R","Drama"
"412","6/4/2010","Killers",7.5e+07,47059963,95572749,"Lionsgate","PG-13","Action"
"413","8/14/2015","The Man From U.N.C.L.E.",7.5e+07,45445109,105445109,"Warner Bros.","PG-13","Action"
"414","3/7/2003","Tears of the Sun",7.5e+07,43632458,85632458,"Sony Pictures","R","Action"
"415","7/21/2006","Lady in the Water",7.5e+07,42285169,72785169,"Warner Bros.","PG-13","Drama"
"416","12/17/2004","Spanglish",7.5e+07,42044321,54344321,"Sony Pictures","PG-13","Comedy"
"417","12/17/1999","Anna and the King",7.5e+07,39251128,39251128,"20th Century Fox","PG-13","Drama"
"418","7/7/1995","First Knight",7.5e+07,37361412,127361412,"Sony Pictures","PG-13","Drama"
"419","3/25/2011","Sucker Punch",7.5e+07,36392502,89758389,"Warner Bros.","PG-13","Action"
"420","3/11/2005","Hostage",7.5e+07,34636443,77636443,"Miramax","R","Action"
"421","6/13/2003","Hollywood Homicide",7.5e+07,30207785,50409753,"Sony Pictures","PG-13","Action"
"422","6/16/2000","Titan A.E.",7.5e+07,22751979,36751979,"20th Century Fox","PG","Adventure"
"423","12/17/2004","Flight of the Phoenix",7.5e+07,21009180,34009180,"20th Century Fox","PG-13","Adventure"
"424","10/23/1998","Soldier",7.5e+07,14623082,14623082,"Warner Bros.","R","Action"
"425","1/15/1999","Virus",7.5e+07,14010690,30626690,"Universal","R","Action"
"426","2/23/2001","Monkeybone",7.5e+07,5409517,5409517,"20th Century Fox","PG-13","Comedy"
"427","7/10/2015","Minions",7.4e+07,336045770,1162781621,"Universal","PG","Adventure"
"428","5/20/2016","The Angry Birds Movie",7.3e+07,107509366,352829528,"Sony Pictures","PG","Adventure"
"429","2/13/1998","Sphere",7.3e+07,37068294,50168294,"Warner Bros.","PG-13","Horror"
"430","7/27/2007","The Simpsons Movie",72500000,183135014,527071022,"20th Century Fox","PG-13","Adventure"
"431","2/8/2008","Fool's Gold",72500000,70231041,109362966,"Warner Bros.","PG-13","Adventure"
"432","7/31/2009","Funny People",72500000,51855045,71880305,"Universal","R","Comedy"
"433","9/28/2007","The Kingdom",72500000,47467250,86509602,"Universal","R","Action"
"434","6/22/2001","Dr. Dolittle 2",7.2e+07,112950721,176101721,"20th Century Fox","PG","Adventure"
"435","5/24/1995","Braveheart",7.2e+07,75545647,209045244,"Paramount Pictures","R","Drama"
"436","11/4/2005","Jarhead",7.2e+07,62647540,96947540,"Universal","R","Drama"
"437","4/27/2001","Driven",7.2e+07,32616869,54616869,"Warner Bros.","PG-13","Action"
"438","12/21/2001","The Majestic",7.2e+07,27796042,37306334,"Warner Bros.","PG","Drama"
"439","6/25/2004","Two Brothers",7.2e+07,19176754,62176754,"Universal","PG","Drama"
"440","6/26/1998","Doctor Dolittle",71500000,144156605,294156605,"20th Century Fox","PG-13","Adventure"
"441","5/19/2004","Shrek 2",7e+07,441226247,937008132,"Dreamworks SKG","PG","Adventure"
"442","6/9/2006","Cars",7e+07,244082982,461651246,"Walt Disney","G","Adventure"
"443","6/22/1988","Who Framed Roger Rabbit?",7e+07,154112492,351500000,"Walt Disney","PG","Adventure"
"444","8/9/2002","xXx",7e+07,141930000,267200000,"Sony Pictures","PG-13","Action"
"445","6/8/2018","Ocean’s 8",7e+07,139377762,296277762,"Warner Bros.","PG-13","Action"
"446","11/8/1996","Ransom",7e+07,136492681,308700000,"Walt Disney","R","Action"
"447","8/21/2009","Inglourious Basterds",7e+07,120774594,316915264,"Weinstein Co.","R","Action"
"448","12/11/1991","Hook",7e+07,119654823,300854823,"Sony Pictures","PG","Adventure"
"449","7/3/1990","Die Hard 2",7e+07,117323878,239814025,"20th Century Fox","R","Action"
"450","8/8/2003","S.W.A.T.",7e+07,116877597,207154748,"Sony Pictures","PG-13","Action"
"451","11/10/2017","Daddy’s Home 2",7e+07,104029443,175809810,"Paramount Pictures","PG-13","Comedy"
"452","11/19/1999","Sleepy Hollow",7e+07,101068340,207068340,"Paramount Pictures","R","Horror"
"453","3/11/2011","Battle: Los Angeles",7e+07,83552429,213463976,"Sony Pictures","PG-13","Action"
"454","8/13/2004","AVP: Alien Vs. Predator",7e+07,80281096,172543519,"20th Century Fox","PG-13","Horror"
"455","12/25/2011","War Horse",7e+07,79883359,156815529,"Walt Disney","PG-13","Drama"
"456","3/1/2002","We Were Soldiers",7e+07,78120196,114658262,"Paramount Pictures","R","Drama"
"457","2/7/2014","The Monuments Men",7e+07,78031620,158702748,"Sony Pictures","PG-13","Drama"
"458","9/23/2016","Storks",7e+07,72679278,174030321,"Warner Bros.","PG","Adventure"
"459","12/11/1998","Star Trek: Insurrection",7e+07,70187658,117800000,"Paramount Pictures","PG","Adventure"
"460","12/10/2003","Big Fish",7e+07,66432867,123954323,"Sony Pictures","PG-13","Drama"
"461","1/20/2012","Underworld: Awakening",7e+07,62321039,160379930,"Sony Pictures","R","Action"
"462","9/22/2017","The Lego Ninjago Movie",7e+07,59281555,122739546,"Warner Bros.","PG","Adventure"
"463","10/10/2014","Dracula Untold",7e+07,55991880,220241723,"Universal","PG-13","Action"
"464","9/29/2006","The Guardian",7e+07,55011732,94973540,"Walt Disney","PG-13","Action"
"465","8/9/1989","The Abyss",7e+07,54243125,54243125,"20th Century Fox","PG-13","Action"
"466","9/24/2010","Wall Street 2: Money Never Sleeps",7e+07,52474616,137431619,"20th Century Fox","PG-13","Drama"
"467","1/14/2011","The Dilemma",7e+07,48475290,70546865,"Universal","PG-13","Comedy"
"468","12/25/2005","Rumor Has It",7e+07,42996140,88933562,"Warner Bros.","PG-13","Comedy"
"469","11/6/1998","The Siege",7e+07,40934175,116625798,"20th Century Fox","R","Action"
"470","8/10/2007","Stardust",7e+07,38634938,137022245,"Paramount Pictures","PG-13","Adventure"
"471","10/8/1997","Seven Years in Tibet",7e+07,37945884,131445884,"Sony Pictures","PG-13","Drama"
"472","9/14/2007","The Brave One",7e+07,36793804,69792704,"Warner Bros.","R","Drama"
"473","11/1/2002","I Spy",7e+07,33561137,60279822,"Sony Pictures","PG-13","Action"
"474","6/7/2002","Bad Company",7e+07,30157016,69157016,"Walt Disney","PG-13","Action"
"475","10/21/2005","Doom",7e+07,28212337,54612337,"Universal","R","Horror"
"476","9/23/2011","Killer Elite",7e+07,25124986,65409046,"Open Road","R","Action"
"477","1/16/1998","Hard Rain",7e+07,19870567,19870567,"Paramount Pictures","R","Action"
"478","2/15/2002","Hart's War",7e+07,19076815,33076815,"MGM","R","Drama"
"479","2/8/2002","Rollerball",7e+07,18990542,25852508,"MGM","PG-13","Action"
"480","1/10/2014","The Legend of Hercules",7e+07,18848538,58953319,"Lionsgate","PG-13","Adventure"
"481","9/20/2002","Ballistic: Ecks vs. Sever",7e+07,14294842,14294842,"Warner Bros.","R","Action"
"482","8/10/2001","Osmosis Jones",7e+07,13596911,13596911,"Warner Bros.","PG","Adventure"
"483","5/9/2014","Legends of Oz: Dorothy’s Return",7e+07,8462347,20107933,"Clarius Entertainment","PG","Adventure"
"484","5/28/2010","Agora",7e+07,619423,38992292,"Newmarket Films","R","Drama"
"485","7/9/2010","Despicable Me",6.9e+07,251513985,543464573,"Universal","PG","Adventure"
"486","7/30/2010","Dinner for Schmucks",6.9e+07,73026337,86796502,"Paramount Pictures","PG-13","Comedy"
"487","6/30/2010","The Twilight Saga: Eclipse",6.8e+07,300531751,706102828,"Summit Entertainment","PG-13","Drama"
"488","5/31/2002","The Sum of All Fears",6.8e+07,118471320,193500000,"Paramount Pictures","PG-13","Action"
"489","6/26/2015","Ted 2",6.8e+07,81476385,217214143,"Universal","R","Comedy"
"490","2/25/2011","Hall Pass",6.8e+07,45060734,87173475,"Warner Bros.","R","Comedy"
"491","11/22/1995","Money Train",6.8e+07,35324232,77224232,"Sony Pictures","R","Action"
"492","3/21/2003","Dreamcatcher",6.8e+07,33685268,75685268,"Warner Bros.","R","Drama"
"493","8/6/1999","Mystery Men",6.8e+07,29762011,33462011,"Universal","PG-13","Comedy"
"494","11/5/1999","The Insider",6.8e+07,28965197,60265197,"Walt Disney","R","Drama"
"495","12/22/2017","Downsizing",6.8e+07,24449754,48681134,"Paramount Pictures","R","Comedy"
"496","3/2/2012","Doctor Seuss' The Lorax",67500000,214030500,350976753,"Universal","PG","Adventure"
"497","6/22/2012","Abraham Lincoln: Vampire Hunter",67500000,37519139,137489730,"20th Century Fox","R","Horror"
"498","9/20/1996","Last Man Standing",6.7e+07,18115927,18115927,"New Line","R","Action"
"499","8/17/2007","The Last Legion",6.7e+07,5932060,25357771,"Weinstein/Dimension","PG-13","Action"
"500","6/19/1998","The X Files: Fight the Future",6.6e+07,83898313,189176423,"20th Century Fox","PG-13","Action"
"501","3/14/2014","Need for Speed",6.6e+07,43568507,194169619,"Walt Disney","PG-13","Action"
"502","7/24/1998","Saving Private Ryan",6.5e+07,216335085,485035085,"Dreamworks SKG","R","Drama"
"503","11/9/2012","Lincoln",6.5e+07,182207973,273346281,"Walt Disney","PG-13","Drama"
"504","3/15/2002","Ice Age",6.5e+07,176387405,386116343,"20th Century Fox","PG","Adventure"
"505","6/30/1995","Apollo 13",6.5e+07,173772767,335802271,"Universal","PG","Drama"
"506","3/31/1999","The Matrix",6.5e+07,171479930,463517383,"Warner Bros.","R","Action"
"507","11/1/2002","The Santa Clause 2",6.5e+07,139225854,172825854,"Walt Disney","G","Adventure"
"508","6/1/1990","Total Recall",6.5e+07,119394839,261400000,"Sony Pictures","R","Action"
"509","12/18/1998","You've Got Mail",6.5e+07,115821495,250800000,"Warner Bros.","PG","Drama"
"510","12/25/2014","Unbroken",6.5e+07,115637895,163527824,"Universal","PG-13","Drama"
"511","6/5/2015","Spy!",6.5e+07,110825712,233121406,"20th Century Fox","R","Comedy"
"512","11/5/2010","Due Date",6.5e+07,100539043,211739043,"Warner Bros.","R","Comedy"
"513","7/25/2008","Step Brothers",6.5e+07,100468793,128468793,"Sony Pictures","R","Comedy"
"514","12/15/1995","Jumanji",6.5e+07,100458310,262758310,"Sony Pictures","PG","Adventure"
"515","7/17/1998","The Mask of Zorro",6.5e+07,93828745,233700000,"Sony Pictures","PG-13","Adventure"
"516","8/4/2000","Space Cowboys",6.5e+07,90454043,128874043,"Warner Bros.","PG-13","Adventure"
"517","5/28/1993","Cliffhanger",6.5e+07,84049211,2.55e+08,"Sony Pictures","R","Action"
"518","8/12/2016","Pete’s Dragon",6.5e+07,76233151,137768975,"Walt Disney","PG","Adventure"
"519","2/9/1996","Broken Arrow",6.5e+07,70645997,148345997,"20th Century Fox","R","Action"
"520","8/9/2006","World Trade Center",6.5e+07,70278893,163295654,"Paramount Pictures","PG-13","Drama"
"521","7/7/2000","The Kid",6.5e+07,69688384,69688384,"Walt Disney","PG","Comedy"
"522","12/19/2003","Mona Lisa Smile",6.5e+07,63803100,141205169,"Sony Pictures","PG-13","Drama"
"523","5/16/2012","The Dictator",6.5e+07,59650222,180148897,"Paramount Pictures","R","Comedy"
"524","7/16/1999","Eyes Wide Shut",6.5e+07,55691208,104267443,"Warner Bros.","R","Drama"
"525","12/8/2004","Blade: Trinity",6.5e+07,52397389,131353165,"New Line","R","Action"
"526","12/22/2006","We Are Marshall",6.5e+07,43545364,43545364,"Warner Bros.","PG","Drama"
"527","9/14/2012","Resident Evil: Retribution",6.5e+07,42345531,238940997,"Sony Pictures","R","Action"
"528","3/20/1998","Primary Colors",6.5e+07,39017984,39017984,"Universal","R","Comedy"
"529","10/15/1999","Fight Club",6.5e+07,37030102,100851705,"20th Century Fox","R","Drama"
"530","8/22/2008","Death Race",6.5e+07,36316032,72516819,"Universal","R","Action"
"531","10/11/1996","The Long Kiss Goodnight",6.5e+07,33447612,33447612,"New Line","R","Action"
"532","12/8/2000","Proof of Life",6.5e+07,32598931,62761005,"Warner Bros.","R","Action"
"533","11/11/2005","Zathura",6.5e+07,28045540,58545540,"Sony Pictures","PG","Adventure"
"534","1/14/2005","Elektra",6.5e+07,24409722,56824633,"20th Century Fox","PG-13","Action"
"535","10/23/2009","Astro Boy",6.5e+07,19551067,41636243,"Summit Entertainment","PG","Adventure"
"536","1/24/2014","I, Frankenstein",6.5e+07,19075290,74575290,"Lionsgate","PG-13","Action"
"537","5/24/1991","Hudson Hawk",6.5e+07,17218916,17218916,"Sony Pictures","R","Action"
"538","8/22/2014","Sin City: A Dame to Kill For",6.5e+07,13757804,40650842,"Weinstein Co.","R","Action"
"539","12/25/2016","Live by Night",6.5e+07,10378555,21778555,"Warner Bros.","R","Drama"
"540","10/27/2000","Lucky Numbers",6.5e+07,10014234,10014234,"Paramount Pictures","R","Comedy"
"541","9/23/2005","Oliver Twist",6.5e+07,2070920,26670920,"Sony/TriStar","PG-13","Drama"
"542","9/4/2015","Tian jiang xiong shi",6.5e+07,74070,122519874,"Lionsgate","R","Action"
"543","7/14/2006","Little Man",6.4e+07,58636047,101636047,"Sony Pictures","PG-13","Comedy"
"544","10/8/1999","Random Hearts",6.4e+07,31054924,63200000,"Sony Pictures","R","Drama"
"545","12/27/2006","Perfume: The Story of a Murderer",63700000,2223293,133603463,"Paramount Pictures","R","Drama"
"546","6/11/1993","Jurassic Park",6.3e+07,395708305,1038812584,"Universal","PG-13","Action"
"547","7/25/2002","Austin Powers in Goldmember",6.3e+07,213117789,296338663,"New Line","PG-13","Comedy"
"548","4/1/2011","Hop",6.3e+07,108085305,188657593,"Universal","PG","Adventure"
"549","8/3/1994","Clear and Present Danger",6.2e+07,122012656,207500000,"Paramount Pictures","PG-13","Action"
"550","4/21/2000","U-571",6.2e+07,77086030,127630030,"Universal","PG-13","Action"
"551","6/20/2008","The Love Guru",6.2e+07,32235793,40159017,"Paramount Pictures","PG-13","Comedy"
"552","2/23/2001","3000 Miles to Graceland",6.2e+07,15738632,18708848,"Warner Bros.","R","Drama"
"553","3/30/2007","Blades of Glory",6.1e+07,118594548,145594548,"Paramount Pictures","PG-13","Comedy"
"554","8/2/2013","2 Guns",6.1e+07,75612460,132493015,"Universal","R","Action"
"555","12/22/2004","Meet the Fockers",6e+07,279167575,516567575,"Universal","PG-13","Comedy"
"556","2/7/2014","The Lego Movie",6e+07,257784718,457729388,"Warner Bros.","PG","Adventure"
"557","3/2/2007","Wild Hogs",6e+07,168213584,253555383,"Walt Disney","PG-13","Comedy"
"558","12/25/2008","Marley & Me",6e+07,143153751,247812011,"20th Century Fox","PG","Comedy"
"559","12/10/1999","The Green Mile",6e+07,136801374,290701374,"Warner Bros.","R","Drama"
"560","11/4/2005","Chicken Little",6e+07,135386665,310043823,"Walt Disney","G","Adventure"
"561","6/5/1998","The Truman Show",6e+07,125618201,264118201,"Paramount Pictures","PG","Drama"
"562","9/9/2016","Sully",6e+07,125070033,238552082,"Warner Bros.","PG-13","Drama"
"563","10/9/2009","Couples Retreat",6e+07,109205660,172450423,"Universal","PG-13","Comedy"
"564","11/17/1995","Goldeneye",6e+07,106429941,356429941,"MGM","PG-13","Action"
"565","5/30/2003","The Italian Job",6e+07,106126012,176262839,"Paramount Pictures","PG-13","Adventure"
"566","5/9/2003","Daddy Day Care",6e+07,104148781,164285587,"Sony Pictures","PG","Comedy"
"567","6/18/1999","The General's Daughter",6e+07,102705852,149705852,"Paramount Pictures","R","Drama"
"568","12/18/1998","The Prince of Egypt",6e+07,101413188,218613188,"Dreamworks SKG","PG","Adventure"
"569","8/6/2004","Collateral",6e+07,100170152,217670152,"Dreamworks SKG","R","Action"
"570","7/4/2001","Cats & Dogs",6e+07,93375151,200700000,"Warner Bros.","PG","Comedy"
"571","10/2/1998","Antz",6e+07,90757863,152457863,"Dreamworks SKG","PG","Adventure"
"572","4/19/2002","The Scorpion King",6e+07,90580000,165890634,"Universal","PG-13","Action"
"573","10/15/2010","Red",6e+07,90380162,196439693,"Summit Entertainment","PG-13","Action"
"574","3/5/2004","Starsky & Hutch",6e+07,88200225,170200225,"Warner Bros.","PG-13","Comedy"
"575","6/27/1990","Days of Thunder",6e+07,82670733,157670733,"Paramount Pictures","PG-13","Action"
"576","12/21/2005","Cheaper by the Dozen 2",6e+07,82571173,135015330,"20th Century Fox","PG","Adventure"
"577","8/13/2010","Eat Pray Love",6e+07,80574010,206598789,"Sony Pictures","PG-13","Drama"
"578","12/21/2012","Jack Reacher",6e+07,80070736,217370736,"Paramount Pictures","PG-13","Drama"
"579","12/22/2000","The Family Man",6e+07,75764085,124715863,"Universal","PG-13","Comedy"
"580","12/22/1999","Any Given Sunday",6e+07,75530832,100230832,"Warner Bros.","R","Drama"
"581","5/15/1998","The Horse Whisperer",6e+07,75383563,186883563,"Walt Disney","PG-13","Drama"
"582","2/6/2009","Coraline",6e+07,75286229,126037057,"Focus Features","PG","Adventure"
"583","10/1/2004","Ladder 49",6e+07,74541707,102332848,"Walt Disney","PG-13","Action"
"584","7/28/1999","Deep Blue Sea",6e+07,73648228,165048228,"Warner Bros.","R","Action"
"585","1/17/2003","Kangaroo Jack",6e+07,66723216,90723216,"Warner Bros.","PG","Adventure"
"586","3/4/2016","London Has Fallen",6e+07,62524260,194094168,"Focus Features","R","Action"
"587","3/10/2006","The Shaggy Dog",6e+07,61123569,87123569,"Walt Disney","PG","Comedy"
"588","11/22/1996","Jingle All the Way",6e+07,60592389,129832389,"20th Century Fox","PG","Adventure"
"589","4/2/2004","Hellboy",6e+07,59623958,99823958,"Sony Pictures","PG-13","Action"
"590","10/21/2016","Jack Reacher: Never Go Back",6e+07,58697076,160038407,"Paramount Pictures","PG-13","Action"
"591","5/25/2017","Baywatch",6e+07,58060186,176023296,"Paramount Pictures","R","Comedy"
"592","12/25/1998","A Civil Action",6e+07,56709981,56709981,"Walt Disney","PG-13","Drama"
"593","12/25/2015","Joy",6e+07,56451232,101134059,"20th Century Fox","PG-13","Drama"
"594","8/17/2012","ParaNorman",6e+07,56003051,108119662,"Focus Features","PG","Adventure"
"595","3/1/1996","Up Close & Personal",6e+07,51045801,100645801,"Walt Disney","PG-13","Drama"
"596","12/19/2008","The Tale of Despereaux",6e+07,50877145,90482317,"Universal","G","Adventure"
"597","9/26/2014","The Boxtrolls",6e+07,50837305,111946251,"Focus Features","PG","Adventure"
"598","9/27/2002","The Tuxedo",6e+07,50586000,104429625,"Dreamworks SKG","PG-13","Action"
"599","1/17/2014","Jack Ryan: Shadow Recruit",6e+07,50577412,131377412,"Paramount Pictures","PG-13","Action"
"600","7/14/1995","Under Siege 2: Dark Territory",6e+07,50024083,104324083,"Warner Bros.","R","Action"
"601","11/26/1997","Alien: Resurrection",6e+07,47795018,160700000,"20th Century Fox","R","Action"
"602","10/16/1998","Practical Magic",6e+07,46850558,68336997,"Warner Bros.","PG-13","Comedy"
"603","1/11/2013","Gangster Squad",6e+07,46000903,104100903,"Warner Bros.","R","Drama"
"604","4/7/2017","Smurfs: The Lost Village",6e+07,45020282,197422438,"Sony Pictures","PG","Adventure"
"605","6/19/2009","Year One",6e+07,43337279,57604723,"Sony Pictures","PG-13","Comedy"
"606","1/29/2010","Edge of Darkness",6e+07,43313890,82812456,"Warner Bros.","R","Drama"
"607","12/13/2002","Star Trek: Nemesis",6e+07,43254409,67312826,"Paramount Pictures","PG-13","Adventure"
"608","2/19/2002","Reign of Fire",6e+07,43061982,82150183,"Walt Disney","PG-13","Action"
"609","11/20/2009","Planet 51",6e+07,42194060,108996113,"Sony Pictures","PG","Adventure"
"610","12/11/2009","Invictus",6e+07,37491364,124514011,"Warner Bros.","PG-13","Drama"
"611","2/12/1999","My Favorite Martian",6e+07,36850101,36850101,"Walt Disney","PG","Comedy"
"612","9/21/2012","Trouble with the Curve",6e+07,35763137,47818913,"Warner Bros.","PG-13","Drama"
"613","1/10/1997","The Relic",6e+07,33956608,33956608,"Paramount Pictures","R","Horror"
"614","9/15/2000","Almost Famous",6e+07,32522352,47371191,"Dreamworks SKG","R","Comedy"
"615","12/6/2002","Analyze That",6e+07,32122249,54994757,"Warner Bros.","R","Comedy"
"616","4/24/2009","The Soloist",6e+07,31853584,38522450,"Paramount Pictures","PG-13","Drama"
"617","11/3/2000","The Legend of Bagger Vance",6e+07,30695227,39235486,"Dreamworks SKG","PG-13","Drama"
"618","2/22/2002","Dragonfly",6e+07,30063805,30063805,"Universal","PG-13","Drama"
"619","10/12/2018","First Man",6e+07,30000050,55500050,"Universal","PG-13","Drama"
"620","6/16/2006","Garfield: A Tail of Two Kitties",6e+07,28426747,147985373,"20th Century Fox","PG","Adventure"
"621","4/29/2005","XXX: State of the Union",6e+07,26873932,71073932,"Sony Pictures","PG-13","Action"
"622","8/15/1997","Event Horizon",6e+07,26673242,26673242,"Paramount Pictures","R","Horror"
"623","7/2/2003","Sinbad: Legend of the Seven Seas",6e+07,26483452,80767884,"Dreamworks SKG","PG","Adventure"
"624","3/26/1999","EDtv",6e+07,22508689,35319689,"Universal","PG-13","Comedy"
"625","12/25/2008","The Spirit",6e+07,19806188,39006188,"Lionsgate","PG-13","Action"
"626","10/19/2001","The Last Castle",6e+07,18208078,20541668,"Dreamworks SKG","R","Drama"
"627","1/23/2009","Inkheart",6e+07,17303424,66655938,"Warner Bros.","PG","Adventure"
"628","1/14/2000","Supernova",6e+07,14218868,14816494,"MGM","PG-13","Action"
"629","9/22/2006","Flyboys",6e+07,13090630,14816379,"MGM","PG-13","Drama"
"630","2/14/2014","Winter's Tale",6e+07,12600231,22468620,"Warner Bros.","PG-13","Drama"
"631","10/9/1998","Holy Man",6e+07,12069719,12069719,"Walt Disney","PG","Comedy"
"632","7/11/2008","Meet Dave",6e+07,11803254,50648806,"20th Century Fox","PG","Adventure"
"633","8/12/2005","The Great Raid",6e+07,10166502,10597070,"Miramax","R","Action"
"634","2/24/2017","Rock Dog",6e+07,9420546,24152192,"Lionsgate","PG","Adventure"
"635","1/23/2015","Mortdecai",6e+07,7696134,30396134,"Lionsgate","R","Adventure"
"636","10/24/2003","Beyond Borders",6e+07,4426297,11427090,"Paramount Pictures","R","Drama"
"637","3/23/2018","Sherlock Gnomes",5.9e+07,43242871,87750965,"Paramount Pictures","PG","Adventure"
"638","2/12/2016","Deadpool",5.8e+07,363070709,801029249,"20th Century Fox","R","Action"
"639","12/25/2014","American Sniper",5.8e+07,350126372,547326372,"Warner Bros.","R","Drama"
"640","10/16/2015","Goosebumps",5.8e+07,80069458,158905324,"Sony Pictures","PG","Horror"
"641","5/25/1988","Rambo III",5.8e+07,53715611,188715611,"Sony/TriStar","R","Action"
"642","1/20/2012","Red Tails",5.8e+07,49876377,50365498,"20th Century Fox","PG-13","Action"
"643","6/7/2013","The Internship",5.8e+07,44672764,93672764,"20th Century Fox","PG-13","Comedy"
"644","4/28/2000","The Flintstones in Viva Rock Vegas",5.8e+07,35231365,59431365,"Universal","PG","Adventure"
"645","5/30/2008","Sex and the City",57500000,152647258,415247258,"Warner Bros.","R","Comedy"
"646","9/10/2010","Resident Evil: Afterlife",57500000,60128566,295874190,"Sony Pictures","R","Horror"
"647","6/15/2012","That's My Boy",57500000,36931089,58085235,"Sony Pictures","R","Comedy"
"648","10/17/1997","Devil's Advocate",5.7e+07,61007424,153007424,"Warner Bros.","R","Drama"
"649","2/17/2012","Ghost Rider: Spirit of Vengeance",5.7e+07,51774002,149217355,"Sony Pictures","PG-13","Action"
"650","5/31/1996","Dragonheart",5.7e+07,51364680,104364680,"Universal","PG-13","Adventure"
"651","11/12/2004","After the Sunset",5.7e+07,28328132,38329114,"New Line","PG-13","Action"
"652","8/17/2001","Captain Corelli's Mandolin",5.7e+07,25528495,62097495,"Miramax","R","Drama"
"653","4/11/2003","Anger Management",5.6e+07,135560942,195660942,"Sony Pictures","PG-13","Comedy"
"654","3/4/2005","The Pacifier",5.6e+07,113006880,198006880,"Walt Disney","PG","Comedy"
"655","4/2/2004","Walking Tall",5.6e+07,46213824,47313824,"MGM","PG-13","Action"
"656","7/6/1994","Forrest Gump",5.5e+07,330151138,679850637,"Paramount Pictures","PG-13","Drama"
"657","12/14/2007","Alvin and the Chipmunks",5.5e+07,217326974,362605033,"20th Century Fox","PG","Adventure"
"658","10/6/2000","Meet the Parents",5.5e+07,166225040,330425040,"Universal","PG-13","Comedy"
"659","12/15/2006","The Pursuit of Happyness",5.5e+07,162586036,307311093,"Sony Pictures","PG-13","Drama"
"660","6/10/1995","Pocahontas",5.5e+07,141579773,347100000,"Walt Disney","G","Adventure"
"661","12/15/1978","Superman",5.5e+07,134218018,300200000,"Warner Bros.","PG","Adventure"
"662","6/28/1996","The Nutty Professor",5.5e+07,128814019,273814019,"Universal","PG-13","Comedy"
"663","2/10/2017","Fifty Shades Darker",5.5e+07,114434010,381437482,"Universal","R","Drama"
"664","10/11/2013","Captain Phillips",5.5e+07,107136417,220648184,"Sony Pictures","PG-13","Drama"
"665","7/16/1997","George Of The Jungle",5.5e+07,105263257,174463257,"Walt Disney","PG","Adventure"
"666","8/1/2003","American Wedding",5.5e+07,104354205,126425115,"Universal","R","Comedy"
"667","11/10/2017","Murder on the Orient Express",5.5e+07,102826543,345924923,"20th Century Fox","PG-13","Drama"
"668","9/26/2014","The Equalizer",5.5e+07,101530738,192903624,"Sony Pictures","R","Action"
"669","2/9/2018","Fifty Shades Freed",5.5e+07,100407760,371222158,"Universal","R","Drama"
"670","5/26/1995","Casper",5.5e+07,100328194,282300000,"Universal","PG","Comedy"
"671","4/9/2010","Date Night",5.5e+07,98711404,152269033,"20th Century Fox","PG-13","Comedy"
"672","5/12/1995","Crimson Tide",5.5e+07,91387195,159387195,"Walt Disney","R","Action"
"673","12/9/1994","Disclosure",5.5e+07,83015089,212200000,"Warner Bros.","R","Drama"
"674","4/10/1998","City of Angels",5.5e+07,78750909,198750909,"Warner Bros.","PG-13","Drama"
"675","1/16/2015","Paddington",5.5e+07,76223578,258789097,"Weinstein Co.","PG","Adventure"
"676","4/28/2006","R.V.",5.5e+07,71724497,87473024,"Sony Pictures","PG","Adventure"
"677","10/28/1994","Stargate",5.5e+07,71565669,196565669,"MGM","PG-13","Action"
"678","10/10/2003","Kill Bill: Volume 1",5.5e+07,70098138,176469428,"Miramax","R","Action"
"679","6/17/2011","Mr. Poppers's Penguins",5.5e+07,68224452,189624452,"20th Century Fox","PG","Adventure"
"680","8/13/1999","Bowfinger",5.5e+07,66458769,98699769,"Universal","PG-13","Comedy"
"681","4/16/2004","Kill Bill: Volume 2",5.5e+07,66207920,153535982,"Miramax","R","Action"
"682","12/22/1989","Tango & Cash",5.5e+07,63408614,63408614,"Warner Bros.","R","Action"
"683","7/31/1992","Death Becomes Her",5.5e+07,58422650,149022650,"Universal","PG-13","Comedy"
"684","11/1/2013","Free Birds",5.5e+07,55750480,110387072,"Relativity","PG","Adventure"
"685","5/22/1992","Alien 3",5.5e+07,54927174,158500000,"20th Century Fox","R","Action"
"686","4/18/2008","The Forbidden Kingdom",5.5e+07,52075270,129075270,"Lionsgate","PG-13","Action"
"687","3/21/2014","Muppets Most Wanted",5.5e+07,51178893,79312301,"Walt Disney","PG","Adventure"
"688","8/19/2016","Kubo and the Two Strings",5.5e+07,48023088,77548564,"Focus Features","PG","Adventure"
"689","9/25/1998","Ronin",5.5e+07,41610884,70692101,"MGM","R","Action"
"690","11/24/2010","Burlesque",5.5e+07,39440655,90552675,"Sony Pictures","PG-13","Drama"
"691","10/11/1996","The Ghost and the Darkness",5.5e+07,38564422,38564422,"Paramount Pictures","R","Action"
"692","7/27/2012","The Watch",5.5e+07,34353000,67130045,"20th Century Fox","R","Comedy"
"693","6/4/1999","Instinct",5.5e+07,34105207,34105207,"Walt Disney","R","Drama"
"694","12/12/2003","Stuck On You",5.5e+07,33832741,63537164,"20th Century Fox","PG-13","Comedy"
"695","2/29/2008","Semi-Pro",5.5e+07,33479698,43980363,"New Line","R","Comedy"
"696","10/16/2015","Crimson Peak",5.5e+07,31090320,75466595,"Universal","R","Horror"
"697","4/27/2012","The Pirates! Band of Misfits",5.5e+07,31051126,136143605,"Sony Pictures","PG","Adventure"
"698","12/2/2005","Aeon Flux",5.5e+07,25857987,53913573,"Paramount Pictures","PG-13","Action"
"699","10/12/2007","Elizabeth: The Golden Age",5.5e+07,16285240,74870866,"Universal","PG-13","Drama"
"700","6/12/2009","Imagine That",5.5e+07,16222392,16222392,"Paramount Pictures","PG","Adventure"
"701","2/21/2003","Gods and Generals",5.5e+07,12882934,12923936,"Warner Bros.","PG-13","Drama"
"702","2/1/2013","Bullet to the Head",5.5e+07,9489829,22597969,"Warner Bros.","R","Action"
"703","9/22/2006","All the King's Men",5.5e+07,7221458,9521458,"Sony Pictures","PG-13","Drama"
"704","7/30/2004","Thunderbirds",5.5e+07,6768055,28231444,"Universal","PG","Adventure"
"705","11/26/2004","Un long dimanche de fiançailles",5.5e+07,6167817,69759296,"Warner Bros.","R","Drama"
"706","5/4/2007","Lucky You",5.5e+07,5755286,6521829,"Warner Bros.","PG-13","Drama"
"707","7/22/1998","Lolita",5.5e+07,1147784,1147784,"MGM","R","Drama"
"708","6/19/1981","Superman II",5.4e+07,108185706,108185706,"Warner Bros.","PG","Adventure"
"709","3/22/2002","Blade 2",5.4e+07,81676888,154338601,"New Line","R","Action"
"710","7/14/2006","You, Me and Dupree",5.4e+07,75802010,130402010,"Universal","PG-13","Comedy"
"711","12/19/2008","Seven Pounds",5.4e+07,69951824,166617328,"Sony Pictures","PG-13","Drama"
"712","12/25/1990","The Godfather: Part III",5.4e+07,66520529,66520529,"Paramount Pictures","R","Drama"
"713","10/14/2005","Elizabethtown",5.4e+07,26850426,50719373,"Paramount Pictures","PG-13","Drama"
"714","8/5/2005","The Dukes of Hazzard",5.3e+07,80270227,109848461,"Warner Bros.","PG-13","Comedy"
"715","9/18/2015","Black Mass",5.3e+07,62575678,98837872,"Warner Bros.","R","Drama"
"716","10/20/2006","Flags of Our Fathers",5.3e+07,33602376,63657941,"Paramount Pictures","R","Drama"
"717","4/6/2007","Grindhouse",5.3e+07,25031037,50187789,"Weinstein/Dimension","R","Horror"
"718","10/16/1998","Beloved",5.3e+07,22852487,22852487,"Walt Disney","R","Drama"
"719","12/19/2012","Zero Dark Thirty",52500000,95720716,134612435,"Sony Pictures","R","Drama"
"720","12/25/2002","Catch Me if You Can",5.2e+07,164606800,355612291,"Dreamworks SKG","PG-13","Drama"
"721","11/22/1995","Casino",5.2e+07,42438300,110400000,"Universal","R","Drama"
"722","8/5/2011","The Change-Up",5.2e+07,37243418,75997067,"Universal","R","Comedy"
"723","12/23/1998","The Thin Red Line",5.2e+07,36400491,97709034,"20th Century Fox","R","Drama"
"724","12/22/1999","Man on the Moon",5.2e+07,34580635,47407635,"Universal","R","Drama"
"725","4/16/2003","Bulletproof Monk",5.2e+07,23010607,23010607,"MGM","PG-13","Action"
"726","11/22/2006","Deck the Halls",5.1e+07,35093569,46815807,"20th Century Fox","PG","Comedy"
"727","11/20/2009","The Twilight Saga: New Moon",5e+07,296623634,687557727,"Summit Entertainment","PG-13","Drama"
"728","5/18/2001","Shrek",5e+07,267655011,491812794,"Dreamworks SKG","PG","Adventure"
"729","6/29/2012","Ted",5e+07,218665740,556016627,"Universal","R","Adventure"
"730","6/13/2014","22 Jump Street",5e+07,191719337,331333876,"Sony Pictures","R","Comedy"
"731","6/14/1991","Robin Hood: Prince of Thieves",5e+07,165493908,390500000,"Warner Bros.","PG-13","Adventure"
"732","12/25/2015","Daddy’s Home",5e+07,150357137,242757137,"Paramount Pictures","PG-13","Comedy"
"733","12/25/1998","Patch Adams",5e+07,135014968,202173000,"Universal","PG-13","Comedy"
"734","6/17/2016","Central Intelligence",5e+07,127440871,217196811,"Warner Bros.","PG-13","Comedy"
"735","12/18/2013","Anchorman 2: The Legend Continues",5e+07,127352707,172185754,"Paramount Pictures","PG-13","Comedy"
"736","6/28/2002","Mr. Deeds",5e+07,126293452,171269535,"Sony Pictures","PG-13","Comedy"
"737","3/17/2000","Erin Brockovich",5e+07,125548685,257805243,"Universal","R","Drama"
"738","2/9/2018","Peter Rabbit",5e+07,115234093,347134901,"Sony Pictures","PG","Adventure"
"739","12/19/2008","Yes Man",5e+07,97690976,225990976,"Warner Bros.","PG-13","Comedy"
"740","2/28/2014","Non-Stop",5e+07,91742160,222383055,"Universal","PG-13","Action"
"741","12/25/1998","Stepmom",5e+07,91137662,159745279,"Sony/TriStar","PG-13","Drama"
"742","8/9/2013","Disney Planes",5e+07,90282580,238059569,"Walt Disney","PG","Adventure"
"743","7/28/2017","The Emoji Movie",5e+07,86089513,216508301,"Sony Pictures","PG","Adventure"
"744","7/29/2011","Crazy, Stupid, Love",5e+07,84351197,147142328,"Warner Bros.","PG-13","Comedy"
"745","12/22/2017","The Post",5e+07,81903458,179769457,"20th Century Fox","PG-13","Drama"
"746","2/5/1999","Payback",5e+07,81526121,161626121,"Paramount Pictures","R","Action"
"747","6/9/1995","Congo",5e+07,81022333,152022333,"Paramount Pictures","PG-13","Adventure"
"748","3/18/2005","The Ring Two",5e+07,75941727,161941727,"Dreamworks SKG","PG-13","Horror"
"749","12/23/2011","We Bought a Zoo",5e+07,75624550,118729073,"20th Century Fox","PG","Drama"
"750","9/23/2011","Moneyball",5e+07,75605492,111300835,"Sony Pictures","PG-13","Drama"
"751","6/11/2004","Garfield: The Movie",5e+07,75367693,208094550,"20th Century Fox","PG","Adventure"
"752","11/24/2004","Christmas with the Kranks",5e+07,73701902,96469187,"Sony Pictures","PG","Comedy"
"753","3/17/2006","V for Vendetta",5e+07,70511035,130214162,"Warner Bros.","R","Action"
"754","3/13/2009","Race to Witch Mountain",5e+07,67172595,105103784,"Walt Disney","PG","Adventure"
"755","6/22/2005","Herbie: Fully Loaded",5e+07,66010682,144110682,"Walt Disney","G","Adventure"
"756","2/7/2003","Shanghai Knights",5e+07,60470220,88316835,"Walt Disney","PG-13","Adventure"
"757","7/18/2014","Planes: Fire and Rescue",5e+07,59157732,156399644,"Walt Disney","PG","Adventure"
"758","2/10/2006","Curious George",5e+07,58640119,71052604,"Universal","G","Adventure"
"759","4/6/2012","American Reunion",5e+07,56758835,236799211,"Universal","R","Comedy"
"760","1/25/2013","Hansel & Gretel: Witch Hunters",5e+07,55703475,214949716,"Paramount Pictures","R","Action"
"761","2/18/2011","I am Number Four",5e+07,55100437,146195159,"Walt Disney","PG-13","Adventure"
"762","5/8/2002","Unfaithful",5e+07,52752475,119114494,"20th Century Fox","R","Drama"
"763","9/10/2004","Resident Evil: Apocalypse",5e+07,50740078,125168734,"Sony Pictures","R","Horror"
"764","10/17/2014","The Book of Life",5e+07,50151543,97651543,"20th Century Fox","PG","Adventure"
"765","8/22/1997","G.I. Jane",5e+07,48169156,48169156,"Walt Disney","R","Drama"
"766","10/10/2014","The Judge",5e+07,47119388,76119388,"Warner Bros.","R","Drama"
"767","4/21/2006","Silent Hill",5e+07,46982632,94704227,"Sony Pictures","R","Horror"
"768","8/11/2000","The Replacements",5e+07,44737059,50054511,"Warner Bros.","PG-13","Comedy"
"769","7/29/1998","The Negotiator",5e+07,44705766,49105766,"Warner Bros.","R","Action"
"770","8/19/2016","War Dogs",5e+07,43034523,86234523,"Warner Bros.","R","Comedy"
"771","5/25/1994","Beverly Hills Cop III",5e+07,42586861,119180938,"Paramount Pictures","R","Action"
"772","6/15/1990","Gremlins 2: The New Batch",5e+07,41476097,41476097,"Warner Bros.","PG-13","Comedy"
"773","9/26/1997","The Peacemaker",5e+07,41263140,62967368,"Dreamworks SKG","R","Action"
"774","2/11/2000","The Beach",5e+07,39778599,39778599,"20th Century Fox","R","Drama"
"775","2/18/1994","On Deadly Ground",5e+07,38590458,38590458,"Warner Bros.","R","Action"
"776","11/25/2009","Ninja Assassin",5e+07,38122883,62209892,"Warner Bros.","R","Action"
"777","5/28/2004","Raising Helen",5e+07,37485528,49928680,"Walt Disney","PG-13","Comedy"
"778","9/17/1999","For Love of the Game",5e+07,35188640,46112640,"Universal","PG-13","Drama"
"779","12/11/1998","Jack Frost",5e+07,34645374,34645374,"Warner Bros.","PG","Comedy"
"780","6/4/2010","Marmaduke",5e+07,33644788,89895930,"20th Century Fox","PG","Adventure"
"781","6/28/1996","Striptease",5e+07,33109743,113309743,"Sony Pictures","R","Comedy"
"782","10/6/1995","Assassins",5e+07,30306268,83306268,"Warner Bros.","R","Action"
"783","2/12/2016","Zoolander 2",5e+07,28848693,55348693,"Paramount Pictures","PG-13","Comedy"
"784","1/16/2009","Defiance",5e+07,28644813,52987754,"Paramount Vantage","R","Drama"
"785","3/13/2015","Run All Night",5e+07,26461644,66961644,"Warner Bros.","R","Action"
"786","8/9/1996","Escape from L.A.",5e+07,25426861,25426861,"Paramount Pictures","R","Action"
"787","12/10/2004","The Life Aquatic with Steve Zissou",5e+07,24006726,34806726,"Walt Disney","R","Comedy"
"788","8/4/1999","The Iron Giant",5e+07,23159305,31333917,"Warner Bros.","PG","Adventure"
"789","4/8/2011","Your Highness",5e+07,21596445,26121638,"Universal","R","Comedy"
"790","9/16/2016","Snowden",5e+07,21587519,34841016,"Open Road","R","Drama"
"791","9/30/2011","Dream House",5e+07,21302340,41642166,"Universal","PG-13","Horror"
"792","6/24/2016","Free State of Jones",5e+07,20810036,23237175,"STX Entertainment","R","Drama"
"793","9/4/2009","Gamer",5e+07,20534907,42002029,"Lionsgate","R","Action"
"794","9/30/2005","Into the Blue",5e+07,18782227,41982227,"Sony Pictures","PG-13","Adventure"
"795","7/1/1994","Baby's Day Out",5e+07,16581575,16581575,"20th Century Fox","PG","Adventure"
"796","11/3/1995","Fair Game",5e+07,11497497,26097497,"Warner Bros.","R","Action"
"797","2/25/2011","Drive Angry",5e+07,10721033,41042583,"Summit Entertainment","R","Action"
"798","11/7/1997","Mad City",5e+07,10561038,10561038,"Warner Bros.","PG-13","Drama"
"799","10/13/1995","The Scarlet Letter",5e+07,10359006,10359006,"Walt Disney","R","Drama"
"800","10/14/2005","Domino",5e+07,10169202,22969202,"New Line","R","Action"
"801","2/16/2018","Early Man",5e+07,8267544,44773318,"Lionsgate","PG","Adventure"
"802","11/13/2009","The Boat That Rocked",5e+07,8017467,37472651,"Focus Features","R","Comedy"
"803","1/30/2004","The Big Bounce",5e+07,6471394,6626115,"Warner Bros.","PG-13","Comedy"
"804","3/3/2000","What Planet Are You From?",5e+07,6291602,6291602,"Sony Pictures","R","Comedy"
"805","1/23/2009","Outlander",5e+07,166003,1250617,"Third Rail","R","Adventure"
"806","10/2/2015","Shanghai",5e+07,46425,15505922,"Weinstein Co.","R","Drama"
"807","11/2/2001","The One",4.9e+07,43905746,72689126,"Sony Pictures","PG-13","Action"
"808","3/6/2015","Chappie",4.9e+07,31569268,105002056,"Sony Pictures","R","Action"
"809","7/11/1990","The Adventures of Ford Fairlane",4.9e+07,20423389,20423389,"20th Century Fox","R","Comedy"
"810","5/24/1989","Indiana Jones and the Last Crusade",4.8e+07,197171806,474171806,"Paramount Pictures","PG-13","Adventure"
"811","10/18/2002","The Ring",4.8e+07,129094024,248218486,"Dreamworks SKG","PG-13","Horror"
"812","12/27/2000","Traffic",4.8e+07,124107476,208300000,"USA Films","R","Drama"
"813","1/9/2015","Taken 3",4.8e+07,89256424,327656424,"20th Century Fox","PG-13","Action"
"814","10/1/1999","Three Kings",4.8e+07,60652036,107752036,"Warner Bros.","R","Action"
"815","1/22/2010","Tooth Fairy",4.8e+07,60022256,112610386,"20th Century Fox","PG","Adventure"
"816","8/17/2001","Rat Race",4.8e+07,56607223,86607223,"Paramount Pictures","PG-13","Comedy"
"817","8/13/2001","K-PAX",4.8e+07,50315140,50315140,"Universal","PG-13","Drama"
"818","10/20/2000","Bedazzled",4.8e+07,37879996,90376224,"20th Century Fox","PG-13","Comedy"
"819","6/26/1998","Out of Sight",4.8e+07,37562568,77562568,"Universal","R","Drama"
"820","12/14/1984","The Cotton Club",4.8e+07,25928721,25928721,"Orion Pictures",NA,"Drama"
"821","1/25/2008","Rambo",47500000,42754105,112214531,"Lionsgate","R","Action"
"822","6/15/1990","Dick Tracy",4.7e+07,103738726,162738726,"Walt Disney","PG","Action"
"823","11/11/2016","Arrival",4.7e+07,100546139,203162211,"Paramount Pictures","PG-13","Drama"
"824","6/14/1996","The Cable Guy",4.7e+07,60240295,102825796,"Sony Pictures","PG-13","Comedy"
"825","10/19/2001","Riding in Cars with Boys",4.7e+07,29781453,29781453,"Sony Pictures","PG-13","Drama"
"826","1/5/2007","Happily N'Ever After",4.7e+07,15849032,37923818,"Lionsgate","PG","Adventure"
"827","11/27/2002","Solaris",4.7e+07,14970038,14970038,"20th Century Fox","PG-13","Drama"
"828","6/18/2010","Jonah Hex",4.7e+07,10547117,11022696,"Warner Bros.","PG-13","Action"
"829","2/23/1996","Mary Reilly",4.7e+07,5707094,12900000,"Sony Pictures","R","Drama"
"830","12/23/2016","Silence",46500000,7100177,23727516,"Paramount Pictures","R","Drama"
"831","6/20/1997","My Best Friend's Wedding",4.6e+07,126813153,298923419,"Sony Pictures","PG","Comedy"
"832","11/22/1996","Star Trek: First Contact",4.6e+07,92027888,1.5e+08,"Paramount Pictures","PG-13","Adventure"
"833","7/12/1996","Courage Under Fire",4.6e+07,59003384,100833145,"20th Century Fox","R","Drama"
"834","9/17/1982","Inchon",4.6e+07,4408636,4408636,"MGM",NA,"Drama"
"835","3/21/1997","Liar Liar",4.5e+07,181410615,302710615,"Universal","PG-13","Comedy"
"836","11/20/1998","A Bug's Life",4.5e+07,162798565,363095319,"Walt Disney","G","Adventure"
"837","5/27/1994","The Flintstones",4.5e+07,130531208,358500000,"Universal","PG","Comedy"
"838","10/24/2003","Scary Movie 3",4.5e+07,110000082,155200000,"Miramax/Dimension","PG-13","Comedy"
"839","12/22/2000","Miss Congeniality",4.5e+07,106807667,213420951,"Warner Bros.","PG-13","Comedy"
"840","12/22/2017","Pitch Perfect 3",4.5e+07,104897530,185736412,"Universal","PG-13","Comedy"
"841","7/11/2008","Journey to the Center of the Earth",4.5e+07,101704370,243180937,"Warner Bros.","PG","Adventure"
"842","12/17/1993","The Pelican Brief",4.5e+07,100768056,187995859,"Warner Bros.","PG-13","Drama"
"843","12/25/2007","The Bucket List",4.5e+07,93466502,174807445,"Warner Bros.","PG-13","Comedy"
"844","7/20/1994","The Client",4.5e+07,92115211,117615211,"Warner Bros.","PG-13","Drama"
"845","11/23/2011","The Muppets",4.5e+07,88625922,160971922,"Walt Disney","PG","Adventure"
"846","6/5/1992","Patriot Games",4.5e+07,83287363,178100000,"Paramount Pictures","R","Action"
"847","5/13/2005","Monster-in-Law",4.5e+07,82931301,155931301,"New Line","PG-13","Comedy"
"848","10/5/2001","Training Day",4.5e+07,76261036,104505362,"Warner Bros.","R","Drama"
"849","12/24/1999","Galaxy Quest",4.5e+07,71423726,90523726,"Dreamworks SKG","PG","Adventure"
"850","7/4/2001","Scary Movie 2",4.5e+07,71277420,141189101,"Miramax/Dimension","R","Comedy"
"851","8/21/1998","Blade",4.5e+07,70141876,131237688,"New Line","R","Action"
"852","1/14/2005","Coach Carter",4.5e+07,67264877,76665507,"Paramount Pictures","PG-13","Drama"
"853","4/11/1997","Anaconda",4.5e+07,65598907,136998907,"Sony Pictures","PG-13","Horror"
"854","1/20/2006","Underworld: Evolution",4.5e+07,62318875,113417762,"Sony Pictures","R","Action"
"855","8/4/2000","Coyote Ugly",4.5e+07,60786269,113916474,"Walt Disney","PG-13","Drama"
"856","8/9/1996","Jack",4.5e+07,58617334,58617334,"Walt Disney","PG-13","Drama"
"857","10/7/1994","The Specialist",4.5e+07,57362581,57362581,"Warner Bros.","R","Action"
"858","12/9/2016","Office Christmas Party",4.5e+07,54767494,91340376,"Paramount Pictures","R","Comedy"
"859","11/23/2005","Yours, Mine and Ours",4.5e+07,53359917,72359917,"Paramount Pictures","PG","Comedy"
"860","9/21/2007","Resident Evil: Extinction",4.5e+07,50648679,146162920,"Sony Pictures","R","Action"
"861","12/25/2004","Fat Albert",4.5e+07,48114556,48563556,"20th Century Fox","PG","Comedy"
"862","9/30/1994","The River Wild",4.5e+07,46815000,94215000,"Universal","PG-13","Action"
"863","6/16/2017","All Eyez on Me",4.5e+07,44922302,54876855,"Lionsgate","R","Drama"
"864","1/13/2006","Last Holiday",4.5e+07,38399961,43343247,"Paramount Pictures","PG-13","Comedy"
"865","3/3/2006","16 Blocks",4.5e+07,36895141,65595141,"Warner Bros.","PG-13","Action"
"866","7/14/1995","The Indian in the Cupboard",4.5e+07,35627222,35627222,"Paramount Pictures","PG","Adventure"
"867","7/28/2006","The Ant Bully",4.5e+07,28142535,49610898,"Warner Bros.","PG","Adventure"
"868","7/18/2003","Johnny English",4.5e+07,28013509,163126676,"Universal","PG","Adventure"
"869","12/14/1984","Dune",4.5e+07,27447471,27447471,"Universal",NA,"Action"
"870","7/31/2009","Aliens in the Attic",4.5e+07,25200412,59551283,"20th Century Fox","PG","Adventure"
"871","12/26/2008","Revolutionary Road",4.5e+07,22951340,79604820,"Paramount Vantage","R","Drama"
"872","8/29/2008","Babylon A.D.",4.5e+07,22532572,70216497,"20th Century Fox","PG-13","Action"
"873","11/4/1994","Frankenstein",4.5e+07,22006296,112006296,"Sony Pictures","R","Horror"
"874","10/4/1996","The Glimmer Man",4.5e+07,20404841,36404841,"Warner Bros.","R","Action"
"875","7/17/1996","Multiplicity",4.5e+07,20133326,20133326,"Sony Pictures","PG-13","Comedy"
"876","1/19/2001","The Pledge",4.5e+07,19719930,29406132,"Warner Bros.","R","Drama"
"877","6/7/1996","The Phantom",4.5e+07,17220599,17220599,"Paramount Pictures","PG","Action"
"878","7/1/2005","Rebound",4.5e+07,16809014,17492014,"20th Century Fox","PG","Comedy"
"879","12/20/1995","Nixon",4.5e+07,13668249,34668249,"Walt Disney","R","Drama"
"880","9/21/2012","Dredd",4.5e+07,13414714,41467606,"Lionsgate","R","Action"
"881","10/28/2011","The Rum Diary",4.5e+07,13109815,21544732,"FilmDistrict","R","Drama"
"882","1/30/1998","Deep Rising",4.5e+07,11203026,11203026,"Walt Disney","R","Action"
"883","10/21/2011","Johnny English Reborn",4.5e+07,8406711,164640401,"Universal","PG","Adventure"
"884","9/26/2008","Miracle at St. Anna",4.5e+07,7916887,9676497,"Walt Disney","R","Drama"
"885","4/5/2002","Big Trouble",4.5e+07,7262288,8488871,"Walt Disney","PG-13","Comedy"
"886","12/21/2006","Man cheng jin dai huang jin jia",4.5e+07,6566773,76904429,"Sony Pictures Classics","R","Action"
"887","11/16/2007","Love in the Time of Cholera",4.5e+07,4617608,31077418,"New Line","R","Drama"
"888","5/22/1985","Rambo: First Blood Part II",4.4e+07,150415432,300400000,"Sony/TriStar","R","Action"
"889","10/18/1996","Sleepers",4.4e+07,53300852,165600852,"Warner Bros.","R","Drama"
"890","7/30/2010","Charlie St. Cloud",4.4e+07,31206263,48478084,"Universal","PG-13","Drama"
"891","2/6/2014","The Interview",4.4e+07,6105175,12342632,"Sony Pictures","R","Comedy"
"892","6/28/2013","The Heat",4.3e+07,159581587,229727774,"20th Century Fox","R","Comedy"
"893","12/19/2000","Finding Forrester",4.3e+07,51768623,80013623,"Sony Pictures","PG-13","Drama"
"894","4/14/2000","28 Days",4.3e+07,37035515,62063972,"Sony Pictures","PG-13","Comedy"
"895","5/13/2005","Danny the Dog",4.3e+07,24537621,49037621,"Focus/Rogue Pictures","R","Action"
"896","1/6/2017","A Monster Calls",4.3e+07,3740823,46414964,"Focus Features","PG-13","Drama"
"897","1/28/2011","The Mechanic",42500000,29121498,76347393,"CBS Films","R","Action"
"898","3/16/2012","21 Jump Street",4.2e+07,138447667,202812429,"Sony Pictures","R","Comedy"
"899","6/21/2000","Chicken Run",4.2e+07,106793915,227793915,"Dreamworks SKG","G","Adventure"
"900","7/1/1992","Boomerang",4.2e+07,70052444,131052444,"Paramount Pictures","R","Comedy"
"901","7/10/2009","Brüno",4.2e+07,60054530,138708527,"Universal","R","Comedy"
"902","6/12/1963","Cleopatra",4.2e+07,5.7e+07,7.1e+07,"20th Century Fox","G","Drama"
"903","5/12/2017","Snatched",4.2e+07,45852178,57852177,"20th Century Fox","R","Comedy"
"904","10/12/2012","Here Comes the Boom",4.2e+07,45290318,73239258,"Sony Pictures","PG","Comedy"
"905","7/14/1989","Licence to Kill",4.2e+07,34667015,156167015,"MGM","PG-13","Action"
"906","1/27/2012","One for the Money",4.2e+07,26414527,36197221,"Lionsgate","PG-13","Comedy"
"907","9/16/2005","Lord of War",4.2e+07,24149632,60437727,"Lionsgate","R","Action"
"908","5/28/1993","Super Mario Bros.",4.2e+07,20844907,20844907,"Walt Disney","PG","Action"
"909","10/2/1992","Hero",4.2e+07,19487173,66787173,"Sony Pictures","PG-13","Comedy"
"910","4/18/1997","McHale's Navy",4.2e+07,4408420,4408420,"Universal","PG","Comedy"
"911","5/28/2010","Micmacs",4.2e+07,1259693,11756922,"Sony Pictures Classics","R","Comedy"
"912","11/8/2002","8 Mile",4.1e+07,116724075,245768384,"Universal","R","Drama"
"913","5/11/2001","A Knight’s Tale",4.1e+07,56083966,100622586,"Sony Pictures","PG-13","Adventure"
"914","8/22/2003","The Medallion",4.1e+07,22108977,22108977,"Sony Pictures","PG-13","Comedy"
"915","10/14/2011","The Big Year",4.1e+07,7204138,7684524,"20th Century Fox","PG","Comedy"
"916","7/15/2005","Wedding Crashers",4e+07,209218368,283218368,"New Line","R","Comedy"
"917","2/13/2015","Fifty Shades of Grey",4e+07,166167230,570998101,"Universal","R","Drama"
"918","12/25/2003","Cheaper by the Dozen",4e+07,138614544,190212113,"20th Century Fox","PG","Comedy"
"919","7/25/2014","Lucy",4e+07,126573960,457507776,"Universal","R","Action"
"920","12/25/2013","Lone Survivor",4e+07,125095601,149804632,"Universal","R","Action"
"921","11/22/1989","Back to the Future Part II",4e+07,118450002,3.32e+08,"Universal","PG","Adventure"
"922","9/24/1999","Double Jeopardy",4e+07,116735231,177835231,"Paramount Pictures","R","Action"
"923","7/25/2003","Spy Kids 3-D: Game Over",4e+07,111760631,167851995,"Miramax/Dimension","PG","Adventure"
"924","7/24/1996","A Time to Kill",4e+07,108766007,152266007,"Warner Bros.","R","Drama"
"925","7/1/1992","A League of Their Own",4e+07,107533925,132440066,"Sony Pictures","PG","Comedy"
"926","10/1/2010","The Social Network",4e+07,96962694,224922135,"Sony Pictures","PG-13","Drama"
"927","8/7/2009","Julie & Julia",4e+07,94125426,126646119,"Sony Pictures","PG-13","Comedy"
"928","2/10/2017","John Wick: Chapter Two",4e+07,92029184,171370497,"Lionsgate","R","Action"
"929","1/15/2016","Ride Along 2",4e+07,90862685,124827316,"Universal","PG-13","Comedy"
"930","4/14/2006","Scary Movie 4",4e+07,90710620,178710620,"Weinstein/Dimension","PG-13","Comedy"
"931","3/27/2015","Get Hard",4e+07,90411453,106511453,"Warner Bros.","R","Comedy"
"932","2/4/2000","Scream 3",4e+07,89138076,161838076,"Miramax","R","Horror"
"933","5/24/1990","Back to the Future Part III",4e+07,88055283,244088654,"Universal","PG","Adventure"
"934","11/14/2014","Dumb and Dumber To",4e+07,86208010,156553592,"Universal","PG-13","Comedy"
"935","11/13/1992","Bram Stoker's Dracula",4e+07,82522790,215862692,"Sony Pictures","R","Horror"
"936","2/17/2006","Eight Below",4e+07,81612565,120455994,"Walt Disney","PG","Adventure"
"937","12/24/1999","The Talented Mr. Ripley",4e+07,81292135,128792135,"Paramount Pictures","R","Drama"
"938","9/25/2015","The Intern",4e+07,75764672,197232734,"Warner Bros.","PG-13","Comedy"
"939","9/25/1992","The Last of the Mohicans",4e+07,75505856,75505856,"20th Century Fox","R","Action"
"940","10/29/2004","Ray",4e+07,75305995,124823094,"Universal","PG-13","Drama"
"941","4/1/2005","Sin City",4e+07,74103820,158527918,"Miramax/Dimension","R","Action"
"942","3/20/2009","I Love You, Man",4e+07,72013010,92302502,"Paramount Pictures","R","Comedy"
"943","12/20/1991","JFK",4e+07,70405498,205400000,"Warner Bros.","R","Drama"
"944","1/27/2006","Big Momma's House 2",4e+07,70165972,137047376,"20th Century Fox","PG-13","Comedy"
"945","11/4/2016","Hacksaw Ridge",4e+07,67209615,168940583,"Lionsgate","R","Drama"
"946","3/2/2001","The Mexican",4e+07,66808615,145238250,"Dreamworks SKG","R","Action"
"947","8/28/2009","The Final Destination",4e+07,66477700,187384627,"Warner Bros.","R","Horror"
"948","4/17/2009","17 Again",4e+07,64167069,139474906,"Warner Bros.","PG-13","Comedy"
"949","6/4/2010","Get Him to the Greek",4e+07,61153526,91455875,"Universal","R","Comedy"
"950","11/21/2003","Gothika",4e+07,59588068,141484812,"Warner Bros.","R","Horror"
"951","11/30/2001","Behind Enemy Lines",4e+07,58855732,58855732,"20th Century Fox","PG-13","Action"
"952","8/25/2006","Invincible",4e+07,57806952,58501127,"Walt Disney","PG","Drama"
"953","2/15/2013","Escape From Planet Earth",4e+07,57012977,74156610,"Weinstein Co.","PG","Adventure"
"954","7/10/1998","Small Soldiers",4e+07,55143823,71743823,"Dreamworks SKG","PG-13","Adventure"
"955","7/31/1997","Spawn",4e+07,54979992,87949859,"New Line","PG-13","Action"
"956","11/26/2014","Horrible Bosses 2",4e+07,54445357,105945357,"Warner Bros.","R","Comedy"
"957","1/25/2002","The Count of Monte Cristo",4e+07,54228104,75389090,"Walt Disney","PG-13","Drama"
"958","6/16/2006","The Lake House",4e+07,52330111,114830111,"Warner Bros.","PG","Drama"
"959","7/9/2010","Predators",4e+07,52000688,127234389,"20th Century Fox","R","Action"
"960","8/15/2012","The Odd Life of Timothy Green",4e+07,51853450,55249159,"Walt Disney","PG","Drama"
"961","7/31/1987","The Living Daylights",4e+07,51185000,191200000,"MGM","PG","Action"
"962","12/8/2006","Apocalypto",4e+07,50866635,121032272,"Walt Disney","R","Action"
"963","6/18/1986","Legal Eagles",4e+07,49851591,49851591,"Universal","PG","Comedy"
"964","8/12/2005","The Skeleton Key",4e+07,47907715,92256918,"Universal","PG-13","Horror"
"965","6/20/2014","Jersey Boys",4e+07,47047013,65282732,"Warner Bros.","R","Drama"
"966","11/21/1997","The Rainmaker",4e+07,45916769,45916769,"Paramount Pictures","PG-13","Drama"
"967","2/7/1992","Medicine Man",4e+07,44948240,44948240,"Walt Disney","PG-13","Drama"
"968","12/12/1997","Amistad",4e+07,44212592,58250151,"Dreamworks SKG","R","Drama"
"969","5/30/2014","A Million Ways to Die in The West",4e+07,42720965,86778557,"Universal","R","Comedy"
"970","8/12/2011","Final Destination 5",4e+07,42587643,155011165,"Warner Bros.","R","Horror"
"971","12/25/2007","Aliens vs. Predator - Requiem",4e+07,41797066,128884494,"20th Century Fox","R","Action"
"972","12/25/2007","The Water Horse: Legend of the Deep",4e+07,40412817,103429755,"Sony Pictures","PG","Drama"
"973","7/18/2014","Sex Tape",4e+07,38543473,126069509,"Sony Pictures","R","Comedy"
"974","4/15/2011","Scream 4",4e+07,38180928,95989590,"Weinstein/Dimension","R","Horror"
"975","12/21/1994","Ri¢hie Ri¢h",4e+07,38087756,38087756,"Warner Bros.","PG","Comedy"
"976","8/11/2000","Autumn in New York",4e+07,37752931,90717684,"MGM","PG-13","Drama"
"977","3/18/2011","Paul",4e+07,37412945,101162106,"Universal","R","Comedy"
"978","12/19/2012","The Guilt Trip",4e+07,37134215,41294674,"Paramount Pictures","PG-13","Comedy"
"979","2/18/2000","Hanging Up",4e+07,36037909,51867723,"Sony Pictures","PG-13","Comedy"
"980","3/1/1991","The Doors",4e+07,34416893,34416893,"Sony Pictures","R","Drama"
"981","8/20/1999","Mickey Blue Eyes",4e+07,33864342,53864342,"Warner Bros.","PG-13","Comedy"
"982","10/20/2000","Pay it Forward",4e+07,33508922,55696705,"Warner Bros.","PG-13","Drama"
"983","3/21/2008","Drillbit Taylor",4e+07,32862104,49686263,"Paramount Pictures","PG-13","Comedy"
"984","12/25/2011","Extremely Loud and Incredibly Close",4e+07,31847881,55247881,"Warner Bros.","PG-13","Drama"
"985","7/1/1994","The Shadow",4e+07,31835600,31835600,"Universal","PG-13","Action"
"986","11/10/2010","Morning Glory",4e+07,31011732,59795070,"Paramount Pictures","PG-13","Comedy"
"987","11/9/2005","Get Rich or Die Tryin'",4e+07,30981850,46666955,"Paramount Pictures","R","Drama"
"988","12/25/2013","Grudge Match",4e+07,29807260,69807260,"Warner Bros.","PG-13","Comedy"
"989","4/2/1999","The Out-of-Towners",4e+07,28544120,28544120,"Paramount Pictures","PG-13","Comedy"
"990","8/11/2017","The Nut Job 2: Nutty by Nature",4e+07,28370522,57465156,"Open Road","PG","Adventure"
"991","8/23/1996","The Island of Dr. Moreau",4e+07,27682712,27682712,"New Line","PG-13","Adventure"
"992","9/7/2001","The Musketeer",4e+07,27053815,27053815,"Universal","PG-13","Adventure"
"993","1/27/2017","Resident Evil: The Final Chapter",4e+07,26844692,312825686,"Sony Pictures","R","Action"
"994","2/29/2008","The Other Boleyn Girl",4e+07,26814957,78269970,"Sony Pictures","PG-13","Drama"
"995","6/30/2017","The House",4e+07,25584504,31192743,"Warner Bros.","R","Comedy"
"996","2/16/2001","Sweet November",4e+07,25288103,65754228,"Warner Bros.","PG-13","Drama"
"997","4/5/2007","The Reaping",4e+07,25126214,62226214,"Warner Bros.","R","Horror"
"998","6/3/1994","Renaissance Man",4e+07,24172899,24172899,"Walt Disney","PG-13","Comedy"
"999","5/15/1998","Quest for Camelot",4e+07,22772500,38172500,"Warner Bros.","G","Adventure"
"1000","9/6/2002","City by the Sea",4e+07,22433915,22433915,"Warner Bros.","R","Drama"
"1001","1/15/1999","At First Sight",4e+07,22365133,22365133,"MGM","PG-13","Drama"
"1002","1/16/2004","Torque",4e+07,21176322,46176322,"Warner Bros.","PG-13","Action"
"1003","11/13/2009","Fantastic Mr. Fox",4e+07,21002919,47083412,"20th Century Fox","PG","Adventure"
"1004","2/16/1996","City Hall",4e+07,20278055,20278055,"Sony Pictures","R","Drama"
"1005","2/3/2012","Big Miracle",4e+07,20157300,25268680,"Universal","PG","Drama"
"1006","12/21/2012","The Impossible",4e+07,19019882,169590606,"Lionsgate","PG-13","Drama"
"1007","3/9/2012","A Thousand Words",4e+07,18450127,20790486,"Paramount Pictures","PG-13","Comedy"
"1008","10/20/2006","Marie Antoinette",4e+07,15962471,60862471,"Sony Pictures","PG-13","Drama"
"1009","10/6/2000","Get Carter",4e+07,14967182,19417182,"Warner Bros.","R","Drama"
"1010","4/21/1995","Kiss of Death",4e+07,14942422,14942422,"20th Century Fox","R","Drama"
"1011","5/15/1987","Ishtar",4e+07,14375181,14375181,"Sony Pictures","PG-13","Comedy"
"1012","2/28/1992","Memoirs of an Invisible Man",4e+07,14358033,14358033,"Warner Bros.","PG-13","Comedy"
"1013","10/23/2009","Amelia",4e+07,14279575,19756077,"Fox Searchlight","PG","Drama"
"1014","5/7/2004","New York Minute",4e+07,14018364,21215882,"Warner Bros.","PG","Comedy"
"1015","3/12/1999","The Deep End of the Ocean",4e+07,13508635,13508635,"Sony Pictures","PG-13","Drama"
"1016","8/30/2002","FearDotCom",4e+07,13208023,13208023,"Warner Bros.","R","Horror"
"1017","11/7/2008","Soul Men",4e+07,12082391,12345883,"MGM","R","Comedy"
"1018","8/20/1999","Universal Soldier II: The Return",4e+07,10447421,10717421,"Sony Pictures","R","Action"
"1019","9/25/2009","Pandorum",4e+07,10330853,17033431,"Overture Films","R","Horror"
"1020","9/26/2003","Duplex",4e+07,9652000,10070651,"Miramax","PG-13","Comedy"
"1021","11/27/2002","Extreme Ops",4e+07,4835968,12624471,"Paramount Pictures","PG-13","Action"
"1022","4/6/2001","Just Visiting",4e+07,4777007,16172200,"Walt Disney","PG-13","Comedy"
"1023","3/11/1994","The Hudsucker Proxy",4e+07,2816518,14938149,"Warner Bros.","PG","Comedy"
"1024","11/11/2016","Billy Lynn’s Long Halftime Walk",4e+07,1738477,30230402,"Sony Pictures","R","Drama"
"1025","12/12/2008","Delgo",4e+07,915840,915840,"Freestyle Releasing","PG","Adventure"
"1026","9/7/2007","The Hunting Party",4e+07,876671,7729552,"Weinstein Co.","R","Adventure"
"1027","10/13/2006","Alex Rider: Operation Stormbreaker",4e+07,659210,20722450,"Weinstein Co.","PG","Action"
"1028","11/20/2009","Red Cliff",4e+07,627047,119627047,"Magnolia Pictures","R","Action"
"1029","9/24/2004","The Last Shot",4e+07,463730,463730,"Walt Disney","R","Comedy"
"1030","3/16/2007","Nomad",4e+07,79123,79123,"Weinstein Co.","R","Drama"
"1031","11/11/2016","USS Indianapolis: Men of Courage",4e+07,0,1641255,"Saban Films","R","Drama"
"1032","8/14/2009","The Time Traveler's Wife",3.9e+07,63414846,102332135,"Warner Bros.","PG-13","Drama"
"1033","6/17/1983","Superman III",3.9e+07,59950623,59950623,"Warner Bros.","PG","Adventure"
"1034","2/2/2007","Because I Said So",3.9e+07,42674040,69538833,"Universal","PG-13","Comedy"
"1035","10/5/2012","Frankenweenie",3.9e+07,35287788,81150788,"Walt Disney","PG","Adventure"
"1036","3/29/1996","Sgt. Bilko",3.9e+07,30356589,37956589,"Universal","PG","Comedy"
"1037","9/30/2005","Serenity",3.9e+07,25514517,40319440,"Universal","PG-13","Action"
"1038","2/20/2004","Against the Ropes",3.9e+07,5881504,6429865,"Paramount Pictures","PG-13","Drama"
"1039","8/23/2013","Yi dai zong shi",38600000,6594959,57987299,"Weinstein Co.","PG-13","Action"
"1040","6/22/2001","The Fast and the Furious",3.8e+07,144512310,206512310,"Universal","PG-13","Action"
"1041","9/27/2002","Sweet Home Alabama",3.8e+07,127214072,182365114,"Walt Disney","PG-13","Comedy"
"1042","11/18/1994","Star Trek: Generations",3.8e+07,75671262,1.2e+08,"Paramount Pictures","PG","Adventure"
"1043","4/17/2015","Paul Blart: Mall Cop 2",3.8e+07,71091594,107650646,"Sony Pictures","PG","Adventure"
"1044","12/19/1997","Mouse Hunt",3.8e+07,61894591,61894591,"Dreamworks SKG","PG","Adventure"
"1045","12/23/2016","Why Him?",3.8e+07,60323786,117439538,"20th Century Fox","R","Comedy"
"1046","4/22/2011","Water for Elephants",3.8e+07,58709717,116809717,"20th Century Fox","PG-13","Drama"
"1047","12/29/1999","The Hurricane",3.8e+07,50699241,73956241,"Universal","R","Drama"
"1048","9/6/2013","Riddick",3.8e+07,42025135,94763758,"Universal","R","Action"
"1049","1/22/2016","The 5th Wave",3.8e+07,34912982,111336398,"Sony Pictures","PG-13","Action"
"1050","9/20/2013","Rush",3.8e+07,26947624,98230839,"Universal","R","Drama"
"1051","5/18/2001","Angel Eyes",3.8e+07,24044532,29544532,"Warner Bros.","R","Drama"
"1052","12/21/2001","Joe Somebody",3.8e+07,22770864,24515990,"20th Century Fox","PG","Comedy"
"1053","10/20/2017","Only the Brave",3.8e+07,18340051,24181629,"Sony Pictures","PG-13","Drama"
"1054","9/27/1996","Extreme Measures",3.8e+07,17378193,17378193,"Sony Pictures","R","Drama"
"1055","9/7/2001","Rock Star",3.8e+07,16991902,19317765,"Warner Bros.","R","Drama"
"1056","2/2/1996","White Squall",3.8e+07,10229300,10229300,"Walt Disney","PG-13","Adventure"
"1057","10/10/2008","City of Ember",3.8e+07,7873007,17831558,"20th Century Fox","PG","Adventure"
"1058","10/31/1997","Switchback",3.8e+07,6504442,6504442,"Paramount Pictures","R","Action"
"1059","9/14/2012","The Master",37500000,16247159,50647416,"Weinstein Co.","R","Drama"
"1060","10/10/2008","The Express",37500000,9793406,9813309,"Universal","PG","Drama"
"1061","8/7/2013","We're the Millers",3.7e+07,150394119,267816276,"Warner Bros.","R","Comedy"
"1062","11/25/2015","Creed",3.7e+07,109767581,173567581,"Warner Bros.","PG-13","Drama"
"1063","9/17/2010","The Town",3.7e+07,92186262,152566881,"Warner Bros.","R","Drama"
"1064","9/23/2011","Dolphin Tale",3.7e+07,72286779,96068724,"Warner Bros.","PG","Drama"
"1065","2/23/2018","Game Night",3.7e+07,69001013,117201013,"Warner Bros.","R","Comedy"
"1066","4/23/2004","13 Going On 30",3.7e+07,57139723,97658712,"Sony Pictures","PG-13","Comedy"
"1067","4/4/2008","Nim's Island",3.7e+07,48006762,101857425,"20th Century Fox","PG","Adventure"
"1068","2/26/2010","Cop Out",3.7e+07,44875481,55909910,"Warner Bros.","R","Comedy"
"1069","1/28/2011","The Rite",3.7e+07,33047633,97143987,"Warner Bros.","PG-13","Horror"
"1070","7/18/2008","Space Chimps",3.7e+07,30105968,67029956,"20th Century Fox","G","Adventure"
"1071","12/17/1999","Magnolia",3.7e+07,22450975,48446802,"New Line","R","Drama"
"1072","5/29/2015","Aloha",3.7e+07,21052030,24935799,"Sony Pictures","PG-13","Drama"
"1073","10/5/2018","A Star is Born",3.6e+07,126181246,200881246,"Warner Bros.","R","Drama"
"1074","2/11/2011","Gnomeo and Juliet",3.6e+07,99967670,193737977,"Walt Disney","G","Comedy"
"1075","2/15/2002","John Q",3.6e+07,71026631,102226631,"New Line","PG-13","Drama"
"1076","9/17/1999","Blue Streak",3.6e+07,68208190,117448157,"Sony Pictures","PG-13","Action"
"1077","10/7/1983","Never Say Never Again",3.6e+07,55500000,1.6e+08,"Warner Bros.","PG","Action"
"1078","3/26/2010","Hot Tub Time Machine",3.6e+07,50269859,65967750,"MGM","R","Comedy"
"1079","9/12/2014","Dolphin Tale 2",3.6e+07,42024533,57824533,"Warner Bros.","PG","Drama"
"1080","12/16/2016","Collateral Beauty",3.6e+07,31016021,85315070,"Warner Bros.","PG-13","Drama"
"1081","4/4/2003","A Man Apart",3.6e+07,26500000,43797731,"New Line","R","Action"
"1082","2/25/2000","Reindeer Games",3.6e+07,23360779,23360779,"Miramax","R","Action"
"1083","12/24/1999","Snow Falling on Cedars",3.6e+07,14378353,14378353,"Universal","PG-13","Drama"
"1084","12/20/1996","Ghosts of Mississippi",3.6e+07,13052741,13052741,"Sony Pictures","PG-13","Drama"
"1085","10/24/1997","Gattaca",3.6e+07,12532777,12532777,"Sony Pictures","PG-13","Drama"
"1086","1/28/2000","Isn't She Great",3.6e+07,2954405,2954405,"Universal","R","Comedy"
"1087","1/22/2016","Yip Man 3",3.6e+07,2679437,157300954,"Well Go USA","PG-13","Action"
"1088","5/6/2011","There Be Dragons",3.6e+07,1069334,4020990,"Samuel Goldwyn Films","PG-13","Drama"
"1089","4/14/2017","Queen of the Desert",3.6e+07,0,1578543,"IFC Films","PG-13","Drama"
"1090","3/28/2003","Head of State",35200000,37788228,38283765,"Dreamworks SKG","PG-13","Comedy"
"1091","9/8/2017","It",3.5e+07,327481748,697459228,"Warner Bros.","R","Horror"
"1092","6/5/2009","The Hangover",3.5e+07,277322503,465764086,"Warner Bros.","R","Comedy"
"1093","11/20/2009","The Blind Side",3.5e+07,255959475,305705794,"Warner Bros.","PG-13","Drama"
"1094","6/23/1989","Batman",3.5e+07,251188924,411348924,"Warner Bros.","PG-13","Action"
"1095","5/15/1992","Lethal Weapon 3",3.5e+07,144731527,319700000,"Warner Bros.","R","Action"
"1096","9/18/1998","Rush Hour",3.5e+07,141186864,245300000,"New Line","PG-13","Action"
"1097","2/8/2013","Identity Thief",3.5e+07,134506920,175361578,"Universal","R","Comedy"
"1098","6/30/2006","The Devil Wears Prada",3.5e+07,124740460,326073155,"20th Century Fox","PG-13","Comedy"
"1099","7/8/2011","Horrible Bosses",3.5e+07,117538559,212417601,"Warner Bros.","R","Comedy"
"1100","3/30/2001","Spy Kids",3.5e+07,112692062,197692062,"Miramax/Dimension","PG","Adventure"
"1101","7/17/2015","Trainwreck",3.5e+07,110212700,141123897,"Universal","R","Comedy"
"1102","12/13/2013","Saving Mr. Banks",3.5e+07,83299761,114962525,"Walt Disney","PG-13","Drama"
"1103","12/7/1979","Star Trek: The Motion Picture",3.5e+07,82258456,1.39e+08,"Paramount Pictures","PG","Adventure"
"1104","11/15/1996","The English Patient",3.5e+07,78716374,231710008,"Miramax","R","Drama"
"1105","1/16/2009","Hotel for Dogs",3.5e+07,73178547,122357172,"Paramount Pictures","PG","Adventure"
"1106","3/25/2005","Guess Who",3.5e+07,68915888,102115888,"Sony Pictures","PG-13","Comedy"
"1107","12/21/2012","This is 40",3.5e+07,67544505,90221182,"Universal","R","Comedy"
"1108","9/19/1997","L.A. Confidential",3.5e+07,64604977,126204977,"Warner Bros.","R","Drama"
"1109","7/29/2005","Sky High",3.5e+07,63939454,83109359,"Walt Disney","PG","Adventure"
"1110","9/19/1997","In & Out",3.5e+07,63826569,83226569,"Paramount Pictures","PG-13","Comedy"
"1111","7/7/1995","Species",3.5e+07,60054449,113354449,"MGM","R","Action"
"1112","4/7/2006","The Benchwarmers",3.5e+07,59843754,65063726,"Sony Pictures","PG-13","Comedy"
"1113","10/8/2010","Secretariat",3.5e+07,59699513,60376247,"Walt Disney","PG","Drama"
"1114","3/13/1998","The Man in the Iron Mask",3.5e+07,56968169,56968169,"MGM","PG-13","Adventure"
"1115","5/20/2016","Neighbors 2: Sorority Rising",3.5e+07,55340730,108758521,"Universal","R","Comedy"
"1116","3/23/2007","TMNT",3.5e+07,54149098,96096018,"Warner Bros.","PG","Action"
"1117","10/24/2003","Radio",3.5e+07,52333738,53293628,"Sony Pictures","PG","Drama"
"1118","6/29/2018","Sicario: Day of the Soldado",3.5e+07,50065850,73285196,"Sony Pictures","R","Action"
"1119","11/25/2009","Old Dogs",3.5e+07,49492060,95104304,"Walt Disney","PG","Comedy"
"1120","11/18/1992","Malcolm X",3.5e+07,48169910,48169910,"Warner Bros.","PG-13","Drama"
"1121","1/23/2009","Underworld 3: Rise of the Lycans",3.5e+07,45802315,89102315,"Sony Pictures","R","Action"
"1122","1/19/2018","12 Strong",3.5e+07,45500164,70798829,"Warner Bros.","R","Drama"
"1123","2/28/1997","Donnie Brasco",3.5e+07,41954997,65303052,"Sony Pictures","R","Drama"
"1124","10/17/2008","Max Payne",3.5e+07,40689393,85763888,"20th Century Fox","PG-13","Action"
"1125","3/15/2002","Resident Evil",3.5e+07,40119709,103787401,"Sony Pictures","R","Horror"
"1126","3/26/2004","The Ladykillers",3.5e+07,39692139,77392139,"Walt Disney","R","Comedy"
"1127","12/1/2006","The Nativity Story",3.5e+07,37629831,46309644,"New Line","PG","Drama"
"1128","11/9/2011","J. Edgar",3.5e+07,37306030,84606030,"Warner Bros.","R","Drama"
"1129","11/17/2000","Bounce",3.5e+07,36805288,53425292,"Miramax","PG-13","Drama"
"1130","8/17/2018","Mile 22",3.5e+07,36108758,64708758,"STX Entertainment","R","Action"
"1131","10/13/2017","The Foreigner",3.5e+07,34393507,140783646,"STX Entertainment","R","Action"
"1132","12/3/2004","Closer",3.5e+07,33987757,116177695,"Sony Pictures","R","Drama"
"1133","12/23/1994","Street Fighter",3.5e+07,33423000,99423000,"Universal","PG-13","Action"
"1134","11/21/2001","Black Knight",3.5e+07,33422806,33422806,"20th Century Fox","PG-13","Adventure"
"1135","12/27/2002","The Pianist",3.5e+07,32519322,111854182,"Focus Features","R","Drama"
"1136","5/6/2005","House of Wax",3.5e+07,32064800,70064800,"Warner Bros.","R","Horror"
"1137","6/1/2018","Adrift",3.5e+07,31445011,57931376,"STX Entertainment","PG-13","Drama"
"1138","8/15/2008","Mirrors",3.5e+07,30691439,77220596,"20th Century Fox","R","Horror"
"1139","2/22/2002","Queen of the Damned",3.5e+07,30307804,30307804,"Warner Bros.","R","Horror"
"1140","8/20/2010","Nanny McPhee and the Big Bang",3.5e+07,29197642,97799865,"Universal","PG","Adventure"
"1141","10/12/2018","Goosebumps 2: Haunted Halloween",3.5e+07,28804812,39904812,"Sony Pictures","PG","Horror"
"1142","11/21/1990","Predator 2",3.5e+07,28317513,54768418,"20th Century Fox","R","Action"
"1143","12/5/1980","Flash Gordon",3.5e+07,27107960,27107960,"Universal",NA,"Action"
"1144","3/28/2008","Superhero Movie",3.5e+07,26638520,73026302,"MGM","PG-13","Comedy"
"1145","2/12/1999","Blast from the Past",3.5e+07,26613620,26613620,"New Line","PG-13","Comedy"
"1146","3/26/2004","Jersey Girl",3.5e+07,25266129,37066129,"Miramax","PG-13","Comedy"
"1147","11/9/2001","Heist",3.5e+07,23483357,28906817,"New Line","R","Action"
"1148","12/25/1992","Hoffa",3.5e+07,23365858,28391473,"20th Century Fox","R","Drama"
"1149","3/4/2016","Whiskey Tango Foxtrot",3.5e+07,23083334,25350747,"Paramount Pictures","R","Comedy"
"1150","4/9/2004","Ella Enchanted",3.5e+07,22913677,22913677,"Miramax","PG","Comedy"
"1151","8/21/2015","Hitman: Agent 47",3.5e+07,22467450,81959582,"20th Century Fox","R","Action"
"1152","7/25/2008","The X-Files: I Want to Believe",3.5e+07,20982478,68170792,"20th Century Fox","PG-13","Action"
"1153","8/19/2005","Valiant",3.5e+07,19478106,64188387,"Walt Disney","G","Adventure"
"1154","2/23/2000","Wonder Boys",3.5e+07,19389454,33422485,"Paramount Pictures","R","Comedy"
"1155","2/25/2005","Cursed",3.5e+07,19294901,25114901,"Miramax/Dimension","PG-13","Horror"
"1156","12/21/2007","Walk Hard: The Dewey Cox Story",3.5e+07,18317151,20606053,"Sony Pictures","R","Comedy"
"1157","9/20/2002","The Four Feathers",3.5e+07,18306166,29882645,"Paramount Pictures","PG-13","Drama"
"1158","4/30/2010","Furry Vengeance",3.5e+07,17630465,39340177,"Summit Entertainment","PG","Adventure"
"1159","9/15/2000","Bait",3.5e+07,15325127,15471969,"Warner Bros.","R","Action"
"1160","12/8/2000","Dungeons and Dragons",3.5e+07,15185241,33771965,"New Line","PG-13","Adventure"
"1161","11/9/2007","Lions for Lambs",3.5e+07,14998070,63211088,"United Artists","R","Drama"
"1162","1/18/1991","Flight of the Intruder",3.5e+07,14471440,14471440,"Paramount Pictures","PG-13","Action"
"1163","5/27/2011","The Tree of Life",3.5e+07,13305665,61721826,"Fox Searchlight","PG-13","Drama"
"1164","8/11/2006","Zoom",3.5e+07,11989328,12506188,"Sony Pictures","PG","Adventure"
"1165","12/25/2001","The Shipping News",3.5e+07,11405825,24405825,"Miramax","R","Drama"
"1166","12/18/2009","The Young Victoria",3.5e+07,11001272,31878891,"Apparition","PG","Drama"
"1167","3/28/2014","Sabotage",3.5e+07,10508518,18376443,"Open Road","R","Action"
"1168","9/4/1998","Knock Off",3.5e+07,10319915,10319915,"Sony Pictures","R","Action"
"1169","3/6/2015","Unfinished Business",3.5e+07,10219501,12819501,"20th Century Fox","R","Comedy"
"1170","9/30/2015","The Walk",3.5e+07,10161183,61197045,"Sony Pictures","PG","Drama"
"1171","11/22/2006","The Fountain",3.5e+07,10144010,15461638,"Warner Bros.","PG-13","Drama"
"1172","11/29/2013","Mandela: Long Walk to Freedom",3.5e+07,8323085,29890402,"Weinstein Co.","PG-13","Drama"
"1173","12/5/2008","Punisher: War Zone",3.5e+07,8050977,10157534,"Lionsgate","R","Action"
"1174","11/10/2006","A Good Year",3.5e+07,7459300,42064105,"20th Century Fox","PG-13","Drama"
"1175","3/11/2016","The Brothers Grimsby",3.5e+07,6864016,28721408,"Sony Pictures","R","Comedy"
"1176","5/2/1997","Warriors of Virtue",3.5e+07,6448817,6448817,"MGM","PG","Action"
"1177","9/26/2003","Luther",3.5e+07,5781086,32736879,"RS Entertainment","PG-13","Drama"
"1178","1/28/2011","Biutiful",3.5e+07,5101237,24687524,"Roadside Attractions","R","Drama"
"1179","2/21/1992","Radio Flyer",3.5e+07,4651977,4651977,"Sony Pictures","PG-13","Drama"
"1180","4/22/2016","A Hologram for the King",3.5e+07,4212494,11848058,"Roadside Attractions","R","Drama"
"1181","1/1/1980","Lion of the Desert",3.5e+07,1500000,1500000,"United Film Distrib…",NA,"Drama"
"1182","4/19/1996","Le hussard sur le toit",3.5e+07,1320043,1320043,"Miramax","R","Drama"
"1183","9/14/2012","Stolen",3.5e+07,289773,17967746,"Alchemy","R","Action"
"1184","3/13/2015","The Lovers",3.5e+07,0,11106,"IFC Films","PG-13","Adventure"
"1185","12/25/2011","The Darkest Hour",34800000,21443494,62831715,"Summit Entertainment","PG-13","Action"
"1186","4/10/2015","The Longest Ride",3.4e+07,37446117,63802928,"20th Century Fox","PG-13","Drama"
"1187","9/17/1993","The Age of Innocence",3.4e+07,32014993,32014993,"Sony Pictures","PG","Drama"
"1188","8/14/2009","Gake no ue no Ponyo",3.4e+07,15090399,205312666,"Walt Disney","G","Adventure"
"1189","9/1/1999","Chill Factor",3.4e+07,11263966,11263966,"Warner Bros.","R","Action"
"1190","5/5/2000","I Dreamed of Africa",3.4e+07,6543194,14291999,"Sony Pictures","PG-13","Drama"
"1191","12/4/1981","Reds",33500000,5e+07,5e+07,"Paramount Pictures","PG","Drama"
"1192","12/11/1992","A Few Good Men",3.3e+07,141340178,236500000,"Sony Pictures","R","Drama"
"1193","6/2/2000","Big Momma's House",3.3e+07,117559438,173559438,"20th Century Fox","PG-13","Comedy"
"1194","3/16/2001","Exit Wounds",3.3e+07,51758599,79958599,"Warner Bros.","R","Action"
"1195","7/8/2016","Mike and Dave Need Wedding Dates",3.3e+07,46009673,75909673,"20th Century Fox","R","Comedy"
"1196","7/27/2012","Step Up Revolution",3.3e+07,35074677,165552290,"Lionsgate","PG-13","Drama"
"1197","4/16/2004","The Punisher",3.3e+07,33664370,54533774,"Lionsgate","R","Action"
"1198","4/27/2012","Safe",3.3e+07,17142080,41495213,"Lionsgate","R","Action"
"1199","3/14/2008","Doomsday",3.3e+07,11008770,21621188,"Universal","R","Action"
"1200","4/23/1999","Pushing Tin",3.3e+07,8408835,8408835,"20th Century Fox","R","Comedy"
"1201","5/12/2006","Goal! The Dream Begins",3.3e+07,4283255,27610873,"Walt Disney","PG","Drama"
"1202","5/13/2011","Bridesmaids",32500000,169211718,289632023,"Universal","R","Comedy"
"1203","12/10/2008","The Reader",32500000,34194407,112964875,"Weinstein Co.","R","Drama"
"1204","2/24/2012","Wanderlust",32500000,17288155,24159934,"Universal","R","Comedy"
"1205","11/7/2003","Elf",3.2e+07,173398518,220236410,"New Line","PG","Adventure"
"1206","7/5/1996","Phenomenon",3.2e+07,104636382,152036382,"Walt Disney","PG","Drama"
"1207","6/12/2013","This is the End",3.2e+07,101470202,126539117,"Sony Pictures","R","Comedy"
"1208","1/18/2002","Snow Dogs",3.2e+07,81150692,116898028,"Walt Disney","PG","Adventure"
"1209","6/16/2006","Nacho Libre",3.2e+07,80197993,99296462,"Paramount Pictures","PG","Comedy"
"1210","11/23/1988","Scrooged",3.2e+07,59450353,59450353,"Paramount Pictures","PG-13","Comedy"
"1211","8/27/2010","Takers",3.2e+07,57744720,70587268,"Sony Pictures","PG-13","Drama"
"1212","9/10/1999","Stigmata",3.2e+07,50041732,89441732,"MGM","R","Horror"
"1213","11/10/2000","Men of Honor",3.2e+07,48814909,82339483,"20th Century Fox","R","Drama"
"1214","4/20/2018","I Feel Pretty",3.2e+07,48795601,91569698,"STX Entertainment","PG-13","Comedy"
"1215","9/2/2005","The Transporter 2",3.2e+07,43095856,88978458,"20th Century Fox","PG-13","Action"
"1216","2/18/2011","Big Mommas: Like Father, Like Son",3.2e+07,37915414,82332450,"20th Century Fox","PG-13","Comedy"
"1217","1/15/1993","Alive",3.2e+07,36299670,36299670,"Walt Disney","R","Drama"
"1218","10/21/2005","Dreamer: Inspired by a True Story",3.2e+07,33022286,39498360,"Dreamworks SKG","PG","Drama"
"1219","9/23/2005","A History of Violence",3.2e+07,31493782,61477797,"New Line","R","Drama"
"1220","3/15/2013","The Incredible Burt Wonderstone",3.2e+07,22537881,27392609,"Warner Bros.","PG-13","Comedy"
"1221","3/19/2010","Repo Men",3.2e+07,13942007,18195238,"Universal","R","Action"
"1222","9/14/2007","Dragon Wars: D-War",3.2e+07,10977721,79915361,"Freestyle Releasing","PG-13","Action"
"1223","9/6/1996","Bogus",3.2e+07,4357406,4357406,"Warner Bros.","PG","Comedy"
"1224","12/8/1999","Cradle Will Rock",3.2e+07,2899970,2899970,"Walt Disney","R","Drama"
"1225","12/15/2006","The Good German",3.2e+07,1308696,6678033,"Warner Bros.","R","Drama"
"1226","8/15/1979","Apocalypse Now",31500000,78800000,81250485,"United Artists","R","Action"
"1227","4/15/2016","Criminal",31500000,14708696,38771262,"Lionsgate","R","Action"
"1228","11/2/2012","Flight",3.1e+07,93772375,160558438,"Paramount Pictures","R","Drama"
"1229","12/29/1995","Mr. Holland’s Opus",3.1e+07,82582604,106282604,"Walt Disney","PG","Drama"
"1230","12/18/1985","Out of Africa",3.1e+07,79096868,258210860,"Universal","PG","Drama"
"1231","6/29/1979","Moonraker",3.1e+07,70300000,210300000,"United Artists","PG","Action"
"1232","3/7/2014","The Grand Budapest Hotel",3.1e+07,59076019,164180547,"Fox Searchlight","R","Comedy"
"1233","7/29/2015","Vacation",3.1e+07,58884188,101627989,"Warner Bros.","R","Comedy"
"1234","4/28/2000","Frequency",3.1e+07,44983704,68079671,"New Line","PG-13","Drama"
"1235","9/28/2001","Hearts in Atlantis",3.1e+07,24185781,30885781,"Warner Bros.","PG-13","Drama"
"1236","1/22/2010","Extraordinary Measures",3.1e+07,12482741,15826984,"CBS Films","PG","Drama"
"1237","8/25/2017","Birth of the Dragon",3.1e+07,6901965,7220514,"BH Tilt","PG-13","Action"
"1238","10/20/1995","Get Shorty",30250000,72021008,115021008,"MGM","R","Comedy"
"1239","6/8/1984","Ghostbusters",3e+07,242212467,295212467,"Sony Pictures","PG","Adventure"
"1240","11/22/1995","Toy Story",3e+07,191796233,364545516,"Walt Disney","G","Adventure"
"1241","6/25/1999","Big Daddy",3e+07,163479795,228641283,"Sony Pictures","PG-13","Comedy"
"1242","8/10/2001","American Pie 2",3e+07,145096820,286500000,"Universal","R","Comedy"
"1243","2/10/2012","The Vow",3e+07,125014030,197618160,"Sony Pictures","PG-13","Drama"
"1244","6/10/1994","Speed",3e+07,121248145,283200000,"20th Century Fox","R","Action"
"1245","8/16/2013","Lee Daniels' The Butler",3e+07,116632095,177025498,"Weinstein Co.","PG-13","Drama"
"1246","9/29/2000","Remember the Titans",3e+07,115654751,136706683,"Walt Disney","PG","Drama"
"1247","6/18/2004","Dodgeball: A True Underdog Story",3e+07,114326736,167791704,"20th Century Fox","PG-13","Comedy"
"1248","11/10/1995","Ace Ventura: When Nature Calls",3e+07,108360063,212400000,"Warner Bros.","PG-13","Comedy"
"1249","8/3/2001","The Princess Diaries",3e+07,108244774,165334774,"Walt Disney","G","Comedy"
"1250","3/5/1999","Analyze This",3e+07,106885658,176885658,"Warner Bros.","R","Comedy"
"1251","9/20/1996","The First Wives Club",3e+07,105489203,181489203,"Paramount Pictures","PG","Comedy"
"1252","12/15/2004","Million Dollar Baby",3e+07,100492203,231928227,"Warner Bros.","PG-13","Drama"
"1253","10/8/2003","Mystic River",3e+07,90135191,156822020,"Warner Bros.","R","Drama"
"1254","12/18/2015","Sisters",3e+07,87044645,106030660,"Universal","R","Comedy"
"1255","11/10/1999","Pokemon: The First Movie",3e+07,85744662,163644662,"Warner Bros.","G","Adventure"
"1256","11/19/2004","SpongeBob SquarePants: The Movie",3e+07,85416609,142051255,"Paramount Pictures","PG","Adventure"
"1257","12/4/2009","Up in the Air",3e+07,83823381,166842739,"Paramount Pictures","R","Drama"
"1258","6/25/2004","The Notebook",3e+07,81001787,116025023,"New Line","PG-13","Drama"
"1259","4/10/2009","Hannah Montana the Movie",3e+07,79576189,169173206,"Walt Disney","G","Drama"
"1260","11/17/2000","Rugrats in Paris",3e+07,76501438,103284813,"Paramount Pictures","G","Adventure"
"1261","8/18/2017","The Hitman’s Bodyguard",3e+07,75468583,172779292,"Lionsgate","R","Action"
"1262","12/25/1991","The Prince of Tides",3e+07,74787599,74787599,"Sony Pictures","R","Drama"
"1263","8/12/2005","Four Brothers",3e+07,74494381,92494381,"Paramount Pictures","R","Drama"
"1264","12/23/1994","Legends of the Fall",3e+07,66502573,160502573,"Sony Pictures","R","Drama"
"1265","9/28/2012","Looper",3e+07,66486205,170466405,"Sony Pictures","R","Action"
"1266","12/13/2002","About Schmidt",3e+07,65005217,107054484,"New Line","R","Drama"
"1267","1/17/2014","The Nut Job",3e+07,64251538,122529966,"Open Road","PG","Adventure"
"1268","2/16/2001","Down to Earth",3e+07,64172251,71172251,"Paramount Pictures","PG-13","Comedy"
"1269","8/4/1995","Babe",3e+07,63658910,246100000,"Universal","G","Adventure"
"1270","4/18/2008","Forgetting Sarah Marshall",3e+07,63172463,105173042,"Universal","R","Comedy"
"1271","10/8/2004","Friday Night Lights",3e+07,61255921,61950770,"Universal","PG-13","Drama"
"1272","11/17/1989","Harlem Nights",3e+07,60857262,95857262,"Paramount Pictures","R","Comedy"
"1273","4/25/2008","Baby Mama",3e+07,60494212,64170447,"Universal","PG-13","Comedy"
"1274","5/29/1998","Hope Floats",3e+07,60110313,81529000,"20th Century Fox","PG-13","Drama"
"1275","1/9/2009","Bride Wars",3e+07,58715510,115150424,"20th Century Fox","PG","Comedy"
"1276","8/20/2004","Without a Paddle",3e+07,58156435,65121280,"Paramount Pictures","PG-13","Comedy"
"1277","11/22/2017","Darkest Hour",3e+07,56443120,150355828,"Focus Features","PG-13","Drama"
"1278","9/23/2005","Corpse Bride",3e+07,53359111,114770654,"Warner Bros.","PG","Adventure"
"1279","5/14/2010","Letters to Juliet",3e+07,53032453,82148538,"Summit Entertainment","PG","Drama"
"1280","4/6/2001","Blow",3e+07,52990775,83282296,"New Line","R","Drama"
"1281","2/12/1999","Message in a Bottle",3e+07,52880016,52880016,"Warner Bros.","PG-13","Drama"
"1282","5/11/2018","Life of the Party",3e+07,52856061,65556061,"Warner Bros.","PG-13","Comedy"
"1283","7/24/2015","Southpaw",3e+07,52421953,94207861,"Weinstein Co.","R","Drama"
"1284","6/9/1989","Star Trek V: The Final Frontier",3e+07,52210049,70200000,"Paramount Pictures","PG","Action"
"1285","7/3/2002","Like Mike",3e+07,51432423,62432423,"20th Century Fox","PG","Adventure"
"1286","3/18/1994","Naked Gun 33 1/3: The Final Insult",3e+07,51041856,51041856,"Paramount Pictures","PG-13","Comedy"
"1287","12/7/2007","Atonement",3e+07,50980159,129779728,"Focus Features","R","Drama"
"1288","5/24/1985","A View to a Kill",3e+07,50327960,152627960,"MGM","PG","Action"
"1289","1/14/2005","Racing Stripes",3e+07,49772522,89955540,"Warner Bros.","PG","Adventure"
"1290","1/19/2018","Den of Thieves",3e+07,44947622,79424321,"STX Entertainment","R","Action"
"1291","4/13/2012","The Three Stooges",3e+07,44338224,54052249,"20th Century Fox","PG","Adventure"
"1292","7/21/2000","Pokemon 2000",3e+07,43746923,133946923,"Warner Bros.","G","Adventure"
"1293","10/24/2014","John Wick",3e+07,43037835,76235001,"Lionsgate","R","Action"
"1294","1/13/2006","Glory Road",3e+07,42647449,42799060,"Walt Disney","PG","Drama"
"1295","4/24/2015","The Age of Adaline",3e+07,42629776,69057415,"Lionsgate","PG-13","Drama"
"1296","8/6/2010","Step Up 3D",3e+07,42400223,165889117,"Walt Disney","PG-13","Drama"
"1297","5/29/2009","Drag Me To Hell",3e+07,42100625,91388487,"Universal","PG-13","Horror"
"1298","9/19/2003","Secondhand Lions",3e+07,42070939,47902566,"New Line","PG","Drama"
"1299","11/10/2006","Stranger Than Fiction",3e+07,40435190,53572822,"Sony Pictures","PG-13","Comedy"
"1300","4/8/2011","Hanna",3e+07,40259119,65343694,"Focus Features","PG-13","Drama"
"1301","8/16/2002","Blue Crush",3e+07,40118420,51618420,"Universal","PG-13","Drama"
"1302","10/19/2007","30 Days of Night",3e+07,39568996,80276156,"Sony Pictures","R","Horror"
"1303","9/15/2006","Gridiron Gang",3e+07,38432823,41457834,"Sony Pictures","PG-13","Drama"
"1304","7/20/1988","Midnight Run",3e+07,38413606,81613606,"Universal","R","Action"
"1305","1/25/2008","Meet the Spartans",3e+07,38233676,84646831,"20th Century Fox","PG-13","Comedy"
"1306","11/13/1987","The Running Man",3e+07,38122000,38122000,"Sony/TriStar","R","Action"
"1307","2/9/2018","The 15:17 to Paris",3e+07,36250957,56070897,"Warner Bros.","PG-13","Drama"
"1308","11/21/1997","Mortal Kombat: Annihilation",3e+07,35927406,51327406,"New Line","PG-13","Action"
"1309","4/7/2006","Take the Lead",3e+07,34742066,65390493,"New Line","PG-13","Drama"
"1310","11/24/2010","Love and Other Drugs",3e+07,32367005,102716321,"20th Century Fox","R","Drama"
"1311","6/3/2015","Entourage",3e+07,32363404,46362449,"Warner Bros.","R","Comedy"
"1312","6/1/2001","What's the Worst That Could Happen?",3e+07,32267774,38462071,"MGM","PG-13","Comedy"
"1313","7/2/2014","Deliver Us from Evil",3e+07,30577122,87956618,"Sony Pictures","R","Horror"
"1314","8/1/2014","Get on Up",3e+07,30569935,33339868,"Universal","PG-13","Drama"
"1315","7/15/2011","Winnie the Pooh",3e+07,26692846,50145607,"Walt Disney","G","Adventure"
"1316","5/15/1998","Bulworth",3e+07,26528684,29203383,"20th Century Fox","R","Comedy"
"1317","8/4/1995","Virtuosity",3e+07,23998226,23998226,"Paramount Pictures","R","Action"
"1318","9/14/2018","White Boy Rick",3e+07,23851700,23851700,"Sony Pictures","R","Drama"
"1319","9/18/1998","One True Thing",3e+07,23337196,26708196,"Universal","R","Drama"
"1320","2/4/2011","Sanctum",3e+07,23209310,104283753,"Universal","R","Adventure"
"1321","7/21/2006","My Super Ex-Girlfriend",3e+07,22530295,60772856,"20th Century Fox","PG-13","Comedy"
"1322","8/25/2017","Ballerina",3e+07,21858070,96908157,"Weinstein Co.","PG","Adventure"
"1323","9/17/2004","Mr. 3000",3e+07,21800302,21827296,"Walt Disney","PG-13","Comedy"
"1324","1/19/2005","Assault On Precinct 13",3e+07,20040895,36040895,"Focus/Rogue Pictures","R","Action"
"1325","2/6/1998","The Replacement Killers",3e+07,19035741,19035741,"Sony Pictures","R","Action"
"1326","3/3/2006","Ultraviolet",3e+07,18522064,30962112,"Sony Pictures","PG-13","Action"
"1327","10/21/2005","North Country",3e+07,18324242,23676771,"Warner Bros.","R","Drama"
"1328","10/9/2015","Steve Jobs",3e+07,17766658,35579007,"Universal","R","Drama"
"1329","7/17/2002","Eight Legged Freaks",3e+07,17266505,36722311,"Warner Bros.","PG-13","Comedy"
"1330","7/19/1996","Fled",3e+07,17192205,19892205,"MGM","R","Action"
"1331","6/4/2010","Splice",3e+07,17010170,28542494,"Warner Bros.","R","Horror"
"1332","4/9/2004","The Whole Ten Yards",3e+07,16323969,26323969,"Warner Bros.","PG-13","Comedy"
"1333","8/1/1986","Howard the Duck",3e+07,16295774,16295774,"Universal",NA,"Action"
"1334","10/24/2008","Pride and Glory",3e+07,15740721,43440721,"Warner Bros.","R","Drama"
"1335","8/26/2005","The Cave",3e+07,15007991,27147991,"Sony Pictures","PG-13","Horror"
"1336","6/20/2003","Alex & Emma",3e+07,14208384,15358583,"Warner Bros.","PG-13","Drama"
"1337","12/25/2005","The New World",3e+07,12712093,26184400,"New Line","PG-13","Adventure"
"1338","6/29/2007","Evening",3e+07,12406646,12885574,"Focus Features","PG-13","Drama"
"1339","1/18/2013","The Last Stand",3e+07,12050299,48330757,"Lionsgate","R","Action"
"1340","1/15/1999","In Dreams",3e+07,12017369,12017369,"Dreamworks SKG","R","Horror"
"1341","3/12/1999","Wing Commander",3e+07,11578022,11578022,"20th Century Fox","PG-13","Action"
"1342","4/29/2011","Hoodwinked Too: Hood vs. Evil",3e+07,10143779,23353111,"Weinstein Co.","PG","Adventure"
"1343","4/10/2009","Dragonball Evolution",3e+07,9362785,58228460,"20th Century Fox","PG","Adventure"
"1344","9/9/2005","An Unfinished Life",3e+07,8535575,18535575,"Miramax","PG-13","Drama"
"1345","2/3/2017","The Space Between Us",3e+07,7885294,16481405,"STX Entertainment","PG-13","Drama"
"1346","12/25/2009","The Imaginarium of Doctor Parnassus",3e+07,7689607,64352607,"Sony Pictures Classics","PG-13","Adventure"
"1347","1/14/2011","Barney's Version",3e+07,7502560,8845575,"Sony Pictures Classics","R","Drama"
"1348","6/1/1984","Once Upon a Time in America",3e+07,5321508,5575648,"Warner Bros.","R","Drama"
"1349","1/22/1999","Gloria",3e+07,4167493,4967493,"Sony Pictures","R","Drama"
"1350","12/29/2004","The Merchant of Venice",3e+07,3765585,18765585,"Sony Pictures Classics","R","Drama"
"1351","4/2/2003","The Good Thief",3e+07,3517797,6460758,"Fox Searchlight","R","Drama"
"1352","8/17/2005","Supercross",3e+07,3102550,3252550,"20th Century Fox","PG-13","Action"
"1353","12/29/2006","Miss Potter",3e+07,3005605,35891257,"MGM","PG","Drama"
"1354","5/5/2006","Wu ji",3e+07,669625,35869934,"Warner Independent","PG-13","Action"
"1355","9/23/2011","Machine Gun Preacher",3e+07,538690,3721988,"Relativity","R","Drama"
"1356","2/2/2018","Bilal: A New Breed of Hero",3e+07,490973,648599,"Vertical Entertainment","PG-13","Adventure"
"1357","6/15/2007","DOA: Dead or Alive",3e+07,480314,7755686,"Weinstein/Dimension","PG-13","Action"
"1358","10/7/2011","Xinhai geming",3e+07,135739,8593154,"Variance Films","R","Drama"
"1359","1/30/2015","Wild Card",3e+07,3200,3989464,"Lionsgate","R","Action"
"1360","12/14/2007","Goodbye Bafana",3e+07,0,2717302,"Paramount Vantage",NA,"Drama"
"1361","2/24/2017","Collide",29200000,2280004,5466631,"Open Road","PG-13","Action"
"1362","5/15/2015","Pitch Perfect 2",2.9e+07,184296230,287641616,"Universal","PG-13","Comedy"
"1363","11/18/2005","Walk the Line",2.9e+07,119519402,187707495,"20th Century Fox","PG-13","Drama"
"1364","9/28/2018","Night School",2.9e+07,66906825,84406825,"Universal","PG-13","Comedy"
"1365","4/8/2016","The Boss",2.9e+07,63077560,78652395,"Universal","R","Comedy"
"1366","12/27/1995","Twelve Monkeys",2.9e+07,57141459,168841459,"Universal","R","Drama"
"1367","9/12/2003","Once Upon a Time in Mexico",2.9e+07,56330657,97413527,"Sony Pictures","R","Action"
"1368","8/18/2017","Logan Lucky",2.9e+07,27778642,43886147,"Bleecker Street","PG-13","Comedy"
"1369","8/12/2016","Florence Foster Jenkins",2.9e+07,27383770,56017691,"Paramount Pictures","PG-13","Drama"
"1370","2/13/1998","The Borrowers",2.9e+07,22619589,54045832,"Polygram","PG","Adventure"
"1371","12/5/2008","Frost/Nixon",2.9e+07,18622031,28452945,"Universal","R","Drama"
"1372","11/12/2004","Seed of Chucky",2.9e+07,17016190,24716190,"Focus/Rogue Pictures","R","Horror"
"1373","12/31/2002","Confessions of a Dangerous Mind",2.9e+07,16007718,33013805,"Miramax","R","Drama"
"1374","8/26/2009","Taking Woodstock",2.9e+07,7460204,10066366,"Focus Features","R","Drama"
"1375","11/6/1987","Cry Freedom",2.9e+07,5899797,25899797,"Universal",NA,"Drama"
"1376","9/24/1999","Mumford",28700000,4559569,4559569,"Walt Disney","R","Comedy"
"1377","11/11/1992","Aladdin",2.8e+07,217350219,504050219,"Walt Disney","G","Adventure"
"1378","8/14/2015","Straight Outta Compton",2.8e+07,161197785,202182981,"Universal","R","Drama"
"1379","7/21/2017","Girls Trip",2.8e+07,115108515,140886353,"Universal","R","Comedy"
"1380","11/20/1998","The Rugrats Movie",2.8e+07,100494685,140894685,"Paramount Pictures","G","Adventure"
"1381","7/15/1988","Die Hard",2.8e+07,81350242,139109346,"20th Century Fox","R","Action"
"1382","11/1/2017","A Bad Moms Christmas",2.8e+07,72110659,127710659,"STX Entertainment","R","Comedy"
"1383","2/14/2013","Safe Haven",2.8e+07,71399120,94050951,"Relativity","PG-13","Drama"
"1384","12/11/2015","The Big Short",2.8e+07,70259870,133162752,"Paramount Pictures","R","Drama"
"1385","11/7/2008","Role Models",2.8e+07,67300955,94500826,"Universal","R","Comedy"
"1386","2/6/2004","Miracle",2.8e+07,64378093,64474705,"Walt Disney","PG","Drama"
"1387","1/28/2013","Last Vegas",2.8e+07,63914167,112914167,"CBS Films","PG-13","Comedy"
"1388","6/26/1981","For Your Eyes Only",2.8e+07,54800000,195300000,"Universal","PG","Action"
"1389","6/15/2018","Tag",2.8e+07,54547470,76844788,"Warner Bros.","R","Comedy"
"1390","9/28/2001","Zoolander",2.8e+07,45172250,60780981,"Paramount Pictures","PG-13","Comedy"
"1391","9/16/1994","Timecop",2.8e+07,44853581,102053581,"Universal","R","Action"
"1392","7/16/1993","Hocus Pocus",2.8e+07,39360491,39360491,"Walt Disney","PG","Comedy"
"1393","11/11/2005","Pride & Prejudice",2.8e+07,38372662,126549607,"Focus Features","PG","Drama"
"1394","8/12/2011","30 Minutes or Less",2.8e+07,37053924,40966716,"Sony Pictures","R","Comedy"
"1395","12/22/2000","Dracula 2000",2.8e+07,33000377,33000377,"Miramax","R","Horror"
"1396","4/7/1995","Rob Roy",2.8e+07,31390587,31390587,"MGM","R","Drama"
"1397","8/16/2013","Kick-Ass 2",2.8e+07,28795985,63129909,"Universal","R","Action"
"1398","10/12/2007","We Own the Night",2.8e+07,28563179,55307857,"Sony Pictures","R","Drama"
"1399","9/19/2014","A Walk Among the Tombstones",2.8e+07,26017685,62108587,"Universal","R","Action"
"1400","1/15/2010","The Spy Next Door",2.8e+07,24307106,46752858,"Lionsgate","PG","Adventure"
"1401","4/25/2014","Brick Mansions",2.8e+07,20396829,73421224,"Relativity","PG-13","Action"
"1402","10/1/1999","Mystery, Alaska",2.8e+07,8891623,8891623,"Walt Disney","R","Comedy"
"1403","8/24/2001","John Carpenter's Ghosts of Mars",2.8e+07,8434601,8434601,"Screen Media Films","R","Action"
"1404","7/11/1997","A Simple Wish",2.8e+07,8165213,8165213,"Universal","PG","Comedy"
"1405","10/30/2015","Our Brand is Crisis",2.8e+07,7002261,8592432,"Warner Bros.","R","Drama"
"1406","12/25/1997","Kundun",2.8e+07,5686694,5686694,"Walt Disney","PG-13","Drama"
"1407","6/10/1983","Octopussy",27500000,67900000,187500000,"MGM","PG","Action"
"1408","6/26/2009","My Sister's Keeper",27500000,49200230,96673002,"Warner Bros.","PG-13","Drama"
"1409","2/8/2008","Welcome Home Roscoe Jenkins",27500000,42436517,43607627,"Universal","PG-13","Comedy"
"1410","12/14/1984","A Passage to India",27500000,27187653,27187653,"Sony Pictures",NA,"Drama"
"1411","12/25/2006","Notes on a Scandal",27500000,17510118,50578411,"Fox Searchlight","R","Drama"
"1412","12/25/1994","The Jungle Book",2.7e+07,44342956,44342956,"Walt Disney","PG","Adventure"
"1413","8/19/2011","Spy Kids: All the Time in the World",2.7e+07,38536376,80681183,"Weinstein/Dimension","PG","Adventure"
"1414","10/21/1983","The Right Stuff",2.7e+07,21500000,21500000,"Warner Bros.",NA,"Action"
"1415","7/20/1984","Die Unendliche Geschichte",2.7e+07,21300000,21300000,"Warner Bros.",NA,"Adventure"
"1416","9/19/2008","The Duchess",2.7e+07,13848978,45160110,"Paramount Vantage","PG-13","Drama"
"1417","10/1/2010","Case 39",2.7e+07,13261851,28773827,"Paramount Vantage","R","Horror"
"1418","6/10/2005","The Honeymooners",2.7e+07,12834849,13174426,"Paramount Pictures","PG-13","Comedy"
"1419","6/21/1985","Return to Oz",2.7e+07,10618813,10618813,"Walt Disney","PG","Adventure"
"1420","3/27/1998","The Newton Boys",2.7e+07,10341093,10341093,"20th Century Fox","PG-13","Drama"
"1421","11/2/2007","Martian Child",2.7e+07,7500310,9352089,"New Line","PG","Drama"
"1422","10/18/2002","Formula 51",2.7e+07,5204007,5204007,"Screen Media Films","R","Action"
"1423","11/24/1999","Flawless",2.7e+07,4485485,4485485,"MGM","R","Drama"
"1424","10/17/2008","What Just Happened",2.7e+07,1090947,2412123,"Magnolia Pictures","R","Comedy"
"1425","1/16/2009","Paul Blart: Mall Cop",2.6e+07,146336178,185904750,"Sony Pictures","PG","Adventure"
"1426","8/19/2005","The 40 Year-old Virgin",2.6e+07,109449237,177344230,"Universal","R","Comedy"
"1427","12/21/1990","Kindergarten Cop",2.6e+07,91457688,2.02e+08,"Universal","PG-13","Comedy"
"1428","8/6/2008","Pineapple Express",2.6e+07,87341380,102404019,"Sony Pictures","R","Comedy"
"1429","12/22/1993","Philadelphia",2.6e+07,77324422,201324422,"Sony/TriStar","PG-13","Drama"
"1430","7/31/1998","Ever After: A Cinderella Story",2.6e+07,65705772,65705772,"20th Century Fox","PG","Drama"
"1431","6/15/1977","A Bridge Too Far",2.6e+07,50800000,50800000,"United Artists","PG","Action"
"1432","4/26/2013","Pain & Gain",2.6e+07,49875291,81275291,"Paramount Pictures","R","Action"
"1433","1/31/2003","Final Destination 2",2.6e+07,46896664,90396664,"New Line","R","Horror"
"1434","12/22/2000","O Brother, Where Art Thou?",2.6e+07,45506619,75763814,"Walt Disney","PG-13","Comedy"
"1435","12/29/2004","In Good Company",2.6e+07,45489752,63489752,"Universal","PG-13","Comedy"
"1436","8/29/2012","Lawless",2.6e+07,37397291,54393637,"Weinstein Co.","R","Drama"
"1437","3/29/2002","Clockstoppers",2.6e+07,36985501,38788828,"Paramount Pictures","PG","Adventure"
"1438","12/4/2009","Brothers",2.6e+07,28544157,45043870,"Lionsgate","R","Drama"
"1439","10/17/2014","The Best of Me",2.6e+07,26766213,41059418,"Relativity","PG-13","Drama"
"1440","2/20/2004","Welcome to Mooseport",2.6e+07,14469428,14469428,"20th Century Fox","PG-13","Comedy"
"1441","1/27/1995","Highlander: The Final Dimension",2.6e+07,13738574,13738574,"Miramax","PG-13","Action"
"1442","8/24/2001","The Curse of the Jade Scorpion",2.6e+07,7496522,18496522,"Dreamworks SKG","PG-13","Comedy"
"1443","10/18/2013","The Fifth Estate",2.6e+07,3254172,6154172,"Walt Disney","R","Drama"
"1444","3/21/2014","Blood Ties",2.6e+07,42472,2923959,"Roadside Attractions","R","Drama"
"1445","8/24/1997","The Grimm Brothers' Snow White",2.6e+07,5000,5000,"Gramercy","PG-13","Horror"
"1446","3/17/2015","Accidental Love",2.6e+07,0,135436,"Alchemy","PG-13","Comedy"
"1447","5/17/1996","Flipper",25530000,20080020,30593313,"Universal","PG","Adventure"
"1448","8/31/2005","The Constant Gardener",25500000,33579798,86301599,"Focus Features","R","Drama"
"1449","10/17/2008","W.",25100000,25534493,28575778,"Lionsgate","PG-13","Drama"
"1450","2/25/2004","The Passion of the Christ",2.5e+07,370782930,622341924,"Newmarket Films","R","Drama"
"1451","11/24/1993","Mrs. Doubtfire",2.5e+07,219195051,441286003,"20th Century Fox","PG-13","Comedy"
"1452","12/16/1988","Rain Man",2.5e+07,172825435,412800000,"MGM","R","Comedy"
"1453","8/10/2011","The Help",2.5e+07,169705587,213120004,"Walt Disney","PG-13","Drama"
"1454","12/25/2016","Hidden Figures",2.5e+07,169607287,231771716,"20th Century Fox","PG","Drama"
"1455","12/12/2008","Gran Torino",2.5e+07,148095302,274543085,"Warner Bros.","R","Drama"
"1456","1/17/2014","Ride Along",2.5e+07,134202565,153733800,"Universal","PG-13","Comedy"
"1457","12/15/1993","Schindler’s List",2.5e+07,96067179,321365567,"Universal","R","Drama"
"1458","3/26/2004","Scooby-Doo 2: Monsters Unleashed",2.5e+07,84185387,181185387,"Warner Bros.","PG","Adventure"
"1459","8/15/2003","Freddy vs. Jason",2.5e+07,82622655,114576403,"New Line","R","Horror"
"1460","2/16/2007","Bridge to Terabithia",2.5e+07,82234139,137984788,"Walt Disney","PG","Drama"
"1461","12/21/2001","Jimmy Neutron: Boy Genius",2.5e+07,80936232,102992536,"Paramount Pictures","G","Adventure"
"1462","1/18/2008","Cloverfield",2.5e+07,80048433,171302226,"Paramount Pictures","PG-13","Action"
"1463","2/5/2010","Dear John",2.5e+07,80014842,142033509,"Sony Pictures","PG-13","Drama"
"1464","12/25/2012","Parental Guidance",2.5e+07,77267296,120832383,"20th Century Fox","PG","Adventure"
"1465","6/3/1987","The Untouchables",2.5e+07,76270454,76270454,"Paramount Pictures","R","Action"
"1466","11/9/2007","No Country for Old Men",2.5e+07,74273505,164035753,"Miramax","R","Action"
"1467","1/13/2012","Contraband",2.5e+07,66528000,98406855,"Universal","R","Action"
"1468","1/27/2017","A Dog’s Purpose",2.5e+07,64321890,203731707,"Universal","PG","Drama"
"1469","4/20/2012","The Lucky One",2.5e+07,60457138,96633833,"Warner Bros.","PG-13","Drama"
"1470","3/22/2000","Romeo Must Die",2.5e+07,55973336,91036760,"Warner Bros.","R","Action"
"1471","2/10/2006","Final Destination 3",2.5e+07,54098051,112798051,"New Line","R","Horror"
"1472","4/22/2011","Madea's Big Happy Family",2.5e+07,53345287,54160818,"Lionsgate","PG-13","Drama"
"1473","12/13/2013","Tyler Perry's A Madea Christmas",2.5e+07,52543354,52543354,"Lionsgate","PG-13","Comedy"
"1474","11/12/2004","Finding Neverland",2.5e+07,51676606,115036108,"Miramax","PG","Drama"
"1475","5/23/1986","Cobra",2.5e+07,49042224,49042224,"Cannon","R","Action"
"1476","8/22/2008","The House Bunny",2.5e+07,48237389,71390601,"Sony Pictures","PG-13","Comedy"
"1477","3/14/2003","Agent Cody Banks",2.5e+07,47545060,58240458,"MGM","PG","Adventure"
"1478","1/27/2006","Nanny McPhee",2.5e+07,47279279,128745578,"Universal","PG","Adventure"
"1479","9/19/1990","Goodfellas",2.5e+07,46743809,46777347,"Warner Bros.","R","Drama"
"1480","8/15/2014","The Giver",2.5e+07,45090374,55090374,"Weinstein Co.","PG-13","Drama"
"1481","7/18/1997","Nothing To Lose",2.5e+07,44480039,64594061,"Walt Disney","R","Comedy"
"1482","11/20/1987","The Last Emperor",2.5e+07,43984987,44005073,"Sony Pictures","PG-13","Drama"
"1483","11/20/2015","The Night Before",2.5e+07,43035725,52427346,"Sony Pictures","R","Comedy"
"1484","10/15/1993","The Beverly Hillbillies",2.5e+07,42222647,55598481,"20th Century Fox","PG","Comedy"
"1485","12/27/2002","The Hours",2.5e+07,41675994,97030468,"Paramount Pictures","PG-13","Drama"
"1486","8/22/1997","Money Talks",2.5e+07,41076865,41076865,"New Line","R","Action"
"1487","12/26/2007","There Will Be Blood",2.5e+07,40222514,77208711,"Paramount Vantage","R","Drama"
"1488","12/20/2002","The Wild Thornberrys Movie",2.5e+07,40108697,60694737,"Paramount Pictures","PG","Adventure"
"1489","6/13/2003","Rugrats Go Wild",2.5e+07,39402572,55443032,"Paramount Pictures","PG","Adventure"
"1490","5/31/2002","Undercover Brother",2.5e+07,38230435,40796145,"Universal","PG-13","Comedy"
"1491","7/6/2001","Kiss of the Dragon",2.5e+07,36833473,36833473,"20th Century Fox","R","Action"
"1492","5/16/2014","Million Dollar Arm",2.5e+07,36447959,39217912,"Walt Disney","PG","Drama"
"1493","1/1/2004","Beauty Shop",2.5e+07,36351350,38351350,"MGM","PG-13","Comedy"
"1494","4/4/2003","What a Girl Wants",2.5e+07,35990505,35990505,"Warner Bros.","PG","Comedy"
"1495","8/29/2003","Jeepers Creepers II",2.5e+07,35623801,119923801,"MGM","R","Horror"
"1496","2/28/2003","Cradle 2 the Grave",2.5e+07,34657731,56434942,"Warner Bros.","R","Action"
"1497","8/24/2007","Mr. Bean’s Holiday",2.5e+07,33302167,234981342,"Universal","G","Adventure"
"1498","10/16/1998","Bride of Chucky",2.5e+07,32404188,50692188,"Universal","R","Horror"
"1499","2/17/2017","Fist Fight",2.5e+07,32187017,40287017,"Warner Bros.","R","Comedy"
"1500","11/21/2007","August Rush",2.5e+07,31664162,66015869,"Warner Bros.","PG","Drama"
"1501","12/9/2011","The Sitter",2.5e+07,30542576,38749404,"20th Century Fox","R","Comedy"
"1502","11/6/1998","Elizabeth",2.5e+07,30082699,82150642,"Gramercy","R","Drama"
"1503","1/23/1998","Spice World",2.5e+07,29342592,56042592,"Sony Pictures","PG","Comedy"
"1504","4/11/2014","Draft Day",2.5e+07,28842237,29847480,"Lionsgate","PG-13","Drama"
"1505","9/23/1994","The Shawshank Redemption",2.5e+07,28241469,28307092,"Sony Pictures","R","Drama"
"1506","2/3/2017","Rings",2.5e+07,27793018,82933201,"Paramount Pictures","PG-13","Horror"
"1507","5/22/2009","Dance Flick",2.5e+07,25794018,32224624,"Paramount Pictures","PG-13","Comedy"
"1508","4/20/2001","Crocodile Dundee in Los Angeles",2.5e+07,25590119,39393111,"Paramount Pictures","PG","Adventure"
"1509","7/26/1996","Kingpin",2.5e+07,25023424,32223424,"MGM","R","Comedy"
"1510","3/18/2005","Ice Princess",2.5e+07,24381334,25732334,"Walt Disney","G","Comedy"
"1511","8/26/2011","Don't Be Afraid of the Dark",2.5e+07,24046682,39126427,"FilmDistrict","R","Horror"
"1512","4/23/2010","The Losers",2.5e+07,23591432,29863840,"Warner Bros.","PG-13","Action"
"1513","8/24/2007","War",2.5e+07,22486409,40686409,"Lionsgate","R","Action"
"1514","4/7/1995","Don Juan DeMarco",2.5e+07,22032635,22032635,"New Line","PG-13","Drama"
"1515","4/22/2005","A Lot Like Love",2.5e+07,21835784,41921590,"Walt Disney","PG-13","Comedy"
"1516","5/1/1998","He Got Game",2.5e+07,21567853,22411948,"Walt Disney","R","Drama"
"1517","2/11/2011","The Eagle",2.5e+07,19490041,38993548,"Focus Features","PG-13","Action"
"1518","8/5/2015","Shaun the Sheep",2.5e+07,19375982,101927062,"Lionsgate","PG","Adventure"
"1519","9/2/2011","Shark Night 3D",2.5e+07,18877153,18877153,"Relativity","PG-13","Horror"
"1520","3/24/2017","CHiPS",2.5e+07,18600152,23190697,"Warner Bros.","R","Action"
"1521","10/11/2002","Punch-Drunk Love",2.5e+07,17791031,24591031,"Sony Pictures","R","Comedy"
"1522","2/20/2004","Eurotrip",2.5e+07,17718223,20718223,"Dreamworks SKG","R","Comedy"
"1523","12/22/2017","Father Figures",2.5e+07,17501244,21038826,"Warner Bros.","R","Comedy"
"1524","4/4/2008","The Ruins",2.5e+07,17432844,22910563,"Paramount Pictures","R","Horror"
"1525","12/8/2006","Unaccompanied Minors",2.5e+07,16655224,21970831,"Warner Bros.","PG","Adventure"
"1526","4/1/1988","Bright Lights, Big City",2.5e+07,16118077,16118077,"United Artists","R","Drama"
"1527","11/15/2002","Half Past Dead",2.5e+07,15567860,19233280,"Sony Pictures","PG-13","Action"
"1528","4/18/1986","Legend",2.5e+07,15502112,23506237,"Universal","PG","Adventure"
"1529","7/26/1996","The Adventures of Pinocchio",2.5e+07,15382170,36682170,"New Line","G","Adventure"
"1530","9/30/2005","The Greatest Game Ever Played",2.5e+07,15331289,15468266,"Walt Disney","PG","Drama"
"1531","3/3/2000","The Next Best Thing",2.5e+07,14983572,24355762,"Paramount Pictures","PG-13","Drama"
"1532","10/8/2010","My Soul to Take",2.5e+07,14744435,16727470,"Universal","R","Horror"
"1533","8/15/2008","Fly Me To the Moon",2.5e+07,14543943,43530281,"Summit Entertainment","G","Adventure"
"1534","9/13/1996","Maximum Risk",2.5e+07,14102929,51702929,"Sony Pictures","R","Action"
"1535","9/13/2002","Stealing Harvard",2.5e+07,13973532,13973532,"Sony Pictures","PG-13","Comedy"
"1536","8/3/2007","Hot Rod",2.5e+07,13938332,14334401,"Paramount Pictures","PG-13","Comedy"
"1537","9/9/2011","Warrior",2.5e+07,13657115,24215385,"Lionsgate","PG-13","Drama"
"1538","12/24/1999","Angela's Ashes",2.5e+07,13038660,13038660,"Paramount Pictures","R","Drama"
"1539","9/22/2017","Battle of the Sexes",2.5e+07,12638526,18445094,"Fox Searchlight","PG-13","Drama"
"1540","12/21/2012","Cirque du Soleil: Worlds Away",2.5e+07,12512862,28012862,"Paramount Pictures","PG","Drama"
"1541","11/13/2015","The 33",2.5e+07,12227722,28400715,"Warner Bros.","PG-13","Drama"
"1542","6/21/1985","Lifeforce",2.5e+07,11603545,11603545,"Sony/TriStar","R","Horror"
"1543","4/15/2011","The Conspirator",2.5e+07,11538204,15907411,"Roadside Attractions","PG-13","Drama"
"1544","7/3/2002","The Powerpuff Girls",2.5e+07,11411644,16425701,"Warner Bros.","PG","Adventure"
"1545","6/3/2005","The Lords of Dogtown",2.5e+07,11273517,13424365,"Sony/TriStar","PG-13","Action"
"1546","7/1/1986","Big Trouble in Little China",2.5e+07,11100000,11100000,"20th Century Fox",NA,"Action"
"1547","10/11/1996","Michael Collins",2.5e+07,11092559,27572844,"Warner Bros.","R","Drama"
"1548","3/28/2008","Stop-Loss",2.5e+07,10915744,11229035,"Paramount Pictures","R","Drama"
"1549","10/8/1993","Gettysburg",2.5e+07,10731997,10731997,"New Line","PG","Drama"
"1550","8/13/1999","Brokedown Palace",2.5e+07,10115014,11115766,"20th Century Fox","PG-13","Drama"
"1551","8/16/2002","Possession",2.5e+07,10103647,14805812,"Focus Features","PG-13","Drama"
"1552","5/17/1991","Stone Cold",2.5e+07,9286314,9286314,"Sony Pictures","R","Action"
"1553","11/25/2009","The Road",2.5e+07,8114270,29206732,"Weinstein Co.","R","Drama"
"1554","4/6/2007","The Hoax",2.5e+07,7164995,7164995,"Walt Disney","R","Drama"
"1555","8/17/1984","Sheena",2.5e+07,5778353,5778353,"Sony Pictures",NA,"Adventure"
"1556","3/23/2001","Say It Isn't So",2.5e+07,5516708,5516708,"20th Century Fox","R","Comedy"
"1557","12/7/2005","The World's Fastest Indian",2.5e+07,5128124,18991288,"Magnolia Pictures","PG-13","Drama"
"1558","3/1/1995","Tank Girl",2.5e+07,4064333,4064333,"MGM","R","Action"
"1559","4/22/2005","King's Ransom",2.5e+07,4008527,4049527,"New Line","PG-13","Comedy"
"1560","12/16/2011","Carnage",2.5e+07,2546747,38112154,"Sony Pictures Classics","R","Drama"
"1561","9/1/2017","Tulip Fever",2.5e+07,2455635,6498776,"Weinstein Co.","R","Drama"
"1562","1/6/2006","BloodRayne",2.5e+07,2405420,3605420,"Romar","R","Action"
"1563","11/25/2009","Me and Orson Welles",2.5e+07,1190003,1190003,"Freestyle Releasing","PG-13","Drama"
"1564","9/11/1998","Without Limits",2.5e+07,780326,780326,"Warner Bros.","PG-13","Drama"
"1565","3/22/2013","On the Road",2.5e+07,720828,9313302,"IFC Films","R","Drama"
"1566","6/30/2010","Love Ranch",2.5e+07,137885,146149,NA,"R","Drama"
"1567","7/8/2011","Ironclad",2.5e+07,0,5297411,"ARC Entertainment","R","Action"
"1568","11/26/1986","Star Trek IV: The Voyage Home",2.4e+07,109713132,1.33e+08,"Paramount Pictures","PG","Adventure"
"1569","12/12/1997","Scream 2",2.4e+07,101363301,172363301,"Miramax","R","Horror"
"1570","2/21/2003","Old School",2.4e+07,75155000,86765463,"Dreamworks SKG","R","Comedy"
"1571","12/20/2006","Rocky Balboa",2.4e+07,70269899,156229050,"MGM","PG","Drama"
"1572","12/16/2016","Fences",2.4e+07,57682904,64282881,"Paramount Pictures","PG-13","Drama"
"1573","2/18/2000","The Whole Nine Yards",2.4e+07,57262492,85527867,"Warner Bros.","R","Comedy"
"1574","4/7/2017","Going in Style",2.4e+07,45018541,78673103,"Warner Bros.","PG-13","Comedy"
"1575","7/12/1991","Point Break",2.4e+07,43218387,83531958,"20th Century Fox","R","Action"
"1576","9/20/1991","The Fisher King",2.4e+07,41798224,41798224,"Sony Pictures","R","Drama"
"1577","10/31/2008","Zack and Miri Make a Porno",2.4e+07,31457946,36856306,"Weinstein Co.","R","Comedy"
"1578","1/12/2001","Double Take",2.4e+07,29823162,29823162,"Walt Disney","PG-13","Action"
"1579","12/21/1999","Girl, Interrupted",2.4e+07,28871190,28871190,"Sony Pictures","R","Drama"
"1580","8/20/2010","Piranha 3D",2.4e+07,25003155,83660160,"Weinstein/Dimension","R","Horror"
"1581","11/24/2010","Faster",2.4e+07,23240020,35792945,"CBS Films","R","Action"
"1582","7/14/1999","Muppets From Space",2.4e+07,16304786,16304786,"Sony Pictures","G","Adventure"
"1583","4/7/2000","Ready to Rumble",2.4e+07,12372410,12372410,"Warner Bros.","PG-13","Comedy"
"1584","9/16/2011","I Don't Know How She Does It",2.4e+07,9659074,24474463,"Weinstein Co.","PG-13","Comedy"
"1585","12/24/1999","Play it to the Bone",2.4e+07,8427204,8427204,"Walt Disney","R","Comedy"
"1586","12/17/2004","Beyond the Sea",2.4e+07,6144806,8292914,"Lionsgate","PG-13","Drama"
"1587","6/10/2005","Hauru no ugoku shiro",2.4e+07,4710455,237814327,"Walt Disney","PG","Adventure"
"1588","3/27/1998","Meet the Deedles",2.4e+07,4356126,4356126,"Walt Disney","PG","Comedy"
"1589","8/25/1995","The Thief and the Cobbler",2.4e+07,669276,669276,"Miramax","G","Adventure"
"1590","6/10/2005","The Bridge of San Luis Rey",2.4e+07,49981,1696765,"Fine Line","PG","Drama"
"1591","10/2/2009","Zombieland",23600000,75590286,102236596,"Sony Pictures","R","Comedy"
"1592","11/6/1998","The Waterboy",2.3e+07,161491646,190191646,"Walt Disney","PG-13","Comedy"
"1593","4/7/1995","Bad Boys",2.3e+07,65647413,141247413,"Sony Pictures","R","Action"
"1594","1/16/2015","The Wedding Ringer",2.3e+07,64460211,80171596,"Sony Pictures","R","Comedy"
"1595","3/17/2000","Final Destination",2.3e+07,53302314,112036870,"New Line","R","Horror"
"1596","12/17/1976","King Kong",2.3e+07,52614445,90614445,"Paramount Pictures","PG","Action"
"1597","10/7/2011","The Ides of March",2.3e+07,40962534,77735925,"Sony Pictures","R","Drama"
"1598","2/18/2000","Pitch Black",2.3e+07,39235088,53182088,"USA Films","R","Horror"
"1599","1/10/2014","Her",2.3e+07,25568251,48259031,"Warner Bros.","R","Drama"
"1600","2/17/2012","Kari gurashi no Arietti",2.3e+07,19192510,151496097,"Walt Disney","G","Adventure"
"1601","11/12/1999","Anywhere But Here",2.3e+07,18653615,18653615,"20th Century Fox","PG-13","Drama"
"1602","9/1/2004","Vanity Fair",2.3e+07,16123851,19123851,"Focus Features","PG-13","Drama"
"1603","2/26/2016","Eddie the Eagle",2.3e+07,15789389,45061177,"20th Century Fox","PG-13","Drama"
"1604","7/17/1987","Jaws 4: The Revenge",2.3e+07,15728335,15728335,"Universal","PG-13","Horror"
"1605","8/25/2000","The Crew",2.3e+07,13019253,13019253,"Walt Disney","PG-13","Comedy"
"1606","12/20/1996","Marvin's Room",2.3e+07,12803305,12803305,"Miramax","PG-13","Drama"
"1607","8/22/2008","The Longshots",2.3e+07,11511323,11778396,"MGM","PG","Drama"
"1608","12/3/1999","The End of the Affair",2.3e+07,10660147,10660147,"Sony Pictures","R","Drama"
"1609","9/14/2007","In the Valley of Elah",2.3e+07,6777741,24489150,"Warner Bros.","R","Drama"
"1610","9/25/2009","Coco avant Chanel",2.3e+07,6113834,50813834,"Sony Pictures Classics","PG-13","Drama"
"1611","6/26/2009","Chéri",2.3e+07,2715657,2715657,"Miramax","R","Drama"
"1612","4/25/2008","Rogue",2.3e+07,10452,4673377,"Weinstein Co.","R","Horror"
"1613","6/24/1987","Spaceballs",22700000,38119483,38119483,"MGM","PG","Comedy"
"1614","4/24/2015","The Water Diviner",22500000,4200117,30864649,"Warner Bros.","R","Drama"
"1615","7/13/1990","Ghost",2.2e+07,217631306,517600000,"Paramount Pictures","PG-13","Drama"
"1616","11/11/1994","The Santa Clause",2.2e+07,144833357,189800000,"Walt Disney","PG","Adventure"
"1617","9/28/2007","The Game Plan",2.2e+07,90648202,146590987,"Walt Disney","PG","Comedy"
"1618","3/29/2002","The Rookie",2.2e+07,75600072,80491516,"Walt Disney","G","Drama"
"1619","6/2/1995","The Bridges of Madison County",2.2e+07,71516617,175516617,"Warner Bros.","PG-13","Drama"
"1620","2/28/2014","Son of God",2.2e+07,59700064,70949793,"20th Century Fox","PG-13","Drama"
"1621","6/1/2001","The Animal",2.2e+07,55762229,55762229,"Sony Pictures","PG-13","Comedy"
"1622","12/8/1982","Gandhi",2.2e+07,52767889,127767889,"Sony Pictures","PG","Drama"
"1623","9/19/2003","Underworld",2.2e+07,51970690,95708457,"Sony Pictures","R","Action"
"1624","8/3/2012","Diary of a Wimpy Kid: Dog Days",2.2e+07,49008662,77229695,"20th Century Fox","PG","Adventure"
"1625","12/28/2001","I Am Sam",2.2e+07,40270895,92542418,"New Line","PG-13","Drama"
"1626","11/11/2005","Derailed",2.2e+07,36020063,57520063,"Weinstein Co.","R","Action"
"1627","11/22/2013","Delivery Man",2.2e+07,30659817,70536870,"Walt Disney","PG-13","Comedy"
"1628","2/5/2016","Hail, Caesar!",2.2e+07,30080225,64171419,"Universal","PG-13","Comedy"
"1629","8/24/2001","Jay and Silent Bob Strike Back",2.2e+07,30059386,33762400,"Miramax/Dimension","R","Comedy"
"1630","12/29/1993","Shadowlands",2.2e+07,25842377,25842377,"Savoy","R","Drama"
"1631","8/12/2005","Deuce Bigalow: European Gigolo",2.2e+07,22400154,45273464,"Sony Pictures","R","Comedy"
"1632","5/19/2017","Diary of a Wimpy Kid: The Long Haul",2.2e+07,20738724,35608734,"20th Century Fox","PG","Adventure"
"1633","1/18/2008","Mad Money",2.2e+07,20668843,25044057,"Overture Films","PG-13","Comedy"
"1634","11/27/2013","Homefront",2.2e+07,20158492,51695362,"Open Road","R","Action"
"1635","9/19/2008","Igor",2.2e+07,19528602,31013349,"MGM","PG","Adventure"
"1636","2/9/2001","Saving Silverman",2.2e+07,19351569,25873142,"Sony Pictures","R","Comedy"
"1637","7/2/1999","Summer of Sam",2.2e+07,19288130,19288130,"Walt Disney","R","Drama"
"1638","9/4/2015","The Transporter Refueled",2.2e+07,16029670,69698495,"EuropaCorp","PG-13","Action"
"1639","4/11/2001","Josie and the Pussycats",2.2e+07,14252830,14252830,"Universal","PG-13","Comedy"
"1640","8/22/2012","Hit & Run",2.2e+07,13749300,17216955,"Open Road","R","Comedy"
"1641","10/27/2000","The Little Vampire",2.2e+07,13555988,13555988,"New Line","PG","Adventure"
"1642","10/1/2004","I Heart Huckabees",2.2e+07,12784713,20034713,"Fox Searchlight","R","Comedy"
"1643","11/17/2017","Roman J. Israel, Esq.",2.2e+07,11962712,12967012,"Sony Pictures","PG-13","Drama"
"1644","12/4/2013","Out of the Furnace",2.2e+07,11330849,15434375,"Relativity","R","Drama"
"1645","11/5/1993","RoboCop 3",2.2e+07,10696210,10696210,"Orion Pictures","PG-13","Action"
"1646","8/27/1999","Dudley Do-Right",2.2e+07,9818792,9818792,"Universal","PG","Adventure"
"1647","12/8/2017","Just Getting Started",2.2e+07,6069605,6756452,"Broad Green Pictures","PG-13","Comedy"
"1648","9/21/2001","Megiddo: Omega Code 2",2.2e+07,6047691,6047691,"8X Entertainment","PG-13","Action"
"1649","1/1/1970","Darling Lili",2.2e+07,5e+06,5e+06,NA,NA,"Drama"
"1650","11/23/2005","The Libertine",2.2e+07,4835065,9448623,"Weinstein Co.","R","Drama"
"1651","10/8/2010","Stone",2.2e+07,1810078,4065020,"Overture Films","R","Drama"
"1652","3/3/2006","Joyeux Noël",2.2e+07,1054361,23134075,"Sony Pictures Classics","PG-13","Drama"
"1653","6/24/1977","Sorcerer",21600000,1.2e+07,12005968,"Paramount Pictures","PG","Adventure"
"1654","7/27/2007","Molière",21600000,635733,791154,"Sony Pictures Classics","PG-13","Comedy"
"1655","10/5/2007","Michael Clayton",21500000,49033882,92987651,"Warner Bros.","R","Drama"
"1656","12/20/1996","My Fellow Americans",21500000,22331846,22331846,"Warner Bros.","PG-13","Comedy"
"1657","11/16/2012","Silver Linings Playbook",2.1e+07,132092958,236412453,"Weinstein Co.","R","Drama"
"1658","4/6/2018","Blockers",2.1e+07,59839515,93442495,"Universal","R","Comedy"
"1659","6/30/1999","South Park: Bigger, Longer & Uncut",2.1e+07,52037603,52037603,"Paramount Pictures","R","Comedy"
"1660","6/18/1982","Firefox",2.1e+07,45785720,45785720,"Warner Bros.","PG","Action"
"1661","3/19/1993","Teenage Mutant Ninja Turtles III",2.1e+07,42273609,42273609,"New Line","PG","Adventure"
"1662","9/14/2001","Hardball",2.1e+07,40222729,43728560,"Paramount Pictures","PG-13","Drama"
"1663","11/5/2010","For Colored Girls",2.1e+07,37729698,38017873,"Lionsgate","R","Drama"
"1664","1/5/2007","Freedom Writers",2.1e+07,36605602,43632609,"Paramount Pictures","PG-13","Drama"
"1665","10/11/2002","The Transporter",2.1e+07,25296447,43928932,"20th Century Fox","PG-13","Action"
"1666","3/14/2008","Never Back Down",2.1e+07,24850922,39319801,"Summit Entertainment","PG-13","Action"
"1667","3/12/1999","The Rage: Carrie 2",2.1e+07,17760244,17760244,"MGM","R","Horror"
"1668","8/1/2008","Swing Vote",2.1e+07,16289867,17589867,"Walt Disney","PG-13","Comedy"
"1669","6/5/2009","Away We Go",2.1e+07,9451946,10108016,"Focus Features","R","Comedy"
"1670","9/27/2002","Moonlight Mile",2.1e+07,6830957,6830957,"Walt Disney","PG-13","Drama"
"1671","5/6/2011","The Beaver",2.1e+07,970816,5046038,"Summit Entertainment","PG-13","Comedy"
"1672","2/24/2017","Bitter Harvest",2.1e+07,557241,606162,"Roadside Attractions","R","Drama"
"1673","7/23/1982","The Best Little Whorehouse in Texas",20500000,69701637,69701637,"Universal","R","Comedy"
"1674","8/11/2006","Pulse",20500000,20264436,30241435,"Weinstein/Dimension","R","Horror"
"1675","6/12/1981","Raiders of the Lost Ark",2e+07,225686079,367452079,"Paramount Pictures","PG","Adventure"
"1676","11/20/1992","Home Alone 2: Lost in New York",2e+07,173585516,358994850,"20th Century Fox","PG","Adventure"
"1677","11/16/1977","Close Encounters of the Third Kind",2e+07,169100479,340800479,"Columbia","PG","Adventure"
"1678","5/20/1987","Beverly Hills Cop II",2e+07,153665036,276665036,"Paramount Pictures","R","Action"
"1679","7/19/2013","The Conjuring",2e+07,137400141,318000141,"Warner Bros.","R","Horror"
"1680","3/7/2003","Bringing Down the House",2e+07,132675402,164675402,"Walt Disney","PG-13","Comedy"
"1681","11/17/2017","Wonder",2e+07,132422809,305051118,"Lionsgate","PG","Drama"
"1682","2/14/1992","Wayne's World",2e+07,121697323,183097323,"Paramount Pictures","PG-13","Comedy"
"1683","10/15/2010","Jackass 3D",2e+07,117229692,171685793,"Paramount Pictures","R","Comedy"
"1684","7/29/2016","Bad Moms",2e+07,113257297,180999077,"STX Entertainment","R","Comedy"
"1685","6/16/1978","Jaws 2",2e+07,102922376,208900376,"Universal","PG","Horror"
"1686","10/3/2008","Beverly Hills Chihuahua",2e+07,94514402,154218168,"Walt Disney","PG","Adventure"
"1687","7/2/2014","Tammy",2e+07,84525432,96407655,"Warner Bros.","R","Comedy"
"1688","11/16/2011","The Descendants",2e+07,82624961,175507800,"Fox Searchlight","R","Drama"
"1689","10/3/2003","School of Rock",2e+07,81261177,131944672,"Paramount Pictures","PG-13","Comedy"
"1690","7/16/1993","Free Willy",2e+07,77698625,153698625,"Warner Bros.","PG","Adventure"
"1691","8/18/1995","Mortal Kombat",2e+07,70433227,122133227,"New Line","PG-13","Action"
"1692","6/23/2004","White Chicks",2e+07,69148997,111448997,"Sony Pictures","PG-13","Comedy"
"1693","4/18/2003","Holes",2e+07,67383924,71232214,"Walt Disney","PG","Drama"
"1694","3/31/2010","The Last Song",2e+07,62950384,92678948,"Walt Disney","PG","Drama"
"1695","4/2/2010","Why Did I Get Married Too?",2e+07,60095852,60831067,"Lionsgate","PG-13","Drama"
"1696","10/23/1998","La vita è bella",2e+07,57598247,229385361,"Miramax","PG-13","Drama"
"1697","10/18/2013","12 Years a Slave",2e+07,56671993,181025343,"Fox Searchlight","R","Drama"
"1698","12/13/2002","Drumline",2e+07,56398162,56398162,"20th Century Fox","PG-13","Comedy"
"1699","6/3/2016","Me Before You",2e+07,56245075,208314186,"Warner Bros.","PG-13","Drama"
"1700","4/15/2016","Barbershop: The Next Cut",2e+07,54030051,54404202,"Warner Bros.","PG-13","Comedy"
"1701","12/7/1990","Edward Scissorhands",2e+07,53976987,53976987,"20th Century Fox","PG-13","Comedy"
"1702","1/9/2015","Selma",2e+07,52076908,66776576,"Paramount Pictures","PG-13","Drama"
"1703","2/17/2006","Date Movie",2e+07,48548426,85146165,"20th Century Fox","PG-13","Comedy"
"1704","2/15/2002","Peter Pan: Return to Neverland",2e+07,48430258,109862682,"Walt Disney","G","Adventure"
"1705","2/14/2003","The Jungle Book 2",2e+07,47901582,140122225,"Walt Disney","G","Adventure"
"1706","2/4/2005","Boogeyman",2e+07,46752382,67192859,"Sony Pictures","PG-13","Horror"
"1707","2/11/2000","The Tigger Movie",2e+07,45542421,96147688,"Walt Disney","G","Adventure"
"1708","11/6/2015","Spotlight",2e+07,45055776,92108847,"Open Road","R","Drama"
"1709","6/26/2015","Max",2e+07,42656255,43658157,"Warner Bros.","PG","Adventure"
"1710","3/21/2008","Meet the Browns",2e+07,41975388,41975388,"Lionsgate","PG-13","Comedy"
"1711","7/24/2009","Orphan",2e+07,41596251,78769428,"Warner Bros.","R","Drama"
"1712","11/17/2017","The Star",2e+07,40847995,62758010,"Sony Pictures","PG","Adventure"
"1713","1/26/2007","Epic Movie",2e+07,39739367,86858578,"20th Century Fox","PG-13","Comedy"
"1714","10/13/2006","The Grudge 2",2e+07,39143839,70743839,"Sony Pictures","PG-13","Horror"
"1715","5/14/1982","Conan the Barbarian",2e+07,38264085,79114085,"Universal",NA,"Action"
"1716","8/14/1998","How Stella Got Her Groove Back",2e+07,37672944,37672944,"20th Century Fox","R","Drama"
"1717","7/19/1991","Bill & Ted's Bogus Journey",2e+07,37537675,37537675,"Orion Pictures","PG","Adventure"
"1718","10/13/2006","Man of the Year",2e+07,37442180,41342180,"Universal","PG-13","Comedy"
"1719","2/19/2016","Risen",2e+07,36880033,46255763,"Sony Pictures","PG-13","Drama"
"1720","8/18/2010","Vampires Suck",2e+07,36661504,81424988,"20th Century Fox","PG-13","Comedy"
"1721","3/21/1997","Selena",2e+07,35450113,35450113,"Warner Bros.","PG","Drama"
"1722","11/4/2011","A Very Harold & Kumar 3D Christmas",2e+07,35061031,36265745,"Warner Bros.","R","Comedy"
"1723","1/4/2013","Texas Chainsaw 3D",2e+07,34341945,47666013,"Lionsgate","R","Horror"
"1724","10/27/2006","Babel",2e+07,34302837,132121212,"Paramount Vantage","R","Drama"
"1725","9/19/2014","This is Where I Leave You",2e+07,34296320,41296320,"Warner Bros.","R","Comedy"
"1726","12/12/2008","Doubt",2e+07,33446470,53191101,"Miramax","PG-13","Drama"
"1727","10/15/2004","Team America: World Police",2e+07,32774834,50948811,"Paramount Pictures","R","Comedy"
"1728","4/12/2013","Scary Movie V",2e+07,32015787,78613981,"Weinstein Co.","PG-13","Comedy"
"1729","11/26/2008","Milk",2e+07,31841299,57293371,"Focus Features","R","Drama"
"1730","10/25/2002","Ghost Ship",2e+07,30113491,68349884,"Warner Bros.","R","Horror"
"1731","1/8/2010","Daybreakers",2e+07,30101577,51445503,"Lionsgate","R","Horror"
"1732","3/31/2000","High Fidelity",2e+07,27277055,47881663,"Walt Disney","R","Comedy"
"1733","4/28/2006","Stick It",2e+07,26910736,30399714,"Walt Disney","PG-13","Comedy"
"1734","1/4/2008","One Missed Call",2e+07,26890041,44513466,"Warner Bros.","PG-13","Horror"
"1735","1/12/1996","Eye for an Eye",2e+07,26792700,26792700,"Paramount Pictures","R","Drama"
"1736","8/23/2013","The World's End",2e+07,26004851,47508505,"Focus Features","R","Comedy"
"1737","1/19/1996","From Dusk Till Dawn",2e+07,25728961,25732986,"Miramax/Dimension","R","Horror"
"1738","9/24/2010","You Again",2e+07,25702053,32838945,"Walt Disney","PG","Comedy"
"1739","9/17/2010","Alpha and Omega 3D",2e+07,25107267,48958353,"Lionsgate","PG","Adventure"
"1740","3/24/2006","Stay Alive",2e+07,23086480,23187506,"Walt Disney","PG-13","Horror"
"1741","10/7/2005","2 For the Money",2e+07,22991379,30491379,"Universal","R","Drama"
"1742","8/21/2009","Shorts",2e+07,20919166,29870801,"Warner Bros.","PG","Adventure"
"1743","10/30/1998","Vampires",2e+07,20268825,20268825,"Sony Pictures","R","Horror"
"1744","8/13/2004","Yu-Gi-Oh",2e+07,19762690,28762690,"Warner Bros.","PG","Adventure"
"1745","3/23/2007","Reign Over Me",2e+07,19661987,20081987,"Sony Pictures","R","Drama"
"1746","9/19/2008","My Best Friend's Girl",2e+07,19219250,34787111,"Lionsgate","R","Comedy"
"1747","5/11/2007","Georgia Rule",2e+07,18882880,20819601,"Universal","R","Drama"
"1748","7/31/1981","Under the Rainbow",2e+07,18826490,18826490,"Warner Bros.",NA,"Comedy"
"1749","4/12/1985","Ladyhawke",2e+07,18400000,18400000,"Warner Bros.",NA,"Action"
"1750","9/21/2007","Into the Wild",2e+07,18354356,56822960,"Paramount Vantage","R","Drama"
"1751","9/11/1998","Simon Birch",2e+07,18253415,18310591,"Walt Disney","PG","Drama"
"1752","2/11/2005","Pooh's Heffalump Movie",2e+07,18098433,55686944,"Walt Disney","G","Adventure"
"1753","9/29/2006","School for Scoundrels",2e+07,17807569,17807569,"MGM","PG-13","Comedy"
"1754","10/26/2012","Silent Hill: Revelation 3D",2e+07,17530219,55975672,"Open Road","R","Horror"
"1755","11/3/1995","Home for the Holidays",2e+07,17468887,22119269,"Paramount Pictures","PG-13","Comedy"
"1756","3/31/2017","The Zookeeper’s Wife",2e+07,17445186,24521550,"Focus Features","PG-13","Drama"
"1757","2/20/2009","Fired Up",2e+07,17231291,18608570,"Sony Pictures","PG-13","Comedy"
"1758","4/8/2005","Kung Fu Hustle",2e+07,17104669,102034104,"Sony Pictures Classics","R","Action"
"1759","7/26/2002","The Country Bears",2e+07,16988996,16988996,"Walt Disney","G","Adventure"
"1760","3/16/2007","Dead Silence",2e+07,16574590,20614661,"Universal","R","Horror"
"1761","11/21/2003","21 Grams",2e+07,16248701,59667625,"Focus Features","R","Drama"
"1762","12/14/2007","The Kite Runner",2e+07,15800078,74180745,"Paramount Vantage","PG-13","Drama"
"1763","2/15/1965","The Greatest Story Ever Told",2e+07,15473333,15473333,"MGM","G","Drama"
"1764","3/6/1998","Twilight",2e+07,15055091,15055091,"Paramount Pictures","R","Drama"
"1765","8/29/2008","Disaster Movie",2e+07,14190901,36720752,"Lionsgate","PG-13","Comedy"
"1766","11/14/1997","The Man Who Knew Too Little",2e+07,13801755,13801755,"Warner Bros.","PG","Comedy"
"1767","10/30/2015","Burnt",2e+07,13651946,36780895,"Weinstein Co.","R","Comedy"
"1768","4/30/2004","Envy",2e+07,13548322,14566246,"Dreamworks SKG","PG-13","Comedy"
"1769","10/13/2006","One Night with the King",2e+07,13395961,13725032,"Rocky Mountain Pict…","PG","Drama"
"1770","10/21/1994","Bullets Over Broadway",2e+07,13383747,13383747,"Miramax","R","Comedy"
"1771","11/22/2002","The Quiet American",2e+07,12987647,26348203,"Miramax","R","Drama"
"1772","9/2/2016","The Light Between Oceans",2e+07,12545979,21748977,"Walt Disney","PG-13","Drama"
"1773","10/28/2005","The Weather Man",2e+07,12482775,15466961,"Paramount Pictures","R","Drama"
"1774","8/23/2002","Undisputed",2e+07,12398628,12398628,"Miramax","R","Drama"
"1775","3/27/2009","12 Rounds",2e+07,12234694,17306648,"20th Century Fox","PG-13","Action"
"1776","5/6/1994","3 Ninjas Kick Back",2e+07,11744960,11744960,"Walt Disney","PG","Action"
"1777","2/22/2008","Be Kind Rewind",2e+07,11175164,30894247,"New Line","PG-13","Comedy"
"1778","12/9/2005","Mrs. Henderson Presents",2e+07,11036366,27836366,"Weinstein Co.","R","Comedy"
"1779","12/15/1989","We're No Angels",2e+07,10555348,10555348,"Paramount Pictures","PG-13","Comedy"
"1780","8/31/2007","Death Sentence",2e+07,9534258,16907831,"20th Century Fox","R","Action"
"1781","6/3/2016","Popstar: Never Stop Never Stopping",2e+07,9496130,9537120,"Universal","R","Comedy"
"1782","10/27/2017","Thank You for Your Service",2e+07,9479390,9985316,"Universal","R","Drama"
"1783","12/4/2009","Everybody's Fine",2e+07,9208876,9208876,"Miramax","PG-13","Drama"
"1784","8/27/2004","Superbabies: Baby Geniuses 2",2e+07,9109322,9355369,"Sony Pictures","PG","Adventure"
"1785","9/20/2013","Battle of the Year",2e+07,8888355,16723377,"Sony Pictures","PG-13","Drama"
"1786","4/29/2016","Ratchet and Clank",2e+07,8813410,12769469,"Focus Features","PG","Adventure"
"1787","8/17/2007","Death at a Funeral",2e+07,8580428,46790428,"MGM","R","Comedy"
"1788","9/9/2005","The Man",2e+07,8330720,10393696,"New Line","PG-13","Comedy"
"1789","1/5/2007","Code Name: The Cleaner",2e+07,8135024,8135024,"New Line","PG-13","Comedy"
"1790","12/12/2014","Inherent Vice",2e+07,8110975,14772346,"Warner Bros.","R","Drama"
"1791","4/16/2004","Connie & Carla",2e+07,8047525,8047525,"Universal","PG-13","Comedy"
"1792","10/11/2013","Machete Kills",2e+07,8008161,18273009,"Open Road","R","Action"
"1793","2/24/2006","Doogal",2e+07,7578946,28058652,"Weinstein Co.","G","Adventure"
"1794","9/16/2005","Proof",2e+07,7535331,8284331,"Miramax","PG-13","Drama"
"1795","10/3/2008","An American Carol",2e+07,7013191,7022183,"Vivendi Entertainment","PG-13","Comedy"
"1796","3/14/2003","Willard",2e+07,6882696,6882696,"New Line","PG-13","Horror"
"1797","2/1/2008","Strange Wilderness",2e+07,6575282,6947084,"Paramount Vantage","R","Comedy"
"1798","4/24/2015","Little Boy",2e+07,6485961,17768390,"Open Road","PG-13","Drama"
"1799","10/26/2012","Chasing Mavericks",2e+07,6002756,8300821,"20th Century Fox","PG","Drama"
"1800","12/31/2014","A Most Violent Year",2e+07,5749134,8398291,"A24","R","Drama"
"1801","11/23/2011","A Dangerous Method",2e+07,5702083,14807531,"Sony Pictures Classics","R","Drama"
"1802","8/14/2009","Bandslam",2e+07,5210988,12967829,"Summit Entertainment","PG","Comedy"
"1803","1/28/2005","Alone in the Dark",2e+07,5178569,8178569,"Lionsgate","R","Horror"
"1804","10/29/2004","Birth",2e+07,5005899,14603001,"New Line","R","Drama"
"1805","8/26/2016","Hands of Stone",2e+07,4712792,5032013,"Weinstein Co.","R","Drama"
"1806","10/3/2008","Flash of Genius",2e+07,4442377,4504111,"Universal","PG-13","Drama"
"1807","11/21/2007","I’m Not There",2e+07,4017609,12397613,"Weinstein Co.","R","Drama"
"1808","10/24/2008","Synecdoche, New York",2e+07,3083538,4383538,"Sony Pictures Classics","R","Drama"
"1809","11/3/2017","LBJ",2e+07,2468683,2507181,"Electric Entertainment","R","Drama"
"1810","10/29/1999","Mononoke-hime",2e+07,2374107,150350000,"Miramax","PG-13","Action"
"1811","3/19/2004","Bon Voyage",2e+07,2353728,8361736,"Sony Pictures","PG-13","Comedy"
"1812","11/13/2015","My All-American",2e+07,2246000,2246000,"Clarius Entertainment","PG","Drama"
"1813","8/22/2003","Marci X",2e+07,1646664,1646664,"Paramount Pictures","R","Comedy"
"1814","12/6/2002","Equilibrium",2e+07,1190018,5345869,"Miramax/Dimension","R","Action"
"1815","4/29/2011","Dylan Dog: Dead of Night",2e+07,1186538,6093725,"Omin/Freestyle","PG-13","Horror"
"1816","5/23/2008","The Children of Huang Shi",2e+07,1031872,8221700,"Sony Pictures Classics","R","Drama"
"1817","10/20/2000","The Yards",2e+07,882710,2282710,"Miramax","R","Drama"
"1818","8/6/2010","Middle Men",2e+07,754301,754301,"Paramount Vantage","R","Comedy"
"1819","12/3/2010","All Good Things",2e+07,582024,873617,"Magnolia Pictures","R","Drama"
"1820","11/13/2015","By the Sea",2e+07,538460,3727746,"Universal","R","Drama"
"1821","3/18/2005","Steamboy",2e+07,468867,10468867,"Sony Pictures","PG-13","Action"
"1822","4/22/2005","The Game of Their Lives",2e+07,375474,375474,"IFC Films","PG","Drama"
"1823","12/10/2010","The Tempest",2e+07,277943,277943,"Miramax","PG-13","Drama"
"1824","3/7/2008","長江七號 (CJ7)",2e+07,206678,47300771,"Sony Pictures Classics","PG","Adventure"
"1825","9/18/2009","The Burning Plain",2e+07,200730,1167092,"Magnolia Pictures","R","Drama"
"1826","3/31/2004","The Touch",2e+07,0,5918742,"Miramax","PG-13","Adventure"
"1827","8/29/2014","Dwegons and Leprechauns",2e+07,0,0,NA,"PG","Adventure"
"1828","8/21/2009","Der Baader Meinhof Komplex",19700000,476270,16498827,"Vitagraph Films","R","Action"
"1829","12/1/2017","The Shape of Water",19500000,63859435,189258193,"Fox Searchlight","R","Drama"
"1830","11/23/2012","De rouille et d’os",19500000,2061449,29393634,"Sony Pictures Classics","R","Drama"
"1831","12/20/2006","The Painted Veil",19400000,8060487,15118795,"Warner Independent","PG-13","Drama"
"1832","7/29/2011","The Devil's Double",19100000,1361512,5965646,"Lionsgate","R","Drama"
"1833","7/3/1985","Back to the Future",1.9e+07,212259762,385524862,"Universal","PG","Adventure"
"1834","7/7/2000","Scary Movie",1.9e+07,157019771,277200000,"Miramax/Dimension","R","Comedy"
"1835","6/24/2011","Bad Teacher",1.9e+07,100292856,215448997,"Sony Pictures","R","Comedy"
"1836","8/12/2016","Sausage Party",1.9e+07,97670358,141354394,"Sony Pictures","R","Comedy"
"1837","9/11/2009","I Can Do Bad All By Myself",1.9e+07,51733921,51733921,"Lionsgate","PG-13","Comedy"
"1838","5/23/1980","The Shining",1.9e+07,44017374,44728227,"Warner Bros.","R","Horror"
"1839","10/26/2001","Thirteen Ghosts",1.9e+07,41867960,68467960,"Warner Bros.","R","Horror"
"1840","10/29/1999","House on Haunted Hill",1.9e+07,40846082,65090541,"Warner Bros.","R","Horror"
"1841","1/16/2009","Notorious",1.9e+07,36843682,44972183,"Fox Searchlight","R","Drama"
"1842","11/8/2013","The Book Thief",1.9e+07,21488481,76086711,"20th Century Fox","PG-13","Drama"
"1843","10/19/2007","Gone, Baby, Gone",1.9e+07,20300218,34352162,"Miramax","R","Drama"
"1844","7/26/2000","Thomas and the Magic Railroad",1.9e+07,15911332,15911332,"Destination Films","G","Adventure"
"1845","9/20/2002","Sen to Chihiro no Kamikakushi",1.9e+07,10049886,274949886,"Walt Disney","PG","Adventure"
"1846","10/17/2008","Sex Drive",1.9e+07,8402485,10412485,"Summit Entertainment","R","Comedy"
"1847","1/9/1998","Firestorm",1.9e+07,8123860,8123860,"20th Century Fox","R","Action"
"1848","3/4/2011","Take Me Home Tonight",1.9e+07,6928068,7576604,"Relativity","R","Comedy"
"1849","9/28/2012","Won't Back Down",1.9e+07,5310554,5745503,"20th Century Fox","PG","Drama"
"1850","6/1/2018","Action Point",1.9e+07,5059608,5103675,"Paramount Pictures","R","Comedy"
"1851","8/16/1996","Kansas City",1.9e+07,1353824,1353824,"New Line","R","Drama"
"1852","6/24/2005","George A. Romero's Land of the Dead",18975000,20700082,47751015,"Universal","R","Horror"
"1853","12/6/2002","Adaptation",18500000,22498520,32531759,"Sony Pictures","R","Comedy"
"1854","10/2/2009","The Invention of Lying",18500000,18451251,32679264,"Warner Bros.","PG-13","Comedy"
"1855","5/22/1998","Fear and Loathing in Las Vegas",18500000,10680275,13711903,"Universal","R","Comedy"
"1856","2/2/2001","Left Behind",18500000,4221341,4221341,"Cloud Ten Pictures","PG-13","Drama"
"1857","11/3/2006","Borat",1.8e+07,128505958,261443242,"20th Century Fox","R","Comedy"
"1858","7/29/1994","The Mask",1.8e+07,119920129,351620129,"New Line","PG-13","Comedy"
"1859","6/3/1988","Big",1.8e+07,114968774,151668774,"20th Century Fox","PG","Comedy"
"1860","7/13/2001","Legally Blonde",1.8e+07,96493426,141809235,"MGM","PG-13","Comedy"
"1861","4/30/2004","Mean Girls",1.8e+07,86047227,130953026,"Paramount Pictures","PG-13","Comedy"
"1862","6/1/1984","Star Trek III: The Search for Spock",1.8e+07,76471046,8.7e+07,"Paramount Pictures","PG","Adventure"
"1863","9/9/2005","The Exorcism of Emily Rose",1.8e+07,75072454,144529078,"Sony Pictures","PG-13","Horror"
"1864","12/10/1999","Deuce Bigalow: Male Gigolo",1.8e+07,65535067,92935067,"Walt Disney","R","Comedy"
"1865","1/1/2004","Barbershop 2: Back in Business",1.8e+07,65070412,65842412,"MGM","PG-13","Comedy"
"1866","12/16/2005","The Family Stone",1.8e+07,60062868,92357499,"20th Century Fox","PG-13","Comedy"
"1867","6/12/1987","Predator",1.8e+07,59735548,98267558,"20th Century Fox","R","Action"
"1868","3/25/2016","My Big Fat Greek Wedding 2",1.8e+07,59689605,92057814,"Universal","PG-13","Comedy"
"1869","3/25/2011","Diary of a Wimpy Kid: Rodrick Rules",1.8e+07,52698535,73695194,"20th Century Fox","PG","Adventure"
"1870","9/19/1984","Amadeus",1.8e+07,51973029,51973029,"Warner Bros.","R","Drama"
"1871","4/11/2008","Prom Night",1.8e+07,43869350,57193655,"Sony Pictures","PG-13","Horror"
"1872","4/8/2011","Soul Surfer",1.8e+07,43853424,47158652,"Sony Pictures","PG","Drama"
"1873","9/26/2003","Under the Tuscan Sun",1.8e+07,43601508,57490024,"Walt Disney","PG-13","Comedy"
"1874","10/10/1986","Peggy Sue Got Married",1.8e+07,41382841,41382841,"Sony/TriStar","PG-13","Comedy"
"1875","12/26/2001","Gosford Park",1.8e+07,41300105,41300105,"USA Films","R","Comedy"
"1876","1/11/2002","Orange County",1.8e+07,41059716,43308707,"Paramount Pictures","PG-13","Comedy"
"1877","7/26/2013","Blue Jasmine",1.8e+07,33404871,102912961,"Sony Pictures Classics","PG-13","Comedy"
"1878","4/28/2006","United 93",1.8e+07,31567134,77635035,"Universal","R","Drama"
"1879","12/5/2003","Honey",1.8e+07,30272254,62646763,"Universal","PG-13","Drama"
"1880","5/24/1996","Spy Hard",1.8e+07,26936265,26936265,"Walt Disney","PG-13","Comedy"
"1881","8/7/2015","Ricki and the Flash",1.8e+07,26839498,41166033,"Sony Pictures","PG-13","Drama"
"1882","12/13/1989","Glory",1.8e+07,26593580,26593580,"Sony Pictures","R","Action"
"1883","6/29/1984","Conan the Destroyer",1.8e+07,26400000,26400000,"Universal",NA,"Action"
"1884","11/13/2015","Love the Coopers",1.8e+07,26302731,42227490,"CBS Films","PG-13","Comedy"
"1885","6/24/1970","Catch-22",1.8e+07,24911670,24911670,"Paramount Pictures",NA,"Comedy"
"1886","4/10/2009","Observe and Report",1.8e+07,24007324,27148898,"Warner Bros.","R","Comedy"
"1887","9/18/2009","Love Happens",1.8e+07,22965110,36133014,"Universal","PG-13","Drama"
"1888","12/4/1985","Young Sherlock Holmes",1.8e+07,19739000,19739000,"Paramount Pictures","PG-13","Adventure"
"1889","11/5/2010","127 Hours",1.8e+07,18335230,60217171,"Fox Searchlight","R","Drama"
"1890","5/19/2000","Small Time Crooks",1.8e+07,17266359,29934477,"Dreamworks SKG","PG","Comedy"
"1891","5/12/2000","Center Stage",1.8e+07,17200925,21361109,"Sony Pictures","PG-13","Drama"
"1892","1/15/2016","Norm of the North",1.8e+07,17062499,30535660,"Lionsgate","PG","Adventure"
"1893","2/6/2004","Catch That Kid",1.8e+07,16703799,16959614,"20th Century Fox","PG","Adventure"
"1894","8/16/2013","Jobs",1.8e+07,16131410,43402515,"Open Road","PG-13","Drama"
"1895","10/26/2001","Life as a House",1.8e+07,15652637,23889158,"New Line","R","Drama"
"1896","1/8/2010","Youth in Revolt",1.8e+07,15285588,19685588,"Weinstein/Dimension","R","Comedy"
"1897","7/25/2014","And So It Goes",1.8e+07,15160801,17868801,"Clarius Entertainment","PG-13","Comedy"
"1898","7/10/2009","I Love You, Beth Cooper",1.8e+07,14800725,16382538,"20th Century Fox","PG-13","Comedy"
"1899","1/31/2014","Labor Day",1.8e+07,13371528,14189810,"Paramount Pictures","PG-13","Drama"
"1900","9/26/1997","The Ice Storm",1.8e+07,8038061,16011975,"Fox Searchlight","R","Drama"
"1901","10/15/2004","Being Julia",1.8e+07,7739049,14488705,"Sony Pictures","R","Drama"
"1902","3/22/1989","Troop Beverly Hills",1.8e+07,7190505,7190505,"Sony Pictures",NA,"Comedy"
"1903","2/21/1986","Nine 1/2 Weeks",1.8e+07,6734844,6734844,"MGM",NA,"Drama"
"1904","1/15/2010","The Last Station",1.8e+07,6617867,15696146,"Sony Pictures Classics","R","Drama"
"1905","6/26/1981","Dragonslayer",1.8e+07,6e+06,6e+06,"Paramount Pictures",NA,"Action"
"1906","9/30/1994","Ed Wood",1.8e+07,5828466,5828466,"Walt Disney","R","Comedy"
"1907","6/6/2008","Mongol",1.8e+07,5705761,27147349,"Picturehouse","R","Drama"
"1908","10/8/2008","RocknRolla",1.8e+07,5700626,27794339,"Warner Bros.","R","Action"
"1909","6/25/1982","Megaforce",1.8e+07,5675599,5675599,"20th Century Fox",NA,"Action"
"1910","8/20/2010","Mao's Last Dancer",1.8e+07,4806750,25941437,"Samuel Goldwyn Films","PG","Drama"
"1911","4/11/2014","The Railway Man",1.8e+07,4438438,23910210,"Weinstein Co.","R","Drama"
"1912","12/29/1995","Restoration",1.8e+07,4100000,4100000,"Miramax","R","Drama"
"1913","3/18/2016","Midnight Special",1.8e+07,3712282,7680250,"Warner Bros.","PG-13","Drama"
"1914","11/25/2016","Miss Sloane",1.8e+07,3500605,7727952,"EuropaCorp","R","Drama"
"1915","3/17/2017","T2: Trainspotting",1.8e+07,2402004,42091497,"Sony Pictures","R","Drama"
"1916","4/25/1986","8 Million Ways to Die",1.8e+07,1305114,1305114,"Sony Pictures",NA,"Action"
"1917","9/22/2006","Renaissance",1.8e+07,70644,2401413,"Miramax","R","Action"
"1918","4/15/2016","I Am Wrath",1.8e+07,0,309608,"Saban Films","R","Action"
"1919","8/22/2014","The Prince",1.8e+07,0,0,"Lionsgate","R","Action"
"1920","6/28/1985","Red Sonja",17900000,6905861,6908640,"MGM","PG-13","Action"
"1921","8/17/2007","Superbad",17500000,121463226,169955142,"Sony Pictures","R","Comedy"
"1922","2/20/2009","Madea Goes To Jail",17500000,90508336,90508336,"Lionsgate","PG-13","Comedy"
"1923","2/14/2008","Step Up 2 the Streets",17500000,58017783,148586910,"Walt Disney","PG-13","Drama"
"1924","1/13/2006","Hoodwinked",17500000,51386611,109843390,"Weinstein Co.","PG","Adventure"
"1925","11/21/2007","Hitman",17500000,39687694,99135571,"20th Century Fox","R","Action"
"1926","12/22/2004","Hotel Rwanda",17500000,23519128,36521223,"MGM","PG-13","Drama"
"1927","8/25/2006","Beerfest",17500000,19185184,20159316,"Warner Bros.","R","Comedy"
"1928","4/25/2003","City of Ghosts",17500000,325491,325491,"MGM","R","Drama"
"1929","4/6/2018","A Quiet Place",1.7e+07,188024361,334524361,"Paramount Pictures","PG-13","Horror"
"1930","8/10/2001","The Others",1.7e+07,96522687,207765056,"Miramax","PG-13","Horror"
"1931","7/18/1986","Aliens",1.7e+07,85160248,183316455,"20th Century Fox","R","Action"
"1932","8/13/2014","Let’s Be Cops",1.7e+07,82390774,136890774,"20th Century Fox","R","Comedy"
"1933","10/17/1997","I Know What You Did Last Summer",1.7e+07,72250091,125250091,"Sony Pictures","R","Horror"
"1934","10/22/2004","Sideways",1.7e+07,71502303,109793192,"Fox Searchlight","R","Drama"
"1935","11/15/2013","The Best Man Holiday",1.7e+07,70525195,72835710,"Universal","R","Comedy"
"1936","9/28/2012","Pitch Perfect",1.7e+07,65001093,116044347,"Universal","PG-13","Comedy"
"1937","8/5/1998","Halloween: H2O",1.7e+07,55041738,55041738,"Miramax","R","Horror"
"1938","4/5/2013","Evil Dead",1.7e+07,54239856,97778356,"Sony Pictures","R","Horror"
"1939","8/27/2004","Jet Li's Hero",1.7e+07,53652140,177535958,"Miramax","PG-13","Action"
"1940","10/29/2010","Saw 3D",1.7e+07,45710178,133735284,"Lionsgate","R","Horror"
"1941","2/20/2015","McFarland, USA",1.7e+07,44480275,45707924,"Walt Disney","PG","Drama"
"1942","11/11/2016","Almost Christmas",1.7e+07,42065185,42493506,"Universal","PG-13","Drama"
"1943","3/10/2006","The Hills Have Eyes",1.7e+07,41778863,70355813,"Fox Searchlight","R","Horror"
"1944","10/10/2003","Good Boy!",1.7e+07,37667746,45312217,"MGM","PG","Adventure"
"1945","1/26/2007","Smokin' Aces",1.7e+07,35662731,57263440,"Universal","R","Comedy"
"1946","10/2/1998","A Night at the Roxbury",1.7e+07,30331165,30331165,"Paramount Pictures","PG-13","Comedy"
"1947","3/4/2011","Beastly",1.7e+07,27865571,38028230,"CBS Films","PG-13","Drama"
"1948","7/9/1982","Tron",1.7e+07,26918576,26918576,"Walt Disney",NA,"Action"
"1949","8/20/2010","Lottery Ticket",1.7e+07,24719879,24719879,"Warner Bros.","PG-13","Comedy"
"1950","9/5/2003","Dickie Roberts: Former Child Star",1.7e+07,22734486,23734486,"Paramount Pictures","PG-13","Comedy"
"1951","3/31/2006","ATL",1.7e+07,21170563,21170563,"Warner Bros.","PG-13","Comedy"
"1952","8/24/2001","Summer Catch",1.7e+07,19693891,19693891,"Warner Bros.","PG-13","Comedy"
"1953","12/11/1998","A Simple Plan",1.7e+07,16316273,16316273,"Paramount Pictures","R","Drama"
"1954","11/27/2002","Wes Craven Presents: They",1.7e+07,12840842,16140842,"Miramax/Dimension","PG-13","Horror"
"1955","7/24/1987","Superman IV: The Quest for Peace",1.7e+07,11227824,11227824,"Warner Bros.","PG","Action"
"1956","1/25/2008","How She Move",1.7e+07,7070641,8607815,"Paramount Vantage","PG-13","Drama"
"1957","2/24/2006","Running Scared",1.7e+07,6855137,9729088,"New Line","R","Action"
"1958","8/24/2012","The Apparition",1.7e+07,4936819,10637281,"Warner Bros.","PG-13","Horror"
"1959","4/30/2004","Bobby Jones: Stroke of Genius",1.7e+07,2694071,2694071,"Film Foundry","PG","Drama"
"1960","12/25/2010","L'illusionniste",1.7e+07,2231474,8609949,"Sony Pictures Classics","PG","Adventure"
"1961","1/1/1981","Roar",1.7e+07,2110050,2110050,NA,"PG","Adventure"
"1962","10/17/2003","Veronica Guerin",1.7e+07,1569918,9438074,"Walt Disney","R","Drama"
"1963","6/10/2016","Genius",1.7e+07,1361045,6942889,"Roadside Attractions","PG-13","Drama"
"1964","6/26/2015","Escobar: Paradise Lost",1.7e+07,195792,3917679,"RADiUS-TWC","R","Drama"
"1965","3/11/2016","The Young Messiah",16800000,6469813,7313697,"Focus Features","PG-13","Drama"
"1966","11/27/1991","My Girl",16500000,58011485,58011485,"Sony Pictures","PG-13","Comedy"
"1967","12/11/1987","Wall Street",16500000,43848100,43848100,"20th Century Fox","R","Drama"
"1968","12/11/1995","Sense and Sensibility",16500000,42993774,134993774,"Sony Pictures","PG","Drama"
"1969","8/18/2006","The Illusionist",16500000,39868642,83792062,"Yari Film Group Rel…","PG-13","Drama"
"1970","12/19/2003","House of Sand and Fog",16500000,13005485,16157923,"Dreamworks SKG","R","Drama"
"1971","9/21/2007","Sydney White",16500000,11892415,13636339,"Universal","PG-13","Comedy"
"1972","6/2/1989","Dead Poets Society",16400000,95860116,239500000,"Walt Disney","PG","Drama"
"1973","12/16/1994","Dumb & Dumber",1.6e+07,127175374,246400000,"New Line","PG-13","Comedy"
"1974","5/19/2000","Road Trip",1.6e+07,68525609,119739110,"Dreamworks SKG","R","Comedy"
"1975","12/8/1982","The Verdict",1.6e+07,53977250,53977250,"20th Century Fox","R","Drama"
"1976","1/15/1999","Varsity Blues",1.6e+07,52894169,54294169,"Paramount Pictures","R","Drama"
"1977","5/25/2012","Moonrise Kingdom",1.6e+07,45512466,68848446,"Focus Features","PG-13","Drama"
"1978","11/25/2011","The Artist",1.6e+07,44667095,128256712,"Weinstein Co.","PG-13","Drama"
"1979","8/2/2002","The Master of Disguise",1.6e+07,40363530,40363530,"Sony Pictures","PG","Adventure"
"1980","12/29/2006","El Laberinto del Fauno",1.6e+07,37634615,87041569,"Picturehouse","R","Horror"
"1981","2/2/2007","The Messengers",1.6e+07,35374833,53774833,"Sony Pictures","PG-13","Horror"
"1982","3/2/2001","See Spot Run",1.6e+07,33357476,43057552,"Warner Bros.","PG","Adventure"
"1983","8/9/1991","Double Impact",1.6e+07,29090445,29090445,"Sony Pictures","R","Action"
"1984","6/27/2001","Baby Boy",1.6e+07,28734552,28734552,"Sony Pictures","R","Drama"
"1985","4/11/2001","Joe Dirt",1.6e+07,27087695,30987695,"Sony Pictures","PG-13","Comedy"
"1986","9/12/2008","The Women",1.6e+07,26902075,50103808,"Picturehouse","PG-13","Comedy"
"1987","4/20/2007","Hot Fuzz",1.6e+07,23618786,81742618,"Focus Features","R","Comedy"
"1988","8/15/2008","Vicky Cristina Barcelona",1.6e+07,23216709,104504817,"MGM","PG-13","Comedy"
"1989","6/13/2018","Superfly",1.6e+07,20537137,20723581,"Sony Pictures","R","Action"
"1990","3/12/2010","Remember Me",1.6e+07,19068240,56506120,"Summit Entertainment","PG-13","Drama"
"1991","10/11/2002","White Oleander",1.6e+07,16357770,21657770,"Warner Bros.","PG-13","Drama"
"1992","3/3/2000","Drowning Mona",1.6e+07,15427192,15980376,"Destination Films","PG-13","Comedy"
"1993","1/30/1987","Radio Days",1.6e+07,14792779,14792779,"Orion Pictures",NA,"Comedy"
"1994","7/18/2003","How to Deal",1.6e+07,14108518,14108518,"New Line","PG-13","Drama"
"1995","5/28/2004","Soul Plane",1.6e+07,13922211,14553807,"MGM","R","Comedy"
"1996","12/9/1988","My Stepmother Is an Alien",1.6e+07,13854000,13854000,"Sony Pictures","PG-13","Comedy"
"1997","6/29/2012","People Like Us",1.6e+07,12431792,12617472,"Walt Disney","PG-13","Drama"
"1998","9/3/2004","The Cookout",1.6e+07,11540112,11540112,"Lionsgate","PG-13","Comedy"
"1999","10/19/1979","Meteor",1.6e+07,8400000,8400000,"American Internatio…",NA,"Action"
"2000","3/7/1986","Highlander",1.6e+07,5900000,12900000,"20th Century Fox","R","Action"
"2001","11/18/2016","Bleed for This",1.6e+07,5083906,6603926,"Open Road","R","Drama"
"2002","9/15/2000","Duets",1.6e+07,4734235,6615452,"Walt Disney","R","Drama"
"2003","8/13/1999","Detroit Rock City",1.6e+07,4217115,5825314,"New Line","R","Comedy"
"2004","10/19/2007","Things We Lost in the Fire",1.6e+07,3287315,8120148,"Paramount Pictures","R","Drama"
"2005","5/16/2014","The Immigrant",1.6e+07,2013456,7585011,"RADiUS-TWC","R","Drama"
"2006","8/15/1997","Steel",1.6e+07,1686429,1686429,"Warner Bros.","PG-13","Action"
"2007","12/21/2005","The White Countess",1.6e+07,1669971,2814566,"Sony Pictures Classics","PG-13","Drama"
"2008","10/1/2014","Men, Women and Children",1.6e+07,705908,1685403,"Paramount Pictures","R","Comedy"
"2009","12/31/2008","Good",1.6e+07,31631,31631,"ThinkFilm","R","Drama"
"2010","6/21/2002","Juwanna Mann",15600000,13571817,13771817,"Warner Bros.","PG-13","Comedy"
"2011","6/8/2007","La Môme",15500000,10299782,88611837,"Picturehouse","PG-13","Drama"
"2012","11/15/2002","Ararat",15500000,1693000,1693000,"Miramax","R","Drama"
"2013","4/22/2005","Madison",15500000,517262,517262,"MGM","PG","Drama"
"2014","2/26/2010","The Yellow Handkerchief",15500000,318623,318623,"Samuel Goldwyn Films","PG-13","Drama"
"2015","3/31/2006","Slither",15250000,7802450,12930343,"Universal","R","Horror"
"2016","11/16/1990","Home Alone",1.5e+07,285761243,476684675,"20th Century Fox","PG","Comedy"
"2017","12/5/1984","Beverly Hills Cop",1.5e+07,234760478,316300000,"Paramount Pictures","R","Action"
"2018","5/16/1986","Top Gun",1.5e+07,179800601,356799634,"Paramount Pictures","PG","Action"
"2019","12/17/1982","Tootsie",1.5e+07,177200000,177200000,"Sony Pictures","PG","Comedy"
"2020","11/25/1987","3 Men and a Baby",1.5e+07,167780960,167780960,"Walt Disney","PG","Comedy"
"2021","11/26/2010","The King’s Speech",1.5e+07,138797449,430821168,"Weinstein Co.","R","Drama"
"2022","9/15/1999","American Beauty",1.5e+07,130058047,356258047,"Dreamworks SKG","R","Drama"
"2023","12/8/2000","Crouching Tiger, Hidden Dragon",1.5e+07,128067808,213514672,"Sony Pictures Classics","PG-13","Action"
"2024","12/9/1988","Twins",1.5e+07,111936388,216600000,"Universal","PG","Comedy"
"2025","12/20/1996","Scream",1.5e+07,103046663,173046663,"Miramax","R","Horror"
"2026","8/11/2017","Annabelle: Creation",1.5e+07,102092201,305385888,"Warner Bros.","R","Horror"
"2027","10/25/2013","Jackass Presents: Bad Grandpa",1.5e+07,102003019,160903019,"Paramount Pictures","R","Comedy"
"2028","6/28/1978","Heaven Can Wait",1.5e+07,98800000,98800000,"Paramount Pictures","PG","Comedy"
"2029","12/18/1985","The Color Purple",1.5e+07,93589701,93589701,"Warner Bros.","PG-13","Drama"
"2030","11/28/2014","The Imitation Game",1.5e+07,91125143,227773686,"Weinstein Co.","PG-13","Drama"
"2031","3/30/1988","Beetlejuice",1.5e+07,73326666,73326666,"Warner Bros.","PG","Comedy"
"2032","11/18/1959","Ben-Hur",1.5e+07,7.3e+07,7.3e+07,"MGM","G","Adventure"
"2033","1/18/2013","Mama",1.5e+07,71628180,148095566,"Universal","PG-13","Horror"
"2034","10/10/1980","Private Benjamin",1.5e+07,69847348,69847348,"Warner Bros.","R","Comedy"
"2035","3/7/1980","Coal Miner's Daughter",1.5e+07,67182787,67182787,"Universal","PG","Drama"
"2036","3/6/1987","Lethal Weapon",1.5e+07,65192350,120192350,"Warner Bros.","R","Action"
"2037","3/19/2010","Diary of a Wimpy Kid",1.5e+07,64003625,76954311,"20th Century Fox","PG","Adventure"
"2038","7/29/1983","National Lampoon’s Vacation",1.5e+07,61400000,61400000,"Warner Bros.","R","Comedy"
"2039","9/30/2006","The Queen",1.5e+07,56441711,128885873,"Miramax","PG-13","Drama"
"2040","12/21/1994","Little Women",1.5e+07,50003303,50003303,"Sony Pictures","PG","Drama"
"2041","1/1/1979","The Deer Hunter",1.5e+07,5e+07,50009253,"Universal","R","Drama"
"2042","2/3/2006","When a Stranger Calls",1.5e+07,47860214,67215435,"Sony Pictures","PG-13","Horror"
"2043","2/8/2002","Big Fat Liar",1.5e+07,47811275,52461017,"Universal","PG","Adventure"
"2044","8/15/1997","Cop Land",1.5e+07,44906632,63706632,"Miramax","R","Drama"
"2045","12/25/1997","Wag the Dog",1.5e+07,43057470,64252038,"New Line","R","Drama"
"2046","5/2/2003","The Lizzie McGuire Movie",1.5e+07,42734455,55534455,"Walt Disney","PG","Adventure"
"2047","12/25/1998","The Faculty",1.5e+07,40283321,40283321,"Miramax","R","Horror"
"2048","6/9/1993","What's Love Got to Do With It",1.5e+07,39100956,39100956,"Walt Disney","R","Drama"
"2049","12/14/2001","Not Another Teen Movie",1.5e+07,37882551,62401343,"Sony Pictures","R","Comedy"
"2050","12/3/2014","Wild",1.5e+07,37880356,52460543,"Fox Searchlight","R","Drama"
"2051","12/16/1962","Lawrence of Arabia",1.5e+07,37495385,69995385,"Sony Pictures","PG","Adventure"
"2052","11/7/2014","The Theory of Everything",1.5e+07,35893537,123327692,"Focus Features","PG-13","Drama"
"2053","9/16/2011","Drive",1.5e+07,35060689,81357930,"FilmDistrict","R","Action"
"2054","4/18/2003","Malibu's Most Wanted",1.5e+07,34308901,34499204,"Warner Bros.","PG-13","Comedy"
"2055","4/28/2000","Where the Heart Is",1.5e+07,33771174,40862054,"20th Century Fox","PG-13","Drama"
"2056","8/28/2009","Halloween 2",1.5e+07,33392973,38512850,"Weinstein/Dimension","R","Horror"
"2057","3/13/2009","The Last House on the Left",1.5e+07,32752215,46526243,"Universal","R","Horror"
"2058","2/18/2005","Because of Winn-Dixie",1.5e+07,32647042,33508485,"20th Century Fox","PG","Comedy"
"2059","9/25/1987","The Princess Bride",1.5e+07,30857000,30858487,"20th Century Fox","PG","Adventure"
"2060","7/12/2002","Halloween: Resurrection",1.5e+07,30259652,37659652,"Miramax/Dimension","R","Horror"
"2061","12/25/2007","The Great Debaters",1.5e+07,30226144,30261293,"Weinstein Co.","PG-13","Drama"
"2062","8/22/2014","When the Game Stands Tall",1.5e+07,30127963,30138912,"Sony Pictures","PG","Drama"
"2063","5/11/2007","28 Weeks Later",1.5e+07,28638916,64232714,"20th Century Fox","R","Horror"
"2064","4/21/2000","Love and Basketball",1.5e+07,27441122,27709625,"New Line","PG-13","Drama"
"2065","10/27/2000","Book of Shadows: Blair Witch 2",1.5e+07,26421314,47721314,"Artisan","R","Horror"
"2066","10/10/1997","Boogie Nights",1.5e+07,26410771,43111725,"New Line","R","Drama"
"2067","7/23/2010","Ramona and Beezus",1.5e+07,26167002,27469621,"20th Century Fox","G","Adventure"
"2068","11/5/1993","The Remains of the Day",1.5e+07,22954968,63954968,"Sony Pictures","PG","Drama"
"2069","1/15/1993","Nowhere to Run",1.5e+07,22189039,52189039,"Sony Pictures","R","Action"
"2070","9/22/2000","Urban Legends: Final Cut",1.5e+07,21468807,38574362,"Sony Pictures","R","Horror"
"2071","3/29/2013","The Place Beyond the Pines",1.5e+07,21403519,47011449,"Focus Features","R","Drama"
"2072","10/20/2006","Flicka",1.5e+07,21000147,21896367,"20th Century Fox","PG","Drama"
"2073","3/23/2007","The Hills Have Eyes II",1.5e+07,20804166,37466538,"20th Century Fox","R","Horror"
"2074","4/29/2016","Keanu",1.5e+07,20591853,20688141,"Warner Bros.","R","Comedy"
"2075","12/22/2010","Country Strong",1.5e+07,20218921,20601987,"Sony Pictures","PG-13","Drama"
"2076","10/11/2002","Tuck Everlasting",1.5e+07,19161999,19344615,"Walt Disney","PG","Drama"
"2077","10/13/2006","The Marine",1.5e+07,18844784,22165608,"20th Century Fox","PG-13","Action"
"2078","3/6/1998","The Big Lebowski",1.5e+07,17498804,46189568,"Gramercy","R","Comedy"
"2079","6/26/2009","The Hurt Locker",1.5e+07,17017811,49894223,"Summit Entertainment","R","Drama"
"2080","11/2/2012","The Man with the Iron Fists",1.5e+07,15634090,22018988,"Universal","R","Action"
"2081","5/11/1984","Firestarter",1.5e+07,15136870,15136870,"Universal",NA,"Horror"
"2082","4/20/2001","Freddy Got Fingered",1.5e+07,14249005,14249005,"20th Century Fox","R","Comedy"
"2083","8/19/2011","One Day",1.5e+07,13843771,59168692,"Focus Features","PG-13","Drama"
"2084","6/25/2004","De-Lovely",1.5e+07,13337299,18524496,"MGM","PG-13","Drama"
"2085","10/2/2009","Whip It",1.5e+07,13077184,18889972,"Fox Searchlight","PG-13","Comedy"
"2086","9/1/2000","Highlander: Endgame",1.5e+07,12801190,12801190,"Miramax/Dimension","R","Action"
"2087","1/20/2017","The Founder",1.5e+07,12786053,24408130,"Weinstein Co.","PG-13","Drama"
"2088","4/25/2003","Confidence",1.5e+07,12212417,12212417,"Lionsgate","R","Drama"
"2089","10/11/2002","Knockaround Guys",1.5e+07,11660180,12419700,"New Line","R","Drama"
"2090","8/27/1999","The Muse",1.5e+07,11614954,11614954,"October Films","PG-13","Comedy"
"2091","4/3/1998","Barney's Great Adventure",1.5e+07,11156471,11156471,"Polygram","G","Adventure"
"2092","3/1/1989","New York Stories",1.5e+07,10763469,10763469,"Walt Disney","PG","Drama"
"2093","3/24/2000","Here on Earth",1.5e+07,10494147,10845127,"20th Century Fox","PG-13","Drama"
"2094","10/8/2004","Raise Your Voice",1.5e+07,10411980,14811980,"New Line","PG","Drama"
"2095","4/23/1993","The Dark Half",1.5e+07,9579068,9579068,"Orion Pictures","R","Horror"
"2096","3/2/2007","Black Snake Moan",1.5e+07,9396870,10951153,"Paramount Vantage","R","Drama"
"2097","2/21/2003","Dark Blue",1.5e+07,9237470,12262065,"MGM","R","Drama"
"2098","6/22/2007","A Mighty Heart",1.5e+07,9176787,19153568,"Paramount Vantage","R","Drama"
"2099","3/21/2003","Boat Trip",1.5e+07,8586376,14933713,"Artisan","R","Comedy"
"2100","5/22/2002","The Importance of Being Earnest",1.5e+07,8378141,8378141,"Miramax","PG","Comedy"
"2101","5/5/2006","Hoot",1.5e+07,8117637,8224998,"New Line","PG","Adventure"
"2102","2/8/2008","In Bruges",1.5e+07,7800825,34533783,"Focus Features","R","Comedy"
"2103","1/4/2013","Promised Land",1.5e+07,7597898,12394562,"Focus Features","R","Drama"
"2104","10/8/2001","Mulholland Drive",1.5e+07,7219578,20785973,"Universal","R","Drama"
"2105","8/20/2008","The Rocker",1.5e+07,6409528,8767338,"20th Century Fox","PG-13","Comedy"
"2106","9/24/1999","Jakob the Liar",1.5e+07,4956401,4956401,"Sony Pictures","PG-13","Drama"
"2107","10/21/2005","Kiss Kiss, Bang Bang",1.5e+07,4235837,16829464,"Warner Bros.","R","Comedy"
"2108","4/30/1999","Idle Hands",1.5e+07,4023741,4023741,"Sony Pictures","R","Horror"
"2109","1/26/2007","Blood and Chocolate",1.5e+07,3526588,6551310,"MGM","PG-13","Horror"
"2110","9/22/2010","You Will Meet a Tall Dark Stranger",1.5e+07,3247816,34247816,"Sony Pictures Classics","R","Drama"
"2111","9/15/2010","Never Let Me Go",1.5e+07,2434652,11173718,"Fox Searchlight","R","Drama"
"2112","9/9/2016","The Disappointments Room",1.5e+07,2423467,3144688,"Relativity","R","Horror"
"2113","12/25/2003","The Company",1.5e+07,2281585,3396508,"Sony Pictures","PG-13","Drama"
"2114","10/22/1999","Crazy in Alabama",1.5e+07,1954202,1954202,"Sony Pictures","PG-13","Drama"
"2115","1/17/1986","The Clan of the Cave Bear",1.5e+07,1953732,1953732,"Warner Bros.",NA,"Adventure"
"2116","6/2/2006","Banlieue 13",1.5e+07,1200216,11599903,"Magnolia Pictures","R","Action"
"2117","11/12/1999","Felicia's Journey",1.5e+07,824295,1970268,"Artisan","PG-13","Drama"
"2118","1/25/2002","Metropolis",1.5e+07,673414,1405032,"Sony Pictures","PG-13","Adventure"
"2119","4/26/2013","The Reluctant Fundamentalist",1.5e+07,528731,528731,"IFC Films","R","Drama"
"2120","2/6/2004","The Return",1.5e+07,501752,5953886,"Kino International","PG-13","Drama"
"2121","7/25/2003","Buffalo Soldiers",1.5e+07,353743,353743,"Miramax","R","Comedy"
"2122","8/27/2010","Centurion",1.5e+07,123570,7885048,"Magnolia Pictures","R","Action"
"2123","10/23/2009","Ong-Bak 2",1.5e+07,102458,7583050,"Magnolia Pictures","R","Action"
"2124","9/6/2013","Winnie Mandela",1.5e+07,61847,61847,"Image Entertainment","R","Drama"
"2125","11/4/2011","The Son of No One",1.5e+07,30680,1148578,"Anchor Bay Entertai…","R","Drama"
"2126","10/25/2002","All the Queen's Men",1.5e+07,22723,22723,"Strand","PG-13","Comedy"
"2127","2/17/2017","In Dubious Battle",1.5e+07,0,214182,"Momentum Pictures","R","Drama"
"2128","7/1/2015","Magic Mike XXL",14500000,66013057,123709460,"Warner Bros.","R","Comedy"
"2129","11/1/1996","Romeo+Juliet",14500000,46338728,147542381,"20th Century Fox","PG-13","Drama"
"2130","7/22/2011","Elle s'appelait Sarah",14500000,7691700,25480031,"Weinstein Co.","PG-13","Drama"
"2131","6/5/2015","Freedom",14500000,0,872757,"ARC Entertainment","R","Drama"
"2132","11/12/2008","Slumdog Millionaire",1.4e+07,141330703,384530440,"Fox Searchlight","R","Drama"
"2133","12/17/1974","Towering Inferno",1.4e+07,1.16e+08,139700000,"20th Century Fox","PG","Action"
"2134","5/25/1988","Crocodile Dundee 2",1.4e+07,109306210,239606210,"Paramount Pictures","PG","Adventure"
"2135","12/20/1989","Born on the Fourth of July",1.4e+07,70001698,70001698,"Universal","R","Drama"
"2136","10/1/1993","Cool Runnings",1.4e+07,68856263,155056263,"Walt Disney","PG","Adventure"
"2137","1/12/2007","Stomp the Yard",1.4e+07,61356221,75525718,"Sony Pictures","PG-13","Drama"
"2138","1/16/2009","My Bloody Valentine",1.4e+07,51545952,102836002,"Lionsgate","R","Horror"
"2139","8/31/2012","The Possession",1.4e+07,49130588,82925064,"Lionsgate","PG-13","Horror"
"2140","10/22/1982","First Blood",1.4e+07,47212904,125212904,"Orion Pictures","R","Action"
"2141","7/13/1977","The Spy Who Loved Me",1.4e+07,46800000,185400000,"United Artists","PG","Action"
"2142","9/25/1998","Urban Legend",1.4e+07,38116707,72571864,"Sony Pictures","R","Horror"
"2143","12/9/1981","Taps",1.4e+07,35856053,35856053,"20th Century Fox","PG","Drama"
"2144","2/24/2012","Tyler Perry's Good Deeds",1.4e+07,35025791,35579177,"Lionsgate","PG-13","Drama"
"2145","1/18/1991","White Fang",1.4e+07,34729091,34729091,"Walt Disney","PG","Adventure"
"2146","12/21/1988","Dangerous Liaisons",1.4e+07,34700000,34700000,"Warner Bros.","R","Drama"
"2147","10/8/1999","Superstar",1.4e+07,30628981,30628981,"Paramount Pictures","PG-13","Comedy"
"2148","1/13/2012","The Iron Lady",1.4e+07,29959436,115592104,"Weinstein Co.","PG-13","Drama"
"2149","7/23/1993","Poetic Justice",1.4e+07,27450453,27450453,"Sony Pictures","R","Drama"
"2150","10/4/2002","Jonah: A VeggieTales Movie",1.4e+07,25571351,25608779,"Artisan","G","Adventure"
"2151","3/8/2002","All About the Benjamins",1.4e+07,25482931,25873145,"New Line","R","Comedy"
"2152","6/17/1977","Exorcist II: The Heretic",1.4e+07,25011000,25011000,"Warner Bros.",NA,"Horror"
"2153","3/12/2010","Our Family Wedding",1.4e+07,20255281,21410546,"Fox Searchlight","PG-13","Comedy"
"2154","10/27/1995","Vampire in Brooklyn",1.4e+07,19637147,19637147,"Paramount Pictures","R","Horror"
"2155","5/5/2006","An American Haunting",1.4e+07,16298046,30443277,"Freestyle Releasing","PG-13","Horror"
"2156","10/25/1996","Thinner",1.4e+07,15171475,15171475,"Paramount Pictures","R","Horror"
"2157","5/14/1999","Tea with Mussolini",1.4e+07,14395874,14395874,"MGM","PG","Drama"
"2158","4/26/2002","Jason X",1.4e+07,13121555,16951798,"New Line","R","Horror"
"2159","5/13/1994","Crooklyn",1.4e+07,13024170,13024170,"Universal","PG-13","Comedy"
"2160","2/20/2015","Hot Tub Time Machine 2",1.4e+07,12314651,12452601,"Paramount Pictures","R","Comedy"
"2161","11/17/2006","Bobby",1.4e+07,11242801,20597806,"MGM","R","Drama"
"2162","10/26/2012","Fun Size",1.4e+07,9409538,11166615,"Paramount Pictures","PG-13","Comedy"
"2163","11/30/2007","Le Scaphandre et le Papillon",1.4e+07,5990075,22754472,"Miramax","PG-13","Drama"
"2164","10/6/2006","Little Children",1.4e+07,5463019,14121177,"New Line","R","Drama"
"2165","4/21/2000","Gossip",1.4e+07,5108820,12591270,"Warner Bros.","R","Drama"
"2166","3/26/1999","A Walk on the Moon",1.4e+07,4741987,4741987,"Miramax","R","Drama"
"2167","10/23/2015","Suffragette",1.4e+07,4702420,34044909,"Focus Features","PG-13","Drama"
"2168","12/19/2014","Mr. Turner",1.4e+07,3958546,25187026,"Sony Pictures Classics","R","Drama"
"2169","9/7/2001","Soul Survivors",1.4e+07,3100650,4288246,"Artisan","PG-13","Horror"
"2170","3/31/1995","Jefferson in Paris",1.4e+07,2461628,2461628,"Walt Disney","PG-13","Drama"
"2171","1/1/1978","Caravans",1.4e+07,1e+06,1e+06,"Universal",NA,"Adventure"
"2172","9/26/2008","The Lucky Ones",1.4e+07,266967,266967,"Lionsgate","R","Drama"
"2173","9/30/2011","Margaret",1.4e+07,47185,623292,"Fox Searchlight","R","Drama"
"2174","12/9/2005","Brokeback Mountain",13900000,83043761,177012173,"Focus Features","R","Drama"
"2175","7/1/1995","Clueless",13700000,56598476,56598476,"Paramount Pictures","PG-13","Comedy"
"2176","3/30/1990","Teenage Mutant Ninja Turtles",13500000,135265915,2.02e+08,"New Line","PG","Adventure"
"2177","11/8/2002","Far From Heaven",13500000,15901849,29027914,"Focus Features","PG-13","Drama"
"2178","10/12/2012","Seven Psychopaths",13500000,15024049,33035736,"CBS Films","R","Comedy"
"2179","11/22/2000","Quills",13500000,7060876,11732088,"Fox Searchlight","R","Drama"
"2180","1/29/1982","The Border",13500000,6118683,6118683,"Universal",NA,"Drama"
"2181","2/18/2005","Der Untergang",13500000,5501940,93631744,"Newmarket Films","R","Drama"
"2182","3/2/2001","The Caveman's Valentine",13500000,687081,892506,"Focus Features","R","Drama"
"2183","4/1/2011","The Last Godfather",13400000,164247,164247,"Roadside Attractions","PG-13","Comedy"
"2184","12/17/2004","Mar adentro",13300000,2086345,39686345,"Fine Line","PG-13","Drama"
"2185","12/23/1987","Good Morning Vietnam",1.3e+07,123922370,123922370,"Walt Disney","R","Comedy"
"2186","1/12/2001","Save the Last Dance",1.3e+07,91038276,122244329,"Paramount Pictures","PG-13","Drama"
"2187","7/4/2018","The First Purge",1.3e+07,69086325,136112145,"Universal","R","Horror"
"2188","3/16/2016","Miracles from Heaven",1.3e+07,61705123,73798720,"Sony Pictures","PG","Drama"
"2189","2/11/2000","Snow Day",1.3e+07,60008303,62452927,"Paramount Pictures","PG","Adventure"
"2190","6/24/2016","The Shallows",1.3e+07,55121623,118888025,"Sony Pictures","PG-13","Drama"
"2191","7/17/1987","RoboCop",1.3e+07,53424681,53424681,"Orion Pictures","R","Action"
"2192","11/21/2007","This Christmas",1.3e+07,49121934,49733545,"Sony Pictures","PG-13","Drama"
"2193","12/15/2000","Dude, Where's My Car?",1.3e+07,46729374,73180297,"20th Century Fox","PG-13","Comedy"
"2194","10/10/2014","St. Vincent",1.3e+07,44137712,54837234,"Weinstein Co.","PG-13","Comedy"
"2195","7/2/2014","Earth to Echo",1.3e+07,38934842,42174545,"Relativity","PG","Adventure"
"2196","5/10/2002","The New Guy",1.3e+07,28972187,28972187,"Sony Pictures","PG-13","Comedy"
"2197","2/5/1993","Loaded Weapon 1",1.3e+07,27979399,27979399,"New Line","PG-13","Comedy"
"2198","3/12/1999","Baby Geniuses",1.3e+07,27151490,27151490,"Sony Pictures","PG","Adventure"
"2199","4/24/1998","The Big Hit",1.3e+07,27066941,27066941,"Sony Pictures","R","Action"
"2200","11/9/1990","Child's Play 2",1.3e+07,26904572,34166572,"Universal","R","Horror"
"2201","7/10/1996","Harriet the Spy",1.3e+07,26570048,26570048,"Paramount Pictures","PG","Adventure"
"2202","3/1/2013","21 and Over",1.3e+07,25682380,42195766,"Relativity","R","Comedy"
"2203","11/21/2007","The Mist",1.3e+07,25593755,57189408,"MGM","R","Horror"
"2204","9/21/2012","The Perks of Being a Wallflower",1.3e+07,17742948,33069303,"Lionsgate","PG-13","Drama"
"2205","6/29/2001","crazy/beautiful",1.3e+07,16929123,19929123,"Walt Disney","PG-13","Drama"
"2206","10/16/2015","Room",1.3e+07,14677674,36262783,"A24","R","Drama"
"2207","10/16/2015","Woodlawn",1.3e+07,14394097,14403703,"Pure Flix Entertain…","PG","Drama"
"2208","12/20/2006","Letters from Iwo Jima",1.3e+07,13756082,67867998,"Warner Bros.","R","Drama"
"2209","2/23/2007","The Astronaut Farmer",1.3e+07,11003643,11141213,"Warner Bros.","PG","Drama"
"2210","6/12/1998","Dirty Work",1.3e+07,10020081,10020081,"MGM","PG-13","Comedy"
"2211","9/9/2016","Robinson Crusoe",1.3e+07,8005586,33490316,"Lionsgate","PG","Adventure"
"2212","4/13/1994","Serial Mom",1.3e+07,7881335,7881335,"Savoy","R","Comedy"
"2213","8/4/1999","Dick",1.3e+07,6276869,6276869,"Sony Pictures","PG-13","Comedy"
"2214","11/10/1999","Light It Up",1.3e+07,5871603,5871603,"20th Century Fox","R","Drama"
"2215","8/24/2001","Bubble Boy",1.3e+07,5002310,5002310,"Walt Disney","PG-13","Comedy"
"2216","5/4/2007","Paris, je t'aime",1.3e+07,4857374,5175088,"First Look","R","Drama"
"2217","8/24/2007","Resurrecting the Champ",1.3e+07,3172382,3260555,"Yari Film Group Rel…","PG-13","Drama"
"2218","3/2/2001","The Widow of St. Pierre",1.3e+07,3058380,3058380,"Lionsgate","R","Drama"
"2219","12/4/2015","Youth",1.3e+07,2703296,24002112,"Fox Searchlight","R","Drama"
"2220","2/26/2010","Un Prophète",1.3e+07,2087720,19910624,"Sony Pictures Classics","R","Drama"
"2221","12/3/2010","I Love You, Phillip Morris",1.3e+07,2037459,23014027,"Roadside Attractions","R","Comedy"
"2222","7/24/2015","The Vatican Tapes",1.3e+07,1784763,14999638,"Lionsgate","PG-13","Horror"
"2223","3/17/2006","Find Me Guilty",1.3e+07,1173673,2898225,"Freestyle Releasing","R","Drama"
"2224","10/13/2006","Infamous",1.3e+07,1151330,2613717,"Warner Independent","R","Drama"
"2225","7/29/2011","Attack the Block",1.3e+07,1024175,6459183,"Sony Pictures","R","Action"
"2226","12/23/2011","In The Land of Blood and Honey",1.3e+07,303877,509193,"FilmDistrict","R","Drama"
"2227","6/18/2010","The Killer Inside Me",1.3e+07,217277,3617277,"IFC Films","R","Drama"
"2228","9/12/2014","The Drop",12600000,10724389,19054534,"Fox Searchlight","R","Drama"
"2229","9/3/2010","Machete",12500000,26593646,46370970,"20th Century Fox","R","Action"
"2230","12/19/2002","Antwone Fisher",12500000,21078145,23367586,"Fox Searchlight","PG-13","Drama"
"2231","2/12/1982","La Guerre du feu",12500000,20959585,20959585,"20th Century Fox",NA,"Adventure"
"2232","11/22/2002","The Emperor's Club",12500000,14060950,16193713,"Universal","PG-13","Drama"
"2233","9/11/2009","Sorority Row",12500000,11965282,26735797,"Summit Entertainment","R","Horror"
"2234","9/30/1992","Glengarry Glen Ross",12500000,10725228,10725228,"New Line","R","Drama"
"2235","11/7/2008","The Boy in the Striped Pyjamas",12500000,9046156,44083403,"Miramax","PG-13","Drama"
"2236","4/2/1982","Cat People",12500000,7e+06,2.1e+07,"Universal","R","Drama"
"2237","5/25/1979","The Prisoner of Zenda",12500000,7e+06,7e+06,"Universal",NA,"Comedy"
"2238","10/15/2010","Conviction",12500000,6797696,11826980,"Fox Searchlight","R","Drama"
"2239","10/12/2007","Lars and the Real Girl",12500000,5956480,11277119,"MGM","PG-13","Comedy"
"2240","5/21/2010","Solitary Man",12500000,4360548,4360548,"Anchor Bay Entertai…","R","Drama"
"2241","12/31/1997","Oscar and Lucinda",12500000,1612957,1612957,"Fox Searchlight","R","Drama"
"2242","11/1/1996","The Funeral",12500000,1212799,1412799,"October Films","R","Drama"
"2243","9/3/2004","Tae Guik Gi: The Brotherhood of War",12500000,1110186,69826708,"IDP Distribution","R","Drama"
"2244","4/16/2010","The Perfect Game",12500000,1089445,3931367,"Slowhand Cinema","PG","Drama"
"2245","11/18/1988","The Land Before Time",12300000,48092846,81972846,"Universal","G","Adventure"
"2246","6/20/1975","Jaws",1.2e+07,2.6e+08,470700000,"Universal","PG","Horror"
"2247","12/26/1973","The Exorcist",1.2e+07,204868002,402735134,"Warner Bros.","R","Horror"
"2248","6/6/2014","The Fault in Our Stars",1.2e+07,124872350,307166834,"20th Century Fox","PG-13","Drama"
"2249","7/9/1999","American Pie",1.2e+07,101800948,234723148,"Universal","R","Comedy"
"2250","4/16/2014","Heaven is for Real",1.2e+07,91386097,100916299,"Sony Pictures","PG","Drama"
"2251","12/12/1986","The Golden Child",1.2e+07,79817937,79817937,"Paramount Pictures","PG-13","Action"
"2252","6/4/1982","Star Trek II: The Wrath of Khan",1.2e+07,78912963,95800000,"Paramount Pictures","PG","Adventure"
"2253","9/13/2002","Barbershop",1.2e+07,75781642,77063461,"MGM","PG-13","Comedy"
"2254","2/4/1994","Ace Ventura: Pet Detective",1.2e+07,72217396,107217396,"Warner Bros.","PG-13","Comedy"
"2255","2/24/2012","Act of Valor",1.2e+07,70012847,82497035,"Relativity","R","Action"
"2256","8/11/2006","Step Up",1.2e+07,65328121,110989157,"Walt Disney","PG-13","Drama"
"2257","12/20/1996","Beavis and Butt-Head Do America",1.2e+07,63118386,63118386,"Paramount Pictures","PG-13","Adventure"
"2258","11/25/2016","Lion",1.2e+07,51739495,149875676,"Weinstein Co.","PG-13","Drama"
"2259","12/25/1997","Jackie Brown",1.2e+07,39673162,74727492,"Miramax","R","Drama"
"2260","11/22/2013","Philomena",1.2e+07,37709979,98963392,"Weinstein Co.","PG-13","Drama"
"2261","11/6/1981","Time Bandits",1.2e+07,37400000,37400000,"Avco Embassy",NA,"Adventure"
"2262","7/24/2015","Paper Towns",1.2e+07,32000304,85512300,"20th Century Fox","PG-13","Drama"
"2263","10/10/2008","Quarantine",1.2e+07,31691811,41924774,"Sony Pictures","R","Horror"
"2264","8/21/2002","One Hour Photo",1.2e+07,31597131,52223306,"Fox Searchlight","R","Drama"
"2265","4/7/2004","Johnson Family Vacation",1.2e+07,31203964,31286759,"Fox Searchlight","PG-13","Comedy"
"2266","12/21/2001","How High",1.2e+07,31155435,31222395,"Universal","R","Comedy"
"2267","10/7/1960","Spartacus",1.2e+07,3e+07,6e+07,"Universal","PG-13","Action"
"2268","9/1/2006","Crank",1.2e+07,27838408,43924923,"Lionsgate","R","Action"
"2269","12/11/1992","The Muppet Christmas Carol",1.2e+07,27281507,27492918,"Walt Disney","G","Comedy"
"2270","10/25/2002","Frida",1.2e+07,25885000,56131239,"Miramax","R","Drama"
"2271","12/12/2014","Top Five",1.2e+07,25317379,26001741,"Paramount Pictures","R","Comedy"
"2272","9/11/1998","Rounders",1.2e+07,22921898,22921898,"Miramax","R","Drama"
"2273","1/30/2015","Project Almanac",1.2e+07,22348241,32909437,"Paramount Pictures","PG-13","Adventure"
"2274","1/13/1995","Tales from the Crypt: Demon Knight",1.2e+07,21089146,21089146,"Universal","R","Horror"
"2275","3/11/2005","The Upside of Anger",1.2e+07,18761993,28915761,"New Line","R","Drama"
"2276","3/3/2006","Aquamarine",1.2e+07,18597342,22978953,"20th Century Fox","PG","Comedy"
"2277","11/15/2013","Nebraska",1.2e+07,17654912,24761360,"Paramount Pictures","R","Drama"
"2278","1/9/2004","My Baby's Daddy",1.2e+07,17321573,17322212,"Miramax","PG-13","Comedy"
"2279","10/5/2001","Max Keeble's Big Move",1.2e+07,17292381,17292381,"Walt Disney","PG","Adventure"
"2280","12/9/2011","Young Adult",1.2e+07,16311571,22750356,"Paramount Pictures","R","Comedy"
"2281","7/14/2017","Wish Upon",1.2e+07,14301505,23477345,"Broad Green Pictures","PG-13","Horror"
"2282","8/6/1997","Def Jam's How To Be a Player",1.2e+07,14010363,14010363,"Gramercy","R","Comedy"
"2283","10/30/1998","Living Out Loud",1.2e+07,12905901,12905901,"New Line","R","Drama"
"2284","10/3/2008","Rachel Getting Married",1.2e+07,12796861,17475475,"Sony Pictures Classics","R","Drama"
"2285","3/20/1981","The Postman Always Rings Twice",1.2e+07,12200000,44200000,"Paramount Pictures",NA,"Drama"
"2286","12/12/2003","Girl with a Pearl Earring",1.2e+07,11634362,43274797,"Lionsgate","PG-13","Drama"
"2287","2/10/1982","Das Boot",1.2e+07,11487676,84970337,"Sony Pictures","R","Drama"
"2288","12/3/2004","House of Flying Daggers",1.2e+07,11050094,92863945,"Sony Pictures Classics","PG-13","Action"
"2289","3/22/2002","Sorority Boys",1.2e+07,10198766,12516222,"Walt Disney","R","Comedy"
"2290","10/13/2017","Marshall",1.2e+07,10051659,10116816,"Open Road","PG-13","Drama"
"2291","12/5/2008","Cadillac Records",1.2e+07,8195551,8942516,"Sony Pictures","R","Drama"
"2292","5/12/2000","Screwed",1.2e+07,6982680,6982680,"Universal","PG-13","Comedy"
"2293","10/20/2006","Running With Scissors",1.2e+07,6860000,8706701,"Sony Pictures","R","Comedy"
"2294","9/3/1993","Fortress",1.2e+07,6730578,46730578,"Miramax","R","Action"
"2295","11/17/2006","For Your Consideration",1.2e+07,5549923,5549923,"Warner Independent","PG-13","Comedy"
"2296","11/20/1998","Celebrity",1.2e+07,5078660,6200000,"Miramax","R","Comedy"
"2297","6/6/1986","Invaders from Mars",1.2e+07,4884663,4984663,"Cannon",NA,"Horror"
"2298","3/22/1996","Girl 6",1.2e+07,4880941,4880941,"Fox Searchlight","R","Comedy"
"2299","2/22/2008","Charlie Bartlett",1.2e+07,3950294,5295909,"MGM","R","Comedy"
"2300","2/13/2009","Two Lovers",1.2e+07,3149034,16349034,"Magnolia Pictures","R","Drama"
"2301","2/15/2002","Last Orders",1.2e+07,2326407,2326407,"Sony Pictures Classics","R","Drama"
"2302","3/9/2007","Gwoemul",1.2e+07,2201923,92618117,"Magnolia Pictures","R","Action"
"2303","11/13/1981","The Pursuit of D.B. Cooper",1.2e+07,2104164,2104164,"Universal",NA,"Adventure"
"2304","3/19/1999","Ravenous",1.2e+07,2062406,2062406,"20th Century Fox","R","Horror"
"2305","6/14/2002","The Dangerous Lives of Altar Boys",1.2e+07,1779284,1779284,"ThinkFilm","R","Drama"
"2306","3/1/2013","Stoker",1.2e+07,1703125,12034913,"Fox Searchlight","R","Drama"
"2307","3/7/2008","Married Life",1.2e+07,1506998,2975188,"Sony Pictures Classics","PG-13","Drama"
"2308","3/11/2011","Kill the Irishman",1.2e+07,1188194,1188194,"Anchor Bay Entertai…","R","Drama"
"2309","9/30/2005","Duma",1.2e+07,870067,994790,"Warner Bros.","PG","Adventure"
"2310","4/20/2012","Darling Companion",1.2e+07,793352,1200346,"Sony Pictures Classics","PG-13","Comedy"
"2311","6/4/2010","Ondine",1.2e+07,550472,557545,"Magnolia Pictures","PG-13","Drama"
"2312","4/18/2008","Life Before Her Eyes",1.2e+07,303439,7203439,"Magnolia Pictures","R","Drama"
"2313","10/31/1997","Critical Care",1.2e+07,220175,220175,NA,"R","Drama"
"2314","9/28/2007","Trade",1.2e+07,214202,1513388,"Roadside Attractions","R","Drama"
"2315","1/6/2006","Fateless",1.2e+07,196857,196857,"ThinkFilm","R","Drama"
"2316","9/3/2010","San qiang pai an jing qi",1.2e+07,190946,310946,"Sony Pictures Classics","R","Drama"
"2317","9/17/1999","Breakfast of Champions",1.2e+07,178287,178287,"Walt Disney","R","Comedy"
"2318","3/9/2001","Company Man",1.2e+07,146028,622273,NA,"PG-13","Comedy"
"2319","11/7/2009","Nanjing! Nanjing!",1.2e+07,122558,20122558,"Kino International","R","Drama"
"2320","10/9/2015","Trash",1.2e+07,17484,6553186,"Focus Features","R","Adventure"
"2321","8/19/2011","5 Days of War",1.2e+07,17479,87793,"Anchor Bay Entertai…","R","Drama"
"2322","11/11/2015","10 Days in a Madhouse",1.2e+07,14616,14616,"Cafe Pictures","R","Drama"
"2323","9/23/2016","The Dressmaker",11900000,2022115,24041617,"Broad Green Pictures","R","Drama"
"2324","12/10/1999","Diamonds",11900000,81897,81897,"Miramax","PG-13","Comedy"
"2325","3/20/1998","Madadayo",11900000,48856,48856,"WinStar Cinema",NA,"Drama"
"2326","11/20/2015","Carol",11800000,12711491,42895440,"Weinstein Co.","R","Drama"
"2327","4/21/1989","Pet Sematary",11500000,57469179,57469179,"Paramount Pictures","R","Horror"
"2328","1/22/2016","Dirty Grandpa",11500000,35593113,105241410,"Lionsgate","R","Comedy"
"2329","10/9/2009","St. Trinian’s",11400000,15000,29830239,"NeoClassics Films","PG-13","Comedy"
"2330","5/25/1977","Star Wars Ep. IV: A New Hope",1.1e+07,460998007,786598007,"20th Century Fox","PG","Adventure"
"2331","6/8/1984","Gremlins",1.1e+07,148168459,148199515,"Warner Bros.","PG","Comedy"
"2332","12/22/1965","Doctor Zhivago",1.1e+07,111721000,111859493,"MGM","PG-13","Drama"
"2333","12/10/2010","The Fighter",1.1e+07,93617009,129262388,"Paramount Pictures","R","Drama"
"2334","12/27/1991","Fried Green Tomatoes",1.1e+07,81204830,81204830,"Universal","PG-13","Drama"
"2335","9/22/2006","Jackass: Number Two",1.1e+07,72778712,85278712,"Paramount Pictures","R","Comedy"
"2336","3/13/1992","My Cousin Vinny",1.1e+07,52929168,52929168,"20th Century Fox","R","Comedy"
"2337","8/22/2014","If I Stay",1.1e+07,50474843,78356170,"Warner Bros.","PG-13","Drama"
"2338","4/7/1989","Major League",1.1e+07,49793054,49793054,"Paramount Pictures","R","Comedy"
"2339","1/25/2002","A Walk to Remember",1.1e+07,41227069,46060915,"Warner Bros.","PG","Drama"
"2340","12/29/1995","Dead Man Walking",1.1e+07,39387284,83088295,"Gramercy","R","Drama"
"2341","11/4/2015","Brooklyn",1.1e+07,38322743,62076141,"Fox Searchlight","PG-13","Drama"
"2342","3/5/1999","Cruel Intentions",1.1e+07,38230075,75803716,"Sony Pictures","R","Drama"
"2343","10/17/2008","The Secret Life of Bees",1.1e+07,37780486,39994347,"Fox Searchlight","PG-13","Drama"
"2344","4/1/2015","Woman in Gold",1.1e+07,33307793,57019592,"Weinstein Co.","PG-13","Drama"
"2345","6/12/1981","History of the World: Part I",1.1e+07,31672000,31672000,"20th Century Fox",NA,"Comedy"
"2346","10/23/2009","Saw VI",1.1e+07,27693292,69752402,"Lionsgate","R","Horror"
"2347","10/12/2001","Corky Romano",1.1e+07,23978402,25116103,"Walt Disney","PG-13","Comedy"
"2348","4/13/1978","F.I.S.T",1.1e+07,20388920,20388920,"United Artists",NA,"Drama"
"2349","1/1/1975","Barry Lyndon",1.1e+07,2e+07,20169934,"Warner Bros.","PG","Drama"
"2350","1/11/2013","Quartet",1.1e+07,18388357,56178935,"Weinstein Co.","PG-13","Comedy"
"2351","11/21/2001","Out Cold",1.1e+07,13906394,14786394,"Walt Disney","PG-13","Comedy"
"2352","10/13/2000","The Ladies Man",1.1e+07,13592872,13719474,"Paramount Pictures","R","Comedy"
"2353","3/30/2001","Tomcats",1.1e+07,13558739,13558739,"Sony Pictures","R","Comedy"
"2354","12/6/2013","Inside Llewyn Davis",1.1e+07,13248209,32943247,"CBS Films","R","Drama"
"2355","2/19/1993","Army of Darkness",1.1e+07,11502976,21502976,"Universal","R","Horror"
"2356","11/12/2004","Kinsey",1.1e+07,10214647,17443529,"Fox Searchlight","R","Drama"
"2357","12/25/1993","What's Eating Gilbert Grape",1.1e+07,9170214,9170214,"Paramount Pictures","PG-13","Drama"
"2358","2/1/2002","Slackers",1.1e+07,4814244,5942218,"Sony Pictures","R","Comedy"
"2359","9/26/2003","The Gospel of John",1.1e+07,4068087,4234355,"ThinkFilm","PG-13","Drama"
"2360","10/10/2004","Vera Drake",1.1e+07,3753806,13353855,"Fine Line","R","Drama"
"2361","1/31/2003","The Guru",1.1e+07,3051221,24150550,"Universal","R","Comedy"
"2362","12/14/1995","Othello",1.1e+07,2844379,2844379,"Sony Pictures","R","Drama"
"2363","5/12/1995","The Perez Family",1.1e+07,2794056,2794056,"Goldwyn Entertainment","R","Comedy"
"2364","1/1/1970","The Molly Maguires",1.1e+07,2200000,2200000,NA,"PG","Drama"
"2365","1/1/1991","Return to the Blue Lagoon",1.1e+07,2e+06,2e+06,NA,"PG-13","Adventure"
"2366","9/7/2007","Romance and Cigarettes",1.1e+07,551002,3231251,"Borotoro","R","Comedy"
"2367","11/10/2006","Copying Beethoven",1.1e+07,355968,6586324,"MGM","PG-13","Drama"
"2368","8/26/2011","Brighton Rock",1.1e+07,229653,229653,"IFC Films","R","Drama"
"2369","5/4/2012","LOL",1.1e+07,0,10431506,"Lionsgate","PG-13","Comedy"
"2370","10/24/2008","Saw V",10800000,56746769,118209778,"Lionsgate","R","Horror"
"2371","5/25/2012","Les Intouchables",10800000,13182281,484873045,"Weinstein Co.","R","Comedy"
"2372","4/27/2007","Jindabyne",10800000,399879,2862544,"Sony Pictures Classics","R","Drama"
"2373","6/4/1982","Poltergeist",10700000,74706019,121706019,"MGM","PG","Horror"
"2374","6/18/1999","An Ideal Husband",10700000,18542974,31341183,"Miramax","PG-13","Comedy"
"2375","12/25/2004","Darkness",10600000,22163442,34409206,"Miramax/Dimension","PG-13","Horror"
"2376","6/11/1982","ET: The Extra-Terrestrial",10500000,435110554,792965326,"Universal","PG","Drama"
"2377","4/2/1968","2001: A Space Odyssey",10500000,58583410,70576492,"MGM","G","Adventure"
"2378","4/20/2007","In the Land of Women",10500000,11052958,14140402,"Warner Bros.","PG-13","Comedy"
"2379","2/20/2004","The Blue Butterfly",10400000,1610194,1610194,"Alliance Films","PG","Drama"
"2380","2/18/1983","Lovesick",10100000,10143618,10143618,"Warner Bros.",NA,"Comedy"
"2381","8/24/2007","September Dawn",10100000,1066555,1066555,"Black Diamond Pictures","R","Drama"
"2382","12/5/1997","Good Will Hunting",1e+07,138433435,225925989,"Miramax","R","Drama"
"2383","10/22/2004","The Grudge",1e+07,110359362,187281115,"Sony Pictures","PG-13","Horror"
"2384","8/26/2016","Don’t Breathe",1e+07,89217875,159047649,"Sony Pictures","R","Horror"
"2385","6/26/1981","Stripes",1e+07,85300000,85300000,"Columbia","R","Comedy"
"2386","10/27/2006","Saw III",1e+07,80238724,163876815,"Lionsgate","R","Horror"
"2387","7/1/2016","The Purge: Election Year",1e+07,79042440,118557124,"Universal","R","Horror"
"2388","5/18/2018","Book Club",1e+07,68566296,89643819,"Paramount Pictures","PG-13","Comedy"
"2389","8/25/2000","Bring it On",1e+07,68353550,90453550,"Universal","PG-13","Comedy"
"2390","10/26/2007","Saw IV",1e+07,63300095,135759694,"Lionsgate","R","Horror"
"2391","2/24/2006","Madea's Family Reunion",1e+07,63257940,63320521,"Lionsgate","PG-13","Comedy"
"2392","1/7/2005","White Noise",1e+07,56094360,92094360,"Universal","PG-13","Drama"
"2393","10/17/1986","The Color of Money",1e+07,52293000,52293000,"Walt Disney","R","Drama"
"2394","6/5/2015","Insidious Chapter 3",1e+07,52218558,120678444,"Focus Features","PG-13","Horror"
"2395","10/2/1992","The Mighty Ducks",1e+07,50752337,50752337,"Walt Disney","PG","Comedy"
"2396","11/3/2017","Lady Bird",1e+07,48958273,78610769,"A24","R","Drama"
"2397","5/4/2012","The Best Exotic Marigold Hotel",1e+07,46383639,134639780,"Fox Searchlight","PG-13","Comedy"
"2398","6/8/2018","Hereditary",1e+07,44069456,70090779,"A24","R","Horror"
"2399","3/16/2018","Love, Simon",1e+07,40826341,65521685,"20th Century Fox","PG-13","Drama"
"2400","2/17/1989","Bill & Ted's Excellent Adventure",1e+07,40485039,40485039,"Orion Pictures","PG","Adventure"
"2401","10/4/1962","The Longest Day",1e+07,39100000,50100000,"20th Century Fox","G","Action"
"2402","2/16/1996","Happy Gilmore",1e+07,38623460,41004412,"Universal","PG-13","Comedy"
"2403","10/27/2017","Jigsaw",1e+07,38052832,102499582,"Lionsgate","R","Horror"
"2404","8/31/2001","Jeepers Creepers",1e+07,37904175,58939035,"MGM","R","Horror"
"2405","6/28/1985","St. Elmo’s Fire",1e+07,37800000,37800000,"Sony Pictures","R","Drama"
"2406","2/16/2001","Recess: School's Out",1e+07,36696761,44451470,"Walt Disney","G","Adventure"
"2407","7/10/1985","Mad Max Beyond Thunderdome",1e+07,36230219,36230219,"Warner Bros.","PG-13","Action"
"2408","1/22/2016","The Boy",1e+07,35819556,68220952,"STX Entertainment","PG-13","Horror"
"2409","10/4/1985","Commando",1e+07,35073978,35073978,"20th Century Fox","R","Action"
"2410","5/19/2017","Everything, Everything",1e+07,34121140,61604439,"Warner Bros.","PG-13","Drama"
"2411","9/17/2010","Devil",1e+07,33679655,63354114,"Universal","PG-13","Horror"
"2412","11/22/2002","Friday After Next",1e+07,33253609,33526835,"New Line","R","Comedy"
"2413","3/22/1985","The Last Dragon",1e+07,3.3e+07,3.3e+07,"Sony Pictures",NA,"Action"
"2414","4/28/2017","How to Be a Latin Lover",1e+07,32149404,62556228,"Lionsgate","PG-13","Comedy"
"2415","3/6/1992","The Lawnmower Man",1e+07,32100816,32100816,"New Line","R","Action"
"2416","10/3/2008","Nick and Norah's Infinite Playlist",1e+07,31487293,33886017,"Sony Pictures","PG-13","Drama"
"2417","12/19/2003","Calendar Girls",1e+07,31011616,93074616,"Walt Disney","PG-13","Comedy"
"2418","11/12/1999","Dogma",1e+07,30651422,43948865,"Lionsgate","R","Comedy"
"2419","9/20/2002","The Banger Sisters",1e+07,30306281,38067218,"20th Century Fox","R","Comedy"
"2420","5/19/1989","Road House",1e+07,30050028,30050028,"United Artists","R","Action"
"2421","7/27/2018","Teen Titans Go! To The Movies",1e+07,29562341,51411600,"Warner Bros.","PG","Adventure"
"2422","6/24/1983","Twilight Zone: The Movie",1e+07,29500000,29500000,"Warner Bros.","PG","Horror"
"2423","11/23/1994","A Low Down Dirty Shame",1e+07,29317886,29317886,"Walt Disney","R","Action"
"2424","9/6/2002","Swimfan",1e+07,28564995,34084228,"20th Century Fox","PG-13","Drama"
"2425","10/6/2006","Employee of the Month",1e+07,28444855,38364855,"Lionsgate","PG-13","Comedy"
"2426","8/21/2015","Sinister 2",1e+07,27740955,54104225,"Focus Features","R","Horror"
"2427","3/25/1983","The Outsiders",1e+07,25697647,25697647,"Warner Bros.","PG-13","Drama"
"2428","6/12/1998","Can't Hardly Wait",1e+07,25358996,25358996,"Sony Pictures","PG-13","Comedy"
"2429","4/26/2013","Mud",1e+07,21590086,31556959,"Roadside Attractions","PG-13","Drama"
"2430","9/16/2016","Blair Witch",1e+07,20777061,37478274,"Lionsgate","R","Horror"
"2431","10/21/1983","The Dead Zone",1e+07,20766000,20766000,"Paramount Pictures",NA,"Horror"
"2432","2/2/2001","Valentine",1e+07,20384136,20384136,"Warner Bros.","R","Horror"
"2433","6/9/2006","A Prairie Home Companion",1e+07,20342852,26716191,"Picturehouse","PG-13","Comedy"
"2434","2/23/2007","Reno 911!: Miami",1e+07,20342161,21851362,"20th Century Fox","R","Comedy"
"2435","7/24/1998","Jane Austen's Mafia",1e+07,19843795,30143795,"Walt Disney","PG-13","Comedy"
"2436","2/25/1994","Sugar Hill",1e+07,18272447,18423914,"20th Century Fox","R","Drama"
"2437","6/20/2008","Kit Kittredge: An American Girl",1e+07,17657973,17657973,"Picturehouse","G","Drama"
"2438","9/27/1985","Invasion U.S.A.",1e+07,17536256,17536256,"Cannon","R","Action"
"2439","9/23/2005","Roll Bounce",1e+07,17380866,17433072,"Fox Searchlight","PG-13","Comedy"
"2440","1/19/1990","Tremors",1e+07,16667084,16667084,"Universal","PG-13","Action"
"2441","8/3/1990","Mo' Better Blues",1e+07,16153000,16153000,"Universal","R","Drama"
"2442","1/25/2002","Kung Pow: Enter the Fist",1e+07,16033556,17033556,"20th Century Fox","PG-13","Comedy"
"2443","10/7/2016","The Birth of a Nation",1e+07,15861566,16891011,"Fox Searchlight","R","Drama"
"2444","5/30/2003","Wrong Turn",1e+07,15417771,28649556,"20th Century Fox","R","Horror"
"2445","5/16/1980","The Long Riders",1e+07,15198912,15198912,"United Artists",NA,"Action"
"2446","3/12/1999","The Corruptor",1e+07,15164492,15164492,"New Line","R","Action"
"2447","8/14/2009","The Goods: Live Hard, Sell Hard",1e+07,15122676,15297318,"Paramount Vantage","R","Comedy"
"2448","11/23/2011","My Week with Marilyn",1e+07,14597405,34240572,"Weinstein Co.","R","Drama"
"2449","12/25/2014","Big Eyes",1e+07,14482031,27317872,"Weinstein Co.","PG-13","Drama"
"2450","6/28/2002","Hey Arnold! The Movie",1e+07,13684949,13684949,"Paramount Pictures","PG","Adventure"
"2451","3/14/1997","Love Jones",1e+07,12554569,12554569,"New Line","R","Drama"
"2452","1/20/2006","End of the Spear",1e+07,11748661,11924041,"M Power Releasing","PG-13","Drama"
"2453","10/20/2000","The Legend of Drunken Master",1e+07,11546543,11546543,"Miramax","R","Action"
"2454","7/23/1999","Drop Dead Gorgeous",1e+07,10571408,10571408,"New Line","PG-13","Comedy"
"2455","4/3/1998","The Spanish Prisoner",1e+07,10162034,13835130,"Sony Pictures Classics","PG","Drama"
"2456","6/11/1999","Le Violon rouge",1e+07,10019109,10019109,"Lionsgate","R","Drama"
"2457","7/9/2004","Sleepover",1e+07,9408183,9408183,"MGM","PG","Adventure"
"2458","1/25/2013","Movie 43",1e+07,8840453,31164747,"Relativity","R","Comedy"
"2459","5/21/2010","MacGruber",1e+07,8525600,8629895,"Universal","R","Comedy"
"2460","7/18/2003","Dirty Pretty Things",1e+07,8112414,14156753,"Miramax","R","Drama"
"2461","3/14/2014","Bad Words",1e+07,7779614,7843145,"Focus Features","R","Comedy"
"2462","3/27/2015","While We're Young",1e+07,7582065,14956484,"A24","R","Comedy"
"2463","2/1/2008","Over Her Dead Body",1e+07,7570127,21596074,"New Line","PG-13","Comedy"
"2464","10/24/2001","Bones",1e+07,7316658,8378853,"New Line","R","Horror"
"2465","2/11/2011","Cedar Rapids",1e+07,6861102,7862131,"Fox Searchlight","R","Comedy"
"2466","11/30/2012","The Collection",1e+07,6810754,8890094,"LD Distribution","R","Horror"
"2467","10/30/1998","American History X",1e+07,6719864,6719864,"New Line","R","Drama"
"2468","1/16/2004","Teacher's Pet: The Movie",1e+07,6491969,6491969,"Walt Disney","PG","Adventure"
"2469","10/15/1999","The Straight Story",1e+07,6197866,6197866,"Walt Disney","G","Drama"
"2470","5/3/2002","Deuces Wild",1e+07,6044618,6244618,"MGM","R","Drama"
"2471","3/28/2008","Run, Fatboy, Run",1e+07,6003262,33512260,"Picturehouse","PG-13","Comedy"
"2472","12/18/1981","Heartbeeps",1e+07,6e+06,6e+06,"Universal",NA,"Comedy"
"2473","3/20/2015","Danny Collins",1e+07,5637066,7501132,"Bleecker Street","R","Comedy"
"2474","7/4/2007","Rescue Dawn",1e+07,5490423,7037886,"MGM","PG-13","Action"
"2475","4/5/2000","Black and White",1e+07,5241315,5241315,"Sony Pictures","R","Drama"
"2476","6/18/2010","Io sono l’amore",1e+07,5005465,15121528,"Magnolia Pictures","R","Drama"
"2477","6/15/2018","Gotti",1e+07,4286367,6089100,"Vertical Entertainment","R","Drama"
"2478","3/16/2012","Jeff, Who Lives at Home",1e+07,4269426,4708127,"Paramount Vantage","R","Comedy"
"2479","9/30/2016","Denial",1e+07,4073448,9263940,"Bleecker Street","PG-13","Drama"
"2480","3/30/2016","Everybody Wants Some",1e+07,3400278,5437126,"Paramount Pictures","R","Comedy"
"2481","10/4/1996","Crash",1e+07,3357324,3357324,"Fine Line","R","Drama"
"2482","10/12/2012","Atlas Shrugged: Part II",1e+07,3336053,3336053,"Atlas Distribution","PG-13","Drama"
"2483","2/4/1994","Romeo Is Bleeding",1e+07,3275585,3275585,"Gramercy","R","Drama"
"2484","10/8/1999","The Limey",1e+07,3193102,6030047,"Artisan","R","Drama"
"2485","11/14/2014","Rosewater",1e+07,3128941,3185717,"Open Road","R","Drama"
"2486","12/22/2000","The House of Mirth",1e+07,3041803,5149131,"Sony Pictures Classics","PG","Drama"
"2487","5/1/1987","Malone",1e+07,3e+06,3e+06,"Orion Pictures","R","Action"
"2488","6/2/2006","Peaceful Warrior",1e+07,2893666,3260179,"Universal","PG-13","Drama"
"2489","9/9/2011","Bucky Larson: Born to Be a Star",1e+07,2529395,2529395,"Sony Pictures","R","Comedy"
"2490","10/6/2000","Bamboozled",1e+07,2185266,2373937,"New Line","R","Drama"
"2491","5/3/2013","The Iceman",1e+07,1930282,3623609,"Alchemy","R","Drama"
"2492","4/21/2017","Free Fire",1e+07,1799322,3793739,"A24","R","Action"
"2493","6/24/2011","A Better Life",1e+07,1759252,1884251,"Summit Entertainment","PG-13","Drama"
"2494","2/28/2003","Spider",1e+07,1641788,1641788,"Sony Pictures Classics","R","Drama"
"2495","12/27/2002","Nicholas Nickleby",1e+07,1562800,1562800,"United Artists","PG","Drama"
"2496","3/21/2014","50 to 1",1e+07,1069454,1069454,"Ten Furlongs","PG-13","Drama"
"2497","5/2/2003","Owning Mahowny",1e+07,1011054,1011054,"Sony Pictures Classics","R","Drama"
"2498","10/19/2007","The Ten Commandments",1e+07,952820,1051907,"Rocky Mountain Pict…","PG","Adventure"
"2499","9/7/2007","The Brothers Solomon",1e+07,900926,900926,"Sony Pictures","R","Comedy"
"2500","4/4/2008","My Blueberry Nights",1e+07,866778,22198996,"Weinstein Co.","PG-13","Drama"
"2501","8/6/1999","Illuminata",1e+07,836641,836641,"Artisan","R","Drama"
"2502","1/20/2012","Coriolanus",1e+07,749641,2179623,"Weinstein Co.","R","Drama"
"2503","10/4/2013","Parkland",1e+07,641439,1616353,"Exclusive Releasing","PG-13","Drama"
"2504","4/2/2004","Shaolin Soccer",1e+07,488872,42776032,"Miramax","PG-13","Comedy"
"2505","9/14/2007","King of California",1e+07,268461,1165102,"First Look","PG-13","Drama"
"2506","10/24/1997","Rien ne va plus",1e+07,245359,5045359,"New Yorker",NA,"Comedy"
"2507","8/14/1998","La femme de chambre du Titanic",1e+07,244465,244465,"MGM",NA,"Drama"
"2508","12/17/2004","Imaginary Heroes",1e+07,228524,290875,"Sony Pictures Classics","R","Drama"
"2509","5/3/2013","Cinco de Mayo, La Batalla",1e+07,173472,173472,"Lionsgate","R","Action"
"2510","10/29/2010","Welcome to the Rileys",1e+07,152857,355919,"Samuel Goldwyn Films","R","Drama"
"2511","9/9/2016","Kicks",1e+07,150191,150191,"Focus World","R","Adventure"
"2512","6/1/2012","High School",1e+07,139034,248133,"Anchor Bay Entertai…","R","Comedy"
"2513","5/18/2007","Severance",1e+07,137221,5950002,"Magnolia Pictures","R","Comedy"
"2514","4/23/2010","Joheunnom nabbeunnom isanghannom",1e+07,128486,42226657,NA,"R","Action"
"2515","8/26/1994","Police Academy 7: Mission to Moscow",1e+07,126247,126247,"Warner Bros.","PG","Comedy"
"2516","2/19/2010","Blood Done Sign My Name",1e+07,109383,109383,"Paladin","PG-13","Drama"
"2517","10/23/2009","Motherhood",1e+07,93388,723388,"Freestyle Releasing","PG-13","Comedy"
"2518","10/15/2004","Eulogy",1e+07,70527,70527,"Artisan","R","Comedy"
"2519","11/7/2014","Elsa & Fred",1e+07,67657,109144,"Alchemy","PG-13","Comedy"
"2520","8/28/2009","The Open Road",1e+07,19716,19716,"Anchor Bay Entertai…","PG-13","Drama"
"2521","7/10/2015","Strangerland",1e+07,17472,161097,"Alchemy","R","Drama"
"2522","10/16/2009","Janky Promoters",1e+07,9069,9069,"Third Rail","R","Comedy"
"2523","12/21/2007","Blonde Ambition",1e+07,6422,1537479,"First Look","PG-13","Comedy"
"2524","10/8/2010","It's a Wonderful Afterlife",1e+07,0,1642939,"UTV Communications","PG-13","Comedy"
"2525","8/21/2009","Fifty Dead Men Walking",1e+07,0,997921,"Phase 4 Films","R","Drama"
"2526","9/26/2014","Plastic",1e+07,0,575371,"ARC Entertainment","R","Action"
"2527","2/2/2007","Partition",1e+07,0,0,NA,NA,"Drama"
"2528","4/13/2012","Detention",1e+07,0,0,"Samuel Goldwyn Films","R","Comedy"
"2529","2/7/2014","Nurse 3D",1e+07,0,0,"Lionsgate","R","Horror"
"2530","7/21/2015","American Heist",1e+07,0,0,"Lionsgate","R","Action"
"2531","12/19/2012","Amour",9700000,6738954,36787044,"Sony Pictures Classics","PG-13","Drama"
"2532","4/28/2006","The Lost City",9600000,2484186,5256839,"Magnolia Pictures","R","Drama"
"2533","1/12/2000","Next Friday",9500000,57176582,59675307,"New Line","R","Comedy"
"2534","6/13/1967","You Only Live Twice",9500000,43100000,111600000,"MGM","PG","Action"
"2535","6/10/1988","Poltergeist III",9500000,14114000,14114000,"MGM","PG-13","Horror"
"2536","3/19/2010","The Runaways",9500000,3573673,5278632,"Apparition","R","Drama"
"2537","10/30/2009","Gentlemen Broncos",9500000,115155,119955,"Fox Searchlight","PG-13","Comedy"
"2538","11/7/1963","It's a Mad Mad Mad Mad World",9400000,46300000,6e+07,NA,NA,"Comedy"
"2539","11/3/2006","Volver",9400000,12899867,87226613,"Sony Pictures Classics","R","Comedy"
"2540","8/7/1981","Heavy Metal",9300000,19571091,19571091,"Sony Pictures",NA,"Adventure"
"2541","12/29/1995","Richard III",9200000,2684904,4199334,"MGM","R","Drama"
"2542","5/25/1979","Alien",9e+06,80930630,203630630,"20th Century Fox","R","Horror"
"2543","12/29/1965","Thunderball",9e+06,63600000,141200000,"MGM","PG","Action"
"2544","11/6/1996","Set It Off",9e+06,36049108,36049108,"New Line","R","Drama"
"2545","10/21/2016","Ouija: Origin of Evil",9e+06,35144505,81831866,"Universal","PG-13","Horror"
"2546","11/9/1988","Child's Play",9e+06,33244684,44196684,"United Artists","R","Horror"
"2547","1/30/2015","Black or White",9e+06,21571189,21971021,"Relativity","PG-13","Drama"
"2548","7/30/2004","Harold & Kumar Go to White Castle",9e+06,18225165,18225165,"New Line","R","Comedy"
"2549","10/13/2000","The Contender",9e+06,17804273,17804273,"Dreamworks SKG","R","Drama"
"2550","2/18/2000","Boiler Room",9e+06,16963963,28773637,"New Line","R","Drama"
"2551","12/5/2006","Black Christmas",9e+06,16235738,16235738,"MGM","R","Horror"
"2552","11/18/2016","The Edge of Seventeen",9e+06,14431633,19096003,"STX Entertainment","R","Drama"
"2553","12/2/2016","Jackie",9e+06,13960394,29345883,"Fox Searchlight","R","Drama"
"2554","3/16/1984","The Ice Pirates",9e+06,13075390,13075390,"MGM/UA Classics",NA,"Comedy"
"2555","11/8/1989","Henry V",9e+06,10161099,10176701,"Goldwyn Entertainment","PG-13","Action"
"2556","11/4/2016","Loving",9e+06,7710234,12898064,"Focus Features","PG-13","Drama"
"2557","11/28/2007","The Savages",9e+06,6623082,10642023,"Fox Searchlight","R","Drama"
"2558","4/16/2003","Chasing Papi",9e+06,6126237,12657377,"20th Century Fox","PG","Comedy"
"2559","9/8/2000","The Way of the Gun",9e+06,6047856,13061935,"Artisan","R","Action"
"2560","8/22/2008","Hamlet 2",9e+06,4886216,4934104,"Focus Features","R","Comedy"
"2561","9/13/2002","Igby Goes Down",9e+06,4777465,4777465,"MGM","R","Comedy"
"2562","4/29/1994","PCU",9e+06,4333569,4333569,"20th Century Fox","PG-13","Comedy"
"2563","3/9/2007","The Ultimate Gift",9e+06,3438735,3438735,"Film Foundry","PG","Drama"
"2564","9/29/2000","Beautiful",9e+06,3134509,3134509,"Destination Films","PG-13","Drama"
"2565","6/1/2007","Gracie",9e+06,2956339,3922043,"Picturehouse","PG-13","Drama"
"2566","8/26/2016","Greater",9e+06,2000093,2000093,"Hammond Entertainment","PG","Drama"
"2567","8/18/2006","Trust the Man",9e+06,1530535,2548378,"Fox Searchlight","R","Comedy"
"2568","5/14/2010","Princess Kaiulani",9e+06,883887,883887,"Roadside Attractions","PG","Drama"
"2569","5/6/2016","Dheepan",9e+06,248795,7704357,"Sundance Selects","R","Drama"
"2570","10/25/2002","All or Nothing",9e+06,184255,184255,"MGM","R","Drama"
"2571","11/22/2006","Opal Dream",9e+06,14443,14443,"Strand","PG","Drama"
"2572","5/8/2015","Skin Trade",9e+06,1242,1242,"Magnolia Pictures","R","Action"
"2573","1/20/2015","Veronika Decides to Die",9e+06,0,2243,"Entertainment One","R","Drama"
"2574","10/10/1968","Barbarella",9e+06,0,0,"Paramount Pictures","PG","Adventure"
"2575","2/26/2011","Ultramarines",8900000,0,0,"Codex Pictures","R","Action"
"2576","9/26/1986","Crocodile Dundee",8800000,174803506,328203506,"Paramount Pictures","PG-13","Comedy"
"2577","11/18/2016","Manchester by the Sea",8500000,47695371,77733867,"Roadside Attractions","R","Drama"
"2578","12/16/2009","Crazy Heart",8500000,39471742,47417251,"Fox Searchlight","R","Drama"
"2579","8/15/2008","Star Wars: The Clone Wars",8500000,35161554,68695443,"Warner Bros.","PG","Adventure"
"2580","2/20/2015","The DUFF",8500000,34030343,43528634,"CBS Films","PG-13","Comedy"
"2581","7/31/1987","The Lost Boys",8500000,32222567,32222567,"Warner Bros.","R","Horror"
"2582","11/7/1979","The Rose",8500000,29200000,29200000,"20th Century Fox",NA,"Drama"
"2583","3/1/1991","Haakon Haakonsen",8500000,15024232,15024232,"Walt Disney","PG","Adventure"
"2584","3/9/2007","The Namesake",8500000,13610521,20288774,"Fox Searchlight","PG-13","Drama"
"2585","2/27/2004","Club Dread",8500000,5001655,7573551,"Fox Searchlight","R","Comedy"
"2586","9/17/2009","Bright Star",8500000,4444637,17220091,"Apparition","PG","Drama"
"2587","6/13/2014","The Rover",8500000,1109199,3180252,"A24","R","Drama"
"2588","11/1/2016","A.C.O.R.N.S.: Operation Crackdown",8500000,0,1353287,"Viva Entertainment","PG","Adventure"
"2589","2/12/2010","My Name is Khan",8470000,4046336,42355526,"Fox Searchlight","PG-13","Drama"
"2590","6/4/1999","Limbo",8300000,2160710,2598224,"Sony Pictures","R","Drama"
"2591","4/16/2010","The City of Your Final Destination",8300000,493296,1353296,"Hyde Park Films","PG-13","Drama"
"2592","11/24/2006","Kurtlar vadisi - Irak",8300000,0,24906717,NA,NA,"Action"
"2593","10/14/1994","Pulp Fiction",8e+06,107928762,212928762,"Miramax","R","Drama"
"2594","6/22/1984","The Karate Kid",8e+06,90815558,90815558,"Sony Pictures","PG","Action"
"2595","6/22/1979","The Muppet Movie",8e+06,76657000,76657000,"Associated Film Dis…","G","Adventure"
"2596","3/9/1984","Splash",8e+06,62599495,62599495,"Walt Disney","PG","Comedy"
"2597","7/26/2006","Little Miss Sunshine",8e+06,59891098,100642353,"Fox Searchlight","R","Comedy"
"2598","9/17/2010","Easy A",8e+06,58401464,76200721,"Sony Pictures","PG-13","Comedy"
"2599","8/8/1986","Stand by Me",8e+06,52287414,52287414,"Sony Pictures","R","Drama"
"2600","6/27/2003","28 Days Later…",8e+06,45064915,82955633,"Fox Searchlight","R","Horror"
"2601","6/22/1979","Escape from Alcatraz",8e+06,4.3e+07,4.3e+07,"Paramount Pictures","PG","Drama"
"2602","1/30/2004","You Got Served",8e+06,40066497,50811858,"Sony Pictures","PG-13","Drama"
"2603","3/13/1992","Howards End",8e+06,26124872,26317943,"Sony Pictures Classics","PG","Drama"
"2604","3/21/2008","Shutter",8e+06,25928550,47782426,"20th Century Fox","PG-13","Horror"
"2605","12/25/1981","Modern Problems",8e+06,24474312,24474312,"20th Century Fox",NA,"Comedy"
"2606","12/18/1969","On Her Majesty's Secret Service",8e+06,22800000,8.2e+07,"MGM","PG","Action"
"2607","11/10/1982","Creepshow",8e+06,20036244,20036244,"Warner Bros.",NA,"Horror"
"2608","4/28/2006","Akeelah and the Bee",8e+06,18848430,18959424,"Lionsgate","PG","Drama"
"2609","10/14/1994","Wes Craven's New Nightmare",8e+06,18090181,18090181,"New Line","R","Horror"
"2610","10/1/1999","Drive Me Crazy",8e+06,17843379,22591451,"20th Century Fox","PG-13","Comedy"
"2611","9/18/2013","Enough Said",8e+06,17550872,25621449,"Fox Searchlight","PG-13","Comedy"
"2612","1/16/1998","Half Baked",8e+06,17394881,17394881,"Universal","R","Comedy"
"2613","6/27/2014","Begin Again",8e+06,16170632,68838736,"Weinstein Co.","R","Drama"
"2614","5/19/2006","See No Evil",8e+06,15032800,18828036,"Lionsgate","R","Horror"
"2615","8/7/2002","The Good Girl",8e+06,14018296,16585503,"Fox Searchlight","R","Drama"
"2616","4/29/2011","Prom",8e+06,10130219,10763183,"Walt Disney","PG","Comedy"
"2617","4/22/1994","The Inkwell",8e+06,8864699,8864699,"Walt Disney","R","Comedy"
"2618","12/29/2000","Shadow of the Vampire",8e+06,8279017,8279017,"Lionsgate","R","Drama"
"2619","6/12/2015","Me and Earl and the Dying Girl",8e+06,6758416,9266180,"Fox Searchlight","PG-13","Drama"
"2620","10/8/2010","It's Kind of a Funny Story",8e+06,6363628,6632950,"Focus Features","PG-13","Comedy"
"2621","5/12/2000","Held Up",8e+06,4714090,4714090,"Trimark","PG-13","Comedy"
"2622","12/30/2015","Anomalisa",8e+06,3759286,5538273,"Paramount Pictures","R","Adventure"
"2623","12/23/2005","Caché",8e+06,3647381,19891331,"Sony Pictures Classics","R","Drama"
"2624","12/29/2010","Another Year",8e+06,3205706,20005613,"Sony Pictures Classics","PG-13","Drama"
"2625","1/1/1991","Showdown in Little Tokyo",8e+06,2275557,2275557,NA,"R","Action"
"2626","11/19/2010","Made in Dagenham",8e+06,1095369,15644196,"Sony Pictures Classics","R","Drama"
"2627","1/24/1997","Prefontaine",8e+06,590817,590817,"Walt Disney","PG-13","Drama"
"2628","10/28/1983","The Wicked Lady",8e+06,589308,589308,"Cannon","R","Drama"
"2629","5/11/2007","Brooklyn Rules",8e+06,458232,458232,"Lionsgate","R","Drama"
"2630","10/24/2003","The Singing Detective",8e+06,336456,524747,"Paramount Pictures","R","Comedy"
"2631","6/15/2007","Fido",8e+06,298110,456814,"Lionsgate","R","Horror"
"2632","9/16/2011","Restless",8e+06,163753,2772511,"Sony Pictures Classics","PG-13","Drama"
"2633","5/18/2007","The Wendell Baker Story",8e+06,127188,127188,"ThinkFilm","PG-13","Comedy"
"2634","10/29/2010","Wild Target",8e+06,109338,5314194,"Freestyle Releasing","PG-13","Comedy"
"2635","5/22/2015","Aloft",8e+06,53086,53086,"Sony Pictures Classics","R","Drama"
"2636","10/14/2011","Fireflies in the Garden",8e+06,36884,3587191,NA,"R","Drama"
"2637","4/27/2001","Akira",8e+06,19585,19585,NA,"R","Action"
"2638","9/29/2017","Don Gato, el inicio de la pandilla",8e+06,0,4598934,"Viva Entertainment","PG","Adventure"
"2639","11/30/2007","Maurice Richard",8e+06,0,0,"Palm Pictures","PG","Drama"
"2640","5/6/2016","Code of Honor",8e+06,0,0,"Lionsgate Premiere","R","Action"
"2641","2/23/1990","The Blood of Heroes",7700000,882290,882290,"New Line",NA,"Action"
"2642","12/13/1989","Driving Miss Daisy",7500000,106593296,106593296,"Warner Bros.","PG","Drama"
"2643","9/26/1997","Soul Food",7500000,43492389,43492389,"20th Century Fox","R","Comedy"
"2644","2/23/1996","Rumble in the Bronx",7500000,32281907,36238752,"New Line","R","Action"
"2645","6/8/2007","Hostel: Part II",7500000,17544812,33606409,"Lionsgate","R","Horror"
"2646","10/9/2009","An Education",7500000,12574914,29652736,"Sony Pictures Classics","PG-13","Drama"
"2647","9/4/2009","Extract",7500000,10823158,10849158,"Miramax","R","Comedy"
"2648","10/21/2005","Shopgirl",7500000,10284523,11758418,"Walt Disney","R","Drama"
"2649","3/9/1984","The Hotel New Hampshire",7500000,5142858,5142858,"Orion Pictures",NA,"Drama"
"2650","3/8/2002","Men with Brooms",7500000,4239767,4239767,"Artisan","R","Comedy"
"2651","2/22/2008","Witless Protection",7500000,4151836,4151836,"Lionsgate","PG-13","Comedy"
"2652","11/24/2004","The Work and the Glory",7500000,3347647,3347647,"Excel Entertainment","PG","Drama"
"2653","12/21/2011","Albert Nobbs",7500000,3014696,8539003,"Roadside Attractions","R","Drama"
"2654","6/24/2016","The Neon Demon",7500000,1333124,3559803,"Broad Green Pictures","R","Horror"
"2655","7/24/2003","Masked and Anonymous",7500000,533344,555335,"Sony Pictures","PG-13","Drama"
"2656","4/13/2018","Borg vs McEnroe",7500000,231346,3257078,"Neon","R","Drama"
"2657","5/15/2015","Pound of Flesh",7500000,0,0,"Entertainment One","R","Action"
"2658","12/25/2007","Persepolis",7300000,4443403,25397460,"Sony Pictures Classics","PG-13","Drama"
"2659","5/27/2011","Die Welle",7250000,0,35122948,"IFC Films",NA,"Drama"
"2660","10/15/1999","The Omega Code",7200000,12610552,12678312,"Providence Entertai…","PG-13","Action"
"2661","12/5/2007","Juno",7e+06,143495265,231450102,"Fox Searchlight","PG-13","Comedy"
"2662","3/15/1972","The Godfather",7e+06,134966411,268500000,"Paramount Pictures","R","Drama"
"2663","6/29/2012","Magic Mike",7e+06,113721571,170549753,"Warner Bros.","R","Comedy"
"2664","4/15/1983","Flashdance",7e+06,90463574,201463574,"Paramount Pictures","R","Drama"
"2665","3/16/2018","I Can Only Imagine",7e+06,83482352,85430011,"Roadside Attractions","PG","Drama"
"2666","11/12/1993","The Piano",7e+06,40157856,40168957,"Miramax","R","Drama"
"2667","6/27/1973","Live and Let Die",7e+06,35400000,161800000,"MGM","PG","Action"
"2668","1/12/2000","My Dog Skip",7e+06,34099640,35795319,"Warner Bros.","PG","Drama"
"2669","1/24/2003","Darkness Falls",7e+06,32539681,47289758,"Sony Pictures","PG-13","Horror"
"2670","10/7/2005","Good Night, and Good Luck",7e+06,31501218,56586901,"Warner Independent","PG","Drama"
"2671","9/30/2005","Capote",7e+06,28750530,49924079,"Sony Pictures Classics","R","Drama"
"2672","3/29/1974","The Great Gatsby",7e+06,26533200,26533200,NA,NA,"Drama"
"2673","8/25/1995","Desperado",7e+06,25532388,25532388,"Sony Pictures","R","Action"
"2674","4/11/2001","Kingdom Come",7e+06,23247539,23393939,"Fox Searchlight","PG","Comedy"
"2675","12/20/1974","The Man with the Golden Gun",7e+06,2.1e+07,97600000,"MGM","PG","Action"
"2676","2/12/1988","Action Jackson",7e+06,20257000,20257000,"Lorimar Motion Pict…","R","Action"
"2677","5/13/1983","Breathless",7e+06,19910002,19910002,"Orion Pictures","R","Action"
"2678","6/19/2015","Dope",7e+06,17506470,18190831,"Open Road","R","Comedy"
"2679","7/22/2005","The Devil's Rejects",7e+06,17044981,20940428,"Lionsgate","R","Horror"
"2680","1/17/2014","Devil's Due",7e+06,15821461,36146087,"20th Century Fox","R","Horror"
"2681","3/22/1996","Flirting with Disaster",7e+06,14853474,16149180,"Miramax","R","Comedy"
"2682","11/14/2014","Beyond the Lights",7e+06,14618727,14618727,"Relativity","PG-13","Drama"
"2683","7/31/1992","Buffy the Vampire Slayer",7e+06,14231669,14231669,"20th Century Fox","PG-13","Horror"
"2684","8/25/1999","In Too Deep",7e+06,14026509,15471229,"Gramercy","R","Drama"
"2685","4/11/2003","House of 1,000 Corpses",7e+06,12634962,17005466,"Lionsgate","R","Horror"
"2686","10/11/1985","Silver Bullet",7e+06,10803211,10803211,"Paramount Pictures","R","Horror"
"2687","10/10/2003","House of the Dead",7e+06,10199354,13767816,"Artisan","R","Horror"
"2688","10/2/2009","A Serious Man",7e+06,9228788,30360570,"Focus Features","R","Comedy"
"2689","12/11/2009","A Single Man",7e+06,9176000,28142379,"Weinstein Co.","R","Drama"
"2690","1/10/1991","Warlock",7e+06,8824553,8824553,"Trimark","R","Horror"
"2691","8/12/1988","The Last Temptation of Christ",7e+06,8373585,8373585,"Universal",NA,"Drama"
"2692","6/18/2010","Cyrus",7e+06,7468936,10062896,"Fox Searchlight","R","Comedy"
"2693","9/1/1999","Outside Providence",7e+06,7309628,7824358,"Miramax","R","Comedy"
"2694","11/29/2002","Rabbit-Proof Fence",7e+06,6199600,16866928,"Miramax","PG","Drama"
"2695","7/27/2007","Who's Your Caddy?",7e+06,5694308,5694308,"MGM","PG-13","Comedy"
"2696","5/1/1992","Split Second",7e+06,5430822,5430822,"InterStar Releasing","R","Action"
"2697","12/14/2001","The Other Side of Heaven",7e+06,4720371,4720371,"Excel Entertainment","PG","Drama"
"2698","9/28/1990","Dark Angel",7e+06,4372561,4372561,"Triumph Releasing",NA,"Action"
"2699","6/27/1986","American Anthem",7e+06,3571624,3571624,"Sony Pictures","PG-13","Drama"
"2700","5/2/2008","Redbelt",7e+06,2344847,2667084,"Sony Pictures Classics","R","Action"
"2701","8/27/1999","A Dog of Flanders",7e+06,2165637,2165637,"Warner Bros.","PG","Drama"
"2702","10/18/2002","Auto Focus",7e+06,2062066,2703821,"Sony Pictures Classics","R","Drama"
"2703","10/21/2011","The Mighty Macs",7e+06,1891936,1891936,"Quaker Media","G","Drama"
"2704","12/22/2010","Somewhere",7e+06,1785645,17023121,"Focus Features","R","Drama"
"2705","1/13/2012","We Need to Talk About Kevin",7e+06,1738692,10765283,"Oscilloscope Pictures","R","Drama"
"2706","2/2/2007","Factory Girl",7e+06,1661464,1661464,"MGM","R","Drama"
"2707","11/15/2013","The Christmas Candle",7e+06,1632000,1933829,"Echolight Studios","PG","Adventure"
"2708","9/25/2009","I Hope They Serve Beer in Hell",7e+06,1429299,1429453,"Freestyle Releasing","R","Comedy"
"2709","4/8/1983","Losin' It",7e+06,1246141,1246141,NA,"R","Comedy"
"2710","5/7/2010","Mother and Child",7e+06,1110509,6537179,"Sony Pictures Classics","R","Drama"
"2711","7/12/1996","Les Visiteurs",7e+06,659000,98754000,"Miramax","R","Comedy"
"2712","10/2/2015","Freeheld",7e+06,546201,1732228,"Lionsgate","PG-13","Drama"
"2713","4/2/2014","Dom Hemingway",7e+06,523511,1857458,"Fox Searchlight","R","Comedy"
"2714","7/30/2010","The Extra Man",7e+06,453377,492108,"Magnolia Pictures","R","Comedy"
"2715","5/13/2011","Hesher",7e+06,382946,382946,"Wrekin Hill Enterta…","R","Drama"
"2716","3/13/1998","Chairman of the Board",7e+06,306715,306715,"Trimark","PG-13","Comedy"
"2717","2/14/2003","Gerry",7e+06,254683,719699,"ThinkFilm","R","Drama"
"2718","1/21/2000","The Boondock Saints",7e+06,30471,411874,"Indican Pictures","R","Action"
"2719","12/12/2008","The Kings of Appletown",7e+06,0,0,NA,"PG","Action"
"2720","9/21/2012","House at the End of the Street",6900000,31611916,44103982,"Relativity","PG-13","Horror"
"2721","9/24/1993","Dazed and Confused",6900000,7950889,7950889,"Universal","R","Comedy"
"2722","9/17/2010","Incendies",6800000,6857096,16038343,"Sony Pictures Classics","R","Drama"
"2723","8/5/2005","The Chumscrubber",6800000,49526,49526,"Picturehouse","R","Drama"
"2724","9/19/2008","Tropa de Elite",6537890,8744,14319195,"IFC Films","R","Action"
"2725","10/3/2014","Annabelle",6500000,84273813,256862920,"Warner Bros.","R","Horror"
"2726","7/12/1991","Boyz n the Hood",6500000,56190094,56215095,"Sony Pictures","R","Drama"
"2727","7/24/1987","La Bamba",6500000,54215416,54215416,"Sony Pictures","PG-13","Drama"
"2728","5/22/1981","The Four Seasons",6500000,42488161,42488161,"Universal","PG","Comedy"
"2729","4/2/1993","The Adventures of Huck Finn",6500000,24103594,24103594,"Walt Disney","PG","Adventure"
"2730","4/7/2006","Friends with Money",6500000,13368437,18110152,"Sony Pictures Classics","R","Comedy"
"2731","10/22/1999","Bats",6500000,10155691,10155691,"Sony Pictures","R","Horror"
"2732","3/7/2003","Nowhere in Africa",6500000,6173485,6173485,"Zeitgeist","R","Drama"
"2733","5/31/2013","The East",6500000,2274649,3027956,"Fox Searchlight","PG-13","Drama"
"2734","11/13/2009","The Messenger",6500000,1109660,1744952,"Oscilloscope Pictures","R","Drama"
"2735","7/23/2004","A Home at the End of the World",6500000,1029017,1033810,"Warner Independent","R","Drama"
"2736","10/26/1984","The Terminator",6400000,38019031,78019031,"Orion Pictures","R","Action"
"2737","2/27/2004","Good Bye, Lenin!",6400000,4063859,79384539,"Sony Pictures Classics","R","Comedy"
"2738","10/10/2007","Control",6400000,871577,8902141,"Weinstein Co.","R","Drama"
"2739","10/9/2009","The Damned United",6400000,449865,4199874,"Sony Pictures Classics","R","Drama"
"2740","2/22/2008","Die Fälscher",6250000,5488570,20199663,"Sony Pictures Classics","R","Drama"
"2741","1/15/1988","Return of the Living Dead Part II",6200000,9205924,9205924,"Lorimar Motion Pict…",NA,"Horror"
"2742","10/20/1995","Mallrats",6100000,2108367,2108367,"Gramercy","R","Comedy"
"2743","12/19/1986","Platoon",6e+06,137963328,137978395,"Orion Pictures","R","Drama"
"2744","9/19/1980","Ordinary People",6e+06,52302978,52302978,"Paramount Pictures","R","Drama"
"2745","10/17/1956","Around the World in 80 Days",6e+06,4.2e+07,4.2e+07,"United Artists","PG","Adventure"
"2746","7/25/1980","Caddyshack",6e+06,39846344,39846344,"Warner Bros.",NA,"Comedy"
"2747","3/23/2001","The Brothers",6e+06,27457409,27958191,"Sony Pictures","R","Comedy"
"2748","12/17/2008","The Wrestler",6e+06,26238243,46634275,"Fox Searchlight","R","Drama"
"2749","6/30/1989","Do the Right Thing",6e+06,26004026,26004026,"Universal","R","Comedy"
"2750","7/10/1981","Escape from New York",6e+06,25244700,25244700,"Avco Embassy","R","Action"
"2751","7/16/1999","The Wood",6e+06,25059640,25059640,"Paramount Pictures","R","Comedy"
"2752","8/16/1995","The Usual Suspects",6e+06,23341568,34449356,"Gramercy","R","Drama"
"2753","4/5/2002","National Lampoon’s Van Wilder",6e+06,21305259,39241323,"Artisan","R","Comedy"
"2754","9/27/2000","Best in Show",6e+06,18621249,20695413,"Warner Bros.","PG-13","Comedy"
"2755","9/27/2006","The Last King of Scotland",6e+06,17606684,49155371,"Fox Searchlight","R","Drama"
"2756","4/16/2003","A Mighty Wind",6e+06,17583468,18504539,"Warner Bros.","PG-13","Comedy"
"2757","2/12/1988","School Daze",6e+06,14545844,14545844,"Sony Pictures","R","Drama"
"2758","8/8/2007","Daddy Day Camp",6e+06,13235267,18209872,"Sony Pictures","PG","Comedy"
"2759","10/21/1988","Mystic Pizza",6e+06,12793213,12793213,"Samuel Goldwyn Films","R","Comedy"
"2760","3/20/1998","Mr. Nice Guy",6e+06,12716953,31716953,"New Line","PG-13","Action"
"2761","4/24/1998","Sliding Doors",6e+06,11911200,58809149,"Miramax","PG-13","Drama"
"2762","5/24/1995","Tales from the Hood",6e+06,11784569,11784569,"Savoy","R","Horror"
"2763","9/7/2012","The Words",6e+06,11494838,16369708,"CBS Films","PG-13","Drama"
"2764","12/15/2000","Pollock",6e+06,8596914,10557291,"Sony Pictures Classics","R","Drama"
"2765","3/19/2010","City Island",6e+06,6671283,8173486,"Anchor Bay Entertai…","PG-13","Comedy"
"2766","3/16/2012","Casa de mi Padre",6e+06,5909483,8446952,"Lionsgate","R","Comedy"
"2767","7/29/2011","The Guard",6e+06,5359774,21197454,"Sony Pictures Classics","R","Comedy"
"2768","8/29/2008","College",6e+06,4694491,6176114,"MGM","R","Comedy"
"2769","9/22/2006","La science des rêves",6e+06,4670644,15137932,"Warner Independent","R","Comedy"
"2770","3/13/2009","Miss March",6e+06,4543320,4713059,"20th Century Fox","R","Comedy"
"2771","7/18/2014","Wish I Was Here",6e+06,3591299,6591365,"Focus Features","R","Comedy"
"2772","12/21/2006","Venus",6e+06,3347411,7818479,"Miramax","R","Drama"
"2773","3/14/2014","Veronica Mars",6e+06,3322127,3485383,"Warner Bros.","PG-13","Drama"
"2774","10/31/2003","Shattered Glass",6e+06,2207975,3456602,"Lionsgate","PG-13","Drama"
"2775","7/3/2008","The Wackness",6e+06,2077046,3330012,"Sony Pictures Classics","R","Comedy"
"2776","11/16/2001","Novocaine",6e+06,2025238,2522928,"Artisan","R","Comedy"
"2777","7/15/2011","Snow Flower and the Secret Fan",6e+06,1348205,11348205,"Fox Searchlight","PG-13","Drama"
"2778","12/7/2001","The Business of Strangers",6e+06,1030920,1290920,"IFC Films","R","Drama"
"2779","4/29/2011","Jûsan-nin no shikaku",6e+06,802778,18727440,"Magnolia Pictures","R","Action"
"2780","3/25/2011","The 5th Quarter",6e+06,408159,408159,"Rocky Mountain Pict…","PG","Drama"
"2781","2/2/1979","The First Great Train Robbery",6e+06,391942,391942,"United Artists",NA,"Action"
"2782","11/10/2006","Come Early Morning",6e+06,119452,119452,"IDP/Goldwyn/Roadside","R","Drama"
"2783","4/2/2010","The Greatest",6e+06,115862,117796,"Paladin","R","Drama"
"2784","9/5/2008","Surfer, Dude",6e+06,36497,36497,"Anchor Bay Entertai…","R","Comedy"
"2785","1/23/2015","Song One",6e+06,32251,437089,"Cinedigm/Film Arcade","PG-13","Drama"
"2786","2/4/1983","Videodrome",5952000,2120439,2120439,"Universal",NA,"Horror"
"2787","3/18/2011","Winter in Wartime",5800000,542860,9662214,"Sony Pictures Classics","R","Drama"
"2788","9/8/2006","Tom yum goong",5700000,12044087,43044087,"Weinstein Co.","R","Action"
"2789","9/7/2012","The Inbetweeners",5700000,35955,86051320,"Wrekin Hill Enterta…","R","Comedy"
"2790","3/12/2003","Bend it Like Beckham",5600000,32543449,74566042,"Fox Searchlight","PG-13","Drama"
"2791","9/1/2006","Crossover",5600000,7009668,7009668,"Sony Pictures","PG-13","Drama"
"2792","6/21/2002","Sunshine State",5600000,3064356,3281898,"Sony Pictures Classics","PG-13","Drama"
"2793","12/25/1973","The Sting",5500000,159616327,159616327,"Universal","PG","Comedy"
"2794","9/25/1981","Chariots of Fire",5500000,61558162,61865947,"Warner Bros.","PG","Drama"
"2795","2/25/2005","Diary of a Mad Black Woman",5500000,50406346,50458356,"Lionsgate","PG-13","Drama"
"2796","11/22/1996","Shine",5500000,35811509,36672493,"Fine Line","PG-13","Drama"
"2797","9/28/2018","Hell Fest",5500000,10751601,12527795,"CBS Films","R","Horror"
"2798","6/6/2003","Mambo Italiano",5500000,9282750,12399772,"Goldwyn Entertainment","R","Comedy"
"2799","7/20/2001","Ghost World",5500000,6217849,8761608,"MGM","R","Comedy"
"2800","12/14/2001","Iris",5500000,5580479,15155021,"Miramax","R","Drama"
"2801","11/26/2004","Les Choristes",5500000,3629758,83529758,"Miramax","PG-13","Drama"
"2802","10/3/2003","Wonderland",5500000,1060512,1060512,"Lionsgate","R","Drama"
"2803","4/1/2011","Haevnen",5500000,1008098,15867314,"Sony Pictures Classics","R","Drama"
"2804","5/17/2002","Harvard Man",5500000,56653,56653,NA,"R","Drama"
"2805","7/15/2011","Salvation Boulevard",5500000,28468,28468,"IFC Films","R","Comedy"
"2806","8/3/2007","The Ten",5250000,769726,786677,"ThinkFilm","R","Comedy"
"2807","2/24/2017","The Girl with all the Gifts",5250000,0,4802379,"Saban Films","R","Horror"
"2808","8/5/2005","Saint Ralph",5200000,795126,1695126,"Samuel Goldwyn Films","PG-13","Comedy"
"2809","4/22/2011","Dum Maaro Dum",5200000,564489,11633427,"Fox Searchlight","R","Drama"
"2810","10/3/1980","Somewhere in Time",5100000,9709597,9709597,"Universal",NA,"Drama"
"2811","2/24/2017","Get Out",5e+06,176040665,255363701,"Universal","R","Horror"
"2812","1/20/2017","Split",5e+06,138141585,278306227,"Universal","PG-13","Horror"
"2813","10/21/2011","Paranormal Activity 3",5e+06,104028807,202053386,"Paramount Pictures","R","Horror"
"2814","10/28/2005","Saw II",5e+06,87025093,152925093,"Lionsgate","R","Horror"
"2815","9/13/2013","Insidious Chapter 2",5e+06,83586447,161921515,"FilmDistrict","PG-13","Horror"
"2816","7/22/2016","Lights Out",5e+06,67268835,148868835,"Warner Bros.","PG-13","Horror"
"2817","10/25/2002","Jackass: The Movie",5e+06,64282312,79282312,"Paramount Pictures","R","Comedy"
"2818","10/13/2017","Happy Death Day",5e+06,55683845,125013000,"Universal","PG-13","Horror"
"2819","10/19/2012","Paranormal Activity 4",5e+06,53900335,140619520,"Paramount Pictures","R","Horror"
"2820","10/24/2014","Ouija",5e+06,50856010,103300632,"Universal","PG-13","Horror"
"2821","8/30/2013","No se Aceptan Devoluciones",5e+06,44467206,100486616,"Lionsgate","PG-13","Comedy"
"2822","5/16/1975","The Return of the Pink Panther",5e+06,41833347,41833347,"MGM",NA,"Comedy"
"2823","12/24/2003","Monster",5e+06,34469210,64240813,"Newmarket Films","R","Drama"
"2824","12/23/1954","20,000 Leagues Under the Sea",5e+06,28200000,28200000,"Walt Disney","G","Adventure"
"2825","4/11/2014","Oculus",5e+06,27695246,44115496,"Relativity","R","Horror"
"2826","11/1/2013","Dallas Buyers Club",5e+06,27298285,60611845,"Focus Features","R","Drama"
"2827","2/27/2015","The Lazarus Effect",5e+06,25801570,35341814,"Lionsgate","PG-13","Horror"
"2828","3/16/2001","Memento",5e+06,25544867,39723096,"Newmarket Films","R","Drama"
"2829","8/26/2011","Our Idiot Brother",5e+06,24814830,25861249,"Weinstein Co.","R","Comedy"
"2830","7/21/2006","Clerks II",5e+06,24148068,27342246,"MGM","R","Comedy"
"2831","4/8/1998","The Players Club",5e+06,23047939,23047939,"New Line","R","Drama"
"2832","10/13/2000","Billy Elliot",5e+06,21995263,109263464,"Focus Features","PG-13","Drama"
"2833","7/5/2013","The Way Way Back",5e+06,21502690,26853810,"Fox Searchlight","PG-13","Comedy"
"2834","4/1/2016","God’s Not Dead 2",5e+06,20773069,23562057,"Pure Flix Entertain…","PG","Drama"
"2835","12/17/1997","The Apostle",5e+06,20733485,21277770,"October Films","PG-13","Drama"
"2836","11/3/1982","The Man From Snowy River",5e+06,20659423,20659423,"20th Century Fox",NA,"Drama"
"2837","10/23/1991","House Party 2",5e+06,19438638,19438638,"New Line","R","Comedy"
"2838","3/26/1999","Doug's 1st Movie",5e+06,19421271,19421271,"Walt Disney","G","Adventure"
"2839","9/18/1981","Mommie Dearest",5e+06,19032000,25032000,"Paramount Pictures",NA,"Drama"
"2840","1/16/2015","Still Alice",5e+06,18656400,41699612,"Sony Pictures Classics","PG-13","Drama"
"2841","3/23/2018","Paul, Apostle of Christ",5e+06,17547999,23389835,"Sony Pictures","PG-13","Drama"
"2842","10/10/2014","Addicted",5e+06,17390770,17499242,"Lionsgate","R","Drama"
"2843","8/31/2001","O (Othello)",5e+06,16017403,16017403,"Lionsgate","R","Drama"
"2844","11/7/1997","Eve's Bayou",5e+06,14843425,14843425,"Trimark","R","Drama"
"2845","4/10/1981","Nighthawks",5e+06,14600000,19600000,"Universal",NA,"Action"
"2846","6/9/2017","It Comes at Night",5e+06,13985117,19720203,"A24","R","Horror"
"2847","3/15/2002","Y Tu Mamá También",5e+06,13649881,33649881,"IFC Films","R","Drama"
"2848","9/24/2004","Shaun of the Dead",5e+06,13542874,30332385,"Focus/Rogue Pictures","R","Comedy"
"2849","6/21/1996","Lone Star",5e+06,12961389,12961389,"Sony Pictures Classics","R","Drama"
"2850","3/27/1986","April Fool's Day",5e+06,12947763,12947763,"Paramount Pictures",NA,"Horror"
"2851","4/2/1982","Diner",5e+06,12592907,12592907,"MGM",NA,"Comedy"
"2852","3/3/2017","Before I Fall",5e+06,12241072,18945682,"Open Road","PG-13","Drama"
"2853","4/15/1983","Lone Wolf McQuade",5e+06,12232628,12232628,"Orion Pictures",NA,"Action"
"2854","3/13/2009","Sunshine Cleaning",5e+06,12062558,17329337,"Overture Films","R","Comedy"
"2855","1/29/2016","Fifty Shades of Black",5e+06,11686940,22113075,"Open Road","R","Comedy"
"2856","8/20/1982","The Beastmaster",5e+06,10751126,10751126,"MGM",NA,"Action"
"2857","1/9/2009","Not Easily Broken",5e+06,10572742,10732909,"Sony Pictures","PG-13","Drama"
"2858","5/9/2014","Moms’ Night Out",5e+06,10429707,10537341,"Sony Pictures","PG","Adventure"
"2859","3/17/2017","The Belko Experiment",5e+06,10166820,10803839,"BH Tilt","R","Horror"
"2860","10/6/2000","Digimon: The Movie",5e+06,9628751,16628751,"20th Century Fox","PG","Adventure"
"2861","5/28/2004","Saved!",5e+06,8886160,10206551,"MGM","PG-13","Comedy"
"2862","5/9/2003","Les invasions barbares",5e+06,8460000,25913869,"Miramax","R","Drama"
"2863","12/22/1978","Force 10 from Navarone",5e+06,7100000,7100000,"American Internatio…",NA,"Action"
"2864","4/27/2001","The Forsaken",5e+06,6755271,6755271,"Sony Pictures","R","Horror"
"2865","7/21/1989","UHF",5e+06,6157157,6157157,NA,NA,"Comedy"
"2866","1/6/2006","Grandma’s Boy",5e+06,6090172,6590172,"20th Century Fox","R","Comedy"
"2867","8/14/1998","Slums of Beverly Hills",5e+06,5502773,5502773,"Fox Searchlight","R","Comedy"
"2868","7/13/2001","Made",5e+06,5308707,5476060,"Artisan","R","Comedy"
"2869","9/11/2015","90 Minutes in Heaven",5e+06,4816142,4816142,"Samuel Goldwyn Films","PG-13","Drama"
"2870","5/12/2006","Keeping Up with the Steins",5e+06,4339241,4414753,"Miramax","PG-13","Comedy"
"2871","10/10/1997","The Sweet Hereafter",5e+06,4306697,7951247,"Fine Line","R","Drama"
"2872","8/6/2008","Bottle Shock",5e+06,4078607,4815890,"Freestyle Releasing","PG-13","Drama"
"2873","2/25/2011","Des Hommes et Des Dieux",5e+06,3954651,46263525,"Sony Pictures Classics","PG-13","Drama"
"2874","8/27/1982","Jekyll and Hyde... Together Again",5e+06,3707583,3707583,"Universal",NA,"Comedy"
"2875","3/3/2017","Table 19",5e+06,3614896,4620399,"Fox Searchlight","PG-13","Comedy"
"2876","4/15/2016","Green Room",5e+06,3220371,3807503,"A24","R","Horror"
"2877","11/16/1994","Heavenly Creatures",5e+06,3046086,5438120,"Miramax","R","Drama"
"2878","5/13/2011","Everything Must Go",5e+06,2712131,2821010,"Roadside Attractions","PG","Drama"
"2879","12/17/2010","Rabbit Hole",5e+06,2229058,6205034,"Lionsgate","PG-13","Drama"
"2880","12/28/2016","Paterson",5e+06,2141423,10761547,"Bleecker Street","R","Comedy"
"2881","1/30/1998","Zero Effect",5e+06,2080693,2080693,"Sony Pictures","R","Comedy"
"2882","9/12/2014","Atlas Shrugged: Who Is John Galt?",5e+06,851690,851690,"Atlas Distribution","PG-13","Drama"
"2883","8/29/2003","Party Monster",5e+06,742898,894030,"ContentFilm","R","Comedy"
"2884","2/21/1996","Bottle Rocket",5e+06,407488,407488,"Sony Pictures","R","Action"
"2885","8/16/2013","Ain't Them Bodies Saints",5e+06,391611,1075009,"IFC Films","R","Drama"
"2886","1/17/1997","Albino Alligator",5e+06,353480,353480,"Miramax","R","Drama"
"2887","9/26/2014","Jimi: All is By My Side",5e+06,340911,927074,"XLrator Media","R","Drama"
"2888","9/10/2010","Lovely, Still",5e+06,127564,282687,"Monterey Media","PG","Drama"
"2889","11/16/2007","Redacted",5e+06,65388,861325,"Magnolia Pictures","R","Drama"
"2890","10/17/2014","Rudderless",5e+06,56001,567219,"Samuel Goldwyn Films","R","Drama"
"2891","8/14/2009","Grace",5e+06,8297,8297,"Anchor Bay Entertai…","R","Horror"
"2892","9/2/2016","Yoga Hosers",5e+06,0,2199,"Invincible Pictures","PG-13","Adventure"
"2893","11/21/2014","Reach Me",5e+06,0,0,"Alchemy","R","Drama"
"2894","8/18/2014","Henry & Me",5e+06,0,0,"Distrib Films","PG","Adventure"
"2895","1/23/2015","Mommy",4900000,3498695,17536004,"Roadside Attractions","R","Drama"
"2896","11/20/1996","Sling Blade",4833610,24475416,34175000,"Miramax","R","Drama"
"2897","1/6/2006","Hostel",4800000,47326473,82241110,"Lionsgate","R","Horror"
"2898","9/30/2011","Take Shelter",4750000,1728953,4972016,"Sony Pictures Classics","R","Drama"
"2899","8/22/1986","The Texas Chainsaw Massacre 2",4700000,8025872,8025872,"Cannon",NA,"Horror"
"2900","4/22/1988","Lady in White",4700000,1705139,1705139,"New Century Vista F…",NA,"Horror"
"2901","3/4/2005","Dear Frankie",4600000,1340891,3099369,"Miramax","PG-13","Drama"
"2902","12/29/2004","The Assassination of Richard Nixon",4600000,708776,4880143,"ThinkFilm","R","Drama"
"2903","6/24/2011","Le nom des gens",4600000,514237,9261711,"Music Box Films","R","Comedy"
"2904","3/23/1984","Police Academy",4500000,81198894,81198894,"Warner Bros.","R","Comedy"
"2905","6/20/1980","The Blue Lagoon",4500000,47923795,47923795,"Universal","R","Drama"
"2906","8/13/1982","Fast Times at Ridgemont High",4500000,27092880,27092880,"Universal",NA,"Comedy"
"2907","9/28/1996","Secrets & Lies",4500000,13417292,13417292,"October Films","R","Drama"
"2908","12/19/2002","25th Hour",4500000,13084595,25344490,"Walt Disney","R","Drama"
"2909","9/13/1985","After Hours",4500000,10609321,10609321,"Warner Bros.",NA,"Comedy"
"2910","10/24/2008","Låt den rätte komma in",4500000,2122085,12247682,"Magnolia Pictures","R","Horror"
"2911","2/12/1999","Tango",4500000,1687311,5428387,"Sony Pictures Classics","PG-13","Drama"
"2912","4/23/1986","Salvador",4500000,1500000,1500000,"Hemdale",NA,"Drama"
"2913","10/26/2001","Donnie Darko",4500000,1480006,7510877,"Newmarket Films","R","Drama"
"2914","9/2/2011","Salvando al Soldado Perez",4500000,1400726,9330465,"Lionsgate","PG-13","Action"
"2915","3/27/1998","Karakter",4500000,713413,713413,"Sony Pictures Classics","R","Drama"
"2916","10/7/2011","Blackthorn",4500000,200558,1217307,"Magnolia Pictures","R","Adventure"
"2917","5/8/2015","Maggie",4500000,187112,664346,"Roadside Attractions","PG-13","Drama"
"2918","4/18/2003","Lilja 4-ever",4500000,181655,4556982,"Newmarket Films","R","Drama"
"2919","4/9/2010","After.Life",4500000,108596,2481925,NA,"R","Horror"
"2920","3/1/2013","The Sweeney",4500000,26345,8000366,"Entertainment One","R","Action"
"2921","9/4/2014","Falcon Rising",4500000,11774,11774,"Freestyle Releasing","R","Adventure"
"2922","12/1/2017","Daisy Winters",4500000,0,0,"Hannover House","PG-13","Drama"
"2923","11/19/1975","One Flew Over the Cuckoo's Nest",4400000,108981275,108997629,"MGM","R","Drama"
"2924","6/25/1976","Silent Movie",4400000,36145695,36145695,"20th Century Fox",NA,"Comedy"
"2925","6/6/2003","Whale Rider",4300000,20779666,39374600,"Newmarket Films","PG-13","Drama"
"2926","6/13/2001","Sexy Beast",4300000,6946056,10158355,"Fox Searchlight","R","Drama"
"2927","10/19/1990","Night of the Living Dead",4200000,5835247,5835247,"Sony Pictures","R","Horror"
"2928","8/13/2010","Animal Kingdom",4200000,1044039,8078683,"Sony Pictures Classics","R","Drama"
"2929","10/21/2011","Cargo",4200000,0,313230,"Persona Films","R","Drama"
"2930","3/6/1998","Love and Death on Long Island",4030000,2542264,2542264,"Lionsgate","PG-13","Drama"
"2931","3/19/1982","Porky's",4e+06,109492484,109492484,"20th Century Fox","R","Comedy"
"2932","2/5/1953","Peter Pan",4e+06,87400000,87400000,"RKO Radio Pictures","PG","Adventure"
"2933","11/25/1992","The Crying Game",4e+06,62546695,62546695,"Miramax","R","Drama"
"2934","9/12/2003","Lost in Translation",4e+06,44585453,117085297,"Focus Features","R","Drama"
"2935","4/20/1977","Annie Hall",4e+06,38251425,38251425,"United Artists",NA,"Comedy"
"2936","10/27/1995","Leaving Las Vegas",4e+06,31983777,49800000,"MGM","R","Drama"
"2937","12/26/2001","Monster's Ball",4e+06,31273922,43766463,"Lionsgate","R","Drama"
"2938","7/11/2014","Boyhood",4e+06,25379975,57273049,"IFC Films","R","Drama"
"2939","7/9/2010","The Kids Are All Right",4e+06,20811365,36275469,"Focus Features","R","Comedy"
"2940","8/17/1979","Life of Brian",4e+06,20008693,20008693,"Warner Bros.","R","Comedy"
"2941","4/18/2014","A Haunted House 2",4e+06,17329487,21206861,"Open Road","R","Comedy"
"2942","3/1/2013","The Last Exorcism Part II",4e+06,15179303,25448707,"CBS Films","PG-13","Horror"
"2943","12/17/1974","The Front Page",4e+06,1.5e+07,1.5e+07,"Universal",NA,"Comedy"
"2944","8/16/1985","The Return of the Living Dead",4e+06,14237880,14237880,"Orion Pictures","R","Horror"
"2945","8/4/2000","Saving Grace",4e+06,12178602,27786849,"Fine Line","R","Comedy"
"2946","8/8/1963","The Great Escape",4e+06,11744471,11744471,"MGM",NA,"Drama"
"2947","5/13/2016","The Darkness",4e+06,10753574,10898293,"High Top Releasing","PG-13","Horror"
"2948","11/14/2001","The Wash",4e+06,10097096,10097096,"Lionsgate","R","Comedy"
"2949","3/1/2000","3 Strikes",4e+06,9821335,9821335,"MGM","R","Comedy"
"2950","4/11/2008","The Visitor",4e+06,9427026,19174817,"Overture Films","PG-13","Comedy"
"2951","11/26/2003","The Cooler",4e+06,8291572,11131455,"Lionsgate","R","Drama"
"2952","8/4/2006","The Night Listener",4e+06,7836393,10770993,"Miramax","R","Drama"
"2953","2/3/1995","The Jerky Boys",4e+06,7555256,7555256,"Walt Disney","R","Comedy"
"2954","12/28/2007","El orfanato",4e+06,7159147,79250193,"Picturehouse","R","Horror"
"2955","5/25/2007","Bug",4e+06,7006708,8302995,"Lionsgate","R","Drama"
"2956","11/17/2006","Let's Go to Prison",4e+06,4630045,4630045,"Universal","R","Comedy"
"2957","12/25/1995","Four Rooms",4e+06,4301000,4301000,"Miramax","R","Comedy"
"2958","9/20/2002","Secretary",4e+06,4046737,9413956,"Lionsgate","R","Drama"
"2959","12/1/1988","Talk Radio",4e+06,3468572,3468572,"Universal",NA,"Drama"
"2960","1/31/1997","Waiting for Guffman",4e+06,2922988,2922988,"Sony Pictures Classics","R","Comedy"
"2961","9/10/1999","Love Stinks",4e+06,2793776,2793776,"Independent Artists","R","Comedy"
"2962","9/16/2005","Thumbsucker",4e+06,1328679,1919197,"Sony Pictures Classics","R","Comedy"
"2963","9/23/2011","Red State",4e+06,1065429,1983596,"Smodshow Productions","R","Horror"
"2964","9/30/2005","MirrorMask",4e+06,864959,973613,"Samuel Goldwyn Films","PG","Drama"
"2965","2/28/2003","Poolhall Junkies",4e+06,563711,563711,"Gold Circle Films","R","Drama"
"2966","3/7/2014","The Face of Love",4e+06,385069,1158877,"IFC Films","PG-13","Drama"
"2967","4/11/2014","Joe",4e+06,373375,373375,"Roadside Attractions","R","Drama"
"2968","3/4/1988","Prison",4e+06,354704,354704,"Empire Pictures",NA,"Horror"
"2969","5/8/2009","Adoration",4e+06,294244,384244,"Sony Pictures Classics","R","Drama"
"2970","1/28/2000","The Big Tease",4e+06,185577,185577,"Warner Bros.","R","Comedy"
"2971","4/10/2015","Desert Dancer",4e+06,155271,338109,"Relativity","PG-13","Drama"
"2972","1/30/2015","Guten Tag, Ramon",4e+06,154356,4854356,"20th Century Fox","PG-13","Drama"
"2973","6/19/2015","Manglehorn",4e+06,132270,797439,"IFC Films","PG-13","Drama"
"2974","4/2/2010","Tau ming chong",4e+06,129078,38899792,NA,"R","Action"
"2975","4/2/2010","Tau ming chong",4e+06,129078,38899792,NA,"R","Action"
"2976","4/1/2011","Trust",4e+06,120016,120016,"Alchemy","R","Drama"
"2977","12/22/2000","An Everlasting Piece",4e+06,75078,75078,"Dreamworks SKG","R","Comedy"
"2978","4/22/2011","Stake Land",4e+06,33245,679482,"IFC Films","R","Horror"
"2979","12/27/2002","Sonny",4e+06,17639,17639,NA,"R","Drama"
"2980","11/18/2011","Another Happy Day",4e+06,9120,978527,"Phase 4 Films","R","Drama"
"2981","6/1/2012","The Loved Ones",4e+06,0,12302,"Paramount Pictures","R","Horror"
"2982","7/11/2014","The Perfect Wave",4e+06,0,0,NA,"PG","Drama"
"2983","12/15/1939","Gone with the Wind",3900000,198680470,390525192,"MGM","G","Drama"
"2984","1/1/1976","Network",3800000,23689877,23689877,"MGM",NA,"Drama"
"2985","1/14/2011","Down for Life",3800000,41914,41914,"B.D. Fox Independent","R","Drama"
"2986","4/30/2010","The Good Heart",3800000,20930,340930,"Magnolia Pictures","R","Drama"
"2987","10/5/2018","Hevi reissu",3800000,9079,9079,"Music Box Films",NA,"Comedy"
"2988","8/11/2006","Casa de Areia",3750000,539285,1178175,"Sony Pictures Classics","R","Drama"
"2989","2/19/2010","Defendor",3750000,44462,44462,NA,"R","Drama"
"2990","11/21/2006","The History Boys",3700000,2730296,13447998,"Fox Searchlight","R","Comedy"
"2991","7/4/1980","Airplane!",3500000,83453539,83453539,"Paramount Pictures","PG","Comedy"
"2992","8/13/1997","The Full Monty",3500000,45950122,261249383,"Fox Searchlight","R","Comedy"
"2993","5/26/1993","Menace II Society",3500000,27731527,27731527,"New Line","R","Action"
"2994","4/26/1995","Friday",3500000,27467564,27936778,"New Line","R","Comedy"
"2995","2/19/2016","The Witch",3500000,25138705,40454520,"A24","R","Horror"
"2996","12/6/2002","Empire",3500000,17504595,18495444,"Universal","R","Drama"
"2997","1/19/2018","Forever My Girl",3500000,16376066,16376066,"Roadside Attractions","PG","Drama"
"2998","5/1/1987","Creepshow 2",3500000,1.4e+07,1.4e+07,"New World","R","Horror"
"2999","1/1/1967","In Cold Blood",3500000,1.3e+07,13007551,NA,"R","Drama"
"3000","5/27/1998","I Got the Hook-Up!",3500000,10317779,10317779,"Miramax","R","Comedy"
"3001","11/6/1998","Gods and Monsters",3500000,6451628,6451628,"Lionsgate","R","Drama"
"3002","3/13/1987","Evil Dead II",3500000,5923044,5923044,"Rosebud Releasing",NA,"Horror"
"3003","6/29/2001","Pootie Tang",3500000,3293258,3293258,"Paramount Pictures","PG-13","Comedy"
"3004","12/2/2016","Believe",3500000,890303,890303,"Smith Global Media","PG","Drama"
"3005","4/19/2000","La otra conquista",3500000,886410,886410,"Hombre de Oro","R","Drama"
"3006","9/30/2016","American Honey",3500000,663247,2611750,"A24","R","Drama"
"3007","6/10/2011","Trolljegeren",3500000,253444,5706638,"Magnet Pictures","PG","Horror"
"3008","9/14/2007","Ira and Abby",3500000,221096,221096,"Magnolia Pictures","R","Comedy"
"3009","1/8/2016","The Masked Saint",3500000,182695,182695,"Freestyle Releasing","PG-13","Action"
"3010","2/17/2006","Winter Passing",3500000,107492,113783,"Focus Features","R","Drama"
"3011","3/25/2005","D.E.B.S.",3500000,96793,96793,"Samuel Goldwyn Films","PG-13","Action"
"3012","9/17/1999","Taxman",3500000,9871,9871,NA,NA,"Comedy"
"3013","5/17/2013","Jagten",3450000,687185,18309793,"Magnolia Pictures","R","Drama"
"3014","10/21/2011","Margin Call",3400000,5353586,20433227,"Roadside Attractions","R","Drama"
"3015","9/26/2008","Choke",3400000,2926565,4124277,"Fox Searchlight","R","Comedy"
"3016","2/16/1956","Carousel",3380000,0,3220,"20th Century Fox",NA,"Drama"
"3017","10/10/2014","Whiplash",3300000,13092006,37825230,"Sony Pictures Classics","R","Drama"
"3018","10/26/2007","Bella",3300000,8093373,12405473,"Roadside Attractions","PG-13","Drama"
"3019","1/17/2003","Cidade de Deus",3300000,7563397,32059295,"Miramax","R","Drama"
"3020","11/18/1983","A Christmas Story",3250000,20605209,20605209,"MGM","PG","Comedy"
"3021","8/20/1982","Class of 1984",3250000,6965361,6965361,"United Film Distrib…",NA,"Drama"
"3022","7/16/2004","Maria Full of Grace",3200000,6529624,14441158,"New Line","R","Drama"
"3023","6/3/2011","Beginners",3200000,5790894,14314407,"Focus Features","R","Drama"
"3024","4/22/2016","The Meddler",3200000,4267219,5526942,"Sony Pictures Classics","PG-13","Comedy"
"3025","7/29/2009","Adam",3200000,2283291,2834485,"Fox Searchlight","PG-13","Drama"
"3026","9/22/2006","Feast",3200000,56131,690872,"Weinstein/Dimension","R","Horror"
"3027","1/1/1946","It’s a Wonderful Life",3180000,6600000,10768908,NA,"PG","Drama"
"3028","7/19/1996","Trainspotting",3100000,16501785,71558971,"Miramax","R","Drama"
"3029","7/28/1978","National Lampoon's Animal House",3e+06,141600000,141600000,"Universal","R","Comedy"
"3030","10/20/2010","Paranormal Activity 2",3e+06,84752907,177512032,"Paramount Pictures","R","Horror"
"3031","8/28/2015","War Room",3e+06,67790117,73975239,"Sony Pictures","PG","Drama"
"3032","12/22/1964","Goldfinger",3e+06,51100000,124900000,"MGM","PG","Action"
"3033","12/18/1957","The Bridge on the River Kwai",3e+06,33300000,33300000,"Sony Pictures","PG","Drama"
"3034","1/1/1978","Coming Home",3e+06,32653000,32653000,"United Artists",NA,"Drama"
"3035","11/20/1998","Waking Ned Devine",3e+06,24793251,55193251,"20th Century Fox","PG","Comedy"
"3036","8/1/1997","Air Bud",3e+06,24646936,27788649,"Walt Disney","PG","Adventure"
"3037","6/10/1975","Love and Death",3e+06,20123742,20123742,"MGM",NA,"Comedy"
"3038","4/6/2001","Pokemon 3: The Movie",3e+06,17052128,68452128,"Warner Bros.","G","Adventure"
"3039","4/27/1990","Spaced Invaders",3e+06,1.5e+07,1.5e+07,"Walt Disney","PG","Adventure"
"3040","10/25/1985","Krush Groove",3e+06,11052713,11052713,"Warner Bros.","R","Drama"
"3041","5/8/2009","Next Day Air",3e+06,10027047,10172519,"Summit Entertainment","R","Comedy"
"3042","11/4/1998","Belly",3e+06,9639390,9639390,"Artisan","R","Drama"
"3043","5/12/1999","Trippin’",3e+06,9017070,9017070,"October Films","R","Comedy"
"3044","5/24/2013","Before Midnight",3e+06,8110621,23251930,"Sony Pictures Classics","R","Drama"
"3045","11/20/1987","Teen Wolf Too",3e+06,7888000,7888000,"Atlantic",NA,"Comedy"
"3046","7/31/2009","The Collector",3e+06,7712114,10473836,"Freestyle Releasing","R","Horror"
"3047","7/8/1988","Phantasm II",3e+06,7282851,7282851,"Universal",NA,"Horror"
"3048","10/1/2004","Woman Thou Art Loosed",3e+06,6879730,6879730,"Magnolia Pictures","R","Drama"
"3049","10/18/2002","Real Women Have Curves",3e+06,5853194,7777790,"Newmarket Films","PG-13","Comedy"
"3050","4/28/2006","Water",3e+06,5529144,11322573,"Fox Searchlight","PG-13","Drama"
"3051","7/22/2016","Don’t Think Twice",3e+06,4417983,4417983,"Film Arcade","R","Comedy"
"3052","6/24/2016","Swiss Army Man",3e+06,4210454,5837111,"A24","R","Drama"
"3053","4/14/2000","East is East",3e+06,4170647,30438635,"Miramax","R","Comedy"
"3054","9/1/2000","Whipped",3e+06,4142507,4142507,"Destination Films","R","Comedy"
"3055","2/28/1997","Kama Sutra",3e+06,4109095,4109095,"Trimark","R","Drama"
"3056","5/17/2013","Frances Ha",3e+06,4067398,11262769,"IFC Films","R","Comedy"
"3057","9/24/1993","Warlock: The Armageddon",3e+06,3902679,3902679,"Trimark","R","Horror"
"3058","9/13/1978","Days of Heaven",3e+06,3446749,3660880,"Paramount Pictures",NA,"Drama"
"3059","4/22/2016","Compadres",3e+06,3127773,7445044,"Lionsgate","R","Comedy"
"3060","8/9/1996","Basquiat",3e+06,2962051,2962051,"Miramax","R","Drama"
"3061","2/24/2006","Tsotsi",3e+06,2912606,11537539,"Miramax","R","Drama"
"3062","4/9/2010","Letters to God",3e+06,2848587,3237452,"Vivendi Entertainment","PG","Drama"
"3063","9/19/2014","Tusk",3e+06,1821983,1857688,"A24","R","Horror"
"3064","10/24/2003","Elephant",3e+06,1266955,10051516,"Fine Line","R","Drama"
"3065","9/7/2012","Bachelorette",3e+06,446770,12577401,"Weinstein Co.","R","Comedy"
"3066","9/5/2008","Everybody Wants to Be Italian",3e+06,351416,351416,"Roadside Attractions","R","Comedy"
"3067","9/9/2011","Creature",3e+06,331000,331000,"The Bubble Factory","R","Horror"
"3068","8/23/1996","Freeway",3e+06,295493,295493,"Roxie Releasing","R","Comedy"
"3069","2/12/1993","Dead Alive",3e+06,242623,242623,"Trimark",NA,"Horror"
"3070","10/1/2010","Chain Letter",3e+06,205842,1022453,"New Films Cinema","R","Horror"
"3071","3/2/2012","Tim and Eric's Billion Dollar Movie",3e+06,201436,223652,"Magnet Pictures","R","Comedy"
"3072","11/9/2007","Holly",3e+06,163069,163069,"Priority Films","R","Drama"
"3073","3/21/2008","The Grand",3e+06,115879,115879,"Anchor Bay Entertai…","R","Comedy"
"3074","3/17/2006","Sommersturm",3e+06,95204,95204,"Regent Releasing","R","Drama"
"3075","8/15/2014","Fort McCoy",3e+06,78948,78948,"Monterey Media","R","Drama"
"3076","8/4/1999","The Gambler",3e+06,51773,101773,NA,"R","Drama"
"3077","9/4/2015","Before We Go",3e+06,37151,483938,"Radius","PG-13","Drama"
"3078","9/9/2011","Tanner Hall",3e+06,5073,5073,"Anchor Bay Entertai…","R","Drama"
"3079","9/30/2005","My Big Fat Independent Movie",3e+06,4655,4655,"Big Fat Movies","R","Comedy"
"3080","6/27/2014","They Came Together",3e+06,0,82780,"Lionsgate","R","Comedy"
"3081","10/1/2010","Barry Munday",3e+06,0,0,"Magnolia Pictures","R","Comedy"
"3082","11/20/1998","Central do Brasil",2900000,5969553,17006158,"Sony Pictures Classics","R","Drama"
"3083","6/10/2005","High Tension",2850000,3681066,6435262,"Lionsgate","R","Horror"
"3084","12/15/1974","Young Frankenstein",2800000,86300000,86300000,"20th Century Fox","PG","Comedy"
"3085","6/25/1976","The Omen",2800000,48570885,48570885,"20th Century Fox","R","Horror"
"3086","7/22/2005","Hustle & Flow",2800000,22202809,23591783,"Paramount Vantage","R","Drama"
"3087","9/15/2006","Artie Lange's Beer League",2800000,475000,475000,"Freestyle Releasing","R","Comedy"
"3088","2/15/2008","Diary of the Dead",2750000,952620,5394447,"Weinstein Co.","R","Horror"
"3089","10/17/1979","The Black Stallion",2700000,37799643,37799643,"United Artists","G","Drama"
"3090","6/13/1997","Ulee's Gold",2700000,9054736,15600000,"Orion Pictures","R","Drama"
"3091","2/7/1974","Blazing Saddles",2600000,119500000,119500000,"Warner Bros.","R","Comedy"
"3092","5/2/2014","Ida",2600000,3827060,15298355,"Music Box Films","PG-13","Drama"
"3093","1/1/1987","Maurice",2600000,3147950,3198308,NA,"R","Drama"
"3094","12/7/2007","Timber Falls",2600000,0,71248,"Slowhand Cinema","R","Horror"
"3095","1/11/2013","A Haunted House",2500000,40041683,59922558,"Open Road","R","Comedy"
"3096","7/28/2004","Garden State",2500000,26782316,36028802,"Fox Searchlight","R","Drama"
"3097","10/4/1996","That Thing You Do!",2500000,25857416,34557416,"20th Century Fox","PG","Drama"
"3098","10/30/1981","Halloween II",2500000,25533818,25533818,"Universal",NA,"Horror"
"3099","10/22/1982","Halloween 3: Season of the Witch",2500000,14400000,14400000,"Universal",NA,"Horror"
"3100","8/2/2013","The Spectacular Now",2500000,6852971,6916951,"A24","R","Drama"
"3101","1/27/1995","Before Sunrise",2500000,5274005,5686742,"Sony Pictures","R","Drama"
"3102","6/24/2016","Hunt for the Wilderpeople",2500000,5205471,23845533,"The Orchard","PG-13","Comedy"
"3103","8/17/2012","Robot & Frank",2500000,3317468,4934356,"Samuel Goldwyn Films","PG-13","Drama"
"3104","6/16/2000","Jesus' Son",2500000,1282084,1687548,"Lionsgate","R","Drama"
"3105","5/27/2005","Saving Face",2500000,1187266,1269705,"Sony Pictures Classics","R","Comedy"
"3106","6/20/2008","Brick Lane",2500000,1094998,3838486,"Sony Pictures Classics","PG-13","Drama"
"3107","8/24/2007","Eye of the Dolphin",2500000,72210,72260,"Monterey Media","PG-13","Drama"
"3108","8/16/2013","Underdogs",2500000,35017,35017,"Freestyle Releasing","PG","Drama"
"3109","6/21/2013","Alien Uprising",2500000,0,0,"Phase 4 Films","R","Action"
"3110","5/13/2011","Go For It!",2450000,180237,182358,"Lionsgate","PG-13","Drama"
"3111","10/16/1996","Get on the Bus",2400000,5691854,5691854,"Sony Pictures","R","Drama"
"3112","9/1/2006","Idiocracy",2400000,444093,500296,"20th Century Fox","R","Comedy"
"3113","3/20/2015","Do You Believe?",2300000,12985600,14305450,"Pure Flix Entertain…","PG-13","Drama"
"3114","5/1/1998","Dancer, Texas Pop. 81",2300000,574838,574838,"Sony Pictures","PG","Comedy"
"3115","9/5/2014","Frontera",2300000,59696,59696,"Magnolia Pictures","PG-13","Drama"
"3116","8/26/2011","Redemption Road",2300000,29384,29384,"Freestyle Releasing","PG-13","Drama"
"3117","2/9/1940","Pinocchio",2289247,84300000,84300000,"Walt Disney","G","Adventure"
"3118","8/13/1982","Friday the 13th Part 3",2250000,36690067,36690067,"Paramount Pictures",NA,"Horror"
"3119","10/9/1971","The French Connection",2200000,41158757,41158757,NA,NA,"Drama"
"3120","2/9/2007","The Last Sin Eater",2200000,388390,388390,"20th Century Fox","PG-13","Drama"
"3121","7/13/2001","Bully",2100000,881824,1381824,"Lionsgate","R","Drama"
"3122","10/16/2016","Mi America",2100000,3330,3330,"Industrial House Films","R","Drama"
"3123","9/30/2011","Courageous",2e+06,34522221,35185884,"Sony Pictures","PG-13","Drama"
"3124","4/8/1964","From Russia With Love",2e+06,24800000,78900000,"MGM","PG","Action"
"3125","5/21/1982","Mad Max 2: The Road Warrior",2e+06,24600832,24600832,"Warner Bros.",NA,"Action"
"3126","8/2/1967","In the Heat of the Night",2e+06,24379978,24407647,"MGM",NA,"Drama"
"3127","12/17/1973","Sleeper",2e+06,18344729,18344729,"MGM",NA,"Comedy"
"3128","3/13/2015","It Follows",2e+06,14674077,23250755,"RADiUS-TWC","R","Horror"
"3129","3/9/2012","Silent House",2e+06,12739737,16610760,"Open Road","R","Horror"
"3130","10/8/1999","Boys Don't Cry",2e+06,11540607,20741000,"Fox Searchlight","R","Drama"
"3131","2/9/2007","Das Leben der Anderen",2e+06,11284657,81197047,"Sony Pictures Classics","R","Drama"
"3132","12/31/1986","Witchboard",2e+06,7369373,7369373,"Cinema Guild",NA,"Horror"
"3133","6/26/1998","Smoke Signals",2e+06,6719300,7756617,"Miramax","PG-13","Comedy"
"3134","6/11/2010","Winter's Bone",2e+06,6531503,16131551,"Roadside Attractions","R","Drama"
"3135","8/15/2003","American Splendor",2e+06,6003587,8685632,"Fine Line","R","Drama"
"3136","10/6/2017","The Florida Project",2e+06,5904366,11303040,"A24","R","Drama"
"3137","8/25/2017","All Saints",2e+06,5802208,5941994,"Sony Pictures","PG","Drama"
"3138","7/2/2004","Before Sunset",2e+06,5792822,11217346,"Warner Independent","R","Drama"
"3139","3/30/2001","Amores Perros",2e+06,5383834,20883834,"Lionsgate","R","Drama"
"3140","8/20/2003","Thirteen",2e+06,4601043,9505996,"Fox Searchlight","R","Drama"
"3141","6/17/2005","Me and You and Everyone We Know",2e+06,3885134,9615464,"IFC Films","R","Drama"
"3142","8/28/2015","We Are Your Friends",2e+06,3591417,10166209,"Warner Bros.","R","Drama"
"3143","11/10/2006","Harsh Times",2e+06,3337931,6225304,"MGM","R","Drama"
"3144","3/3/2000","Ghost Dog: The Way of the Samurai",2e+06,3330230,10672492,"Artisan","R","Drama"
"3145","9/18/2015","Captive",2e+06,2583301,2791973,"Paramount Pictures","PG-13","Drama"
"3146","8/2/2002","Full Frontal",2e+06,2512846,3438804,"Miramax","R","Comedy"
"3147","6/8/2018","Hearts Beat Loud",2e+06,2386254,2420962,"Gunpowder & Sky","PG-13","Drama"
"3148","1/20/2017","The Resurrection of Gavin Stone",2e+06,2303792,2303792,"High Top Releasing","PG","Comedy"
"3149","6/28/2006","Strangers with Candy",2e+06,2072645,2077844,"ThinkFilm","R","Comedy"
"3150","5/2/2008","Son of Rambow: A Home Movie",2e+06,1785505,11263263,"Paramount Vantage","PG-13","Comedy"
"3151","8/7/2015","The Diary of a Teenage Girl",2e+06,1477002,2279959,"Sony Pictures Classics","R","Drama"
"3152","4/30/1999","Get Real",2e+06,1152411,1152411,"Paramount Pictures","R","Comedy"
"3153","4/8/2011","Meek's Cutoff",2e+06,977772,1869928,"Oscilloscope Pictures","PG","Drama"
"3154","9/28/2001","Dinner Rush",2e+06,638227,1075504,"Access Motion Pictu…","R","Drama"
"3155","9/24/2010","The Virginity Hit",2e+06,636706,636706,"Sony Pictures","R","Comedy"
"3156","4/15/2005","House of D",2e+06,388532,466106,"Lionsgate","PG-13","Drama"
"3157","1/18/2008","Teeth",2e+06,347578,2350641,"Roadside Attractions","R","Comedy"
"3158","7/26/1996","Stonewall",2e+06,304602,304602,"Strand","R","Drama"
"3159","9/8/2006","Sherrybaby",2e+06,199176,759504,"IFC Films","R","Drama"
"3160","4/15/2005","It's All Gone Pete Tong",2e+06,120620,2226603,"Matson","R","Drama"
"3161","4/15/1998","24 7: Twenty Four Seven",2e+06,72544,72544,"October Films","R","Comedy"
"3162","2/3/2017","Growing up Smith",2e+06,35312,35312,"Good Deed Entertain…","PG-13","Comedy"
"3163","3/20/2009","Super Capers",2e+06,30955,30955,"Roadside Attractions","PG","Adventure"
"3164","1/1/1993","Return of the Living Dead 3",2e+06,21000,21000,NA,NA,"Horror"
"3165","2/10/2006","London",2e+06,12667,12667,"IDP/Goldwyn/Roadside","R","Drama"
"3166","10/31/2008","Eden Lake",2e+06,7321,4294373,"Third Rail","R","Horror"
"3167","6/23/2006","Say Uncle",2e+06,5361,5361,"TLA Releasing","R","Comedy"
"3168","9/9/2011","Grave Encounters",2e+06,0,2151887,"TriBeca Films",NA,"Horror"
"3169","4/28/1971","Bananas",2e+06,0,0,"MGM","PG-13","Comedy"
"3170","7/7/2007","Rockaway",2e+06,0,0,"Off-Hollywood Distr…","R","Drama"
"3171","2/8/2013","Small Apartments",2e+06,0,0,"Morocco Junction Pi…","R","Comedy"
"3172","7/8/2016","The Dog Lover",2e+06,0,0,"ESX Entertainment","PG","Drama"
"3173","10/8/2010","Nowhere Boy",1900000,1445366,7785229,"Weinstein Co.","R","Drama"
"3174","7/11/2003","Northfork",1900000,1420578,1445140,"Paramount Vantage","PG-13","Drama"
"3175","4/24/2015","Brotherly Love",1900000,478595,478595,"Freestyle Releasing","R","Drama"
"3176","6/3/2011","Submarine",1900000,467602,4581937,"Weinstein Co.","R","Comedy"
"3177","8/27/2010","The Last Exorcism",1800000,41034350,70165900,"Lionsgate","PG-13","Horror"
"3178","11/16/1976","Carrie",1800000,25878153,25878153,"United Artists",NA,"Horror"
"3179","11/9/1984","A Nightmare on Elm Street",1800000,25504513,25504513,"New Line","R","Horror"
"3180","6/27/2012","Beasts of the Southern Wild",1800000,12795746,23265132,"Fox Searchlight","PG-13","Drama"
"3181","11/15/2002","El crimen de padre Amaro",1800000,5719000,5719000,"Goldwyn Entertainment","R","Drama"
"3182","6/15/2001","Songcatcher",1800000,3050934,3050934,"Lionsgate","PG-13","Drama"
"3183","8/23/2011","Higher Ground",1800000,841056,842693,"Sony Pictures Classics","R","Drama"
"3184","10/8/2010","I Spit on Your Grave",1750000,93051,1278471,"Anchor Bay Entertai…","R","Horror"
"3185","11/23/2001","In the Bedroom",1700000,35930604,42137871,"Miramax","R","Drama"
"3186","3/19/2008","La misma luna",1700000,12590147,23271741,"Weinstein Co.","PG-13","Drama"
"3187","2/28/2014","The Lunchbox",1700000,4231500,12231500,"Sony Pictures Classics","PG","Drama"
"3188","10/4/2013","Grace Unplugged",1700000,2507159,2507159,"Roadside Attractions","PG","Drama"
"3189","10/1/1999","Happy, Texas",1700000,2039192,2891228,"Miramax","PG-13","Comedy"
"3190","12/18/2015","Saul fia",1700000,1777043,9696537,"Sony Pictures Classics","R","Drama"
"3191","6/17/2005","My Summer of Love",1700000,1000915,4727375,"Focus Features","R","Drama"
"3192","6/24/2005","Yes",1700000,396035,661221,"Sony Pictures Classics","R","Drama"
"3193","4/9/1999","Foolish",1600000,6026908,6026908,"Artisan","R","Comedy"
"3194","1/27/2006","Bubble",1600000,145382,145382,"Magnolia Pictures","R","Drama"
"3195","1/15/1999","Mississippi Mermaid",1600000,27795,2627795,"MGM","R","Drama"
"3196","11/4/2005","I Love Your Work",1600000,3264,3264,"ThinkFilm","R","Comedy"
"3197","4/1/2011","Insidious",1500000,54009150,99870886,"FilmDistrict","R","Horror"
"3198","10/21/2016","Moonlight",1500000,27854931,65322266,"A24","R","Drama"
"3199","9/12/2003","Cabin Fever",1500000,21158188,30351664,"Lionsgate","R","Horror"
"3200","9/8/1989","Kickboxer",1500000,14533681,14533681,"Cannon","R","Action"
"3201","2/26/1988","Bloodsport",1500000,11806119,11806119,"Cannon","R","Action"
"3202","10/5/2005","The Squid and the Whale",1500000,7372734,11191423,"IDP/Goldwyn/Roadside","R","Drama"
"3203","4/20/1979","Dawn of the Dead",1500000,5100000,5.5e+07,"United Film Distrib…",NA,"Horror"
"3204","9/23/1994","Exotica",1500000,5046118,5046118,"Miramax","R","Drama"
"3205","7/26/2013","The To Do List",1500000,3491669,4128828,"CBS Films","R","Comedy"
"3206","6/26/1998","Buffalo '66",1500000,2380606,2380606,"Lionsgate","R","Comedy"
"3207","3/2/1984","Repo Man",1500000,2300000,2300000,"Universal",NA,"Comedy"
"3208","10/21/2016","I’m Not Ashamed",1500000,2082980,2082980,"Pure Flix Entertain…","PG-13","Drama"
"3209","4/19/2002","Nueve Reinas",1500000,1222889,12412889,"Sony Pictures Classics","R","Drama"
"3210","4/19/2013","The Lords of Salem",1500000,1165881,1541131,"Anchor Bay Entertai…","R","Horror"
"3211","3/25/2005","The Ballad of Jack and Rose",1500000,712294,1126258,"IFC Films","R","Drama"
"3212","5/17/2002","The Believer",1500000,406035,1840248,"Sony Pictures","R","Drama"
"3213","3/7/2008","Snow Angels",1500000,402858,414404,"Warner Independent","R","Drama"
"3214","2/11/2011","MOOZ-lum",1500000,362239,372239,"Peace Film LLC","PG-13","Drama"
"3215","8/19/2011","Amigo",1500000,184705,184705,"Variance Films","R","Drama"
"3216","9/7/2007","Hatchet",1500000,175281,240396,"Anchor Bay Entertai…","R","Horror"
"3217","10/31/2008","My Name is Bruce",1500000,173066,173066,"Image Entertainment","R","Horror"
"3218","2/5/1936","Modern Times",1500000,163245,165049,"Kino International","G","Comedy"
"3219","5/11/2007","The Salon",1500000,139084,139084,"Freestyle Releasing","PG-13","Comedy"
"3220","3/22/2002","Stolen Summer",1500000,119841,119841,"Miramax","PG","Drama"
"3221","9/28/2005","Forty Shades of Blue",1500000,75828,172569,"Vitagraph Films","R","Drama"
"3222","10/9/2009","Trucker",1500000,52429,52429,"Monterey Media","R","Drama"
"3223","7/20/2018","Teefa in Trouble",1500000,0,98806,"Yash Raj Films",NA,"Action"
"3224","3/17/2006","Fetching Cody",1500000,0,0,NA,NA,"Drama"
"3225","6/3/2011","The Lion of Judah",1500000,0,0,"Rocky Mountain Pict…","PG","Adventure"
"3226","11/20/2015","Mustang",1400000,845464,5545484,"Cohen Media Group","PG-13","Drama"
"3227","4/29/2005","The Holy Girl",1400000,304124,1261792,"Fine Line","R","Drama"
"3228","10/9/1998","Festen",1300000,1647780,1647780,"October Films","R","Comedy"
"3229","10/11/1996","Trees Lounge",1300000,749741,749741,"Orion Classics","R","Drama"
"3230","3/23/2007","Journey from the Fall",1300000,635305,635305,"Imaginasian","R","Drama"
"3231","5/5/2000","The Basket",1300000,609042,609042,"MGM","PG","Drama"
"3232","3/15/1985","Def-Con 4",1300000,210904,210904,"New World",NA,"Action"
"3233","4/30/1981","Friday the 13th Part 2",1250000,21722776,21722776,"Paramount Pictures",NA,"Horror"
"3234","8/31/1984","C.H.U.D.",1250000,4700000,4700000,"New World",NA,"Horror"
"3235","4/19/2013","Filly Brown",1250000,2850357,2940411,"Lionsgate","R","Drama"
"3236","10/29/2004","Saw",1200000,55968727,103880027,"Lionsgate","R","Horror"
"3237","8/4/1989","Sex, Lies, and Videotape",1200000,24741667,36741667,"Miramax","R","Drama"
"3238","2/15/2002","Super Troopers",1200000,18492362,23046142,"Fox Searchlight","R","Comedy"
"3239","2/22/2002","Monsoon Wedding",1200000,13876974,27025600,"USA Films","R","Comedy"
"3240","11/10/2000","You Can Count on Me",1200000,9180275,10827356,"Paramount Vantage","R","Drama"
"3241","4/19/2013","Home Run",1200000,2859955,2859955,"Samuel Goldwyn Films","PG-13","Drama"
"3242","7/7/2000","But I'm a Cheerleader",1200000,2205627,2509344,"Lionsgate","R","Comedy"
"3243","4/13/2012","Blue Like Jazz",1200000,595018,595018,"Roadside Attractions","PG-13","Comedy"
"3244","8/28/2015","Que Horas Ela Volta?",1200000,376976,3247411,"Oscilloscope Pictures","R","Drama"
"3245","11/19/1982","Q",1200000,255000,255000,"United Film Distrib…",NA,"Horror"
"3246","6/18/2004","Grand Theft Parsons",1200000,0,0,"Swipe Films","PG-13","Drama"
"3247","9/7/2012","Crowsnest",1200000,0,0,"IFC Midnight","R","Horror"
"3248","9/14/2012","Airborne",1200000,0,0,"Image Entertainment",NA,"Horror"
"3249","3/21/2014","God’s Not Dead",1150000,60755732,63777092,"Pure Flix Entertain…","PG","Drama"
"3250","10/7/2005","Waiting...",1125000,16124543,18673274,"Lionsgate","R","Comedy"
"3251","12/25/2005","Wolf Creek",1100000,16186348,29005064,"Weinstein Co.","R","Horror"
"3252","2/11/2005","Ong-Bak",1100000,4563167,24062965,"Magnolia Pictures","R","Action"
"3253","3/23/2012","Serbuan maut",1100000,4105123,9297407,"Sony Pictures Classics","R","Action"
"3254","9/4/1987","The Offspring",1100000,1355728,1355728,"Moviestore Entertai…","R","Horror"
"3255","5/18/2012","Beyond the Black Rainbow",1100000,56491,56491,"Mongrel Media","R","Drama"
"3256","1/23/1943","Casablanca",1039000,10462500,10462500,"Warner Bros.","PG","Drama"
"3257","11/21/1976","Rocky",1e+06,117235147,2.25e+08,"United Artists","PG","Drama"
"3258","1/6/2012","The Devil Inside",1e+06,53262945,101759490,"Paramount Pictures","R","Horror"
"3259","4/17/2015","Unfriended",1e+06,32789645,62869004,"Universal","R","Horror"
"3260","2/8/1976","Taxi Driver",1e+06,28262574,28316211,"Columbia","R","Drama"
"3261","2/1/1980","The Fog",1e+06,21378361,21378361,"Avco Embassy",NA,"Horror"
"3262","8/23/2013","You're Next",1e+06,18494006,26887177,"Lionsgate","R","Horror"
"3263","5/25/2012","Chernobyl Diaries",1e+06,18119640,42411721,"Warner Bros.","R","Horror"
"3264","4/10/1981","The Howling",1e+06,17985000,17985000,"Avco Embassy",NA,"Horror"
"3265","5/8/1963","Dr. No",1e+06,16067035,59567035,"MGM","PG","Action"
"3266","9/18/1987","Hellraiser",1e+06,14564000,14575148,"New World","R","Horror"
"3267","8/18/2000","Godzilla 2000",1e+06,10037390,10037390,"Sony Pictures","PG","Action"
"3268","12/29/2010","Blue Valentine",1e+06,9737892,16566240,"Weinstein Co.","R","Drama"
"3269","1/20/2006","Transamerica",1e+06,9015303,16553163,"Weinstein Co.","R","Drama"
"3270","1/1/1970","Beyond the Valley of the Dolls",1e+06,9e+06,9e+06,"20th Century Fox",NA,"Comedy"
"3271","7/20/2018","Unfriended: Dark Web",1e+06,8783985,9620953,"OTL Releasing","R","Horror"
"3272","9/25/2015","The Green Inferno",1e+06,7192291,12931569,"High Top Releasing","R","Horror"
"3273","10/19/2012","The Sessions",1e+06,6002451,11495204,"Fox Searchlight","R","Drama"
"3274","3/23/2012","October Baby",1e+06,5355847,5391992,"Five & Two Pictures","PG-13","Drama"
"3275","9/12/2014","The Skeleton Twins",1e+06,5284309,5797192,"Lionsgate/Roadside …","R","Drama"
"3276","8/3/2005","Junebug",1e+06,2678010,3553253,"Sony Pictures Classics","R","Drama"
"3277","8/1/2008","Frozen River",1e+06,2511476,6030129,"Sony Pictures Classics","R","Drama"
"3278","11/21/2001","Sidewalks of New York",1e+06,2402459,3575308,"Paramount Vantage","R","Comedy"
"3279","4/24/1998","Two Girls and a Guy",1e+06,2057193,2315026,"Fox Searchlight","R","Drama"
"3280","9/18/2009","The Secrets of Jonathan Sperry",1e+06,1355079,1355079,"Five & Two Pictures","PG","Drama"
"3281","9/19/2003","Bubba Ho-Tep",1e+06,1239183,1492895,"Vitagraph Films","R","Comedy"
"3282","12/7/2001","No Man's Land",1e+06,1067481,2684207,"MGM","R","Drama"
"3283","10/9/1998","Slam",1e+06,1009819,1087521,"Trimark","R","Drama"
"3284","8/18/2017","Patti Cake$",1e+06,800148,1471090,"Fox Searchlight","R","Comedy"
"3285","12/1/2000","Panic",1e+06,779137,1425707,"Roxie Releasing","R","Drama"
"3286","5/9/2014","Palo Alto",1e+06,767732,1156309,"TriBeca Films","R","Drama"
"3287","7/29/2011","The Future",1e+06,568662,1239174,"Roadside Attractions","R","Drama"
"3288","2/14/2003","All the Real Girls",1e+06,549666,703020,"Sony Pictures Classics","R","Drama"
"3289","10/24/2014","23 Blast",1e+06,549185,549185,"Abramorama Films","PG-13","Drama"
"3290","6/20/1997","Dream With The Fishes",1e+06,542909,542909,"Sony Pictures Classics","R","Drama"
"3291","5/2/2003","Blue Car",1e+06,464126,475367,"Miramax","R","Drama"
"3292","10/19/2007","Wristcutters: A Love Story",1e+06,446165,473769,"Autonomous Films","R","Comedy"
"3293","5/5/2000","Luminarias",1e+06,428535,428535,NA,"R","Comedy"
"3294","7/18/2014","I Origins",1e+06,336472,852399,"Fox Searchlight","R","Drama"
"3295","8/22/2003","The Battle of Shaker Heights",1e+06,280351,839145,"Miramax","PG-13","Comedy"
"3296","12/30/2002","Love Liza",1e+06,213137,213137,NA,"R","Drama"
"3297","8/22/2001","Lisa Picard is Famous",1e+06,113433,113433,NA,"PG-13","Comedy"
"3298","10/30/2009","The House of the Devil",1e+06,101215,102812,"Magnolia Pictures","R","Horror"
"3299","6/1/2012","Hardflip",1e+06,96734,96734,"Rocky Mountain Pict…","PG-13","Drama"
"3300","3/11/2016","Creative Control",1e+06,63014,63014,"Magnolia Pictures","R","Drama"
"3301","10/17/2014","Camp X-Ray",1e+06,9837,9837,"IFC Films","R","Drama"
"3302","11/21/2008","Special",1e+06,7202,26822,"Revolver Entertainment","R","Drama"
"3303","4/10/2015","The Sisterhood of Night",1e+06,6870,6870,"Freestyle Releasing","PG-13","Drama"
"3304","3/18/2005","The Helix…Loaded",1e+06,3700,3700,"Romar","R","Comedy"
"3305","5/15/2015","Childless",1e+06,1036,1036,"Monterey Media","R","Drama"
"3306","4/21/2006","In Her Line of Fire",1e+06,884,884,"Regent Releasing","R","Action"
"3307","9/15/2006","Jimmy and Judy",1e+06,0,0,"Outrider Pictures","R","Action"
"3308","7/17/2009","The Poker House",1e+06,0,0,"Phase 4 Films","R","Drama"
"3309","9/23/2005","Proud",1e+06,0,0,"Castle Hill Product…","PG","Drama"
"3310","12/31/2008","Steppin: The Movie",1e+06,0,0,"Weinstein Co.","PG-13","Comedy"
"3311","1/29/2010","Zombies of Mass Destruction",1e+06,0,0,"After Dark","R","Comedy"
"3312","4/14/2006","Hard Candy",950000,1024640,8267066,"Lionsgate","R","Horror"
"3313","9/27/2002","Charly",950000,814666,814666,"Excel Entertainment","PG","Comedy"
"3314","4/13/2012","L!fe Happens",930000,30905,30905,"PMK*BNC","R","Comedy"
"3315","5/12/2017","Lowriders",916000,6179955,6188421,"BH Tilt","PG-13","Drama"
"3316","7/12/2013","Fruitvale Station",9e+05,16098998,17549645,"Weinstein Co.","R","Drama"
"3317","4/1/2016","Meet the Blacks",9e+05,9097072,9097072,"Freestyle Releasing","R","Comedy"
"3318","8/26/2011","Circumstance",9e+05,454121,958978,"Roadside Attractions","R","Drama"
"3319","8/25/2006","The Quiet",9e+05,381420,381420,"Sony Pictures Classics","R","Drama"
"3320","8/13/1942","Bambi",858000,102797000,2.68e+08,"RKO Radio Pictures","G","Drama"
"3321","8/31/2012","For a Good Time, Call",850000,1251749,1386088,"Focus Features","R","Comedy"
"3322","1/30/2004","Latter Days",850000,833118,865708,"TLA Releasing","R","Drama"
"3323","10/25/2002","Time Changer",825000,1500711,1500711,"Five & Two Pictures","PG","Drama"
"3324","12/30/2011","Jodaeiye Nader az Simin",8e+05,7098492,24426169,"Sony Pictures Classics","PG-13","Drama"
"3325","5/10/1996","Welcome to the Dollhouse",8e+05,4198137,5034794,"Sony Pictures Classics","R","Comedy"
"3326","3/28/2003","Raising Victor Vargas",8e+05,2073984,2900578,"Samuel Goldwyn Films","R","Drama"
"3327","10/1/1993","Ruby in Paradise",8e+05,1001437,1001437,NA,"R","Drama"
"3328","5/7/2004","The Mudge Boy",8e+05,62544,62544,"Strand","R","Drama"
"3329","8/6/2004","Saints and Soldiers",780000,1310470,1310470,"Excel Entertainment","PG-13","Drama"
"3330","8/11/1973","American Graffiti",777000,1.15e+08,1.4e+08,"Universal","PG","Drama"
"3331","6/8/2012","Safety Not Guaranteed",750000,4010957,4422318,"FilmDistrict","R","Comedy"
"3332","2/3/2012","The Innkeepers",750000,78396,1011535,"Magnolia Pictures","R","Horror"
"3333","8/29/2014","Il conformista",750000,59656,89609,"Kino Lorber","R","Drama"
"3334","7/1/2005","Undead",750000,41196,229250,"Lionsgate","R","Horror"
"3335","10/11/2013","All the Boys Love Mandy Lane",750000,0,1960521,"Radius","R","Horror"
"3336","6/25/1968","La mariée était en noir",747000,44566,44566,"Film Forum",NA,"Drama"
"3337","8/11/2006","Half Nelson",7e+05,2697938,4911725,"ThinkFilm","R","Drama"
"3338","6/19/1998","Hav Plenty",650000,2301777,2301777,"Miramax","R","Comedy"
"3339","7/14/1999","The Blair Witch Project",6e+05,140539099,248300000,"Artisan","R","Horror"
"3340","8/10/1977","The Kentucky Fried Movie",6e+05,1.5e+07,2e+07,"United Film Distrib…",NA,"Comedy"
"3341","10/31/2000","Mercy Streets",6e+05,173599,173599,NA,"PG-13","Drama"
"3342","7/2/1999","Broken Vessels",6e+05,15030,85343,NA,"R","Drama"
"3343","5/22/2015","Drunk Wedding",6e+05,3301,3301,"Paramount Pictures","R","Comedy"
"3344","8/11/1964","A Hard Day's Night",560000,1537860,1626784,"Universal","G","Comedy"
"3345","5/9/1980","Friday the 13th",550000,39754601,59754601,"Paramount Pictures",NA,"Horror"
"3346","9/26/2008","Fireproof",5e+05,33456317,33473297,"Samuel Goldwyn Films","PG","Drama"
"3347","11/15/1974","Benji",5e+05,31559560,31559560,NA,"G","Adventure"
"3348","10/3/2003","The Station Agent",5e+05,5801558,9470209,"Miramax","R","Drama"
"3349","1/22/2010","To Save a Life",5e+05,3777210,3824868,"Samuel Goldwyn Films","PG-13","Drama"
"3350","2/1/2002","The Singles Ward",5e+05,1250798,1250798,"Halestorm Entertain…","PG","Comedy"
"3351","1/30/2004","Osama",5e+05,1127331,1971479,"MGM","PG-13","Drama"
"3352","6/9/2000","Groove",5e+05,1115313,1167524,"Sony Pictures Classics","R","Comedy"
"3353","1/31/2003","The R.M.",5e+05,1111615,1111615,"Halestone","PG","Comedy"
"3354","7/30/1999","Twin Falls Idaho",5e+05,985341,1027228,"Sony Pictures Classics","R","Drama"
"3355","8/20/2004","Mean Creek",5e+05,603951,1348750,"Paramount Vantage","R","Drama"
"3356","8/23/2013","Drinking Buddies",5e+05,343706,407100,"Magnolia Pictures","R","Drama"
"3357","2/13/1998","Hurricane Streets",5e+05,334041,367582,"MGM",NA,"Drama"
"3358","8/29/2003","Civil Brand",5e+05,254293,254293,"Lionsgate","R","Drama"
"3359","10/29/2010","Monsters",5e+05,237301,5639730,"Magnet Pictures","R","Drama"
"3360","3/24/2006","Lonesome Jim",5e+05,154187,602789,"IFC Films","R","Comedy"
"3361","12/11/2015","O Menino e o Mundo",5e+05,129479,271893,"GKIDS","PG","Adventure"
"3362","1/1/1991","Johnny Suede",5e+05,55000,55000,"Miramax","R","Drama"
"3363","10/21/2005","The Californians",5e+05,4134,4134,"Fabrication Films","PG","Drama"
"3364","11/2/2001","Everything Put Together",5e+05,0,7890,NA,"R","Drama"
"3365","9/25/2009","Paranormal Activity",450000,107918810,194183034,"Paramount Pictures","R","Horror"
"3366","3/31/2006","Brick",450000,2075743,4243996,"Focus/Rogue Pictures","R","Drama"
"3367","8/22/1997","Sunday",450000,410919,450349,NA,NA,"Drama"
"3368","8/11/2006","Conversations with Other Women",450000,379418,1297745,"Fabrication Films","R","Comedy"
"3369","8/3/1990","Metropolitan",430000,2938000,2938000,NA,"PG-13","Comedy"
"3370","6/11/2004","Napoleon Dynamite",4e+05,44540956,46122713,"Fox Searchlight","PG","Comedy"
"3371","5/10/1975","Monty Python and the Holy Grail",4e+05,3427696,5028948,NA,NA,"Comedy"
"3372","8/2/2006","Quinceanera",4e+05,1692693,2797199,"Sony Pictures Classics","R","Drama"
"3373","10/24/2008","Heroes",4e+05,655538,655538,"Eros Entertainment","R","Adventure"
"3374","1/1/1983","E tu vivrai nel terrore - L'aldilà",4e+05,126387,126387,NA,NA,"Horror"
"3375","7/27/2001","Jackpot",4e+05,44452,44452,NA,"R","Drama"
"3376","12/10/2004","Fabled",4e+05,31425,31425,"Indican Pictures","R","Horror"
"3377","10/13/2005","The Dark Hours",4e+05,423,423,"Freestyle Releasing","R","Horror"
"3378","4/1/1986","My Beautiful Laundrette",4e+05,0,0,"Orion Classics",NA,"Drama"
"3379","1/1/1980","Maniac",350000,1e+07,1e+07,"Analysis",NA,"Horror"
"3380","1/1/1987","American Ninja 2: The Confrontation",350000,4e+06,4e+06,NA,NA,"Action"
"3381","4/13/1957","12 Angry Men",340000,0,0,"United Artists",NA,"Drama"
"3382","10/17/1978","Halloween",325000,4.7e+07,7e+07,"Compass International","R","Horror"
"3383","11/24/1999","Tumbleweeds",312000,1350248,1788168,"Fine Line","PG-13","Drama"
"3384","3/10/2000","God's Army",3e+05,2637726,2652515,"Excel Entertainment","PG","Drama"
"3385","10/17/2003","Pieces of April",3e+05,2528664,3571253,"MGM","PG-13","Comedy"
"3386","9/20/1996","When The Cat's Away",3e+05,1652472,2525984,"Sony Pictures Classics","R","Comedy"
"3387","12/10/2008","Wendy and Lucy",3e+05,865695,1416046,"Oscilloscope Pictures","R","Drama"
"3388","9/11/1998","Let's Talk About Sex",3e+05,373615,373615,"Fine Line",NA,"Comedy"
"3389","7/15/2005","First Morning",3e+05,87264,87264,"Illuminare","PG-13","Drama"
"3390","3/11/2011","3 Backyards",3e+05,39475,39475,"Screen Media Films","R","Drama"
"3391","8/7/1998","First Love, Last Rites",3e+05,10876,10876,"Strand","R","Drama"
"3392","5/6/2005","Fighting Tommy Riley",3e+05,10514,10514,"Freestyle Releasing","R","Drama"
"3393","8/17/2012","Compliance",270000,319285,830700,"Magnolia Pictures","R","Drama"
"3394","6/28/2002","Lovely and Amazing",250000,4210379,4613482,"Lionsgate","R","Drama"
"3395","4/28/2017","Sleight",250000,3930990,3934450,"High Top Releasing","R","Action"
"3396","4/11/2003","Better Luck Tomorrow",250000,3802390,3809226,"Paramount Pictures","R","Drama"
"3397","10/28/2011","Like Crazy",250000,3395391,3728400,"Paramount Pictures","PG-13","Drama"
"3398","7/14/2000","Chuck&Buck",250000,1055671,1157672,"Artisan","R","Drama"
"3399","3/28/1997","Love and Other Catastrophes",250000,212285,743216,"Fox Searchlight","R","Comedy"
"3400","8/28/1998","I Married a Strange Person",250000,203134,203134,"Lionsgate",NA,"Comedy"
"3401","7/22/2005","November",250000,191862,191862,"Sony Pictures Classics","R","Drama"


================================================
FILE: ch_regr_mult_and_log/figures/eoce/movie_returns_by_genre/horror_movies_conds.R
================================================
# load packages ----------------------------------------------------------------
library(tidyverse)
library(lubridate)
library(openintro)
library(broom)

# load data --------------------------------------------------------------------
movie_profit <- read_csv("movie_profit.csv") %>%
  select(-X1)


# fix dates --------------------------------------------------------------------
movie_profit <- movie_profit %>%
  mutate(
    release_date = mdy(release_date),
    release_year = year(release_date),
    oct_release = ifelse(month(release_date) == 10, "yes", "no"),
    dom_gross_to_prod = domestic_gross / production_budget,
    ww_gross_to_prod = worldwide_gross / production_budget
    ) 

# subset for movies after 2000 -------------------------------------------------
movie_profit_2000 <- movie_profit %>%
  filter(
    release_year >= 2010,
    release_year < 2019
    )

# mlr --------------------------------------------------------------------------

m <- lm(ww_gross_to_prod ~ release_year + genre, data = movie_profit_2000)
m_aug <- augment(m)

# residuals against fitted -----------------------------------------------------

cols <- c(
  "Action" = COL[1,1],
  "Adventure" = COL[2,1],
  "Comedy" = COL[3,1],
  "Drama" = COL[4,1],
  "Horror" = COL[5,1]
)

ggplot(m_aug, aes(y = .fitted, x = ww_gross_to_prod, color = genre)) + 
  geom_point(alpha = 0.5) +
  facet_wrap(~genre, scales = "free_x") +
  theme_minimal() + 
  labs(x = "Actual ROI", y = "Predicted ROI", color = "Genre") +
  scale_color_manual(values = cols) +
  guides(color = FALSE) +
  geom_abline(yintercept = 0, slope = 1)
  
ggsave(filename = "horror_movies_by_genre.pdf",
       width = 5.5, height = 4.3)


================================================
FILE: ch_regr_mult_and_log/figures/eoce/movie_returns_by_genre/movie_profit.csv
================================================
"","release_date","movie","production_budget","domestic_gross","worldwide_gross","distributor","mpaa_rating","genre"
"1","6/22/2007","Evan Almighty",1.75e+08,100289690,174131329,"Universal","PG","Comedy"
"2","7/28/1995","Waterworld",1.75e+08,88246220,264246220,"Universal","PG-13","Action"
"3","5/12/2017","King Arthur: Legend of the Sword",1.75e+08,39175066,139950708,"Warner Bros.","PG-13","Adventure"
"4","12/25/2013","47 Ronin",1.75e+08,38362475,151716815,"Universal","PG-13","Action"
"5","6/22/2018","Jurassic World: Fallen Kingdom",1.7e+08,416769345,1304866322,"Universal","PG-13","Action"
"6","8/1/2014","Guardians of the Galaxy",1.7e+08,333172112,771051335,"Walt Disney","PG-13","Action"
"7","5/7/2010","Iron Man 2",1.7e+08,312433331,621156389,"Paramount Pictures","PG-13","Action"
"8","4/4/2014","Captain America: The Winter Soldier",1.7e+08,259746958,714401889,"Walt Disney","PG-13","Action"
"9","7/11/2014","Dawn of the Planet of the Apes",1.7e+08,208545589,710644566,"20th Century Fox","PG-13","Adventure"
"10","11/10/2004","The Polar Express",1.7e+08,186493587,310634169,"Warner Bros.","G","Adventure"
"11","6/1/2012","Snow White and the Huntsman",1.7e+08,155136755,401021746,"Universal","PG-13","Adventure"
"12","7/1/2003","Terminator 3: Rise of the Machines",1.7e+08,150358296,433058296,"Warner Bros.","R","Action"
"13","5/7/2004","Van Helsing",1.7e+08,120150546,300150546,"Universal","PG-13","Action"
"14","5/22/2015","Tomorrowland",1.7e+08,93436322,207283457,"Walt Disney","PG","Adventure"
"15","5/27/2016","Alice Through the Looking Glass",1.7e+08,77042381,276934087,"Walt Disney","PG","Adventure"
"16","5/21/2010","Shrek Forever After",1.65e+08,238736787,756244673,"Paramount Pictures","PG","Adventure"
"17","11/4/2016","Doctor Strange",1.65e+08,232641920,676486457,"Walt Disney","PG-13","Action"
"18","11/7/2014","Big Hero 6",1.65e+08,222527828,652127828,"Walt Disney","PG","Adventure"
"19","3/26/2010","How to Train Your Dragon",1.65e+08,217581232,494870992,"Paramount Pictures","PG","Adventure"
"20","11/2/2012","Wreck-It Ralph",1.65e+08,189412677,496511521,"Walt Disney","PG","Adventure"
"21","11/5/2014","Interstellar",1.65e+08,188017894,667752422,"Paramount Pictures","PG-13","Adventure"
"22","6/24/2016","Independence Day: Resurgence",1.65e+08,103144286,384413934,"20th Century Fox","PG-13","Action"
"23","7/29/2011","Cowboys and Aliens",1.63e+08,100368560,176038324,"Universal","PG-13","Action"
"24","5/17/2007","Shrek the Third",1.6e+08,322719944,807330936,"Paramount Pictures","PG","Adventure"
"25","5/24/2013","Fast and Furious 6",1.6e+08,238679850,789300444,"Universal","PG-13","Action"
"26","6/3/2011","X-Men: First Class",1.6e+08,146408305,355408305,"20th Century Fox","PG-13","Action"
"27","12/25/2008","The Curious Case of Benjamin Button",1.6e+08,127509326,329631958,"Paramount Pictures","PG-13","Drama"
"28","7/14/2010","The Sorcerer's Apprentice",1.6e+08,63150991,217986320,"Walt Disney","PG","Adventure"
"29","5/12/2006","Poseidon",1.6e+08,60674817,181674817,"Warner Bros.","PG-13","Adventure"
"30","6/10/2016","Warcraft",1.6e+08,47225655,425547111,"Universal","PG-13","Action"
"31","12/21/2018","Aquaman",1.6e+08,0,0,"Warner Bros.","PG-13","Action"
"32","9/30/2016","Deepwater Horizon",1.56e+08,61433527,122631306,"Lionsgate","PG-13","Drama"
"33","7/1/2015","Terminator: Genisys",1.55e+08,89760956,432150894,"Paramount Pictures","PG-13","Action"
"34","3/23/2018","Pacific Rim: Uprising",1.55e+08,59185715,290241338,"Universal","PG-13","Action"
"35","11/24/2004","Alexander",1.55e+08,34297191,167297191,"Warner Bros.","R","Action"
"36","7/14/2017","War for the Planet of the Apes",1.52e+08,146880162,489592267,"20th Century Fox","PG-13","Action"
"37","5/25/2001","Pearl Harbor",151500000,198539855,449239855,"Walt Disney","PG-13","Action"
"38","7/2/2007","Transformers",1.51e+08,319246193,708272592,"Paramount Pictures","PG-13","Action"
"39","6/2/2017","Wonder Woman",1.5e+08,412563408,821133378,"Warner Bros.","PG-13","Action"
"40","3/4/2016","Zootopia",1.5e+08,341268248,1019706594,"Walt Disney","PG","Adventure"
"41","11/18/2005","Harry Potter and the Goblet of Fire",1.5e+08,290013036,896911078,"Warner Bros.","PG-13","Adventure"
"42","5/15/2003","The Matrix Reloaded",1.5e+08,281553689,738576929,"Warner Bros.","R","Action"
"43","12/14/2007","I am Legend",1.5e+08,256393010,585532684,"Warner Bros.","PG-13","Horror"
"44","7/1/2008","Hancock",1.5e+08,227946274,624234272,"Sony Pictures","PG-13","Action"
"45","7/15/2005","Charlie and the Chocolate Factory",1.5e+08,206459076,475825484,"Warner Bros.","PG","Adventure"
"46","6/29/2007","Ratatouille",1.5e+08,206445654,626549695,"Walt Disney","G","Adventure"
"47","11/8/2013","Thor: The Dark World",1.5e+08,206362140,644602516,"Walt Disney","PG-13","Action"
"48","6/15/2005","Batman Begins",1.5e+08,205343774,359142722,"Warner Bros.","PG-13","Action"
"49","7/31/2015","Mission: Impossible—Rogue Nation",1.5e+08,195042377,689388363,"Paramount Pictures","PG-13","Action"
"50","7/21/2017","Dunkirk",1.5e+08,190068280,499900860,"Warner Bros.","PG-13","Action"
"51","5/6/2011","Thor",1.5e+08,181030624,449326618,"Paramount Pictures","PG-13","Action"
"52","11/7/2008","Madagascar: Escape 2 Africa",1.5e+08,180174880,599680774,"Paramount Pictures","PG","Adventure"
"53","5/1/2009","X-Men Origins: Wolverine",1.5e+08,179883157,374825760,"20th Century Fox","PG-13","Action"
"54","5/26/2011","Kung Fu Panda 2",1.5e+08,165249063,664837547,"Paramount Pictures","PG","Adventure"
"55","5/15/2015","Mad Max: Fury Road",1.5e+08,153636354,370651733,"Warner Bros.","R","Action"
"56","8/10/2018","The Meg",1.5e+08,142700791,527100791,"Warner Bros.","PG-13","Action"
"57","11/5/2003","The Matrix Revolutions",1.5e+08,139270910,427300260,"Warner Bros.","R","Action"
"58","3/29/2018","Ready Player One",1.5e+08,137018455,578621729,"Warner Bros.","PG-13","Adventure"
"59","5/5/2006","Mission: Impossible III",1.5e+08,133501348,397501348,"Paramount Pictures","PG-13","Action"
"60","5/14/2004","Troy",1.5e+08,133298577,484161265,"Warner Bros.","R","Action"
"61","7/1/2010","The Last Airbender",1.5e+08,131772187,319713881,"Paramount Pictures","PG","Adventure"
"62","11/2/2007","Bee Movie",1.5e+08,126631277,287594577,"Paramount Pictures","PG","Adventure"
"63","7/24/2009","G-Force",1.5e+08,119436770,292817841,"Walt Disney","PG","Adventure"
"64","11/21/2008","Bolt",1.5e+08,114053579,328015029,"Walt Disney","PG","Adventure"
"65","3/30/2012","Wrath of the Titans",1.5e+08,83670083,305270083,"Warner Bros.","PG-13","Adventure"
"66","11/16/2007","Beowulf",1.5e+08,82280579,195080579,"Paramount Pictures","PG-13","Adventure"
"67","2/12/2010","The Wolfman",1.5e+08,62189884,142634358,"Universal","R","Horror"
"68","2/17/2017","The Great Wall",1.5e+08,45157105,334550106,"Universal","PG-13","Action"
"69","10/9/2015","Pan",1.5e+08,35088320,151543635,"Warner Bros.","PG","Adventure"
"70","3/11/2011","Mars Needs Moms",1.5e+08,21392758,39549758,"Walt Disney","PG","Adventure"
"71","11/3/2006","Flushed Away",1.49e+08,64665672,179357126,"Paramount Pictures","PG","Adventure"
"72","6/8/2012","Madagascar 3: Europe's Most Wanted",1.45e+08,216391482,746921271,"Paramount Pictures","PG","Adventure"
"73","6/13/2014","How to Train Your Dragon 2",1.45e+08,177002924,614586270,"20th Century Fox","PG","Adventure"
"74","6/16/1999","Tarzan",1.45e+08,171091819,448191819,"Walt Disney","G","Adventure"
"75","3/7/2014","Mr. Peabody & Sherman",1.45e+08,111506430,269806430,"20th Century Fox","PG","Adventure"
"76","11/21/2012","Rise of the Guardians",1.45e+08,103412758,306900902,"Paramount Pictures","PG","Adventure"
"77","11/22/2002","Die Another Day",1.42e+08,160942139,431942139,"MGM","PG-13","Action"
"78","5/8/2009","Star Trek",1.4e+08,257730019,385680446,"Paramount Pictures","PG-13","Adventure"
"79","7/1/1998","Armageddon",1.4e+08,201578182,554600000,"Walt Disney","PG-13","Adventure"
"80","7/3/2002","Men in Black 2",1.4e+08,190418803,441767803,"Sony Pictures","PG-13","Action"
"81","7/22/2011","Captain America: The First Avenger",1.4e+08,176654505,370569776,"Paramount Pictures","PG-13","Action"
"82","1/29/2016","Kung Fu Panda 3",1.4e+08,143528619,518418751,"20th Century Fox","PG","Adventure"
"83","7/10/1998","Lethal Weapon 4",1.4e+08,130444603,285400000,"Warner Bros.","R","Action"
"84","3/27/2013","G.I. Joe: Retaliation",1.4e+08,122523060,375740705,"Paramount Pictures","PG-13","Action"
"85","12/5/2003","The Last Samurai",1.4e+08,111110575,456810575,"Warner Bros.","R","Action"
"86","12/21/2005","Fun With Dick And Jane",1.4e+08,110550000,203018919,"Sony Pictures","PG-13","Comedy"
"87","12/12/2014","Exodus: Gods and Kings",1.4e+08,65014513,268314513,"20th Century Fox","PG-13","Drama"
"88","7/1/2016","The BFG",1.4e+08,55483770,199676255,"Walt Disney","PG","Adventure"
"89","2/26/2016","Gods of Egypt",1.4e+08,31153464,138587563,"Lionsgate","PG-13","Adventure"
"90","5/3/2002","Spider-Man",1.39e+08,403706375,821706375,"Sony Pictures","PG-13","Adventure"
"91","3/6/2009","Watchmen",1.38e+08,107509799,186976250,"Warner Bros.","R","Action"
"92","7/29/2005","Stealth",1.38e+08,32116746,76416746,"Sony Pictures","PG-13","Action"
"93","6/13/2008","The Incredible Hulk",137500000,134806913,265573859,"Universal","PG-13","Adventure"
"94","6/20/2003","Hulk",1.37e+08,132177234,245075434,"Universal","PG-13","Action"
"95","7/11/2001","Final Fantasy: The Spirits Within",1.37e+08,32131830,85131830,"Sony Pictures","PG-13","Adventure"
"96","3/22/2013","The Croods",1.35e+08,187168425,573068425,"20th Century Fox","PG","Adventure"
"97","12/25/2015","The Revenant",1.35e+08,183637894,532950503,"20th Century Fox","R","Adventure"
"98","11/19/1999","The World is Not Enough",1.35e+08,126930660,361730660,"MGM","PG-13","Action"
"99","3/4/2011","Rango",1.35e+08,123477607,245724600,"Paramount Pictures","PG","Adventure"
"100","7/17/2013","Turbo",1.35e+08,83028130,286896578,"20th Century Fox","PG","Adventure"
"101","11/18/2011","Happy Feet Two",1.35e+08,64006466,157956466,"Warner Bros.","PG","Adventure"
"102","7/28/2006","Miami Vice",1.35e+08,63478838,163818556,"Universal","R","Action"
"103","6/29/2005","War of the Worlds",1.32e+08,234280354,606836535,"Paramount Pictures","PG-13","Action"
"104","11/26/2014","Penguins of Madagascar",1.32e+08,83350911,367650911,"20th Century Fox","PG","Adventure"
"105","11/22/2013","The Hunger Games: Catching Fire",1.3e+08,424668047,864868047,"Lionsgate","PG-13","Adventure"
"106","7/6/2018","Ant-Man and the Wasp",1.3e+08,216565229,617176819,"Walt Disney","PG-13","Action"
"107","6/6/2008","Kung Fu Panda",1.3e+08,215434591,631910531,"Paramount Pictures","PG","Adventure"
"108","7/17/2015","Ant-Man",1.3e+08,180202163,518860086,"Walt Disney","PG-13","Action"
"109","3/27/2015","Home",1.3e+08,177397510,386031994,"20th Century Fox","PG","Adventure"
"110","10/28/2011","Puss in Boots",1.3e+08,149260504,554987477,"Paramount Pictures","PG","Adventure"
"111","11/5/2010","Megamind",1.3e+08,148415853,321887208,"Paramount Pictures","PG","Adventure"
"112","7/18/2003","Bad Boys II",1.3e+08,138540870,273271982,"Sony Pictures","R","Action"
"113","4/11/2014","Rio 2",1.3e+08,131538435,492846291,"20th Century Fox","G","Adventure"
"114","3/28/2014","Noah",1.3e+08,101200044,352831065,"Paramount Pictures","PG-13","Drama"
"115","12/21/2011","The Adventures of Tintin",1.3e+08,77591831,373993951,"Paramount Pictures","PG","Adventure"
"116","5/31/2013","After Earth",1.3e+08,60522097,251499665,"Sony Pictures","PG-13","Action"
"117","11/26/2008","Australia",1.3e+08,49554002,215080810,"20th Century Fox","PG-13","Drama"
"118","7/19/2013","R.I.P.D.",1.3e+08,33618855,79076678,"Universal","PG-13","Action"
"119","5/19/2000","Dinosaur",127500000,137748063,356148063,"Walt Disney","PG","Adventure"
"120","3/3/2017","Logan",1.27e+08,226277068,615476965,"20th Century Fox","R","Action"
"121","5/2/2003","X-Men 2",1.25e+08,214949694,406875536,"20th Century Fox","PG-13","Action"
"122","4/29/2011","Fast Five",1.25e+08,210031325,630163454,"Universal","PG-13","Action"
"123","12/16/2011","Sherlock Holmes: A Game of Shadows",1.25e+08,186848418,535663443,"Warner Bros.","PG-13","Action"
"124","5/28/2004","The Day After Tomorrow",1.25e+08,186740799,556319450,"20th Century Fox","PG-13","Adventure"
"125","3/31/2017","The Boss Baby",1.25e+08,175003033,510888357,"20th Century Fox","PG","Adventure"
"126","4/1/2010","Clash of the Titans",1.25e+08,163214888,493214888,"Warner Bros.","PG-13","Action"
"127","11/4/2016","Trolls",1.25e+08,153707064,344527425,"20th Century Fox","PG","Adventure"
"128","5/19/1998","Godzilla",1.25e+08,136314294,3.76e+08,"Sony Pictures","PG-13","Action"
"129","6/8/2012","Prometheus",1.25e+08,126477084,402448265,"20th Century Fox","R","Adventure"
"130","6/20/1997","Batman & Robin",1.25e+08,107325195,238317814,"Warner Bros.","PG-13","Action"
"131","7/13/2018","Skyscraper",1.25e+08,67796355,304034615,"Universal","PG","Action"
"132","12/21/2016","Assassin’s Creed",1.25e+08,54647948,240497948,"20th Century Fox","PG-13","Action"
"133","1/13/2017","Monster Trucks",1.25e+08,33370166,61642798,"Paramount Pictures","PG-13","Adventure"
"134","8/27/1999","The 13th Warrior",1.25e+08,32698899,61698899,"Walt Disney","R","Action"
"135","11/17/2000","How the Grinch Stole Christmas",1.23e+08,260044825,345141403,"Universal","PG","Adventure"
"136","5/24/2000","Mission: Impossible 2",1.2e+08,215409889,549588516,"Paramount Pictures","PG-13","Action"
"137","6/30/2000","The Perfect Storm",1.2e+08,182618434,328711434,"Warner Bros.","PG-13","Drama"
"138","7/29/2016","Jason Bourne",1.2e+08,162192920,416197059,"Universal","PG-13","Action"
"139","11/21/2012","Life of Pi",1.2e+08,124987022,607258634,"20th Century Fox","PG","Drama"
"140","2/16/2007","Ghost Rider",1.2e+08,115802596,229545589,"Sony Pictures","PG-13","Action"
"141","6/27/2003","Charlie's Angels: Full Throttle",1.2e+08,100814328,227200000,"Sony Pictures","PG-13","Action"
"142","4/13/2018","Rampage",1.2e+08,99345950,424745950,"Warner Bros.","PG-13","Action"
"143","8/9/2013","Elysium",1.2e+08,93050117,286192091,"Sony Pictures","R","Action"
"144","3/24/2017","Power Rangers",1.2e+08,85364450,142545357,"Lionsgate","PG-13","Action"
"145","7/19/2002","Stuart Little 2",1.2e+08,64956806,1.66e+08,"Sony Pictures","PG","Adventure"
"146","6/11/2004","The Chronicles of Riddick",1.2e+08,57712751,107212751,"Universal","PG-13","Adventure"
"147","5/9/2008","Speed Racer",1.2e+08,43945766,93394462,"Warner Bros.","PG","Action"
"148","7/22/2005","The Island",1.2e+08,35818913,163018913,"Dreamworks SKG","PG-13","Action"
"149","6/23/2010","Knight and Day",1.17e+08,76423035,258751370,"20th Century Fox","PG-13","Action"
"150","5/19/1999","Star Wars Ep. I: The Phantom Menace",1.15e+08,474544677,1027044677,"20th Century Fox","PG","Adventure"
"151","11/2/2001","Monsters, Inc.",1.15e+08,289423425,559757719,"Walt Disney","G","Adventure"
"152","7/26/2013","The Wolverine",1.15e+08,132556852,416456852,"20th Century Fox","PG-13","Action"
"153","2/7/1997","Dante's Peak",1.15e+08,67163857,178200000,"Universal","PG-13","Drama"
"154","4/22/2016","The Huntsman: Winter’s War",1.15e+08,48003015,165149302,"Universal","PG-13","Action"
"155","6/14/2002","Windtalkers",1.15e+08,40914068,77628265,"MGM","R","Action"
"156","12/25/2010","Gulliver's Travels",1.12e+08,42779261,232017848,"20th Century Fox","PG","Adventure"
"157","12/15/2017","Ferdinand",1.11e+08,84410380,289867087,"20th Century Fox","PG","Adventure"
"158","5/18/2018","Deadpool 2",1.1e+08,318491426,733809601,"20th Century Fox","R","Action"
"159","12/22/2006","Night at the Museum",1.1e+08,250863268,579446407,"20th Century Fox","PG","Adventure"
"160","6/10/2005","Mr. and Mrs. Smith",1.1e+08,186336279,486124090,"20th Century Fox","PG-13","Action"
"161","5/29/2015","San Andreas",1.1e+08,155190832,457199280,"Warner Bros.","PG-13","Adventure"
"162","7/29/2011","The Smurfs",1.1e+08,142614158,563749323,"Sony Pictures","PG","Adventure"
"163","6/27/2007","Live Free or Die Hard",1.1e+08,134529403,382288147,"20th Century Fox","PG-13","Action"
"164","3/20/2015","The Divergent Series: Insurgent",1.1e+08,130179072,295075882,"Lionsgate","PG-13","Action"
"165","12/10/2004","Ocean's Twelve",1.1e+08,125531634,362989076,"Warner Bros.","PG-13","Adventure"
"166","12/19/1997","Tomorrow Never Dies",1.1e+08,125304276,339504276,"MGM","PG-13","Action"
"167","6/28/2000","The Patriot",1.1e+08,113330342,215300000,"Sony Pictures","R","Drama"
"168","3/7/2014","300: Rise of an Empire",1.1e+08,106580051,330780051,"Warner Bros.","R","Action"
"169","1/14/2011","The Green Hornet",1.1e+08,98780042,229155503,"Sony Pictures","PG-13","Action"
"170","10/7/2011","Real Steel",1.1e+08,85463309,263880341,"Walt Disney","PG-13","Action"
"171","6/11/2010","The A-Team",1.1e+08,77222099,177241171,"20th Century Fox","PG-13","Action"
"172","7/31/2013","The Smurfs 2",1.1e+08,71017784,348547523,"Sony Pictures","PG","Adventure"
"173","3/18/2016","The Divergent Series: Allegiant",1.1e+08,66184051,171871661,"Lionsgate","PG-13","Action"
"174","6/12/2009","The Taking of Pelham 123",1.1e+08,65452312,152364370,"Sony Pictures","R","Action"
"175","11/1/2013","Ender's Game",1.1e+08,61737191,127983283,"Lionsgate","PG-13","Adventure"
"176","4/2/2004","Home on the Range",1.1e+08,50026353,76482461,"Walt Disney","PG","Adventure"
"177","6/13/1997","Speed 2: Cruise Control",1.1e+08,48097081,150468000,"20th Century Fox","PG-13","Action"
"178","5/6/2005","Kingdom of Heaven",1.1e+08,47398413,218853353,"20th Century Fox","R","Adventure"
"179","3/31/2017","Ghost in the Shell",1.1e+08,40563557,167918847,"Paramount Pictures","PG-13","Action"
"180","11/21/2003","The Cat in the Hat",1.09e+08,101018283,133818283,"Universal","PG","Adventure"
"181","12/25/2001","Ali",1.09e+08,58183966,87683966,"Sony Pictures","R","Drama"
"182","11/23/2016","Allied",1.06e+08,40098064,119285656,"Paramount Pictures","R","Drama"
"183","7/16/2004","I, Robot",1.05e+08,144801023,348629585,"20th Century Fox","PG-13","Action"
"184","12/17/1999","Stuart Little",1.05e+08,140015224,298815224,"Sony Pictures","PG","Adventure"
"185","11/25/2009","The Princess and the Frog",1.05e+08,104400899,270997378,"Walt Disney","G","Adventure"
"186","3/7/2008","10,000 B.C.",1.05e+08,94784201,269065678,"Warner Bros.","PG-13","Adventure"
"187","7/22/2016","Ice Age: Collision Course",1.05e+08,64063008,403092412,"20th Century Fox","PG","Adventure"
"188","9/22/2017","Kingsman: The Golden Circle",1.04e+08,100234838,408822328,"20th Century Fox","R","Action"
"189","6/9/2000","Gone in 60 Seconds",103300000,101643008,232643008,"Walt Disney","PG-13","Action"
"190","5/23/2013","The Hangover 3",1.03e+08,112200072,362000072,"Warner Bros.","R","Comedy"
"191","3/9/2018","A Wrinkle in Time",1.03e+08,100478608,133401882,"Walt Disney","PG","Adventure"
"192","7/1/2009","Public Enemies",102500000,97104620,212282709,"Universal","R","Drama"
"193","11/17/2006","Casino Royale",1.02e+08,167365000,594420283,"Sony Pictures","PG-13","Action"
"194","6/21/2002","Minority Report",1.02e+08,132024714,358824714,"20th Century Fox","PG-13","Action"
"195","10/26/2012","Cloud Atlas",1.02e+08,27108272,130673154,"Warner Bros.","R","Drama"
"196","7/2/1991","Terminator 2: Judgment Day",1e+08,203464105,515419827,"Sony Pictures","R","Action"
"197","6/16/1995","Batman Forever",1e+08,184031112,336529144,"Warner Bros.","PG-13","Action"
"198","7/27/2001","Planet of the Apes",1e+08,180011740,362211740,"20th Century Fox","PG-13","Adventure"
"199","11/19/2004","National Treasure",1e+08,173005002,331323410,"Walt Disney","PG","Adventure"
"200","10/5/2018","Venom",1e+08,171125095,461825095,"Sony Pictures","PG-13","Action"
"201","12/22/2010","Little Fockers",1e+08,148438600,310650574,"Universal","PG-13","Comedy"
"202","7/15/1994","True Lies",1e+08,146282411,365300000,"20th Century Fox","R","Action"
"203","11/2/2007","American Gangster",1e+08,130164645,267985456,"Universal","R","Drama"
"204","9/18/2009","Cloudy with a Chance of Meatballs",1e+08,124870275,236827677,"Sony Pictures","PG","Adventure"
"205","8/6/2010","The Other Guys",1e+08,119219978,170936470,"Sony Pictures","PG-13","Comedy"
"206","5/24/2013","Epic",1e+08,107518682,262794441,"20th Century Fox","PG","Adventure"
"207","6/21/1996","Eraser",1e+08,101295562,234400000,"Warner Bros.","R","Action"
"208","6/21/1996","The Hunchback of Notre Dame",1e+08,100138851,325500000,"Walt Disney","G","Adventure"
"209","12/15/2000","The Emperor's New Groove",1e+08,89296573,169296573,"Walt Disney","G","Adventure"
"210","8/17/2012","The Expendables 2",1e+08,85028192,311979256,"Lionsgate","R","Action"
"211","10/16/2009","Where the Wild Things Are",1e+08,77233467,99123656,"Warner Bros.","PG","Adventure"
"212","12/15/2006","Eragon",1e+08,75030163,249488115,"20th Century Fox","PG","Adventure"
"213","7/25/2014","Hercules",1e+08,72688614,243388614,"Paramount Pictures","PG-13","Action"
"214","11/24/1999","End of Days",1e+08,66889043,212026975,"Universal","R","Action"
"215","6/11/2004","The Stepford Wives",1e+08,59475623,96221971,"Paramount Pictures","PG-13","Comedy"
"216","6/8/2007","Surf's Up",1e+08,58867694,145395745,"Sony Pictures","PG","Adventure"
"217","12/8/2006","Blood Diamond",1e+08,57377916,171377916,"Warner Bros.","R","Action"
"218","11/7/1997","Starship Troopers",1e+08,54768952,121100000,"Sony Pictures","R","Action"
"219","6/5/2009","Land of the Lost",1e+08,49438370,69548641,"Universal","PG-13","Comedy"
"220","7/23/2004","Catwoman",1e+08,40202379,82145379,"Warner Bros.","PG-13","Action"
"221","8/15/2014","The Expendables 3",1e+08,39322544,209461378,"Lionsgate","PG-13","Action"
"222","11/27/2002","Treasure Planet",1e+08,38120554,91800000,"Walt Disney","PG","Adventure"
"223","3/12/2010","Green Zone",1e+08,35497337,97523020,"Universal","R","Drama"
"224","10/20/2017","Geostorm",1e+08,33700160,220800160,"Warner Bros.","PG-13","Action"
"225","12/11/2015","In the Heart of the Sea",1e+08,25020758,89693309,"Warner Bros.","PG-13","Adventure"
"226","2/18/2005","Son of the Mask",1e+08,17018422,59918422,"New Line","PG","Adventure"
"227","8/16/2002","The Adventures of Pluto Nash",1e+08,4411102,7094995,"Warner Bros.","PG-13","Comedy"
"228","1/20/2012","Jin líng shí san chai",1e+08,311434,98227017,"Wrekin Hill Enterta…","R","Drama"
"229","3/15/2019","Wonder Park",1e+08,0,0,"Paramount Pictures","PG","Adventure"
"230","11/6/2015","The Peanuts Movie",9.9e+07,130178411,250091610,"20th Century Fox","G","Adventure"
"231","5/4/2001","The Mummy Returns",9.8e+07,202007640,435040395,"Universal","PG-13","Adventure"
"232","12/20/2002","Gangs of New York",9.7e+07,77730500,183124621,"Miramax","R","Drama"
"233","5/19/2017","Alien: Covenant",9.7e+07,74262031,238521247,"20th Century Fox","R","Horror"
"234","3/13/2015","Cinderella",9.5e+07,201151353,534551353,"Walt Disney","PG","Drama"
"235","7/13/2012","Ice Age: Continental Drift",9.5e+07,161321843,879765137,"20th Century Fox","PG","Adventure"
"236","12/28/2001","Black Hawk Down",9.5e+07,108638745,159691085,"Sony Pictures","R","Action"
"237","5/27/2010","Sex and the City 2",9.5e+07,95347692,294680778,"Warner Bros.","R","Comedy"
"238","8/10/2012","The Campaign",9.5e+07,86907746,104907746,"Warner Bros.","R","Comedy"
"239","11/12/2010","Unstoppable",9.5e+07,81562942,165720921,"20th Century Fox","PG-13","Action"
"240","5/9/1997","The Fifth Element",9.5e+07,63570862,263898761,"Sony Pictures","PG-13","Action"
"241","3/31/2000","The Road to El Dorado",9.5e+07,50802661,65700000,"Dreamworks SKG","PG","Adventure"
"242","12/11/2009","The Lovely Bones",9.5e+07,44114232,94894448,"Paramount Pictures","PG-13","Drama"
"243","2/6/2015","Seventh Son",9.5e+07,17725785,109485785,"Universal","PG-13","Adventure"
"244","5/30/2003","Finding Nemo",9.4e+07,380529370,936429370,"Walt Disney","G","Adventure"
"245","6/15/2001","Lara Croft: Tomb Raider",9.4e+07,131144183,273330185,"Paramount Pictures","PG-13","Adventure"
"246","2/13/2015","Kingsman: The Secret Service",9.4e+07,128261724,404561724,"20th Century Fox","R","Action"
"247","7/18/2001","Jurassic Park III",9.3e+07,181166115,365900000,"Universal","PG-13","Action"
"248","8/5/2011","Rise of the Planet of the Apes",9.3e+07,176760185,482860185,"20th Century Fox","PG-13","Adventure"
"249","2/14/2008","The Spiderwick Chronicles",92500000,71195053,162839667,"Paramount Pictures","PG","Adventure"
"250","11/5/2004","The Incredibles",9.2e+07,261441092,614726752,"Walt Disney","PG","Adventure"
"251","2/14/2013","A Good Day to Die Hard",9.2e+07,67349198,304249198,"20th Century Fox","R","Action"
"252","12/22/1995","Cutthroat Island",9.2e+07,10017322,18517322,"MGM","PG-13","Adventure"
"253","12/25/2013","The Secret Life of Walter Mitty",9.1e+07,58236838,187861183,"20th Century Fox","PG","Adventure"
"254","12/20/2017","Jumanji: Welcome to the Jungle",9e+07,404508916,961758540,"Sony Pictures","PG-13","Adventure"
"255","7/1/1997","Men in Black",9e+07,250690539,587790539,"Sony Pictures","PG-13","Adventure"
"256","11/19/1999","Toy Story 2",9e+07,245852179,511358276,"Walt Disney","G","Adventure"
"257","8/3/2001","Rush Hour 2",9e+07,226164286,347425832,"New Line","PG-13","Action"
"258","12/25/2009","Sherlock Holmes",9e+07,209028679,498438212,"Warner Bros.","PG-13","Adventure"
"259","7/1/2009","Ice Age: Dawn of the Dinosaurs",9e+07,196573705,859701857,"20th Century Fox","PG","Adventure"
"260","4/15/2011","Rio",9e+07,143619809,487519809,"20th Century Fox","G","Adventure"
"261","10/6/2006","The Departed",9e+07,132384315,289660619,"Warner Bros.","R","Drama"
"262","11/3/2000","Charlie's Angels",9e+07,125305545,259736090,"Sony Pictures","PG-13","Action"
"263","6/19/1998","Mulan",9e+07,120620254,303500000,"Walt Disney","G","Adventure"
"264","8/13/2008","Tropic Thunder",9e+07,110515313,191145256,"Paramount Pictures","R","Comedy"
"265","7/11/1997","Contact",9e+07,100920329,165900000,"Warner Bros.","PG","Drama"
"266","6/6/2008","You Don't Mess With the Zohan",9e+07,100018837,202910991,"Sony Pictures","PG-13","Comedy"
"267","5/19/1995","Die Hard: With a Vengeance",9e+07,100012499,366101666,"20th Century Fox","R","Action"
"268","6/8/2001","Atlantis: The Lost Empire",9e+07,84052762,186049020,"Walt Disney","PG","Adventure"
"269","7/24/2015","Pixels",9e+07,78765986,244041804,"Sony Pictures","PG-13","Adventure"
"270","6/29/2001","Artificial Intelligence: AI",9e+07,78616689,235900000,"Warner Bros.","PG-13","Drama"
"271","11/26/2003","The Haunted Mansion",9e+07,75817994,155750628,"Walt Disney","PG","Adventure"
"272","8/4/2000","Hollow Man",9e+07,73209340,191200000,"Sony Pictures","R","Horror"
"273","8/7/2013","Percy Jackson: Sea of Monsters",9e+07,68559554,200859554,"20th Century Fox","PG","Adventure"
"274","11/21/2001","Spy Game",9e+07,62362560,143049560,"Universal","R","Action"
"275","4/4/1997","The Saint",9e+07,61363304,169400000,"Paramount Pictures","PG-13","Action"
"276","3/10/2000","Mission to Mars",9e+07,60874615,1.06e+08,"Walt Disney","PG","Adventure"
"277","12/17/1999","Bicentennial Man",9e+07,58220776,87420776,"Walt Disney","PG","Drama"
"278","3/16/2018","Tomb Raider",9e+07,57421715,272648985,"Warner Bros.","PG-13","Action"
"279","7/7/2004","King Arthur",9e+07,51877963,203877963,"Walt Disney","PG-13","Adventure"
"280","4/25/1997","Volcano",9e+07,47546796,120100000,"20th Century Fox","PG-13","Action"
"281","7/19/2002","K-19: The Widowmaker",9e+07,35168966,65716126,"Paramount Pictures","PG-13","Action"
"282","4/21/2017","The Promise",9e+07,8224288,10551417,"Open Road","PG-13","Drama"
"283","5/10/1996","Twister",8.8e+07,241688385,495700000,"Warner Bros.","PG-13","Action"
"284","6/3/2005","Cinderella Man",8.8e+07,61649911,105021488,"Universal","PG-13","Drama"
"285","9/14/2018","The Predator",8.8e+07,50787159,127987159,"20th Century Fox","R","Action"
"286","7/8/2005","Fantastic Four",87500000,154696080,333132750,"20th Century Fox","PG-13","Action"
"287","2/9/2001","Hannibal",8.7e+07,165092266,350100280,"MGM","R","Horror"
"288","7/25/2003","Seabiscuit",8.6e+07,120277854,148715342,"Universal","PG-13","Drama"
"289","12/22/2000","Cast Away",8.5e+07,233632142,427230516,"20th Century Fox","PG-13","Drama"
"290","11/17/2006","Happy Feet",8.5e+07,198000317,385000317,"Warner Bros.","PG","Adventure"
"291","7/25/1997","Air Force One",8.5e+07,172956409,315268353,"Sony Pictures","R","Action"
"292","4/3/2009","Fast & Furious",8.5e+07,155064265,363064265,"Universal","PG-13","Action"
"293","3/14/2008","Horton Hears a Who",8.5e+07,154529439,299477886,"20th Century Fox","G","Adventure"
"294","3/21/2014","Divergent",8.5e+07,150947895,276014965,"Lionsgate","PG-13","Adventure"
"295","9/28/2012","Hotel Transylvania",8.5e+07,148313048,378505812,"Sony Pictures","PG","Adventure"
"296","7/20/2007","I Now Pronounce You Chuck and Larry",8.5e+07,119725280,185708462,"Universal","PG-13","Comedy"
"297","6/8/2007","Ocean's Thirteen",8.5e+07,117144465,311744465,"Warner Bros.","PG-13","Adventure"
"298","11/20/1998","Enemy of the State",8.5e+07,111549836,250649836,"Walt Disney","R","Action"
"299","9/29/2006","Open Season",8.5e+07,85105259,191932158,"Sony Pictures","PG","Adventure"
"300","11/4/2011","Tower Heist",8.5e+07,78046570,150422946,"Universal","PG-13","Comedy"
"301","11/22/2000","102 Dalmatians",8.5e+07,66941559,66941559,"Walt Disney","G","Adventure"
"302","3/30/2012","Mirror Mirror",8.5e+07,64935167,173613482,"Relativity","PG","Adventure"
"303","12/9/2005","Memoirs of a Geisha",8.5e+07,57010853,161510853,"Sony Pictures","PG-13","Drama"
"304","3/16/2001","Enemy at the Gates",8.5e+07,51396781,96971293,"Paramount Pictures","R","Drama"
"305","6/18/1993","Last Action Hero",8.5e+07,50016394,137298489,"Sony Pictures","PG-13","Action"
"306","9/26/2003","The Rundown",8.5e+07,47641743,80831893,"Universal","PG-13","Action"
"307","11/23/2011","Arthur Christmas",8.5e+07,46462469,149717124,"Sony Pictures","PG","Adventure"
"308","1/20/2017","xXx: Return of Xander Cage",8.5e+07,44898413,345044476,"Paramount Pictures","PG-13","Action"
"309","11/13/1998","Meet Joe Black",8.5e+07,44650003,44650003,"Universal","PG-13","Drama"
"310","2/8/2002","Collateral Damage",8.5e+07,40048332,78353508,"Warner Bros.","R","Action"
"311","3/15/2002","Showtime",8.5e+07,37948765,78948765,"Warner Bros.","PG-13","Comedy"
"312","6/30/1995","Judge Dredd",8.5e+07,34687912,113487912,"Walt Disney","R","Action"
"313","8/13/2010","Scott Pilgrim vs. The World",8.5e+07,31611316,48056764,"Universal","PG-13","Comedy"
"314","3/28/2003","The Core",8.5e+07,31111260,74132631,"Paramount Pictures","PG-13","Action"
"315","5/9/1997","Father's Day",8.5e+07,28681080,35681080,"Warner Bros.","PG-13","Comedy"
"316","6/14/2002","Scooby-Doo",8.4e+07,153294164,276294164,"Warner Bros.","PG","Adventure"
"317","7/28/2000","Nutty Professor II: The Klumps",8.4e+07,123307945,166307945,"Universal","PG-13","Comedy"
"318","7/19/2013","RED 2",8.4e+07,53262560,141507355,"Lionsgate","PG-13","Action"
"319","6/23/2006","Click",82500000,137355633,237685089,"Sony Pictures","PG-13","Comedy"
"320","12/15/2006","Charlotte's Web",82500000,82985708,143985708,"Paramount Pictures","G","Drama"
"321","2/14/2008","Jumper",82500000,80172128,222640812,"20th Century Fox","PG-13","Adventure"
"322","7/11/2008","Hellboy II: The Golden Army",82500000,75986503,160388063,"Universal","PG-13","Action"
"323","5/27/2005","The Longest Yard",8.2e+07,158119460,191558505,"Paramount Pictures","PG-13","Comedy"
"324","8/13/2010","The Expendables",8.2e+07,103068524,268268174,"Lionsgate","R","Action"
"325","11/17/2000","The 6th Day",8.2e+07,34543701,96024898,"Sony Pictures","PG-13","Action"
"326","5/23/2003","Bruce Almighty",8.1e+07,242704995,484468608,"Universal","PG-13","Comedy"
"327","5/26/2011","The Hangover Part II",8e+07,254464305,586464305,"Warner Bros.","R","Comedy"
"328","5/21/1996","Mission: Impossible",8e+07,180981886,457697994,"Paramount Pictures","PG-13","Action"
"329","2/10/2017","The Lego Batman Movie",8e+07,175750384,310692896,"Warner Bros.","PG","Adventure"
"330","9/25/2015","Hotel Transylvania 2",8e+07,169700110,469500298,"Sony Pictures","PG","Adventure"
"331","6/18/1992","Batman Returns",8e+07,162833635,266824291,"Warner Bros.","PG-13","Action"
"332","5/7/1999","The Mummy",8e+07,155385488,416385488,"Universal","PG-13","Adventure"
"333","5/19/2006","Over the Hedge",8e+07,155019340,343397247,"Paramount Pictures","PG","Adventure"
"334","6/21/2002","Lilo & Stitch",8e+07,145771527,245800000,"Walt Disney","PG","Adventure"
"335","5/8/1998","Deep Impact",8e+07,140464664,349464664,"Paramount Pictures","PG-13","Adventure"
"336","7/12/2013","Grown Ups 2",8e+07,133668525,247023808,"Sony Pictures","PG-13","Comedy"
"337","6/20/2008","Get Smart",8e+07,130319208,226739416,"Warner Bros.","PG-13","Comedy"
"338","3/11/2005","Robots",8e+07,128200012,260700012,"20th Century Fox","PG","Adventure"
"339","11/26/2008","Four Christmases",8e+07,120146040,168311558,"Warner Bros.","PG-13","Comedy"
"340","6/27/1997","Face/Off",8e+07,112276146,241200000,"Paramount Pictures","R","Action"
"341","12/25/2008","Bedtime Stories",8e+07,110101975,221468935,"Walt Disney","PG","Adventure"
"342","7/12/2002","Road to Perdition",8e+07,104054514,183354514,"Dreamworks SKG","R","Drama"
"343","2/14/2003","Daredevil",8e+07,102543518,182782518,"20th Century Fox","PG-13","Action"
"344","6/6/1997","Con Air",8e+07,101117573,224117573,"Walt Disney","R","Action"
"345","12/17/2010","Yogi Bear",8e+07,100246011,204774690,"Warner Bros.","PG","Adventure"
"346","12/25/2003","Cold Mountain",8e+07,95632614,165173909,"Miramax","R","Drama"
"347","1/15/2010","The Book of Eli",8e+07,94835059,158750817,"Warner Bros.","R","Action"
"348","11/26/1997","Flubber",8e+07,92993801,177993801,"Walt Disney","PG","Comedy"
"349","7/23/1999","The Haunting",8e+07,91188905,180188905,"Dreamworks SKG","PG-13","Horror"
"350","11/15/1996","Space Jam",8e+07,90463534,250200000,"Warner Bros.","PG","Adventure"
"351","10/17/2014","Fury",8e+07,85817906,210315681,"Sony Pictures","R","Drama"
"352","2/10/2006","The Pink Panther",8e+07,82226474,158926474,"Sony Pictures","PG","Adventure"
"353","12/12/2008","The Day the Earth Stood Still",8e+07,79366978,233066978,"20th Century Fox","PG-13","Adventure"
"354","5/24/2002","Spirit: Stallion of the Cimarron",8e+07,73215310,106515310,"Dreamworks SKG","G","Adventure"
"355","6/8/2001","Swordfish",8e+07,69772969,147080413,"Warner Bros.","R","Action"
"356","4/3/1998","Lost In Space",8e+07,69117629,136047317,"New Line","PG-13","Adventure"
"357","9/28/2018","Smallfoot",8e+07,66361035,137161035,"Warner Bros.","PG","Adventure"
"358","6/24/2005","Bewitched",8e+07,63313159,131159306,"Sony Pictures","PG-13","Comedy"
"359","3/8/2002","The Time Machine",8e+07,56684819,98983590,"Dreamworks SKG","PG-13","Adventure"
"360","10/2/1998","What Dreams May Come",8e+07,55485043,71485043,"Polygram","PG-13","Drama"
"361","12/25/1998","Mighty Joe Young",8e+07,50632037,50632037,"Walt Disney","PG","Adventure"
"362","10/28/2005","The Legend of Zorro",8e+07,45575336,141475336,"Sony Pictures","PG","Adventure"
"363","11/10/2000","Little Nicky",8e+07,39442871,58270391,"New Line","PG-13","Comedy"
"364","9/25/2009","Surrogates",8e+07,38577772,119668350,"Walt Disney","PG-13","Action"
"365","6/8/2001","Evolution",8e+07,38311134,98341932,"Dreamworks SKG","PG-13","Comedy"
"366","8/26/2005","The Brothers Grimm",8e+07,37899638,105299638,"Miramax/Dimension","PG-13","Adventure"
"367","12/13/1996","Mars Attacks!",8e+07,37771017,101371017,"Warner Bros.","PG-13","Comedy"
"368","4/14/2006","The Wild",8e+07,37384046,99010667,"Walt Disney","G","Adventure"
"369","12/20/2013","Walking with Dinosaurs",8e+07,36076121,123386322,"20th Century Fox","PG","Adventure"
"370","12/22/2000","Thirteen Days",8e+07,34566746,66554547,"New Line","PG-13","Drama"
"371","12/6/1996","Daylight",8e+07,32908290,158908290,"Universal","PG-13","Action"
"372","10/23/2015","The Last Witch Hunter",8e+07,27367660,131437876,"Lionsgate","PG-13","Action"
"373","2/21/2014","Pompeii",8e+07,23169033,108469033,"Sony Pictures","PG-13","Drama"
"374","11/14/2003","Looney Tunes: Back in Action",8e+07,20950820,54540662,"Warner Bros.","PG","Adventure"
"375","11/26/2003","Timeline",8e+07,19480739,26703184,"Paramount Pictures","PG-13","Adventure"
"376","11/25/1998","Babe: Pig in the City",8e+07,18319860,69131860,"Universal","G","Adventure"
"377","12/25/1997","The Postman",8e+07,17650704,20841123,"Warner Bros.","R","Action"
"378","11/10/2000","Red Planet",8e+07,17480890,33463969,"Warner Bros.","PG-13","Action"
"379","1/12/2007","Arthur et les Minimoys",8e+07,15132763,113325743,"Weinstein Co.","PG","Adventure"
"380","9/2/2005","A Sound of Thunder",8e+07,1900451,6300451,"Warner Bros.","PG-13","Action"
"381","6/15/1994","The Lion King",79300000,421785283,986332275,"Walt Disney","G","Adventure"
"382","2/10/2012","Journey 2: The Mysterious Island",7.9e+07,103860290,318146162,"Warner Bros.","PG","Adventure"
"383","11/11/2011","Jack and Jill",7.9e+07,74158157,150519217,"Sony Pictures","PG","Comedy"
"384","12/21/2001","A Beautiful Mind",7.8e+07,170708996,317668058,"Universal","PG-13","Drama"
"385","9/27/2013","Cloudy with a Chance of Meatballs 2",7.8e+07,119793567,274392649,"Sony Pictures","PG","Adventure"
"386","8/20/2004","Exorcist: The Beginning",7.8e+07,41814863,43957541,"Warner Bros.","R","Horror"
"387","2/12/2016","The Little Prince",77500000,1311213,102029819,"Entertainment One","PG","Adventure"
"388","7/3/2013","Despicable Me 2",7.6e+07,368065385,975216835,"Universal","PG","Adventure"
"389","6/6/2003","2 Fast 2 Furious",7.6e+07,127120058,236410607,"Universal","PG-13","Action"
"390","7/8/2016","The Secret Life of Pets",7.5e+07,368384330,886767422,"Universal","PG","Adventure"
"391","7/2/1996","Independence Day",7.5e+07,306169255,817400878,"20th Century Fox","PG-13","Adventure"
"392","12/21/2016","Sing",7.5e+07,270329045,634547945,"Universal","PG","Adventure"
"393","6/30/2017","Despicable Me 3",7.5e+07,264624300,1034520868,"Universal","PG","Adventure"
"394","5/22/1997","The Lost World: Jurassic Park",7.5e+07,229086679,618638999,"Universal","PG-13","Action"
"395","3/31/2006","Ice Age: The Meltdown",7.5e+07,195330621,651899282,"20th Century Fox","PG","Adventure"
"396","5/27/2005","Madagascar",7.5e+07,193595521,556559566,"Dreamworks SKG","PG","Adventure"
"397","6/25/2010","Grown Ups",7.5e+07,162001186,272223430,"Sony Pictures","PG-13","Comedy"
"398","10/1/2004","Shark Tale",7.5e+07,161412000,371917043,"Dreamworks SKG","PG","Adventure"
"399","7/14/2000","X-Men",7.5e+07,157299717,296872367,"20th Century Fox","PG-13","Action"
"400","6/27/2008","Wanted",7.5e+07,134508551,342416460,"Universal","R","Action"
"401","6/7/1996","The Rock",7.5e+07,134069511,336069511,"Walt Disney","R","Action"
"402","8/3/2018","Christopher Robin",7.5e+07,98677443,186977443,"Walt Disney","PG","Adventure"
"403","7/23/1999","Inspector Gadget",7.5e+07,97387965,97387965,"Walt Disney","PG","Adventure"
"404","11/11/2011","Immortals",7.5e+07,83504017,211562435,"Relativity","R","Action"
"405","6/18/2004","The Terminal",7.5e+07,77073959,218673959,"Dreamworks SKG","PG-13","Drama"
"406","2/18/2005","Constantine",7.5e+07,75976178,221594911,"Warner Bros.","R","Action"
"407","7/21/2006","Monster House",7.5e+07,73661010,141267370,"Sony Pictures","PG","Adventure"
"408","12/8/2000","Vertical Limit",7.5e+07,68473360,213500000,"Sony Pictures","PG-13","Action"
"409","12/21/2007","Charlie Wilson's War",7.5e+07,66661095,119512771,"Universal","R","Drama"
"410","3/4/2005","Be Cool",7.5e+07,55849401,94944017,"MGM","PG-13","Comedy"
"411","12/23/2005","Munich",7.5e+07,47379090,131492772,"Universal","R","Drama"
"412","6/4/2010","Killers",7.5e+07,47059963,95572749,"Lionsgate","PG-13","Action"
"413","8/14/2015","The Man From U.N.C.L.E.",7.5e+07,45445109,105445109,"Warner Bros.","PG-13","Action"
"414","3/7/2003","Tears of the Sun",7.5e+07,43632458,85632458,"Sony Pictures","R","Action"
"415","7/21/2006","Lady in the Water",7.5e+07,42285169,72785169,"Warner Bros.","PG-13","Drama"
"416","12/17/2004","Spanglish",7.5e+07,42044321,54344321,"Sony Pictures","PG-13","Comedy"
"417","12/17/1999","Anna and the King",7.5e+07,39251128,39251128,"20th Century Fox","PG-13","Drama"
"418","7/7/1995","First Knight",7.5e+07,37361412,127361412,"Sony Pictures","PG-13","Drama"
"419","3/25/2011","Sucker Punch",7.5e+07,36392502,89758389,"Warner Bros.","PG-13","Action"
"420","3/11/2005","Hostage",7.5e+07,34636443,77636443,"Miramax","R","Action"
"421","6/13/2003","Hollywood Homicide",7.5e+07,30207785,50409753,"Sony Pictures","PG-13","Action"
"422","6/16/2000","Titan A.E.",7.5e+07,22751979,36751979,"20th Century Fox","PG","Adventure"
"423","12/17/2004","Flight of the Phoenix",7.5e+07,21009180,34009180,"20th Century Fox","PG-13","Adventure"
"424","10/23/1998","Soldier",7.5e+07,14623082,14623082,"Warner Bros.","R","Action"
"425","1/15/1999","Virus",7.5e+07,14010690,30626690,"Universal","R","Action"
"426","2/23/2001","Monkeybone",7.5e+07,5409517,5409517,"20th Century Fox","PG-13","Comedy"
"427","7/10/2015","Minions",7.4e+07,336045770,1162781621,"Universal","PG","Adventure"
"428","5/20/2016","The Angry Birds Movie",7.3e+07,107509366,352829528,"Sony Pictures","PG","Adventure"
"429","2/13/1998","Sphere",7.3e+07,37068294,50168294,"Warner Bros.","PG-13","Horror"
"430","7/27/2007","The Simpsons Movie",72500000,183135014,527071022,"20th Century Fox","PG-13","Adventure"
"431","2/8/2008","Fool's Gold",72500000,70231041,109362966,"Warner Bros.","PG-13","Adventure"
"432","7/31/2009","Funny People",72500000,51855045,71880305,"Universal","R","Comedy"
"433","9/28/2007","The Kingdom",72500000,47467250,86509602,"Universal","R","Action"
"434","6/22/2001","Dr. Dolittle 2",7.2e+07,112950721,176101721,"20th Century Fox","PG","Adventure"
"435","5/24/1995","Braveheart",7.2e+07,75545647,209045244,"Paramount Pictures","R","Drama"
"436","11/4/2005","Jarhead",7.2e+07,62647540,96947540,"Universal","R","Drama"
"437","4/27/2001","Driven",7.2e+07,32616869,54616869,"Warner Bros.","PG-13","Action"
"438","12/21/2001","The Majestic",7.2e+07,27796042,37306334,"Warner Bros.","PG","Drama"
"439","6/25/2004","Two Brothers",7.2e+07,19176754,62176754,"Universal","PG","Drama"
"440","6/26/1998","Doctor Dolittle",71500000,144156605,294156605,"20th Century Fox","PG-13","Adventure"
"441","5/19/2004","Shrek 2",7e+07,441226247,937008132,"Dreamworks SKG","PG","Adventure"
"442","6/9/2006","Cars",7e+07,244082982,461651246,"Walt Disney","G","Adventure"
"443","6/22/1988","Who Framed Roger Rabbit?",7e+07,154112492,351500000,"Walt Disney","PG","Adventure"
"444","8/9/2002","xXx",7e+07,141930000,267200000,"Sony Pictures","PG-13","Action"
"445","6/8/2018","Ocean’s 8",7e+07,139377762,296277762,"Warner Bros.","PG-13","Action"
"446","11/8/1996","Ransom",7e+07,136492681,308700000,"Walt Disney","R","Action"
"447","8/21/2009","Inglourious Basterds",7e+07,120774594,316915264,"Weinstein Co.","R","Action"
"448","12/11/1991","Hook",7e+07,119654823,300854823,"Sony Pictures","PG","Adventure"
"449","7/3/1990","Die Hard 2",7e+07,117323878,239814025,"20th Century Fox","R","Action"
"450","8/8/2003","S.W.A.T.",7e+07,116877597,207154748,"Sony Pictures","PG-13","Action"
"451","11/10/2017","Daddy’s Home 2",7e+07,104029443,175809810,"Paramount Pictures","PG-13","Comedy"
"452","11/19/1999","Sleepy Hollow",7e+07,101068340,207068340,"Paramount Pictures","R","Horror"
"453","3/11/2011","Battle: Los Angeles",7e+07,83552429,213463976,"Sony Pictures","PG-13","Action"
"454","8/13/2004","AVP: Alien Vs. Predator",7e+07,80281096,172543519,"20th Century Fox","PG-13","Horror"
"455","12/25/2011","War Horse",7e+07,79883359,156815529,"Walt Disney","PG-13","Drama"
"456","3/1/2002","We Were Soldiers",7e+07,78120196,114658262,"Paramount Pictures","R","Drama"
"457","2/7/2014","The Monuments Men",7e+07,78031620,158702748,"Sony Pictures","PG-13","Drama"
"458","9/23/2016","Storks",7e+07,72679278,174030321,"Warner Bros.","PG","Adventure"
"459","12/11/1998","Star Trek: Insurrection",7e+07,70187658,117800000,"Paramount Pictures","PG","Adventure"
"460","12/10/2003","Big Fish",7e+07,66432867,123954323,"Sony Pictures","PG-13","Drama"
"461","1/20/2012","Underworld: Awakening",7e+07,62321039,160379930,"Sony Pictures","R","Action"
"462","9/22/2017","The Lego Ninjago Movie",7e+07,59281555,122739546,"Warner Bros.","PG","Adventure"
"463","10/10/2014","Dracula Untold",7e+07,55991880,220241723,"Universal","PG-13","Action"
"464","9/29/2006","The Guardian",7e+07,55011732,94973540,"Walt Disney","PG-13","Action"
"465","8/9/1989","The Abyss",7e+07,54243125,54243125,"20th Century Fox","PG-13","Action"
"466","9/24/2010","Wall Street 2: Money Never Sleeps",7e+07,52474616,137431619,"20th Century Fox","PG-13","Drama"
"467","1/14/2011","The Dilemma",7e+07,48475290,70546865,"Universal","PG-13","Comedy"
"468","12/25/2005","Rumor Has It",7e+07,42996140,88933562,"Warner Bros.","PG-13","Comedy"
"469","11/6/1998","The Siege",7e+07,40934175,116625798,"20th Century Fox","R","Action"
"470","8/10/2007","Stardust",7e+07,38634938,137022245,"Paramount Pictures","PG-13","Adventure"
"471","10/8/1997","Seven Years in Tibet",7e+07,37945884,131445884,"Sony Pictures","PG-13","Drama"
"472","9/14/2007","The Brave One",7e+07,36793804,69792704,"Warner Bros.","R","Drama"
"473","11/1/2002","I Spy",7e+07,33561137,60279822,"Sony Pictures","PG-13","Action"
"474","6/7/2002","Bad Company",7e+07,30157016,69157016,"Walt Disney","PG-13","Action"
"475","10/21/2005","Doom",7e+07,28212337,54612337,"Universal","R","Horror"
"476","9/23/2011","Killer Elite",7e+07,25124986,65409046,"Open Road","R","Action"
"477","1/16/1998","Hard Rain",7e+07,19870567,19870567,"Paramount Pictures","R","Action"
"478","2/15/2002","Hart's War",7e+07,19076815,33076815,"MGM","R","Drama"
"479","2/8/2002","Rollerball",7e+07,18990542,25852508,"MGM","PG-13","Action"
"480","1/10/2014","The Legend of Hercules",7e+07,18848538,58953319,"Lionsgate","PG-13","Adventure"
"481","9/20/2002","Ballistic: Ecks vs. Sever",7e+07,14294842,14294842,"Warner Bros.","R","Action"
"482","8/10/2001","Osmosis Jones",7e+07,13596911,13596911,"Warner Bros.","PG","Adventure"
"483","5/9/2014","Legends of Oz: Dorothy’s Return",7e+07,8462347,20107933,"Clarius Entertainment","PG","Adventure"
"484","5/28/2010","Agora",7e+07,619423,38992292,"Newmarket Films","R","Drama"
"485","7/9/2010","Despicable Me",6.9e+07,251513985,543464573,"Universal","PG","Adventure"
"486","7/30/2010","Dinner for Schmucks",6.9e+07,73026337,86796502,"Paramount Pictures","PG-13","Comedy"
"487","6/30/2010","The Twilight Saga: Eclipse",6.8e+07,300531751,706102828,"Summit Entertainment","PG-13","Drama"
"488","5/31/2002","The Sum of All Fears",6.8e+07,118471320,193500000,"Paramount Pictures","PG-13","Action"
"489","6/26/2015","Ted 2",6.8e+07,81476385,217214143,"Universal","R","Comedy"
"490","2/25/2011","Hall Pass",6.8e+07,45060734,87173475,"Warner Bros.","R","Comedy"
"491","11/22/1995","Money Train",6.8e+07,35324232,77224232,"Sony Pictures","R","Action"
"492","3/21/2003","Dreamcatcher",6.8e+07,33685268,75685268,"Warner Bros.","R","Drama"
"493","8/6/1999","Mystery Men",6.8e+07,29762011,33462011,"Universal","PG-13","Comedy"
"494","11/5/1999","The Insider",6.8e+07,28965197,60265197,"Walt Disney","R","Drama"
"495","12/22/2017","Downsizing",6.8e+07,24449754,48681134,"Paramount Pictures","R","Comedy"
"496","3/2/2012","Doctor Seuss' The Lorax",67500000,214030500,350976753,"Universal","PG","Adventure"
"497","6/22/2012","Abraham Lincoln: Vampire Hunter",67500000,37519139,137489730,"20th Century Fox","R","Horror"
"498","9/20/1996","Last Man Standing",6.7e+07,18115927,18115927,"New Line","R","Action"
"499","8/17/2007","The Last Legion",6.7e+07,5932060,25357771,"Weinstein/Dimension","PG-13","Action"
"500","6/19/1998","The X Files: Fight the Future",6.6e+07,83898313,189176423,"20th Century Fox","PG-13","Action"
"501","3/14/2014","Need for Speed",6.6e+07,43568507,194169619,"Walt Disney","PG-13","Action"
"502","7/24/1998","Saving Private Ryan",6.5e+07,216335085,485035085,"Dreamworks SKG","R","Drama"
"503","11/9/2012","Lincoln",6.5e+07,182207973,273346281,"Walt Disney","PG-13","Drama"
"504","3/15/2002","Ice Age",6.5e+07,176387405,386116343,"20th Century Fox","PG","Adventure"
"505","6/30/1995","Apollo 13",6.5e+07,173772767,335802271,"Universal","PG","Drama"
"506","3/31/1999","The Matrix",6.5e+07,171479930,463517383,"Warner Bros.","R","Action"
"507","11/1/2002","The Santa Clause 2",6.5e+07,139225854,172825854,"Walt Disney","G","Adventure"
"508","6/1/1990","Total Recall",6.5e+07,119394839,261400000,"Sony Pictures","R","Action"
"509","12/18/1998","You've Got Mail",6.5e+07,115821495,250800000,"Warner Bros.","PG","Drama"
"510","12/25/2014","Unbroken",6.5e+07,115637895,163527824,"Universal","PG-13","Drama"
"511","6/5/2015","Spy!",6.5e+07,110825712,233121406,"20th Century Fox","R","Comedy"
"512","11/5/2010","Due Date",6.5e+07,100539043,211739043,"Warner Bros.","R","Comedy"
"513","7/25/2008","Step Brothers",6.5e+07,100468793,128468793,"Sony Pictures","R","Comedy"
"514","12/15/1995","Jumanji",6.5e+07,100458310,262758310,"Sony Pictures","PG","Adventure"
"515","7/17/1998","The Mask of Zorro",6.5e+07,93828745,233700000,"Sony Pictures","PG-13","Adventure"
"516","8/4/2000","Space Cowboys",6.5e+07,90454043,128874043,"Warner Bros.","PG-13","Adventure"
"517","5/28/1993","Cliffhanger",6.5e+07,84049211,2.55e+08,"Sony Pictures","R","Action"
"518","8/12/2016","Pete’s Dragon",6.5e+07,76233151,137768975,"Walt Disney","PG","Adventure"
"519","2/9/1996","Broken Arrow",6.5e+07,70645997,148345997,"20th Century Fox","R","Action"
"520","8/9/2006","World Trade Center",6.5e+07,70278893,163295654,"Paramount Pictures","PG-13","Drama"
"521","7/7/2000","The Kid",6.5e+07,69688384,69688384,"Walt Disney","PG","Comedy"
"522","12/19/2003","Mona Lisa Smile",6.5e+07,63803100,141205169,"Sony Pictures","PG-13","Drama"
"523","5/16/2012","The Dictator",6.5e+07,59650222,180148897,"Paramount Pictures","R","Comedy"
"524","7/16/1999","Eyes Wide Shut",6.5e+07,55691208,104267443,"Warner Bros.","R","Drama"
"525","12/8/2004","Blade: Trinity",6.5e+07,52397389,131353165,"New Line","R","Action"
"526","12/22/2006","We Are Marshall",6.5e+07,43545364,43545364,"Warner Bros.","PG","Drama"
"527","9/14/2012","Resident Evil: Retribution",6.5e+07,42345531,238940997,"Sony Pictures","R","Action"
"528","3/20/1998","Primary Colors",6.5e+07,39017984,39017984,"Universal","R","Comedy"
"529","10/15/1999","Fight Club",6.5e+07,37030102,100851705,"20th Century Fox","R","Drama"
"530","8/22/2008","Death Race",6.5e+07,36316032,72516819,"Universal","R","Action"
"531","10/11/1996","The Long Kiss Goodnight",6.5e+07,33447612,33447612,"New Line","R","Action"
"532","12/8/2000","Proof of Life",6.5e+07,32598931,62761005,"Warner Bros.","R","Action"
"533","11/11/2005","Zathura",6.5e+07,28045540,58545540,"Sony Pictures","PG","Adventure"
"534","1/14/2005","Elektra",6.5e+07,24409722,56824633,"20th Century Fox","PG-13","Action"
"535","10/23/2009","Astro Boy",6.5e+07,19551067,41636243,"Summit Entertainment","PG","Adventure"
"536","1/24/2014","I, Frankenstein",6.5e+07,19075290,74575290,"Lionsgate","PG-13","Action"
"537","5/24/1991","Hudson Hawk",6.5e+07,17218916,17218916,"Sony Pictures","R","Action"
"538","8/22/2014","Sin City: A Dame to Kill For",6.5e+07,13757804,40650842,"Weinstein Co.","R","Action"
"539","12/25/2016","Live by Night",6.5e+07,10378555,21778555,"Warner Bros.","R","Drama"
"540","10/27/2000","Lucky Numbers",6.5e+07,10014234,10014234,"Paramount Pictures","R","Comedy"
"541","9/23/2005","Oliver Twist",6.5e+07,2070920,26670920,"Sony/TriStar","PG-13","Drama"
"542","9/4/2015","Tian jiang xiong shi",6.5e+07,74070,122519874,"Lionsgate","R","Action"
"543","7/14/2006","Little Man",6.4e+07,58636047,101636047,"Sony Pictures","PG-13","Comedy"
"544","10/8/1999","Random Hearts",6.4e+07,31054924,63200000,"Sony Pictures","R","Drama"
"545","12/27/2006","Perfume: The Story of a Murderer",63700000,2223293,133603463,"Paramount Pictures","R","Drama"
"546","6/11/1993","Jurassic Park",6.3e+07,395708305,1038812584,"Universal","PG-13","Action"
"547","7/25/2002","Austin Powers in Goldmember",6.3e+07,213117789,296338663,"New Line","PG-13","Comedy"
"548","4/1/2011","Hop",6.3e+07,108085305,188657593,"Universal","PG","Adventure"
"549","8/3/1994","Clear and Present Danger",6.2e+07,122012656,207500000,"Paramount Pictures","PG-13","Action"
"550","4/21/2000","U-571",6.2e+07,77086030,127630030,"Universal","PG-13","Action"
"551","6/20/2008","The Love Guru",6.2e+07,32235793,40159017,"Paramount Pictures","PG-13","Comedy"
"552","2/23/2001","3000 Miles to Graceland",6.2e+07,15738632,18708848,"Warner Bros.","R","Drama"
"553","3/30/2007","Blades of Glory",6.1e+07,118594548,145594548,"Paramount Pictures","PG-13","Comedy"
"554","8/2/2013","2 Guns",6.1e+07,75612460,132493015,"Universal","R","Action"
"555","12/22/2004","Meet the Fockers",6e+07,279167575,516567575,"Universal","PG-13","Comedy"
"556","2/7/2014","The Lego Movie",6e+07,257784718,457729388,"Warner Bros.","PG","Adventure"
"557","3/2/2007","Wild Hogs",6e+07,168213584,253555383,"Walt Disney","PG-13","Comedy"
"558","12/25/2008","Marley & Me",6e+07,143153751,247812011,"20th Century Fox","PG","Comedy"
"559","12/10/1999","The Green Mile",6e+07,136801374,290701374,"Warner Bros.","R","Drama"
"560","11/4/2005","Chicken Little",6e+07,135386665,310043823,"Walt Disney","G","Adventure"
"561","6/5/1998","The Truman Show",6e+07,125618201,264118201,"Paramount Pictures","PG","Drama"
"562","9/9/2016","Sully",6e+07,125070033,238552082,"Warner Bros.","PG-13","Drama"
"563","10/9/2009","Couples Retreat",6e+07,109205660,172450423,"Universal","PG-13","Comedy"
"564","11/17/1995","Goldeneye",6e+07,106429941,356429941,"MGM","PG-13","Action"
"565","5/30/2003","The Italian Job",6e+07,106126012,176262839,"Paramount Pictures","PG-13","Adventure"
"566","5/9/2003","Daddy Day Care",6e+07,104148781,164285587,"Sony Pictures","PG","Comedy"
"567","6/18/1999","The General's Daughter",6e+07,102705852,149705852,"Paramount Pictures","R","Drama"
"568","12/18/1998","The Prince of Egypt",6e+07,101413188,218613188,"Dreamworks SKG","PG","Adventure"
"569","8/6/2004","Collateral",6e+07,100170152,217670152,"Dreamworks SKG","R","Action"
"570","7/4/2001","Cats & Dogs",6e+07,93375151,200700000,"Warner Bros.","PG","Comedy"
"571","10/2/1998","Antz",6e+07,90757863,152457863,"Dreamworks SKG","PG","Adventure"
"572","4/19/2002","The Scorpion King",6e+07,90580000,165890634,"Universal","PG-13","Action"
"573","10/15/2010","Red",6e+07,90380162,196439693,"Summit Entertainment","PG-13","Action"
"574","3/5/2004","Starsky & Hutch",6e+07,88200225,170200225,"Warner Bros.","PG-13","Comedy"
"575","6/27/1990","Days of Thunder",6e+07,82670733,157670733,"Paramount Pictures","PG-13","Action"
"576","12/21/2005","Cheaper by the Dozen 2",6e+07,82571173,135015330,"20th Century Fox","PG","Adventure"
"577","8/13/2010","Eat Pray Love",6e+07,80574010,206598789,"Sony Pictures","PG-13","Drama"
"578","12/21/2012","Jack Reacher",6e+07,80070736,217370736,"Paramount Pictures","PG-13","Drama"
"579","12/22/2000","The Family Man",6e+07,75764085,124715863,"Universal","PG-13","Comedy"
"580","12/22/1999","Any Given Sunday",6e+07,75530832,100230832,"Warner Bros.","R","Drama"
"581","5/15/1998","The Horse Whisperer",6e+07,75383563,186883563,"Walt Disney","PG-13","Drama"
"582","2/6/2009","Coraline",6e+07,75286229,126037057,"Focus Features","PG","Adventure"
"583","10/1/2004","Ladder 49",6e+07,74541707,102332848,"Walt Disney","PG-13","Action"
"584","7/28/1999","Deep Blue Sea",6e+07,73648228,165048228,"Warner Bros.","R","Action"
"585","1/17/2003","Kangaroo Jack",6e+07,66723216,90723216,"Warner Bros.","PG","Adventure"
"586","3/4/2016","London Has Fallen",6e+07,62524260,194094168,"Focus Features","R","Action"
"587","3/10/2006","The Shaggy Dog",6e+07,61123569,87123569,"Walt Disney","PG","Comedy"
"588","11/22/1996","Jingle All the Way",6e+07,60592389,129832389,"20th Century Fox","PG","Adventure"
"589","4/2/2004","Hellboy",6e+07,59623958,99823958,"Sony Pictures","PG-13","Action"
"590","10/21/2016","Jack Reacher: Never Go Back",6e+07,58697076,160038407,"Paramount Pictures","PG-13","Action"
"591","5/25/2017","Baywatch",6e+07,58060186,176023296,"Paramount Pictures","R","Comedy"
"592","12/25/1998","A Civil Action",6e+07,56709981,56709981,"Walt Disney","PG-13","Drama"
"593","12/25/2015","Joy",6e+07,56451232,101134059,"20th Century Fox","PG-13","Drama"
"594","8/17/2012","ParaNorman",6e+07,56003051,108119662,"Focus Features","PG","Adventure"
"595","3/1/1996","Up Close & Personal",6e+07,51045801,100645801,"Walt Disney","PG-13","Drama"
"596","12/19/2008","The Tale of Despereaux",6e+07,50877145,90482317,"Universal","G","Adventure"
"597","9/26/2014","The Boxtrolls",6e+07,50837305,111946251,"Focus Features","PG","Adventure"
"598","9/27/2002","The Tuxedo",6e+07,50586000,104429625,"Dreamworks SKG","PG-13","Action"
"599","1/17/2014","Jack Ryan: Shadow Recruit",6e+07,50577412,131377412,"Paramount Pictures","PG-13","Action"
"600","7/14/1995","Under Siege 2: Dark Territory",6e+07,50024083,104324083,"Warner Bros.","R","Action"
"601","11/26/1997","Alien: Resurrection",6e+07,47795018,160700000,"20th Century Fox","R","Action"
"602","10/16/1998","Practical Magic",6e+07,46850558,68336997,"Warner Bros.","PG-13","Comedy"
"603","1/11/2013","Gangster Squad",6e+07,46000903,104100903,"Warner Bros.","R","Drama"
"604","4/7/2017","Smurfs: The Lost Village",6e+07,45020282,197422438,"Sony Pictures","PG","Adventure"
"605","6/19/2009","Year One",6e+07,43337279,57604723,"Sony Pictures","PG-13","Comedy"
"606","1/29/2010","Edge of Darkness",6e+07,43313890,82812456,"Warner Bros.","R","Drama"
"607","12/13/2002","Star Trek: Nemesis",6e+07,43254409,67312826,"Paramount Pictures","PG-13","Adventure"
"608","2/19/2002","Reign of Fire",6e+07,43061982,82150183,"Walt Disney","PG-13","Action"
"609","11/20/2009","Planet 51",6e+07,42194060,108996113,"Sony Pictures","PG","Adventure"
"610","12/11/2009","Invictus",6e+07,37491364,124514011,"Warner Bros.","PG-13","Drama"
"611","2/12/1999","My Favorite Martian",6e+07,36850101,36850101,"Walt Disney","PG","Comedy"
"612","9/21/2012","Trouble with the Curve",6e+07,35763137,47818913,"Warner Bros.","PG-13","Drama"
"613","1/10/1997","The Relic",6e+07,33956608,33956608,"Paramount Pictures","R","Horror"
"614","9/15/2000","Almost Famous",6e+07,32522352,47371191,"Dreamworks SKG","R","Comedy"
"615","12/6/2002","Analyze That",6e+07,32122249,54994757,"Warner Bros.","R","Comedy"
"616","4/24/2009","The Soloist",6e+07,31853584,38522450,"Paramount Pictures","PG-13","Drama"
"617","11/3/2000","The Legend of Bagger Vance",6e+07,30695227,39235486,"Dreamworks SKG","PG-13","Drama"
"618","2/22/2002","Dragonfly",6e+07,30063805,30063805,"Universal","PG-13","Drama"
"619","10/12/2018","First Man",6e+07,30000050,55500050,"Universal","PG-13","Drama"
"620","6/16/2006","Garfield: A Tail of Two Kitties",6e+07,28426747,147985373,"20th Century Fox","PG","Adventure"
"621","4/29/2005","XXX: State of the Union",6e+07,26873932,71073932,"Sony Pictures","PG-13","Action"
"622","8/15/1997","Event Horizon",6e+07,26673242,26673242,"Paramount Pictures","R","Horror"
"623","7/2/2003","Sinbad: Legend of the Seven Seas",6e+07,26483452,80767884,"Dreamworks SKG","PG","Adventure"
"624","3/26/1999","EDtv",6e+07,22508689,35319689,"Universal","PG-13","Comedy"
"625","12/25/2008","The Spirit",6e+07,19806188,39006188,"Lionsgate","PG-13","Action"
"626","10/19/2001","The Last Castle",6e+07,18208078,20541668,"Dreamworks SKG","R","Drama"
"627","1/23/2009","Inkheart",6e+07,17303424,66655938,"Warner Bros.","PG","Adventure"
"628","1/14/2000","Supernova",6e+07,14218868,14816494,"MGM","PG-13","Action"
"629","9/22/2006","Flyboys",6e+07,13090630,14816379,"MGM","PG-13","Drama"
"630","2/14/2014","Winter's Tale",6e+07,12600231,22468620,"Warner Bros.","PG-13","Drama"
"631","10/9/1998","Holy Man",6e+07,12069719,12069719,"Walt Disney","PG","Comedy"
"632","7/11/2008","Meet Dave",6e+07,11803254,50648806,"20th Century Fox","PG","Adventure"
"633","8/12/2005","The Great Raid",6e+07,10166502,10597070,"Miramax","R","Action"
"634","2/24/2017","Rock Dog",6e+07,9420546,24152192,"Lionsgate","PG","Adventure"
"635","1/23/2015","Mortdecai",6e+07,7696134,30396134,"Lionsgate","R","Adventure"
"636","10/24/2003","Beyond Borders",6e+07,4426297,11427090,"Paramount Pictures","R","Drama"
"637","3/23/2018","Sherlock Gnomes",5.9e+07,43242871,87750965,"Paramount Pictures","PG","Adventure"
"638","2/12/2016","Deadpool",5.8e+07,363070709,801029249,"20th Century Fox","R","Action"
"639","12/25/2014","American Sniper",5.8e+07,350126372,547326372,"Warner Bros.","R","Drama"
"640","10/16/2015","Goosebumps",5.8e+07,80069458,158905324,"Sony Pictures","PG","Horror"
"641","5/25/1988","Rambo III",5.8e+07,53715611,188715611,"Sony/TriStar","R","Action"
"642","1/20/2012","Red Tails",5.8e+07,49876377,50365498,"20th Century Fox","PG-13","Action"
"643","6/7/2013","The Internship",5.8e+07,44672764,93672764,"20th Century Fox","PG-13","Comedy"
"644","4/28/2000","The Flintstones in Viva Rock Vegas",5.8e+07,35231365,59431365,"Universal","PG","Adventure"
"645","5/30/2008","Sex and the City",57500000,152647258,415247258,"Warner Bros.","R","Comedy"
"646","9/10/2010","Resident Evil: Afterlife",57500000,60128566,295874190,"Sony Pictures","R","Horror"
"647","6/15/2012","That's My Boy",57500000,36931089,58085235,"Sony Pictures","R","Comedy"
"648","10/17/1997","Devil's Advocate",5.7e+07,61007424,153007424,"Warner Bros.","R","Drama"
"649","2/17/2012","Ghost Rider: Spirit of Vengeance",5.7e+07,51774002,149217355,"Sony Pictures","PG-13","Action"
"650","5/31/1996","Dragonheart",5.7e+07,51364680,104364680,"Universal","PG-13","Adventure"
"651","11/12/2004","After the Sunset",5.7e+07,28328132,38329114,"New Line","PG-13","Action"
"652","8/17/2001","Captain Corelli's Mandolin",5.7e+07,25528495,62097495,"Miramax","R","Drama"
"653","4/11/2003","Anger Management",5.6e+07,135560942,195660942,"Sony Pictures","PG-13","Comedy"
"654","3/4/2005","The Pacifier",5.6e+07,113006880,198006880,"Walt Disney","PG","Comedy"
"655","4/2/2004","Walking Tall",5.6e+07,46213824,47313824,"MGM","PG-13","Action"
"656","7/6/1994","Forrest Gump",5.5e+07,330151138,679850637,"Paramount Pictures","PG-13","Drama"
"657","12/14/2007","Alvin and the Chipmunks",5.5e+07,217326974,362605033,"20th Century Fox","PG","Adventure"
"658","10/6/2000","Meet the Parents",5.5e+07,166225040,330425040,"Universal","PG-13","Comedy"
"659","12/15/2006","The Pursuit of Happyness",5.5e+07,162586036,307311093,"Sony Pictures","PG-13","Drama"
"660","6/10/1995","Pocahontas",5.5e+07,141579773,347100000,"Walt Disney","G","Adventure"
"661","12/15/1978","Superman",5.5e+07,134218018,300200000,"Warner Bros.","PG","Adventure"
"662","6/28/1996","The Nutty Professor",5.5e+07,128814019,273814019,"Universal","PG-13","Comedy"
"663","2/10/2017","Fifty Shades Darker",5.5e+07,114434010,381437482,"Universal","R","Drama"
"664","10/11/2013","Captain Phillips",5.5e+07,107136417,220648184,"Sony Pictures","PG-13","Drama"
"665","7/16/1997","George Of The Jungle",5.5e+07,105263257,174463257,"Walt Disney","PG","Adventure"
"666","8/1/2003","American Wedding",5.5e+07,104354205,126425115,"Universal","R","Comedy"
"667","11/10/2017","Murder on the Orient Express",5.5e+07,102826543,345924923,"20th Century Fox","PG-13","Drama"
"668","9/26/2014","The Equalizer",5.5e+07,101530738,192903624,"Sony Pictures","R","Action"
"669","2/9/2018","Fifty Shades Freed",5.5e+07,100407760,371222158,"Universal","R","Drama"
"670","5/26/1995","Casper",5.5e+07,100328194,282300000,"Universal","PG","Comedy"
"671","4/9/2010","Date Night",5.5e+07,98711404,152269033,"20th Century Fox","PG-13","Comedy"
"672","5/12/1995","Crimson Tide",5.5e+07,91387195,159387195,"Walt Disney","R","Action"
"673","12/9/1994","Disclosure",5.5e+07,83015089,212200000,"Warner Bros.","R","Drama"
"674","4/10/1998","City of Angels",5.5e+07,78750909,198750909,"Warner Bros.","PG-13","Drama"
"675","1/16/2015","Paddington",5.5e+07,76223578,258789097,"Weinstein Co.","PG","Adventure"
"676","4/28/2006","R.V.",5.5e+07,71724497,87473024,"Sony Pictures","PG","Adventure"
"677","10/28/1994","Stargate",5.5e+07,71565669,196565669,"MGM","PG-13","Action"
"678","10/10/2003","Kill Bill: Volume 1",5.5e+07,70098138,176469428,"Miramax","R","Action"
"679","6/17/2011","Mr. Poppers's Penguins",5.5e+07,68224452,189624452,"20th Century Fox","PG","Adventure"
"680","8/13/1999","Bowfinger",5.5e+07,66458769,98699769,"Universal","PG-13","Comedy"
"681","4/16/2004","Kill Bill: Volume 2",5.5e+07,66207920,153535982,"Miramax","R","Action"
"682","12/22/1989","Tango & Cash",5.5e+07,63408614,63408614,"Warner Bros.","R","Action"
"683","7/31/1992","Death Becomes Her",5.5e+07,58422650,149022650,"Universal","PG-13","Comedy"
"684","11/1/2013","Free Birds",5.5e+07,55750480,110387072,"Relativity","PG","Adventure"
"685","5/22/1992","Alien 3",5.5e+07,54927174,158500000,"20th Century Fox","R","Action"
"686","4/18/2008","The Forbidden Kingdom",5.5e+07,52075270,129075270,"Lionsgate","PG-13","Action"
"687","3/21/2014","Muppets Most Wanted",5.5e+07,51178893,79312301,"Walt Disney","PG","Adventure"
"688","8/19/2016","Kubo and the Two Strings",5.5e+07,48023088,77548564,"Focus Features","PG","Adventure"
"689","9/25/1998","Ronin",5.5e+07,41610884,70692101,"MGM","R","Action"
"690","11/24/2010","Burlesque",5.5e+07,39440655,90552675,"Sony Pictures","PG-13","Drama"
"691","10/11/1996","The Ghost and the Darkness",5.5e+07,38564422,38564422,"Paramount Pictures","R","Action"
"692","7/27/2012","The Watch",5.5e+07,34353000,67130045,"20th Century Fox","R","Comedy"
"693","6/4/1999","Instinct",5.5e+07,34105207,34105207,"Walt Disney","R","Drama"
"694","12/12/2003","Stuck On You",5.5e+07,33832741,63537164,"20th Century Fox","PG-13","Comedy"
"695","2/29/2008","Semi-Pro",5.5e+07,33479698,43980363,"New Line","R","Comedy"
"696","10/16/2015","Crimson Peak",5.5e+07,31090320,75466595,"Universal","R","Horror"
"697","4/27/2012","The Pirates! Band of Misfits",5.5e+07,31051126,136143605,"Sony Pictures","PG","Adventure"
"698","12/2/2005","Aeon Flux",5.5e+07,25857987,53913573,"Paramount Pictures","PG-13","Action"
"699","10/12/2007","Elizabeth: The Golden Age",5.5e+07,16285240,74870866,"Universal","PG-13","Drama"
"700","6/12/2009","Imagine That",5.5e+07,16222392,16222392,"Paramount Pictures","PG","Adventure"
"701","2/21/2003","Gods and Generals",5.5e+07,12882934,12923936,"Warner Bros.","PG-13","Drama"
"702","2/1/2013","Bullet to the Head",5.5e+07,9489829,22597969,"Warner Bros.","R","Action"
"703","9/22/2006","All the King's Men",5.5e+07,7221458,9521458,"Sony Pictures","PG-13","Drama"
"704","7/30/2004","Thunderbirds",5.5e+07,6768055,28231444,"Universal","PG","Adventure"
"705","11/26/2004","Un long dimanche de fiançailles",5.5e+07,6167817,69759296,"Warner Bros.","R","Drama"
"706","5/4/2007","Lucky You",5.5e+07,5755286,6521829,"Warner Bros.","PG-13","Drama"
"707","7/22/1998","Lolita",5.5e+07,1147784,1147784,"MGM","R","Drama"
"708","6/19/1981","Superman II",5.4e+07,108185706,108185706,"Warner Bros.","PG","Adventure"
"709","3/22/2002","Blade 2",5.4e+07,81676888,154338601,"New Line","R","Action"
"710","7/14/2006","You, Me and Dupree",5.4e+07,75802010,130402010,"Universal","PG-13","Comedy"
"711","12/19/2008","Seven Pounds",5.4e+07,69951824,166617328,"Sony Pictures","PG-13","Drama"
"712","12/25/1990","The Godfather: Part III",5.4e+07,66520529,66520529,"Paramount Pictures","R","Drama"
"713","10/14/2005","Elizabethtown",5.4e+07,26850426,50719373,"Paramount Pictures","PG-13","Drama"
"714","8/5/2005","The Dukes of Hazzard",5.3e+07,80270227,109848461,"Warner Bros.","PG-13","Comedy"
"715","9/18/2015","Black Mass",5.3e+07,62575678,98837872,"Warner Bros.","R","Drama"
"716","10/20/2006","Flags of Our Fathers",5.3e+07,33602376,63657941,"Paramount Pictures","R","Drama"
"717","4/6/2007","Grindhouse",5.3e+07,25031037,50187789,"Weinstein/Dimension","R","Horror"
"718","10/16/1998","Beloved",5.3e+07,22852487,22852487,"Walt Disney","R","Drama"
"719","12/19/2012","Zero Dark Thirty",52500000,95720716,134612435,"Sony Pictures","R","Drama"
"720","12/25/2002","Catch Me if You Can",5.2e+07,164606800,355612291,"Dreamworks SKG","PG-13","Drama"
"721","11/22/1995","Casino",5.2e+07,42438300,110400000,"Universal","R","Drama"
"722","8/5/2011","The Change-Up",5.2e+07,37243418,75997067,"Universal","R","Comedy"
"723","12/23/1998","The Thin Red Line",5.2e+07,36400491,97709034,"20th Century Fox","R","Drama"
"724","12/22/1999","Man on the Moon",5.2e+07,34580635,47407635,"Universal","R","Drama"
"725","4/16/2003","Bulletproof Monk",5.2e+07,23010607,23010607,"MGM","PG-13","Action"
"726","11/22/2006","Deck the Halls",5.1e+07,35093569,46815807,"20th Century Fox","PG","Comedy"
"727","11/20/2009","The Twilight Saga: New Moon",5e+07,296623634,687557727,"Summit Entertainment","PG-13","Drama"
"728","5/18/2001","Shrek",5e+07,267655011,491812794,"Dreamworks SKG","PG","Adventure"
"729","6/29/2012","Ted",5e+07,218665740,556016627,"Universal","R","Adventure"
"730","6/13/2014","22 Jump Street",5e+07,191719337,331333876,"Sony Pictures","R","Comedy"
"731","6/14/1991","Robin Hood: Prince of Thieves",5e+07,165493908,390500000,"Warner Bros.","PG-13","Adventure"
"732","12/25/2015","Daddy’s Home",5e+07,150357137,242757137,"Paramount Pictures","PG-13","Comedy"
"733","12/25/1998","Patch Adams",5e+07,135014968,202173000,"Universal","PG-13","Comedy"
"734","6/17/2016","Central Intelligence",5e+07,127440871,217196811,"Warner Bros.","PG-13","Comedy"
"735","12/18/2013","Anchorman 2: The Legend Continues",5e+07,127352707,172185754,"Paramount Pictures","PG-13","Comedy"
"736","6/28/2002","Mr. Deeds",5e+07,126293452,171269535,"Sony Pictures","PG-13","Comedy"
"737","3/17/2000","Erin Brockovich",5e+07,125548685,257805243,"Universal","R","Drama"
"738","2/9/2018","Peter Rabbit",5e+07,115234093,347134901,"Sony Pictures","PG","Adventure"
"739","12/19/2008","Yes Man",5e+07,97690976,225990976,"Warner Bros.","PG-13","Comedy"
"740","2/28/2014","Non-Stop",5e+07,91742160,222383055,"Universal","PG-13","Action"
"741","12/25/1998","Stepmom",5e+07,91137662,159745279,"Sony/TriStar","PG-13","Drama"
"742","8/9/2013","Disney Planes",5e+07,90282580,238059569,"Walt Disney","PG","Adventure"
"743","7/28/2017","The Emoji Movie",5e+07,86089513,216508301,"Sony Pictures","PG","Adventure"
"744","7/29/2011","Crazy, Stupid, Love",5e+07,84351197,147142328,"Warner Bros.","PG-13","Comedy"
"745","12/22/2017","The Post",5e+07,81903458,179769457,"20th Century Fox","PG-13","Drama"
"746","2/5/1999","Payback",5e+07,81526121,161626121,"Paramount Pictures","R","Action"
"747","6/9/1995","Congo",5e+07,81022333,152022333,"Paramount Pictures","PG-13","Adventure"
"748","3/18/2005","The Ring Two",5e+07,75941727,161941727,"Dreamworks SKG","PG-13","Horror"
"749","12/23/2011","We Bought a Zoo",5e+07,75624550,118729073,"20th Century Fox","PG","Drama"
"750","9/23/2011","Moneyball",5e+07,75605492,111300835,"Sony Pictures","PG-13","Drama"
"751","6/11/2004","Garfield: The Movie",5e+07,75367693,208094550,"20th Century Fox","PG","Adventure"
"752","11/24/2004","Christmas with the Kranks",5e+07,73701902,96469187,"Sony Pictures","PG","Comedy"
"753","3/17/2006","V for Vendetta",5e+07,70511035,130214162,"Warner Bros.","R","Action"
"754","3/13/2009","Race to Witch Mountain",5e+07,67172595,105103784,"Walt Disney","PG","Adventure"
"755","6/22/2005","Herbie: Fully Loaded",5e+07,66010682,144110682,"Walt Disney","G","Adventure"
"756","2/7/2003","Shanghai Knights",5e+07,60470220,88316835,"Walt Disney","PG-13","Adventure"
"757","7/18/2014","Planes: Fire and Rescue",5e+07,59157732,156399644,"Walt Disney","PG","Adventure"
"758","2/10/2006","Curious George",5e+07,58640119,71052604,"Universal","G","Adventure"
"759","4/6/2012","American Reunion",5e+07,56758835,236799211,"Universal","R","Comedy"
"760","1/25/2013","Hansel & Gretel: Witch Hunters",5e+07,55703475,214949716,"Paramount Pictures","R","Action"
"761","2/18/2011","I am Number Four",5e+07,55100437,146195159,"Walt Disney","PG-13","Adventure"
"762","5/8/2002","Unfaithful",5e+07,52752475,119114494,"20th Century Fox","R","Drama"
"763","9/10/2004","Resident Evil: Apocalypse",5e+07,50740078,125168734,"Sony Pictures","R","Horror"
"764","10/17/2014","The Book of Life",5e+07,50151543,97651543,"20th Century Fox","PG","Adventure"
"765","8/22/1997","G.I. Jane",5e+07,48169156,48169156,"Walt Disney","R","Drama"
"766","10/10/2014","The Judge",5e+07,47119388,76119388,"Warner Bros.","R","Drama"
"767","4/21/2006","Silent Hill",5e+07,46982632,94704227,"Sony Pictures","R","Horror"
"768","8/11/2000","The Replacements",5e+07,44737059,50054511,"Warner Bros.","PG-13","Comedy"
"769","7/29/1998","The Negotiator",5e+07,44705766,49105766,"Warner Bros.","R","Action"
"770","8/19/2016","War Dogs",5e+07,43034523,86234523,"Warner Bros.","R","Comedy"
"771","5/25/1994","Beverly Hills Cop III",5e+07,42586861,119180938,"Paramount Pictures","R","Action"
"772","6/15/1990","Gremlins 2: The New Batch",5e+07,41476097,41476097,"Warner Bros.","PG-13","Comedy"
"773","9/26/1997","The Peacemaker",5e+07,41263140,62967368,"Dreamworks SKG","R","Action"
"774","2/11/2000","The Beach",5e+07,39778599,39778599,"20th Century Fox","R","Drama"
"775","2/18/1994","On Deadly Ground",5e+07,38590458,38590458,"Warner Bros.","R","Action"
"776","11/25/2009","Ninja Assassin",5e+07,38122883,62209892,"Warner Bros.","R","Action"
"777","5/28/2004","Raising Helen",5e+07,37485528,49928680,"Walt Disney","PG-13","Comedy"
"778","9/17/1999","For Love of the Game",5e+07,35188640,46112640,"Universal","PG-13","Drama"
"779","12/11/1998","Jack Frost",5e+07,34645374,34645374,"Warner Bros.","PG","Comedy"
"780","6/4/2010","Marmaduke",5e+07,33644788,89895930,"20th Century Fox","PG","Adventure"
"781","6/28/1996","Striptease",5e+07,33109743,113309743,"Sony Pictures","R","Comedy"
"782","10/6/1995","Assassins",5e+07,30306268,83306268,"Warner Bros.","R","Action"
"783","2/12/2016","Zoolander 2",5e+07,28848693,55348693,"Paramount Pictures","PG-13","Comedy"
"784","1/16/2009","Defiance",5e+07,28644813,52987754,"Paramount Vantage","R","Drama"
"785","3/13/2015","Run All Night",5e+07,26461644,66961644,"Warner Bros.","R","Action"
"786","8/9/1996","Escape from L.A.",5e+07,25426861,25426861,"Paramount Pictures","R","Action"
"787","12/10/2004","The Life Aquatic with Steve Zissou",5e+07,24006726,34806726,"Walt Disney","R","Comedy"
"788","8/4/1999","The Iron Giant",5e+07,23159305,31333917,"Warner Bros.","PG","Adventure"
"789","4/8/2011","Your Highness",5e+07,21596445,26121638,"Universal","R","Comedy"
"790","9/16/2016","Snowden",5e+07,21587519,34841016,"Open Road","R","Drama"
"791","9/30/2011","Dream House",5e+07,21302340,41642166,"Universal","PG-13","Horror"
"792","6/24/2016","Free State of Jones",5e+07,20810036,23237175,"STX Entertainment","R","Drama"
"793","9/4/2009","Gamer",5e+07,20534907,42002029,"Lionsgate","R","Action"
"794","9/30/2005","Into the Blue",5e+07,18782227,41982227,"Sony Pictures","PG-13","Adventure"
"795","7/1/1994","Baby's Day Out",5e+07,16581575,16581575,"20th Century Fox","PG","Adventure"
"796","11/3/1995","Fair Game",5e+07,11497497,26097497,"Warner Bros.","R","Action"
"797","2/25/2011","Drive Angry",5e+07,10721033,41042583,"Summit Entertainment","R","Action"
"798","11/7/1997","Mad City",5e+07,10561038,10561038,"Warner Bros.","PG-13","Drama"
"799","10/13/1995","The Scarlet Letter",5e+07,10359006,10359006,"Walt Disney","R","Drama"
"800","10/14/2005","Domino",5e+07,10169202,22969202,"New Line","R","Action"
"801","2/16/2018","Early Man",5e+07,8267544,44773318,"Lionsgate","PG","Adventure"
"802","11/13/2009","The Boat That Rocked",5e+07,8017467,37472651,"Focus Features","R","Comedy"
"803","1/30/2004","The Big Bounce",5e+07,6471394,6626115,"Warner Bros.","PG-13","Comedy"
"804","3/3/2000","What Planet Are You From?",5e+07,6291602,6291602,"Sony Pictures","R","Comedy"
"805","1/23/2009","Outlander",5e+07,166003,1250617,"Third Rail","R","Adventure"
"806","10/2/2015","Shanghai",5e+07,46425,15505922,"Weinstein Co.","R","Drama"
"807","11/2/2001","The One",4.9e+07,43905746,72689126,"Sony Pictures","PG-13","Action"
"808","3/6/2015","Chappie",4.9e+07,31569268,105002056,"Sony Pictures","R","Action"
"809","7/11/1990","The Adventures of Ford Fairlane",4.9e+07,20423389,20423389,"20th Century Fox","R","Comedy"
"810","5/24/1989","Indiana Jones and the Last Crusade",4.8e+07,197171806,474171806,"Paramount Pictures","PG-13","Adventure"
"811","10/18/2002","The Ring",4.8e+07,129094024,248218486,"Dreamworks SKG","PG-13","Horror"
"812","12/27/2000","Traffic",4.8e+07,124107476,208300000,"USA Films","R","Drama"
"813","1/9/2015","Taken 3",4.8e+07,89256424,327656424,"20th Century Fox","PG-13","Action"
"814","10/1/1999","Three Kings",4.8e+07,60652036,107752036,"Warner Bros.","R","Action"
"815","1/22/2010","Tooth Fairy",4.8e+07,60022256,112610386,"20th Century Fox","PG","Adventure"
"816","8/17/2001","Rat Race",4.8e+07,56607223,86607223,"Paramount Pictures","PG-13","Comedy"
"817","8/13/2001","K-PAX",4.8e+07,50315140,50315140,"Universal","PG-13","Drama"
"818","10/20/2000","Bedazzled",4.8e+07,37879996,90376224,"20th Century Fox","PG-13","Comedy"
"819","6/26/1998","Out of Sight",4.8e+07,37562568,77562568,"Universal","R","Drama"
"820","12/14/1984","The Cotton Club",4.8e+07,25928721,25928721,"Orion Pictures",NA,"Drama"
"821","1/25/2008","Rambo",47500000,42754105,112214531,"Lionsgate","R","Action"
"822","6/15/1990","Dick Tracy",4.7e+07,103738726,162738726,"Walt Disney","PG","Action"
"823","11/11/2016","Arrival",4.7e+07,100546139,203162211,"Paramount Pictures","PG-13","Drama"
"824","6/14/1996","The Cable Guy",4.7e+07,60240295,102825796,"Sony Pictures","PG-13","Comedy"
"825","10/19/2001","Riding in Cars with Boys",4.7e+07,29781453,29781453,"Sony Pictures","PG-13","Drama"
"826","1/5/2007","Happily N'Ever After",4.7e+07,15849032,37923818,"Lionsgate","PG","Adventure"
"827","11/27/2002","Solaris",4.7e+07,14970038,14970038,"20th Century Fox","PG-13","Drama"
"828","6/18/2010","Jonah Hex",4.7e+07,10547117,11022696,"Warner Bros.","PG-13","Action"
"829","2/23/1996","Mary Reilly",4.7e+07,5707094,12900000,"Sony Pictures","R","Drama"
"830","12/23/2016","Silence",46500000,7100177,23727516,"Paramount Pictures","R","Drama"
"831","6/20/1997","My Best Friend's Wedding",4.6e+07,126813153,298923419,"Sony Pictures","PG","Comedy"
"832","11/22/1996","Star Trek: First Contact",4.6e+07,92027888,1.5e+08,"Paramount Pictures","PG-13","Adventure"
"833","7/12/1996","Courage Under Fire",4.6e+07,59003384,100833145,"20th Century Fox","R","Drama"
"834","9/17/1982","Inchon",4.6e+07,4408636,4408636,"MGM",NA,"Drama"
"835","3/21/1997","Liar Liar",4.5e+07,181410615,302710615,"Universal","PG-13","Comedy"
"836","11/20/1998","A Bug's Life",4.5e+07,162798565,363095319,"Walt Disney","G","Adventure"
"837","5/27/1994","The Flintstones",4.5e+07,130531208,358500000,"Universal","PG","Comedy"
"838","10/24/2003","Scary Movie 3",4.5e+07,110000082,155200000,"Miramax/Dimension","PG-13","Comedy"
"839","12/22/2000","Miss Congeniality",4.5e+07,106807667,213420951,"Warner Bros.","PG-13","Comedy"
"840","12/22/2017","Pitch Perfect 3",4.5e+07,104897530,185736412,"Universal","PG-13","Comedy"
"841","7/11/2008","Journey to the Center of the Earth",4.5e+07,101704370,243180937,"Warner Bros.","PG","Adventure"
"842","12/17/1993","The Pelican Brief",4.5e+07,100768056,187995859,"Warner Bros.","PG-13","Drama"
"843","12/25/2007","The Bucket List",4.5e+07,93466502,174807445,"Warner Bros.","PG-13","Comedy"
"844","7/20/1994","The Client",4.5e+07,92115211,117615211,"Warner Bros.","PG-13","Drama"
"845","11/23/2011","The Muppets",4.5e+07,88625922,160971922,"Walt Disney","PG","Adventure"
"846","6/5/1992","Patriot Games",4.5e+07,83287363,178100000,"Paramount Pictures","R","Action"
"847","5/13/2005","Monster-in-Law",4.5e+07,82931301,155931301,"New Line","PG-13","Comedy"
"848","10/5/2001","Training Day",4.5e+07,76261036,104505362,"Warner Bros.","R","Drama"
"849","12/24/1999","Galaxy Quest",4.5e+07,71423726,90523726,"Dreamworks SKG","PG","Adventure"
"850","7/4/2001","Scary Movie 2",4.5e+07,71277420,141189101,"Miramax/Dimension","R","Comedy"
"851","8/21/1998","Blade",4.5e+07,70141876,131237688,"New Line","R","Action"
"852","1/14/2005","Coach Carter",4.5e+07,67264877,76665507,"Paramount Pictures","PG-13","Drama"
"853","4/11/1997","Anaconda",4.5e+07,65598907,136998907,"Sony Pictures","PG-13","Horror"
"854","1/20/2006","Underworld: Evolution",4.5e+07,62318875,113417762,"Sony Pictures","R","Action"
"855","8/4/2000","Coyote Ugly",4.5e+07,60786269,113916474,"Walt Disney","PG-13","Drama"
"856","8/9/1996","Jack",4.5e+07,58617334,58617334,"Walt Disney","PG-13","Drama"
"857","10/7/1994","The Specialist",4.5e+07,57362581,57362581,"Warner Bros.","R","Action"
"858","12/9/2016","Office Christmas Party",4.5e+07,54767494,91340376,"Paramount Pictures","R","Comedy"
"859","11/23/2005","Yours, Mine and Ours",4.5e+07,53359917,72359917,"Paramount Pictures","PG","Comedy"
"860","9/21/2007","Resident Evil: Extinction",4.5e+07,50648679,146162920,"Sony Pictures","R","Action"
"861","12/25/2004","Fat Albert",4.5e+07,48114556,48563556,"20th Century Fox","PG","Comedy"
"862","9/30/1994","The River Wild",4.5e+07,46815000,94215000,"Universal","PG-13","Action"
"863","6/16/2017","All Eyez on Me",4.5e+07,44922302,54876855,"Lionsgate","R","Drama"
"864","1/13/2006","Last Holiday",4.5e+07,38399961,43343247,"Paramount Pictures","PG-13","Comedy"
"865","3/3/2006","16 Blocks",4.5e+07,36895141,65595141,"Warner Bros.","PG-13","Action"
"866","7/14/1995","The Indian in the Cupboard",4.5e+07,35627222,35627222,"Paramount Pictures","PG","Adventure"
"867","7/28/2006","The Ant Bully",4.5e+07,28142535,49610898,"Warner Bros.","PG","Adventure"
"868","7/18/2003","Johnny English",4.5e+07,28013509,163126676,"Universal","PG","Adventure"
"869","12/14/1984","Dune",4.5e+07,27447471,27447471,"Universal",NA,"Action"
"870","7/31/2009","Aliens in the Attic",4.5e+07,25200412,59551283,"20th Century Fox","PG","Adventure"
"871","12/26/2008","Revolutionary Road",4.5e+07,22951340,79604820,"Paramount Vantage","R","Drama"
"872","8/29/2008","Babylon A.D.",4.5e+07,22532572,70216497,"20th Century Fox","PG-13","Action"
"873","11/4/1994","Frankenstein",4.5e+07,22006296,112006296,"Sony Pictures","R","Horror"
"874","10/4/1996","The Glimmer Man",4.5e+07,20404841,36404841,"Warner Bros.","R","Action"
"875","7/17/1996","Multiplicity",4.5e+07,20133326,20133326,"Sony Pictures","PG-13","Comedy"
"876","1/19/2001","The Pledge",4.5e+07,19719930,29406132,"Warner Bros.","R","Drama"
"877","6/7/1996","The Phantom",4.5e+07,17220599,17220599,"Paramount Pictures","PG","Action"
"878","7/1/2005","Rebound",4.5e+07,16809014,17492014,"20th Century Fox","PG","Comedy"
"879","12/20/1995","Nixon",4.5e+07,13668249,34668249,"Walt Disney","R","Drama"
"880","9/21/2012","Dredd",4.5e+07,13414714,41467606,"Lionsgate","R","Action"
"881","10/28/2011","The Rum Diary",4.5e+07,13109815,21544732,"FilmDistrict","R","Drama"
"882","1/30/1998","Deep Rising",4.5e+07,11203026,11203026,"Walt Disney","R","Action"
"883","10/21/2011","Johnny English Reborn",4.5e+07,8406711,164640401,"Universal","PG","Adventure"
"884","9/26/2008","Miracle at St. Anna",4.5e+07,7916887,9676497,"Walt Disney","R","Drama"
"885","4/5/2002","Big Trouble",4.5e+07,7262288,8488871,"Walt Disney","PG-13","Comedy"
"886","12/21/2006","Man cheng jin dai huang jin jia",4.5e+07,6566773,76904429,"Sony Pictures Classics","R","Action"
"887","11/16/2007","Love in the Time of Cholera",4.5e+07,4617608,31077418,"New Line","R","Drama"
"888","5/22/1985","Rambo: First Blood Part II",4.4e+07,150415432,300400000,"Sony/TriStar","R","Action"
"889","10/18/1996","Sleepers",4.4e+07,53300852,165600852,"Warner Bros.","R","Drama"
"890","7/30/2010","Charlie St. Cloud",4.4e+07,31206263,48478084,"Universal","PG-13","Drama"
"891","2/6/2014","The Interview",4.4e+07,6105175,12342632,"Sony Pictures","R","Comedy"
"892","6/28/2013","The Heat",4.3e+07,159581587,229727774,"20th Century Fox","R","Comedy"
"893","12/19/2000","Finding Forrester",4.3e+07,51768623,80013623,"Sony Pictures","PG-13","Drama"
"894","4/14/2000","28 Days",4.3e+07,37035515,62063972,"Sony Pictures","PG-13","Comedy"
"895","5/13/2005","Danny the Dog",4.3e+07,24537621,49037621,"Focus/Rogue Pictures","R","Action"
"896","1/6/2017","A Monster Calls",4.3e+07,3740823,46414964,"Focus Features","PG-13","Drama"
"897","1/28/2011","The Mechanic",42500000,29121498,76347393,"CBS Films","R","Action"
"898","3/16/2012","21 Jump Street",4.2e+07,138447667,202812429,"Sony Pictures","R","Comedy"
"899","6/21/2000","Chicken Run",4.2e+07,106793915,227793915,"Dreamworks SKG","G","Adventure"
"900","7/1/1992","Boomerang",4.2e+07,70052444,131052444,"Paramount Pictures","R","Comedy"
"901","7/10/2009","Brüno",4.2e+07,60054530,138708527,"Universal","R","Comedy"
"902","6/12/1963","Cleopatra",4.2e+07,5.7e+07,7.1e+07,"20th Century Fox","G","Drama"
"903","5/12/2017","Snatched",4.2e+07,45852178,57852177,"20th Century Fox","R","Comedy"
"904","10/12/2012","Here Comes the Boom",4.2e+07,45290318,73239258,"Sony Pictures","PG","Comedy"
"905","7/14/1989","Licence to Kill",4.2e+07,34667015,156167015,"MGM","PG-13","Action"
"906","1/27/2012","One for the Money",4.2e+07,26414527,36197221,"Lionsgate","PG-13","Comedy"
"907","9/16/2005","Lord of War",4.2e+07,24149632,60437727,"Lionsgate","R","Action"
"908","5/28/1993","Super Mario Bros.",4.2e+07,20844907,20844907,"Walt Disney","PG","Action"
"909","10/2/1992","Hero",4.2e+07,19487173,66787173,"Sony Pictures","PG-13","Comedy"
"910","4/18/1997","McHale's Navy",4.2e+07,4408420,4408420,"Universal","PG","Comedy"
"911","5/28/2010","Micmacs",4.2e+07,1259693,11756922,"Sony Pictures Classics","R","Comedy"
"912","11/8/2002","8 Mile",4.1e+07,116724075,245768384,"Universal","R","Drama"
"913","5/11/2001","A Knight’s Tale",4.1e+07,56083966,100622586,"Sony Pictures","PG-13","Adventure"
"914","8/22/2003","The Medallion",4.1e+07,22108977,22108977,"Sony Pictures","PG-13","Comedy"
"915","10/14/2011","The Big Year",4.1e+07,7204138,7684524,"20th Century Fox","PG","Comedy"
"916","7/15/2005","Wedding Crashers",4e+07,209218368,283218368,"New Line","R","Comedy"
"917","2/13/2015","Fifty Shades of Grey",4e+07,166167230,570998101,"Universal","R","Drama"
"918","12/25/2003","Cheaper by the Dozen",4e+07,138614544,190212113,"20th Century Fox","PG","Comedy"
"919","7/25/2014","Lucy",4e+07,126573960,457507776,"Universal","R","Action"
"920","12/25/2013","Lone Survivor",4e+07,125095601,149804632,"Universal","R","Action"
"921","11/22/1989","Back to the Future Part II",4e+07,118450002,3.32e+08,"Universal","PG","Adventure"
"922","9/24/1999","Double Jeopardy",4e+07,116735231,177835231,"Paramount Pictures","R","Action"
"923","7/25/2003","Spy Kids 3-D: Game Over",4e+07,111760631,167851995,"Miramax/Dimension","PG","Adventure"
"924","7/24/1996","A Time to Kill",4e+07,108766007,152266007,"Warner Bros.","R","Drama"
"925","7/1/1992","A League of Their Own",4e+07,107533925,132440066,"Sony Pictures","PG","Comedy"
"926","10/1/2010","The Social Network",4e+07,96962694,224922135,"Sony Pictures","PG-13","Drama"
"927","8/7/2009","Julie & Julia",4e+07,94125426,126646119,"Sony Pictures","PG-13","Comedy"
"928","2/10/2017","John Wick: Chapter Two",4e+07,92029184,171370497,"Lionsgate","R","Action"
"929","1/15/2016","Ride Along 2",4e+07,90862685,124827316,"Universal","PG-13","Comedy"
"930","4/14/2006","Scary Movie 4",4e+07,90710620,178710620,"Weinstein/Dimension","PG-13","Comedy"
"931","3/27/2015","Get Hard",4e+07,90411453,106511453,"Warner Bros.","R","Comedy"
"932","2/4/2000","Scream 3",4e+07,89138076,161838076,"Miramax","R","Horror"
"933","5/24/1990","Back to the Future Part III",4e+07,88055283,244088654,"Universal","PG","Adventure"
"934","11/14/2014","Dumb and Dumber To",4e+07,86208010,156553592,"Universal","PG-13","Comedy"
"935","11/13/1992","Bram Stoker's Dracula",4e+07,82522790,215862692,"Sony Pictures","R","Horror"
"936","2/17/2006","Eight Below",4e+07,81612565,120455994,"Walt Disney","PG","Adventure"
"937","12/24/1999","The Talented Mr. Ripley",4e+07,81292135,128792135,"Paramount Pictures","R","Drama"
"938","9/25/2015","The Intern",4e+07,75764672,197232734,"Warner Bros.","PG-13","Comedy"
"939","9/25/1992","The Last of the Mohicans",4e+07,75505856,75505856,"20th Century Fox","R","Action"
"940","10/29/2004","Ray",4e+07,75305995,124823094,"Universal","PG-13","Drama"
"941","4/1/2005","Sin City",4e+07,74103820,158527918,"Miramax/Dimension","R","Action"
"942","3/20/2009","I Love You, Man",4e+07,72013010,92302502,"Paramount Pictures","R","Comedy"
"943","12/20/1991","JFK",4e+07,70405498,205400000,"Warner Bros.","R","Drama"
"944","1/27/2006","Big Momma's House 2",4e+07,70165972,137047376,"20th Century Fox","PG-13","Comedy"
"945","11/4/2016","Hacksaw Ridge",4e+07,67209615,168940583,"Lionsgate","R","Drama"
"946","3/2/2001","The Mexican",4e+07,66808615,145238250,"Dreamworks SKG","R","Action"
"947","8/28/2009","The Final Destination",4e+07,66477700,187384627,"Warner Bros.","R","Horror"
"948","4/17/2009","17 Again",4e+07,64167069,139474906,"Warner Bros.","PG-13","Comedy"
"949","6/4/2010","Get Him to the Greek",4e+07,61153526,91455875,"Universal","R","Comedy"
"950","11/21/2003","Gothika",4e+07,59588068,141484812,"Warner Bros.","R","Horror"
"951","11/30/2001","Behind Enemy Lines",4e+07,58855732,58855732,"20th Century Fox","PG-13","Action"
"952","8/25/2006","Invincible",4e+07,57806952,58501127,"Walt Disney","PG","Drama"
"953","2/15/2013","Escape From Planet Earth",4e+07,57012977,74156610,"Weinstein Co.","PG","Adventure"
"954","7/10/1998","Small Soldiers",4e+07,55143823,71743823,"Dreamworks SKG","PG-13","Adventure"
"955","7/31/1997","Spawn",4e+07,54979992,87949859,"New Line","PG-13","Action"
"956","11/26/2014","Horrible Bosses 2",4e+07,54445357,105945357,"Warner Bros.","R","Comedy"
"957","1/25/2002","The Count of Monte Cristo",4e+07,54228104,75389090,"Walt Disney","PG-13","Drama"
"958","6/16/2006","The Lake House",4e+07,52330111,114830111,"Warner Bros.","PG","Drama"
"959","7/9/2010","Predators",4e+07,52000688,127234389,"20th Century Fox","R","Action"
"960","8/15/2012","The Odd Life of Timothy Green",4e+07,51853450,55249159,"Walt Disney","PG","Drama"
"961","7/31/1987","The Living Daylights",4e+07,51185000,191200000,"MGM","PG","Action"
"962","12/8/2006","Apocalypto",4e+07,50866635,121032272,"Walt Disney","R","Action"
"963","6/18/1986","Legal Eagles",4e+07,49851591,49851591,"Universal","PG","Comedy"
"964","8/12/2005","The Skeleton Key",4e+07,47907715,92256918,"Universal","PG-13","Horror"
"965","6/20/2014","Jersey Boys",4e+07,47047013,65282732,"Warner Bros.","R","Drama"
"966","11/21/1997","The Rainmaker",4e+07,45916769,45916769,"Paramount Pictures","PG-13","Drama"
"967","2/7/1992","Medicine Man",4e+07,44948240,44948240,"Walt Disney","PG-13","Drama"
"968","12/12/1997","Amistad",4e+07,44212592,58250151,"Dreamworks SKG","R","Drama"
"969","5/30/2014","A Million Ways to Die in The West",4e+07,42720965,86778557,"Universal","R","Comedy"
"970","8/12/2011","Final Destination 5",4e+07,42587643,155011165,"Warner Bros.","R","Horror"
"971","12/25/2007","Aliens vs. Predator - Requiem",4e+07,41797066,128884494,"20th Century Fox","R","Action"
"972","12/25/2007","The Water Horse: Legend of the Deep",4e+07,40412817,103429755,"Sony Pictures","PG","Drama"
"973","7/18/2014","Sex Tape",4e+07,38543473,126069509,"Sony Pictures","R","Comedy"
"974","4/15/2011","Scream 4",4e+07,38180928,95989590,"Weinstein/Dimension","R","Horror"
"975","12/21/1994","Ri¢hie Ri¢h",4e+07,38087756,38087756,"Warner Bros.","PG","Comedy"
"976","8/11/2000","Autumn in New York",4e+07,37752931,90717684,"MGM","PG-13","Drama"
"977","3/18/2011","Paul",4e+07,37412945,101162106,"Universal","R","Comedy"
"978","12/19/2012","The Guilt Trip",4e+07,37134215,41294674,"Paramount Pictures","PG-13","Comedy"
"979","2/18/2000","Hanging Up",4e+07,36037909,51867723,"Sony Pictures","PG-13","Comedy"
"980","3/1/1991","The Doors",4e+07,34416893,34416893,"Sony Pictures","R","Drama"
"981","8/20/1999","Mickey Blue Eyes",4e+07,33864342,53864342,"Warner Bros.","PG-13","Comedy"
"982","10/20/2000","Pay it Forward",4e+07,33508922,55696705,"Warner Bros.","PG-13","Drama"
"983","3/21/2008","Drillbit Taylor",4e+07,32862104,49686263,"Paramount Pictures","PG-13","Comedy"
"984","12/25/2011","Extremely Loud and Incredibly Close",4e+07,31847881,55247881,"Warner Bros.","PG-13","Drama"
"985","7/1/1994","The Shadow",4e+07,31835600,31835600,"Universal","PG-13","Action"
"986","11/10/2010","Morning Glory",4e+07,31011732,59795070,"Paramount Pictures","PG-13","Comedy"
"987","11/9/2005","Get Rich or Die Tryin'",4e+07,30981850,46666955,"Paramount Pictures","R","Drama"
"988","12/25/2013","Grudge Match",4e+07,29807260,69807260,"Warner Bros.","PG-13","Comedy"
"989","4/2/1999","The Out-of-Towners",4e+07,28544120,28544120,"Paramount Pictures","PG-13","Comedy"
"990","8/11/2017","The Nut Job 2: Nutty by Nature",4e+07,28370522,57465156,"Open Road","PG","Adventure"
"991","8/23/1996","The Island of Dr. Moreau",4e+07,27682712,27682712,"New Line","PG-13","Adventure"
"992","9/7/2001","The Musketeer",4e+07,27053815,27053815,"Universal","PG-13","Adventure"
"993","1/27/2017","Resident Evil: The Final Chapter",4e+07,26844692,312825686,"Sony Pictures","R","Action"
"994","2/29/2008","The Other Boleyn Girl",4e+07,26814957,78269970,"Sony Pictures","PG-13","Drama"
"995","6/30/2017","The House",4e+07,25584504,31192743,"Warner Bros.","R","Comedy"
"996","2/16/2001","Sweet November",4e+07,25288103,65754228,"Warner Bros.","PG-13","Drama"
"997","4/5/2007","The Reaping",4e+07,25126214,62226214,"Warner Bros.","R","Horror"
"998","6/3/1994","Renaissance Man",4e+07,24172899,24172899,"Walt Disney","PG-13","Comedy"
"999","5/15/1998","Quest for Camelot",4e+07,22772500,38172500,"Warner Bros.","G","Adventure"
"1000","9/6/2002","City by the Sea",4e+07,22433915,22433915,"Warner Bros.","R","Drama"
"1001","1/15/1999","At First Sight",4e+07,22365133,22365133,"MGM","PG-13","Drama"
"1002","1/16/2004","Torque",4e+07,21176322,46176322,"Warner Bros.","PG-13","Action"
"1003","11/13/2009","Fantastic Mr. Fox",4e+07,21002919,47083412,"20th Century Fox","PG","Adventure"
"1004","2/16/1996","City Hall",4e+07,20278055,20278055,"Sony Pictures","R","Drama"
"1005","2/3/2012","Big Miracle",4e+07,20157300,25268680,"Universal","PG","Drama"
"1006","12/21/2012","The Impossible",4e+07,19019882,169590606,"Lionsgate","PG-13","Drama"
"1007","3/9/2012","A Thousand Words",4e+07,18450127,20790486,"Paramount Pictures","PG-13","Comedy"
"1008","10/20/2006","Marie Antoinette",4e+07,15962471,60862471,"Sony Pictures","PG-13","Drama"
"1009","10/6/2000","Get Carter",4e+07,14967182,19417182,"Warner Bros.","R","Drama"
"1010","4/21/1995","Kiss of Death",4e+07,14942422,14942422,"20th Century Fox","R","Drama"
"1011","5/15/1987","Ishtar",4e+07,14375181,14375181,"Sony Pictures","PG-13","Comedy"
"1012","2/28/1992","Memoirs of an Invisible Man",4e+07,14358033,14358033,"Warner Bros.","PG-13","Comedy"
"1013","10/23/2009","Amelia",4e+07,14279575,19756077,"Fox Searchlight","PG","Drama"
"1014","5/7/2004","New York Minute",4e+07,14018364,21215882,"Warner Bros.","PG","Comedy"
"1015","3/12/1999","The Deep End of the Ocean",4e+07,13508635,13508635,"Sony Pictures","PG-13","Drama"
"1016","8/30/2002","FearDotCom",4e+07,13208023,13208023,"Warner Bros.","R","Horror"
"1017","11/7/2008","Soul Men",4e+07,12082391,12345883,"MGM","R","Comedy"
"1018","8/20/1999","Universal Soldier II: The Return",4e+07,10447421,10717421,"Sony Pictures","R","Action"
"1019","9/25/2009","Pandorum",4e+07,10330853,17033431,"Overture Films","R","Horror"
"1020","9/26/2003","Duplex",4e+07,9652000,10070651,"Miramax","PG-13","Comedy"
"1021","11/27/2002","Extreme Ops",4e+07,4835968,12624471,"Paramount Pictures","PG-13","Action"
"1022","4/6/2001","Just Visiting",4e+07,4777007,16172200,"Walt Disney","PG-13","Comedy"
"1023","3/11/1994","The Hudsucker Proxy",4e+07,2816518,14938149,"Warner Bros.","PG","Comedy"
"1024","11/11/2016","Billy Lynn’s Long Halftime Walk",4e+07,1738477,30230402,"Sony Pictures","R","Drama"
"1025","12/12/2008","Delgo",4e+07,915840,915840,"Freestyle Releasing","PG","Adventure"
"1026","9/7/2007","The Hunting Party",4e+07,876671,7729552,"Weinstein Co.","R","Adventure"
"1027","10/13/2006","Alex Rider: Operation Stormbreaker",4e+07,659210,20722450,"Weinstein Co.","PG","Action"
"1028","11/20/2009","Red Cliff",4e+07,627047,119627047,"Magnolia Pictures","R","Action"
"1029","9/24/2004","The Last Shot",4e+07,463730,463730,"Walt Disney","R","Comedy"
"1030","3/16/2007","Nomad",4e+07,79123,79123,"Weinstein Co.","R","Drama"
"1031","11/11/2016","USS Indianapolis: Men of Courage",4e+07,0,1641255,"Saban Films","R","Drama"
"1032","8/14/2009","The Time Traveler's Wife",3.9e+07,63414846,102332135,"Warner Bros.","PG-13","Drama"
"1033","6/17/1983","Superman III",3.9e+07,59950623,59950623,"Warner Bros.","PG","Adventure"
"1034","2/2/2007","Because I Said So",3.9e+07,42674040,69538833,"Universal","PG-13","Comedy"
"1035","10/5/2012","Frankenweenie",3.9e+07,35287788,81150788,"Walt Disney","PG","Adventure"
"1036","3/29/1996","Sgt. Bilko",3.9e+07,30356589,37956589,"Universal","PG","Comedy"
"1037","9/30/2005","Serenity",3.9e+07,25514517,40319440,"Universal","PG-13","Action"
"1038","2/20/2004","Against the Ropes",3.9e+07,5881504,6429865,"Paramount Pictures","PG-13","Drama"
"1039","8/23/2013","Yi dai zong shi",38600000,6594959,57987299,"Weinstein Co.","PG-13","Action"
"1040","6/22/2001","The Fast and the Furious",3.8e+07,144512310,206512310,"Universal","PG-13","Action"
"1041","9/27/2002","Sweet Home Alabama",3.8e+07,127214072,182365114,"Walt Disney","PG-13","Comedy"
"1042","11/18/1994","Star Trek: Generations",3.8e+07,75671262,1.2e+08,"Paramount Pictures","PG","Adventure"
"1043","4/17/2015","Paul Blart: Mall Cop 2",3.8e+07,71091594,107650646,"Sony Pictures","PG","Adventure"
"1044","12/19/1997","Mouse Hunt",3.8e+07,61894591,61894591,"Dreamworks SKG","PG","Adventure"
"1045","12/23/2016","Why Him?",3.8e+07,60323786,117439538,"20th Century Fox","R","Comedy"
"1046","4/22/2011","Water for Elephants",3.8e+07,58709717,116809717,"20th Century Fox","PG-13","Drama"
"1047","12/29/1999","The Hurricane",3.8e+07,50699241,73956241,"Universal","R","Drama"
"1048","9/6/2013","Riddick",3.8e+07,42025135,94763758,"Universal","R","Action"
"1049","1/22/2016","The 5th Wave",3.8e+07,34912982,111336398,"Sony Pictures","PG-13","Action"
"1050","9/20/2013","Rush",3.8e+07,26947624,98230839,"Universal","R","Drama"
"1051","5/18/2001","Angel Eyes",3.8e+07,24044532,29544532,"Warner Bros.","R","Drama"
"1052","12/21/2001","Joe Somebody",3.8e+07,22770864,24515990,"20th Century Fox","PG","Comedy"
"1053","10/20/2017","Only the Brave",3.8e+07,18340051,24181629,"Sony Pictures","PG-13","Drama"
"1054","9/27/1996","Extreme Measures",3.8e+07,17378193,17378193,"Sony Pictures","R","Drama"
"1055","9/7/2001","Rock Star",3.8e+07,16991902,19317765,"Warner Bros.","R","Drama"
"1056","2/2/1996","White Squall",3.8e+07,10229300,10229300,"Walt Disney","PG-13","Adventure"
"1057","10/10/2008","City of Ember",3.8e+07,7873007,17831558,"20th Century Fox","PG","Adventure"
"1058","10/31/1997","Switchback",3.8e+07,6504442,6504442,"Paramount Pictures","R","Action"
"1059","9/14/2012","The Master",37500000,16247159,50647416,"Weinstein Co.","R","Drama"
"1060","10/10/2008","The Express",37500000,9793406,9813309,"Universal","PG","Drama"
"1061","8/7/2013","We're the Millers",3.7e+07,150394119,267816276,"Warner Bros.","R","Comedy"
"1062","11/25/2015","Creed",3.7e+07,109767581,173567581,"Warner Bros.","PG-13","Drama"
"1063","9/17/2010","The Town",3.7e+07,92186262,152566881,"Warner Bros.","R","Drama"
"1064","9/23/2011","Dolphin Tale",3.7e+07,72286779,96068724,"Warner Bros.","PG","Drama"
"1065","2/23/2018","Game Night",3.7e+07,69001013,117201013,"Warner Bros.","R","Comedy"
"1066","4/23/2004","13 Going On 30",3.7e+07,57139723,97658712,"Sony Pictures","PG-13","Comedy"
"1067","4/4/2008","Nim's Island",3.7e+07,48006762,101857425,"20th Century Fox","PG","Adventure"
"1068","2/26/2010","Cop Out",3.7e+07,44875481,55909910,"Warner Bros.","R","Comedy"
"1069","1/28/2011","The Rite",3.7e+07,33047633,97143987,"Warner Bros.","PG-13","Horror"
"1070","7/18/2008","Space Chimps",3.7e+07,30105968,67029956,"20th Century Fox","G","Adventure"
"1071","12/17/1999","Magnolia",3.7e+07,22450975,48446802,"New Line","R","Drama"
"1072","5/29/2015","Aloha",3.7e+07,21052030,24935799,"Sony Pictures","PG-13","Drama"
"1073","10/5/2018","A Star is Born",3.6e+07,126181246,200881246,"Warner Bros.","R","Drama"
"1074","2/11/2011","Gnomeo and Juliet",3.6e+07,99967670,193737977,"Walt Disney","G","Comedy"
"1075","2/15/2002","John Q",3.6e+07,71026631,102226631,"New Line","PG-13","Drama"
"1076","9/17/1999","Blue Streak",3.6e+07,68208190,117448157,"Sony Pictures","PG-13","Action"
"1077","10/7/1983","Never Say Never Again",3.6e+07,55500000,1.6e+08,"Warner Bros.","PG","Action"
"1078","3/26/2010","Hot Tub Time Machine",3.6e+07,50269859,65967750,"MGM","R","Comedy"
"1079","9/12/2014","Dolphin Tale 2",3.6e+07,42024533,57824533,"Warner Bros.","PG","Drama"
"1080","12/16/2016","Collateral Beauty",3.6e+07,31016021,85315070,"Warner Bros.","PG-13","Drama"
"1081","4/4/2003","A Man Apart",3.6e+07,26500000,43797731,"New Line","R","Action"
"1082","2/25/2000","Reindeer Games",3.6e+07,23360779,23360779,"Miramax","R","Action"
"1083","12/24/1999","Snow Falling on Cedars",3.6e+07,14378353,14378353,"Universal","PG-13","Drama"
"1084","12/20/1996","Ghosts of Mississippi",3.6e+07,13052741,13052741,"Sony Pictures","PG-13","Drama"
"1085","10/24/1997","Gattaca",3.6e+07,12532777,12532777,"Sony Pictures","PG-13","Drama"
"1086","1/28/2000","Isn't She Great",3.6e+07,2954405,2954405,"Universal","R","Comedy"
"1087","1/22/2016","Yip Man 3",3.6e+07,2679437,157300954,"Well Go USA","PG-13","Action"
"1088","5/6/2011","There Be Dragons",3.6e+07,1069334,4020990,"Samuel Goldwyn Films","PG-13","Drama"
"1089","4/14/2017","Queen of the Desert",3.6e+07,0,1578543,"IFC Films","PG-13","Drama"
"1090","3/28/2003","Head of State",35200000,37788228,38283765,"Dreamworks SKG","PG-13","Comedy"
"1091","9/8/2017","It",3.5e+07,327481748,697459228,"Warner Bros.","R","Horror"
"1092","6/5/2009","The Hangover",3.5e+07,277322503,465764086,"Warner Bros.","R","Comedy"
"1093","11/20/2009","The Blind Side",3.5e+07,255959475,305705794,"Warner Bros.","PG-13","Drama"
"1094","6/23/1989","Batman",3.5e+07,251188924,411348924,"Warner Bros.","PG-13","Action"
"1095","5/15/1992","Lethal Weapon 3",3.5e+07,144731527,319700000,"Warner Bros.","R","Action"
"1096","9/18/1998","Rush Hour",3.5e+07,141186864,245300000,"New Line","PG-13","Action"
"1097","2/8/2013","Identity Thief",3.5e+07,134506920,175361578,"Universal","R","Comedy"
"1098","6/30/2006","The Devil Wears Prada",3.5e+07,124740460,326073155,"20th Century Fox","PG-13","Comedy"
"1099","7/8/2011","Horrible Bosses",3.5e+07,117538559,212417601,"Warner Bros.","R","Comedy"
"1100","3/30/2001","Spy Kids",3.5e+07,112692062,197692062,"Miramax/Dimension","PG","Adventure"
"1101","7/17/2015","Trainwreck",3.5e+07,110212700,141123897,"Universal","R","Comedy"
"1102","12/13/2013","Saving Mr. Banks",3.5e+07,83299761,114962525,"Walt Disney","PG-13","Drama"
"1103","12/7/1979","Star Trek: The Motion Picture",3.5e+07,82258456,1.39e+08,"Paramount Pictures","PG","Adventure"
"1104","11/15/1996","The English Patient",3.5e+07,78716374,231710008,"Miramax","R","Drama"
"1105","1/16/2009","Hotel for Dogs",3.5e+07,73178547,122357172,"Paramount Pictures","PG","Adventure"
"1106","3/25/2005","Guess Who",3.5e+07,68915888,102115888,"Sony Pictures","PG-13","Comedy"
"1107","12/21/2012","This is 40",3.5e+07,67544505,90221182,"Universal","R","Comedy"
"1108","9/19/1997","L.A. Confidential",3.5e+07,64604977,126204977,"Warner Bros.","R","Drama"
"1109","7/29/2005","Sky High",3.5e+07,63939454,83109359,"Walt Disney","PG","Adventure"
"1110","9/19/1997","In & Out",3.5e+07,63826569,83226569,"Paramount Pictures","PG-13","Comedy"
"1111","7/7/1995","Species",3.5e+07,60054449,113354449,"MGM","R","Action"
"1112","4/7/2006","The Benchwarmers",3.5e+07,59843754,65063726,"Sony Pictures","PG-13","Comedy"
"1113","10/8/2010","Secretariat",3.5e+07,59699513,60376247,"Walt Disney","PG","Drama"
"1114","3/13/1998","The Man in the Iron Mask",3.5e+07,56968169,56968169,"MGM","PG-13","Adventure"
"1115","5/20/2016","Neighbors 2: Sorority Rising",3.5e+07,55340730,108758521,"Universal","R","Comedy"
"1116","3/23/2007","TMNT",3.5e+07,54149098,96096018,"Warner Bros.","PG","Action"
"1117","10/24/2003","Radio",3.5e+07,52333738,53293628,"Sony Pictures","PG","Drama"
"1118","6/29/2018","Sicario: Day of the Soldado",3.5e+07,50065850,73285196,"Sony Pictures","R","Action"
"1119","11/25/2009","Old Dogs",3.5e+07,49492060,95104304,"Walt Disney","PG","Comedy"
"1120","11/18/1992","Malcolm X",3.5e+07,48169910,48169910,"Warner Bros.","PG-13","Drama"
"1121","1/23/2009","Underworld 3: Rise of the Lycans",3.5e+07,45802315,89102315,"Sony Pictures","R","Action"
"1122","1/19/2018","12 Strong",3.5e+07,45500164,70798829,"Warner Bros.","R","Drama"
"1123","2/28/1997","Donnie Brasco",3.5e+07,41954997,65303052,"Sony Pictures","R","Drama"
"1124","10/17/2008","Max Payne",3.5e+07,40689393,85763888,"20th Century Fox","PG-13","Action"
"1125","3/15/2002","Resident Evil",3.5e+07,40119709,103787401,"Sony Pictures","R","Horror"
"1126","3/26/2004","The Ladykillers",3.5e+07,39692139,77392139,"Walt Disney","R","Comedy"
"1127","12/1/2006","The Nativity Story",3.5e+07,37629831,46309644,"New Line","PG","Drama"
"1128","11/9/2011","J. Edgar",3.5e+07,37306030,84606030,"Warner Bros.","R","Drama"
"1129","11/17/2000","Bounce",3.5e+07,36805288,53425292,"Miramax","PG-13","Drama"
"1130","8/17/2018","Mile 22",3.5e+07,36108758,64708758,"STX Entertainment","R","Action"
"1131","10/13/2017","The Foreigner",3.5e+07,34393507,140783646,"STX Entertainment","R","Action"
"1132","12/3/2004","Closer",3.5e+07,33987757,116177695,"Sony Pictures","R","Drama"
"1133","12/23/1994","Street Fighter",3.5e+07,33423000,99423000,"Universal","PG-13","Action"
"1134","11/21/2001","Black Knight",3.5e+07,33422806,33422806,"20th Century Fox","PG-13","Adventure"
"1135","12/27/2002","The Pianist",3.5e+07,32519322,111854182,"Focus Features","R","Drama"
"1136","5/6/2005","House of Wax",3.5e+07,32064800,70064800,"Warner Bros.","R","Horror"
"1137","6/1/2018","Adrift",3.5e+07,31445011,57931376,"STX Entertainment","PG-13","Drama"
"1138","8/15/2008","Mirrors",3.5e+07,30691439,77220596,"20th Century Fox","R","Horror"
"1139","2/22/2002","Queen of the Damned",3.5e+07,30307804,30307804,"Warner Bros.","R","Horror"
"1140","8/20/2010","Nanny McPhee and the Big Bang",3.5e+07,29197642,97799865,"Universal","PG","Adventure"
"1141","10/12/2018","Goosebumps 2: Haunted Halloween",3.5e+07,28804812,39904812,"Sony Pictures","PG","Horror"
"1142","11/21/1990","Predator 2",3.5e+07,28317513,54768418,"20th Century Fox","R","Action"
"1143","12/5/1980","Flash Gordon",3.5e+07,27107960,27107960,"Universal",NA,"Action"
"1144","3/28/2008","Superhero Movie",3.5e+07,26638520,73026302,"MGM","PG-13","Comedy"
"1145","2/12/1999","Blast from the Past",3.5e+07,26613620,26613620,"New Line","PG-13","Comedy"
"1146","3/26/2004","Jersey Girl",3.5e+07,25266129,37066129,"Miramax","PG-13","Comedy"
"1147","11/9/2001","Heist",3.5e+07,23483357,28906817,"New Line","R","Action"
"1148","12/25/1992","Hoffa",3.5e+07,23365858,28391473,"20th Century Fox","R","Drama"
"1149","3/4/2016","Whiskey Tango Foxtrot",3.5e+07,23083334,25350747,"Paramount Pictures","R","Comedy"
"1150","4/9/2004","Ella Enchanted",3.5e+07,22913677,22913677,"Miramax","PG","Comedy"
"1151","8/21/2015","Hitman: Agent 47",3.5e+07,22467450,81959582,"20th Century Fox","R","Action"
"1152","7/25/2008","The X-Files: I Want to Believe",3.5e+07,20982478,68170792,"20th Century Fox","PG-13","Action"
"1153","8/19/2005","Valiant",3.5e+07,19478106,64188387,"Walt Disney","G","Adventure"
"1154","2/23/2000","Wonder Boys",3.5e+07,19389454,33422485,"Paramount Pictures","R","Comedy"
"1155","2/25/2005","Cursed",3.5e+07,19294901,25114901,"Miramax/Dimension","PG-13","Horror"
"1156","12/21/2007","Walk Hard: The Dewey Cox Story",3.5e+07,18317151,20606053,"Sony Pictures","R","Comedy"
"1157","9/20/2002","The Four Feathers",3.5e+07,18306166,29882645,"Paramount Pictures","PG-13","Drama"
"1158","4/30/2010","Furry Vengeance",3.5e+07,17630465,39340177,"Summit Entertainment","PG","Adventure"
"1159","9/15/2000","Bait",3.5e+07,15325127,15471969,"Warner Bros.","R","Action"
"1160","12/8/2000","Dungeons and Dragons",3.5e+07,15185241,33771965,"New Line","PG-13","Adventure"
"1161","11/9/2007","Lions for Lambs",3.5e+07,14998070,63211088,"United Artists","R","Drama"
"1162","1/18/1991","Flight of the Intruder",3.5e+07,14471440,14471440,"Paramount Pictures","PG-13","Action"
"1163","5/27/2011","The Tree of Life",3.5e+07,13305665,61721826,"Fox Searchlight","PG-13","Drama"
"1164","8/11/2006","Zoom",3.5e+07,11989328,12506188,"Sony Pictures","PG","Adventure"
"1165","12/25/2001","The Shipping News",3.5e+07,11405825,24405825,"Miramax","R","Drama"
"1166","12/18/2009","The Young Victoria",3.5e+07,11001272,31878891,"Apparition","PG","Drama"
"1167","3/28/2014","Sabotage",3.5e+07,10508518,18376443,"Open Road","R","Action"
"1168","9/4/1998","Knock Off",3.5e+07,10319915,10319915,"Sony Pictures","R","Action"
"1169","3/6/2015","Unfinished Business",3.5e+07,10219501,12819501,"20th Century Fox","R","Comedy"
"1170","9/30/2015","The Walk",3.5e+07,10161183,61197045,"Sony Pictures","PG","Drama"
"1171","11/22/2006","The Fountain",3.5e+07,10144010,15461638,"Warner Bros.","PG-13","Drama"
"1172","11/29/2013","Mandela: Long Walk to Freedom",3.5e+07,8323085,29890402,"Weinstein Co.","PG-13","Drama"
"1173","12/5/2008","Punisher: War Zone",3.5e+07,8050977,10157534,"Lionsgate","R","Action"
"1174","11/10/2006","A Good Year",3.5e+07,7459300,42064105,"20th Century Fox","PG-13","Drama"
"1175","3/11/2016","The Brothers Grimsby",3.5e+07,6864016,28721408,"Sony Pictures","R","Comedy"
"1176","5/2/1997","Warriors of Virtue",3.5e+07,6448817,6448817,"MGM","PG","Action"
"1177","9/26/2003","Luther",3.5e+07,5781086,32736879,"RS Entertainment","PG-13","Drama"
"1178","1/28/2011","Biutiful",3.5e+07,5101237,24687524,"Roadside Attractions","R","Drama"
"1179","2/21/1992","Radio Flyer",3.5e+07,4651977,4651977,"Sony Pictures","PG-13","Drama"
"1180","4/22/2016","A Hologram for the King",3.5e+07,4212494,11848058,"Roadside Attractions","R","Drama"
"1181","1/1/1980","Lion of the Desert",3.5e+07,1500000,1500000,"United Film Distrib…",NA,"Drama"
"1182","4/19/1996","Le hussard sur le toit",3.5e+07,1320043,1320043,"Miramax","R","Drama"
"1183","9/14/2012","Stolen",3.5e+07,289773,17967746,"Alchemy","R","Action"
"1184","3/13/2015","The Lovers",3.5e+07,0,11106,"IFC Films","PG-13","Adventure"
"1185","12/25/2011","The Darkest Hour",34800000,21443494,62831715,"Summit Entertainment","PG-13","Action"
"1186","4/10/2015","The Longest Ride",3.4e+07,37446117,63802928,"20th Century Fox","PG-13","Drama"
"1187","9/17/1993","The Age of Innocence",3.4e+07,32014993,32014993,"Sony Pictures","PG","Drama"
"1188","8/14/2009","Gake no ue no Ponyo",3.4e+07,15090399,205312666,"Walt Disney","G","Adventure"
"1189","9/1/1999","Chill Factor",3.4e+07,11263966,11263966,"Warner Bros.","R","Action"
"1190","5/5/2000","I Dreamed of Africa",3.4e+07,6543194,14291999,"Sony Pictures","PG-13","Drama"
"1191","12/4/1981","Reds",33500000,5e+07,5e+07,"Paramount Pictures","PG","Drama"
"1192","12/11/1992","A Few Good Men",3.3e+07,141340178,236500000,"Sony Pictures","R","Drama"
"1193","6/2/2000","Big Momma's House",3.3e+07,117559438,173559438,"20th Century Fox","PG-13","Comedy"
"1194","3/16/2001","Exit Wounds",3.3e+07,51758599,79958599,"Warner Bros.","R","Action"
"1195","7/8/2016","Mike and Dave Need Wedding Dates",3.3e+07,46009673,75909673,"20th Century Fox","R","Comedy"
"1196","7/27/2012","Step Up Revolution",3.3e+07,35074677,165552290,"Lionsgate","PG-13","Drama"
"1197","4/16/2004","The Punisher",3.3e+07,33664370,54533774,"Lionsgate","R","Action"
"1198","4/27/2012","Safe",3.3e+07,17142080,41495213,"Lionsgate","R","Action"
"1199","3/14/2008","Doomsday",3.3e+07,11008770,21621188,"Universal","R","Action"
"1200","4/23/1999","Pushing Tin",3.3e+07,8408835,8408835,"20th Century Fox","R","Comedy"
"1201","5/12/2006","Goal! The Dream Begins",3.3e+07,4283255,27610873,"Walt Disney","PG","Drama"
"1202","5/13/2011","Bridesmaids",32500000,169211718,289632023,"Universal","R","Comedy"
"1203","12/10/2008","The Reader",32500000,34194407,112964875,"Weinstein Co.","R","Drama"
"1204","2/24/2012","Wanderlust",32500000,17288155,24159934,"Universal","R","Comedy"
"1205","11/7/2003","Elf",3.2e+07,173398518,220236410,"New Line","PG","Adventure"
"1206","7/5/1996","Phenomenon",3.2e+07,104636382,152036382,"Walt Disney","PG","Drama"
"1207","6/12/2013","This is the End",3.2e+07,101470202,126539117,"Sony Pictures","R","Comedy"
"1208","1/18/2002","Snow Dogs",3.2e+07,81150692,116898028,"Walt Disney","PG","Adventure"
"1209","6/16/2006","Nacho Libre",3.2e+07,80197993,99296462,"Paramount Pictures","PG","Comedy"
"1210","11/23/1988","Scrooged",3.2e+07,59450353,59450353,"Paramount Pictures","PG-13","Comedy"
"1211","8/27/2010","Takers",3.2e+07,57744720,70587268,"Sony Pictures","PG-13","Drama"
"1212","9/10/1999","Stigmata",3.2e+07,50041732,89441732,"MGM","R","Horror"
"1213","11/10/2000","Men of Honor",3.2e+07,48814909,82339483,"20th Century Fox","R","Drama"
"1214","4/20/2018","I Feel Pretty",3.2e+07,48795601,91569698,"STX Entertainment","PG-13","Comedy"
"1215","9/2/2005","The Transporter 2",3.2e+07,43095856,88978458,"20th Century Fox","PG-13","Action"
"1216","2/18/2011","Big Mommas: Like Father, Like Son",3.2e+07,37915414,82332450,"20th Century Fox","PG-13","Comedy"
"1217","1/15/1993","Alive",3.2e+07,36299670,36299670,"Walt Disney","R","Drama"
"1218","10/21/2005","Dreamer: Inspired by a True Story",3.2e+07,33022286,39498360,"Dreamworks SKG","PG","Drama"
"1219","9/23/2005","A History of Violence",3.2e+07,31493782,61477797,"New Line","R","Drama"
"1220","3/15/2013","The Incredible Burt Wonderstone",3.2e+07,22537881,27392609,"Warner Bros.","PG-13","Comedy"
"1221","3/19/2010","Repo Men",3.2e+07,13942007,18195238,"Universal","R","Action"
"1222","9/14/2007","Dragon Wars: D-War",3.2e+07,10977721,79915361,"Freestyle Releasing","PG-13","Action"
"1223","9/6/1996","Bogus",3.2e+07,4357406,4357406,"Warner Bros.","PG","Comedy"
"1224","12/8/1999","Cradle Will Rock",3.2e+07,2899970,2899970,"Walt Disney","R","Drama"
"1225","12/15/2006","The Good German",3.2e+07,1308696,6678033,"Warner Bros.","R","Drama"
"1226","8/15/1979","Apocalypse Now",31500000,78800000,81250485,"United Artists","R","Action"
"1227","4/15/2016","Criminal",31500000,14708696,38771262,"Lionsgate","R","Action"
"1228","11/2/2012","Flight",3.1e+07,93772375,160558438,"Paramount Pictures","R","Drama"
"1229","12/29/1995","Mr. Holland’s Opus",3.1e+07,82582604,106282604,"Walt Disney","PG","Drama"
"1230","12/18/1985","Out of Africa",3.1e+07,79096868,258210860,"Universal","PG","Drama"
"1231","6/29/1979","Moonraker",3.1e+07,70300000,210300000,"United Artists","PG","Action"
"1232","3/7/2014","The Grand Budapest Hotel",3.1e+07,59076019,164180547,"Fox Searchlight","R","Comedy"
"1233","7/29/2015","Vacation",3.1e+07,58884188,101627989,"Warner Bros.","R","Comedy"
"1234","4/28/2000","Frequency",3.1e+07,44983704,68079671,"New Line","PG-13","Drama"
"1235","9/28/2001","Hearts in Atlantis",3.1e+07,24185781,30885781,"Warner Bros.","PG-13","Drama"
"1236","1/22/2010","Extraordinary Measures",3.1e+07,12482741,15826984,"CBS Films","PG","Drama"
"1237","8/25/2017","Birth of the Dragon",3.1e+07,6901965,7220514,"BH Tilt","PG-13","Action"
"1238","10/20/1995","Get Shorty",30250000,72021008,115021008,"MGM","R","Comedy"
"1239","6/8/1984","Ghostbusters",3e+07,242212467,295212467,"Sony Pictures","PG","Adventure"
"1240","11/22/1995","Toy Story",3e+07,191796233,364545516,"Walt Disney","G","Adventure"
"1241","6/25/1999","Big Daddy",3e+07,163479795,228641283,"Sony Pictures","PG-13","Comedy"
"1242","8/10/2001","American Pie 2",3e+07,145096820,286500000,"Universal","R","Comedy"
"1243","2/10/2012","The Vow",3e+07,125014030,197618160,"Sony Pictures","PG-13","Drama"
"1244","6/10/1994","Speed",3e+07,121248145,283200000,"20th Century Fox","R","Action"
"1245","8/16/2013","Lee Daniels' The Butler",3e+07,116632095,177025498,"Weinstein Co.","PG-13","Drama"
"1246","9/29/2000","Remember the Titans",3e+07,115654751,136706683,"Walt Disney","PG","Drama"
"1247","6/18/2004","Dodgeball: A True Underdog Story",3e+07,114326736,167791704,"20th Century Fox","PG-13","Comedy"
"1248","11/10/1995","Ace Ventura: When Nature Calls",3e+07,108360063,212400000,"Warner Bros.","PG-13","Comedy"
"1249","8/3/2001","The Princess Diaries",3e+07,108244774,165334774,"Walt Disney","G","Comedy"
"1250","3/5/1999","Analyze This",3e+07,106885658,176885658,"Warner Bros.","R","Comedy"
"1251","9/20/1996","The First Wives Club",3e+07,105489203,181489203,"Paramount Pictures","PG","Comedy"
"1252","12/15/2004","Million Dollar Baby",3e+07,100492203,231928227,"Warner Bros.","PG-13","Drama"
"1253","10/8/2003","Mystic River",3e+07,90135191,156822020,"Warner Bros.","R","Drama"
"1254","12/18/2015","Sisters",3e+07,87044645,106030660,"Universal","R","Comedy"
"1255","11/10/1999","Pokemon: The First Movie",3e+07,85744662,163644662,"Warner Bros.","G","Adventure"
"1256","11/19/2004","SpongeBob SquarePants: The Movie",3e+07,85416609,142051255,"Paramount Pictures","PG","Adventure"
"1257","12/4/2009","Up in the Air",3e+07,83823381,166842739,"Paramount Pictures","R","Drama"
"1258","6/25/2004","The Notebook",3e+07,81001787,116025023,"New Line","PG-13","Drama"
"1259","4/10/2009","Hannah Montana the Movie",3e+07,79576189,169173206,"Walt Disney","G","Drama"
"1260","11/17/2000","Rugrats in Paris",3e+07,76501438,103284813,"Paramount Pictures","G","Adventure"
"1261","8/18/2017","The Hitman’s Bodyguard",3e+07,75468583,172779292,"Lionsgate","R","Action"
"1262","12/25/1991","The Prince of Tides",3e+07,74787599,74787599,"Sony Pictures","R","Drama"
"1263","8/12/2005","Four Brothers",3e+07,74494381,92494381,"Paramount Pictures","R","Drama"
"1264","12/23/1994","Legends of the Fall",3e+07,66502573,160502573,"Sony Pictures","R","Drama"
"1265","9/28/2012","Looper",3e+07,66486205,170466405,"Sony Pictures","R","Action"
"1266","12/13/2002","About Schmidt",3e+07,65005217,107054484,"New Line","R","Drama"
"1267","1/17/2014","The Nut Job",3e+07,64251538,122529966,"Open Road","PG","Adventure"
"1268","2/16/2001","Down to Earth",3e+07,64172251,71172251,"Paramount Pictures","PG-13","Comedy"
"1269","8/4/1995","Babe",3e+07,63658910,246100000,"Universal","G","Adventure"
"1270","4/18/2008","Forgetting Sarah Marshall",3e+07,63172463,105173042,"Universal","R","Comedy"
"1271","10/8/2004","Friday Night Lights",3e+07,61255921,61950770,"Universal","PG-13","Drama"
"1272","11/17/1989","Harlem Nights",3e+07,60857262,95857262,"Paramount Pictures","R","Comedy"
"1273","4/25/2008","Baby Mama",3e+07,60494212,64170447,"Universal","PG-13","Comedy"
"1274","5/29/1998","Hope Floats",3e+07,60110313,81529000,"20th Century Fox","PG-13","Drama"
"1275","1/9/2009","Bride Wars",3e+07,58715510,115150424,"20th Century Fox","PG","Comedy"
"1276","8/20/2004","Without a Paddle",3e+07,58156435,65121280,"Paramount Pictures","PG-13","Comedy"
"1277","11/22/2017","Darkest Hour",3e+07,56443120,150355828,"Focus Features","PG-13","Drama"
"1278","9/23/2005","Corpse Bride",3e+07,53359111,114770654,"Warner Bros.","PG","Adventure"
"1279","5/14/2010","Letters to Juliet",3e+07,53032453,82148538,"Summit Entertainment","PG","Drama"
"1280","4/6/2001","Blow",3e+07,52990775,83282296,"New Line","R","Drama"
"1281","2/12/1999","Message in a Bottle",3e+07,52880016,52880016,"Warner Bros.","PG-13","Drama"
"1282","5/11/2018","Life of the Party",3e+07,52856061,65556061,"Warner Bros.","PG-13","Comedy"
"1283","7/24/2015","Southpaw",3e+07,52421953,94207861,"Weinstein Co.","R","Drama"
"1284","6/9/1989","Star Trek V: The Final Frontier",3e+07,52210049,70200000,"Paramount Pictures","PG","Action"
"1285","7/3/2002","Like Mike",3e+07,51432423,62432423,"20th Century Fox","PG","Adventure"
"1286","3/18/1994","Naked Gun 33 1/3: The Final Insult",3e+07,51041856,51041856,"Paramount Pictures","PG-13","Comedy"
"1287","12/7/2007","Atonement",3e+07,50980159,129779728,"Focus Features","R","Drama"
"1288","5/24/1985","A View to a Kill",3e+07,50327960,152627960,"MGM","PG","Action"
"1289","1/14/2005","Racing Stripes",3e+07,49772522,89955540,"Warner Bros.","PG","Adventure"
"1290","1/19/2018","Den of Thieves",3e+07,44947622,79424321,"STX Entertainment","R","Action"
"1291","4/13/2012","The Three Stooges",3e+07,44338224,54052249,"20th Century Fox","PG","Adventure"
"1292","7/21/2000","Pokemon 2000",3e+07,43746923,133946923,"Warner Bros.","G","Adventure"
"1293","10/24/2014","John Wick",3e+07,43037835,76235001,"Lionsgate","R","Action"
"1294","1/13/2006","Glory Road",3e+07,42647449,42799060,"Walt Disney","PG","Drama"
"1295","4/24/2015","The Age of Adaline",3e+07,42629776,69057415,"Lionsgate","PG-13","Drama"
"1296","8/6/2010","Step Up 3D",3e+07,42400223,165889117,"Walt Disney","PG-13","Drama"
"1297","5/29/2009","Drag Me To Hell",3e+07,42100625,91388487,"Universal","PG-13","Horror"
"1298","9/19/2003","Secondhand Lions",3e+07,42070939,47902566,"New Line","PG","Drama"
"1299","11/10/2006","Stranger Than Fiction",3e+07,40435190,53572822,"Sony Pictures","PG-13","Comedy"
"1300","4/8/2011","Hanna",3e+07,40259119,65343694,"Focus Features","PG-13","Drama"
"1301","8/16/2002","Blue Crush",3e+07,40118420,51618420,"Universal","PG-13","Drama"
"1302","10/19/2007","30 Days of Night",3e+07,39568996,80276156,"Sony Pictures","R","Horror"
"1303","9/15/2006","Gridiron Gang",3e+07,38432823,41457834,"Sony Pictures","PG-13","Drama"
"1304","7/20/1988","Midnight Run",3e+07,38413606,81613606,"Universal","R","Action"
"1305","1/25/2008","Meet the Spartans",3e+07,38233676,84646831,"20th Century Fox","PG-13","Comedy"
"1306","11/13/1987","The Running Man",3e+07,38122000,38122000,"Sony/TriStar","R","Action"
"1307","2/9/2018","The 15:17 to Paris",3e+07,36250957,56070897,"Warner Bros.","PG-13","Drama"
"1308","11/21/1997","Mortal Kombat: Annihilation",3e+07,35927406,51327406,"New Line","PG-13","Action"
"1309","4/7/2006","Take the Lead",3e+07,34742066,65390493,"New Line","PG-13","Drama"
"1310","11/24/2010","Love and Other Drugs",3e+07,32367005,102716321,"20th Century Fox","R","Drama"
"1311","6/3/2015","Entourage",3e+07,32363404,46362449,"Warner Bros.","R","Comedy"
"1312","6/1/2001","What's the Worst That Could Happen?",3e+07,32267774,38462071,"MGM","PG-13","Comedy"
"1313","7/2/2014","Deliver Us from Evil",3e+07,30577122,87956618,"Sony Pictures","R","Horror"
"1314","8/1/2014","Get on Up",3e+07,30569935,33339868,"Universal","PG-13","Drama"
"1315","7/15/2011","Winnie the Pooh",3e+07,26692846,50145607,"Walt Disney","G","Adventure"
"1316","5/15/1998","Bulworth",3e+07,26528684,29203383,"20th Century Fox","R","Comedy"
"1317","8/4/1995","Virtuosity",3e+07,23998226,23998226,"Paramount Pictures","R","Action"
"1318","9/14/2018","White Boy Rick",3e+07,23851700,23851700,"Sony Pictures","R","Drama"
"1319","9/18/1998","One True Thing",3e+07,23337196,26708196,"Universal","R","Drama"
"1320","2/4/2011","Sanctum",3e+07,23209310,104283753,"Universal","R","Adventure"
"1321","7/21/2006","My Super Ex-Girlfriend",3e+07,22530295,60772856,"20th Century Fox","PG-13","Comedy"
"1322","8/25/2017","Ballerina",3e+07,21858070,96908157,"Weinstein Co.","PG","Adventure"
"1323","9/17/2004","Mr. 3000",3e+07,21800302,21827296,"Walt Disney","PG-13","Comedy"
"1324","1/19/2005","Assault On Precinct 13",3e+07,20040895,36040895,"Focus/Rogue Pictures","R","Action"
"1325","2/6/1998","The Replacement Killers",3e+07,19035741,19035741,"Sony Pictures","R","Action"
"1326","3/3/2006","Ultraviolet",3e+07,18522064,30962112,"Sony Pictures","PG-13","Action"
"1327","10/21/2005","North Country",3e+07,18324242,23676771,"Warner Bros.","R","Drama"
"1328","10/9/2015","Steve Jobs",3e+07,17766658,35579007,"Universal","R","Drama"
"1329","7/17/2002","Eight Legged Freaks",3e+07,17266505,36722311,"Warner Bros.","PG-13","Comedy"
"1330","7/19/1996","Fled",3e+07,17192205,19892205,"MGM","R","Action"
"1331","6/4/2010","Splice",3e+07,17010170,28542494,"Warner Bros.","R","Horror"
"1332","4/9/2004","The Whole Ten Yards",3e+07,16323969,26323969,"Warner Bros.","PG-13","Comedy"
"1333","8/1/1986","Howard the Duck",3e+07,16295774,16295774,"Universal",NA,"Action"
"1334","10/24/2008","Pride and Glory",3e+07,15740721,43440721,"Warner Bros.","R","Drama"
"1335","8/26/2005","The Cave",3e+07,15007991,27147991,"Sony Pictures","PG-13","Horror"
"1336","6/20/2003","Alex & Emma",3e+07,14208384,15358583,"Warner Bros.","PG-13","Drama"
"1337","12/25/2005","The New World",3e+07,12712093,26184400,"New Line","PG-13","Adventure"
"1338","6/29/2007","Evening",3e+07,12406646,12885574,"Focus Features","PG-13","Drama"
"1339","1/18/2013","The Last Stand",3e+07,12050299,48330757,"Lionsgate","R","Action"
"1340","1/15/1999","In Dreams",3e+07,12017369,12017369,"Dreamworks SKG","R","Horror"
"1341","3/12/1999","Wing Commander",3e+07,11578022,11578022,"20th Century Fox","PG-13","Action"
"1342","4/29/2011","Hoodwinked Too: Hood vs. Evil",3e+07,10143779,23353111,"Weinstein Co.","PG","Adventure"
"1343","4/10/2009","Dragonball Evolution",3e+07,9362785,58228460,"20th Century Fox","PG","Adventure"
"1344","9/9/2005","An Unfinished Life",3e+07,8535575,18535575,"Miramax","PG-13","Drama"
"1345","2/3/2017","The Space Between Us",3e+07,7885294,16481405,"STX Entertainment","PG-13","Drama"
"1346","12/25/2009","The Imaginarium of Doctor Parnassus",3e+07,7689607,64352607,"Sony Pictures Classics","PG-13","Adventure"
"1347","1/14/2011","Barney's Version",3e+07,7502560,8845575,"Sony Pictures Classics","R","Drama"
"1348","6/1/1984","Once Upon a Time in America",3e+07,5321508,5575648,"Warner Bros.","R","Drama"
"1349","1/22/1999","Gloria",3e+07,4167493,4967493,"Sony Pictures","R","Drama"
"1350","12/29/2004","The Merchant of Venice",3e+07,3765585,18765585,"Sony Pictures Classics","R","Drama"
"1351","4/2/2003","The Good Thief",3e+07,3517797,6460758,"Fox Searchlight","R","Drama"
"1352","8/17/2005","Supercross",3e+07,3102550,3252550,"20th Century Fox","PG-13","Action"
"1353","12/29/2006","Miss Potter",3e+07,3005605,35891257,"MGM","PG","Drama"
"1354","5/5/2006","Wu ji",3e+07,669625,35869934,"Warner Independent","PG-13","Action"
"1355","9/23/2011","Machine Gun Preacher",3e+07,538690,3721988,"Relativity","R","Drama"
"1356","2/2/2018","Bilal: A New Breed of Hero",3e+07,490973,648599,"Vertical Entertainment","PG-13","Adventure"
"1357","6/15/2007","DOA: Dead or Alive",3e+07,480314,7755686,"Weinstein/Dimension","PG-13","Action"
"1358","10/7/2011","Xinhai geming",3e+07,135739,8593154,"Variance Films","R","Drama"
"1359","1/30/2015","Wild Card",3e+07,3200,3989464,"Lionsgate","R","Action"
"1360","12/14/2007","Goodbye Bafana",3e+07,0,2717302,"Paramount Vantage",NA,"Drama"
"1361","2/24/2017","Collide",29200000,2280004,5466631,"Open Road","PG-13","Action"
"1362","5/15/2015","Pitch Perfect 2",2.9e+07,184296230,287641616,"Universal","PG-13","Comedy"
"1363","11/18/2005","Walk the Line",2.9e+07,119519402,187707495,"20th Century Fox","PG-13","Drama"
"1364","9/28/2018","Night School",2.9e+07,66906825,84406825,"Universal","PG-13","Comedy"
"1365","4/8/2016","The Boss",2.9e+07,63077560,78652395,"Universal","R","Comedy"
"1366","12/27/1995","Twelve Monkeys",2.9e+07,57141459,168841459,"Universal","R","Drama"
"1367","9/12/2003","Once Upon a Time in Mexico",2.9e+07,56330657,97413527,"Sony Pictures","R","Action"
"1368","8/18/2017","Logan Lucky",2.9e+07,27778642,43886147,"Bleecker Street","PG-13","Comedy"
"1369","8/12/2016","Florence Foster Jenkins",2.9e+07,27383770,56017691,"Paramount Pictures","PG-13","Drama"
"1370","2/13/1998","The Borrowers",2.9e+07,22619589,54045832,"Polygram","PG","Adventure"
"1371","12/5/2008","Frost/Nixon",2.9e+07,18622031,28452945,"Universal","R","Drama"
"1372","11/12/2004","Seed of Chucky",2.9e+07,17016190,24716190,"Focus/Rogue Pictures","R","Horror"
"1373","12/31/2002","Confessions of a Dangerous Mind",2.9e+07,16007718,33013805,"Miramax","R","Drama"
"1374","8/26/2009","Taking Woodstock",2.9e+07,7460204,10066366,"Focus Features","R","Drama"
"1375","11/6/1987","Cry Freedom",2.9e+07,5899797,25899797,"Universal",NA,"Drama"
"1376","9/24/1999","Mumford",28700000,4559569,4559569,"Walt Disney","R","Comedy"
"1377","11/11/1992","Aladdin",2.8e+07,217350219,504050219,"Walt Disney","G","Adventure"
"1378","8/14/2015","Straight Outta Compton",2.8e+07,161197785,202182981,"Universal","R","Drama"
"1379","7/21/2017","Girls Trip",2.8e+07,115108515,140886353,"Universal","R","Comedy"
"1380","11/20/1998","The Rugrats Movie",2.8e+07,100494685,140894685,"Paramount Pictures","G","Adventure"
"1381","7/15/1988","Die Hard",2.8e+07,81350242,139109346,"20th Century Fox","R","Action"
"1382","11/1/2017","A Bad Moms Christmas",2.8e+07,72110659,127710659,"STX Entertainment","R","Comedy"
"1383","2/14/2013","Safe Haven",2.8e+07,71399120,94050951,"Relativity","PG-13","Drama"
"1384","12/11/2015","The Big Short",2.8e+07,70259870,133162752,"Paramount Pictures","R","Drama"
"1385","11/7/2008","Role Models",2.8e+07,67300955,94500826,"Universal","R","Comedy"
"1386","2/6/2004","Miracle",2.8e+07,64378093,64474705,"Walt Disney","PG","Drama"
"1387","1/28/2013","Last Vegas",2.8e+07,63914167,112914167,"CBS Films","PG-13","Comedy"
"1388","6/26/1981","For Your Eyes Only",2.8e+07,54800000,195300000,"Universal","PG","Action"
"1389","6/15/2018","Tag",2.8e+07,54547470,76844788,"Warner Bros.","R","Comedy"
"1390","9/28/2001","Zoolander",2.8e+07,45172250,60780981,"Paramount Pictures","PG-13","Comedy"
"1391","9/16/1994","Timecop",2.8e+07,44853581,102053581,"Universal","R","Action"
"1392","7/16/1993","Hocus Pocus",2.8e+07,39360491,39360491,"Walt Disney","PG","Comedy"
"1393","11/11/2005","Pride & Prejudice",2.8e+07,38372662,126549607,"Focus Features","PG","Drama"
"1394","8/12/2011","30 Minutes or Less",2.8e+07,37053924,40966716,"Sony Pictures","R","Comedy"
"1395","12/22/2000","Dracula 2000",2.8e+07,33000377,33000377,"Miramax","R","Horror"
"1396","4/7/1995","Rob Roy",2.8e+07,31390587,31390587,"MGM","R","Drama"
"1397","8/16/2013","Kick-Ass 2",2.8e+07,28795985,63129909,"Universal","R","Action"
"1398","10/12/2007","We Own the Night",2.8e+07,28563179,55307857,"Sony Pictures","R","Drama"
"1399","9/19/2014","A Walk Among the Tombstones",2.8e+07,26017685,62108587,"Universal","R","Action"
"1400","1/15/2010","The Spy Next Door",2.8e+07,24307106,46752858,"Lionsgate","PG","Adventure"
"1401","4/25/2014","Brick Mansions",2.8e+07,20396829,73421224,"Relativity","PG-13","Action"
"1402","10/1/1999","Mystery, Alaska",2.8e+07,8891623,8891623,"Walt Disney","R","Comedy"
"1403","8/24/2001","John Carpenter's Ghosts of Mars",2.8e+07,8434601,8434601,"Screen Media Films","R","Action"
"1404","7/11/1997","A Simple Wish",2.8e+07,8165213,8165213,"Universal","PG","Comedy"
"1405","10/30/2015","Our Brand is Crisis",2.8e+07,7002261,8592432,"Warner Bros.","R","Drama"
"1406","12/25/1997","Kundun",2.8e+07,5686694,5686694,"Walt Disney","PG-13","Drama"
"1407","6/10/1983","Octopussy",27500000,67900000,187500000,"MGM","PG","Action"
"1408","6/26/2009","My Sister's Keeper",27500000,49200230,96673002,"Warner Bros.","PG-13","Drama"
"1409","2/8/2008","Welcome Home Roscoe Jenkins",27500000,42436517,43607627,"Universal","PG-13","Comedy"
"1410","12/14/1984","A Passage to India",27500000,27187653,27187653,"Sony Pictures",NA,"Drama"
"1411","12/25/2006","Notes on a Scandal",27500000,17510118,50578411,"Fox Searchlight","R","Drama"
"1412","12/25/1994","The Jungle Book",2.7e+07,44342956,44342956,"Walt Disney","PG","Adventure"
"1413","8/19/2011","Spy Kids: All the Time in the World",2.7e+07,38536376,80681183,"Weinstein/Dimension","PG","Adventure"
"1414","10/21/1983","The Right Stuff",2.7e+07,21500000,21500000,"Warner Bros.",NA,"Action"
"1415","7/20/1984","Die Unendliche Geschichte",2.7e+07,21300000,21300000,"Warner Bros.",NA,"Adventure"
"1416","9/19/2008","The Duchess",2.7e+07,13848978,45160110,"Paramount Vantage","PG-13","Drama"
"1417","10/1/2010","Case 39",2.7e+07,13261851,28773827,"Paramount Vantage","R","Horror"
"1418","6/10/2005","The Honeymooners",2.7e+07,12834849,13174426,"Paramount Pictures","PG-13","Comedy"
"1419","6/21/1985","Return to Oz",2.7e+07,10618813,10618813,"Walt Disney","PG","Adventure"
"1420","3/27/1998","The Newton Boys",2.7e+07,10341093,10341093,"20th Century Fox","PG-13","Drama"
"1421","11/2/2007","Martian Child",2.7e+07,7500310,9352089,"New Line","PG","Drama"
"1422","10/18/2002","Formula 51",2.7e+07,5204007,5204007,"Screen Media Films","R","Action"
"1423","11/24/1999","Flawless",2.7e+07,4485485,4485485,"MGM","R","Drama"
"1424","10/17/2008","What Just Happened",2.7e+07,1090947,2412123,"Magnolia Pictures","R","Comedy"
"1425","1/16/2009","Paul Blart: Mall Cop",2.6e+07,146336178,185904750,"Sony Pictures","PG","Adventure"
"1426","8/19/2005","The 40 Year-old Virgin",2.6e+07,109449237,177344230,"Universal","R","Comedy"
"1427","12/21/1990","Kindergarten Cop",2.6e+07,91457688,2.02e+08,"Universal","PG-13","Comedy"
"1428","8/6/2008","Pineapple Express",2.6e+07,87341380,102404019,"Sony Pictures","R","Comedy"
"1429","12/22/1993","Philadelphia",2.6e+07,77324422,201324422,"Sony/TriStar","PG-13","Drama"
"1430","7/31/1998","Ever After: A Cinderella Story",2.6e+07,65705772,65705772,"20th Century Fox","PG","Drama"
"1431","6/15/1977","A Bridge Too Far",2.6e+07,50800000,50800000,"United Artists","PG","Action"
"1432","4/26/2013","Pain & Gain",2.6e+07,49875291,81275291,"Paramount Pictures","R","Action"
"1433","1/31/2003","Final Destination 2",2.6e+07,46896664,90396664,"New Line","R","Horror"
"1434","12/22/2000","O Brother, Where Art Thou?",2.6e+07,45506619,75763814,"Walt Disney","PG-13","Comedy"
"1435","12/29/2004","In Good Company",2.6e+07,45489752,63489752,"Universal","PG-13","Comedy"
"1436","8/29/2012","Lawless",2.6e+07,37397291,54393637,"Weinstein Co.","R","Drama"
"1437","3/29/2002","Clockstoppers",2.6e+07,36985501,38788828,"Paramount Pictures","PG","Adventure"
"1438","12/4/2009","Brothers",2.6e+07,28544157,45043870,"Lionsgate","R","Drama"
"1439","10/17/2014","The Best of Me",2.6e+07,26766213,41059418,"Relativity","PG-13","Drama"
"1440","2/20/2004","Welcome to Mooseport",2.6e+07,14469428,14469428,"20th Century Fox","PG-13","Comedy"
"1441","1/27/1995","Highlander: The Final Dimension",2.6e+07,13738574,13738574,"Miramax","PG-13","Action"
"1442","8/24/2001","The Curse of the Jade Scorpion",2.6e+07,7496522,18496522,"Dreamworks SKG","PG-13","Comedy"
"1443","10/18/2013","The Fifth Estate",2.6e+07,3254172,6154172,"Walt Disney","R","Drama"
"1444","3/21/2014","Blood Ties",2.6e+07,42472,2923959,"Roadside Attractions","R","Drama"
"1445","8/24/1997","The Grimm Brothers' Snow White",2.6e+07,5000,5000,"Gramercy","PG-13","Horror"
"1446","3/17/2015","Accidental Love",2.6e+07,0,135436,"Alchemy","PG-13","Comedy"
"1447","5/17/1996","Flipper",25530000,20080020,30593313,"Universal","PG","Adventure"
"1448","8/31/2005","The Constant Gardener",25500000,33579798,86301599,"Focus Features","R","Drama"
"1449","10/17/2008","W.",25100000,25534493,28575778,"Lionsgate","PG-13","Drama"
"1450","2/25/2004","The Passion of the Christ",2.5e+07,370782930,622341924,"Newmarket Films","R","Drama"
"1451","11/24/1993","Mrs. Doubtfire",2.5e+07,219195051,441286003,"20th Century Fox","PG-13","Comedy"
"1452","12/16/1988","Rain Man",2.5e+07,172825435,412800000,"MGM","R","Comedy"
"1453","8/10/2011","The Help",2.5e+07,169705587,213120004,"Walt Disney","PG-13","Drama"
"1454","12/25/2016","Hidden Figures",2.5e+07,169607287,231771716,"20th Century Fox","PG","Drama"
"1455","12/12/2008","Gran Torino",2.5e+07,148095302,274543085,"Warner Bros.","R","Drama"
"1456","1/17/2014","Ride Along",2.5e+07,134202565,153733800,"Universal","PG-13","Comedy"
"1457","12/15/1993","Schindler’s List",2.5e+07,96067179,321365567,"Universal","R","Drama"
"1458","3/26/2004","Scooby-Doo 2: Monsters Unleashed",2.5e+07,84185387,181185387,"Warner Bros.","PG","Adventure"
"1459","8/15/2003","Freddy vs. Jason",2.5e+07,82622655,114576403,"New Line","R","Horror"
"1460","2/16/2007","Bridge to Terabithia",2.5e+07,82234139,137984788,"Walt Disney","PG","Drama"
"1461","12/21/2001","Jimmy Neutron: Boy Genius",2.5e+07,80936232,102992536,"Paramount Pictures","G","Adventure"
"1462","1/18/2008","Cloverfield",2.5e+07,80048433,171302226,"Paramount Pictures","PG-13","Action"
"1463","2/5/2010","Dear John",2.5e+07,80014842,142033509,"Sony Pictures","PG-13","Drama"
"1464","12/25/2012","Parental Guidance",2.5e+07,77267296,120832383,"20th Century Fox","PG","Adventure"
"1465","6/3/1987","The Untouchables",2.5e+07,76270454,76270454,"Paramount Pictures","R","Action"
"1466","11/9/2007","No Country for Old Men",2.5e+07,74273505,164035753,"Miramax","R","Action"
"1467","1/13/2012","Contraband",2.5e+07,66528000,98406855,"Universal","R","Action"
"1468","1/27/2017","A Dog’s Purpose",2.5e+07,64321890,203731707,"Universal","PG","Drama"
"1469","4/20/2012","The Lucky One",2.5e+07,60457138,96633833,"Warner Bros.","PG-13","Drama"
"1470","3/22/2000","Romeo Must Die",2.5e+07,55973336,91036760,"Warner Bros.","R","Action"
"1471","2/10/2006","Final Destination 3",2.5e+07,54098051,112798051,"New Line","R","Horror"
"1472","4/22/2011","Madea's Big Happy Family",2.5e+07,53345287,54160818,"Lionsgate","PG-13","Drama"
"1473","12/13/2013","Tyler Perry's A Madea Christmas",2.5e+07,52543354,52543354,"Lionsgate","PG-13","Comedy"
"1474","11/12/2004","Finding Neverland",2.5e+07,51676606,115036108,"Miramax","PG","Drama"
"1475","5/23/1986","Cobra",2.5e+07,49042224,49042224,"Cannon","R","Action"
"1476","8/22/2008","The House Bunny",2.5e+07,48237389,71390601,"Sony Pictures","PG-13","Comedy"
"1477","3/14/2003","Agent Cody Banks",2.5e+07,47545060,58240458,"MGM","PG","Adventure"
"1478","1/27/2006","Nanny McPhee",2.5e+07,47279279,128745578,"Universal","PG","Adventure"
"1479","9/19/1990","Goodfellas",2.5e+07,46743809,46777347,"Warner Bros.","R","Drama"
"1480","8/15/2014","The Giver",2.5e+07,45090374,55090374,"Weinstein Co.","PG-13","Drama"
"1481","7/18/1997","Nothing To Lose",2.5e+07,44480039,64594061,"Walt Disney","R","Comedy"
"1482","11/20/1987","The Last Emperor",2.5e+07,43984987,44005073,"Sony Pictures","PG-13","Drama"
"1483","11/20/2015","The Night Before",2.5e+07,43035725,52427346,"Sony Pictures","R","Comedy"
"1484","10/15/1993","The Beverly Hillbillies",2.5e+07,42222647,55598481,"20th Century Fox","PG","Comedy"
"1485","12/27/2002","The Hours",2.5e+07,41675994,97030468,"Paramount Pictures","PG-13","Drama"
"1486","8/22/1997","Money Talks",2.5e+07,41076865,41076865,"New Line","R","Action"
"1487","12/26/2007","There Will Be Blood",2.5e+07,40222514,77208711,"Paramount Vantage","R","Drama"
"1488","12/20/2002","The Wild Thornberrys Movie",2.5e+07,40108697,60694737,"Paramount Pictures","PG","Adventure"
"1489","6/13/2003","Rugrats Go Wild",2.5e+07,39402572,55443032,"Paramount Pictures","PG","Adventure"
"1490","5/31/2002","Undercover Brother",2.5e+07,38230435,40796145,"Universal","PG-13","Comedy"
"1491","7/6/2001","Kiss of the Dragon",2.5e+07,36833473,36833473,"20th Century Fox","R","Action"
"1492","5/16/2014","Million Dollar Arm",2.5e+07,36447959,39217912,"Walt Disney","PG","Drama"
"1493","1/1/2004","Beauty Shop",2.5e+07,36351350,38351350,"MGM","PG-13","Comedy"
"1494","4/4/2003","What a Girl Wants",2.5e+07,35990505,35990505,"Warner Bros.","PG","Comedy"
"1495","8/29/2003","Jeepers Creepers II",2.5e+07,35623801,119923801,"MGM","R","Horror"
"1496","2/28/2003","Cradle 2 the Grave",2.5e+07,34657731,56434942,"Warner Bros.","R","Action"
"1497","8/24/2007","Mr. Bean’s Holiday",2.5e+07,33302167,234981342,"Universal","G","Adventure"
"1498","10/16/1998","Bride of Chucky",2.5e+07,32404188,50692188,"Universal","R","Horror"
"1499","2/17/2017","Fist Fight",2.5e+07,32187017,40287017,"Warner Bros.","R","Comedy"
"1500","11/21/2007","August Rush",2.5e+07,31664162,66015869,"Warner Bros.","PG","Drama"
"1501","12/9/2011","The Sitter",2.5e+07,30542576,38749404,"20th Century Fox","R","Comedy"
"1502","11/6/1998","Elizabeth",2.5e+07,30082699,82150642,"Gramercy","R","Drama"
"1503","1/23/1998","Spice World",2.5e+07,29342592,56042592,"Sony Pictures","PG","Comedy"
"1504","4/11/2014","Draft Day",2.5e+07,28842237,29847480,"Lionsgate","PG-13","Drama"
"1505","9/23/1994","The Shawshank Redemption",2.5e+07,28241469,28307092,"Sony Pictures","R","Drama"
"1506","2/3/2017","Rings",2.5e+07,27793018,82933201,"Paramount Pictures","PG-13","Horror"
"1507","5/22/2009","Dance Flick",2.5e+07,25794018,32224624,"Paramount Pictures","PG-13","Comedy"
"1508","4/20/2001","Crocodile Dundee in Los Angeles",2.5e+07,25590119,39393111,"Paramount Pictures","PG","Adventure"
"1509","7/26/1996","Kingpin",2.5e+07,25023424,32223424,"MGM","R","Comedy"
"1510","3/18/2005","Ice Princess",2.5e+07,24381334,25732334,"Walt Disney","G","Comedy"
"1511","8/26/2011","Don't Be Afraid of the Dark",2.5e+07,24046682,39126427,"FilmDistrict","R","Horror"
"1512","4/23/2010","The Losers",2.5e+07,23591432,29863840,"Warner Bros.","PG-13","Action"
"1513","8/24/2007","War",2.5e+07,22486409,40686409,"Lionsgate","R","Action"
"1514","4/7/1995","Don Juan DeMarco",2.5e+07,22032635,22032635,"New Line","PG-13","Drama"
"1515","4/22/2005","A Lot Like Love",2.5e+07,21835784,41921590,"Walt Disney","PG-13","Comedy"
"1516","5/1/1998","He Got Game",2.5e+07,21567853,22411948,"Walt Disney","R","Drama"
"1517","2/11/2011","The Eagle",2.5e+07,19490041,38993548,"Focus Features","PG-13","Action"
"1518","8/5/2015","Shaun the Sheep",2.5e+07,19375982,101927062,"Lionsgate","PG","Adventure"
"1519","9/2/2011","Shark Night 3D",2.5e+07,18877153,18877153,"Relativity","PG-13","Horror"
"1520","3/24/2017","CHiPS",2.5e+07,18600152,23190697,"Warner Bros.","R","Action"
"1521","10/11/2002","Punch-Drunk Love",2.5e+07,17791031,24591031,"Sony Pictures","R","Comedy"
"1522","2/20/2004","Eurotrip",2.5e+07,17718223,20718223,"Dreamworks SKG","R","Comedy"
"1523","12/22/2017","Father Figures",2.5e+07,17501244,21038826,"Warner Bros.","R","Comedy"
"1524","4/4/2008","The Ruins",2.5e+07,17432844,22910563,"Paramount Pictures","R","Horror"
"1525","12/8/2006","Unaccompanied Minors",2.5e+07,16655224,21970831,"Warner Bros.","PG","Adventure"
"1526","4/1/1988","Bright Lights, Big City",2.5e+07,16118077,16118077,"United Artists","R","Drama"
"1527","11/15/2002","Half Past Dead",2.5e+07,15567860,19233280,"Sony Pictures","PG-13","Action"
"1528","4/18/1986","Legend",2.5e+07,15502112,23506237,"Universal","PG","Adventure"
"1529","7/26/1996","The Adventures of Pinocchio",2.5e+07,15382170,36682170,"New Line","G","Adventure"
"1530","9/30/2005","The Greatest Game Ever Played",2.5e+07,15331289,15468266,"Walt Disney","PG","Drama"
"1531","3/3/2000","The Next Best Thing",2.5e+07,14983572,24355762,"Paramount Pictures","PG-13","Drama"
"1532","10/8/2010","My Soul to Take",2.5e+07,14744435,16727470,"Universal","R","Horror"
"1533","8/15/2008","Fly Me To the Moon",2.5e+07,14543943,43530281,"Summit Entertainment","G","Adventure"
"1534","9/13/1996","Maximum Risk",2.5e+07,14102929,51702929,"Sony Pictures","R","Action"
"1535","9/13/2002","Stealing Harvard",2.5e+07,13973532,13973532,"Sony Pictures","PG-13","Comedy"
"1536","8/3/2007","Hot Rod",2.5e+07,13938332,14334401,"Paramount Pictures","PG-13","Comedy"
"1537","9/9/2011","Warrior",2.5e+07,13657115,24215385,"Lionsgate","PG-13","Drama"
"1538","12/24/1999","Angela's Ashes",2.5e+07,13038660,13038660,"Paramount Pictures","R","Drama"
"1539","9/22/2017","Battle of the Sexes",2.5e+07,12638526,18445094,"Fox Searchlight","PG-13","Drama"
"1540","12/21/2012","Cirque du Soleil: Worlds Away",2.5e+07,12512862,28012862,"Paramount Pictures","PG","Drama"
"1541","11/13/2015","The 33",2.5e+07,12227722,28400715,"Warner Bros.","PG-13","Drama"
"1542","6/21/1985","Lifeforce",2.5e+07,11603545,11603545,"Sony/TriStar","R","Horror"
"1543","4/15/2011","The Conspirator",2.5e+07,11538204,15907411,"Roadside Attractions","PG-13","Drama"
"1544","7/3/2002","The Powerpuff Girls",2.5e+07,11411644,16425701,"Warner Bros.","PG","Adventure"
"1545","6/3/2005","The Lords of Dogtown",2.5e+07,11273517,13424365,"Sony/TriStar","PG-13","Action"
"1546","7/1/1986","Big Trouble in Little China",2.5e+07,11100000,11100000,"20th Century Fox",NA,"Action"
"1547","10/11/1996","Michael Collins",2.5e+07,11092559,27572844,"Warner Bros.","R","Drama"
"1548","3/28/2008","Stop-Loss",2.5e+07,10915744,11229035,"Paramount Pictures","R","Drama"
"1549","10/8/1993","Gettysburg",2.5e+07,10731997,10731997,"New Line","PG","Drama"
"1550","8/13/1999","Brokedown Palace",2.5e+07,10115014,11115766,"20th Century Fox","PG-13","Drama"
"1551","8/16/2002","Possession",2.5e+07,10103647,14805812,"Focus Features","PG-13","Drama"
"1552","5/17/1991","Stone Cold",2.5e+07,9286314,9286314,"Sony Pictures","R","Action"
"1553","11/25/2009","The Road",2.5e+07,8114270,29206732,"Weinstein Co.","R","Drama"
"1554","4/6/2007","The Hoax",2.5e+07,7164995,7164995,"Walt Disney","R","Drama"
"1555","8/17/1984","Sheena",2.5e+07,5778353,5778353,"Sony Pictures",NA,"Adventure"
"1556","3/23/2001","Say It Isn't So",2.5e+07,5516708,5516708,"20th Century Fox","R","Comedy"
"1557","12/7/2005","The World's Fastest Indian",2.5e+07,5128124,18991288,"Magnolia Pictures","PG-13","Drama"
"1558","3/1/1995","Tank Girl",2.5e+07,4064333,4064333,"MGM","R","Action"
"1559","4/22/2005","King's Ransom",2.5e+07,4008527,4049527,"New Line","PG-13","Comedy"
"1560","12/16/2011","Carnage",2.5e+07,2546747,38112154,"Sony Pictures Classics","R","Drama"
"1561","9/1/2017","Tulip Fever",2.5e+07,2455635,6498776,"Weinstein Co.","R","Drama"
"1562","1/6/2006","BloodRayne",2.5e+07,2405420,3605420,"Romar","R","Action"
"1563","11/25/2009","Me and Orson Welles",2.5e+07,1190003,1190003,"Freestyle Releasing","PG-13","Drama"
"1564","9/11/1998","Without Limits",2.5e+07,780326,780326,"Warner Bros.","PG-13","Drama"
"1565","3/22/2013","On the Road",2.5e+07,720828,9313302,"IFC Films","R","Drama"
"1566","6/30/2010","Love Ranch",2.5e+07,137885,146149,NA,"R","Drama"
"1567","7/8/2011","Ironclad",2.5e+07,0,5297411,"ARC Entertainment","R","Action"
"1568","11/26/1986","Star Trek IV: The Voyage Home",2.4e+07,109713132,1.33e+08,"Paramount Pictures","PG","Adventure"
"1569","12/12/1997","Scream 2",2.4e+07,101363301,172363301,"Miramax","R","Horror"
"1570","2/21/2003","Old School",2.4e+07,75155000,86765463,"Dreamworks SKG","R","Comedy"
"1571","12/20/2006","Rocky Balboa",2.4e+07,70269899,156229050,"MGM","PG","Drama"
"1572","12/16/2016","Fences",2.4e+07,57682904,64282881,"Paramount Pictures","PG-13","Drama"
"1573","2/18/2000","The Whole Nine Yards",2.4e+07,57262492,85527867,"Warner Bros.","R","Comedy"
"1574","4/7/2017","Going in Style",2.4e+07,45018541,78673103,"Warner Bros.","PG-13","Comedy"
"1575","7/12/1991","Point Break",2.4e+07,43218387,83531958,"20th Century Fox","R","Action"
"1576","9/20/1991","The Fisher King",2.4e+07,41798224,41798224,"Sony Pictures","R","Drama"
"1577","10/31/2008","Zack and Miri Make a Porno",2.4e+07,31457946,36856306,"Weinstein Co.","R","Comedy"
"1578","1/12/2001","Double Take",2.4e+07,29823162,29823162,"Walt Disney","PG-13","Action"
"1579","12/21/1999","Girl, Interrupted",2.4e+07,28871190,28871190,"Sony Pictures","R","Drama"
"1580","8/20/2010","Piranha 3D",2.4e+07,25003155,83660160,"Weinstein/Dimension","R","Horror"
"1581","11/24/2010","Faster",2.4e+07,23240020,35792945,"CBS Films","R","Action"
"1582","7/14/1999","Muppets From Space",2.4e+07,16304786,16304786,"Sony Pictures","G","Adventure"
"1583","4/7/2000","Ready to Rumble",2.4e+07,12372410,12372410,"Warner Bros.","PG-13","Comedy"
"1584","9/16/2011","I Don't Know How She Does It",2.4e+07,9659074,24474463,"Weinstein Co.","PG-13","Comedy"
"1585","12/24/1999","Play it to the Bone",2.4e+07,8427204,8427204,"Walt Disney","R","Comedy"
"1586","12/17/2004","Beyond the Sea",2.4e+07,6144806,8292914,"Lionsgate","PG-13","Drama"
"1587","6/10/2005","Hauru no ugoku shiro",2.4e+07,4710455,237814327,"Walt Disney","PG","Adventure"
"1588","3/27/1998","Meet the Deedles",2.4e+07,4356126,4356126,"Walt Disney","PG","Comedy"
"1589","8/25/1995","The Thief and the Cobbler",2.4e+07,669276,669276,"Miramax","G","Adventure"
"1590","6/10/2005","The Bridge of San Luis Rey",2.4e+07,49981,1696765,"Fine Line","PG","Drama"
"1591","10/2/2009","Zombieland",23600000,75590286,102236596,"Sony Pictures","R","Comedy"
"1592","11/6/1998","The Waterboy",2.3e+07,161491646,190191646,"Walt Disney","PG-13","Comedy"
"1593","4/7/1995","Bad Boys",2.3e+07,65647413,141247413,"Sony Pictures","R","Action"
"1594","1/16/2015","The Wedding Ringer",2.3e+07,64460211,80171596,"Sony Pictures","R","Comedy"
"1595","3/17/2000","Final Destination",2.3e+07,53302314,112036870,"New Line","R","Horror"
"1596","12/17/1976","King Kong",2.3e+07,52614445,90614445,"Paramount Pictures","PG","Action"
"1597","10/7/2011","The Ides of March",2.3e+07,40962534,77735925,"Sony Pictures","R","Drama"
"1598","2/18/2000","Pitch Black",2.3e+07,39235088,53182088,"USA Films","R","Horror"
"1599","1/10/2014","Her",2.3e+07,25568251,48259031,"Warner Bros.","R","Drama"
"1600","2/17/2012","Kari gurashi no Arietti",2.3e+07,19192510,151496097,"Walt Disney","G","Adventure"
"1601","11/12/1999","Anywhere But Here",2.3e+07,18653615,18653615,"20th Century Fox","PG-13","Drama"
"1602","9/1/2004","Vanity Fair",2.3e+07,16123851,19123851,"Focus Features","PG-13","Drama"
"1603","2/26/2016","Eddie the Eagle",2.3e+07,15789389,45061177,"20th Century Fox","PG-13","Drama"
"1604","7/17/1987","Jaws 4: The Revenge",2.3e+07,15728335,15728335,"Universal","PG-13","Horror"
"1605","8/25/2000","The Crew",2.3e+07,13019253,13019253,"Walt Disney","PG-13","Comedy"
"1606","12/20/1996","Marvin's Room",2.3e+07,12803305,12803305,"Miramax","PG-13","Drama"
"1607","8/22/2008","The Longshots",2.3e+07,11511323,11778396,"MGM","PG","Drama"
"1608","12/3/1999","The End of the Affair",2.3e+07,10660147,10660147,"Sony Pictures","R","Drama"
"1609","9/14/2007","In the Valley of Elah",2.3e+07,6777741,24489150,"Warner Bros.","R","Drama"
"1610","9/25/2009","Coco avant Chanel",2.3e+07,6113834,50813834,"Sony Pictures Classics","PG-13","Drama"
"1611","6/26/2009","Chéri",2.3e+07,2715657,2715657,"Miramax","R","Drama"
"1612","4/25/2008","Rogue",2.3e+07,10452,4673377,"Weinstein Co.","R","Horror"
"1613","6/24/1987","Spaceballs",22700000,38119483,38119483,"MGM","PG","Comedy"
"1614","4/24/2015","The Water Diviner",22500000,4200117,30864649,"Warner Bros.","R","Drama"
"1615","7/13/1990","Ghost",2.2e+07,217631306,517600000,"Paramount Pictures","PG-13","Drama"
"1616","11/11/1994","The Santa Clause",2.2e+07,144833357,189800000,"Walt Disney","PG","Adventure"
"1617","9/28/2007","The Game Plan",2.2e+07,90648202,146590987,"Walt Disney","PG","Comedy"
"1618","3/29/2002","The Rookie",2.2e+07,75600072,80491516,"Walt Disney","G","Drama"
"1619","6/2/1995","The Bridges of Madison County",2.2e+07,71516617,175516617,"Warner Bros.","PG-13","Drama"
"1620","2/28/2014","Son of God",2.2e+07,59700064,70949793,"20th Century Fox","PG-13","Drama"
"1621","6/1/2001","The Animal",2.2e+07,55762229,55762229,"Sony Pictures","PG-13","Comedy"
"1622","12/8/1982","Gandhi",2.2e+07,52767889,127767889,"Sony Pictures","PG","Drama"
"1623","9/19/2003","Underworld",2.2e+07,51970690,95708457,"Sony Pictures","R","Action"
"1624","8/3/2012","Diary of a Wimpy Kid: Dog Days",2.2e+07,49008662,77229695,"20th Century Fox","PG","Adventure"
"1625","12/28/2001","I Am Sam",2.2e+07,40270895,92542418,"New Line","PG-13","Drama"
"1626","11/11/2005","Derailed",2.2e+07,36020063,57520063,"Weinstein Co.","R","Action"
"1627","11/22/2013","Delivery Man",2.2e+07,30659817,70536870,"Walt Disney","PG-13","Comedy"
"1628","2/5/2016","Hail, Caesar!",2.2e+07,30080225,64171419,"Universal","PG-13","Comedy"
"1629","8/24/2001","Jay and Silent Bob Strike Back",2.2e+07,30059386,33762400,"Miramax/Dimension","R","Comedy"
"1630","12/29/1993","Shadowlands",2.2e+07,25842377,25842377,"Savoy","R","Drama"
"1631","8/12/2005","Deuce Bigalow: European Gigolo",2.2e+07,22400154,45273464,"Sony Pictures","R","Comedy"
"1632","5/19/2017","Diary of a Wimpy Kid: The Long Haul",2.2e+07,20738724,35608734,"20th Century Fox","PG","Adventure"
"1633","1/18/2008","Mad Money",2.2e+07,20668843,25044057,"Overture Films","PG-13","Comedy"
"1634","11/27/2013","Homefront",2.2e+07,20158492,51695362,"Open Road","R","Action"
"1635","9/19/2008","Igor",2.2e+07,19528602,31013349,"MGM","PG","Adventure"
"1636","2/9/2001","Saving Silverman",2.2e+07,19351569,25873142,"Sony Pictures","R","Comedy"
"1637","7/2/1999","Summer of Sam",2.2e+07,19288130,19288130,"Walt Disney","R","Drama"
"1638","9/4/2015","The Transporter Refueled",2.2e+07,16029670,69698495,"EuropaCorp","PG-13","Action"
"1639","4/11/2001","Josie and the Pussycats",2.2e+07,14252830,14252830,"Universal","PG-13","Comedy"
"1640","8/22/2012","Hit & Run",2.2e+07,13749300,17216955,"Open Road","R","Comedy"
"1641","10/27/2000","The Little Vampire",2.2e+07,13555988,13555988,"New Line","PG","Adventure"
"1642","10/1/2004","I Heart Huckabees",2.2e+07,12784713,20034713,"Fox Searchlight","R","Comedy"
"1643","11/17/2017","Roman J. Israel, Esq.",2.2e+07,11962712,12967012,"Sony Pictures","PG-13","Drama"
"1644","12/4/2013","Out of the Furnace",2.2e+07,11330849,15434375,"Relativity","R","Drama"
"1645","11/5/1993","RoboCop 3",2.2e+07,10696210,10696210,"Orion Pictures","PG-13","Action"
"1646","8/27/1999","Dudley Do-Right",2.2e+07,9818792,9818792,"Universal","PG","Adventure"
"1647","12/8/2017","Just Getting Started",2.2e+07,6069605,6756452,"Broad Green Pictures","PG-13","Comedy"
"1648","9/21/2001","Megiddo: Omega Code 2",2.2e+07,6047691,6047691,"8X Entertainment","PG-13","Action"
"1649","1/1/1970","Darling Lili",2.2e+07,5e+06,5e+06,NA,NA,"Drama"
"1650","11/23/2005","The Libertine",2.2e+07,4835065,9448623,"Weinstein Co.","R","Drama"
"1651","10/8/2010","Stone",2.2e+07,1810078,4065020,"Overture Films","R","Drama"
"1652","3/3/2006","Joyeux Noël",2.2e+07,1054361,23134075,"Sony Pictures Classics","PG-13","Drama"
"1653","6/24/1977","Sorcerer",21600000,1.2e+07,12005968,"Paramount Pictures","PG","Adventure"
"1654","7/27/2007","Molière",21600000,635733,791154,"Sony Pictures Classics","PG-13","Comedy"
"1655","10/5/2007","Michael Clayton",21500000,49033882,92987651,"Warner Bros.","R","Drama"
"1656","12/20/1996","My Fellow Americans",21500000,22331846,22331846,"Warner Bros.","PG-13","Comedy"
"1657","11/16/2012","Silver Linings Playbook",2.1e+07,132092958,236412453,"Weinstein Co.","R","Drama"
"1658","4/6/2018","Blockers",2.1e+07,59839515,93442495,"Universal","R","Comedy"
"1659","6/30/1999","South Park: Bigger, Longer & Uncut",2.1e+07,52037603,52037603,"Paramount Pictures","R","Comedy"
"1660","6/18/1982","Firefox",2.1e+07,45785720,45785720,"Warner Bros.","PG","Action"
"1661","3/19/1993","Teenage Mutant Ninja Turtles III",2.1e+07,42273609,42273609,"New Line","PG","Adventure"
"1662","9/14/2001","Hardball",2.1e+07,40222729,43728560,"Paramount Pictures","PG-13","Drama"
"1663","11/5/2010","For Colored Girls",2.1e+07,37729698,38017873,"Lionsgate","R","Drama"
"1664","1/5/2007","Freedom Writers",2.1e+07,36605602,43632609,"Paramount Pictures","PG-13","Drama"
"1665","10/11/2002","The Transporter",2.1e+07,25296447,43928932,"20th Century Fox","PG-13","Action"
"1666","3/14/2008","Never Back Down",2.1e+07,24850922,39319801,"Summit Entertainment","PG-13","Action"
"1667","3/12/1999","The Rage: Carrie 2",2.1e+07,17760244,17760244,"MGM","R","Horror"
"1668","8/1/2008","Swing Vote",2.1e+07,16289867,17589867,"Walt Disney","PG-13","Comedy"
"1669","6/5/2009","Away We Go",2.1e+07,9451946,10108016,"Focus Features","R","Comedy"
"1670","9/27/2002","Moonlight Mile",2.1e+07,6830957,6830957,"Walt Disney","PG-13","Drama"
"1671","5/6/2011","The Beaver",2.1e+07,970816,5046038,"Summit Entertainment","PG-13","Comedy"
"1672","2/24/2017","Bitter Harvest",2.1e+07,557241,606162,"Roadside Attractions","R","Drama"
"1673","7/23/1982","The Best Little Whorehouse in Texas",20500000,69701637,69701637,"Universal","R","Comedy"
"1674","8/11/2006","Pulse",20500000,20264436,30241435,"Weinstein/Dimension","R","Horror"
"1675","6/12/1981","Raiders of the Lost Ark",2e+07,225686079,367452079,"Paramount Pictures","PG","Adventure"
"1676","11/20/1992","Home Alone 2: Lost in New York",2e+07,173585516,358994850,"20th Century Fox","PG","Adventure"
"1677","11/16/1977","Close Encounters of the Third Kind",2e+07,169100479,340800479,"Columbia","PG","Adventure"
"1678","5/20/1987","Beverly Hills Cop II",2e+07,153665036,276665036,"Paramount Pictures","R","Action"
"1679","7/19/2013","The Conjuring",2e+07,137400141,318000141,"Warner Bros.","R","Horror"
"1680","3/7/2003","Bringing Down the House",2e+07,132675402,164675402,"Walt Disney","PG-13","Comedy"
"1681","11/17/2017","Wonder",2e+07,132422809,305051118,"Lionsgate","PG","Drama"
"1682","2/14/1992","Wayne's World",2e+07,121697323,183097323,"Paramount Pictures","PG-13","Comedy"
"1683","10/15/2010","Jackass 3D",2e+07,117229692,171685793,"Paramount Pictures","R","Comedy"
"1684","7/29/2016","Bad Moms",2e+07,113257297,180999077,"STX Entertainment","R","Comedy"
"1685","6/16/1978","Jaws 2",2e+07,102922376,208900376,"Universal","PG","Horror"
"1686","10/3/2008","Beverly Hills Chihuahua",2e+07,94514402,154218168,"Walt Disney","PG","Adventure"
"1687","7/2/2014","Tammy",2e+07,84525432,96407655,"Warner Bros.","R","Comedy"
"1688","11/16/2011","The Descendants",2e+07,82624961,175507800,"Fox Searchlight","R","Drama"
"1689","10/3/2003","School of Rock",2e+07,81261177,131944672,"Paramount Pictures","PG-13","Comedy"
"1690","7/16/1993","Free Willy",2e+07,77698625,153698625,"Warner Bros.","PG","Adventure"
"1691","8/18/1995","Mortal Kombat",2e+07,70433227,122133227,"New Line","PG-13","Action"
"1692","6/23/2004","White Chicks",2e+07,69148997,111448997,"Sony Pictures","PG-13","Comedy"
"1693","4/18/2003","Holes",2e+07,67383924,71232214,"Walt Disney","PG","Drama"
"1694","3/31/2010","The Last Song",2e+07,62950384,92678948,"Walt Disney","PG","Drama"
"1695","4/2/2010","Why Did I Get Married Too?",2e+07,60095852,60831067,"Lionsgate","PG-13","Drama"
"1696","10/23/1998","La vita è bella",2e+07,57598247,229385361,"Miramax","PG-13","Drama"
"1697","10/18/2013","12 Years a Slave",2e+07,56671993,181025343,"Fox Searchlight","R","Drama"
"1698","12/13/2002","Drumline",2e+07,56398162,56398162,"20th Century Fox","PG-13","Comedy"
"1699","6/3/2016","Me Before You",2e+07,56245075,208314186,"Warner Bros.","PG-13","Drama"
"1700","4/15/2016","Barbershop: The Next Cut",2e+07,54030051,54404202,"Warner Bros.","PG-13","Comedy"
"1701","12/7/1990","Edward Scissorhands",2e+07,53976987,53976987,"20th Century Fox","PG-13","Comedy"
"1702","1/9/2015","Selma",2e+07,52076908,66776576,"Paramount Pictures","PG-13","Drama"
"1703","2/17/2006","Date Movie",2e+07,48548426,85146165,"20th Century Fox","PG-13","Comedy"
"1704","2/15/2002","Peter Pan: Return to Neverland",2e+07,48430258,109862682,"Walt Disney","G","Adventure"
"1705","2/14/2003","The Jungle Book 2",2e+07,47901582,140122225,"Walt Disney","G","Adventure"
"1706","2/4/2005","Boogeyman",2e+07,46752382,67192859,"Sony Pictures","PG-13","Horror"
"1707","2/11/2000","The Tigger Movie",2e+07,45542421,96147688,"Walt Disney","G","Adventure"
"1708","11/6/2015","Spotlight",2e+07,45055776,92108847,"Open Road","R","Drama"
"1709","6/26/2015","Max",2e+07,42656255,43658157,"Warner Bros.","PG","Adventure"
"1710","3/21/2008","Meet the Browns",2e+07,41975388,41975388,"Lionsgate","PG-13","Comedy"
"1711","7/24/2009","Orphan",2e+07,41596251,78769428,"Warner Bros.","R","Drama"
"1712","11/17/2017","The Star",2e+07,40847995,62758010,"Sony Pictures","PG","Adventure"
"1713","1/26/2007","Epic Movie",2e+07,39739367,86858578,"20th Century Fox","PG-13","Comedy"
"1714","10/13/2006","The Grudge 2",2e+07,39143839,70743839,"Sony Pictures","PG-13","Horror"
"1715","5/14/1982","Conan the Barbarian",2e+07,38264085,79114085,"Universal",NA,"Action"
"1716","8/14/1998","How Stella Got Her Groove Back",2e+07,37672944,37672944,"20th Century Fox","R","Drama"
"1717","7/19/1991","Bill & Ted's Bogus Journey",2e+07,37537675,37537675,"Orion Pictures","PG","Adventure"
"1718","10/13/2006","Man of the Year",2e+07,37442180,41342180,"Universal","PG-13","Comedy"
"1719","2/19/2016","Risen",2e+07,36880033,46255763,"Sony Pictures","PG-13","Drama"
"1720","8/18/2010","Vampires Suck",2e+07,36661504,81424988,"20th Century Fox","PG-13","Comedy"
"1721","3/21/1997","Selena",2e+07,35450113,35450113,"Warner Bros.","PG","Drama"
"1722","11/4/2011","A Very Harold & Kumar 3D Christmas",2e+07,35061031,36265745,"Warner Bros.","R","Comedy"
"1723","1/4/2013","Texas Chainsaw 3D",2e+07,34341945,47666013,"Lionsgate","R","Horror"
"1724","10/27/2006","Babel",2e+07,34302837,132121212,"Paramount Vantage","R","Drama"
"1725","9/19/2014","This is Where I Leave You",2e+07,34296320,41296320,"Warner Bros.","R","Comedy"
"1726","12/12/2008","Doubt",2e+07,33446470,53191101,"Miramax","PG-13","Drama"
"1727","10/15/2004","Team America: World Police",2e+07,32774834,50948811,"Paramount Pictures","R","Comedy"
"1728","4/12/2013","Scary Movie V",2e+07,32015787,78613981,"Weinstein Co.","PG-13","Comedy"
"1729","11/26/2008","Milk",2e+07,31841299,57293371,"Focus Features","R","Drama"
"1730","10/25/2002","Ghost Ship",2e+07,30113491,68349884,"Warner Bros.","R","Horror"
"1731","1/8/2010","Daybreakers",2e+07,30101577,51445503,"Lionsgate","R","Horror"
"1732","3/31/2000","High Fidelity",2e+07,27277055,47881663,"Walt Disney","R","Comedy"
"1733","4/28/2006","Stick It",2e+07,26910736,30399714,"Walt Disney","PG-13","Comedy"
"1734","1/4/2008","One Missed Call",2e+07,26890041,44513466,"Warner Bros.","PG-13","Horror"
"1735","1/12/1996","Eye for an Eye",2e+07,26792700,26792700,"Paramount Pictures","R","Drama"
"1736","8/23/2013","The World's End",2e+07,26004851,47508505,"Focus Features","R","Comedy"
"1737","1/19/1996","From Dusk Till Dawn",2e+07,25728961,25732986,"Miramax/Dimension","R","Horror"
"1738","9/24/2010","You Again",2e+07,25702053,32838945,"Walt Disney","PG","Comedy"
"1739","9/17/2010","Alpha and Omega 3D",2e+07,25107267,48958353,"Lionsgate","PG","Adventure"
"1740","3/24/2006","Stay Alive",2e+07,23086480,23187506,"Walt Disney","PG-13","Horror"
"1741","10/7/2005","2 For the Money",2e+07,22991379,30491379,"Universal","R","Drama"
"1742","8/21/2009","Shorts",2e+07,20919166,29870801,"Warner Bros.","PG","Adventure"
"1743","10/30/1998","Vampires",2e+07,20268825,20268825,"Sony Pictures","R","Horror"
"1744","8/13/2004","Yu-Gi-Oh",2e+07,19762690,28762690,"Warner Bros.","PG","Adventure"
"1745","3/23/2007","Reign Over Me",2e+07,19661987,20081987,"Sony Pictures","R","Drama"
"1746","9/19/2008","My Best Friend's Girl",2e+07,19219250,34787111,"Lionsgate","R","Comedy"
"1747","5/11/2007","Georgia Rule",2e+07,18882880,20819601,"Universal","R","Drama"
"1748","7/31/1981","Under the Rainbow",2e+07,18826490,18826490,"Warner Bros.",NA,"Comedy"
"1749","4/12/1985","Ladyhawke",2e+07,18400000,18400000,"Warner Bros.",NA,"Action"
"1750","9/21/2007","Into the Wild",2e+07,18354356,56822960,"Paramount Vantage","R","Drama"
"1751","9/11/1998","Simon Birch",2e+07,18253415,18310591,"Walt Disney","PG","Drama"
"1752","2/11/2005","Pooh's Heffalump Movie",2e+07,18098433,55686944,"Walt Disney","G","Adventure"
"1753","9/29/2006","School for Scoundrels",2e+07,17807569,17807569,"MGM","PG-13","Comedy"
"1754","10/26/2012","Silent Hill: Revelation 3D",2e+07,17530219,55975672,"Open Road","R","Horror"
"1755","11/3/1995","Home for the Holidays",2e+07,17468887,22119269,"Paramount Pictures","PG-13","Comedy"
"1756","3/31/2017","The Zookeeper’s Wife",2e+07,17445186,24521550,"Focus Features","PG-13","Drama"
"1757","2/20/2009","Fired Up",2e+07,17231291,18608570,"Sony Pictures","PG-13","Comedy"
"1758","4/8/2005","Kung Fu Hustle",2e+07,17104669,102034104,"Sony Pictures Classics","R","Action"
"1759","7/26/2002","The Country Bears",2e+07,16988996,16988996,"Walt Disney","G","Adventure"
"1760","3/16/2007","Dead Silence",2e+07,16574590,20614661,"Universal","R","Horror"
"1761","11/21/2003","21 Grams",2e+07,16248701,59667625,"Focus Features","R","Drama"
"1762","12/14/2007","The Kite Runner",2e+07,15800078,74180745,"Paramount Vantage","PG-13","Drama"
"1763","2/15/1965","The Greatest Story Ever Told",2e+07,15473333,15473333,"MGM","G","Drama"
"1764","3/6/1998","Twilight",2e+07,15055091,15055091,"Paramount Pictures","R","Drama"
"1765","8/29/2008","Disaster Movie",2e+07,14190901,36720752,"Lionsgate","PG-13","Comedy"
"1766","11/14/1997","The Man Who Knew Too Little",2e+07,13801755,13801755,"Warner Bros.","PG","Comedy"
"1767","10/30/2015","Burnt",2e+07,13651946,36780895,"Weinstein Co.","R","Comedy"
"1768","4/30/2004","Envy",2e+07,13548322,14566246,"Dreamworks SKG","PG-13","Comedy"
"1769","10/13/2006","One Night with the King",2e+07,13395961,13725032,"Rocky Mountain Pict…","PG","Drama"
"1770","10/21/1994","Bullets Over Broadway",2e+07,13383747,13383747,"Miramax","R","Comedy"
"1771","11/22/2002","The Quiet American",2e+07,12987647,26348203,"Miramax","R","Drama"
"1772","9/2/2016","The Light Between Oceans",2e+07,12545979,21748977,"Walt Disney","PG-13","Drama"
"1773","10/28/2005","The Weather Man",2e+07,12482775,15466961,"Paramount Pictures","R","Drama"
"1774","8/23/2002","Undisputed",2e+07,12398628,12398628,"Miramax","R","Drama"
"1775","3/27/2009","12 Rounds",2e+07,12234694,17306648,"20th Century Fox","PG-13","Action"
"1776","5/6/1994","3 Ninjas Kick Back",2e+07,11744960,11744960,"Walt Disney","PG","Action"
"1777","2/22/2008","Be Kind Rewind",2e+07,11175164,30894247,"New Line","PG-13","Comedy"
"1778","12/9/2005","Mrs. Henderson Presents",2e+07,11036366,27836366,"Weinstein Co.","R","Comedy"
"1779","12/15/1989","We're No Angels",2e+07,10555348,10555348,"Paramount Pictures","PG-13","Comedy"
"1780","8/31/2007","Death Sentence",2e+07,9534258,16907831,"20th Century Fox","R","Action"
"1781","6/3/2016","Popstar: Never Stop Never Stopping",2e+07,9496130,9537120,"Universal","R","Comedy"
"1782","10/27/2017","Thank You for Your Service",2e+07,9479390,9985316,"Universal","R","Drama"
"1783","12/4/2009","Everybody's Fine",2e+07,9208876,9208876,"Miramax","PG-13","Drama"
"1784","8/27/2004","Superbabies: Baby Geniuses 2",2e+07,9109322,9355369,"Sony Pictures","PG","Adventure"
"1785","9/20/2013","Battle of the Year",2e+07,8888355,16723377,"Sony Pictures","PG-13","Drama"
"1786","4/29/2016","Ratchet and Clank",2e+07,8813410,12769469,"Focus Features","PG","Adventure"
"1787","8/17/2007","Death at a Funeral",2e+07,8580428,46790428,"MGM","R","Comedy"
"1788","9/9/2005","The Man",2e+07,8330720,10393696,"New Line","PG-13","Comedy"
"1789","1/5/2007","Code Name: The Cleaner",2e+07,8135024,8135024,"New Line","PG-13","Comedy"
"1790","12/12/2014","Inherent Vice",2e+07,8110975,14772346,"Warner Bros.","R","Drama"
"1791","4/16/2004","Connie & Carla",2e+07,8047525,8047525,"Universal","PG-13","Comedy"
"1792","10/11/2013","Machete Kills",2e+07,8008161,18273009,"Open Road","R","Action"
"1793","2/24/2006","Doogal",2e+07,7578946,28058652,"Weinstein Co.","G","Adventure"
"1794","9/16/2005","Proof",2e+07,7535331,8284331,"Miramax","PG-13","Drama"
"1795","10/3/2008","An American Carol",2e+07,7013191,7022183,"Vivendi Entertainment","PG-13","Comedy"
"1796","3/14/2003","Willard",2e+07,6882696,6882696,"New Line","PG-13","Horror"
"1797","2/1/2008","Strange Wilderness",2e+07,6575282,6947084,"Paramount Vantage","R","Comedy"
"1798","4/24/2015","Little Boy",2e+07,6485961,17768390,"Open Road","PG-13","Drama"
"1799","10/26/2012","Chasing Mavericks",2e+07,6002756,8300821,"20th Century Fox","PG","Drama"
"1800","12/31/2014","A Most Violent Year",2e+07,5749134,8398291,"A24","R","Drama"
"1801","11/23/2011","A Dangerous Method",2e+07,5702083,14807531,"Sony Pictures Classics","R","Drama"
"1802","8/14/2009","Bandslam",2e+07,5210988,12967829,"Summit Entertainment","PG","Comedy"
"1803","1/28/2005","Alone in the Dark",2e+07,5178569,8178569,"Lionsgate","R","Horror"
"1804","10/29/2004","Birth",2e+07,5005899,14603001,"New Line","R","Drama"
"1805","8/26/2016","Hands of Stone",2e+07,4712792,5032013,"Weinstein Co.","R","Drama"
"1806","10/3/2008","Flash of Genius",2e+07,4442377,4504111,"Universal","PG-13","Drama"
"1807","11/21/2007","I’m Not There",2e+07,4017609,12397613,"Weinstein Co.","R","Drama"
"1808","10/24/2008","Synecdoche, New York",2e+07,3083538,4383538,"Sony Pictures Classics","R","Drama"
"1809","11/3/2017","LBJ",2e+07,2468683,2507181,"Electric Entertainment","R","Drama"
"1810","10/29/1999","Mononoke-hime",2e+07,2374107,150350000,"Miramax","PG-13","Action"
"1811","3/19/2004","Bon Voyage",2e+07,2353728,8361736,"Sony Pictures","PG-13","Comedy"
"1812","11/13/2015","My All-American",2e+07,2246000,2246000,"Clarius Entertainment","PG","Drama"
"1813","8/22/2003","Marci X",2e+07,1646664,1646664,"Paramount Pictures","R","Comedy"
"1814","12/6/2002","Equilibrium",2e+07,1190018,5345869,"Miramax/Dimension","R","Action"
"1815","4/29/2011","Dylan Dog: Dead of Night",2e+07,1186538,6093725,"Omin/Freestyle","PG-13","Horror"
"1816","5/23/2008","The Children of Huang Shi",2e+07,1031872,8221700,"Sony Pictures Classics","R","Drama"
"1817","10/20/2000","The Yards",2e+07,882710,2282710,"Miramax","R","Drama"
"1818","8/6/2010","Middle Men",2e+07,754301,754301,"Paramount Vantage","R","Comedy"
"1819","12/3/2010","All Good Things",2e+07,582024,873617,"Magnolia Pictures","R","Drama"
"1820","11/13/2015","By the Sea",2e+07,538460,3727746,"Universal","R","Drama"
"1821","3/18/2005","Steamboy",2e+07,468867,10468867,"Sony Pictures","PG-13","Action"
"1822","4/22/2005","The Game of Their Lives",2e+07,375474,375474,"IFC Films","PG","Drama"
"1823","12/10/2010","The Tempest",2e+07,277943,277943,"Miramax","PG-13","Drama"
"1824","3/7/2008","長江七號 (CJ7)",2e+07,206678,47300771,"Sony Pictures Classics","PG","Adventure"
"1825","9/18/2009","The Burning Plain",2e+07,200730,1167092,"Magnolia Pictures","R","Drama"
"1826","3/31/2004","The Touch",2e+07,0,5918742,"Miramax","PG-13","Adventure"
"1827","8/29/2014","Dwegons and Leprechauns",2e+07,0,0,NA,"PG","Adventure"
"1828","8/21/2009","Der Baader Meinhof Komplex",19700000,476270,16498827,"Vitagraph Films","R","Action"
"1829","12/1/2017","The Shape of Water",19500000,63859435,189258193,"Fox Searchlight","R","Drama"
"1830","11/23/2012","De rouille et d’os",19500000,2061449,29393634,"Sony Pictures Classics","R","Drama"
"1831","12/20/2006","The Painted Veil",19400000,8060487,15118795,"Warner Independent","PG-13","Drama"
"1832","7/29/2011","The Devil's Double",19100000,1361512,5965646,"Lionsgate","R","Drama"
"1833","7/3/1985","Back to the Future",1.9e+07,212259762,385524862,"Universal","PG","Adventure"
"1834","7/7/2000","Scary Movie",1.9e+07,157019771,277200000,"Miramax/Dimension","R","Comedy"
"1835","6/24/2011","Bad Teacher",1.9e+07,100292856,215448997,"Sony Pictures","R","Comedy"
"1836","8/12/2016","Sausage Party",1.9e+07,97670358,141354394,"Sony Pictures","R","Comedy"
"1837","9/11/2009","I Can Do Bad All By Myself",1.9e+07,51733921,51733921,"Lionsgate","PG-13","Comedy"
"1838","5/23/1980","The Shining",1.9e+07,44017374,44728227,"Warner Bros.","R","Horror"
"1839","10/26/2001","Thirteen Ghosts",1.9e+07,41867960,68467960,"Warner Bros.","R","Horror"
"1840","10/29/1999","House on Haunted Hill",1.9e+07,40846082,65090541,"Warner Bros.","R","Horror"
"1841","1/16/2009","Notorious",1.9e+07,36843682,44972183,"Fox Searchlight","R","Drama"
"1842","11/8/2013","The Book Thief",1.9e+07,21488481,76086711,"20th Century Fox","PG-13","Drama"
"1843","10/19/2007","Gone, Baby, Gone",1.9e+07,20300218,34352162,"Miramax","R","Drama"
"1844","7/26/2000","Thomas and the Magic Railroad",1.9e+07,15911332,15911332,"Destination Films","G","Adventure"
"1845","9/20/2002","Sen to Chihiro no Kamikakushi",1.9e+07,10049886,274949886,"Walt Disney","PG","Adventure"
"1846","10/17/2008","Sex Drive",1.9e+07,8402485,10412485,"Summit Entertainment","R","Comedy"
"1847","1/9/1998","Firestorm",1.9e+07,8123860,8123860,"20th Century Fox","R","Action"
"1848","3/4/2011","Take Me Home Tonight",1.9e+07,6928068,7576604,"Relativity","R","Comedy"
"1849","9/28/2012","Won't Back Down",1.9e+07,5310554,5745503,"20th Century Fox","PG","Drama"
"1850","6/1/2018","Action Point",1.9e+07,5059608,5103675,"Paramount Pictures","R","Comedy"
"1851","8/16/1996","Kansas City",1.9e+07,1353824,1353824,"New Line","R","Drama"
"1852","6/24/2005","George A. Romero's Land of the Dead",18975000,20700082,47751015,"Universal","R","Horror"
"1853","12/6/2002","Adaptation",18500000,22498520,32531759,"Sony Pictures","R","Comedy"
"1854","10/2/2009","The Invention of Lying",18500000,18451251,32679264,"Warner Bros.","PG-13","Comedy"
"1855","5/22/1998","Fear and Loathing in Las Vegas",18500000,10680275,13711903,"Universal","R","Comedy"
"1856","2/2/2001","Left Behind",18500000,4221341,4221341,"Cloud Ten Pictures","PG-13","Drama"
"1857","11/3/2006","Borat",1.8e+07,128505958,261443242,"20th Century Fox","R","Comedy"
"1858","7/29/1994","The Mask",1.8e+07,119920129,351620129,"New Line","PG-13","Comedy"
"1859","6/3/1988","Big",1.8e+07,114968774,151668774,"20th Century Fox","PG","Comedy"
"1860","7/13/2001","Legally Blonde",1.8e+07,96493426,141809235,"MGM","PG-13","Comedy"
"1861","4/30/2004","Mean Girls",1.8e+07,86047227,130953026,"Paramount Pictures","PG-13","Comedy"
"1862","6/1/1984","Star Trek III: The Search for Spock",1.8e+07,76471046,8.7e+07,"Paramount Pictures","PG","Adventure"
"1863","9/9/2005","The Exorcism of Emily Rose",1.8e+07,75072454,144529078,"Sony Pictures","PG-13","Horror"
"1864","12/10/1999","Deuce Bigalow: Male Gigolo",1.8e+07,65535067,92935067,"Walt Disney","R","Comedy"
"1865","1/1/2004","Barbershop 2: Back in Business",1.8e+07,65070412,65842412,"MGM","PG-13","Comedy"
"1866","12/16/2005","The Family Stone",1.8e+07,60062868,92357499,"20th Century Fox","PG-13","Comedy"
"1867","6/12/1987","Predator",1.8e+07,59735548,98267558,"20th Century Fox","R","Action"
"1868","3/25/2016","My Big Fat Greek Wedding 2",1.8e+07,59689605,92057814,"Universal","PG-13","Comedy"
"1869","3/25/2011","Diary of a Wimpy Kid: Rodrick Rules",1.8e+07,52698535,73695194,"20th Century Fox","PG","Adventure"
"1870","9/19/1984","Amadeus",1.8e+07,51973029,51973029,"Warner Bros.","R","Drama"
"1871","4/11/2008","Prom Night",1.8e+07,43869350,57193655,"Sony Pictures","PG-13","Horror"
"1872","4/8/2011","Soul Surfer",1.8e+07,43853424,47158652,"Sony Pictures","PG","Drama"
"1873","9/26/2003","Under the Tuscan Sun",1.8e+07,43601508,57490024,"Walt Disney","PG-13","Comedy"
"1874","10/10/1986","Peggy Sue Got Married",1.8e+07,41382841,41382841,"Sony/TriStar","PG-13","Comedy"
"1875","12/26/2001","Gosford Park",1.8e+07,41300105,41300105,"USA Films","R","Comedy"
"1876","1/11/2002","Orange County",1.8e+07,41059716,43308707,"Paramount Pictures","PG-13","Comedy"
"1877","7/26/2013","Blue Jasmine",1.8e+07,33404871,102912961,"Sony Pictures Classics","PG-13","Comedy"
"1878","4/28/2006","United 93",1.8e+07,31567134,77635035,"Universal","R","Drama"
"1879","12/5/2003","Honey",1.8e+07,30272254,62646763,"Universal","PG-13","Drama"
"1880","5/24/1996","Spy Hard",1.8e+07,26936265,26936265,"Walt Disney","PG-13","Comedy"
"1881","8/7/2015","Ricki and the Flash",1.8e+07,26839498,41166033,"Sony Pictures","PG-13","Drama"
"1882","12/13/1989","Glory",1.8e+07,26593580,26593580,"Sony Pictures","R","Action"
"1883","6/29/1984","Conan the Destroyer",1.8e+07,26400000,26400000,"Universal",NA,"Action"
"1884","11/13/2015","Love the Coopers",1.8e+07,26302731,42227490,"CBS Films","PG-13","Comedy"
"1885","6/24/1970","Catch-22",1.8e+07,24911670,24911670,"Paramount Pictures",NA,"Comedy"
"1886","4/10/2009","Observe and Report",1.8e+07,24007324,27148898,"Warner Bros.","R","Comedy"
"1887","9/18/2009","Love Happens",1.8e+07,22965110,36133014,"Universal","PG-13","Drama"
"1888","12/4/1985","Young Sherlock Holmes",1.8e+07,19739000,19739000,"Paramount Pictures","PG-13","Adventure"
"1889","11/5/2010","127 Hours",1.8e+07,18335230,60217171,"Fox Searchlight","R","Drama"
"1890","5/19/2000","Small Time Crooks",1.8e+07,17266359,29934477,"Dreamworks SKG","PG","Comedy"
"1891","5/12/2000","Center Stage",1.8e+07,17200925,21361109,"Sony Pictures","PG-13","Drama"
"1892","1/15/2016","Norm of the North",1.8e+07,17062499,30535660,"Lionsgate","PG","Adventure"
"1893","2/6/2004","Catch That Kid",1.8e+07,16703799,16959614,"20th Century Fox","PG","Adventure"
"1894","8/16/2013","Jobs",1.8e+07,16131410,43402515,"Open Road","PG-13","Drama"
"1895","10/26/2001","Life as a House",1.8e+07,15652637,23889158,"New Line","R","Drama"
"1896","1/8/2010","Youth in Revolt",1.8e+07,15285588,19685588,"Weinstein/Dimension","R","Comedy"
"1897","7/25/2014","And So It Goes",1.8e+07,15160801,17868801,"Clarius Entertainment","PG-13","Comedy"
"1898","7/10/2009","I Love You, Beth Cooper",1.8e+07,14800725,16382538,"20th Century Fox","PG-13","Comedy"
"1899","1/31/2014","Labor Day",1.8e+07,13371528,14189810,"Paramount Pictures","PG-13","Drama"
"1900","9/26/1997","The Ice Storm",1.8e+07,8038061,16011975,"Fox Searchlight","R","Drama"
"1901","10/15/2004","Being Julia",1.8e+07,7739049,14488705,"Sony Pictures","R","Drama"
"1902","3/22/1989","Troop Beverly Hills",1.8e+07,7190505,7190505,"Sony Pictures",NA,"Comedy"
"1903","2/21/1986","Nine 1/2 Weeks",1.8e+07,6734844,6734844,"MGM",NA,"Drama"
"1904","1/15/2010","The Last Station",1.8e+07,6617867,15696146,"Sony Pictures Classics","R","Drama"
"1905","6/26/1981","Dragonslayer",1.8e+07,6e+06,6e+06,"Paramount Pictures",NA,"Action"
"1906","9/30/1994","Ed Wood",1.8e+07,5828466,5828466,"Walt Disney","R","Comedy"
"1907","6/6/2008","Mongol",1.8e+07,5705761,27147349,"Picturehouse","R","Drama"
"1908","10/8/2008","RocknRolla",1.8e+07,5700626,27794339,"Warner Bros.","R","Action"
"1909","6/25/1982","Megaforce",1.8e+07,5675599,5675599,"20th Century Fox",NA,"Action"
"1910","8/20/2010","Mao's Last Dancer",1.8e+07,4806750,25941437,"Samuel Goldwyn Films","PG","Drama"
"1911","4/11/2014","The Railway Man",1.8e+07,4438438,23910210,"Weinstein Co.","R","Drama"
"1912","12/29/1995","Restoration",1.8e+07,4100000,4100000,"Miramax","R","Drama"
"1913","3/18/2016","Midnight Special",1.8e+07,3712282,7680250,"Warner Bros.","PG-13","Drama"
"1914","11/25/2016","Miss Sloane",1.8e+07,3500605,7727952,"EuropaCorp","R","Drama"
"1915","3/17/2017","T2: Trainspotting",1.8e+07,2402004,42091497,"Sony Pictures","R","Drama"
"1916","4/25/1986","8 Million Ways to Die",1.8e+07,1305114,1305114,"Sony Pictures",NA,"Action"
"1917","9/22/2006","Renaissance",1.8e+07,70644,2401413,"Miramax","R","Action"
"1918","4/15/2016","I Am Wrath",1.8e+07,0,309608,"Saban Films","R","Action"
"1919","8/22/2014","The Prince",1.8e+07,0,0,"Lionsgate","R","Action"
"1920","6/28/1985","Red Sonja",17900000,6905861,6908640,"MGM","PG-13","Action"
"1921","8/17/2007","Superbad",17500000,121463226,169955142,"Sony Pictures","R","Comedy"
"1922","2/20/2009","Madea Goes To Jail",17500000,90508336,90508336,"Lionsgate","PG-13","Comedy"
"1923","2/14/2008","Step Up 2 the Streets",17500000,58017783,148586910,"Walt Disney","PG-13","Drama"
"1924","1/13/2006","Hoodwinked",17500000,51386611,109843390,"Weinstein Co.","PG","Adventure"
"1925","11/21/2007","Hitman",17500000,39687694,99135571,"20th Century Fox","R","Action"
"1926","12/22/2004","Hotel Rwanda",17500000,23519128,36521223,"MGM","PG-13","Drama"
"1927","8/25/2006","Beerfest",17500000,19185184,20159316,"Warner Bros.","R","Comedy"
"1928","4/25/2003","City of Ghosts",17500000,325491,325491,"MGM","R","Drama"
"1929","4/6/2018","A Quiet Place",1.7e+07,188024361,334524361,"Paramount Pictures","PG-13","Horror"
"1930","8/10/2001","The Others",1.7e+07,96522687,207765056,"Miramax","PG-13","Horror"
"1931","7/18/1986","Aliens",1.7e+07,85160248,183316455,"20th Century Fox","R","Action"
"1932","8/13/2014","Let’s Be Cops",1.7e+07,82390774,136890774,"20th Century Fox","R","Comedy"
"1933","10/17/1997","I Know What You Did Last Summer",1.7e+07,72250091,125250091,"Sony Pictures","R","Horror"
"1934","10/22/2004","Sideways",1.7e+07,71502303,109793192,"Fox Searchlight","R","Drama"
"1935","11/15/2013","The Best Man Holiday",1.7e+07,70525195,72835710,"Universal","R","Comedy"
"1936","9/28/2012","Pitch Perfect",1.7e+07,65001093,116044347,"Universal","PG-13","Comedy"
"1937","8/5/1998","Halloween: H2O",1.7e+07,55041738,55041738,"Miramax","R","Horror"
"1938","4/5/2013","Evil Dead",1.7e+07,54239856,97778356,"Sony Pictures","R","Horror"
"1939","8/27/2004","Jet Li's Hero",1.7e+07,53652140,177535958,"Miramax","PG-13","Action"
"1940","10/29/2010","Saw 3D",1.7e+07,45710178,133735284,"Lionsgate","R","Horror"
"1941","2/20/2015","McFarland, USA",1.7e+07,44480275,45707924,"Walt Disney","PG","Drama"
"1942","11/11/2016","Almost Christmas",1.7e+07,42065185,42493506,"Universal","PG-13","Drama"
"1943","3/10/2006","The Hills Have Eyes",1.7e+07,41778863,70355813,"Fox Searchlight","R","Horror"
"1944","10/10/2003","Good Boy!",1.7e+07,37667746,45312217,"MGM","PG","Adventure"
"1945","1/26/2007","Smokin' Aces",1.7e+07,35662731,57263440,"Universal","R","Comedy"
"1946","10/2/1998","A Night at the Roxbury",1.7e+07,30331165,30331165,"Paramount Pictures","PG-13","Comedy"
"1947","3/4/2011","Beastly",1.7e+07,27865571,38028230,"CBS Films","PG-13","Drama"
"1948","7/9/1982","Tron",1.7e+07,26918576,26918576,"Walt Disney",NA,"Action"
"1949","8/20/2010","Lottery Ticket",1.7e+07,24719879,24719879,"Warner Bros.","PG-13","Comedy"
"1950","9/5/2003","Dickie Roberts: Former Child Star",1.7e+07,22734486,23734486,"Paramount Pictures","PG-13","Comedy"
"1951","3/31/2006","ATL",1.7e+07,21170563,21170563,"Warner Bros.","PG-13","Comedy"
"1952","8/24/2001","Summer Catch",1.7e+07,19693891,19693891,"Warner Bros.","PG-13","Comedy"
"1953","12/11/1998","A Simple Plan",1.7e+07,16316273,16316273,"Paramount Pictures","R","Drama"
"1954","11/27/2002","Wes Craven Presents: They",1.7e+07,12840842,16140842,"Miramax/Dimension","PG-13","Horror"
"1955","7/24/1987","Superman IV: The Quest for Peace",1.7e+07,11227824,11227824,"Warner Bros.","PG","Action"
"1956","1/25/2008","How She Move",1.7e+07,7070641,8607815,"Paramount Vantage","PG-13","Drama"
"1957","2/24/2006","Running Scared",1.7e+07,6855137,9729088,"New Line","R","Action"
"1958","8/24/2012","The Apparition",1.7e+07,4936819,10637281,"Warner Bros.","PG-13","Horror"
"1959","4/30/2004","Bobby Jones: Stroke of Genius",1.7e+07,2694071,2694071,"Film Foundry","PG","Drama"
"1960","12/25/2010","L'illusionniste",1.7e+07,2231474,8609949,"Sony Pictures Classics","PG","Adventure"
"1961","1/1/1981","Roar",1.7e+07,2110050,2110050,NA,"PG","Adventure"
"1962","10/17/2003","Veronica Guerin",1.7e+07,1569918,9438074,"Walt Disney","R","Drama"
"1963","6/10/2016","Genius",1.7e+07,1361045,6942889,"Roadside Attractions","PG-13","Drama"
"1964","6/26/2015","Escobar: Paradise Lost",1.7e+07,195792,3917679,"RADiUS-TWC","R","Drama"
"1965","3/11/2016","The Young Messiah",16800000,6469813,7313697,"Focus Features","PG-13","Drama"
"1966","11/27/1991","My Girl",16500000,58011485,58011485,"Sony Pictures","PG-13","Comedy"
"1967","12/11/1987","Wall Street",16500000,43848100,43848100,"20th Century Fox","R","Drama"
"1968","12/11/1995","Sense and Sensibility",16500000,42993774,134993774,"Sony Pictures","PG","Drama"
"1969","8/18/2006","The Illusionist",16500000,39868642,83792062,"Yari Film Group Rel…","PG-13","Drama"
"1970","12/19/2003","House of Sand and Fog",16500000,13005485,16157923,"Dreamworks SKG","R","Drama"
"1971","9/21/2007","Sydney White",16500000,11892415,13636339,"Universal","PG-13","Comedy"
"1972","6/2/1989","Dead Poets Society",16400000,95860116,239500000,"Walt Disney","PG","Drama"
"1973","12/16/1994","Dumb & Dumber",1.6e+07,127175374,246400000,"New Line","PG-13","Comedy"
"1974","5/19/2000","Road Trip",1.6e+07,68525609,119739110,"Dreamworks SKG","R","Comedy"
"1975","12/8/1982","The Verdict",1.6e+07,53977250,53977250,"20th Century Fox","R","Drama"
"1976","1/15/1999","Varsity Blues",1.6e+07,52894169,54294169,"Paramount Pictures","R","Drama"
"1977","5/25/2012","Moonrise Kingdom",1.6e+07,45512466,68848446,"Focus Features","PG-13","Drama"
"1978","11/25/2011","The Artist",1.6e+07,44667095,128256712,"Weinstein Co.","PG-13","Drama"
"1979","8/2/2002","The Master of Disguise",1.6e+07,40363530,40363530,"Sony Pictures","PG","Adventure"
"1980","12/29/2006","El Laberinto del Fauno",1.6e+07,37634615,87041569,"Picturehouse","R","Horror"
"1981","2/2/2007","The Messengers",1.6e+07,35374833,53774833,"Sony Pictures","PG-13","Horror"
"1982","3/2/2001","See Spot Run",1.6e+07,33357476,43057552,"Warner Bros.","PG","Adventure"
"1983","8/9/1991","Double Impact",1.6e+07,29090445,29090445,"Sony Pictures","R","Action"
"1984","6/27/2001","Baby Boy",1.6e+07,28734552,28734552,"Sony Pictures","R","Drama"
"1985","4/11/2001","Joe Dirt",1.6e+07,27087695,30987695,"Sony Pictures","PG-13","Comedy"
"1986","9/12/2008","The Women",1.6e+07,26902075,50103808,"Picturehouse","PG-13","Comedy"
"1987","4/20/2007","Hot Fuzz",1.6e+07,23618786,81742618,"Focus Features","R","Comedy"
"1988","8/15/2008","Vicky Cristina Barcelona",1.6e+07,23216709,104504817,"MGM","PG-13","Comedy"
"1989","6/13/2018","Superfly",1.6e+07,20537137,20723581,"Sony Pictures","R","Action"
"1990","3/12/2010","Remember Me",1.6e+07,19068240,56506120,"Summit Entertainment","PG-13","Drama"
"1991","10/11/2002","White Oleander",1.6e+07,16357770,21657770,"Warner Bros.","PG-13","Drama"
"1992","3/3/2000","Drowning Mona",1.6e+07,15427192,15980376,"Destination Films","PG-13","Comedy"
"1993","1/30/1987","Radio Days",1.6e+07,14792779,14792779,"Orion Pictures",NA,"Comedy"
"1994","7/18/2003","How to Deal",1.6e+07,14108518,14108518,"New Line","PG-13","Drama"
"1995","5/28/2004","Soul Plane",1.6e+07,13922211,14553807,"MGM","R","Comedy"
"1996","12/9/1988","My Stepmother Is an Alien",1.6e+07,13854000,13854000,"Sony Pictures","PG-13","Comedy"
"1997","6/29/2012","People Like Us",1.6e+07,12431792,12617472,"Walt Disney","PG-13","Drama"
"1998","9/3/2004","The Cookout",1.6e+07,11540112,11540112,"Lionsgate","PG-13","Comedy"
"1999","10/19/1979","Meteor",1.6e+07,8400000,8400000,"American Internatio…",NA,"Action"
"2000","3/7/1986","Highlander",1.6e+07,5900000,12900000,"20th Century Fox","R","Action"
"2001","11/18/2016","Bleed for This",1.6e+07,5083906,6603926,"Open Road","R","Drama"
"2002","9/15/2000","Duets",1.6e+07,4734235,6615452,"Walt Disney","R","Drama"
"2003","8/13/1999","Detroit Rock City",1.6e+07,4217115,5825314,"New Line","R","Comedy"
"2004","10/19/2007","Things We Lost in the Fire",1.6e+07,3287315,8120148,"Paramount Pictures","R","Drama"
"2005","5/16/2014","The Immigrant",1.6e+07,2013456,7585011,"RADiUS-TWC","R","Drama"
"2006","8/15/1997","Steel",1.6e+07,1686429,1686429,"Warner Bros.","PG-13","Action"
"2007","12/21/2005","The White Countess",1.6e+07,1669971,2814566,"Sony Pictures Classics","PG-13","Drama"
"2008","10/1/2014","Men, Women and Children",1.6e+07,705908,1685403,"Paramount Pictures","R","Comedy"
"2009","12/31/2008","Good",1.6e+07,31631,31631,"ThinkFilm","R","Drama"
"2010","6/21/2002","Juwanna Mann",15600000,13571817,13771817,"Warner Bros.","PG-13","Comedy"
"2011","6/8/2007","La Môme",15500000,10299782,88611837,"Picturehouse","PG-13","Drama"
"2012","11/15/2002","Ararat",15500000,1693000,1693000,"Miramax","R","Drama"
"2013","4/22/2005","Madison",15500000,517262,517262,"MGM","PG","Drama"
"2014","2/26/2010","The Yellow Handkerchief",15500000,318623,318623,"Samuel Goldwyn Films","PG-13","Drama"
"2015","3/31/2006","Slither",15250000,7802450,12930343,"Universal","R","Horror"
"2016","11/16/1990","Home Alone",1.5e+07,285761243,476684675,"20th Century Fox","PG","Comedy"
"2017","12/5/1984","Beverly Hills Cop",1.5e+07,234760478,316300000,"Paramount Pictures","R","Action"
"2018","5/16/1986","Top Gun",1.5e+07,179800601,356799634,"Paramount Pictures","PG","Action"
"2019","12/17/1982","Tootsie",1.5e+07,177200000,177200000,"Sony Pictures","PG","Comedy"
"2020","11/25/1987","3 Men and a Baby",1.5e+07,167780960,167780960,"Walt Disney","PG","Comedy"
"2021","11/26/2010","The King’s Speech",1.5e+07,138797449,430821168,"Weinstein Co.","R","Drama"
"2022","9/15/1999","American Beauty",1.5e+07,130058047,356258047,"Dreamworks SKG","R","Drama"
"2023","12/8/2000","Crouching Tiger, Hidden Dragon",1.5e+07,128067808,213514672,"Sony Pictures Classics","PG-13","Action"
"2024","12/9/1988","Twins",1.5e+07,111936388,216600000,"Universal","PG","Comedy"
"2025","12/20/1996","Scream",1.5e+07,103046663,173046663,"Miramax","R","Horror"
"2026","8/11/2017","Annabelle: Creation",1.5e+07,102092201,305385888,"Warner Bros.","R","Horror"
"2027","10/25/2013","Jackass Presents: Bad Grandpa",1.5e+07,102003019,160903019,"Paramount Pictures","R","Comedy"
"2028","6/28/1978","Heaven Can Wait",1.5e+07,98800000,98800000,"Paramount Pictures","PG","Comedy"
"2029","12/18/1985","The Color Purple",1.5e+07,93589701,93589701,"Warner Bros.","PG-13","Drama"
"2030","11/28/2014","The Imitation Game",1.5e+07,91125143,227773686,"Weinstein Co.","PG-13","Drama"
"2031","3/30/1988","Beetlejuice",1.5e+07,73326666,73326666,"Warner Bros.","PG","Comedy"
"2032","11/18/1959","Ben-Hur",1.5e+07,7.3e+07,7.3e+07,"MGM","G","Adventure"
"2033","1/18/2013","Mama",1.5e+07,71628180,148095566,"Universal","PG-13","Horror"
"2034","10/10/1980","Private Benjamin",1.5e+07,69847348,69847348,"Warner Bros.","R","Comedy"
"2035","3/7/1980","Coal Miner's Daughter",1.5e+07,67182787,67182787,"Universal","PG","Drama"
"2036","3/6/1987","Lethal Weapon",1.5e+07,65192350,120192350,"Warner Bros.","R","Action"
"2037","3/19/2010","Diary of a Wimpy Kid",1.5e+07,64003625,76954311,"20th Century Fox","PG","Adventure"
"2038","7/29/1983","National Lampoon’s Vacation",1.5e+07,61400000,61400000,"Warner Bros.","R","Comedy"
"2039","9/30/2006","The Queen",1.5e+07,56441711,128885873,"Miramax","PG-13","Drama"
"2040","12/21/1994","Little Women",1.5e+07,50003303,50003303,"Sony Pictures","PG","Drama"
"2041","1/1/1979","The Deer Hunter",1.5e+07,5e+07,50009253,"Universal","R","Drama"
"2042","2/3/2006","When a Stranger Calls",1.5e+07,47860214,67215435,"Sony Pictures","PG-13","Horror"
"2043","2/8/2002","Big Fat Liar",1.5e+07,47811275,52461017,"Universal","PG","Adventure"
"2044","8/15/1997","Cop Land",1.5e+07,44906632,63706632,"Miramax","R","Drama"
"2045","12/25/1997","Wag the Dog",1.5e+07,43057470,64252038,"New Line","R","Drama"
"2046","5/2/2003","The Lizzie McGuire Movie",1.5e+07,42734455,55534455,"Walt Disney","PG","Adventure"
"2047","12/25/1998","The Faculty",1.5e+07,40283321,40283321,"Miramax","R","Horror"
"2048","6/9/1993","What's Love Got to Do With It",1.5e+07,39100956,39100956,"Walt Disney","R","Drama"
"2049","12/14/2001","Not Another Teen Movie",1.5e+07,37882551,62401343,"Sony Pictures","R","Comedy"
"2050","12/3/2014","Wild",1.5e+07,37880356,52460543,"Fox Searchlight","R","Drama"
"2051","12/16/1962","Lawrence of Arabia",1.5e+07,37495385,69995385,"Sony Pictures","PG","Adventure"
"2052","11/7/2014","The Theory of Everything",1.5e+07,35893537,123327692,"Focus Features","PG-13","Drama"
"2053","9/16/2011","Drive",1.5e+07,35060689,81357930,"FilmDistrict","R","Action"
"2054","4/18/2003","Malibu's Most Wanted",1.5e+07,34308901,34499204,"Warner Bros.","PG-13","Comedy"
"2055","4/28/2000","Where the Heart Is",1.5e+07,33771174,40862054,"20th Century Fox","PG-13","Drama"
"2056","8/28/2009","Halloween 2",1.5e+07,33392973,38512850,"Weinstein/Dimension","R","Horror"
"2057","3/13/2009","The Last House on the Left",1.5e+07,32752215,46526243,"Universal","R","Horror"
"2058","2/18/2005","Because of Winn-Dixie",1.5e+07,32647042,33508485,"20th Century Fox","PG","Comedy"
"2059","9/25/1987","The Princess Bride",1.5e+07,30857000,30858487,"20th Century Fox","PG","Adventure"
"2060","7/12/2002","Halloween: Resurrection",1.5e+07,30259652,37659652,"Miramax/Dimension","R","Horror"
"2061","12/25/2007","The Great Debaters",1.5e+07,30226144,30261293,"Weinstein Co.","PG-13","Drama"
"2062","8/22/2014","When the Game Stands Tall",1.5e+07,30127963,30138912,"Sony Pictures","PG","Drama"
"2063","5/11/2007","28 Weeks Later",1.5e+07,28638916,64232714,"20th Century Fox","R","Horror"
"2064","4/21/2000","Love and Basketball",1.5e+07,27441122,27709625,"New Line","PG-13","Drama"
"2065","10/27/2000","Book of Shadows: Blair Witch 2",1.5e+07,26421314,47721314,"Artisan","R","Horror"
"2066","10/10/1997","Boogie Nights",1.5e+07,26410771,43111725,"New Line","R","Drama"
"2067","7/23/2010","Ramona and Beezus",1.5e+07,26167002,27469621,"20th Century Fox","G","Adventure"
"2068","11/5/1993","The Remains of the Day",1.5e+07,22954968,63954968,"Sony Pictures","PG","Drama"
"2069","1/15/1993","Nowhere to Run",1.5e+07,22189039,52189039,"Sony Pictures","R","Action"
"2070","9/22/2000","Urban Legends: Final Cut",1.5e+07,21468807,38574362,"Sony Pictures","R","Horror"
"2071","3/29/2013","The Place Beyond the Pines",1.5e+07,21403519,47011449,"Focus Features","R","Drama"
"2072","10/20/2006","Flicka",1.5e+07,21000147,21896367,"20th Century Fox","PG","Drama"
"2073","3/23/2007","The Hills Have Eyes II",1.5e+07,20804166,37466538,"20th Century Fox","R","Horror"
"2074","4/29/2016","Keanu",1.5e+07,20591853,20688141,"Warner Bros.","R","Comedy"
"2075","12/22/2010","Country Strong",1.5e+07,20218921,20601987,"Sony Pictures","PG-13","Drama"
"2076","10/11/2002","Tuck Everlasting",1.5e+07,19161999,19344615,"Walt Disney","PG","Drama"
"2077","10/13/2006","The Marine",1.5e+07,18844784,22165608,"20th Century Fox","PG-13","Action"
"2078","3/6/1998","The Big Lebowski",1.5e+07,17498804,46189568,"Gramercy","R","Comedy"
"2079","6/26/2009","The Hurt Locker",1.5e+07,17017811,49894223,"Summit Entertainment","R","Drama"
"2080","11/2/2012","The Man with the Iron Fists",1.5e+07,15634090,22018988,"Universal","R","Action"
"2081","5/11/1984","Firestarter",1.5e+07,15136870,15136870,"Universal",NA,"Horror"
"2082","4/20/2001","Freddy Got Fingered",1.5e+07,14249005,14249005,"20th Century Fox","R","Comedy"
"2083","8/19/2011","One Day",1.5e+07,13843771,59168692,"Focus Features","PG-13","Drama"
"2084","6/25/2004","De-Lovely",1.5e+07,13337299,18524496,"MGM","PG-13","Drama"
"2085","10/2/2009","Whip It",1.5e+07,13077184,18889972,"Fox Searchlight","PG-13","Comedy"
"2086","9/1/2000","Highlander: Endgame",1.5e+07,12801190,12801190,"Miramax/Dimension","R","Action"
"2087","1/20/2017","The Founder",1.5e+07,12786053,24408130,"Weinstein Co.","PG-13","Drama"
"2088","4/25/2003","Confidence",1.5e+07,12212417,12212417,"Lionsgate","R","Drama"
"2089","10/11/2002","Knockaround Guys",1.5e+07,11660180,12419700,"New Line","R","Drama"
"2090","8/27/1999","The Muse",1.5e+07,11614954,11614954,"October Films","PG-13","Comedy"
"2091","4/3/1998","Barney's Great Adventure",1.5e+07,11156471,11156471,"Polygram","G","Adventure"
"2092","3/1/1989","New York Stories",1.5e+07,10763469,10763469,"Walt Disney","PG","Drama"
"2093","3/24/2000","Here on Earth",1.5e+07,10494147,10845127,"20th Century Fox","PG-13","Drama"
"2094","10/8/2004","Raise Your Voice",1.5e+07,10411980,14811980,"New Line","PG","Drama"
"2095","4/23/1993","The Dark Half",1.5e+07,9579068,9579068,"Orion Pictures","R","Horror"
"2096","3/2/2007","Black Snake Moan",1.5e+07,9396870,10951153,"Paramount Vantage","R","Drama"
"2097","2/21/2003","Dark Blue",1.5e+07,9237470,12262065,"MGM","R","Drama"
"2098","6/22/2007","A Mighty Heart",1.5e+07,9176787,19153568,"Paramount Vantage","R","Drama"
"2099","3/21/2003","Boat Trip",1.5e+07,8586376,14933713,"Artisan","R","Comedy"
"2100","5/22/2002","The Importance of Being Earnest",1.5e+07,8378141,8378141,"Miramax","PG","Comedy"
"2101","5/5/2006","Hoot",1.5e+07,8117637,8224998,"New Line","PG","Adventure"
"2102","2/8/2008","In Bruges",1.5e+07,7800825,34533783,"Focus Features","R","Comedy"
"2103","1/4/2013","Promised Land",1.5e+07,7597898,12394562,"Focus Features","R","Drama"
"2104","10/8/2001","Mulholland Drive",1.5e+07,7219578,20785973,"Universal","R","Drama"
"2105","8/20/2008","The Rocker",1.5e+07,6409528,8767338,"20th Century Fox","PG-13","Comedy"
"2106","9/24/1999","Jakob the Liar",1.5e+07,4956401,4956401,"Sony Pictures","PG-13","Drama"
"2107","10/21/2005","Kiss Kiss, Bang Bang",1.5e+07,4235837,16829464,"Warner Bros.","R","Comedy"
"2108","4/30/1999","Idle Hands",1.5e+07,4023741,4023741,"Sony Pictures","R","Horror"
"2109","1/26/2007","Blood and Chocolate",1.5e+07,3526588,6551310,"MGM","PG-13","Horror"
"2110","9/22/2010","You Will Meet a Tall Dark Stranger",1.5e+07,3247816,34247816,"Sony Pictures Classics","R","Drama"
"2111","9/15/2010","Never Let Me Go",1.5e+07,2434652,11173718,"Fox Searchlight","R","Drama"
"2112","9/9/2016","The Disappointments Room",1.5e+07,2423467,3144688,"Relativity","R","Horror"
"2113","12/25/2003","The Company",1.5e+07,2281585,3396508,"Sony Pictures","PG-13","Drama"
"2114","10/22/1999","Crazy in Alabama",1.5e+07,1954202,1954202,"Sony Pictures","PG-13","Drama"
"2115","1/17/1986","The Clan of the Cave Bear",1.5e+07,1953732,1953732,"Warner Bros.",NA,"Adventure"
"2116","6/2/2006","Banlieue 13",1.5e+07,1200216,11599903,"Magnolia Pictures","R","Action"
"2117","11/12/1999","Felicia's Journey",1.5e+07,824295,1970268,"Artisan","PG-13","Drama"
"2118","1/25/2002","Metropolis",1.5e+07,673414,1405032,"Sony Pictures","PG-13","Adventure"
"2119","4/26/2013","The Reluctant Fundamentalist",1.5e+07,528731,528731,"IFC Films","R","Drama"
"2120","2/6/2004","The Return",1.5e+07,501752,5953886,"Kino International","PG-13","Drama"
"2121","7/25/2003","Buffalo Soldiers",1.5e+07,353743,353743,"Miramax","R","Comedy"
"2122","8/27/2010","Centurion",1.5e+07,123570,7885048,"Magnolia Pictures","R","Action"
"2123","10/23/2009","Ong-Bak 2",1.5e+07,102458,7583050,"Magnolia Pictures","R","Action"
"2124","9/6/2013","Winnie Mandela",1.5e+07,61847,61847,"Image Entertainment","R","Drama"
"2125","11/4/2011","The Son of No One",1.5e+07,30680,1148578,"Anchor Bay Entertai…","R","Drama"
"2126","10/25/2002","All the Queen's Men",1.5e+07,22723,22723,"Strand","PG-13","Comedy"
"2127","2/17/2017","In Dubious Battle",1.5e+07,0,214182,"Momentum Pictures","R","Drama"
"2128","7/1/2015","Magic Mike XXL",14500000,66013057,123709460,"Warner Bros.","R","Comedy"
"2129","11/1/1996","Romeo+Juliet",14500000,46338728,147542381,"20th Century Fox","PG-13","Drama"
"2130","7/22/2011","Elle s'appelait Sarah",14500000,7691700,25480031,"Weinstein Co.","PG-13","Drama"
"2131","6/5/2015","Freedom",14500000,0,872757,"ARC Entertainment","R","Drama"
"2132","11/12/2008","Slumdog Millionaire",1.4e+07,141330703,384530440,"Fox Searchlight","R","Drama"
"2133","12/17/1974","Towering Inferno",1.4e+07,1.16e+08,139700000,"20th Century Fox","PG","Action"
"2134","5/25/1988","Crocodile Dundee 2",1.4e+07,109306210,239606210,"Paramount Pictures","PG","Adventure"
"2135","12/20/1989","Born on the Fourth of July",1.4e+07,70001698,70001698,"Universal","R","Drama"
"2136","10/1/1993","Cool Runnings",1.4e+07,68856263,155056263,"Walt Disney","PG","Adventure"
"2137","1/12/2007","Stomp the Yard",1.4e+07,61356221,75525718,"Sony Pictures","PG-13","Drama"
"2138","1/16/2009","My Bloody Valentine",1.4e+07,51545952,102836002,"Lionsgate","R","Horror"
"2139","8/31/2012","The Possession",1.4e+07,49130588,82925064,"Lionsgate","PG-13","Horror"
"2140","10/22/1982","First Blood",1.4e+07,47212904,125212904,"Orion Pictures","R","Action"
"2141","7/13/1977","The Spy Who Loved Me",1.4e+07,46800000,185400000,"United Artists","PG","Action"
"2142","9/25/1998","Urban Legend",1.4e+07,38116707,72571864,"Sony Pictures","R","Horror"
"2143","12/9/1981","Taps",1.4e+07,35856053,35856053,"20th Century Fox","PG","Drama"
"2144","2/24/2012","Tyler Perry's Good Deeds",1.4e+07,35025791,35579177,"Lionsgate","PG-13","Drama"
"2145","1/18/1991","White Fang",1.4e+07,34729091,34729091,"Walt Disney","PG","Adventure"
"2146","12/21/1988","Dangerous Liaisons",1.4e+07,34700000,34700000,"Warner Bros.","R","Drama"
"2147","10/8/1999","Superstar",1.4e+07,30628981,30628981,"Paramount Pictures","PG-13","Comedy"
"2148","1/13/2012","The Iron Lady",1.4e+07,29959436,115592104,"Weinstein Co.","PG-13","Drama"
"2149","7/23/1993","Poetic Justice",1.4e+07,27450453,27450453,"Sony Pictures","R","Drama"
"2150","10/4/2002","Jonah: A VeggieTales Movie",1.4e+07,25571351,25608779,"Artisan","G","Adventure"
"2151","3/8/2002","All About the Benjamins",1.4e+07,25482931,25873145,"New Line","R","Comedy"
"2152","6/17/1977","Exorcist II: The Heretic",1.4e+07,25011000,25011000,"Warner Bros.",NA,"Horror"
"2153","3/12/2010","Our Family Wedding",1.4e+07,20255281,21410546,"Fox Searchlight","PG-13","Comedy"
"2154","10/27/1995","Vampire in Brooklyn",1.4e+07,19637147,19637147,"Paramount Pictures","R","Horror"
"2155","5/5/2006","An American Haunting",1.4e+07,16298046,30443277,"Freestyle Releasing","PG-13","Horror"
"2156","10/25/1996","Thinner",1.4e+07,15171475,15171475,"Paramount Pictures","R","Horror"
"2157","5/14/1999","Tea with Mussolini",1.4e+07,14395874,14395874,"MGM","PG","Drama"
"2158","4/26/2002","Jason X",1.4e+07,13121555,16951798,"New Line","R","Horror"
"2159","5/13/1994","Crooklyn",1.4e+07,13024170,13024170,"Universal","PG-13","Comedy"
"2160","2/20/2015","Hot Tub Time Machine 2",1.4e+07,12314651,12452601,"Paramount Pictures","R","Comedy"
"2161","11/17/2006","Bobby",1.4e+07,11242801,20597806,"MGM","R","Drama"
"2162","10/26/2012","Fun Size",1.4e+07,9409538,11166615,"Paramount Pictures","PG-13","Comedy"
"2163","11/30/2007","Le Scaphandre et le Papillon",1.4e+07,5990075,22754472,"Miramax","PG-13","Drama"
"2164","10/6/2006","Little Children",1.4e+07,5463019,14121177,"New Line","R","Drama"
"2165","4/21/2000","Gossip",1.4e+07,5108820,12591270,"Warner Bros.","R","Drama"
"2166","3/26/1999","A Walk on the Moon",1.4e+07,4741987,4741987,"Miramax","R","Drama"
"2167","10/23/2015","Suffragette",1.4e+07,4702420,34044909,"Focus Features","PG-13","Drama"
"2168","12/19/2014","Mr. Turner",1.4e+07,3958546,25187026,"Sony Pictures Classics","R","Drama"
"2169","9/7/2001","Soul Survivors",1.4e+07,3100650,4288246,"Artisan","PG-13","Horror"
"2170","3/31/1995","Jefferson in Paris",1.4e+07,2461628,2461628,"Walt Disney","PG-13","Drama"
"2171","1/1/1978","Caravans",1.4e+07,1e+06,1e+06,"Universal",NA,"Adventure"
"2172","9/26/2008","The Lucky Ones",1.4e+07,266967,266967,"Lionsgate","R","Drama"
"2173","9/30/2011","Margaret",1.4e+07,47185,623292,"Fox Searchlight","R","Drama"
"2174","12/9/2005","Brokeback Mountain",13900000,83043761,177012173,"Focus Features","R","Drama"
"2175","7/1/1995","Clueless",13700000,56598476,56598476,"Paramount Pictures","PG-13","Comedy"
"2176","3/30/1990","Teenage Mutant Ninja Turtles",13500000,135265915,2.02e+08,"New Line","PG","Adventure"
"2177","11/8/2002","Far From Heaven",13500000,15901849,29027914,"Focus Features","PG-13","Drama"
"2178","10/12/2012","Seven Psychopaths",13500000,15024049,33035736,"CBS Films","R","Comedy"
"2179","11/22/2000","Quills",13500000,7060876,11732088,"Fox Searchlight","R","Drama"
"2180","1/29/1982","The Border",13500000,6118683,6118683,"Universal",NA,"Drama"
"2181","2/18/2005","Der Untergang",13500000,5501940,93631744,"Newmarket Films","R","Drama"
"2182","3/2/2001","The Caveman's Valentine",13500000,687081,892506,"Focus Features","R","Drama"
"2183","4/1/2011","The Last Godfather",13400000,164247,164247,"Roadside Attractions","PG-13","Comedy"
"2184","12/17/2004","Mar adentro",13300000,2086345,39686345,"Fine Line","PG-13","Drama"
"2185","12/23/1987","Good Morning Vietnam",1.3e+07,123922370,123922370,"Walt Disney","R","Comedy"
"2186","1/12/2001","Save the Last Dance",1.3e+07,91038276,122244329,"Paramount Pictures","PG-13","Drama"
"2187","7/4/2018","The First Purge",1.3e+07,69086325,136112145,"Universal","R","Horror"
"2188","3/16/2016","Miracles from Heaven",1.3e+07,61705123,73798720,"Sony Pictures","PG","Drama"
"2189","2/11/2000","Snow Day",1.3e+07,60008303,62452927,"Paramount Pictures","PG","Adventure"
"2190","6/24/2016","The Shallows",1.3e+07,55121623,118888025,"Sony Pictures","PG-13","Drama"
"2191","7/17/1987","RoboCop",1.3e+07,53424681,53424681,"Orion Pictures","R","Action"
"2192","11/21/2007","This Christmas",1.3e+07,49121934,49733545,"Sony Pictures","PG-13","Drama"
"2193","12/15/2000","Dude, Where's My Car?",1.3e+07,46729374,73180297,"20th Century Fox","PG-13","Comedy"
"2194","10/10/2014","St. Vincent",1.3e+07,44137712,54837234,"Weinstein Co.","PG-13","Comedy"
"2195","7/2/2014","Earth to Echo",1.3e+07,38934842,42174545,"Relativity","PG","Adventure"
"2196","5/10/2002","The New Guy",1.3e+07,28972187,28972187,"Sony Pictures","PG-13","Comedy"
"2197","2/5/1993","Loaded Weapon 1",1.3e+07,27979399,27979399,"New Line","PG-13","Comedy"
"2198","3/12/1999","Baby Geniuses",1.3e+07,27151490,27151490,"Sony Pictures","PG","Adventure"
"2199","4/24/1998","The Big Hit",1.3e+07,27066941,27066941,"Sony Pictures","R","Action"
"2200","11/9/1990","Child's Play 2",1.3e+07,26904572,34166572,"Universal","R","Horror"
"2201","7/10/1996","Harriet the Spy",1.3e+07,26570048,26570048,"Paramount Pictures","PG","Adventure"
"2202","3/1/2013","21 and Over",1.3e+07,25682380,42195766,"Relativity","R","Comedy"
"2203","11/21/2007","The Mist",1.3e+07,25593755,57189408,"MGM","R","Horror"
"2204","9/21/2012","The Perks of Being a Wallflower",1.3e+07,17742948,33069303,"Lionsgate","PG-13","Drama"
"2205","6/29/2001","crazy/beautiful",1.3e+07,16929123,19929123,"Walt Disney","PG-13","Drama"
"2206","10/16/2015","Room",1.3e+07,14677674,36262783,"A24","R","Drama"
"2207","10/16/2015","Woodlawn",1.3e+07,14394097,14403703,"Pure Flix Entertain…","PG","Drama"
"2208","12/20/2006","Letters from Iwo Jima",1.3e+07,13756082,67867998,"Warner Bros.","R","Drama"
"2209","2/23/2007","The Astronaut Farmer",1.3e+07,11003643,11141213,"Warner Bros.","PG","Drama"
"2210","6/12/1998","Dirty Work",1.3e+07,10020081,10020081,"MGM","PG-13","Comedy"
"2211","9/9/2016","Robinson Crusoe",1.3e+07,8005586,33490316,"Lionsgate","PG","Adventure"
"2212","4/13/1994","Serial Mom",1.3e+07,7881335,7881335,"Savoy","R","Comedy"
"2213","8/4/1999","Dick",1.3e+07,6276869,6276869,"Sony Pictures","PG-13","Comedy"
"2214","11/10/1999","Light It Up",1.3e+07,5871603,5871603,"20th Century Fox","R","Drama"
"2215","8/24/2001","Bubble Boy",1.3e+07,5002310,5002310,"Walt Disney","PG-13","Comedy"
"2216","5/4/2007","Paris, je t'aime",1.3e+07,4857374,5175088,"First Look","R","Drama"
"2217","8/24/2007","Resurrecting the Champ",1.3e+07,3172382,3260555,"Yari Film Group Rel…","PG-13","Drama"
"2218","3/2/2001","The Widow of St. Pierre",1.3e+07,3058380,3058380,"Lionsgate","R","Drama"
"2219","12/4/2015","Youth",1.3e+07,2703296,24002112,"Fox Searchlight","R","Drama"
"2220","2/26/2010","Un Prophète",1.3e+07,2087720,19910624,"Sony Pictures Classics","R","Drama"
"2221","12/3/2010","I Love You, Phillip Morris",1.3e+07,2037459,23014027,"Roadside Attractions","R","Comedy"
"2222","7/24/2015","The Vatican Tapes",1.3e+07,1784763,14999638,"Lionsgate","PG-13","Horror"
"2223","3/17/2006","Find Me Guilty",1.3e+07,1173673,2898225,"Freestyle Releasing","R","Drama"
"2224","10/13/2006","Infamous",1.3e+07,1151330,2613717,"Warner Independent","R","Drama"
"2225","7/29/2011","Attack the Block",1.3e+07,1024175,6459183,"Sony Pictures","R","Action"
"2226","12/23/2011","In The Land of Blood and Honey",1.3e+07,303877,509193,"FilmDistrict","R","Drama"
"2227","6/18/2010","The Killer Inside Me",1.3e+07,217277,3617277,"IFC Films","R","Drama"
"2228","9/12/2014","The Drop",12600000,10724389,19054534,"Fox Searchlight","R","Drama"
"2229","9/3/2010","Machete",12500000,26593646,46370970,"20th Century Fox","R","Action"
"2230","12/19/2002","Antwone Fisher",12500000,21078145,23367586,"Fox Searchlight","PG-13","Drama"
"2231","2/12/1982","La Guerre du feu",12500000,20959585,20959585,"20th Century Fox",NA,"Adventure"
"2232","11/22/2002","The Emperor's Club",12500000,14060950,16193713,"Universal","PG-13","Drama"
"2233","9/11/2009","Sorority Row",12500000,11965282,26735797,"Summit Entertainment","R","Horror"
"2234","9/30/1992","Glengarry Glen Ross",12500000,10725228,10725228,"New Line","R","Drama"
"2235","11/7/2008","The Boy in the Striped Pyjamas",12500000,9046156,44083403,"Miramax","PG-13","Drama"
"2236","4/2/1982","Cat People",12500000,7e+06,2.1e+07,"Universal","R","Drama"
"2237","5/25/1979","The Prisoner of Zenda",12500000,7e+06,7e+06,"Universal",NA,"Comedy"
"2238","10/15/2010","Conviction",12500000,6797696,11826980,"Fox Searchlight","R","Drama"
"2239","10/12/2007","Lars and the Real Girl",12500000,5956480,11277119,"MGM","PG-13","Comedy"
"2240","5/21/2010","Solitary Man",12500000,4360548,4360548,"Anchor Bay Entertai…","R","Drama"
"2241","12/31/1997","Oscar and Lucinda",12500000,1612957,1612957,"Fox Searchlight","R","Drama"
"2242","11/1/1996","The Funeral",12500000,1212799,1412799,"October Films","R","Drama"
"2243","9/3/2004","Tae Guik Gi: The Brotherhood of War",12500000,1110186,69826708,"IDP Distribution","R","Drama"
"2244","4/16/2010","The Perfect Game",12500000,1089445,3931367,"Slowhand Cinema","PG","Drama"
"2245","11/18/1988","The Land Before Time",12300000,48092846,81972846,"Universal","G","Adventure"
"2246","6/20/1975","Jaws",1.2e+07,2.6e+08,470700000,"Universal","PG","Horror"
"2247","12/26/1973","The Exorcist",1.2e+07,204868002,402735134,"Warner Bros.","R","Horror"
"2248","6/6/2014","The Fault in Our Stars",1.2e+07,124872350,307166834,"20th Century Fox","PG-13","Drama"
"2249","7/9/1999","American Pie",1.2e+07,101800948,234723148,"Universal","R","Comedy"
"2250","4/16/2014","Heaven is for Real",1.2e+07,91386097,100916299,"Sony Pictures","PG","Drama"
"2251","12/12/1986","The Golden Child",1.2e+07,79817937,79817937,"Paramount Pictures","PG-13","Action"
"2252","6/4/1982","Star Trek II: The Wrath of Khan",1.2e+07,78912963,95800000,"Paramount Pictures","PG","Adventure"
"2253","9/13/2002","Barbershop",1.2e+07,75781642,77063461,"MGM","PG-13","Comedy"
"2254","2/4/1994","Ace Ventura: Pet Detective",1.2e+07,72217396,107217396,"Warner Bros.","PG-13","Comedy"
"2255","2/24/2012","Act of Valor",1.2e+07,70012847,82497035,"Relativity","R","Action"
"2256","8/11/2006","Step Up",1.2e+07,65328121,110989157,"Walt Disney","PG-13","Drama"
"2257","12/20/1996","Beavis and Butt-Head Do America",1.2e+07,63118386,63118386,"Paramount Pictures","PG-13","Adventure"
"2258","11/25/2016","Lion",1.2e+07,51739495,149875676,"Weinstein Co.","PG-13","Drama"
"2259","12/25/1997","Jackie Brown",1.2e+07,39673162,74727492,"Miramax","R","Drama"
"2260","11/22/2013","Philomena",1.2e+07,37709979,98963392,"Weinstein Co.","PG-13","Drama"
"2261","11/6/1981","Time Bandits",1.2e+07,37400000,37400000,"Avco Embassy",NA,"Adventure"
"2262","7/24/2015","Paper Towns",1.2e+07,32000304,85512300,"20th Century Fox","PG-13","Drama"
"2263","10/10/2008","Quarantine",1.2e+07,31691811,41924774,"Sony Pictures","R","Horror"
"2264","8/21/2002","One Hour Photo",1.2e+07,31597131,52223306,"Fox Searchlight","R","Drama"
"2265","4/7/2004","Johnson Family Vacation",1.2e+07,31203964,31286759,"Fox Searchlight","PG-13","Comedy"
"2266","12/21/2001","How High",1.2e+07,31155435,31222395,"Universal","R","Comedy"
"2267","10/7/1960","Spartacus",1.2e+07,3e+07,6e+07,"Universal","PG-13","Action"
"2268","9/1/2006","Crank",1.2e+07,27838408,43924923,"Lionsgate","R","Action"
"2269","12/11/1992","The Muppet Christmas Carol",1.2e+07,27281507,27492918,"Walt Disney","G","Comedy"
"2270","10/25/2002","Frida",1.2e+07,25885000,56131239,"Miramax","R","Drama"
"2271","12/12/2014","Top Five",1.2e+07,25317379,26001741,"Paramount Pictures","R","Comedy"
"2272","9/11/1998","Rounders",1.2e+07,22921898,22921898,"Miramax","R","Drama"
"2273","1/30/2015","Project Almanac",1.2e+07,22348241,32909437,"Paramount Pictures","PG-13","Adventure"
"2274","1/13/1995","Tales from the Crypt: Demon Knight",1.2e+07,21089146,21089146,"Universal","R","Horror"
"2275","3/11/2005","The Upside of Anger",1.2e+07,18761993,28915761,"New Line","R","Drama"
"2276","3/3/2006","Aquamarine",1.2e+07,18597342,22978953,"20th Century Fox","PG","Comedy"
"2277","11/15/2013","Nebraska",1.2e+07,17654912,24761360,"Paramount Pictures","R","Drama"
"2278","1/9/2004","My Baby's Daddy",1.2e+07,17321573,17322212,"Miramax","PG-13","Comedy"
"2279","10/5/2001","Max Keeble's Big Move",1.2e+07,17292381,17292381,"Walt Disney","PG","Adventure"
"2280","12/9/2011","Young Adult",1.2e+07,16311571,22750356,"Paramount Pictures","R","Comedy"
"2281","7/14/2017","Wish Upon",1.2e+07,14301505,23477345,"Broad Green Pictures","PG-13","Horror"
"2282","8/6/1997","Def Jam's How To Be a Player",1.2e+07,14010363,14010363,"Gramercy","R","Comedy"
"2283","10/30/1998","Living Out Loud",1.2e+07,12905901,12905901,"New Line","R","Drama"
"2284","10/3/2008","Rachel Getting Married",1.2e+07,12796861,17475475,"Sony Pictures Classics","R","Drama"
"2285","3/20/1981","The Postman Always Rings Twice",1.2e+07,12200000,44200000,"Paramount Pictures",NA,"Drama"
"2286","12/12/2003","Girl with a Pearl Earring",1.2e+07,11634362,43274797,"Lionsgate","PG-13","Drama"
"2287","2/10/1982","Das Boot",1.2e+07,11487676,84970337,"Sony Pictures","R","Drama"
"2288","12/3/2004","House of Flying Daggers",1.2e+07,11050094,92863945,"Sony Pictures Classics","PG-13","Action"
"2289","3/22/2002","Sorority Boys",1.2e+07,10198766,12516222,"Walt Disney","R","Comedy"
"2290","10/13/2017","Marshall",1.2e+07,10051659,10116816,"Open Road","PG-13","Drama"
"2291","12/5/2008","Cadillac Records",1.2e+07,8195551,8942516,"Sony Pictures","R","Drama"
"2292","5/12/2000","Screwed",1.2e+07,6982680,6982680,"Universal","PG-13","Comedy"
"2293","10/20/2006","Running With Scissors",1.2e+07,6860000,8706701,"Sony Pictures","R","Comedy"
"2294","9/3/1993","Fortress",1.2e+07,6730578,46730578,"Miramax","R","Action"
"2295","11/17/2006","For Your Consideration",1.2e+07,5549923,5549923,"Warner Independent","PG-13","Comedy"
"2296","11/20/1998","Celebrity",1.2e+07,5078660,6200000,"Miramax","R","Comedy"
"2297","6/6/1986","Invaders from Mars",1.2e+07,4884663,4984663,"Cannon",NA,"Horror"
"2298","3/22/1996","Girl 6",1.2e+07,4880941,4880941,"Fox Searchlight","R","Comedy"
"2299","2/22/2008","Charlie Bartlett",1.2e+07,3950294,5295909,"MGM","R","Comedy"
"2300","2/13/2009","Two Lovers",1.2e+07,3149034,16349034,"Magnolia Pictures","R","Drama"
"2301","2/15/2002","Last Orders",1.2e+07,2326407,2326407,"Sony Pictures Classics","R","Drama"
"2302","3/9/2007","Gwoemul",1.2e+07,2201923,92618117,"Magnolia Pictures","R","Action"
"2303","11/13/1981","The Pursuit of D.B. Cooper",1.2e+07,2104164,2104164,"Universal",NA,"Adventure"
"2304","3/19/1999","Ravenous",1.2e+07,2062406,2062406,"20th Century Fox","R","Horror"
"2305","6/14/2002","The Dangerous Lives of Altar Boys",1.2e+07,1779284,1779284,"ThinkFilm","R","Drama"
"2306","3/1/2013","Stoker",1.2e+07,1703125,12034913,"Fox Searchlight","R","Drama"
"2307","3/7/2008","Married Life",1.2e+07,1506998,2975188,"Sony Pictures Classics","PG-13","Drama"
"2308","3/11/2011","Kill the Irishman",1.2e+07,1188194,1188194,"Anchor Bay Entertai…","R","Drama"
"2309","9/30/2005","Duma",1.2e+07,870067,994790,"Warner Bros.","PG","Adventure"
"2310","4/20/2012","Darling Companion",1.2e+07,793352,1200346,"Sony Pictures Classics","PG-13","Comedy"
"2311","6/4/2010","Ondine",1.2e+07,550472,557545,"Magnolia Pictures","PG-13","Drama"
"2312","4/18/2008","Life Before Her Eyes",1.2e+07,303439,7203439,"Magnolia Pictures","R","Drama"
"2313","10/31/1997","Critical Care",1.2e+07,220175,220175,NA,"R","Drama"
"2314","9/28/2007","Trade",1.2e+07,214202,1513388,"Roadside Attractions","R","Drama"
"2315","1/6/2006","Fateless",1.2e+07,196857,196857,"ThinkFilm","R","Drama"
"2316","9/3/2010","San qiang pai an jing qi",1.2e+07,190946,310946,"Sony Pictures Classics","R","Drama"
"2317","9/17/1999","Breakfast of Champions",1.2e+07,178287,178287,"Walt Disney","R","Comedy"
"2318","3/9/2001","Company Man",1.2e+07,146028,622273,NA,"PG-13","Comedy"
"2319","11/7/2009","Nanjing! Nanjing!",1.2e+07,122558,20122558,"Kino International","R","Drama"
"2320","10/9/2015","Trash",1.2e+07,17484,6553186,"Focus Features","R","Adventure"
"2321","8/19/2011","5 Days of War",1.2e+07,17479,87793,"Anchor Bay Entertai…","R","Drama"
"2322","11/11/2015","10 Days in a Madhouse",1.2e+07,14616,14616,"Cafe Pictures","R","Drama"
"2323","9/23/2016","The Dressmaker",11900000,2022115,24041617,"Broad Green Pictures","R","Drama"
"2324","12/10/1999","Diamonds",11900000,81897,81897,"Miramax","PG-13","Comedy"
"2325","3/20/1998","Madadayo",11900000,48856,48856,"WinStar Cinema",NA,"Drama"
"2326","11/20/2015","Carol",11800000,12711491,42895440,"Weinstein Co.","R","Drama"
"2327","4/21/1989","Pet Sematary",11500000,57469179,57469179,"Paramount Pictures","R","Horror"
"2328","1/22/2016","Dirty Grandpa",11500000,35593113,105241410,"Lionsgate","R","Comedy"
"2329","10/9/2009","St. Trinian’s",11400000,15000,29830239,"NeoClassics Films","PG-13","Comedy"
"2330","5/25/1977","Star Wars Ep. IV: A New Hope",1.1e+07,460998007,786598007,"20th Century Fox","PG","Adventure"
"2331","6/8/1984","Gremlins",1.1e+07,148168459,148199515,"Warner Bros.","PG","Comedy"
"2332","12/22/1965","Doctor Zhivago",1.1e+07,111721000,111859493,"MGM","PG-13","Drama"
"2333","12/10/2010","The Fighter",1.1e+07,93617009,129262388,"Paramount Pictures","R","Drama"
"2334","12/27/1991","Fried Green Tomatoes",1.1e+07,81204830,81204830,"Universal","PG-13","Drama"
"2335","9/22/2006","Jackass: Number Two",1.1e+07,72778712,85278712,"Paramount Pictures","R","Comedy"
"2336","3/13/1992","My Cousin Vinny",1.1e+07,52929168,52929168,"20th Century Fox","R","Comedy"
"2337","8/22/2014","If I Stay",1.1e+07,50474843,78356170,"Warner Bros.","PG-13","Drama"
"2338","4/7/1989","Major League",1.1e+07,49793054,49793054,"Paramount Pictures","R","Comedy"
"2339","1/25/2002","A Walk to Remember",1.1e+07,41227069,46060915,"Warner Bros.","PG","Drama"
"2340","12/29/1995","Dead Man Walking",1.1e+07,39387284,83088295,"Gramercy","R","Drama"
"2341","11/4/2015","Brooklyn",1.1e+07,38322743,62076141,"Fox Searchlight","PG-13","Drama"
"2342","3/5/1999","Cruel Intentions",1.1e+07,38230075,75803716,"Sony Pictures","R","Drama"
"2343","10/17/2008","The Secret Life of Bees",1.1e+07,37780486,39994347,"Fox Searchlight","PG-13","Drama"
"2344","4/1/2015","Woman in Gold",1.1e+07,33307793,57019592,"Weinstein Co.","PG-13","Drama"
"2345","6/12/1981","History of the World: Part I",1.1e+07,31672000,31672000,"20th Century Fox",NA,"Comedy"
"2346","10/23/2009","Saw VI",1.1e+07,27693292,69752402,"Lionsgate","R","Horror"
"2347","10/12/2001","Corky Romano",1.1e+07,23978402,25116103,"Walt Disney","PG-13","Comedy"
"2348","4/13/1978","F.I.S.T",1.1e+07,20388920,20388920,"United Artists",NA,"Drama"
"2349","1/1/1975","Barry Lyndon",1.1e+07,2e+07,20169934,"Warner Bros.","PG","Drama"
"2350","1/11/2013","Quartet",1.1e+07,18388357,56178935,"Weinstein Co.","PG-13","Comedy"
"2351","11/21/2001","Out Cold",1.1e+07,13906394,14786394,"Walt Disney","PG-13","Comedy"
"2352","10/13/2000","The Ladies Man",1.1e+07,13592872,13719474,"Paramount Pictures","R","Comedy"
"2353","3/30/2001","Tomcats",1.1e+07,13558739,13558739,"Sony Pictures","R","Comedy"
"2354","12/6/2013","Inside Llewyn Davis",1.1e+07,13248209,32943247,"CBS Films","R","Drama"
"2355","2/19/1993","Army of Darkness",1.1e+07,11502976,21502976,"Universal","R","Horror"
"2356","11/12/2004","Kinsey",1.1e+07,10214647,17443529,"Fox Searchlight","R","Drama"
"2357","12/25/1993","What's Eating Gilbert Grape",1.1e+07,9170214,9170214,"Paramount Pictures","PG-13","Drama"
"2358","2/1/2002","Slackers",1.1e+07,4814244,5942218,"Sony Pictures","R","Comedy"
"2359","9/26/2003","The Gospel of John",1.1e+07,4068087,4234355,"ThinkFilm","PG-13","Drama"
"2360","10/10/2004","Vera Drake",1.1e+07,3753806,13353855,"Fine Line","R","Drama"
"2361","1/31/2003","The Guru",1.1e+07,3051221,24150550,"Universal","R","Comedy"
"2362","12/14/1995","Othello",1.1e+07,2844379,2844379,"Sony Pictures","R","Drama"
"2363","5/12/1995","The Perez Family",1.1e+07,2794056,2794056,"Goldwyn Entertainment","R","Comedy"
"2364","1/1/1970","The Molly Maguires",1.1e+07,2200000,2200000,NA,"PG","Drama"
"2365","1/1/1991","Return to the Blue Lagoon",1.1e+07,2e+06,2e+06,NA,"PG-13","Adventure"
"2366","9/7/2007","Romance and Cigarettes",1.1e+07,551002,3231251,"Borotoro","R","Comedy"
"2367","11/10/2006","Copying Beethoven",1.1e+07,355968,6586324,"MGM","PG-13","Drama"
"2368","8/26/2011","Brighton Rock",1.1e+07,229653,229653,"IFC Films","R","Drama"
"2369","5/4/2012","LOL",1.1e+07,0,10431506,"Lionsgate","PG-13","Comedy"
"2370","10/24/2008","Saw V",10800000,56746769,118209778,"Lionsgate","R","Horror"
"2371","5/25/2012","Les Intouchables",10800000,13182281,484873045,"Weinstein Co.","R","Comedy"
"2372","4/27/2007","Jindabyne",10800000,399879,2862544,"Sony Pictures Classics","R","Drama"
"2373","6/4/1982","Poltergeist",10700000,74706019,121706019,"MGM","PG","Horror"
"2374","6/18/1999","An Ideal Husband",10700000,18542974,31341183,"Miramax","PG-13","Comedy"
"2375","12/25/2004","Darkness",10600000,22163442,34409206,"Miramax/Dimension","PG-13","Horror"
"2376","6/11/1982","ET: The Extra-Terrestrial",10500000,435110554,792965326,"Universal","PG","Drama"
"2377","4/2/1968","2001: A Space Odyssey",10500000,58583410,70576492,"MGM","G","Adventure"
"2378","4/20/2007","In the Land of Women",10500000,11052958,14140402,"Warner Bros.","PG-13","Comedy"
"2379","2/20/2004","The Blue Butterfly",10400000,1610194,1610194,"Alliance Films","PG","Drama"
"2380","2/18/1983","Lovesick",10100000,10143618,10143618,"Warner Bros.",NA,"Comedy"
"2381","8/24/2007","September Dawn",10100000,1066555,1066555,"Black Diamond Pictures","R","Drama"
"2382","12/5/1997","Good Will Hunting",1e+07,138433435,225925989,"Miramax","R","Drama"
"2383","10/22/2004","The Grudge",1e+07,110359362,187281115,"Sony Pictures","PG-13","Horror"
"2384","8/26/2016","Don’t Breathe",1e+07,89217875,159047649,"Sony Pictures","R","Horror"
"2385","6/26/1981","Stripes",1e+07,85300000,85300000,"Columbia","R","Comedy"
"2386","10/27/2006","Saw III",1e+07,80238724,163876815,"Lionsgate","R","Horror"
"2387","7/1/2016","The Purge: Election Year",1e+07,79042440,118557124,"Universal","R","Horror"
"2388","5/18/2018","Book Club",1e+07,68566296,89643819,"Paramount Pictures","PG-13","Comedy"
"2389","8/25/2000","Bring it On",1e+07,68353550,90453550,"Universal","PG-13","Comedy"
"2390","10/26/2007","Saw IV",1e+07,63300095,135759694,"Lionsgate","R","Horror"
"2391","2/24/2006","Madea's Family Reunion",1e+07,63257940,63320521,"Lionsgate","PG-13","Comedy"
"2392","1/7/2005","White Noise",1e+07,56094360,92094360,"Universal","PG-13","Drama"
"2393","10/17/1986","The Color of Money",1e+07,52293000,52293000,"Walt Disney","R","Drama"
"2394","6/5/2015","Insidious Chapter 3",1e+07,52218558,120678444,"Focus Features","PG-13","Horror"
"2395","10/2/1992","The Mighty Ducks",1e+07,50752337,50752337,"Walt Disney","PG","Comedy"
"2396","11/3/2017","Lady Bird",1e+07,48958273,78610769,"A24","R","Drama"
"2397","5/4/2012","The Best Exotic Marigold Hotel",1e+07,46383639,134639780,"Fox Searchlight","PG-13","Comedy"
"2398","6/8/2018","Hereditary",1e+07,44069456,70090779,"A24","R","Horror"
"2399","3/16/2018","Love, Simon",1e+07,40826341,65521685,"20th Century Fox","PG-13","Drama"
"2400","2/17/1989","Bill & Ted's Excellent Adventure",1e+07,40485039,40485039,"Orion Pictures","PG","Adventure"
"2401","10/4/1962","The Longest Day",1e+07,39100000,50100000,"20th Century Fox","G","Action"
"2402","2/16/1996","Happy Gilmore",1e+07,38623460,41004412,"Universal","PG-13","Comedy"
"2403","10/27/2017","Jigsaw",1e+07,38052832,102499582,"Lionsgate","R","Horror"
"2404","8/31/2001","Jeepers Creepers",1e+07,37904175,58939035,"MGM","R","Horror"
"2405","6/28/1985","St. Elmo’s Fire",1e+07,37800000,37800000,"Sony Pictures","R","Drama"
"2406","2/16/2001","Recess: School's Out",1e+07,36696761,44451470,"Walt Disney","G","Adventure"
"2407","7/10/1985","Mad Max Beyond Thunderdome",1e+07,36230219,36230219,"Warner Bros.","PG-13","Action"
"2408","1/22/2016","The Boy",1e+07,35819556,68220952,"STX Entertainment","PG-13","Horror"
"2409","10/4/1985","Commando",1e+07,35073978,35073978,"20th Century Fox","R","Action"
"2410","5/19/2017","Everything, Everything",1e+07,34121140,61604439,"Warner Bros.","PG-13","Drama"
"2411","9/17/2010","Devil",1e+07,33679655,63354114,"Universal","PG-13","Horror"
"2412","11/22/2002","Friday After Next",1e+07,33253609,33526835,"New Line","R","Comedy"
"2413","3/22/1985","The Last Dragon",1e+07,3.3e+07,3.3e+07,"Sony Pictures",NA,"Action"
"2414","4/28/2017","How to Be a Latin Lover",1e+07,32149404,62556228,"Lionsgate","PG-13","Comedy"
"2415","3/6/1992","The Lawnmower Man",1e+07,32100816,32100816,"New Line","R","Action"
"2416","10/3/2008","Nick and Norah's Infinite Playlist",1e+07,31487293,33886017,"Sony Pictures","PG-13","Drama"
"2417","12/19/2003","Calendar Girls",1e+07,31011616,93074616,"Walt Disney","PG-13","Comedy"
"2418","11/12/1999","Dogma",1e+07,30651422,43948865,"Lionsgate","R","Comedy"
"2419","9/20/2002","The Banger Sisters",1e+07,30306281,38067218,"20th Century Fox","R","Comedy"
"2420","5/19/1989","Road House",1e+07,30050028,30050028,"United Artists","R","Action"
"2421","7/27/2018","Teen Titans Go! To The Movies",1e+07,29562341,51411600,"Warner Bros.","PG","Adventure"
"2422","6/24/1983","Twilight Zone: The Movie",1e+07,29500000,29500000,"Warner Bros.","PG","Horror"
"2423","11/23/1994","A Low Down Dirty Shame",1e+07,29317886,29317886,"Walt Disney","R","Action"
"2424","9/6/2002","Swimfan",1e+07,28564995,34084228,"20th Century Fox","PG-13","Drama"
"2425","10/6/2006","Employee of the Month",1e+07,28444855,38364855,"Lionsgate","PG-13","Comedy"
"2426","8/21/2015","Sinister 2",1e+07,27740955,54104225,"Focus Features","R","Horror"
"2427","3/25/1983","The Outsiders",1e+07,25697647,25697647,"Warner Bros.","PG-13","Drama"
"2428","6/12/1998","Can't Hardly Wait",1e+07,25358996,25358996,"Sony Pictures","PG-13","Comedy"
"2429","4/26/2013","Mud",1e+07,21590086,31556959,"Roadside Attractions","PG-13","Drama"
"2430","9/16/2016","Blair Witch",1e+07,20777061,37478274,"Lionsgate","R","Horror"
"2431","10/21/1983","The Dead Zone",1e+07,20766000,20766000,"Paramount Pictures",NA,"Horror"
"2432","2/2/2001","Valentine",1e+07,20384136,20384136,"Warner Bros.","R","Horror"
"2433","6/9/2006","A Prairie Home Companion",1e+07,20342852,26716191,"Picturehouse","PG-13","Comedy"
"2434","2/23/2007","Reno 911!: Miami",1e+07,20342161,21851362,"20th Century Fox","R","Comedy"
"2435","7/24/1998","Jane Austen's Mafia",1e+07,19843795,30143795,"Walt Disney","PG-13","Comedy"
"2436","2/25/1994","Sugar Hill",1e+07,18272447,18423914,"20th Century Fox","R","Drama"
"2437","6/20/2008","Kit Kittredge: An American Girl",1e+07,17657973,17657973,"Picturehouse","G","Drama"
"2438","9/27/1985","Invasion U.S.A.",1e+07,17536256,17536256,"Cannon","R","Action"
"2439","9/23/2005","Roll Bounce",1e+07,17380866,17433072,"Fox Searchlight","PG-13","Comedy"
"2440","1/19/1990","Tremors",1e+07,16667084,16667084,"Universal","PG-13","Action"
"2441","8/3/1990","Mo' Better Blues",1e+07,16153000,16153000,"Universal","R","Drama"
"2442","1/25/2002","Kung Pow: Enter the Fist",1e+07,16033556,17033556,"20th Century Fox","PG-13","Comedy"
"2443","10/7/2016","The Birth of a Nation",1e+07,15861566,16891011,"Fox Searchlight","R","Drama"
"2444","5/30/2003","Wrong Turn",1e+07,15417771,28649556,"20th Century Fox","R","Horror"
"2445","5/16/1980","The Long Riders",1e+07,15198912,15198912,"United Artists",NA,"Action"
"2446","3/12/1999","The Corruptor",1e+07,15164492,15164492,"New Line","R","Action"
"2447","8/14/2009","The Goods: Live Hard, Sell Hard",1e+07,15122676,15297318,"Paramount Vantage","R","Comedy"
"2448","11/23/2011","My Week with Marilyn",1e+07,14597405,34240572,"Weinstein Co.","R","Drama"
"2449","12/25/2014","Big Eyes",1e+07,14482031,27317872,"Weinstein Co.","PG-13","Drama"
"2450","6/28/2002","Hey Arnold! The Movie",1e+07,13684949,13684949,"Paramount Pictures","PG","Adventure"
"2451","3/14/1997","Love Jones",1e+07,12554569,12554569,"New Line","R","Drama"
"2452","1/20/2006","End of the Spear",1e+07,11748661,11924041,"M Power Releasing","PG-13","Drama"
"2453","10/20/2000","The Legend of Drunken Master",1e+07,11546543,11546543,"Miramax","R","Action"
"2454","7/23/1999","Drop Dead Gorgeous",1e+07,10571408,10571408,"New Line","PG-13","Comedy"
"2455","4/3/1998","The Spanish Prisoner",1e+07,10162034,13835130,"Sony Pictures Classics","PG","Drama"
"2456","6/11/1999","Le Violon rouge",1e+07,10019109,10019109,"Lionsgate","R","Drama"
"2457","7/9/2004","Sleepover",1e+07,9408183,9408183,"MGM","PG","Adventure"
"2458","1/25/2013","Movie 43",1e+07,8840453,31164747,"Relativity","R","Comedy"
"2459","5/21/2010","MacGruber",1e+07,8525600,8629895,"Universal","R","Comedy"
"2460","7/18/2003","Dirty Pretty Things",1e+07,8112414,14156753,"Miramax","R","Drama"
"2461","3/14/2014","Bad Words",1e+07,7779614,7843145,"Focus Features","R","Comedy"
"2462","3/27/2015","While We're Young",1e+07,7582065,14956484,"A24","R","Comedy"
"2463","2/1/2008","Over Her Dead Body",1e+07,7570127,21596074,"New Line","PG-13","Comedy"
"2464","10/24/2001","Bones",1e+07,7316658,8378853,"New Line","R","Horror"
"2465","2/11/2011","Cedar Rapids",1e+07,6861102,7862131,"Fox Searchlight","R","Comedy"
"2466","11/30/2012","The Collection",1e+07,6810754,8890094,"LD Distribution","R","Horror"
"2467","10/30/1998","American History X",1e+07,6719864,6719864,"New Line","R","Drama"
"2468","1/16/2004","Teacher's Pet: The Movie",1e+07,6491969,6491969,"Walt Disney","PG","Adventure"
"2469","10/15/1999","The Straight Story",1e+07,6197866,6197866,"Walt Disney","G","Drama"
"2470","5/3/2002","Deuces Wild",1e+07,6044618,6244618,"MGM","R","Drama"
"2471","3/28/2008","Run, Fatboy, Run",1e+07,6003262,33512260,"Picturehouse","PG-13","Comedy"
"2472","12/18/1981","Heartbeeps",1e+07,6e+06,6e+06,"Universal",NA,"Comedy"
"2473","3/20/2015","Danny Collins",1e+07,5637066,7501132,"Bleecker Street","R","Comedy"
"2474","7/4/2007","Rescue Dawn",1e+07,5490423,7037886,"MGM","PG-13","Action"
"2475","4/5/2000","Black and White",1e+07,5241315,5241315,"Sony Pictures","R","Drama"
"2476","6/18/2010","Io sono l’amore",1e+07,5005465,15121528,"Magnolia Pictures","R","Drama"
"2477","6/15/2018","Gotti",1e+07,4286367,6089100,"Vertical Entertainment","R","Drama"
"2478","3/16/2012","Jeff, Who Lives at Home",1e+07,4269426,4708127,"Paramount Vantage","R","Comedy"
"2479","9/30/2016","Denial",1e+07,4073448,9263940,"Bleecker Street","PG-13","Drama"
"2480","3/30/2016","Everybody Wants Some",1e+07,3400278,5437126,"Paramount Pictures","R","Comedy"
"2481","10/4/1996","Crash",1e+07,3357324,3357324,"Fine Line","R","Drama"
"2482","10/12/2012","Atlas Shrugged: Part II",1e+07,3336053,3336053,"Atlas Distribution","PG-13","Drama"
"2483","2/4/1994","Romeo Is Bleeding",1e+07,3275585,3275585,"Gramercy","R","Drama"
"2484","10/8/1999","The Limey",1e+07,3193102,6030047,"Artisan","R","Drama"
"2485","11/14/2014","Rosewater",1e+07,3128941,3185717,"Open Road","R","Drama"
"2486","12/22/2000","The House of Mirth",1e+07,3041803,5149131,"Sony Pictures Classics","PG","Drama"
"2487","5/1/1987","Malone",1e+07,3e+06,3e+06,"Orion Pictures","R","Action"
"2488","6/2/2006","Peaceful Warrior",1e+07,2893666,3260179,"Universal","PG-13","Drama"
"2489","9/9/2011","Bucky Larson: Born to Be a Star",1e+07,2529395,2529395,"Sony Pictures","R","Comedy"
"2490","10/6/2000","Bamboozled",1e+07,2185266,2373937,"New Line","R","Drama"
"2491","5/3/2013","The Iceman",1e+07,1930282,3623609,"Alchemy","R","Drama"
"2492","4/21/2017","Free Fire",1e+07,1799322,3793739,"A24","R","Action"
"2493","6/24/2011","A Better Life",1e+07,1759252,1884251,"Summit Entertainment","PG-13","Drama"
"2494","2/28/2003","Spider",1e+07,1641788,1641788,"Sony Pictures Classics","R","Drama"
"2495","12/27/2002","Nicholas Nickleby",1e+07,1562800,1562800,"United Artists","PG","Drama"
"2496","3/21/2014","50 to 1",1e+07,1069454,1069454,"Ten Furlongs","PG-13","Drama"
"2497","5/2/2003","Owning Mahowny",1e+07,1011054,1011054,"Sony Pictures Classics","R","Drama"
"2498","10/19/2007","The Ten Commandments",1e+07,952820,1051907,"Rocky Mountain Pict…","PG","Adventure"
"2499","9/7/2007","The Brothers Solomon",1e+07,900926,900926,"Sony Pictures","R","Comedy"
"2500","4/4/2008","My Blueberry Nights",1e+07,866778,22198996,"Weinstein Co.","PG-13","Drama"
"2501","8/6/1999","Illuminata",1e+07,836641,836641,"Artisan","R","Drama"
"2502","1/20/2012","Coriolanus",1e+07,749641,2179623,"Weinstein Co.","R","Drama"
"2503","10/4/2013","Parkland",1e+07,641439,1616353,"Exclusive Releasing","PG-13","Drama"
"2504","4/2/2004","Shaolin Soccer",1e+07,488872,42776032,"Miramax","PG-13","Comedy"
"2505","9/14/2007","King of California",1e+07,268461,1165102,"First Look","PG-13","Drama"
"2506","10/24/1997","Rien ne va plus",1e+07,245359,5045359,"New Yorker",NA,"Comedy"
"2507","8/14/1998","La femme de chambre du Titanic",1e+07,244465,244465,"MGM",NA,"Drama"
"2508","12/17/2004","Imaginary Heroes",1e+07,228524,290875,"Sony Pictures Classics","R","Drama"
"2509","5/3/2013","Cinco de Mayo, La Batalla",1e+07,173472,173472,"Lionsgate","R","Action"
"2510","10/29/2010","Welcome to the Rileys",1e+07,152857,355919,"Samuel Goldwyn Films","R","Drama"
"2511","9/9/2016","Kicks",1e+07,150191,150191,"Focus World","R","Adventure"
"2512","6/1/2012","High School",1e+07,139034,248133,"Anchor Bay Entertai…","R","Comedy"
"2513","5/18/2007","Severance",1e+07,137221,5950002,"Magnolia Pictures","R","Comedy"
"2514","4/23/2010","Joheunnom nabbeunnom isanghannom",1e+07,128486,42226657,NA,"R","Action"
"2515","8/26/1994","Police Academy 7: Mission to Moscow",1e+07,126247,126247,"Warner Bros.","PG","Comedy"
"2516","2/19/2010","Blood Done Sign My Name",1e+07,109383,109383,"Paladin","PG-13","Drama"
"2517","10/23/2009","Motherhood",1e+07,93388,723388,"Freestyle Releasing","PG-13","Comedy"
"2518","10/15/2004","Eulogy",1e+07,70527,70527,"Artisan","R","Comedy"
"2519","11/7/2014","Elsa & Fred",1e+07,67657,109144,"Alchemy","PG-13","Comedy"
"2520","8/28/2009","The Open Road",1e+07,19716,19716,"Anchor Bay Entertai…","PG-13","Drama"
"2521","7/10/2015","Strangerland",1e+07,17472,161097,"Alchemy","R","Drama"
"2522","10/16/2009","Janky Promoters",1e+07,9069,9069,"Third Rail","R","Comedy"
"2523","12/21/2007","Blonde Ambition",1e+07,6422,1537479,"First Look","PG-13","Comedy"
"2524","10/8/2010","It's a Wonderful Afterlife",1e+07,0,1642939,"UTV Communications","PG-13","Comedy"
"2525","8/21/2009","Fifty Dead Men Walking",1e+07,0,997921,"Phase 4 Films","R","Drama"
"2526","9/26/2014","Plastic",1e+07,0,575371,"ARC Entertainment","R","Action"
"2527","2/2/2007","Partition",1e+07,0,0,NA,NA,"Drama"
"2528","4/13/2012","Detention",1e+07,0,0,"Samuel Goldwyn Films","R","Comedy"
"2529","2/7/2014","Nurse 3D",1e+07,0,0,"Lionsgate","R","Horror"
"2530","7/21/2015","American Heist",1e+07,0,0,"Lionsgate","R","Action"
"2531","12/19/2012","Amour",9700000,6738954,36787044,"Sony Pictures Classics","PG-13","Drama"
"2532","4/28/2006","The Lost City",9600000,2484186,5256839,"Magnolia Pictures","R","Drama"
"2533","1/12/2000","Next Friday",9500000,57176582,59675307,"New Line","R","Comedy"
"2534","6/13/1967","You Only Live Twice",9500000,43100000,111600000,"MGM","PG","Action"
"2535","6/10/1988","Poltergeist III",9500000,14114000,14114000,"MGM","PG-13","Horror"
"2536","3/19/2010","The Runaways",9500000,3573673,5278632,"Apparition","R","Drama"
"2537","10/30/2009","Gentlemen Broncos",9500000,115155,119955,"Fox Searchlight","PG-13","Comedy"
"2538","11/7/1963","It's a Mad Mad Mad Mad World",9400000,46300000,6e+07,NA,NA,"Comedy"
"2539","11/3/2006","Volver",9400000,12899867,87226613,"Sony Pictures Classics","R","Comedy"
"2540","8/7/1981","Heavy Metal",9300000,19571091,19571091,"Sony Pictures",NA,"Adventure"
"2541","12/29/1995","Richard III",9200000,2684904,4199334,"MGM","R","Drama"
"2542","5/25/1979","Alien",9e+06,80930630,203630630,"20th Century Fox","R","Horror"
"2543","12/29/1965","Thunderball",9e+06,63600000,141200000,"MGM","PG","Action"
"2544","11/6/1996","Set It Off",9e+06,36049108,36049108,"New Line","R","Drama"
"2545","10/21/2016","Ouija: Origin of Evil",9e+06,35144505,81831866,"Universal","PG-13","Horror"
"2546","11/9/1988","Child's Play",9e+06,33244684,44196684,"United Artists","R","Horror"
"2547","1/30/2015","Black or White",9e+06,21571189,21971021,"Relativity","PG-13","Drama"
"2548","7/30/2004","Harold & Kumar Go to White Castle",9e+06,18225165,18225165,"New Line","R","Comedy"
"2549","10/13/2000","The Contender",9e+06,17804273,17804273,"Dreamworks SKG","R","Drama"
"2550","2/18/2000","Boiler Room",9e+06,16963963,28773637,"New Line","R","Drama"
"2551","12/5/2006","Black Christmas",9e+06,16235738,16235738,"MGM","R","Horror"
"2552","11/18/2016","The Edge of Seventeen",9e+06,14431633,19096003,"STX Entertainment","R","Drama"
"2553","12/2/2016","Jackie",9e+06,13960394,29345883,"Fox Searchlight","R","Drama"
"2554","3/16/1984","The Ice Pirates",9e+06,13075390,13075390,"MGM/UA Classics",NA,"Comedy"
"2555","11/8/1989","Henry V",9e+06,10161099,10176701,"Goldwyn Entertainment","PG-13","Action"
"2556","11/4/2016","Loving",9e+06,7710234,12898064,"Focus Features","PG-13","Drama"
"2557","11/28/2007","The Savages",9e+06,6623082,10642023,"Fox Searchlight","R","Drama"
"2558","4/16/2003","Chasing Papi",9e+06,6126237,12657377,"20th Century Fox","PG","Comedy"
"2559","9/8/2000","The Way of the Gun",9e+06,6047856,13061935,"Artisan","R","Action"
"2560","8/22/2008","Hamlet 2",9e+06,4886216,4934104,"Focus Features","R","Comedy"
"2561","9/13/2002","Igby Goes Down",9e+06,4777465,4777465,"MGM","R","Comedy"
"2562","4/29/1994","PCU",9e+06,4333569,4333569,"20th Century Fox","PG-13","Comedy"
"2563","3/9/2007","The Ultimate Gift",9e+06,3438735,3438735,"Film Foundry","PG","Drama"
"2564","9/29/2000","Beautiful",9e+06,3134509,3134509,"Destination Films","PG-13","Drama"
"2565","6/1/2007","Gracie",9e+06,2956339,3922043,"Picturehouse","PG-13","Drama"
"2566","8/26/2016","Greater",9e+06,2000093,2000093,"Hammond Entertainment","PG","Drama"
"2567","8/18/2006","Trust the Man",9e+06,1530535,2548378,"Fox Searchlight","R","Comedy"
"2568","5/14/2010","Princess Kaiulani",9e+06,883887,883887,"Roadside Attractions","PG","Drama"
"2569","5/6/2016","Dheepan",9e+06,248795,7704357,"Sundance Selects","R","Drama"
"2570","10/25/2002","All or Nothing",9e+06,184255,184255,"MGM","R","Drama"
"2571","11/22/2006","Opal Dream",9e+06,14443,14443,"Strand","PG","Drama"
"2572","5/8/2015","Skin Trade",9e+06,1242,1242,"Magnolia Pictures","R","Action"
"2573","1/20/2015","Veronika Decides to Die",9e+06,0,2243,"Entertainment One","R","Drama"
"2574","10/10/1968","Barbarella",9e+06,0,0,"Paramount Pictures","PG","Adventure"
"2575","2/26/2011","Ultramarines",8900000,0,0,"Codex Pictures","R","Action"
"2576","9/26/1986","Crocodile Dundee",8800000,174803506,328203506,"Paramount Pictures","PG-13","Comedy"
"2577","11/18/2016","Manchester by the Sea",8500000,47695371,77733867,"Roadside Attractions","R","Drama"
"2578","12/16/2009","Crazy Heart",8500000,39471742,47417251,"Fox Searchlight","R","Drama"
"2579","8/15/2008","Star Wars: The Clone Wars",8500000,35161554,68695443,"Warner Bros.","PG","Adventure"
"2580","2/20/2015","The DUFF",8500000,34030343,43528634,"CBS Films","PG-13","Comedy"
"2581","7/31/1987","The Lost Boys",8500000,32222567,32222567,"Warner Bros.","R","Horror"
"2582","11/7/1979","The Rose",8500000,29200000,29200000,"20th Century Fox",NA,"Drama"
"2583","3/1/1991","Haakon Haakonsen",8500000,15024232,15024232,"Walt Disney","PG","Adventure"
"2584","3/9/2007","The Namesake",8500000,13610521,20288774,"Fox Searchlight","PG-13","Drama"
"2585","2/27/2004","Club Dread",8500000,5001655,7573551,"Fox Searchlight","R","Comedy"
"2586","9/17/2009","Bright Star",8500000,4444637,17220091,"Apparition","PG","Drama"
"2587","6/13/2014","The Rover",8500000,1109199,3180252,"A24","R","Drama"
"2588","11/1/2016","A.C.O.R.N.S.: Operation Crackdown",8500000,0,1353287,"Viva Entertainment","PG","Adventure"
"2589","2/12/2010","My Name is Khan",8470000,4046336,42355526,"Fox Searchlight","PG-13","Drama"
"2590","6/4/1999","Limbo",8300000,2160710,2598224,"Sony Pictures","R","Drama"
"2591","4/16/2010","The City of Your Final Destination",8300000,493296,1353296,"Hyde Park Films","PG-13","Drama"
"2592","11/24/2006","Kurtlar vadisi - Irak",8300000,0,24906717,NA,NA,"Action"
"2593","10/14/1994","Pulp Fiction",8e+06,107928762,212928762,"Miramax","R","Drama"
"2594","6/22/1984","The Karate Kid",8e+06,90815558,90815558,"Sony Pictures","PG","Action"
"2595","6/22/1979","The Muppet Movie",8e+06,76657000,76657000,"Associated Film Dis…","G","Adventure"
"2596","3/9/1984","Splash",8e+06,62599495,62599495,"Walt Disney","PG","Comedy"
"2597","7/26/2006","Little Miss Sunshine",8e+06,59891098,100642353,"Fox Searchlight","R","Comedy"
"2598","9/17/2010","Easy A",8e+06,58401464,76200721,"Sony Pictures","PG-13","Comedy"
"2599","8/8/1986","Stand by Me",8e+06,52287414,52287414,"Sony Pictures","R","Drama"
"2600","6/27/2003","28 Days Later…",8e+06,45064915,82955633,"Fox Searchlight","R","Horror"
"2601","6/22/1979","Escape from Alcatraz",8e+06,4.3e+07,4.3e+07,"Paramount Pictures","PG","Drama"
"2602","1/30/2004","You Got Served",8e+06,40066497,50811858,"Sony Pictures","PG-13","Drama"
"2603","3/13/1992","Howards End",8e+06,26124872,26317943,"Sony Pictures Classics","PG","Drama"
"2604","3/21/2008","Shutter",8e+06,25928550,47782426,"20th Century Fox","PG-13","Horror"
"2605","12/25/1981","Modern Problems",8e+06,24474312,24474312,"20th Century Fox",NA,"Comedy"
"2606","12/18/1969","On Her Majesty's Secret Service",8e+06,22800000,8.2e+07,"MGM","PG","Action"
"2607","11/10/1982","Creepshow",8e+06,20036244,20036244,"Warner Bros.",NA,"Horror"
"2608","4/28/2006","Akeelah and the Bee",8e+06,18848430,18959424,"Lionsgate","PG","Drama"
"2609","10/14/1994","Wes Craven's New Nightmare",8e+06,18090181,18090181,"New Line","R","Horror"
"2610","10/1/1999","Drive Me Crazy",8e+06,17843379,22591451,"20th Century Fox","PG-13","Comedy"
"2611","9/18/2013","Enough Said",8e+06,17550872,25621449,"Fox Searchlight","PG-13","Comedy"
"2612","1/16/1998","Half Baked",8e+06,17394881,17394881,"Universal","R","Comedy"
"2613","6/27/2014","Begin Again",8e+06,16170632,68838736,"Weinstein Co.","R","Drama"
"2614","5/19/2006","See No Evil",8e+06,15032800,18828036,"Lionsgate","R","Horror"
"2615","8/7/2002","The Good Girl",8e+06,14018296,16585503,"Fox Searchlight","R","Drama"
"2616","4/29/2011","Prom",8e+06,10130219,10763183,"Walt Disney","PG","Comedy"
"2617","4/22/1994","The Inkwell",8e+06,8864699,8864699,"Walt Disney","R","Comedy"
"2618","12/29/2000","Shadow of the Vampire",8e+06,8279017,8279017,"Lionsgate","R","Drama"
"2619","6/12/2015","Me and Earl and the Dying Girl",8e+06,6758416,9266180,"Fox Searchlight","PG-13","Drama"
"2620","10/8/2010","It's Kind of a Funny Story",8e+06,6363628,6632950,"Focus Features","PG-13","Comedy"
"2621","5/12/2000","Held Up",8e+06,4714090,4714090,"Trimark","PG-13","Comedy"
"2622","12/30/2015","Anomalisa",8e+06,3759286,5538273,"Paramount Pictures","R","Adventure"
"2623","12/23/2005","Caché",8e+06,3647381,19891331,"Sony Pictures Classics","R","Drama"
"2624","12/29/2010","Another Year",8e+06,3205706,20005613,"Sony Pictures Classics","PG-13","Drama"
"2625","1/1/1991","Showdown in Little Tokyo",8e+06,2275557,2275557,NA,"R","Action"
"2626","11/19/2010","Made in Dagenham",8e+06,1095369,15644196,"Sony Pictures Classics","R","Drama"
"2627","1/24/1997","Prefontaine",8e+06,590817,590817,"Walt Disney","PG-13","Drama"
"2628","10/28/1983","The Wicked Lady",8e+06,589308,589308,"Cannon","R","Drama"
"2629","5/11/2007","Brooklyn Rules",8e+06,458232,458232,"Lionsgate","R","Drama"
"2630","10/24/2003","The Singing Detective",8e+06,336456,524747,"Paramount Pictures","R","Comedy"
"2631","6/15/2007","Fido",8e+06,298110,456814,"Lionsgate","R","Horror"
"2632","9/16/2011","Restless",8e+06,163753,2772511,"Sony Pictures Classics","PG-13","Drama"
"2633","5/18/2007","The Wendell Baker Story",8e+06,127188,127188,"ThinkFilm","PG-13","Comedy"
"2634","10/29/2010","Wild Target",8e+06,109338,5314194,"Freestyle Releasing","PG-13","Comedy"
"2635","5/22/2015","Aloft",8e+06,53086,53086,"Sony Pictures Classics","R","Drama"
"2636","10/14/2011","Fireflies in the Garden",8e+06,36884,3587191,NA,"R","Drama"
"2637","4/27/2001","Akira",8e+06,19585,19585,NA,"R","Action"
"2638","9/29/2017","Don Gato, el inicio de la pandilla",8e+06,0,4598934,"Viva Entertainment","PG","Adventure"
"2639","11/30/2007","Maurice Richard",8e+06,0,0,"Palm Pictures","PG","Drama"
"2640","5/6/2016","Code of Honor",8e+06,0,0,"Lionsgate Premiere","R","Action"
"2641","2/23/1990","The Blood of Heroes",7700000,882290,882290,"New Line",NA,"Action"
"2642","12/13/1989","Driving Miss Daisy",7500000,106593296,106593296,"Warner Bros.","PG","Drama"
"2643","9/26/1997","Soul Food",7500000,43492389,43492389,"20th Century Fox","R","Comedy"
"2644","2/23/1996","Rumble in the Bronx",7500000,32281907,36238752,"New Line","R","Action"
"2645","6/8/2007","Hostel: Part II",7500000,17544812,33606409,"Lionsgate","R","Horror"
"2646","10/9/2009","An Education",7500000,12574914,29652736,"Sony Pictures Classics","PG-13","Drama"
"2647","9/4/2009","Extract",7500000,10823158,10849158,"Miramax","R","Comedy"
"2648","10/21/2005","Shopgirl",7500000,10284523,11758418,"Walt Disney","R","Drama"
"2649","3/9/1984","The Hotel New Hampshire",7500000,5142858,5142858,"Orion Pictures",NA,"Drama"
"2650","3/8/2002","Men with Brooms",7500000,4239767,4239767,"Artisan","R","Comedy"
"2651","2/22/2008","Witless Protection",7500000,4151836,4151836,"Lionsgate","PG-13","Comedy"
"2652","11/24/2004","The Work and the Glory",7500000,3347647,3347647,"Excel Entertainment","PG","Drama"
"2653","12/21/2011","Albert Nobbs",7500000,3014696,8539003,"Roadside Attractions","R","Drama"
"2654","6/24/2016","The Neon Demon",7500000,1333124,3559803,"Broad Green Pictures","R","Horror"
"2655","7/24/2003","Masked and Anonymous",7500000,533344,555335,"Sony Pictures","PG-13","Drama"
"2656","4/13/2018","Borg vs McEnroe",7500000,231346,3257078,"Neon","R","Drama"
"2657","5/15/2015","Pound of Flesh",7500000,0,0,"Entertainment One","R","Action"
"2658","12/25/2007","Persepolis",7300000,4443403,25397460,"Sony Pictures Classics","PG-13","Drama"
"2659","5/27/2011","Die Welle",7250000,0,35122948,"IFC Films",NA,"Drama"
"2660","10/15/1999","The Omega Code",7200000,12610552,12678312,"Providence Entertai…","PG-13","Action"
"2661","12/5/2007","Juno",7e+06,143495265,231450102,"Fox Searchlight","PG-13","Comedy"
"2662","3/15/1972","The Godfather",7e+06,134966411,268500000,"Paramount Pictures","R","Drama"
"2663","6/29/2012","Magic Mike",7e+06,113721571,170549753,"Warner Bros.","R","Comedy"
"2664","4/15/1983","Flashdance",7e+06,90463574,201463574,"Paramount Pictures","R","Drama"
"2665","3/16/2018","I Can Only Imagine",7e+06,83482352,85430011,"Roadside Attractions","PG","Drama"
"2666","11/12/1993","The Piano",7e+06,40157856,40168957,"Miramax","R","Drama"
"2667","6/27/1973","Live and Let Die",7e+06,35400000,161800000,"MGM","PG","Action"
"2668","1/12/2000","My Dog Skip",7e+06,34099640,35795319,"Warner Bros.","PG","Drama"
"2669","1/24/2003","Darkness Falls",7e+06,32539681,47289758,"Sony Pictures","PG-13","Horror"
"2670","10/7/2005","Good Night, and Good Luck",7e+06,31501218,56586901,"Warner Independent","PG","Drama"
"2671","9/30/2005","Capote",7e+06,28750530,49924079,"Sony Pictures Classics","R","Drama"
"2672","3/29/1974","The Great Gatsby",7e+06,26533200,26533200,NA,NA,"Drama"
"2673","8/25/1995","Desperado",7e+06,25532388,25532388,"Sony Pictures","R","Action"
"2674","4/11/2001","Kingdom Come",7e+06,23247539,23393939,"Fox Searchlight","PG","Comedy"
"2675","12/20/1974","The Man with the Golden Gun",7e+06,2.1e+07,97600000,"MGM","PG","Action"
"2676","2/12/1988","Action Jackson",7e+06,20257000,20257000,"Lorimar Motion Pict…","R","Action"
"2677","5/13/1983","Breathless",7e+06,19910002,19910002,"Orion Pictures","R","Action"
"2678","6/19/2015","Dope",7e+06,17506470,18190831,"Open Road","R","Comedy"
"2679","7/22/2005","The Devil's Rejects",7e+06,17044981,20940428,"Lionsgate","R","Horror"
"2680","1/17/2014","Devil's Due",7e+06,15821461,36146087,"20th Century Fox","R","Horror"
"2681","3/22/1996","Flirting with Disaster",7e+06,14853474,16149180,"Miramax","R","Comedy"
"2682","11/14/2014","Beyond the Lights",7e+06,14618727,14618727,"Relativity","PG-13","Drama"
"2683","7/31/1992","Buffy the Vampire Slayer",7e+06,14231669,14231669,"20th Century Fox","PG-13","Horror"
"2684","8/25/1999","In Too Deep",7e+06,14026509,15471229,"Gramercy","R","Drama"
"2685","4/11/2003","House of 1,000 Corpses",7e+06,12634962,17005466,"Lionsgate","R","Horror"
"2686","10/11/1985","Silver Bullet",7e+06,10803211,10803211,"Paramount Pictures","R","Horror"
"2687","10/10/2003","House of the Dead",7e+06,10199354,13767816,"Artisan","R","Horror"
"2688","10/2/2009","A Serious Man",7e+06,9228788,30360570,"Focus Features","R","Comedy"
"2689","12/11/2009","A Single Man",7e+06,9176000,28142379,"Weinstein Co.","R","Drama"
"2690","1/10/1991","Warlock",7e+06,8824553,8824553,"Trimark","R","Horror"
"2691","8/12/1988","The Last Temptation of Christ",7e+06,8373585,8373585,"Universal",NA,"Drama"
"2692","6/18/2010","Cyrus",7e+06,7468936,10062896,"Fox Searchlight","R","Comedy"
"2693","9/1/1999","Outside Providence",7e+06,7309628,7824358,"Miramax","R","Comedy"
"2694","11/29/2002","Rabbit-Proof Fence",7e+06,6199600,16866928,"Miramax","PG","Drama"
"2695","7/27/2007","Who's Your Caddy?",7e+06,5694308,5694308,"MGM","PG-13","Comedy"
"2696","5/1/1992","Split Second",7e+06,5430822,5430822,"InterStar Releasing","R","Action"
"2697","12/14/2001","The Other Side of Heaven",7e+06,4720371,4720371,"Excel Entertainment","PG","Drama"
"2698","9/28/1990","Dark Angel",7e+06,4372561,4372561,"Triumph Releasing",NA,"Action"
"2699","6/27/1986","American Anthem",7e+06,3571624,3571624,"Sony Pictures","PG-13","Drama"
"2700","5/2/2008","Redbelt",7e+06,2344847,2667084,"Sony Pictures Classics","R","Action"
"2701","8/27/1999","A Dog of Flanders",7e+06,2165637,2165637,"Warner Bros.","PG","Drama"
"2702","10/18/2002","Auto Focus",7e+06,2062066,2703821,"Sony Pictures Classics","R","Drama"
"2703","10/21/2011","The Mighty Macs",7e+06,1891936,1891936,"Quaker Media","G","Drama"
"2704","12/22/2010","Somewhere",7e+06,1785645,17023121,"Focus Features","R","Drama"
"2705","1/13/2012","We Need to Talk About Kevin",7e+06,1738692,10765283,"Oscilloscope Pictures","R","Drama"
"2706","2/2/2007","Factory Girl",7e+06,1661464,1661464,"MGM","R","Drama"
"2707","11/15/2013","The Christmas Candle",7e+06,1632000,1933829,"Echolight Studios","PG","Adventure"
"2708","9/25/2009","I Hope They Serve Beer in Hell",7e+06,1429299,1429453,"Freestyle Releasing","R","Comedy"
"2709","4/8/1983","Losin' It",7e+06,1246141,1246141,NA,"R","Comedy"
"2710","5/7/2010","Mother and Child",7e+06,1110509,6537179,"Sony Pictures Classics","R","Drama"
"2711","7/12/1996","Les Visiteurs",7e+06,659000,98754000,"Miramax","R","Comedy"
"2712","10/2/2015","Freeheld",7e+06,546201,1732228,"Lionsgate","PG-13","Drama"
"2713","4/2/2014","Dom Hemingway",7e+06,523511,1857458,"Fox Searchlight","R","Comedy"
"2714","7/30/2010","The Extra Man",7e+06,453377,492108,"Magnolia Pictures","R","Comedy"
"2715","5/13/2011","Hesher",7e+06,382946,382946,"Wrekin Hill Enterta…","R","Drama"
"2716","3/13/1998","Chairman of the Board",7e+06,306715,306715,"Trimark","PG-13","Comedy"
"2717","2/14/2003","Gerry",7e+06,254683,719699,"ThinkFilm","R","Drama"
"2718","1/21/2000","The Boondock Saints",7e+06,30471,411874,"Indican Pictures","R","Action"
"2719","12/12/2008","The Kings of Appletown",7e+06,0,0,NA,"PG","Action"
"2720","9/21/2012","House at the End of the Street",6900000,31611916,44103982,"Relativity","PG-13","Horror"
"2721","9/24/1993","Dazed and Confused",6900000,7950889,7950889,"Universal","R","Comedy"
"2722","9/17/2010","Incendies",6800000,6857096,16038343,"Sony Pictures Classics","R","Drama"
"2723","8/5/2005","The Chumscrubber",6800000,49526,49526,"Picturehouse","R","Drama"
"2724","9/19/2008","Tropa de Elite",6537890,8744,14319195,"IFC Films","R","Action"
"2725","10/3/2014","Annabelle",6500000,84273813,256862920,"Warner Bros.","R","Horror"
"2726","7/12/1991","Boyz n the Hood",6500000,56190094,56215095,"Sony Pictures","R","Drama"
"2727","7/24/1987","La Bamba",6500000,54215416,54215416,"Sony Pictures","PG-13","Drama"
"2728","5/22/1981","The Four Seasons",6500000,42488161,42488161,"Universal","PG","Comedy"
"2729","4/2/1993","The Adventures of Huck Finn",6500000,24103594,24103594,"Walt Disney","PG","Adventure"
"2730","4/7/2006","Friends with Money",6500000,13368437,18110152,"Sony Pictures Classics","R","Comedy"
"2731","10/22/1999","Bats",6500000,10155691,10155691,"Sony Pictures","R","Horror"
"2732","3/7/2003","Nowhere in Africa",6500000,6173485,6173485,"Zeitgeist","R","Drama"
"2733","5/31/2013","The East",6500000,2274649,3027956,"Fox Searchlight","PG-13","Drama"
"2734","11/13/2009","The Messenger",6500000,1109660,1744952,"Oscilloscope Pictures","R","Drama"
"2735","7/23/2004","A Home at the End of the World",6500000,1029017,1033810,"Warner Independent","R","Drama"
"2736","10/26/1984","The Terminator",6400000,38019031,78019031,"Orion Pictures","R","Action"
"2737","2/27/2004","Good Bye, Lenin!",6400000,4063859,79384539,"Sony Pictures Classics","R","Comedy"
"2738","10/10/2007","Control",6400000,871577,8902141,"Weinstein Co.","R","Drama"
"2739","10/9/2009","The Damned United",6400000,449865,4199874,"Sony Pictures Classics","R","Drama"
"2740","2/22/2008","Die Fälscher",6250000,5488570,20199663,"Sony Pictures Classics","R","Drama"
"2741","1/15/1988","Return of the Living Dead Part II",6200000,9205924,9205924,"Lorimar Motion Pict…",NA,"Horror"
"2742","10/20/1995","Mallrats",6100000,2108367,2108367,"Gramercy","R","Comedy"
"2743","12/19/1986","Platoon",6e+06,137963328,137978395,"Orion Pictures","R","Drama"
"2744","9/19/1980","Ordinary People",6e+06,52302978,52302978,"Paramount Pictures","R","Drama"
"2745","10/17/1956","Around the World in 80 Days",6e+06,4.2e+07,4.2e+07,"United Artists","PG","Adventure"
"2746","7/25/1980","Caddyshack",6e+06,39846344,39846344,"Warner Bros.",NA,"Comedy"
"2747","3/23/2001","The Brothers",6e+06,27457409,27958191,"Sony Pictures","R","Comedy"
"2748","12/17/2008","The Wrestler",6e+06,26238243,46634275,"Fox Searchlight","R","Drama"
"2749","6/30/1989","Do the Right Thing",6e+06,26004026,26004026,"Universal","R","Comedy"
"2750","7/10/1981","Escape from New York",6e+06,25244700,25244700,"Avco Embassy","R","Action"
"2751","7/16/1999","The Wood",6e+06,25059640,25059640,"Paramount Pictures","R","Comedy"
"2752","8/16/1995","The Usual Suspects",6e+06,23341568,34449356,"Gramercy","R","Drama"
"2753","4/5/2002","National Lampoon’s Van Wilder",6e+06,21305259,39241323,"Artisan","R","Comedy"
"2754","9/27/2000","Best in Show",6e+06,18621249,20695413,"Warner Bros.","PG-13","Comedy"
"2755","9/27/2006","The Last King of Scotland",6e+06,17606684,49155371,"Fox Searchlight","R","Drama"
"2756","4/16/2003","A Mighty Wind",6e+06,17583468,18504539,"Warner Bros.","PG-13","Comedy"
"2757","2/12/1988","School Daze",6e+06,14545844,14545844,"Sony Pictures","R","Drama"
"2758","8/8/2007","Daddy Day Camp",6e+06,13235267,18209872,"Sony Pictures","PG","Comedy"
"2759","10/21/1988","Mystic Pizza",6e+06,12793213,12793213,"Samuel Goldwyn Films","R","Comedy"
"2760","3/20/1998","Mr. Nice Guy",6e+06,12716953,31716953,"New Line","PG-13","Action"
"2761","4/24/1998","Sliding Doors",6e+06,11911200,58809149,"Miramax","PG-13","Drama"
"2762","5/24/1995","Tales from the Hood",6e+06,11784569,11784569,"Savoy","R","Horror"
"2763","9/7/2012","The Words",6e+06,11494838,16369708,"CBS Films","PG-13","Drama"
"2764","12/15/2000","Pollock",6e+06,8596914,10557291,"Sony Pictures Classics","R","Drama"
"2765","3/19/2010","City Island",6e+06,6671283,8173486,"Anchor Bay Entertai…","PG-13","Comedy"
"2766","3/16/2012","Casa de mi Padre",6e+06,5909483,8446952,"Lionsgate","R","Comedy"
"2767","7/29/2011","The Guard",6e+06,5359774,21197454,"Sony Pictures Classics","R","Comedy"
"2768","8/29/2008","College",6e+06,4694491,6176114,"MGM","R","Comedy"
"2769","9/22/2006","La science des rêves",6e+06,4670644,15137932,"Warner Independent","R","Comedy"
"2770","3/13/2009","Miss March",6e+06,4543320,4713059,"20th Century Fox","R","Comedy"
"2771","7/18/2014","Wish I Was Here",6e+06,3591299,6591365,"Focus Features","R","Comedy"
"2772","12/21/2006","Venus",6e+06,3347411,7818479,"Miramax","R","Drama"
"2773","3/14/2014","Veronica Mars",6e+06,3322127,3485383,"Warner Bros.","PG-13","Drama"
"2774","10/31/2003","Shattered Glass",6e+06,2207975,3456602,"Lionsgate","PG-13","Drama"
"2775","7/3/2008","The Wackness",6e+06,2077046,3330012,"Sony Pictures Classics","R","Comedy"
"2776","11/16/2001","Novocaine",6e+06,2025238,2522928,"Artisan","R","Comedy"
"2777","7/15/2011","Snow Flower and the Secret Fan",6e+06,1348205,11348205,"Fox Searchlight","PG-13","Drama"
"2778","12/7/2001","The Business of Strangers",6e+06,1030920,1290920,"IFC Films","R","Drama"
"2779","4/29/2011","Jûsan-nin no shikaku",6e+06,802778,18727440,"Magnolia Pictures","R","Action"
"2780","3/25/2011","The 5th Quarter",6e+06,408159,408159,"Rocky Mountain Pict…","PG","Drama"
"2781","2/2/1979","The First Great Train Robbery",6e+06,391942,391942,"United Artists",NA,"Action"
"2782","11/10/2006","Come Early Morning",6e+06,119452,119452,"IDP/Goldwyn/Roadside","R","Drama"
"2783","4/2/2010","The Greatest",6e+06,115862,117796,"Paladin","R","Drama"
"2784","9/5/2008","Surfer, Dude",6e+06,36497,36497,"Anchor Bay Entertai…","R","Comedy"
"2785","1/23/2015","Song One",6e+06,32251,437089,"Cinedigm/Film Arcade","PG-13","Drama"
"2786","2/4/1983","Videodrome",5952000,2120439,2120439,"Universal",NA,"Horror"
"2787","3/18/2011","Winter in Wartime",5800000,542860,9662214,"Sony Pictures Classics","R","Drama"
"2788","9/8/2006","Tom yum goong",5700000,12044087,43044087,"Weinstein Co.","R","Action"
"2789","9/7/2012","The Inbetweeners",5700000,35955,86051320,"Wrekin Hill Enterta…","R","Comedy"
"2790","3/12/2003","Bend it Like Beckham",5600000,32543449,74566042,"Fox Searchlight","PG-13","Drama"
"2791","9/1/2006","Crossover",5600000,7009668,7009668,"Sony Pictures","PG-13","Drama"
"2792","6/21/2002","Sunshine State",5600000,3064356,3281898,"Sony Pictures Classics","PG-13","Drama"
"2793","12/25/1973","The Sting",5500000,159616327,159616327,"Universal","PG","Comedy"
"2794","9/25/1981","Chariots of Fire",5500000,61558162,61865947,"Warner Bros.","PG","Drama"
"2795","2/25/2005","Diary of a Mad Black Woman",5500000,50406346,50458356,"Lionsgate","PG-13","Drama"
"2796","11/22/1996","Shine",5500000,35811509,36672493,"Fine Line","PG-13","Drama"
"2797","9/28/2018","Hell Fest",5500000,10751601,12527795,"CBS Films","R","Horror"
"2798","6/6/2003","Mambo Italiano",5500000,9282750,12399772,"Goldwyn Entertainment","R","Comedy"
"2799","7/20/2001","Ghost World",5500000,6217849,8761608,"MGM","R","Comedy"
"2800","12/14/2001","Iris",5500000,5580479,15155021,"Miramax","R","Drama"
"2801","11/26/2004","Les Choristes",5500000,3629758,83529758,"Miramax","PG-13","Drama"
"2802","10/3/2003","Wonderland",5500000,1060512,1060512,"Lionsgate","R","Drama"
"2803","4/1/2011","Haevnen",5500000,1008098,15867314,"Sony Pictures Classics","R","Drama"
"2804","5/17/2002","Harvard Man",5500000,56653,56653,NA,"R","Drama"
"2805","7/15/2011","Salvation Boulevard",5500000,28468,28468,"IFC Films","R","Comedy"
"2806","8/3/2007","The Ten",5250000,769726,786677,"ThinkFilm","R","Comedy"
"2807","2/24/2017","The Girl with all the Gifts",5250000,0,4802379,"Saban Films","R","Horror"
"2808","8/5/2005","Saint Ralph",5200000,795126,1695126,"Samuel Goldwyn Films","PG-13","Comedy"
"2809","4/22/2011","Dum Maaro Dum",5200000,564489,11633427,"Fox Searchlight","R","Drama"
"2810","10/3/1980","Somewhere in Time",5100000,9709597,9709597,"Universal",NA,"Drama"
"2811","2/24/2017","Get Out",5e+06,176040665,255363701,"Universal","R","Horror"
"2812","1/20/2017","Split",5e+06,138141585,278306227,"Universal","PG-13","Horror"
"2813","10/21/2011","Paranormal Activity 3",5e+06,104028807,202053386,"Paramount Pictures","R","Horror"
"2814","10/28/2005","Saw II",5e+06,87025093,152925093,"Lionsgate","R","Horror"
"2815","9/13/2013","Insidious Chapter 2",5e+06,83586447,161921515,"FilmDistrict","PG-13","Horror"
"2816","7/22/2016","Lights Out",5e+06,67268835,148868835,"Warner Bros.","PG-13","Horror"
"2817","10/25/2002","Jackass: The Movie",5e+06,64282312,79282312,"Paramount Pictures","R","Comedy"
"2818","10/13/2017","Happy Death Day",5e+06,55683845,125013000,"Universal","PG-13","Horror"
"2819","10/19/2012","Paranormal Activity 4",5e+06,53900335,140619520,"Paramount Pictures","R","Horror"
"2820","10/24/2014","Ouija",5e+06,50856010,103300632,"Universal","PG-13","Horror"
"2821","8/30/2013","No se Aceptan Devoluciones",5e+06,44467206,100486616,"Lionsgate","PG-13","Comedy"
"2822","5/16/1975","The Return of the Pink Panther",5e+06,41833347,41833347,"MGM",NA,"Comedy"
"2823","12/24/2003","Monster",5e+06,34469210,64240813,"Newmarket Films","R","Drama"
"2824","12/23/1954","20,000 Leagues Under the Sea",5e+06,28200000,28200000,"Walt Disney","G","Adventure"
"2825","4/11/2014","Oculus",5e+06,27695246,44115496,"Relativity","R","Horror"
"2826","11/1/2013","Dallas Buyers Club",5e+06,27298285,60611845,"Focus Features","R","Drama"
"2827","2/27/2015","The Lazarus Effect",5e+06,25801570,35341814,"Lionsgate","PG-13","Horror"
"2828","3/16/2001","Memento",5e+06,25544867,39723096,"Newmarket Films","R","Drama"
"2829","8/26/2011","Our Idiot Brother",5e+06,24814830,25861249,"Weinstein Co.","R","Comedy"
"2830","7/21/2006","Clerks II",5e+06,24148068,27342246,"MGM","R","Comedy"
"2831","4/8/1998","The Players Club",5e+06,23047939,23047939,"New Line","R","Drama"
"2832","10/13/2000","Billy Elliot",5e+06,21995263,109263464,"Focus Features","PG-13","Drama"
"2833","7/5/2013","The Way Way Back",5e+06,21502690,26853810,"Fox Searchlight","PG-13","Comedy"
"2834","4/1/2016","God’s Not Dead 2",5e+06,20773069,23562057,"Pure Flix Entertain…","PG","Drama"
"2835","12/17/1997","The Apostle",5e+06,20733485,21277770,"October Films","PG-13","Drama"
"2836","11/3/1982","The Man From Snowy River",5e+06,20659423,20659423,"20th Century Fox",NA,"Drama"
"2837","10/23/1991","House Party 2",5e+06,19438638,19438638,"New Line","R","Comedy"
"2838","3/26/1999","Doug's 1st Movie",5e+06,19421271,19421271,"Walt Disney","G","Adventure"
"2839","9/18/1981","Mommie Dearest",5e+06,19032000,25032000,"Paramount Pictures",NA,"Drama"
"2840","1/16/2015","Still Alice",5e+06,18656400,41699612,"Sony Pictures Classics","PG-13","Drama"
"2841","3/23/2018","Paul, Apostle of Christ",5e+06,17547999,23389835,"Sony Pictures","PG-13","Drama"
"2842","10/10/2014","Addicted",5e+06,17390770,17499242,"Lionsgate","R","Drama"
"2843","8/31/2001","O (Othello)",5e+06,16017403,16017403,"Lionsgate","R","Drama"
"2844","11/7/1997","Eve's Bayou",5e+06,14843425,14843425,"Trimark","R","Drama"
"2845","4/10/1981","Nighthawks",5e+06,14600000,19600000,"Universal",NA,"Action"
"2846","6/9/2017","It Comes at Night",5e+06,13985117,19720203,"A24","R","Horror"
"2847","3/15/2002","Y Tu Mamá También",5e+06,13649881,33649881,"IFC Films","R","Drama"
"2848","9/24/2004","Shaun of the Dead",5e+06,13542874,30332385,"Focus/Rogue Pictures","R","Comedy"
"2849","6/21/1996","Lone Star",5e+06,12961389,12961389,"Sony Pictures Classics","R","Drama"
"2850","3/27/1986","April Fool's Day",5e+06,12947763,12947763,"Paramount Pictures",NA,"Horror"
"2851","4/2/1982","Diner",5e+06,12592907,12592907,"MGM",NA,"Comedy"
"2852","3/3/2017","Before I Fall",5e+06,12241072,18945682,"Open Road","PG-13","Drama"
"2853","4/15/1983","Lone Wolf McQuade",5e+06,12232628,12232628,"Orion Pictures",NA,"Action"
"2854","3/13/2009","Sunshine Cleaning",5e+06,12062558,17329337,"Overture Films","R","Comedy"
"2855","1/29/2016","Fifty Shades of Black",5e+06,11686940,22113075,"Open Road","R","Comedy"
"2856","8/20/1982","The Beastmaster",5e+06,10751126,10751126,"MGM",NA,"Action"
"2857","1/9/2009","Not Easily Broken",5e+06,10572742,10732909,"Sony Pictures","PG-13","Drama"
"2858","5/9/2014","Moms’ Night Out",5e+06,10429707,10537341,"Sony Pictures","PG","Adventure"
"2859","3/17/2017","The Belko Experiment",5e+06,10166820,10803839,"BH Tilt","R","Horror"
"2860","10/6/2000","Digimon: The Movie",5e+06,9628751,16628751,"20th Century Fox","PG","Adventure"
"2861","5/28/2004","Saved!",5e+06,8886160,10206551,"MGM","PG-13","Comedy"
"2862","5/9/2003","Les invasions barbares",5e+06,8460000,25913869,"Miramax","R","Drama"
"2863","12/22/1978","Force 10 from Navarone",5e+06,7100000,7100000,"American Internatio…",NA,"Action"
"2864","4/27/2001","The Forsaken",5e+06,6755271,6755271,"Sony Pictures","R","Horror"
"2865","7/21/1989","UHF",5e+06,6157157,6157157,NA,NA,"Comedy"
"2866","1/6/2006","Grandma’s Boy",5e+06,6090172,6590172,"20th Century Fox","R","Comedy"
"2867","8/14/1998","Slums of Beverly Hills",5e+06,5502773,5502773,"Fox Searchlight","R","Comedy"
"2868","7/13/2001","Made",5e+06,5308707,5476060,"Artisan","R","Comedy"
"2869","9/11/2015","90 Minutes in Heaven",5e+06,4816142,4816142,"Samuel Goldwyn Films","PG-13","Drama"
"2870","5/12/2006","Keeping Up with the Steins",5e+06,4339241,4414753,"Miramax","PG-13","Comedy"
"2871","10/10/1997","The Sweet Hereafter",5e+06,4306697,7951247,"Fine Line","R","Drama"
"2872","8/6/2008","Bottle Shock",5e+06,4078607,4815890,"Freestyle Releasing","PG-13","Drama"
"2873","2/25/2011","Des Hommes et Des Dieux",5e+06,3954651,46263525,"Sony Pictures Classics","PG-13","Drama"
"2874","8/27/1982","Jekyll and Hyde... Together Again",5e+06,3707583,3707583,"Universal",NA,"Comedy"
"2875","3/3/2017","Table 19",5e+06,3614896,4620399,"Fox Searchlight","PG-13","Comedy"
"2876","4/15/2016","Green Room",5e+06,3220371,3807503,"A24","R","Horror"
"2877","11/16/1994","Heavenly Creatures",5e+06,3046086,5438120,"Miramax","R","Drama"
"2878","5/13/2011","Everything Must Go",5e+06,2712131,2821010,"Roadside Attractions","PG","Drama"
"2879","12/17/2010","Rabbit Hole",5e+06,2229058,6205034,"Lionsgate","PG-13","Drama"
"2880","12/28/2016","Paterson",5e+06,2141423,10761547,"Bleecker Street","R","Comedy"
"2881","1/30/1998","Zero Effect",5e+06,2080693,2080693,"Sony Pictures","R","Comedy"
"2882","9/12/2014","Atlas Shrugged: Who Is John Galt?",5e+06,851690,851690,"Atlas Distribution","PG-13","Drama"
"2883","8/29/2003","Party Monster",5e+06,742898,894030,"ContentFilm","R","Comedy"
"2884","2/21/1996","Bottle Rocket",5e+06,407488,407488,"Sony Pictures","R","Action"
"2885","8/16/2013","Ain't Them Bodies Saints",5e+06,391611,1075009,"IFC Films","R","Drama"
"2886","1/17/1997","Albino Alligator",5e+06,353480,353480,"Miramax","R","Drama"
"2887","9/26/2014","Jimi: All is By My Side",5e+06,340911,927074,"XLrator Media","R","Drama"
"2888","9/10/2010","Lovely, Still",5e+06,127564,282687,"Monterey Media","PG","Drama"
"2889","11/16/2007","Redacted",5e+06,65388,861325,"Magnolia Pictures","R","Drama"
"2890","10/17/2014","Rudderless",5e+06,56001,567219,"Samuel Goldwyn Films","R","Drama"
"2891","8/14/2009","Grace",5e+06,8297,8297,"Anchor Bay Entertai…","R","Horror"
"2892","9/2/2016","Yoga Hosers",5e+06,0,2199,"Invincible Pictures","PG-13","Adventure"
"2893","11/21/2014","Reach Me",5e+06,0,0,"Alchemy","R","Drama"
"2894","8/18/2014","Henry & Me",5e+06,0,0,"Distrib Films","PG","Adventure"
"2895","1/23/2015","Mommy",4900000,3498695,17536004,"Roadside Attractions","R","Drama"
"2896","11/20/1996","Sling Blade",4833610,24475416,34175000,"Miramax","R","Drama"
"2897","1/6/2006","Hostel",4800000,47326473,82241110,"Lionsgate","R","Horror"
"2898","9/30/2011","Take Shelter",4750000,1728953,4972016,"Sony Pictures Classics","R","Drama"
"2899","8/22/1986","The Texas Chainsaw Massacre 2",4700000,8025872,8025872,"Cannon",NA,"Horror"
"2900","4/22/1988","Lady in White",4700000,1705139,1705139,"New Century Vista F…",NA,"Horror"
"2901","3/4/2005","Dear Frankie",4600000,1340891,3099369,"Miramax","PG-13","Drama"
"2902","12/29/2004","The Assassination of Richard Nixon",4600000,708776,4880143,"ThinkFilm","R","Drama"
"2903","6/24/2011","Le nom des gens",4600000,514237,9261711,"Music Box Films","R","Comedy"
"2904","3/23/1984","Police Academy",4500000,81198894,81198894,"Warner Bros.","R","Comedy"
"2905","6/20/1980","The Blue Lagoon",4500000,47923795,47923795,"Universal","R","Drama"
"2906","8/13/1982","Fast Times at Ridgemont High",4500000,27092880,27092880,"Universal",NA,"Comedy"
"2907","9/28/1996","Secrets & Lies",4500000,13417292,13417292,"October Films","R","Drama"
"2908","12/19/2002","25th Hour",4500000,13084595,25344490,"Walt Disney","R","Drama"
"2909","9/13/1985","After Hours",4500000,10609321,10609321,"Warner Bros.",NA,"Comedy"
"2910","10/24/2008","Låt den rätte komma in",4500000,2122085,12247682,"Magnolia Pictures","R","Horror"
"2911","2/12/1999","Tango",4500000,1687311,5428387,"Sony Pictures Classics","PG-13","Drama"
"2912","4/23/1986","Salvador",4500000,1500000,1500000,"Hemdale",NA,"Drama"
"2913","10/26/2001","Donnie Darko",4500000,1480006,7510877,"Newmarket Films","R","Drama"
"2914","9/2/2011","Salvando al Soldado Perez",4500000,1400726,9330465,"Lionsgate","PG-13","Action"
"2915","3/27/1998","Karakter",4500000,713413,713413,"Sony Pictures Classics","R","Drama"
"2916","10/7/2011","Blackthorn",4500000,200558,1217307,"Magnolia Pictures","R","Adventure"
"2917","5/8/2015","Maggie",4500000,187112,664346,"Roadside Attractions","PG-13","Drama"
"2918","4/18/2003","Lilja 4-ever",4500000,181655,4556982,"Newmarket Films","R","Drama"
"2919","4/9/2010","After.Life",4500000,108596,2481925,NA,"R","Horror"
"2920","3/1/2013","The Sweeney",4500000,26345,8000366,"Entertainment One","R","Action"
"2921","9/4/2014","Falcon Rising",4500000,11774,11774,"Freestyle Releasing","R","Adventure"
"2922","12/1/2017","Daisy Winters",4500000,0,0,"Hannover House","PG-13","Drama"
"2923","11/19/1975","One Flew Over the Cuckoo's Nest",4400000,108981275,108997629,"MGM","R","Drama"
"2924","6/25/1976","Silent Movie",4400000,36145695,36145695,"20th Century Fox",NA,"Comedy"
"2925","6/6/2003","Whale Rider",4300000,20779666,39374600,"Newmarket Films","PG-13","Drama"
"2926","6/13/2001","Sexy Beast",4300000,6946056,10158355,"Fox Searchlight","R","Drama"
"2927","10/19/1990","Night of the Living Dead",4200000,5835247,5835247,"Sony Pictures","R","Horror"
"2928","8/13/2010","Animal Kingdom",4200000,1044039,8078683,"Sony Pictures Classics","R","Drama"
"2929","10/21/2011","Cargo",4200000,0,313230,"Persona Films","R","Drama"
"2930","3/6/1998","Love and Death on Long Island",4030000,2542264,2542264,"Lionsgate","PG-13","Drama"
"2931","3/19/1982","Porky's",4e+06,109492484,109492484,"20th Century Fox","R","Comedy"
"2932","2/5/1953","Peter Pan",4e+06,87400000,87400000,"RKO Radio Pictures","PG","Adventure"
"2933","11/25/1992","The Crying Game",4e+06,62546695,62546695,"Miramax","R","Drama"
"2934","9/12/2003","Lost in Translation",4e+06,44585453,117085297,"Focus Features","R","Drama"
"2935","4/20/1977","Annie Hall",4e+06,38251425,38251425,"United Artists",NA,"Comedy"
"2936","10/27/1995","Leaving Las Vegas",4e+06,31983777,49800000,"MGM","R","Drama"
"2937","12/26/2001","Monster's Ball",4e+06,31273922,43766463,"Lionsgate","R","Drama"
"2938","7/11/2014","Boyhood",4e+06,25379975,57273049,"IFC Films","R","Drama"
"2939","7/9/2010","The Kids Are All Right",4e+06,20811365,36275469,"Focus Features","R","Comedy"
"2940","8/17/1979","Life of Brian",4e+06,20008693,20008693,"Warner Bros.","R","Comedy"
"2941","4/18/2014","A Haunted House 2",4e+06,17329487,21206861,"Open Road","R","Comedy"
"2942","3/1/2013","The Last Exorcism Part II",4e+06,15179303,25448707,"CBS Films","PG-13","Horror"
"2943","12/17/1974","The Front Page",4e+06,1.5e+07,1.5e+07,"Universal",NA,"Comedy"
"2944","8/16/1985","The Return of the Living Dead",4e+06,14237880,14237880,"Orion Pictures","R","Horror"
"2945","8/4/2000","Saving Grace",4e+06,12178602,27786849,"Fine Line","R","Comedy"
"2946","8/8/1963","The Great Escape",4e+06,11744471,11744471,"MGM",NA,"Drama"
"2947","5/13/2016","The Darkness",4e+06,10753574,10898293,"High Top Releasing","PG-13","Horror"
"2948","11/14/2001","The Wash",4e+06,10097096,10097096,"Lionsgate","R","Comedy"
"2949","3/1/2000","3 Strikes",4e+06,9821335,9821335,"MGM","R","Comedy"
"2950","4/11/2008","The Visitor",4e+06,9427026,19174817,"Overture Films","PG-13","Comedy"
"2951","11/26/2003","The Cooler",4e+06,8291572,11131455,"Lionsgate","R","Drama"
"2952","8/4/2006","The Night Listener",4e+06,7836393,10770993,"Miramax","R","Drama"
"2953","2/3/1995","The Jerky Boys",4e+06,7555256,7555256,"Walt Disney","R","Comedy"
"2954","12/28/2007","El orfanato",4e+06,7159147,79250193,"Picturehouse","R","Horror"
"2955","5/25/2007","Bug",4e+06,7006708,8302995,"Lionsgate","R","Drama"
"2956","11/17/2006","Let's Go to Prison",4e+06,4630045,4630045,"Universal","R","Comedy"
"2957","12/25/1995","Four Rooms",4e+06,4301000,4301000,"Miramax","R","Comedy"
"2958","9/20/2002","Secretary",4e+06,4046737,9413956,"Lionsgate","R","Drama"
"2959","12/1/1988","Talk Radio",4e+06,3468572,3468572,"Universal",NA,"Drama"
"2960","1/31/1997","Waiting for Guffman",4e+06,2922988,2922988,"Sony Pictures Classics","R","Comedy"
"2961","9/10/1999","Love Stinks",4e+06,2793776,2793776,"Independent Artists","R","Comedy"
"2962","9/16/2005","Thumbsucker",4e+06,1328679,1919197,"Sony Pictures Classics","R","Comedy"
"2963","9/23/2011","Red State",4e+06,1065429,1983596,"Smodshow Productions","R","Horror"
"2964","9/30/2005","MirrorMask",4e+06,864959,973613,"Samuel Goldwyn Films","PG","Drama"
"2965","2/28/2003","Poolhall Junkies",4e+06,563711,563711,"Gold Circle Films","R","Drama"
"2966","3/7/2014","The Face of Love",4e+06,385069,1158877,"IFC Films","PG-13","Drama"
"2967","4/11/2014","Joe",4e+06,373375,373375,"Roadside Attractions","R","Drama"
"2968","3/4/1988","Prison",4e+06,354704,354704,"Empire Pictures",NA,"Horror"
"2969","5/8/2009","Adoration",4e+06,294244,384244,"Sony Pictures Classics","R","Drama"
"2970","1/28/2000","The Big Tease",4e+06,185577,185577,"Warner Bros.","R","Comedy"
"2971","4/10/2015","Desert Dancer",4e+06,155271,338109,"Relativity","PG-13","Drama"
"2972","1/30/2015","Guten Tag, Ramon",4e+06,154356,4854356,"20th Century Fox","PG-13","Drama"
"2973","6/19/2015","Manglehorn",4e+06,132270,797439,"IFC Films","PG-13","Drama"
"2974","4/2/2010","Tau ming chong",4e+06,129078,38899792,NA,"R","Action"
"2975","4/2/2010","Tau ming chong",4e+06,129078,38899792,NA,"R","Action"
"2976","4/1/2011","Trust",4e+06,120016,120016,"Alchemy","R","Drama"
"2977","12/22/2000","An Everlasting Piece",4e+06,75078,75078,"Dreamworks SKG","R","Comedy"
"2978","4/22/2011","Stake Land",4e+06,33245,679482,"IFC Films","R","Horror"
"2979","12/27/2002","Sonny",4e+06,17639,17639,NA,"R","Drama"
"2980","11/18/2011","Another Happy Day",4e+06,9120,978527,"Phase 4 Films","R","Drama"
"2981","6/1/2012","The Loved Ones",4e+06,0,12302,"Paramount Pictures","R","Horror"
"2982","7/11/2014","The Perfect Wave",4e+06,0,0,NA,"PG","Drama"
"2983","12/15/1939","Gone with the Wind",3900000,198680470,390525192,"MGM","G","Drama"
"2984","1/1/1976","Network",3800000,23689877,23689877,"MGM",NA,"Drama"
"2985","1/14/2011","Down for Life",3800000,41914,41914,"B.D. Fox Independent","R","Drama"
"2986","4/30/2010","The Good Heart",3800000,20930,340930,"Magnolia Pictures","R","Drama"
"2987","10/5/2018","Hevi reissu",3800000,9079,9079,"Music Box Films",NA,"Comedy"
"2988","8/11/2006","Casa de Areia",3750000,539285,1178175,"Sony Pictures Classics","R","Drama"
"2989","2/19/2010","Defendor",3750000,44462,44462,NA,"R","Drama"
"2990","11/21/2006","The History Boys",3700000,2730296,13447998,"Fox Searchlight","R","Comedy"
"2991","7/4/1980","Airplane!",3500000,83453539,83453539,"Paramount Pictures","PG","Comedy"
"2992","8/13/1997","The Full Monty",3500000,45950122,261249383,"Fox Searchlight","R","Comedy"
"2993","5/26/1993","Menace II Society",3500000,27731527,27731527,"New Line","R","Action"
"2994","4/26/1995","Friday",3500000,27467564,27936778,"New Line","R","Comedy"
"2995","2/19/2016","The Witch",3500000,25138705,40454520,"A24","R","Horror"
"2996","12/6/2002","Empire",3500000,17504595,18495444,"Universal","R","Drama"
"2997","1/19/2018","Forever My Girl",3500000,16376066,16376066,"Roadside Attractions","PG","Drama"
"2998","5/1/1987","Creepshow 2",3500000,1.4e+07,1.4e+07,"New World","R","Horror"
"2999","1/1/1967","In Cold Blood",3500000,1.3e+07,13007551,NA,"R","Drama"
"3000","5/27/1998","I Got the Hook-Up!",3500000,10317779,10317779,"Miramax","R","Comedy"
"3001","11/6/1998","Gods and Monsters",3500000,6451628,6451628,"Lionsgate","R","Drama"
"3002","3/13/1987","Evil Dead II",3500000,5923044,5923044,"Rosebud Releasing",NA,"Horror"
"3003","6/29/2001","Pootie Tang",3500000,3293258,3293258,"Paramount Pictures","PG-13","Comedy"
"3004","12/2/2016","Believe",3500000,890303,890303,"Smith Global Media","PG","Drama"
"3005","4/19/2000","La otra conquista",3500000,886410,886410,"Hombre de Oro","R","Drama"
"3006","9/30/2016","American Honey",3500000,663247,2611750,"A24","R","Drama"
"3007","6/10/2011","Trolljegeren",3500000,253444,5706638,"Magnet Pictures","PG","Horror"
"3008","9/14/2007","Ira and Abby",3500000,221096,221096,"Magnolia Pictures","R","Comedy"
"3009","1/8/2016","The Masked Saint",3500000,182695,182695,"Freestyle Releasing","PG-13","Action"
"3010","2/17/2006","Winter Passing",3500000,107492,113783,"Focus Features","R","Drama"
"3011","3/25/2005","D.E.B.S.",3500000,96793,96793,"Samuel Goldwyn Films","PG-13","Action"
"3012","9/17/1999","Taxman",3500000,9871,9871,NA,NA,"Comedy"
"3013","5/17/2013","Jagten",3450000,687185,18309793,"Magnolia Pictures","R","Drama"
"3014","10/21/2011","Margin Call",3400000,5353586,20433227,"Roadside Attractions","R","Drama"
"3015","9/26/2008","Choke",3400000,2926565,4124277,"Fox Searchlight","R","Comedy"
"3016","2/16/1956","Carousel",3380000,0,3220,"20th Century Fox",NA,"Drama"
"3017","10/10/2014","Whiplash",3300000,13092006,37825230,"Sony Pictures Classics","R","Drama"
"3018","10/26/2007","Bella",3300000,8093373,12405473,"Roadside Attractions","PG-13","Drama"
"3019","1/17/2003","Cidade de Deus",3300000,7563397,32059295,"Miramax","R","Drama"
"3020","11/18/1983","A Christmas Story",3250000,20605209,20605209,"MGM","PG","Comedy"
"3021","8/20/1982","Class of 1984",3250000,6965361,6965361,"United Film Distrib…",NA,"Drama"
"3022","7/16/2004","Maria Full of Grace",3200000,6529624,14441158,"New Line","R","Drama"
"3023","6/3/2011","Beginners",3200000,5790894,14314407,"Focus Features","R","Drama"
"3024","4/22/2016","The Meddler",3200000,4267219,5526942,"Sony Pictures Classics","PG-13","Comedy"
"3025","7/29/2009","Adam",3200000,2283291,2834485,"Fox Searchlight","PG-13","Drama"
"3026","9/22/2006","Feast",3200000,56131,690872,"Weinstein/Dimension","R","Horror"
"3027","1/1/1946","It’s a Wonderful Life",3180000,6600000,10768908,NA,"PG","Drama"
"3028","7/19/1996","Trainspotting",3100000,16501785,71558971,"Miramax","R","Drama"
"3029","7/28/1978","National Lampoon's Animal House",3e+06,141600000,141600000,"Universal","R","Comedy"
"3030","10/20/2010","Paranormal Activity 2",3e+06,84752907,177512032,"Paramount Pictures","R","Horror"
"3031","8/28/2015","War Room",3e+06,67790117,73975239,"Sony Pictures","PG","Drama"
"3032","12/22/1964","Goldfinger",3e+06,51100000,124900000,"MGM","PG","Action"
"3033","12/18/1957","The Bridge on the River Kwai",3e+06,33300000,33300000,"Sony Pictures","PG","Drama"
"3034","1/1/1978","Coming Home",3e+06,32653000,32653000,"United Artists",NA,"Drama"
"3035","11/20/1998","Waking Ned Devine",3e+06,24793251,55193251,"20th Century Fox","PG","Comedy"
"3036","8/1/1997","Air Bud",3e+06,24646936,27788649,"Walt Disney","PG","Adventure"
"3037","6/10/1975","Love and Death",3e+06,20123742,20123742,"MGM",NA,"Comedy"
"3038","4/6/2001","Pokemon 3: The Movie",3e+06,17052128,68452128,"Warner Bros.","G","Adventure"
"3039","4/27/1990","Spaced Invaders",3e+06,1.5e+07,1.5e+07,"Walt Disney","PG","Adventure"
"3040","10/25/1985","Krush Groove",3e+06,11052713,11052713,"Warner Bros.","R","Drama"
"3041","5/8/2009","Next Day Air",3e+06,10027047,10172519,"Summit Entertainment","R","Comedy"
"3042","11/4/1998","Belly",3e+06,9639390,9639390,"Artisan","R","Drama"
"3043","5/12/1999","Trippin’",3e+06,9017070,9017070,"October Films","R","Comedy"
"3044","5/24/2013","Before Midnight",3e+06,8110621,23251930,"Sony Pictures Classics","R","Drama"
"3045","11/20/1987","Teen Wolf Too",3e+06,7888000,7888000,"Atlantic",NA,"Comedy"
"3046","7/31/2009","The Collector",3e+06,7712114,10473836,"Freestyle Releasing","R","Horror"
"3047","7/8/1988","Phantasm II",3e+06,7282851,7282851,"Universal",NA,"Horror"
"3048","10/1/2004","Woman Thou Art Loosed",3e+06,6879730,6879730,"Magnolia Pictures","R","Drama"
"3049","10/18/2002","Real Women Have Curves",3e+06,5853194,7777790,"Newmarket Films","PG-13","Comedy"
"3050","4/28/2006","Water",3e+06,5529144,11322573,"Fox Searchlight","PG-13","Drama"
"3051","7/22/2016","Don’t Think Twice",3e+06,4417983,4417983,"Film Arcade","R","Comedy"
"3052","6/24/2016","Swiss Army Man",3e+06,4210454,5837111,"A24","R","Drama"
"3053","4/14/2000","East is East",3e+06,4170647,30438635,"Miramax","R","Comedy"
"3054","9/1/2000","Whipped",3e+06,4142507,4142507,"Destination Films","R","Comedy"
"3055","2/28/1997","Kama Sutra",3e+06,4109095,4109095,"Trimark","R","Drama"
"3056","5/17/2013","Frances Ha",3e+06,4067398,11262769,"IFC Films","R","Comedy"
"3057","9/24/1993","Warlock: The Armageddon",3e+06,3902679,3902679,"Trimark","R","Horror"
"3058","9/13/1978","Days of Heaven",3e+06,3446749,3660880,"Paramount Pictures",NA,"Drama"
"3059","4/22/2016","Compadres",3e+06,3127773,7445044,"Lionsgate","R","Comedy"
"3060","8/9/1996","Basquiat",3e+06,2962051,2962051,"Miramax","R","Drama"
"3061","2/24/2006","Tsotsi",3e+06,2912606,11537539,"Miramax","R","Drama"
"3062","4/9/2010","Letters to God",3e+06,2848587,3237452,"Vivendi Entertainment","PG","Drama"
"3063","9/19/2014","Tusk",3e+06,1821983,1857688,"A24","R","Horror"
"3064","10/24/2003","Elephant",3e+06,1266955,10051516,"Fine Line","R","Drama"
"3065","9/7/2012","Bachelorette",3e+06,446770,12577401,"Weinstein Co.","R","Comedy"
"3066","9/5/2008","Everybody Wants to Be Italian",3e+06,351416,351416,"Roadside Attractions","R","Comedy"
"3067","9/9/2011","Creature",3e+06,331000,331000,"The Bubble Factory","R","Horror"
"3068","8/23/1996","Freeway",3e+06,295493,295493,"Roxie Releasing","R","Comedy"
"3069","2/12/1993","Dead Alive",3e+06,242623,242623,"Trimark",NA,"Horror"
"3070","10/1/2010","Chain Letter",3e+06,205842,1022453,"New Films Cinema","R","Horror"
"3071","3/2/2012","Tim and Eric's Billion Dollar Movie",3e+06,201436,223652,"Magnet Pictures","R","Comedy"
"3072","11/9/2007","Holly",3e+06,163069,163069,"Priority Films","R","Drama"
"3073","3/21/2008","The Grand",3e+06,115879,115879,"Anchor Bay Entertai…","R","Comedy"
"3074","3/17/2006","Sommersturm",3e+06,95204,95204,"Regent Releasing","R","Drama"
"3075","8/15/2014","Fort McCoy",3e+06,78948,78948,"Monterey Media","R","Drama"
"3076","8/4/1999","The Gambler",3e+06,51773,101773,NA,"R","Drama"
"3077","9/4/2015","Before We Go",3e+06,37151,483938,"Radius","PG-13","Drama"
"3078","9/9/2011","Tanner Hall",3e+06,5073,5073,"Anchor Bay Entertai…","R","Drama"
"3079","9/30/2005","My Big Fat Independent Movie",3e+06,4655,4655,"Big Fat Movies","R","Comedy"
"3080","6/27/2014","They Came Together",3e+06,0,82780,"Lionsgate","R","Comedy"
"3081","10/1/2010","Barry Munday",3e+06,0,0,"Magnolia Pictures","R","Comedy"
"3082","11/20/1998","Central do Brasil",2900000,5969553,17006158,"Sony Pictures Classics","R","Drama"
"3083","6/10/2005","High Tension",2850000,3681066,6435262,"Lionsgate","R","Horror"
"3084","12/15/1974","Young Frankenstein",2800000,86300000,86300000,"20th Century Fox","PG","Comedy"
"3085","6/25/1976","The Omen",2800000,48570885,48570885,"20th Century Fox","R","Horror"
"3086","7/22/2005","Hustle & Flow",2800000,22202809,23591783,"Paramount Vantage","R","Drama"
"3087","9/15/2006","Artie Lange's Beer League",2800000,475000,475000,"Freestyle Releasing","R","Comedy"
"3088","2/15/2008","Diary of the Dead",2750000,952620,5394447,"Weinstein Co.","R","Horror"
"3089","10/17/1979","The Black Stallion",2700000,37799643,37799643,"United Artists","G","Drama"
"3090","6/13/1997","Ulee's Gold",2700000,9054736,15600000,"Orion Pictures","R","Drama"
"3091","2/7/1974","Blazing Saddles",2600000,119500000,119500000,"Warner Bros.","R","Comedy"
"3092","5/2/2014","Ida",2600000,3827060,15298355,"Music Box Films","PG-13","Drama"
"3093","1/1/1987","Maurice",2600000,3147950,3198308,NA,"R","Drama"
"3094","12/7/2007","Timber Falls",2600000,0,71248,"Slowhand Cinema","R","Horror"
"3095","1/11/2013","A Haunted House",2500000,40041683,59922558,"Open Road","R","Comedy"
"3096","7/28/2004","Garden State",2500000,26782316,36028802,"Fox Searchlight","R","Drama"
"3097","10/4/1996","That Thing You Do!",2500000,25857416,34557416,"20th Century Fox","PG","Drama"
"3098","10/30/1981","Halloween II",2500000,25533818,25533818,"Universal",NA,"Horror"
"3099","10/22/1982","Halloween 3: Season of the Witch",2500000,14400000,14400000,"Universal",NA,"Horror"
"3100","8/2/2013","The Spectacular Now",2500000,6852971,6916951,"A24","R","Drama"
"3101","1/27/1995","Before Sunrise",2500000,5274005,5686742,"Sony Pictures","R","Drama"
"3102","6/24/2016","Hunt for the Wilderpeople",2500000,5205471,23845533,"The Orchard","PG-13","Comedy"
"3103","8/17/2012","Robot & Frank",2500000,3317468,4934356,"Samuel Goldwyn Films","PG-13","Drama"
"3104","6/16/2000","Jesus' Son",2500000,1282084,1687548,"Lionsgate","R","Drama"
"3105","5/27/2005","Saving Face",2500000,1187266,1269705,"Sony Pictures Classics","R","Comedy"
"3106","6/20/2008","Brick Lane",2500000,1094998,3838486,"Sony Pictures Classics","PG-13","Drama"
"3107","8/24/2007","Eye of the Dolphin",2500000,72210,72260,"Monterey Media","PG-13","Drama"
"3108","8/16/2013","Underdogs",2500000,35017,35017,"Freestyle Releasing","PG","Drama"
"3109","6/21/2013","Alien Uprising",2500000,0,0,"Phase 4 Films","R","Action"
"3110","5/13/2011","Go For It!",2450000,180237,182358,"Lionsgate","PG-13","Drama"
"3111","10/16/1996","Get on the Bus",2400000,5691854,5691854,"Sony Pictures","R","Drama"
"3112","9/1/2006","Idiocracy",2400000,444093,500296,"20th Century Fox","R","Comedy"
"3113","3/20/2015","Do You Believe?",2300000,12985600,14305450,"Pure Flix Entertain…","PG-13","Drama"
"3114","5/1/1998","Dancer, Texas Pop. 81",2300000,574838,574838,"Sony Pictures","PG","Comedy"
"3115","9/5/2014","Frontera",2300000,59696,59696,"Magnolia Pictures","PG-13","Drama"
"3116","8/26/2011","Redemption Road",2300000,29384,29384,"Freestyle Releasing","PG-13","Drama"
"3117","2/9/1940","Pinocchio",2289247,84300000,84300000,"Walt Disney","G","Adventure"
"3118","8/13/1982","Friday the 13th Part 3",2250000,36690067,36690067,"Paramount Pictures",NA,"Horror"
"3119","10/9/1971","The French Connection",2200000,41158757,41158757,NA,NA,"Drama"
"3120","2/9/2007","The Last Sin Eater",2200000,388390,388390,"20th Century Fox","PG-13","Drama"
"3121","7/13/2001","Bully",2100000,881824,1381824,"Lionsgate","R","Drama"
"3122","10/16/2016","Mi America",2100000,3330,3330,"Industrial House Films","R","Drama"
"3123","9/30/2011","Courageous",2e+06,34522221,35185884,"Sony Pictures","PG-13","Drama"
"3124","4/8/1964","From Russia With Love",2e+06,24800000,78900000,"MGM","PG","Action"
"3125","5/21/1982","Mad Max 2: The Road Warrior",2e+06,24600832,24600832,"Warner Bros.",NA,"Action"
"3126","8/2/1967","In the Heat of the Night",2e+06,24379978,24407647,"MGM",NA,"Drama"
"3127","12/17/1973","Sleeper",2e+06,18344729,18344729,"MGM",NA,"Comedy"
"3128","3/13/2015","It Follows",2e+06,14674077,23250755,"RADiUS-TWC","R","Horror"
"3129","3/9/2012","Silent House",2e+06,12739737,16610760,"Open Road","R","Horror"
"3130","10/8/1999","Boys Don't Cry",2e+06,11540607,20741000,"Fox Searchlight","R","Drama"
"3131","2/9/2007","Das Leben der Anderen",2e+06,11284657,81197047,"Sony Pictures Classics","R","Drama"
"3132","12/31/1986","Witchboard",2e+06,7369373,7369373,"Cinema Guild",NA,"Horror"
"3133","6/26/1998","Smoke Signals",2e+06,6719300,7756617,"Miramax","PG-13","Comedy"
"3134","6/11/2010","Winter's Bone",2e+06,6531503,16131551,"Roadside Attractions","R","Drama"
"3135","8/15/2003","American Splendor",2e+06,6003587,8685632,"Fine Line","R","Drama"
"3136","10/6/2017","The Florida Project",2e+06,5904366,11303040,"A24","R","Drama"
"3137","8/25/2017","All Saints",2e+06,5802208,5941994,"Sony Pictures","PG","Drama"
"3138","7/2/2004","Before Sunset",2e+06,5792822,11217346,"Warner Independent","R","Drama"
"3139","3/30/2001","Amores Perros",2e+06,5383834,20883834,"Lionsgate","R","Drama"
"3140","8/20/2003","Thirteen",2e+06,4601043,9505996,"Fox Searchlight","R","Drama"
"3141","6/17/2005","Me and You and Everyone We Know",2e+06,3885134,9615464,"IFC Films","R","Drama"
"3142","8/28/2015","We Are Your Friends",2e+06,3591417,10166209,"Warner Bros.","R","Drama"
"3143","11/10/2006","Harsh Times",2e+06,3337931,6225304,"MGM","R","Drama"
"3144","3/3/2000","Ghost Dog: The Way of the Samurai",2e+06,3330230,10672492,"Artisan","R","Drama"
"3145","9/18/2015","Captive",2e+06,2583301,2791973,"Paramount Pictures","PG-13","Drama"
"3146","8/2/2002","Full Frontal",2e+06,2512846,3438804,"Miramax","R","Comedy"
"3147","6/8/2018","Hearts Beat Loud",2e+06,2386254,2420962,"Gunpowder & Sky","PG-13","Drama"
"3148","1/20/2017","The Resurrection of Gavin Stone",2e+06,2303792,2303792,"High Top Releasing","PG","Comedy"
"3149","6/28/2006","Strangers with Candy",2e+06,2072645,2077844,"ThinkFilm","R","Comedy"
"3150","5/2/2008","Son of Rambow: A Home Movie",2e+06,1785505,11263263,"Paramount Vantage","PG-13","Comedy"
"3151","8/7/2015","The Diary of a Teenage Girl",2e+06,1477002,2279959,"Sony Pictures Classics","R","Drama"
"3152","4/30/1999","Get Real",2e+06,1152411,1152411,"Paramount Pictures","R","Comedy"
"3153","4/8/2011","Meek's Cutoff",2e+06,977772,1869928,"Oscilloscope Pictures","PG","Drama"
"3154","9/28/2001","Dinner Rush",2e+06,638227,1075504,"Access Motion Pictu…","R","Drama"
"3155","9/24/2010","The Virginity Hit",2e+06,636706,636706,"Sony Pictures","R","Comedy"
"3156","4/15/2005","House of D",2e+06,388532,466106,"Lionsgate","PG-13","Drama"
"3157","1/18/2008","Teeth",2e+06,347578,2350641,"Roadside Attractions","R","Comedy"
"3158","7/26/1996","Stonewall",2e+06,304602,304602,"Strand","R","Drama"
"3159","9/8/2006","Sherrybaby",2e+06,199176,759504,"IFC Films","R","Drama"
"3160","4/15/2005","It's All Gone Pete Tong",2e+06,120620,2226603,"Matson","R","Drama"
"3161","4/15/1998","24 7: Twenty Four Seven",2e+06,72544,72544,"October Films","R","Comedy"
"3162","2/3/2017","Growing up Smith",2e+06,35312,35312,"Good Deed Entertain…","PG-13","Comedy"
"3163","3/20/2009","Super Capers",2e+06,30955,30955,"Roadside Attractions","PG","Adventure"
"3164","1/1/1993","Return of the Living Dead 3",2e+06,21000,21000,NA,NA,"Horror"
"3165","2/10/2006","London",2e+06,12667,12667,"IDP/Goldwyn/Roadside","R","Drama"
"3166","10/31/2008","Eden Lake",2e+06,7321,4294373,"Third Rail","R","Horror"
"3167","6/23/2006","Say Uncle",2e+06,5361,5361,"TLA Releasing","R","Comedy"
"3168","9/9/2011","Grave Encounters",2e+06,0,2151887,"TriBeca Films",NA,"Horror"
"3169","4/28/1971","Bananas",2e+06,0,0,"MGM","PG-13","Comedy"
"3170","7/7/2007","Rockaway",2e+06,0,0,"Off-Hollywood Distr…","R","Drama"
"3171","2/8/2013","Small Apartments",2e+06,0,0,"Morocco Junction Pi…","R","Comedy"
"3172","7/8/2016","The Dog Lover",2e+06,0,0,"ESX Entertainment","PG","Drama"
"3173","10/8/2010","Nowhere Boy",1900000,1445366,7785229,"Weinstein Co.","R","Drama"
"3174","7/11/2003","Northfork",1900000,1420578,1445140,"Paramount Vantage","PG-13","Drama"
"3175","4/24/2015","Brotherly Love",1900000,478595,478595,"Freestyle Releasing","R","Drama"
"3176","6/3/2011","Submarine",1900000,467602,4581937,"Weinstein Co.","R","Comedy"
"3177","8/27/2010","The Last Exorcism",1800000,41034350,70165900,"Lionsgate","PG-13","Horror"
"3178","11/16/1976","Carrie",1800000,25878153,25878153,"United Artists",NA,"Horror"
"3179","11/9/1984","A Nightmare on Elm Street",1800000,25504513,25504513,"New Line","R","Horror"
"3180","6/27/2012","Beasts of the Southern Wild",1800000,12795746,23265132,"Fox Searchlight","PG-13","Drama"
"3181","11/15/2002","El crimen de padre Amaro",1800000,5719000,5719000,"Goldwyn Entertainment","R","Drama"
"3182","6/15/2001","Songcatcher",1800000,3050934,3050934,"Lionsgate","PG-13","Drama"
"3183","8/23/2011","Higher Ground",1800000,841056,842693,"Sony Pictures Classics","R","Drama"
"3184","10/8/2010","I Spit on Your Grave",1750000,93051,1278471,"Anchor Bay Entertai…","R","Horror"
"3185","11/23/2001","In the Bedroom",1700000,35930604,42137871,"Miramax","R","Drama"
"3186","3/19/2008","La misma luna",1700000,12590147,23271741,"Weinstein Co.","PG-13","Drama"
"3187","2/28/2014","The Lunchbox",1700000,4231500,12231500,"Sony Pictures Classics","PG","Drama"
"3188","10/4/2013","Grace Unplugged",1700000,2507159,2507159,"Roadside Attractions","PG","Drama"
"3189","10/1/1999","Happy, Texas",1700000,2039192,2891228,"Miramax","PG-13","Comedy"
"3190","12/18/2015","Saul fia",1700000,1777043,9696537,"Sony Pictures Classics","R","Drama"
"3191","6/17/2005","My Summer of Love",1700000,1000915,4727375,"Focus Features","R","Drama"
"3192","6/24/2005","Yes",1700000,396035,661221,"Sony Pictures Classics","R","Drama"
"3193","4/9/1999","Foolish",1600000,6026908,6026908,"Artisan","R","Comedy"
"3194","1/27/2006","Bubble",1600000,145382,145382,"Magnolia Pictures","R","Drama"
"3195","1/15/1999","Mississippi Mermaid",1600000,27795,2627795,"MGM","R","Drama"
"3196","11/4/2005","I Love Your Work",1600000,3264,3264,"ThinkFilm","R","Comedy"
"3197","4/1/2011","Insidious",1500000,54009150,99870886,"FilmDistrict","R","Horror"
"3198","10/21/2016","Moonlight",1500000,27854931,65322266,"A24","R","Drama"
"3199","9/12/2003","Cabin Fever",1500000,21158188,30351664,"Lionsgate","R","Horror"
"3200","9/8/1989","Kickboxer",1500000,14533681,14533681,"Cannon","R","Action"
"3201","2/26/1988","Bloodsport",1500000,11806119,11806119,"Cannon","R","Action"
"3202","10/5/2005","The Squid and the Whale",1500000,7372734,11191423,"IDP/Goldwyn/Roadside","R","Drama"
"3203","4/20/1979","Dawn of the Dead",1500000,5100000,5.5e+07,"United Film Distrib…",NA,"Horror"
"3204","9/23/1994","Exotica",1500000,5046118,5046118,"Miramax","R","Drama"
"3205","7/26/2013","The To Do List",1500000,3491669,4128828,"CBS Films","R","Comedy"
"3206","6/26/1998","Buffalo '66",1500000,2380606,2380606,"Lionsgate","R","Comedy"
"3207","3/2/1984","Repo Man",1500000,2300000,2300000,"Universal",NA,"Comedy"
"3208","10/21/2016","I’m Not Ashamed",1500000,2082980,2082980,"Pure Flix Entertain…","PG-13","Drama"
"3209","4/19/2002","Nueve Reinas",1500000,1222889,12412889,"Sony Pictures Classics","R","Drama"
"3210","4/19/2013","The Lords of Salem",1500000,1165881,1541131,"Anchor Bay Entertai…","R","Horror"
"3211","3/25/2005","The Ballad of Jack and Rose",1500000,712294,1126258,"IFC Films","R","Drama"
"3212","5/17/2002","The Believer",1500000,406035,1840248,"Sony Pictures","R","Drama"
"3213","3/7/2008","Snow Angels",1500000,402858,414404,"Warner Independent","R","Drama"
"3214","2/11/2011","MOOZ-lum",1500000,362239,372239,"Peace Film LLC","PG-13","Drama"
"3215","8/19/2011","Amigo",1500000,184705,184705,"Variance Films","R","Drama"
"3216","9/7/2007","Hatchet",1500000,175281,240396,"Anchor Bay Entertai…","R","Horror"
"3217","10/31/2008","My Name is Bruce",1500000,173066,173066,"Image Entertainment","R","Horror"
"3218","2/5/1936","Modern Times",1500000,163245,165049,"Kino International","G","Comedy"
"3219","5/11/2007","The Salon",1500000,139084,139084,"Freestyle Releasing","PG-13","Comedy"
"3220","3/22/2002","Stolen Summer",1500000,119841,119841,"Miramax","PG","Drama"
"3221","9/28/2005","Forty Shades of Blue",1500000,75828,172569,"Vitagraph Films","R","Drama"
"3222","10/9/2009","Trucker",1500000,52429,52429,"Monterey Media","R","Drama"
"3223","7/20/2018","Teefa in Trouble",1500000,0,98806,"Yash Raj Films",NA,"Action"
"3224","3/17/2006","Fetching Cody",1500000,0,0,NA,NA,"Drama"
"3225","6/3/2011","The Lion of Judah",1500000,0,0,"Rocky Mountain Pict…","PG","Adventure"
"3226","11/20/2015","Mustang",1400000,845464,5545484,"Cohen Media Group","PG-13","Drama"
"3227","4/29/2005","The Holy Girl",1400000,304124,1261792,"Fine Line","R","Drama"
"3228","10/9/1998","Festen",1300000,1647780,1647780,"October Films","R","Comedy"
"3229","10/11/1996","Trees Lounge",1300000,749741,749741,"Orion Classics","R","Drama"
"3230","3/23/2007","Journey from the Fall",1300000,635305,635305,"Imaginasian","R","Drama"
"3231","5/5/2000","The Basket",1300000,609042,609042,"MGM","PG","Drama"
"3232","3/15/1985","Def-Con 4",1300000,210904,210904,"New World",NA,"Action"
"3233","4/30/1981","Friday the 13th Part 2",1250000,21722776,21722776,"Paramount Pictures",NA,"Horror"
"3234","8/31/1984","C.H.U.D.",1250000,4700000,4700000,"New World",NA,"Horror"
"3235","4/19/2013","Filly Brown",1250000,2850357,2940411,"Lionsgate","R","Drama"
"3236","10/29/2004","Saw",1200000,55968727,103880027,"Lionsgate","R","Horror"
"3237","8/4/1989","Sex, Lies, and Videotape",1200000,24741667,36741667,"Miramax","R","Drama"
"3238","2/15/2002","Super Troopers",1200000,18492362,23046142,"Fox Searchlight","R","Comedy"
"3239","2/22/2002","Monsoon Wedding",1200000,13876974,27025600,"USA Films","R","Comedy"
"3240","11/10/2000","You Can Count on Me",1200000,9180275,10827356,"Paramount Vantage","R","Drama"
"3241","4/19/2013","Home Run",1200000,2859955,2859955,"Samuel Goldwyn Films","PG-13","Drama"
"3242","7/7/2000","But I'm a Cheerleader",1200000,2205627,2509344,"Lionsgate","R","Comedy"
"3243","4/13/2012","Blue Like Jazz",1200000,595018,595018,"Roadside Attractions","PG-13","Comedy"
"3244","8/28/2015","Que Horas Ela Volta?",1200000,376976,3247411,"Oscilloscope Pictures","R","Drama"
"3245","11/19/1982","Q",1200000,255000,255000,"United Film Distrib…",NA,"Horror"
"3246","6/18/2004","Grand Theft Parsons",1200000,0,0,"Swipe Films","PG-13","Drama"
"3247","9/7/2012","Crowsnest",1200000,0,0,"IFC Midnight","R","Horror"
"3248","9/14/2012","Airborne",1200000,0,0,"Image Entertainment",NA,"Horror"
"3249","3/21/2014","God’s Not Dead",1150000,60755732,63777092,"Pure Flix Entertain…","PG","Drama"
"3250","10/7/2005","Waiting...",1125000,16124543,18673274,"Lionsgate","R","Comedy"
"3251","12/25/2005","Wolf Creek",1100000,16186348,29005064,"Weinstein Co.","R","Horror"
"3252","2/11/2005","Ong-Bak",1100000,4563167,24062965,"Magnolia Pictures","R","Action"
"3253","3/23/2012","Serbuan maut",1100000,4105123,9297407,"Sony Pictures Classics","R","Action"
"3254","9/4/1987","The Offspring",1100000,1355728,1355728,"Moviestore Entertai…","R","Horror"
"3255","5/18/2012","Beyond the Black Rainbow",1100000,56491,56491,"Mongrel Media","R","Drama"
"3256","1/23/1943","Casablanca",1039000,10462500,10462500,"Warner Bros.","PG","Drama"
"3257","11/21/1976","Rocky",1e+06,117235147,2.25e+08,"United Artists","PG","Drama"
"3258","1/6/2012","The Devil Inside",1e+06,53262945,101759490,"Paramount Pictures","R","Horror"
"3259","4/17/2015","Unfriended",1e+06,32789645,62869004,"Universal","R","Horror"
"3260","2/8/1976","Taxi Driver",1e+06,28262574,28316211,"Columbia","R","Drama"
"3261","2/1/1980","The Fog",1e+06,21378361,21378361,"Avco Embassy",NA,"Horror"
"3262","8/23/2013","You're Next",1e+06,18494006,26887177,"Lionsgate","R","Horror"
"3263","5/25/2012","Chernobyl Diaries",1e+06,18119640,42411721,"Warner Bros.","R","Horror"
"3264","4/10/1981","The Howling",1e+06,17985000,17985000,"Avco Embassy",NA,"Horror"
"3265","5/8/1963","Dr. No",1e+06,16067035,59567035,"MGM","PG","Action"
"3266","9/18/1987","Hellraiser",1e+06,14564000,14575148,"New World","R","Horror"
"3267","8/18/2000","Godzilla 2000",1e+06,10037390,10037390,"Sony Pictures","PG","Action"
"3268","12/29/2010","Blue Valentine",1e+06,9737892,16566240,"Weinstein Co.","R","Drama"
"3269","1/20/2006","Transamerica",1e+06,9015303,16553163,"Weinstein Co.","R","Drama"
"3270","1/1/1970","Beyond the Valley of the Dolls",1e+06,9e+06,9e+06,"20th Century Fox",NA,"Comedy"
"3271","7/20/2018","Unfriended: Dark Web",1e+06,8783985,9620953,"OTL Releasing","R","Horror"
"3272","9/25/2015","The Green Inferno",1e+06,7192291,12931569,"High Top Releasing","R","Horror"
"3273","10/19/2012","The Sessions",1e+06,6002451,11495204,"Fox Searchlight","R","Drama"
"3274","3/23/2012","October Baby",1e+06,5355847,5391992,"Five & Two Pictures","PG-13","Drama"
"3275","9/12/2014","The Skeleton Twins",1e+06,5284309,5797192,"Lionsgate/Roadside …","R","Drama"
"3276","8/3/2005","Junebug",1e+06,2678010,3553253,"Sony Pictures Classics","R","Drama"
"3277","8/1/2008","Frozen River",1e+06,2511476,6030129,"Sony Pictures Classics","R","Drama"
"3278","11/21/2001","Sidewalks of New York",1e+06,2402459,3575308,"Paramount Vantage","R","Comedy"
"3279","4/24/1998","Two Girls and a Guy",1e+06,2057193,2315026,"Fox Searchlight","R","Drama"
"3280","9/18/2009","The Secrets of Jonathan Sperry",1e+06,1355079,1355079,"Five & Two Pictures","PG","Drama"
"3281","9/19/2003","Bubba Ho-Tep",1e+06,1239183,1492895,"Vitagraph Films","R","Comedy"
"3282","12/7/2001","No Man's Land",1e+06,1067481,2684207,"MGM","R","Drama"
"3283","10/9/1998","Slam",1e+06,1009819,1087521,"Trimark","R","Drama"
"3284","8/18/2017","Patti Cake$",1e+06,800148,1471090,"Fox Searchlight","R","Comedy"
"3285","12/1/2000","Panic",1e+06,779137,1425707,"Roxie Releasing","R","Drama"
"3286","5/9/2014","Palo Alto",1e+06,767732,1156309,"TriBeca Films","R","Drama"
"3287","7/29/2011","The Future",1e+06,568662,1239174,"Roadside Attractions","R","Drama"
"3288","2/14/2003","All the Real Girls",1e+06,549666,703020,"Sony Pictures Classics","R","Drama"
"3289","10/24/2014","23 Blast",1e+06,549185,549185,"Abramorama Films","PG-13","Drama"
"3290","6/20/1997","Dream With The Fishes",1e+06,542909,542909,"Sony Pictures Classics","R","Drama"
"3291","5/2/2003","Blue Car",1e+06,464126,475367,"Miramax","R","Drama"
"3292","10/19/2007","Wristcutters: A Love Story",1e+06,446165,473769,"Autonomous Films","R","Comedy"
"3293","5/5/2000","Luminarias",1e+06,428535,428535,NA,"R","Comedy"
"3294","7/18/2014","I Origins",1e+06,336472,852399,"Fox Searchlight","R","Drama"
"3295","8/22/2003","The Battle of Shaker Heights",1e+06,280351,839145,"Miramax","PG-13","Comedy"
"3296","12/30/2002","Love Liza",1e+06,213137,213137,NA,"R","Drama"
"3297","8/22/2001","Lisa Picard is Famous",1e+06,113433,113433,NA,"PG-13","Comedy"
"3298","10/30/2009","The House of the Devil",1e+06,101215,102812,"Magnolia Pictures","R","Horror"
"3299","6/1/2012","Hardflip",1e+06,96734,96734,"Rocky Mountain Pict…","PG-13","Drama"
"3300","3/11/2016","Creative Control",1e+06,63014,63014,"Magnolia Pictures","R","Drama"
"3301","10/17/2014","Camp X-Ray",1e+06,9837,9837,"IFC Films","R","Drama"
"3302","11/21/2008","Special",1e+06,7202,26822,"Revolver Entertainment","R","Drama"
"3303","4/10/2015","The Sisterhood of Night",1e+06,6870,6870,"Freestyle Releasing","PG-13","Drama"
"3304","3/18/2005","The Helix…Loaded",1e+06,3700,3700,"Romar","R","Comedy"
"3305","5/15/2015","Childless",1e+06,1036,1036,"Monterey Media","R","Drama"
"3306","4/21/2006","In Her Line of Fire",1e+06,884,884,"Regent Releasing","R","Action"
"3307","9/15/2006","Jimmy and Judy",1e+06,0,0,"Outrider Pictures","R","Action"
"3308","7/17/2009","The Poker House",1e+06,0,0,"Phase 4 Films","R","Drama"
"3309","9/23/2005","Proud",1e+06,0,0,"Castle Hill Product…","PG","Drama"
"3310","12/31/2008","Steppin: The Movie",1e+06,0,0,"Weinstein Co.","PG-13","Comedy"
"3311","1/29/2010","Zombies of Mass Destruction",1e+06,0,0,"After Dark","R","Comedy"
"3312","4/14/2006","Hard Candy",950000,1024640,8267066,"Lionsgate","R","Horror"
"3313","9/27/2002","Charly",950000,814666,814666,"Excel Entertainment","PG","Comedy"
"3314","4/13/2012","L!fe Happens",930000,30905,30905,"PMK*BNC","R","Comedy"
"3315","5/12/2017","Lowriders",916000,6179955,6188421,"BH Tilt","PG-13","Drama"
"3316","7/12/2013","Fruitvale Station",9e+05,16098998,17549645,"Weinstein Co.","R","Drama"
"3317","4/1/2016","Meet the Blacks",9e+05,9097072,9097072,"Freestyle Releasing","R","Comedy"
"3318","8/26/2011","Circumstance",9e+05,454121,958978,"Roadside Attractions","R","Drama"
"3319","8/25/2006","The Quiet",9e+05,381420,381420,"Sony Pictures Classics","R","Drama"
"3320","8/13/1942","Bambi",858000,102797000,2.68e+08,"RKO Radio Pictures","G","Drama"
"3321","8/31/2012","For a Good Time, Call",850000,1251749,1386088,"Focus Features","R","Comedy"
"3322","1/30/2004","Latter Days",850000,833118,865708,"TLA Releasing","R","Drama"
"3323","10/25/2002","Time Changer",825000,1500711,1500711,"Five & Two Pictures","PG","Drama"
"3324","12/30/2011","Jodaeiye Nader az Simin",8e+05,7098492,24426169,"Sony Pictures Classics","PG-13","Drama"
"3325","5/10/1996","Welcome to the Dollhouse",8e+05,4198137,5034794,"Sony Pictures Classics","R","Comedy"
"3326","3/28/2003","Raising Victor Vargas",8e+05,2073984,2900578,"Samuel Goldwyn Films","R","Drama"
"3327","10/1/1993","Ruby in Paradise",8e+05,1001437,1001437,NA,"R","Drama"
"3328","5/7/2004","The Mudge Boy",8e+05,62544,62544,"Strand","R","Drama"
"3329","8/6/2004","Saints and Soldiers",780000,1310470,1310470,"Excel Entertainment","PG-13","Drama"
"3330","8/11/1973","American Graffiti",777000,1.15e+08,1.4e+08,"Universal","PG","Drama"
"3331","6/8/2012","Safety Not Guaranteed",750000,4010957,4422318,"FilmDistrict","R","Comedy"
"3332","2/3/2012","The Innkeepers",750000,78396,1011535,"Magnolia Pictures","R","Horror"
"3333","8/29/2014","Il conformista",750000,59656,89609,"Kino Lorber","R","Drama"
"3334","7/1/2005","Undead",750000,41196,229250,"Lionsgate","R","Horror"
"3335","10/11/2013","All the Boys Love Mandy Lane",750000,0,1960521,"Radius","R","Horror"
"3336","6/25/1968","La mariée était en noir",747000,44566,44566,"Film Forum",NA,"Drama"
"3337","8/11/2006","Half Nelson",7e+05,2697938,4911725,"ThinkFilm","R","Drama"
"3338","6/19/1998","Hav Plenty",650000,2301777,2301777,"Miramax","R","Comedy"
"3339","7/14/1999","The Blair Witch Project",6e+05,140539099,248300000,"Artisan","R","Horror"
"3340","8/10/1977","The Kentucky Fried Movie",6e+05,1.5e+07,2e+07,"United Film Distrib…",NA,"Comedy"
"3341","10/31/2000","Mercy Streets",6e+05,173599,173599,NA,"PG-13","Drama"
"3342","7/2/1999","Broken Vessels",6e+05,15030,85343,NA,"R","Drama"
"3343","5/22/2015","Drunk Wedding",6e+05,3301,3301,"Paramount Pictures","R","Comedy"
"3344","8/11/1964","A Hard Day's Night",560000,1537860,1626784,"Universal","G","Comedy"
"3345","5/9/1980","Friday the 13th",550000,39754601,59754601,"Paramount Pictures",NA,"Horror"
"3346","9/26/2008","Fireproof",5e+05,33456317,33473297,"Samuel Goldwyn Films","PG","Drama"
"3347","11/15/1974","Benji",5e+05,31559560,31559560,NA,"G","Adventure"
"3348","10/3/2003","The Station Agent",5e+05,5801558,9470209,"Miramax","R","Drama"
"3349","1/22/2010","To Save a Life",5e+05,3777210,3824868,"Samuel Goldwyn Films","PG-13","Drama"
"3350","2/1/2002","The Singles Ward",5e+05,1250798,1250798,"Halestorm Entertain…","PG","Comedy"
"3351","1/30/2004","Osama",5e+05,1127331,1971479,"MGM","PG-13","Drama"
"3352","6/9/2000","Groove",5e+05,1115313,1167524,"Sony Pictures Classics","R","Comedy"
"3353","1/31/2003","The R.M.",5e+05,1111615,1111615,"Halestone","PG","Comedy"
"3354","7/30/1999","Twin Falls Idaho",5e+05,985341,1027228,"Sony Pictures Classics","R","Drama"
"3355","8/20/2004","Mean Creek",5e+05,603951,1348750,"Paramount Vantage","R","Drama"
"3356","8/23/2013","Drinking Buddies",5e+05,343706,407100,"Magnolia Pictures","R","Drama"
"3357","2/13/1998","Hurricane Streets",5e+05,334041,367582,"MGM",NA,"Drama"
"3358","8/29/2003","Civil Brand",5e+05,254293,254293,"Lionsgate","R","Drama"
"3359","10/29/2010","Monsters",5e+05,237301,5639730,"Magnet Pictures","R","Drama"
"3360","3/24/2006","Lonesome Jim",5e+05,154187,602789,"IFC Films","R","Comedy"
"3361","12/11/2015","O Menino e o Mundo",5e+05,129479,271893,"GKIDS","PG","Adventure"
"3362","1/1/1991","Johnny Suede",5e+05,55000,55000,"Miramax","R","Drama"
"3363","10/21/2005","The Californians",5e+05,4134,4134,"Fabrication Films","PG","Drama"
"3364","11/2/2001","Everything Put Together",5e+05,0,7890,NA,"R","Drama"
"3365","9/25/2009","Paranormal Activity",450000,107918810,194183034,"Paramount Pictures","R","Horror"
"3366","3/31/2006","Brick",450000,2075743,4243996,"Focus/Rogue Pictures","R","Drama"
"3367","8/22/1997","Sunday",450000,410919,450349,NA,NA,"Drama"
"3368","8/11/2006","Conversations with Other Women",450000,379418,1297745,"Fabrication Films","R","Comedy"
"3369","8/3/1990","Metropolitan",430000,2938000,2938000,NA,"PG-13","Comedy"
"3370","6/11/2004","Napoleon Dynamite",4e+05,44540956,46122713,"Fox Searchlight","PG","Comedy"
"3371","5/10/1975","Monty Python and the Holy Grail",4e+05,3427696,5028948,NA,NA,"Comedy"
"3372","8/2/2006","Quinceanera",4e+05,1692693,2797199,"Sony Pictures Classics","R","Drama"
"3373","10/24/2008","Heroes",4e+05,655538,655538,"Eros Entertainment","R","Adventure"
"3374","1/1/1983","E tu vivrai nel terrore - L'aldilà",4e+05,126387,126387,NA,NA,"Horror"
"3375","7/27/2001","Jackpot",4e+05,44452,44452,NA,"R","Drama"
"3376","12/10/2004","Fabled",4e+05,31425,31425,"Indican Pictures","R","Horror"
"3377","10/13/2005","The Dark Hours",4e+05,423,423,"Freestyle Releasing","R","Horror"
"3378","4/1/1986","My Beautiful Laundrette",4e+05,0,0,"Orion Classics",NA,"Drama"
"3379","1/1/1980","Maniac",350000,1e+07,1e+07,"Analysis",NA,"Horror"
"3380","1/1/1987","American Ninja 2: The Confrontation",350000,4e+06,4e+06,NA,NA,"Action"
"3381","4/13/1957","12 Angry Men",340000,0,0,"United Artists",NA,"Drama"
"3382","10/17/1978","Halloween",325000,4.7e+07,7e+07,"Compass International","R","Horror"
"3383","11/24/1999","Tumbleweeds",312000,1350248,1788168,"Fine Line","PG-13","Drama"
"3384","3/10/2000","God's Army",3e+05,2637726,2652515,"Excel Entertainment","PG","Drama"
"3385","10/17/2003","Pieces of April",3e+05,2528664,3571253,"MGM","PG-13","Comedy"
"3386","9/20/1996","When The Cat's Away",3e+05,1652472,2525984,"Sony Pictures Classics","R","Comedy"
"3387","12/10/2008","Wendy and Lucy",3e+05,865695,1416046,"Oscilloscope Pictures","R","Drama"
"3388","9/11/1998","Let's Talk About Sex",3e+05,373615,373615,"Fine Line",NA,"Comedy"
"3389","7/15/2005","First Morning",3e+05,87264,87264,"Illuminare","PG-13","Drama"
"3390","3/11/2011","3 Backyards",3e+05,39475,39475,"Screen Media Films","R","Drama"
"3391","8/7/1998","First Love, Last Rites",3e+05,10876,10876,"Strand","R","Drama"
"3392","5/6/2005","Fighting Tommy Riley",3e+05,10514,10514,"Freestyle Releasing","R","Drama"
"3393","8/17/2012","Compliance",270000,319285,830700,"Magnolia Pictures","R","Drama"
"3394","6/28/2002","Lovely and Amazing",250000,4210379,4613482,"Lionsgate","R","Drama"
"3395","4/28/2017","Sleight",250000,3930990,3934450,"High Top Releasing","R","Action"
"3396","4/11/2003","Better Luck Tomorrow",250000,3802390,3809226,"Paramount Pictures","R","Drama"
"3397","10/28/2011","Like Crazy",250000,3395391,3728400,"Paramount Pictures","PG-13","Drama"
"3398","7/14/2000","Chuck&Buck",250000,1055671,1157672,"Artisan","R","Drama"
"3399","3/28/1997","Love and Other Catastrophes",250000,212285,743216,"Fox Searchlight","R","Comedy"
"3400","8/28/1998","I Married a Strange Person",250000,203134,203134,"Lionsgate",NA,"Comedy"
"3401","7/22/2005","November",250000,191862,191862,"Sony Pictures Classics","R","Drama"


================================================
FILE: ch_regr_mult_and_log/figures/eoce/possum_classification_model_select/possum_classification_model_select.R
================================================
# load packages -----------------------------------------------------

library(openintro)
library(xtable)

# load data ---------------------------------------------------------

data(possum)

# recode data -------------------------------------------------------

Pop  <- ifelse(possum$pop == "Vic", 1, 0)
Sex  <- ifelse(possum$sex == "m", 1, 0)

# model output ------------------------------------------------------

xtable(glm(Pop ~ Sex + headL + skullW + totalL + tailL, binomial, possum))
xtable(glm(Pop ~ Sex + skullW + totalL + tailL, binomial, possum))

# plot of variables -------------------------------------------------

myPDF("possum_variables.pdf", 8*0.9, 3.7*0.9, 
      mfrow=c(2, 3), mar=c(3.7, 3.5, 0.75, 0.75), mgp=c(2, 0.55, 0))

#_____ sex _____#
par(mar = c(3.7, 3.2, 0.75, 0.75))
histPlot(Sex, breaks = seq(-0.375, 1.375, 0.25), 
         col = COL[1], 
         axes = FALSE, xlab="", ylab="Frequency")
mtext("sex_male", 1, 2.5, cex = 0.7)
axis(1, at = 0:1, labels = c("0\n(Female)", "1\n(Male)"), mgp = c(2, 1.5, 0))
axis(2, at = seq(0, 60, 20))

#_____ head_length _____#
histPlot(possum$headL, breaks = 15, 
         col = COL[1], 
         xlab = "head_length (in mm)", ylab = "Frequency")

#_____ skull_width _____#
histPlot(possum$skullW, breaks=15, 
         col = COL[1], 
         xlab = "skull_width (in mm)", ylab = "Frequency")

#_____ total_length _____#
histPlot(possum$totalL, breaks = 18, 
         col = COL[1], 
         xlab = "total_length (in cm)", ylab = "Frequency", axes = FALSE)
axis(1)
axis(2, at = seq(0, 10, 5))

#_____ tail_length _____#
histPlot(possum$tailL, breaks=18, 
         col = COL[1], 
         xlab = "tail_length (in cm)", ylab = "Frequency")

#_____ population _____#
histPlot(Pop, breaks = seq(-0.375, 1.375, 0.25), 
         col = COL[1], 
         axes = FALSE, xlab = "", ylab = "Frequency")
axis(1, at = 0:1, labels = c("0\n(Not Victoria)", "1\n(Victoria)"), 
     mgp = c(2, 1.5, 0))
mtext("population", 1, 2.5, cex = 0.7)
axis(2, at = seq(0, 60, 20))

dev.off()


================================================
FILE: ch_regr_mult_and_log/figures/eoce/spam_filtering_model_sel/spam_filtering_model_sel.R
================================================
library(openintro)
library(xtable)
d <- email
names(d)

table(d$sent_email, d$spam)
SGlm <- function(form, data = d) {
  m <- glm(
      form,
      data = d,
      family = binomial)
  summary(m)
}

vars <- c(
    "to_multiple", "cc", "attach", "dollar",
    "winner", "inherit", "password", "format",
    "re_subj", "exclaim_subj", "sent_email")
form <- spam ~ 1
for (v in vars) {
  form <- update(form, paste(". ~ . +", v))
}
m <- glm(
    form,
    data = d,
    family = binomial)
summary(m)

# form <- update(form, . ~ . - exclaim_subj - cc)

aic <- c("Drop None" = SGlm(form)$aic)
vars. <- names(unlist(sapply(vars, grep, x = as.character(form)[3], fixed = TRUE)))
for (v in vars.) {
  m. <- update(form, paste(". ~ . -", v))
  aic[v] <- SGlm(m.)$aic
}
# aic <- unlist(aic)
which.min(aic)
# xtable(data.frame(cbind(aic, aic[1] - aic)))
xtable(data.frame(aic))




================================================
FILE: ch_regr_mult_and_log/figures/eoce/spam_filtering_predict/spam_filtering_predict.R
================================================
library(openintro)
library(xtable)
d <- email
names(d)

table(d$sent_email, d$spam)
SGlm <- function(form, data = d) {
  m <- glm(
      form,
      data = d,
      family = binomial)
  summary(m)
}

vars <- c(
    "to_multiple", "cc", "attach", "dollar",
    "winner", "inherit", "password", "format",
    "re_subj", "exclaim_subj", "sent_email")
form <- spam ~ 1
for (v in vars) {
  form <- update(form, paste(". ~ . +", v))
}
form <- update(form, . ~ . - exclaim_subj - cc - inherit - password - sent_email - dollar - attach)
m <- glm(
    form,
    data = d,
    family = binomial)
summary(m)
xtable(summary(m))

which.max(predict(m))
max(predict(m, type = "response"))





================================================
FILE: ch_regr_mult_and_log/figures/loansDiagnostics/loans_analysis.R
================================================
library(xtable)
library(openintro)
d <- loans_full_schema
d$credit_util <- round(ifelse(d$total_credit_limit == 0, 0,
    d$total_credit_utilized / d$total_credit_limit), 4)
d$past_bankr <- (d$public_record_bankrupt > 0) + 0
d$ver_income <- ifelse(d$verified_income == "Verified", "verified",
    ifelse(d$verified_income == "Not Verified", "not", "source_only"))
d$credit_checks <- d$inquiries_last_12m
d$issued <- gsub("-", "", d$issue_month, fixed = TRUE)
these <- d$annual_income %in% 0:1
d$debt_to_income[these] <- d$total_credit_utilized[these] /
    d$annual_income_joint[these]
d$sqrt_debt_to_income <- sqrt(d$debt_to_income)
d$debt_to_income_50 <-
    ifelse(d$debt_to_income > 50, 50, d$debt_to_income)
keep <- c(
    "interest_rate",
    "ver_income",
    "debt_to_income",
    "sqrt_debt_to_income",
    "debt_to_income_50",
    "credit_util",
    "past_bankr",
    "term",
    # "issued",
    "credit_checks")
d <- d[keep]

F <- function(x, sub = 1:length(x)) {
  as.formula(paste("interest_rate ~", paste(x[sub], collapse = "+")))
}
summary(fit <- lm(F(keep[-c(1, 4, 5)]), d))
xtable(summary(fit))

e <- fit$res
f <- fit$fit

int_rate_at <- seq(-30, 30, 5)
IntRateAxis <- function(at) {
  AxisInPercent(2, at)
}
grid_lines_color <- COL[7, 3]
pt_col <- COL[1, 4]

myPDF("loansDiagNormalQuantilePlot.pdf", 4.5, 3.7,
    mgp = c(2.5,0.6,0))
qqnorm(e,
    ylab = "Residuals",
    main = "",
    col = COL[1,2],
    pch = 19)
dev.off()

myPDF("loansDiagNormalHistogram.pdf", 6, 3.7,
    mar = c(3.9, 4, 0.5, 0.5), mgp = c(2.5,0.6,0))
histPlot(e,
    xlab = "Residuals",
    ylab = "",
    col = COL[1],
    axes = FALSE)
AxisInPercent(1, pretty(e))
axis(2)
par(las = 0)
mtext("Frequency", 2, 2.9)
dev.off()

myPDF("ignore-loansDiagInOrder.pdf", 5.65, 3.9,
    mgp = c(2.5, 0.6, 0))
plot(e,
    xlab = "Order of collection",
    ylab = "Residuals",
    axes = FALSE,
    type = "n")
axis(1)
IntRateAxis(int_rate_at)
abline(h = int_rate_at, col = grid_lines_color, lwd = 1)
points(e, col = pt_col, pch = 19)
box()
dev.off()

myPDF("loansDiagEvsF.pdf", 5.65, 4.61,
    mgp = c(2.5, 0.6, 0))
plot(f, e,
    xlab = "Fitted values",
    ylab = "Residuals",
    axes = FALSE)
axis(1)
IntRateAxis(int_rate_at)
abline(h = int_rate_at, col = grid_lines_color, lwd = 1)
points(f, e, col = pt_col, pch = 19)
box()
dev.off()

myPDF("loansDiagEvsAbsF.pdf", 5.5, 3.7,
    mgp = c(2.5, 0.6, 0))
plot(f, abs(e),
    xlab = "Fitted Values",
    ylab = "Absolute Value of Residuals",
    axes = FALSE,
    type = "n")
axis(1)
IntRateAxis(int_rate_at)
abline(h = int_rate_at, col = grid_lines_color, lwd = 1)
points(f, abs(e), col = pt_col, pch = 19)
smooth <- loess(abs(e) ~ f)
o <- order(smooth$x)
lines(smooth$x[o], smooth$fitted[o],
    lwd = 2, col = COL[7,3])
lines(smooth$x[o], smooth$fitted[o],
    lwd = 2, lty = 2, col = COL[2])

box()
dev.off()


PlotCatVar <- function(x, xlab, key) {
  if (missing(key)) {
    key <- unique(d[[x]])
  }
  boxPlot(e, d[[x]],
      xlab = "",
      ylab = "Residuals",
      axes = FALSE,
      lcol = "#00000000",
      col = "#00000000",
      key = key)
  mtext(xlab, 1, line = 2)
  n_levels <- length(unique(d[[x]]))
  axis(1, at = 1:n_levels, key)
  IntRateAxis(int_rate_at)
  abline(h = int_rate_at, col = grid_lines_color, lwd = 1)
  boxPlot(e, d[[x]], add = 1:n_levels, axes = FALSE,
      lcol = COL[1], col = COL[1, 4])
  box()
}

PlotNumVar <- function(x, xlab) {
  plot(d[[x]], e,
      xlab = "",
      ylab = "Residuals",
      axes = FALSE,
      type = "n")
  mtext(xlab, 1, line = 2)
  axis(1)
  IntRateAxis(int_rate_at)
  abline(h = int_rate_at, v = pretty(d[[x]]),
      col = grid_lines_color, lwd = 1)
  points(d[[x]], e, col = pt_col, pch = 19)
  smooth <- loess(e ~ d[[x]])
  o <- order(smooth$x)
  # polygon(smooth$one.delta
  sx <- unique(smooth$x[o])
  sy <- smooth$fitted[o][match(sx, smooth$x[o])]
  lines(sx, sy, lwd = 2, col = COL[7,3])
  lines(sx, sy, lwd = 2, lty = 2, col = COL[2])
  box()
}


mgp <- c(2.5, 0.6, 0)
mar_left <- c(4.1, 3.8, 0.9, 2)
mar_right <- c(4.1, 5.6, 0.9, 0.4)
w <- 7.5
h <- 3.3
myPDF("loansDiagEvsVariables_1.pdf", w, h,
    mgp = mgp, mfrow = c(1, 2), mar = mar_left)
PlotCatVar("ver_income", "Verified Income")
par(mar = mar_right)
PlotNumVar("debt_to_income", "Debt to Income")
dev.off()
myPDF("loansDiagEvsVariables_2.pdf", w, h,
    mgp = mgp, mfrow = c(1, 2), mar = mar_left)
PlotNumVar("credit_util", "Credit Utilization")
par(mar = mar_right)
PlotCatVar("past_bankr", "Any Past Bankruptcy")
dev.off()
myPDF("loansDiagEvsVariables_3.pdf", w, h,
    mgp = mgp, mfrow = c(1, 2), mar = mar_left)
PlotCatVar("term", "Loan Term, in Months", c(36, 60))
par(mar = mar_right)
PlotNumVar("credit_checks", "Credit Checks in Last 12 Months")
dev.off()


myPDF("loansDebtToIncomeHist.pdf", 5, 2.7,
    mar = c(2.9, 4, 0.5, 0.5))
histPlot(d$debt_to_income, breaks = 30, col = COL[1],
    xlab = "", ylab = "Frequency")
mtext("Debt to Income", 1, 1.8)
dev.off()


# Diagnostics when Debt to Income is Transformed
myPDF("loansDiagEvsTransformDebtToIncome.pdf", w, h,
    mar = c(2.9, 4, 0.5, 0.5),
    mfrow = c(1, 2))

# Checking square root transformation
summary(fit <- lm(F(keep[-c(1, 3, 5)]), d))
e <- fit$res
f <- fit$fit
PlotNumVar("sqrt_debt_to_income", "SQRT(Debt to Income)")

# Checking truncation
summary(fit <- lm(F(keep[-c(1, 3, 4)]), d))
e <- fit$res
f <- fit$fit
PlotNumVar("debt_to_income_50", "Debt to Income, Truncated at 50")

dev.off()


================================================
FILE: ch_regr_mult_and_log/figures/loansSingles/intRateVsPastBankrScatter.R
================================================
library(xtable)
library(openintro)
d <- loans_full_schema
d$past_bankr <- (d$public_record_bankrupt > 0) + 0



myPDF("intRateVsPastBankrScatter.pdf", 4.2, 4,
      mar = c(3.7, 3.7, 0, 0.5),
      mgp = c(2.5,0.55,0))
plot(d$past_bankr, d$interest_rate,
     xlim = c(-0.15, 1.15),
     axes = FALSE,
     type = "n",
     xlab = "",
     ylab = "Interest Rate")
at <- seq(0, 30, 5)
abline(h = at, col = COL[7, 3])
points(d$past_bankr, # + runif(nrow(d), -0.05, 0.05),
    d$interest_rate, # + rnorm(nrow(d), sd = 0.5),
    col = COL[1, 4],
    pch = 19,
    cex = 0.7)
AxisInPercent(2, at)
par(mgp = c(2.5, 1.55, 0))
axis(1, at = 0:1, labels = c("0\n(no)", "1\n(yes)"))
par(mgp = c(2.5, 0.55, 0))
mtext("Any Past Bankruptcy", 1, 2.6)
m <- lm(interest_rate ~ past_bankr, data = d)
abline(m, col = COL[5], lwd = 1.5)
dev.off()

summary(m)
xtable(m)


================================================
FILE: ch_regr_mult_and_log/figures/loansSingles/intRateVsVerIncomeScatter.R
================================================
library(xtable)
library(openintro)
d <- loans_full_schema
d$ver_income <- ifelse(d$verified_income == "Verified", "verified",
    ifelse(d$verified_income == "Not Verified", "not", "source_only"))


# This isn't currently correct.
myPDF("intRateVsVerIncomeScatter.pdf", 4.2, 4,
      mar = c(3.7, 3.7, 0, 0.5),
      mgp = c(2.5,0.55,0))
plot(d$ver_income, d$interest_rate,
     xlim = c(-0.15, 1.15),
     axes = FALSE,
     type = "n",
     xlab = "",
     ylab = "Interest Rate")
at <- seq(0, 30, 5)
abline(h = at, col = COL[7, 3])
points(d$ver_income, # + runif(nrow(d), -0.05, 0.05),
    d$interest_rate, # + rnorm(nrow(d), sd = 0.5),
    col = COL[1, 4],
    pch = 19,
    cex = 0.7)
AxisInPercent(2, at)
par(mgp = c(2.5, 1.55, 0))
axis(1, at = 0:1, labels = c("0\n(no)", "1\n(yes)"))
par(mgp = c(2.5, 0.55, 0))
mtext("Verified Income", 1, 2.6)
m <- lm(interest_rate ~ ver_income, data = d)
abline(m, col = COL[5], lwd = 1.5)
dev.off()

summary(m)
xtable(m)


================================================
FILE: ch_regr_mult_and_log/figures/logisticModel/logisticModel.R
================================================
library(openintro)
library(splines)
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"))


m <- glm(callback ~
    job_city + college_degree + years_experience +
    honors + military + email_address +
    race + gender,
    data = d, family = binomial)
m <- glm(callback ~ job_city + years_experience + honors + race,
    data = d, family = binomial)
summary(m)
p  <- predict(m, type = "response")
p. <- p




set.seed(1)
myPDF("logisticModelPredict.pdf", 8, 3,
      mar = c(3.9, 6.5, 0.5, 0.5),
      mgp = c(2.4, 0.55, 0))
noise <- rnorm(nrow(d), sd = 0.08)
plot(p, d$callback + noise,
     xlim = 0:1,
     ylim = c(-0.5, 1.5),
     axes = FALSE,
     xlab = "Predicted Probability",
     ylab = "",
     col = fadeColor(COL[1], "22"),
     pch = 20)
axis(1)
axis(2,
     at = c(0,1),
     labels = c("0 (No Callback)", "1 (Callback)"))
dev.off()



ns1 <- 4
myPDF("logisticModelSpline.pdf", 7.7, 4.4,
      mar = c(3.9, 7, 0.5, 0.2),
      mgp = c(2.4, 0.55, 0))
plot(p, d$callback + noise / 5,
     type = "n",
     xlim = 0:1,
     ylim = c(-0.07, 1.07),
     axes = FALSE,
     xlab = "Predicted Probability",
     ylab = "")
par(las = 0)
mtext("Truth", 2, 5.5)
par(las = 1)
rect(0, 0, 1, 1,
     border = COL[6],
     col = "#00000000",
     lwd = 1.5)
lines(0:1, 0:1,
      lty = 2,
      col = COL[6],
      lwd = 1.5)
points(p, d$callback + noise / 5,
       col = fadeColor(COL[1], "18"),
       pch = 20)
axis(1)
at <- seq(0, 1, length.out = 6)
labels <- c("0 (No Callback)",
            "0.2  ",
            "0.4  ",
            "0.6  ",
            "0.8  ",
            "1 (Callback)")
axis(2, at, labels)
g1 <- lm(d$callback ~ ns(p, ns1))
p  <- seq(min(p), max(p), length.out = 100)
Y  <- predict(g1,
              data.frame(ns(p, ns1)),
              se.fit = TRUE)
yb <- Y$fit - 1.96 * Y$se.fit
yt <- rev(Y$fit + 1.96 * Y$se.fit)
polygon(c(p, rev(p)),
        c(yb, yt),
        col = COL[3, 3],
        border = "#00000000")
lines(p, Y$fit, lwd = 2.5)
arrows(0.15, 0.34,
       0.15, 0.22,
       length = 0.07)
text(0.15, 0.34,
     "Locally-estimated\nprobabilities with\nconfidence bounds",
     cex = 0.75, pos = 3)
arrows(0.4, 0.21,
       max(p) + 0.02, max(p) - 0.08,
       length = 0.07)
text(0.4, 0.19,
     paste("The bounds become wide\nbecause not much data",
           "are found this far right",
           sep = "\n"),
     cex = 0.75, pos = 4)
# arrows(0.83, 0.57,
#        0.8, 0.785,
#        length = 0.07)
text(0.42, 0.63,
     "The smoothed line\nshould fall close to the\ndashed line if the\nlogistic model\nis reasonable",
     cex = 0.75)
dev.off()




p <- p.
ns1 <- 4
myPDF("logisticModelBucketDiag.pdf", 7.7, 4.4,
    mar = c(3.9, 7, 0.5, 0.2),
    mgp = c(2.4, 0.55, 0))
plot(p, d$callback + noise / 5,
     type = "n",
     xlim = 0:1,
     ylim = c(-0.07, 1.07),
     axes = FALSE,
     xlab = "Predicted Probability",
     ylab = "")
par(las = 0)
mtext("Truth", 2, 5.5)
par(las = 1)
rect(0, 0, 1, 1,
     border = COL[6],
     col = "#00000000",
     lwd = 1.5)
lines(0:1, 0:1,
      lty = 2,
      col = COL[6],
      lwd = 1.5)
points(p, d$callback + noise / 5,
       col = fadeColor(COL[1], "18"),
       pch = 20)
axis(1)
at <- seq(0, 1, length.out = 6)
labels <- c("0 (No Callback)",
            "0.2  ",
            "0.4  ",
            "0.6  ",
            "0.8  ",
            "1 (Callback)")
axis(2, at, labels)
eps <- 1e-4
bucket_breaks <- quantile(p, seq(0, 1, 0.01))
bucket_breaks[1] <- bucket_breaks[1] - eps
n_buckets <- length(bucket_breaks) - 1
bucket_breaks[n_buckets] <- bucket_breaks[n_buckets] + 1e3 * eps
bucket_breaks. <- bucket_breaks
k <- 1
for (i in 1:n_buckets) {
  if (abs(bucket_breaks.[i] - bucket_breaks[k]) >= 0.01) {
    k <- k + 1
    bucket_breaks[k] <- bucket_breaks.[i]
  }
}
bucket_breaks <- bucket_breaks[1:k]

n_buckets <- length(bucket_breaks)
xp <- rep(NA, n_buckets)
yp <- rep(NA, n_buckets)
yp_lower <- rep(NA, n_buckets)
yp_upper <- rep(NA, n_buckets)
zs <- qnorm(0.975)
for (i in 1:n_buckets) {
  these <- bucket_breaks[i] < p & p <= bucket_breaks[i + 1]
  xp[i] <- mean(p[these])
  y <- d$callback[these]
  yp[i] <- mean(y)
  yp_lower[i] <- yp[i] - zs * sqrt(yp[i] * (1 - yp[i]) / length(y))
  yp_upper[i] <- yp[i] + zs * sqrt(yp[i] * (1 - yp[i]) / length(y))
}
points(xp, yp, pch = 19, cex = 0.7)
segments(xp, yp_lower, xp, yp_upper)
arrows(0.3, 0.17,
       0.24, 0.22,
       length = 0.07)
text(0.3, 0.15,
    paste("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.",
        sep = "\n"),
    cex = 0.85, pos = 4)
dev.off()



# This plot is still a bit of a mess
ns2 <- 10
myPDF("logisticModelResidual.pdf", 8, 6,
      mar = c(4.9, 6, 5.5, 0.5),
      mgp = c(2.4, 0.55, 0),
      mfrow = 2:1)
noise <- rnorm(nrow(d), sd = 0.08)
p <- p.
res   <- d$callback - p
plot(d$years_experience, res,
     axes = FALSE,
     main = "THIS PLOT IS A BIT OF A MESS",
     xlab = "Time email was sent",
     ylab = "Residual",
     col = COL[1, 4],
     pch = 20)
TR  <- range(as.numeric(d$years_experience))
DR  <- diff(TR)
Mo  <- TR[1] + c(0, DR * 31, DR * 59, DR * 90) / 90
axis(1)
axis(2)
Time <- d$years_experience
g2   <- lm(res ~ ns(Time, ns2))
Time <- seq(TR[1], TR[2], length.out = 200)
Y    <- predict(g2, ns(Time, ns2), se.fit = TRUE)
abline(h = 0, lty = 2, col = "#00000088")
yb <- Y$fit - 1.96 * Y$se.fit
yt <- rev(Y$fit + 1.96 * Y$se.fit)
polygon(c(Time, rev(Time)),
        c(yb, yt),
        col = "#E0E317B5",
        border = "#00000000")
lines(Time, Y$fit, lwd = 1.75)

par(mar = c(3.9, 6, 1.5, 0.5))
noise <- rnorm(nrow(d), sd = 0.08)
res   <- d$callback - p
TR  <- range(as.numeric(d$years_experience))
plot(d$years_experience, res,
     axes = FALSE,
     xlab = "January",
     ylab = "Residual",
     col = "#22558855",
     pch = 20,
     xlim = c(TR[1], quantile(TR, 0.08)))
DR  <- diff(TR)
axis(1)
axis(2)
dev.off()


================================================
FILE: ch_regr_mult_and_log/figures/logitTransformationFigureHoriz/logitTransformationFigureHoriz.R
================================================
library(openintro)
data(COL)
p  <- seq(0.0001, 0.9999, 0.0002)
lp <- log(p/(1-p))

pts  <- seq(0.01, 0.99, length.out = 25)
R    <- c(-6,6)
adj  <- 0.07
adj1 <- 0.02


myPDF("logitTransformationFigureHoriz.pdf", 7, 4,
      mar = c(3.3, 3.4, 0.8, 0.8),
      mgp = c(2.1, 0.55, 0))


plot(lp, p,
     xlab = expression(logit(p[i])),
     ylab = "",
     xlim = c(-5.8, 6.5),
     ylim = c(-0.05, 1.1),
     type = "n")
lines(lp, p,
      type = "l",
      col = COL[5],
      lwd = 1.5)
mtext(expression(p[i]), 2, 2.4)
abline(h = 0:1,
       lty = 2,
       col = COL[1],
       lwd = 1.5)
this <- which.min(abs(p - 0.2))
LP <- c(seq(6, -5, -1))
P <- exp(LP) / (1 + exp(LP))
POS <- c(3, 1, 3, 1, 2, 2, 2, 2, 4, 3, 1, 3)
xOFF <- c()
Round <- c(3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3)
for (i in 1:length(LP)) {
  points(LP[i], P[i], col = COL[4], lwd = 2)
  t1   <- format(round(c(LP, 0.9), Round[i]))[i]
  t2   <- format(round(P, Round[i]))[i]
  text(LP[i], P[i],
       paste0("(", t1, ", ", t2, ")"),
       cex = 0.6,
       pos = POS[i],
       col = COL[5])
  
}
dev.off()


================================================
FILE: ch_regr_mult_and_log/figures/marioKartDiagnostics/marioKartAnalysis.R
================================================
library(xtable)
library(openintro)
data(COL)
data(marioKart)
toss <- which(marioKart$totalPr > 80)
keep <- c("totalPr",
          "cond",
          "stockPhoto",
          "duration",
          "wheels",
          "shipSp")
d <- marioKart[-toss, keep]
d$stockPhoto <- (d$stockPhoto == "yes") + 0
d$cond <- (d$cond == "new") + 0
thisOne <- names(d) == "cond"
names(d)[thisOne] <- "condNew"
d$shipSp <- as.character(d$shipSp)
these <- d$shipSp %in%
         c("firstClass", "priority", "parcel", "media")
d$shipSp[these] <- "usps"
d$shipSp[grep("ups", d$shipSp)] <- "ups"
these <- d$shipSp %in% c("other", "standard")
d$shipSp[these] <- "unknown"
d$shipSp <- as.factor(d$shipSp)
d <- d[,-which(colnames(d) == "shipSp")]

summary(lm(totalPr ~ ., d))
summary(lm(totalPr ~ condNew +
                     stockPhoto +
                     duration +
                     wheels,
           data = d))
fit <- lm(totalPr ~ condNew + stockPhoto + wheels, data = d)
xtable(summary(fit))

e <- fit$res
f <- fit$fit

width <- 4.7
height <- 4


myPDF("mkDiagnosticNormalQuantilePlot.pdf", width, height,
      mgp = c(2.5,0.6,0))
qqnorm(e,
       ylab = "Residuals",
       main = "",
       col = COL[1,2],
       pch = 19)
dev.off()

myPDF("mkDiagResHist.pdf", width, 0.7 * height)
histPlot(e,
    breaks = 12,
    xlab = "Residuals",
    ylab = "Frequency",
    col = COL[1],
    axes = FALSE)
axis(1, pretty(e))
axis(2)
dev.off()


myPDF("mkDiagnosticInOrder.pdf", width, 0.8 * height,
      mgp = c(2.5, 0.6, 0))
plot(e,
     xlab = "Order of Collection",
     ylab = "Residuals",
     axes = FALSE)
axis(1)
AxisInDollars(2, c(-10, 0, 10))
rect(-10, -50, 200, 50,
     col = COL[7,3])
abline(h = seq(-50, 50, 10),
       col = "#FFFFFF",
       lwd = 3)
abline(h = seq(-50, 50, 5),
       col = "#FFFFFF",
       lwd = 1)
points(e, col = COL[1, 2], pch = 19)
box()
dev.off()

myPDF("mkDiagnosticEvsF.pdf", 0.9 * width, 0.9 * height,
      mgp = c(2.5, 0.6, 0))
plot(f, e,
     xlab = "Fitted Values",
     ylab = "Residuals",
     axes = FALSE)
AxisInDollars(1, seq(35, 65, 5))
AxisInDollars(2, seq(-10, 10, 10))
rect(-10, -50, 100, 50,
     col = COL[7, 3])
abline(h = seq(-50, 50, 10),
       col = "#FFFFFF",
       lwd = 3)
abline(h = seq(-50, 50, 5),
       col = "#FFFFFF",
       lwd = 1)
points(f, e,
       col = COL[1, 2],
       pch = 19)
box()
dev.off()

myPDF("mkDiagnosticEvsAbsF.pdf", width, 0.9 * height,
      mgp = c(2.5, 0.6, 0))
plot(f, abs(e),
     xlab = "Fitted Values",
     ylab = "Absolute Value of Residuals",
     axes = FALSE)
AxisInDollars(1, seq(35, 65, 5))
AxisInDollars(2, seq(-10, 10, 5))
rect(-10, -50, 100, 50,
     col = COL[7,3])
abline(h = seq(-50, 50, 10),
       col = "#FFFFFF",
       lwd = 3)
abline(h = seq(-50, 50, 5),
       col = "#FFFFFF",
       lwd = 1)
points(f, abs(e),
       col = COL[1, 2],
       pch = 19)
box()
dev.off()

myPDF("mkDiagnosticEvsVariables.pdf", width, 1.5 * height,
      mgp = c(2, 0.55, 0),
      mfrow = c(3, 1),
      mar = c(4.1, 3.1, 0.9, 0.5))
boxPlot(e, d$condNew,
        xlab = "Condition",
        ylab = "Residuals",
        axes = FALSE)
axis(1, at = 1:2, c("Used", "New"))
AxisInDollars(2, seq(-10, 10, 10))
rect(-10, -50, 100, 50,
     col = COL[7, 3])
abline(h = seq(-50, 50, 10),
       col = "#FFFFFF",
       lwd = 3)
abline(h = seq(-50, 50, 5),
       col = "#FFFFFF",
       lwd = 1)
boxPlot(e, d$condNew,
        add = 1:2,
        axes = FALSE)
dotPlot(e[d$condNew == 0],
        vertical = TRUE,
        at = 1.05,
        add = TRUE,
        col = COL[1, 2],
        pch = 19,
        cex = 0.7)
dotPlot(e[d$condNew == 1],
        vertical = TRUE,
        at = 2.05,
        add = TRUE,
        col = COL[1, 2],
        pch = 19,
        cex = 0.7)
box()

par(mar = c(3.8, 3.1, 1.2, 0.5))
boxPlot(e, d$stockPhoto,
        xlab = "Photo Type",
        ylab = "Residuals",
        axes = FALSE)
axis(1, at = 1:2, c("Unique Photo", "Stock Photo"))
AxisInDollars(2, seq(-10, 10, 10))
rect(-10, -50, 100, 50,
     col = COL[7, 3])
abline(h = seq(-50, 50, 10),
       col = "#FFFFFF",
       lwd = 3)
abline(h = seq(-50, 50, 5),
       col = "#FFFFFF",
       lwd = 1)
boxPlot(e, d$stockPhoto,
        add = 1:2,
        axes = FALSE)
dotPlot(e[d$stockPhoto == 0],
        vertical = TRUE,
        at = 1.05,
        add = TRUE,
        col = COL[1, 2],
        pch = 19,
        cex = 0.7)
dotPlot(e[d$stockPhoto == 1],
        vertical = TRUE,
        at = 2.05,
        add = TRUE,
        col = COL[1, 2],
        pch = 19,
        cex = 0.7)
box()

par(mar = c(3.1, 3.1, 1.2, 0.5))
plot(d$wheels, e,
     xlab = "Number of Wheels",
     ylab = "Residuals",
     axes = FALSE)
axis(1)
AxisInDollars(2, seq(-10, 10, 10))
rect(-10, -50, 100, 50,
     col = COL[7, 3])
abline(h = seq(-50, 50, 10),
       col = "#FFFFFF",
       lwd = 3)
abline(h = seq(-50, 50, 5),
       col = "#FFFFFF",
       lwd = 1)
points(d$wheels, e,
       col = COL[1, 2],
       pch = 19)
box()
dev.off()


fit <- lm(totalPr ~ condNew + wheels + I(wheels^2), d)
plot(fit)


fit1 <- lm(totalPr ~
           duration + condNew + stockPhoto + wheels,
           d)
fit2 <- lm(totalPr ~
           condNew + stockPhoto + wheels,
           d)
anova(fit1, fit2)

fit1 <- lm(totalPr ~ condNew + stockPhoto, d)
fit2 <- lm(totalPr ~ stockPhoto, d)
anova(fit1, fit2)



fit <- lm(totalPr ~
          condNew + stockPhoto + duration + wheels,
          d)
xtable(fit)
summary(fit)
fit <- lm(totalPr ~
          condNew + stockPhoto + wheels,
          d)
xtable(fit)
summary(fit)

# _____ Backward-Selection, Stage 1 _____ #
fit <- lm(totalPr ~
          stockPhoto + duration + wheels,
          d)
summary(fit)
fit <- lm(totalPr ~
          condNew + duration + wheels,
          d)
summary(fit)
fit <- lm(totalPr ~
          condNew + stockPhoto + wheels,
          d)
summary(fit)
fit <- lm(totalPr ~
          condNew + stockPhoto + duration,
          d)
summary(fit)

# _____ Backward-Selection, Stage 2 _____ #
fit <- lm(totalPr ~ stockPhoto + wheels, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ condNew + wheels, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ condNew + stockPhoto, d)
summary(fit)$adj.r.squared


# _____ Forward-Selection, Stage 1 _____ #
fit <- lm(totalPr ~ 1, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ condNew, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ stockPhoto, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ duration, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ wheels, d)
summary(fit)$adj.r.squared

# _____ Forward-Selection, Stage 2 _____ #
fit <- lm(totalPr ~ wheels, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ wheels + condNew, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ wheels + stockPhoto, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ wheels + duration, d)
summary(fit)$adj.r.squared

# _____ Forward-Selection, Stage 3 _____ #
fit <- lm(totalPr ~ wheels + condNew, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ wheels + condNew + stockPhoto, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ wheels + condNew + duration, d)
summary(fit)$adj.r.squared

# _____ Forward-Selection, Stage 4 _____ #
fit <- lm(totalPr ~ wheels + condNew + stockPhoto, d)
summary(fit)$adj.r.squared
fit <- lm(totalPr ~ wheels + condNew + stockPhoto + duration, d)
summary(fit)$adj.r.squared



================================================
FILE: ch_regr_mult_and_log/figures/marioKartSingle/marioKartSingle.R
================================================
library(xtable)
library(openintro)

toss <- which(marioKart$totalPr > 80)
keep <- c("totalPr",
          "cond",
          "stockPhoto",
          "duration",
          "wheels")
d <- marioKart[-toss, keep]
d$stockPhoto <- ifelse(d$stockPhoto == "yes", 1, 0)
d$cond <- ifelse(d$cond == "new", 1, 0)

myPDF("marioKartSingle.pdf", 4.5, 3.5,
      mar = c(3.7, 3.7, 0, 0.5),
      mgp = c(2.5,0.55,0))
plot(d$cond, d$totalPr,
     xlim = c(-0.15, 1.15),
     axes = FALSE,
     col = COL[1, 3],
     pch = 19,
     cex = 1.3,
     xlab = "",
     ylab = "Price")
AxisInDollars(2, at = seq(30, 70, 10))
par(mgp = c(2.5, 1.55, 0))
axis(1, at = 0:1, labels = c("0\n(used)", "1\n(new)"))
par(mgp = c(2.5, 0.55, 0))
mtext("Condition", 1, 2.6)
g <- lm(d$totalPr ~ d$cond)
abline(g, col = COL[5], lwd = 1.5)
dev.off()


================================================
FILE: ch_regr_simple_linear/TeX/ch_regr_simple_linear.tex
================================================
\begin{chapterpage}{Introduction to linear regression}
  \chaptertitle{Introduction to linear \titlebreak{} regression}
  \label{linRegrForTwoVar}
  \label{ch_regr_simple_linear}
  \chaptersection{fitting_line_to_data_section}
  \chaptersection{fittingALineByLSR}
  \chaptersection{typesOfOutliersInLinearRegression}
  \chaptersection{inferenceForLinearRegression}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_regr_simple_linear}


\index{regression|textbf}
\index{regression|(}
\index{linear regression|seealso{regression}}

\chapterintro{Linear regression is a very powerful
  statistical technique.
  Many people have some familiarity with regression just from
  reading the news, where straight lines are overlaid
  on scatterplots.
  Linear models can be used for prediction or to
  evaluate whether there is a linear relationship
  between two numerical variables.}



%__________
\section{Fitting a line, residuals, and correlation}
% \section{Using a line to model data}
\label{fitting_line_to_data_section}

It's helpful to think deeply about the line fitting process.
In this section, we define the form of a linear model,
explore criteria for what makes a good fit,
and introduce a new statistic called
\emph{correlation}\index{correlation}.


\subsection{Fitting a line to data}

Figure~\ref{perfLinearModel} shows two variables whose
relationship can be modeled perfectly with a straight line.
The equation for the line is
\begin{eqnarray*}
y = 5 + 64.96 x
\end{eqnarray*}
Consider what a perfect linear relationship means:
we know the exact value of $y$ just by knowing
the value of $x$.
This is unrealistic in almost any natural process.
For example, if we took family income ($x$),
this value would provide some useful information about
how much financial support a college may offer a prospective
student~($y$).
However, the prediction would be far from perfect,
since other factors play a role in financial support
beyond a family's finances.

\begin{figure}[h]
   \centering
   \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}
   \caption{Requests from twelve separate buyers were
       simultaneously placed with a trading company to purchase
       Target Corporation stock
       (ticker \texttt{TGT}, December 28th, 2018),
       and the total cost of the shares were reported.
       Because the cost is computed using a linear formula,
       the linear fit is perfect.}
   \label{perfLinearModel}
\end{figure}

Linear regression is the statistical method for fitting
a line to data where the relationship between two variables,
$x$ and $y$, can be modeled by a straight line with some error:
\begin{align*}
y = \beta_0 + \beta_1x + \varepsilon
\end{align*}
The values $\beta_0$ and $\beta_1$ represent the model's
parameters\index{parameter}
($\beta$ is the Greek letter
  \emph{beta}\index{Greek!beta@beta ($\beta$)}),
and the error is represented by $\varepsilon$
(the Greek letter \emph{epsilon}\index{Greek!epsilon@epsilon ($\varepsilon$)}).
The parameters are estimated using data,
and we write their point estimates as $b_0$ and $b_1$.
When we use $x$ to predict $y$,
we usually call $x$ the explanatory\index{explanatory variable}
or \term{predictor} variable,
and we call $y$ the response;
we also often drop the $\epsilon$ term when writing down the
model since our main focus is often on the prediction of
the average outcome.

It is rare for all of the data to fall perfectly on a straight line.
Instead, it's more common for data to appear as
a \emph{cloud of points}\index{cloud of points},
such as those examples shown in  Figure~\ref{imperfLinearModel}.
In each case, the data fall around a straight line,
even if none of the observations fall exactly on the line.
The first plot shows a relatively strong downward
linear trend,
where the remaining variability in the data around the
line is minor relative to the strength of the relationship
between $x$ and $y$.
The second plot shows an upward trend that,
while evident, is not as strong as the first.
The last plot shows a very weak downward trend in the data,
so slight we can hardly notice it.
In each of these examples,
we will have some uncertainty regarding our estimates
of the model parameters, $\beta_0$ and $\beta_1$.
For instance, we might wonder, should we move the line
up or down a little, or should we tilt it more or less?
As we move forward in this chapter,
we will learn about criteria for line-fitting,
and we will also learn about the uncertainty associated
with estimates of model parameters.

\begin{figure}
   \centering
   \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}
   \caption{Three data sets where a linear model may be useful
       even though the data do not all fall exactly on the line.}
   \label{imperfLinearModel}
\end{figure}

There are also cases where fitting a straight line to the data,
even if there is a clear relationship between the variables,
is not helpful.
One such case is shown in
Figure~\ref{notGoodAtAllForALinearModel}
where there is a very clear relationship between the variables
even though the trend is not linear.
We discuss \index{nonlinear}nonlinear trends in this chapter
and the next, but details of fitting nonlinear models
are saved for a later course.

\begin{figure}
   \centering
   \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}
   \caption{A linear model is not useful in this nonlinear case.
       These data are from an introductory physics experiment.}
   \label{notGoodAtAllForALinearModel}
\end{figure}




\subsection{Using linear regression to predict possum head lengths}

\index{data!possum|(}

Brushtail possums are a marsupial that lives in Australia,
and a photo of one is shown in
Figure~\ref{brushtail_possum}.
Researchers captured 104 of these animals and took body
measurements before releasing the animals back into the wild.
We consider two of these measurements:
the total length of each possum, from head to tail,
and the length of each possum's head.

\captionsetup{width=0.83\mycaptionwidth}
\begin{figure}[h]
  \centering
  \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}
  \caption{The common brushtail possum of Australia.\vspace{-1mm} \\
      -----------------------------\vspace{-2mm}\\
      {\footnotesize Photo by Greg Schechter
      (\oiRedirect{textbook-flickr_com_schechter_brushtail_possum_5653697137}
          {https://flic.kr/p/9BAFbR}).
      \oiRedirect{textbook-CC_BY_2}
          {CC~BY~2.0~license}.}}
  \label{brushtail_possum}
\end{figure}
\captionsetup{width=\mycaptionwidth}

%Scatterplots were introduced in Chapter~\ref{introductionToData}
%as a graphical technique to present two numerical variables
%simultaneously.
%Such plots permit the relationship between the variables
%to be examined with ease.
Figure~\ref{scattHeadLTotalL} shows a scatterplot for the head
length and total length of the possums.
Each point represents a single possum from the data.
The head and total length variables are associated:
possums with an above average total length also tend to have
above average head lengths.
While the relationship is not perfectly linear, it could
be helpful to partially explain the connection between these
variables with a straight line.

\D{\newpage}

\begin{figure}[h]
  \centering
  \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}
  \caption{A scatterplot showing head length against total length
      for 104 brushtail possums.
      A point representing a possum with head length 94.1mm
      and total length 89cm is highlighted.}
  \label{scattHeadLTotalL}
\end{figure}

%Straight lines should only be used when the data appear to have
%a linear relationship, such as the case shown in the left panel
%of Figure~\ref{scattHeadLTotalLTube}.
%The right panel of Figure~\ref{scattHeadLTotalLTube} shows
%a case where a curved line would be more useful in understanding
%the relationship between the two variables.

%\begin{figure}[h]
%  \centering
%  \Figure{0.95}{scattHeadLTotalLTube}
%  \caption{The figure on the left shows head length versus
%      total length, and reveals that many of the points could
%      be captured by a straight band.
%      On the right, we see that a curved band is more appropriate
%      in this scatterplot.}
%  \label{scattHeadLTotalLTube}
%\end{figure}

We 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
\begin{align*}
\hat{y} = 41 + 0.59x
\end{align*}
A ``hat'' on $y$ is used to signify that this is an estimate.
We can use this line to discuss properties of possums.
For instance, the equation predicts a possum with a total length
of 80 cm will have a head length of
\begin{align*}
\hat{y} &= 41 + 0.59\times 80 \\
	&= 88.2 % mm
\end{align*}
The estimate may be viewed as an average:
the equation predicts that possums with a total length of
80~cm will have an average head length of 88.2~mm.
Absent further information about an 80~cm possum,
the prediction for head length that uses the average
is a reasonable estimate.

\begin{figure}
  \centering
  \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}
      {scattHeadLTotalLLineResiduals}
  \caption{A reasonable linear model was fit to represent
      the relationship between head length and total length.}
  \label{scattHeadLTotalLLine}
\end{figure}

\begin{examplewrap}
\begin{nexample}{What other variables might help us predict the
    head length of a possum besides its length?}
  Perhaps the relationship would be a little different for
  male possums than female possums,
  or perhaps it would differ for possums from one region
  of Australia versus another region.
  In Chapter~\ref{ch_regr_mult_and_log},
  we'll learn about how we can include more than one predictor.
  Before we get there, we first need to better understand
  how to best build a simple linear model with one predictor.
\end{nexample}
\end{examplewrap}


\subsection{Residuals}

\index{residual|(}

\noindent%
\termsub{Residuals}{residual} are the leftover variation in the data after accounting for the model fit:
\begin{align*}
\text{Data} = \text{Fit} + \text{Residual}
\end{align*}
Each observation will have a residual, and three of the
residuals for the linear model we fit for the \data{possum}
data is shown in
Figure~\ref{scattHeadLTotalLLine}.
If an observation is above the regression line, then its residual,
the vertical distance from the observation to the line, is positive.
Observations below the line have negative residuals.
One goal in picking the right linear model is for these residuals
to be as small as possible.

%\begin{figure}[h]
%  \centering
%  \Figures{0.7}{scattHeadLTotalLLine}
%      {scattHeadLTotalLLineResiduals}
%  \caption{The linear model from
%      Figure~\ref{scattHeadLTotalLLine}
%      where 3 residuals are highlighted.}
%  \label{scattHeadLTotalLLineResiduals}
%\end{figure}

Let's look closer at the three residuals featured in
Figure~\ref{scattHeadLTotalLLine}.
The observation marked by an ``$\times$'' has a small,
negative residual of about -1;
the observation marked by ``$+$'' has a large residual of about +7;
and the observation marked by ``$\triangle$'' has a moderate
residual of about -4.
The size of a residual is usually discussed in terms of its
absolute value.
For example, the residual for ``$\triangle$'' is larger than
that of ``$\times$'' because $|-4|$ is larger than $|-1|$.

\begin{onebox}{Residual: difference between observed and expected}
The 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 ($\hat{y}_i$):
\begin{eqnarray*}
e_i = y_i - \hat{y}_i
\end{eqnarray*}
We typically identify $\hat{y}_i$ by plugging $x_i$ into the model.
\end{onebox}

\begin{examplewrap}
\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}.
Check it against the earlier visual estimate,~-1.}
We first compute the predicted value of point ``$\times$'' based on the model:
\begin{eqnarray*}
\hat{y}_{\times} = 41+0.59x_{\times} = 41+0.59\times 77.0 = 86.4
\end{eqnarray*}
Next we compute the difference of the actual head length and the predicted head length:
\begin{eqnarray*}
e_{\times} = y_{\times} - \hat{y}_{\times} = 85.3 -  86.4 = -1.1
\end{eqnarray*}
The model's error is $e_{\times} = -1.1$mm,
which is very close to the visual estimate of -1mm.
The negative residual indicates that the linear model
overpredicted head length for this particular possum.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
If a model underestimates an observation, will the residual be positive or negative? What about if it overestimates the observation?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{exercisewrap}
\begin{nexercise}
Compute the residuals for the ``$+$'' observation $(85.0, 98.6)$
and the ``$\triangle$'' observation $(95.5, 94.0)$ in the figure
using the linear relationship $\hat{y} = 41 + 0.59x$.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{($+$) First compute the predicted value based on
  the model:
  \begin{align*}
  \hat{y}_{+} = 41+0.59x_{+} = 41+0.59\times 85.0 = 91.15
  \end{align*}
  Then the residual is given by
  \begin{align*}
  e_{+} = y_{+} - \hat{y}_{+} = 98.6-91.15=7.45
  \end{align*}
  This was close to the earlier estimate of 7.

\noindent%
($\triangle$) $\hat{y}_{\triangle} = 41+0.59x_{\triangle} = 97.3$.
$e_{\triangle} = y_{\triangle} - \hat{y}_{\triangle} = -3.3$,
close to the estimate of -4.}

Residuals are helpful in evaluating how well a linear model
fits a data set.
We often display them in a \term{residual plot} such as the
one shown in Figure~\ref{scattHeadLTotalLResidualPlot}
for the regression line in Figure~\ref{scattHeadLTotalLLine}.
The residuals are plotted at their original horizontal locations
but with the vertical coordinate as the residual.
For instance, the point $(85.0,98.6)_{+}$ had a residual
of 7.45, so in the residual plot it is placed at $(85.0, 7.45)$.
Creating a residual plot is sort of like tipping the
scatterplot over so the regression line is horizontal. 
\index{data!possum|)}

\begin{figure}[h]
  \centering
  \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}
  \caption{Residual plot for the model in
      Figure~\ref{scattHeadLTotalLLine}.}
  \label{scattHeadLTotalLResidualPlot}
\end{figure}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{One purpose of residual plots is to identify
    characteristics or patterns still apparent in data after
    fitting a model.
    Figure~\ref{sampleLinesAndResPlots} shows three scatterplots
    with linear models in the first row and residual plots in the
    second row.
    Can you identify any patterns remaining in the residuals?}

  In the first data set (first column), the residuals show
  no obvious patterns.
  The residuals appear to be scattered randomly around the
  dashed line that represents 0.

  The second data set shows a pattern in the residuals.
  There is some curvature in the scatterplot, which is more
  obvious in the residual plot.
  We should not use a straight line to model these data.
  Instead, a more advanced technique should be used.

  The last plot shows very little upwards trend, and the
  residuals also show no obvious patterns.
  It is reasonable to try to fit a linear model to the data.
  However, it is unclear whether there is statistically
  significant evidence that the slope parameter is different
  from zero.
  The point estimate of the slope parameter, labeled $b_1$,
  is not zero, but we might wonder if this could just be due
  to chance.
  We will address this sort of scenario in
  Section~\ref{inferenceForLinearRegression}.
\end{nexample}
\end{examplewrap}

\begin{figure}
   \centering
   \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}
   \caption{Sample data with their best fitting lines (top row) and their corresponding residual plots (bottom row).}
   \label{sampleLinesAndResPlots}
\end{figure}

\index{residual|)}


\subsection{Describing linear relationships with correlation}

\index{correlation|(}

\noindent%
We've seen plots with strong linear relationships and
others with very weak linear relationships.
It would be useful if we could quantify the strength of these
linear relationships with a statistic.

\begin{onebox}{Correlation: strength of a linear relationship}
  \termsub{Correlation}{correlation}, which always takes values
  between -1 and 1, describes the strength of the linear
  relationship between two variables.
  We denote the correlation by $R$.
\end{onebox}

We can compute the correlation using a formula, just as we did
with the sample mean and standard deviation.
This formula is rather complex,\footnote{Formally,
  we can compute the correlation for observations $(x_1, y_1)$,
  $(x_2, y_2)$, ..., $(x_n, y_n)$ using the formula
  \begin{align*}
  R = \frac{1}{n-1}
      \sum_{i=1}^{n} \frac{x_i-\bar{x}}{s_x}\frac{y_i-\bar{y}}{s_y}
  \end{align*}
  where $\bar{x}$, $\bar{y}$, $s_x$, and $s_y$ are the sample
  means and standard deviations for each variable.}
and like with other statistics, we generally perform the
calculations on a computer or calculator.
Figure~\ref{posNegCorPlots} shows eight plots and their
corresponding correlations.
Only when the relationship is perfectly linear is the
correlation either -1 or~1.
If~the relationship is strong and positive, the correlation
will be near~+1.
If~it is strong and negative, it will be near~-1.
If~there is no apparent linear relationship between the
variables, then the correlation will be near zero.

\begin{figure}
   \centering
   \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}
   \caption{Sample scatterplots and their correlations.
       The first row shows variables with a positive
       relationship, represented by the trend up and to
       the right.
       The second row shows one plot with an approximately neutral trend
       and three plots with a negative trend.}
   \label{posNegCorPlots}
\end{figure}

The 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}.

\begin{figure}[h]
   \centering
   \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}
   \caption{Sample scatterplots and their correlations.
       In each case, there is a strong relationship between
       the variables.
       However, because the relationship is nonlinear,
       the correlation is relatively weak.}
   \label{corForNonLinearPlots}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
No straight line is a good fit for the data sets
represented in Figure~\ref{corForNonLinearPlots}.
Try drawing nonlinear curves on each plot.
Once you create a curve for each, describe what is important
in your~fit.\footnotemark{}
\index{correlation|)}
\end{nexercise}
\end{exercisewrap}
\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.}

%\begin{examplewrap}
%\begin{nexample}{What other variables might help us predict the
%    head length of a possum besides its length?}
%  Perhaps the relationship would be a little different for
%  male possums than female possums,
%  as shown in Figure~\ref{scattHeadLTotalLSex},
%  Or perhaps it would differ for possums from one region
%  of Australia versus another region.
%  In Chapter~\ref{ch_regr_mult_and_log},
%  we'll learn about how we can include more than one predictor.
%  Before we get there, we first need to better understand
%  how to best build a simple linear model with one predictor.
%\end{nexample}
%\end{examplewrap}
%
%\begin{figure}
%  \centering
%  \Figure{0.6}{scattHeadLTotalLSex}
%  \caption{Possums where the possum's sex is represented
%      by the plotting icon.}
%  \label{scattHeadLTotalLSex}
%\end{figure}


{\input{ch_regr_simple_linear/TeX/line_fitting_residuals_and_correlation.tex}}







%__________________
\section{Least squares regression}
\label{fittingALineByLSR}

\index{least squares regression|(}

Fitting linear models by eye is open to criticism since
it is based on an individual's preference.
In this section, we use \emph{least squares regression}
as a more rigorous approach.


\subsection{Gift aid for freshman at Elmhurst College}

This section considers family income and gift aid data from
a random sample of fifty students in the freshman class of
Elmhurst College in Illinois.
Gift aid is financial aid that does not need to be paid back,
as opposed to a loan.
A scatterplot of the data is shown in
Figure~\ref{elmhurstScatterW2Lines}
along with two linear fits.
The lines follow a negative trend in the data;
students who have higher family incomes tended to have lower
gift aid from the university.

\begin{figure}[h]
  \centering
  \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}
  \caption{Gift aid and family income for a random sample of
      50~freshman students from Elmhurst College.
      Two lines are fit to the data, the solid line being the
      \emph{least squares line}.}
  \label{elmhurstScatterW2Lines}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
Is the correlation positive or negative in Figure~\ref{elmhurstScatterW2Lines}?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}


\subsection{An objective measure for finding the best line}

We begin by thinking about what we mean by ``best''.
Mathematically, we want a line that has small residuals.
The first option that may come to mind is to minimize the
sum of the residual magnitudes:
\begin{align*}
|e_1| + |e_2| + \dots + |e_n|
\end{align*}
which we could accomplish with a computer program.
The resulting dashed line shown in
Figure~\ref{elmhurstScatterW2Lines}
demonstrates this fit can be quite reasonable.
However, a more common practice is to choose the line that
minimizes the sum of the squared residuals:
\begin{align*}
e_{1}^2 + e_{2}^2 + \dots + e_{n}^2
\end{align*}


The line that minimizes this \term{least squares criterion}
is represented as the solid line in
Figure~\ref{elmhurstScatterW2Lines}.
This is commonly called the \term{least squares line}.
The following are three possible reasons to choose this option
instead of trying to minimize the sum of residual magnitudes
without any squaring:
\begin{enumerate}
\item
    It is the most commonly used method.
\item
    Computing the least squares line is widely supported
    in statistical software.
\item
    In many applications, a residual twice as large
    as another residual is more than twice as bad.
    For example, being off by 4 is usually more than twice
    as bad as being off by 2.
    Squaring the residuals accounts for this discrepancy.
\end{enumerate}
The first two reasons are largely for tradition and convenience;
the last reason explains why the least squares criterion
is typically most helpful.\footnote{There
  are applications where the sum of residual magnitudes
  may be more useful, and there are plenty of other criteria
  we might consider.
  However, this book only applies the least squares criterion.}


\subsection{Conditions for the least squares line}

\noindent%
When fitting a least squares line, we generally require
\begin{description}
\setlength{\itemsep}{0mm}
\item[Linearity.]
    The data should show a linear trend.
    If there is a nonlinear trend (e.g. left panel of
    Figure~\ref{whatCanGoWrongWithLinearModel}),
    an advanced regression method from another book
    or later course should be applied.
\item[Nearly normal residuals.]
    Generally, the residuals must be nearly normal.
    When this condition is found to be unreasonable,
    it is usually because of outliers or concerns
    about influential points,
%    The theoretical condition is that the residuals
%    must be normally distributed.
%    The importance of this condition depends on a few factors:
%    \begin{enumerate}[(1)]
%    \item
%        Is there any interest in predicting the range of
%        plausible values for individual observations?
%        If yes, then normality is important.
%    \item
%        Are there very few observations, such as fewer than~30?
%        If yes, then normality is important.
%    \end{enumerate}
%    If the answer is \emph{no} to each of these questions,
%    then
%    However, this condition can be taken with a grain of salt
%    when primarily focused on the trend of the data.
%    When the data's trend is the focus,
%    the number of observations can be modest in number,
%    such as 30 or more, at which point this condition
%    can be somewhat relaxed.
%    Generally, it is important to look for outliers,
    which we'll talk about more in
    Sections~\ref{typesOfOutliersInLinearRegression}.
    An example of a residual that would be a potentially
    concern is shown in
    Figure~\ref{whatCanGoWrongWithLinearModel},
    where one observation is clearly much further from the
    regression line than the others.
\item[Constant variability.]
    The variability of points around the least squares line
    remains roughly constant.
    An example of non-constant variability is shown in the
    third panel of Figure~\ref{whatCanGoWrongWithLinearModel},
    which represents the most common pattern observed
    when this condition fails:
    the variability of $y$ is larger when $x$ is larger.
\item[Independent observations.]
    Be cautious about applying regression to \term{time series}
    data, which are sequential observations in time such as a
    stock price each day.
    Such data may have an underlying structure that should
    be considered in a model and analysis.
    An example of a data set where successive observations
    are not independent is shown in the fourth panel of
    Figure~\ref{whatCanGoWrongWithLinearModel}.
    There are also other instances where correlations within
    the data are important, which is further discussed in
    Chapter~\ref{ch_regr_mult_and_log}.
\end{description}

\begin{figure}[h]
  \centering
  \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}
  \caption{Four examples showing when the methods in this
      chapter are insufficient to apply to the data.
      First panel: linearity fails.
      Second panel: there are outliers, most especially
      one point that is very far away from the line.
      Third panel: the variability of the errors is related
      to the value of $x$.
      Fourth panel: a time series data set is shown,
      where successive observations are highly correlated.}
  \label{whatCanGoWrongWithLinearModel}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
Should we have concerns about applying least squares regression to the Elmhurst data in Figure~\ref{elmhurstScatterW2Lines}?\footnotemark
\end{nexercise}
\end{exercisewrap}
\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.}


\D{\newpage}

\subsection{Finding the least squares line}
\label{findingTheLeastSquaresLineSection}

For the Elmhurst data, we could write the equation of the least squares regression line as
\begin{eqnarray*}
\widehat{aid} = \beta_0 + \beta_{1}\times
    \textit{family\us{}income}
\end{eqnarray*}
Here 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.

As in
Chapters~\ref{ch_foundations_for_inf},
\ref{ch_inference_for_props},
and~\ref{ch_inference_for_means},
the 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:
\begin{itemize}
\item
    The slope of the least squares line can be estimated by
    \begin{align*}
    b_1 = \frac{s_y}{s_x} R
    \end{align*}
    where $R$ is the correlation between the two variables,
    and $s_x$ and $s_y$ are the sample standard deviations
    of the explanatory variable and response, respectively.
\item
    If $\bar{x}$ is the sample mean of the explanatory variable
    and $\bar{y}$ is the sample mean of the vertical variable,
    then the point $(\bar{x}, \bar{y})$ is on the least squares
    line.

    Figure~\ref{summaryStatsElmhurstRegr} shows the sample means
    for the family income and gift aid as \$101,780 and \$19,940,
    respectively.
    We could plot the point $(101.8, 19.94)$ on
    Figure~\vref{elmhurstScatterW2Lines}
    to verify it falls on the least squares line (the solid line).
%     and from the point-slope formula, we can identify $b_0$:
%    \begin{align*}
%    \hat{y} - \bar{y} = b_1 (x - \bar{x})
%    \qquad \to \qquad
%    \hat{y} = (\bar{y} - b_1 \bar{x}) + b_1 x
%    \end{align*}
%    This is the point-slope form of a line,
%    where $b_0 = \bar{y} - b_1 \bar{x}$.
\end{itemize}
Next, we formally find the point estimates $b_0$ and $b_1$
of the parameters $\beta_0$ and $\beta_1$.


\begin{figure}[ht]
\centering
\begin{tabular}{l rr}
\hline
\vspace{-4mm} & & \\
\vspace{0.4mm}	&	\ \ Family Income ($x$)	&
    \ \ Gift Aid ($y$) \\
\hline
  \vspace{-3.9mm} & & \\
mean & $\bar{x} = \text{\$101,780}$ &
    $\bar{y} = \text{\$19,940}$ \\
sd & $s_x = \text{\$63,200}$ &
    $s_y = \text{\$5,460}$ \vspace{0.4mm} \\
\hline
\vspace{-4mm}\ &\\
	& \multicolumn{2}{r}{$R=-0.499$} \\
\hline
\end{tabular}
\caption{Summary statistics for family income and gift aid.}
\label{summaryStatsElmhurstRegr}
\end{figure}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise} \label{findingTheSlopeOfTheLSRLineForIncomeAndAid}
Using the summary statistics in Figure~\ref{summaryStatsElmhurstRegr}, compute the slope for the regression line of gift aid against family income.\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Compute the slope using the summary statistics from Figure~\ref{summaryStatsElmhurstRegr}:
\begin{eqnarray*}
b_1
  = \frac{s_y}{s_x} R
  = \frac{\text{5,460}}{\text{63,200}}(-0.499)
  = -0.0431
\end{eqnarray*}}

You might recall the \term{point-slope} form of a line
from math class, which we can use to find the model fit,
including the estimate of $b_0$.
Given the slope of a line and a point on the line,
$(x_0, y_0)$, the equation for the line can be written as
\begin{align*}
y - y_0 = slope\times (x - x_0)
\end{align*}
%We could plug in $(\bar{x}, \bar{y})$ in for $(x_0, y_0$ and solve for $\hat{y}$ to arrive at the model.
%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. 

\begin{onebox}{Identifying the least squares line from summary statistics}
To identify the least squares line from summary statistics:\vspace{-1mm}
\begin{itemize}
\setlength{\itemsep}{0mm}
\item
    Estimate the slope parameter, $b_1 = (s_y / s_x) R$.
\item
    Noting that the point $(\bar{x}, \bar{y})$ is on the least
    squares line, use $x_0 = \bar{x}$ and $y_0 = \bar{y}$ with
    the point-slope equation: $y - \bar{y} = b_1 (x - \bar{x})$.
\item
    Simplify the equation, which would reveal that
    $b_0 = \bar{y} - b_1 \bar{x}$.
\end{itemize}
\end{onebox}

\begin{examplewrap}
\begin{nexample}{Using the point $(101780, 19940)$
    from the sample means and the slope estimate
    $b_1 = -0.0431$ from Guided
    Practice~\ref{findingTheSlopeOfTheLSRLineForIncomeAndAid},
    find the least-squares line for predicting aid based
    on family income.}
  \label{exampleToFindLSRLineOfElmhurstData}%
  Apply the point-slope equation using $(101.78, 19.94)$
  and the slope $b_1 = -0.0431$:
  \begin{align*}
  y - y_0    &= b_1 (x - x_0) \\
  y - \text{19,940}  &= -0.0431(x - \text{101,780})
  \end{align*}
  Expanding the right side and then adding 19,940 to each side,
  the equation simplifies:
  \begin{align*}
  \widehat{aid} = \text{24,327} - 0.0431 \times
      \textit{family\us{}income}
  \end{align*}
  Here we have replaced $y$ with $\widehat{aid}$ and $x$ with
  \textit{family\us{}income} to put the equation in context.
  The final equation should always include a ``hat''
  on the variable being predicted, whether it is a generic
  ``$y$'' or a named variable like ``$aid$''.
\end{nexample}
\end{examplewrap}

A computer is usually used to compute the least squares line,
and a summary table generated using software for the Elmhurst
regression line is shown in
Figure~\ref{rOutputForIncomeAidLSRLine}.
The first column of numbers provides estimates for ${b}_0$
and ${b}_1$, respectively.
These results match those from
Example~\ref{exampleToFindLSRLineOfElmhurstData}
(with some minor rounding error).

\begin{figure}[ht]
\centering
\begin{tabular}{l rrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
 & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.6mm} & & & & \\
(Intercept) & 24319.3 & 1291.5 & 18.83 & $<$0.0001 \\ 
family\us{}income & -0.0431 & 0.0108 & -3.98 & 0.0002 \\ 
  \hline
\end{tabular}
\caption{Summary of least squares fit for the Elmhurst data.
    Compare the parameter estimates in the first column to
    the results of
    Example~\ref{exampleToFindLSRLineOfElmhurstData}.}
\label{rOutputForIncomeAidLSRLine}
\end{figure}

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{Examine the second, third, and fourth columns
    in Figure~\ref{rOutputForIncomeAidLSRLine}.
    Can you guess what they represent?
    (If you have not reviewed any inference chapter yet,
    skip this example.)}
  We'll describe the meaning of the columns using the
  second row, which corresponds to~$\beta_1$.
  The first column provides the point estimate for $\beta_1$,
  as we calculated in an earlier example: $b_1 = -0.0431$.
  The second column is a standard error for this point estimate:
  $SE_{b_1} = 0.0108$.
  The third column is a $t$-test statistic for the null
  hypothesis that $\beta_1 = 0$: $T = -3.98$.
  The last column is the p-value for the $t$-test statistic
  for the null hypothesis $\beta_1 = 0$ and a two-sided
  alternative hypothesis: 0.0002.
  We will get into more of these details in
  Section~\ref{inferenceForLinearRegression}.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Suppose a high school senior is considering
    Elmhurst College.
    Can she simply use the linear equation that we have estimated
    to calculate her financial aid from the university?}
  She may use it as an estimate, though some qualifiers on this
  approach are important.
  First, the data all come from one freshman class,
  and the way aid is determined by the university may change
  from year to year.
  Second, the equation will provide an imperfect estimate.
  While the linear equation is good at capturing the trend
  in the data, no individual student's aid will be perfectly
  predicted.
\end{nexample}
\end{examplewrap} 

\index{least squares regression|)}


\subsection{Interpreting regression model parameter estimates}

\index{least squares regression!interpreting parameters|(}

\noindent%
Interpreting parameters in a regression model is often one
of the most important steps in the analysis.

\begin{examplewrap}
\begin{nexample}{The intercept and slope estimates for
    the Elmhurst data are $b_0 = \text{24,319}$
    and $b_1 = -0.0431$.
    What do these numbers really mean?}
  Interpreting the slope parameter is helpful in almost any
  application.
  For each additional \$1,000 of family income, we would expect
  a student to receive a net difference of
  $\$\text{1,000}\times (-0.0431) = -\$43.10$ in aid on average,
  i.e. \$43.10 \emph{less}.
  Note that a higher family income corresponds to less aid
  because the coefficient of family income is negative in
  the model.
  We must be cautious in this interpretation:
  while there is a real association, we cannot interpret
  a causal connection between the variables because these
  data are observational.
  That is, increasing a student's family income may not
  cause the student's aid to drop.
  (It would be reasonable to contact the college and ask
  if the relationship is causal,
  i.e. if Elmhurst College's aid decisions are partially
  based on students' family income.)

  The estimated intercept $b_0 = \text{24,319}$
  describes the average aid if a student's family had no income.
  The meaning of the intercept is relevant to this application
  since the family income for some students at Elmhurst is~\$0.
  In other applications, the intercept may have little
  or no practical value if there are no observations where
  $x$ is near zero.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Interpreting parameters estimated by least squares}
  The slope describes the estimated difference in the
  $y$ variable if the explanatory variable $x$ for a case
  happened to be one unit larger.
  The intercept describes the average outcome of $y$ if $x=0$
  \emph{and} the linear model is valid all the way to $x=0$,
  which in many applications is not the case.
\end{onebox}

\index{least squares regression!interpreting parameters|)}


\D{\newpage}

\subsection{Extrapolation is treacherous}

\index{least squares regression!extrapolation|(}

{\em\small When those blizzards hit the East Coast this winter,
it proved to my satisfaction that global warming was a fraud.
That snow was freezing cold.
But in an alarming trend, temperatures this spring have risen.
Consider this: On February $6^{th}$ it was 10 degrees.
Today it hit almost 80. At this rate, by August it will be
220 degrees.
So clearly folks the climate debate rages on.\vspace{0.5mm}}

\noindent\hspace{\textwidth}\hspace{-40mm}Stephen Colbert

\noindent\hspace{\textwidth}\hspace{-40mm}April 6th,
2010\footnote{\oiRedirect{textbook-colbert_extrapolation}
    {www.cc.com/video-clips/l4nkoq}} \\

Linear models can be used to approximate the relationship
between two variables.
However, these models have real limitations.
Linear regression is simply a modeling framework.
The truth is almost always much more complex than our simple line.
For example, we do not know how the data outside of our limited
window will behave.

\begin{examplewrap}
\begin{nexample}{Use the model
    $\widehat{aid}
      = \text{24,319} - 0.0431 \times
          \textit{family\us{}income}$
    to estimate the aid of another freshman student whose
    family had income of \$1~million.}
  We want to calculate the aid for
  $\textit{family\us{}income} = \text{1,000,000}$:
  \begin{align*}
  \text{24,319} - 0.0431\times \textit{family\us{}income}
    = \text{24,319} - 0.0431\times \text{1,000,000}
    = -\text{18,781}
  \end{align*}
  The model predicts this student will have -\$18,781 in aid (!).
  However, Elmhurst College does not offer \emph{negative aid}
  where they select some students to pay extra on top of tuition
  to attend.
\end{nexample}
\end{examplewrap}

Applying a model estimate to values outside of the realm of the
original data is called \term{extrapolation}.
Generally, a linear model is only an approximation of the real
relationship between two variables.
If we extrapolate, we are making an unreliable bet that the
approximate linear relationship will be valid in places where
it has not been analyzed.

\index{least squares regression!extrapolation|)}


\subsection{Using $R^2$ to describe the strength of a fit}

\index{least squares regression!R-squared ($R^2$)|(}

We evaluated the strength of the linear relationship between
two variables earlier using the correlation, $R$.
However, it is more common to explain the strength of a linear
fit using $R^2$, called
\termsub{R-squared}{least squares regression!R-squared ($R^2$)}.
\index{R-squared ($R^2$)|textbf}
If provided with a linear model, we might like to describe how
closely the data cluster around the linear fit.

\begin{figure}[h]
  \centering
  \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}
  \caption{Gift aid and family income for a random sample
      of 50 freshman students from Elmhurst College, shown
      with the least squares regression line.}
  \label{elmhurstScatterWLSROnly}
\end{figure}

\newcommand{\mil}[0]{\text{ million}}
The $R^2$ of a linear model describes the amount of variation
in the response that is explained by the least squares line.
For example, consider the Elmhurst data,
shown in Figure~\ref{elmhurstScatterWLSROnly}.
The variance of the response variable, aid received,
is about $s_{aid}^2 \approx 29.8$ million.
However, if we apply our least squares line, then this model
reduces our uncertainty in predicting aid using a student's
family income.
The variability in the residuals describes how much variation
remains after using the model: $s_{_{RES}}^2 \approx 22.4$ million.
In short, there was a reduction of
\begin{align*}
\frac{s_{aid}^2 - s_{_{RES}}^2}{s_{aid}^2}
  = \frac{\text{29,800,000} - \text{22,400,000}}
      {\text{29,800,000}}
  = \frac{\text{7,500,000}}{\text{29,800,000}}
  = 0.25
\end{align*}
or about  25\% in the data's variation by using information
about family income for predicting aid using a linear model.
This corresponds exactly to the R-squared value:
\begin{align*}
R &= -0.499 &R^2 &= 0.25
\end{align*}

\begin{exercisewrap}
\begin{nexercise}
If a linear model has a very strong negative relationship with
a correlation of -0.97, how much of the variation in the response
is explained by the explanatory variable?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{About $R^2 = (-0.97)^2 = 0.94$ or 94\%
  of the variation is explained by the linear model.}

\index{least squares regression!R-squared ($R^2$)|)}


\subsection{Categorical predictors with two levels}
\label{categoricalPredictorsWithTwoLevels}

Categorical variables are also useful in predicting outcomes.
Here we consider a categorical predictor with two levels
(recall that a \emph{level} is the same as a \emph{category}).
We'll consider Ebay auctions for a video game, \emph{Mario Kart}
for the Nintendo Wii, where both the total price of the auction
and the condition of the game were recorded.
Here we want to predict total price based on game condition,
which takes values \resp{used} and \resp{new}.
A plot of the auction data is shown in Figure~\ref{marioKartNewUsed}.

\begin{figure}[h]
  \centering
  \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}
  \caption{Total auction prices for the video game
      \emph{Mario Kart}, divided into used ($x=0$)
      and new ($x=1$) condition games.
      The least squares regression line is also shown.}
  \label{marioKartNewUsed}
\end{figure}

To incorporate the game condition variable into a regression
equation, we must convert the categories into a numerical form.
We will do so using an \term{indicator variable}
called \var{cond\us{}new}, which takes value 1 when the game
is new and 0 when the game is used.
Using this indicator variable, the linear model may be written as
\begin{align*}
\widehat{price} = \beta_0 + \beta_1 \times \text{\var{cond\us{}new}}
\end{align*}
The parameter estimates are given in
Figure~\ref{marioKartNewUsedRegrSummary},
and the model equation can be summarized as
\begin{align*}
\widehat{price} = 42.87 + 10.90 \times \text{\var{cond\us{}new}}
\end{align*}
For categorical predictors with just two levels,
the linearity assumption will always be satisfied.
However, we must evaluate whether the residuals in
each group are approximately normal and have approximately
equal variance.
As can be seen in Figure~\ref{marioKartNewUsed},
both of these conditions are reasonably satisfied
by the auction data.

\begin{figure}
\centering
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
 & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.6mm} & & & & \\
(Intercept) & 42.87 & 0.81 & 52.67 & $<$0.0001 \\ 
  cond\us{}new & 10.90 & 1.26 & 8.66 & $<$0.0001 \\ 
   \hline
\end{tabular}
\caption{Least squares regression summary for the final auction price against the condition of the game.}
\label{marioKartNewUsedRegrSummary}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Interpret the two parameters estimated in the
    model for the price of \emph{Mario Kart} in eBay auctions.}
  The intercept is the estimated price when \var{cond\us{}new}
  takes value 0, i.e. when the game is in used condition.
  That is, the average selling price of a used version of
  the game is \$42.87.

  The slope indicates that, on average, new games sell for
  about \$10.90 more than used games.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Interpreting model estimates for
    categorical predictors}
  The estimated intercept is the value of the response variable
  for the first category (i.e. the category corresponding to an
  indicator value of~0).
  The estimated slope is the average change in the response
  variable between the two categories.
\end{onebox}

We'll elaborate further on this topic in
Chapter~\ref{ch_regr_mult_and_log},
where we examine the influence of many
predictor variables simultaneously using
multiple regression.


{\input{ch_regr_simple_linear/TeX/fitting_a_line_by_least_squares_regression.tex}}








%__________________
\section{Types of outliers in linear regression}
\label{typesOfOutliersInLinearRegression}

In this section, we identify criteria for determining which
outliers are important and influential.
Outliers in regression are observations that fall far from
the cloud of points.
These points are especially important because they can have
a strong influence on the least squares line. 

\begin{examplewrap}
\begin{nexample}{There are six plots shown in
    Figure~\ref{outlierPlots} along with the least squares
    line and residual plots.
    For~each scatterplot and residual plot pair,
    identify the outliers and note how they influence
    the least squares line.
    Recall that an outlier is any point that doesn't appear
    to belong with the vast majority of the other points.}
  \label{outlierPlotsExample}%
  \begin{itemize}
  %\setlength{\itemsep}{0mm}
  \item[(1)]
      There is one outlier far from the other points,
      though it only appears to slightly influence the~line.
  \item[(2)]
      There is one outlier on the right, though it is quite
      close to the least squares line, which suggests it
      wasn't very influential.
  \item[(3)]
      There is one point far away from the cloud, and this
      outlier appears to pull the least squares line up on
      the right;
      examine how the line around the primary cloud doesn't
      appear to fit very~well.
  \item[(4)]
      There is a primary cloud and then a small secondary
      cloud of four outliers.
      The secondary cloud appears to be influencing the line
      somewhat strongly, making the least square line fit
      poorly almost everywhere.
      There might be an interesting explanation for the dual
      clouds, which is something that could be investigated.
  \item[(5)]
      There is no obvious trend in the main cloud of points
      and the outlier on the right appears to largely control
      the slope of the least squares line.
  \item[(6)]
      There is one outlier far from the cloud.
      However, it falls quite close to the least squares line
      and does not appear to be very influential.
  \end{itemize}
\end{nexample}
\end{examplewrap}

\begin{figure}
  \centering
  \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}
  \caption{Six plots, each with a least squares line and
      residual plot. All data sets have at least one outlier.}
\label{outlierPlots}
\end{figure}

Examine the residual plots in Figure~\ref{outlierPlots}.
You will probably find that there is some trend in the main
clouds of~(3) and~(4).
In these cases, the outliers influenced the slope of the
least squares lines.
In~(5), data with no clear trend were assigned a line with
a large trend simply due to one outlier (!).
 
\begin{onebox}{Leverage}
  Points that fall horizontally away from the center of the
  cloud tend to pull harder on the line, so we call them points
  with \term{high leverage}.\index{leverage}
\end{onebox}

Points that fall horizontally far from the line are points
of high leverage;
these points can strongly influence the slope of the least
squares line.
If one of these high leverage points does appear to actually
invoke its influence on the slope of the line --
as in cases~(3), (4), and (5) of Example~\ref{outlierPlotsExample}
-- then we call it an \term{influential point}.
Usually we can say a point is influential if, had we fitted
the line without it, the influential point would have been
unusually far from the least squares line.

It is tempting to remove outliers.
Don't do this without a very good reason.
Models that ignore exceptional (and interesting) cases often
perform poorly.
For instance, if a financial firm ignored the largest market
swings -- the ``outliers'' --  they would soon go bankrupt
by making poorly thought-out investments.


{\input{ch_regr_simple_linear/TeX/types_of_outliers_in_linear_regression.tex}}








%__________________
\section{Inference for linear regression}
\label{inferenceForLinearRegression}

In this section, we discuss uncertainty in the estimates
of the slope and y-intercept for a regression line.
Just as we identified standard errors for point estimates
in previous chapters, we first discuss standard errors for
these new estimates.


\subsection{Midterm elections and unemployment}

\index{data!midterm elections|(}

Elections for members of the United States House
of Representatives occur every two years, coinciding
every four years with the U.S. Presidential election.
The set of House elections occurring during the middle
of a Presidential term are called
\indexthis{midterm elections}{midterm election}.
In America's two-party system, one political theory
suggests the higher the unemployment rate, the worse
the President's party will do in the midterm elections.

To assess the validity of this claim, we can compile
historical data and look for a connection.
We consider every midterm election from 1898 to 2018,
with the exception of those elections during the Great
Depression.
Figure~\ref{unemploymentAndChangeInHouse} shows these data
and the least-squares regression line: \vspace{-2mm}
\begin{align*}
&\text{\% change in House seats for President's party}  \\
&\qquad\qquad= -7.36 - 0.89 \times \text{(unemployment rate)}
\end{align*}
We consider the percent change in the number of seats
of the President's party (e.g. percent change in the number
of seats for Republicans in 2018) against the unemployment
rate.

Examining the data, there are no clear deviations from
linearity, the constant variance condition,
or substantial outliers.
While the data are collected sequentially, a separate analysis
was used to check for any apparent correlation between successive
observations;
no such correlation was found.

\begin{figure}[h]
  \centering
  \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}
  \caption{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.}
  \label{unemploymentAndChangeInHouse}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
The data for the Great Depression (1934 and 1938) were removed
because the unemployment rate was 21\% and 18\%, respectively.
Do you agree that they should be removed for this investigation?
Why or why not?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We will provide two considerations.
  Each of these points would have very high leverage on any
  least-squares regression line, and years with such high
  unemployment may not help us understand what would happen
  in other years where the unemployment is only modestly high.
  On the other hand, these are exceptional cases, and we would
  be discarding important information if we exclude them from
  a final analysis.}

There is a negative slope in the line shown in
Figure~\ref{unemploymentAndChangeInHouse}.
However, this slope (and the y-intercept) are only estimates
of the parameter values.
We might wonder, is this convincing evidence that the ``true''
linear model has a negative slope?
That is, do the data provide strong evidence that the political
theory is accurate, where the unemployment rate is a useful
predictor of the midterm election?
We can frame this investigation into a statistical hypothesis
test:
\begin{itemize}
\item[$H_0$:]
    $\beta_1 = 0$.
    The true linear model has slope zero.
\item[$H_A$:]
    $\beta_1 \neq 0$.
    The true linear model has a slope different than zero.
    The unemployment is predictive of whether the President's
    party wins or loses seats in the House of Representatives.
\end{itemize}
We would reject $H_0$ in favor of $H_A$ if the data provide
strong evidence that the true slope parameter is different
than zero.
To assess the hypotheses, we identify a standard error
for the estimate, compute an appropriate test statistic,
and identify the p-value.


\subsection{Understanding regression output from software}
\label{testStatisticForTheSlope}

\newcommand{\midtermshouseDF}{27}

Just like other point estimates we have seen before,
we can compute a standard error and test statistic for $b_1$.
We will generally label the test statistic using a $T$,
since it follows the $t$-distribution.

We will rely on statistical software to compute the standard
error and leave the explanation of how this standard error
is determined to a second or third statistics course.
Figure~\ref{midtermUnempRegTable} shows software output for
the least squares regression line in
Figure~\ref{unemploymentAndChangeInHouse}.
The row labeled \emph{unemp} includes the point estimate
and other hypothesis test information for the slope,
which is the coefficient of the unemployment variable.

\begin{figure}[ht]
\centering
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.6mm} & & & & \\
  (Intercept) & -7.3644 & 5.1553 & -1.43 & 0.1646 \\ 
  unemp & -0.8897 & 0.8350 & -1.07 & 0.2961 \\ 
  \hline
  \multicolumn{5}{r}{$df=\midtermshouseDF{}$} \\
\end{tabular}
\caption{Output from statistical software for the regression
    line modeling the midterm election losses for the
    President's party as a response to unemployment.}
\label{midtermUnempRegTable}
\end{figure}

\begin{examplewrap}
\begin{nexample}{What do the first and second columns
    of Figure~\ref{midtermUnempRegTable} represent?}
  The entries in the first column represent the least
  squares estimates, $b_0$ and $b_1$, and the values in
  the second column correspond to the standard errors
  of each estimate.
  Using the estimates, we could write the equation
  for the least square regression line as
  \begin{align*}
  \hat{y} = -7.3644 - 0.8897 x
  \end{align*}
  where $\hat{y}$ in this case represents the predicted
  change in the number of seats for the president's party,
  and $x$ represents the unemployment rate.
\end{nexample}
\end{examplewrap}

\D{\newpage}

We previously used a $t$-test statistic for hypothesis testing
in the context of numerical data.
Regression is very similar.
In the hypotheses we consider, the null value for the slope is~0,
so we can compute the test statistic using the T (or Z) score
formula:
\begin{align*}
T
  = \frac{\text{estimate} - \text{null value}}{\text{SE}}
  = \frac{-0.8897 - 0}{0.8350}
  = -1.07
\end{align*}
This corresponds to the third column of
Figure~\ref{midtermUnempRegTable}.

%\begin{figure}[h]
%  \centering
%  \Figure{0.82}{pValueMidtermUnemp}
%  \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.}
%  \label{pValueMidtermUnemp}
%\end{figure}

\begin{examplewrap}
\begin{nexample}{Use the table in
    Figure~\ref{midtermUnempRegTable}
    to determine the p-value for the hypothesis test.}
  The last column of the table gives the p-value for
  the two-sided hypothesis test for the coefficient of
  the unemployment rate: 0.2961.
  That is, the data do not provide convincing evidence
  that a higher unemployment rate has any correspondence
  with smaller or larger losses for the President's party
  in the House of Representatives in midterm elections.
\end{nexample}
\end{examplewrap}

\index{data!midterm elections|)}

\begin{onebox}{Inference for regression}
  We usually rely on statistical software to identify point
  estimates, standard errors, test statistics, and p-values
  in practice.
  However, be aware that software will not generally
  check whether the method is appropriate, meaning we must
  still verify conditions are met.
\end{onebox}

\begin{examplewrap}
\begin{nexample}{Examine Figure~\vref{elmhurstScatterWLSROnly},
    which relates the Elmhurst College aid and student family
    income.
    How sure are you that the slope is statistically
    significantly different from zero?
    That is, do you think a formal hypothesis test would reject
    the claim that the true slope of the line should be zero?}
  \label{overallAidIncomeInfAssessOfRegrLineSlope}%
  While the relationship between the variables is not perfect,
  there is an evident decreasing trend in the data.
  This suggests the hypothesis test will reject the null claim
  that the slope is zero.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
Figure~\ref{rOutputForIncomeAidLSRLineInInferenceSection}
shows statistical software output from fitting the least
squares regression line shown in
Figure~\ref{elmhurstScatterWLSROnly}.
Use this output to formally evaluate the following
hypotheses.\footnotemark{}
\begin{itemize}
\setlength{\itemsep}{0mm}
\item[$H_0$:]
    The true coefficient for family income is zero.
\item[$H_A$:]
    The true coefficient for family income is not zero.
\end{itemize}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We look in the second row corresponding
  to the family income variable.
  We see the point estimate of the slope of the line is -0.0431,
  the standard error of this estimate is 0.0108, and the $t$-test
  statistic is $T = -3.98$.
  The p-value corresponds exactly to the two-sided test we are
  interested in: 0.0002.
  The p-value is so small that we reject the null hypothesis
  and conclude that family income and financial aid at Elmhurst
  College for freshman entering in the year 2011 are negatively
  correlated and the true slope parameter is indeed less than~0,
  just as we believed in
  Example~\ref{overallAidIncomeInfAssessOfRegrLineSlope}.}

\begin{figure}[ht]
\centering
\begin{tabular}{rrrrr}
  \hline
  \vspace{-3.7mm} & & & & \\
 & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\ 
  \hline
  \vspace{-3.6mm} & & & & \\
(Intercept) & 24319.3 & 1291.5 & 18.83 & $<$0.0001 \\ 
family\us{}income & -0.0431 & 0.0108 & -3.98 & 0.0002 \\ 
   \hline
   \multicolumn{5}{r}{$df=48$} \\
\end{tabular}
\caption{Summary of least squares fit for the Elmhurst
    College data, where we are predicting the gift aid
    by the university based on the family income of
    students.}
\label{rOutputForIncomeAidLSRLineInInferenceSection}
\end{figure}


\newpage

\subsection{Confidence interval for a coefficient}

\index{confidence interval!regression|(}%

Similar to how we can conduct a hypothesis test for
a model coefficient using regression output, we can also
construct a confidence interval for that coefficient.

\begin{examplewrap}
\begin{nexample}{
    Compute the 95\% confidence interval for the
    \var{family\us{}income} coefficient using the
    regression output from
    Table~\ref{rOutputForIncomeAidLSRLineInInferenceSection}.}
  The point estimate is -0.0431 and the standard error is
  $SE = 0.0108$.
  When constructing a confidence interval for a model
  coefficient, we generally use a $t$-distribution.
  The degrees of freedom for the distribution are noted in
  the regression output, $df = 48$, allowing us to identify
  $t_{48}^{\star} = 2.01$ for use in the confidence interval.

  We can now construct the confidence interval in the usual way:
  \begin{align*}
  \text{point estimate} \pm t_{48}^{\star} \times SE
    \qquad\to\qquad -0.0431 \pm 2.01 \times 0.0108
    \qquad\to\qquad (-0.0648, -0.0214)
  \end{align*}
  We are 95\% confident that with each dollar increase in
  \var{family\us{}income}, the university's gift aid is
  predicted to decrease on average by \$0.0214 to \$0.0648.
\end{nexample}
\end{examplewrap}

\begin{onebox}{Confidence intervals for coefficients}
  Confidence intervals for model coefficients can be computed
  using the $t$-distribution:
  \begin{align*}
  b_i \ \pm\ t_{df}^{\star} \times SE_{b_{i}}
  \end{align*}
  where $t_{df}^{\star}$ is the appropriate $t$-value
  corresponding to the confidence level with the
  model's degrees of freedom.
\end{onebox}

On the topic of intervals in this book, we've focused exclusively
on confidence intervals for model parameters.
However, there are other types of intervals that may be
of interest, including
prediction intervals\index{prediction interval}
for a response value
and also
confidence intervals for a
mean response value\index{mean response value}
in the context of regression.
These two interval types are introduced in an online extra
that you may download at
\begin{center}
\oiRedirect{stat_extra_linear_regression_supp}
    {www.openintro.org/d?file=stat\_extra\_linear\_regression\_supp}
\end{center}

\index{confidence interval!regression|)}%
\index{regression|)}


{\input{ch_regr_simple_linear/TeX/inference_for_linear_regression.tex}}


================================================
FILE: ch_regr_simple_linear/TeX/fitting_a_line_by_least_squares_regression.tex
================================================
\exercisesheader{}

% 17

\eoce{\qt{Units of regression\label{regression_units}} Consider a regression 
predicting weight (kg) from height (cm) for a sample of adult males. 
What are the units of the correlation coefficient, the intercept, 
and the slope?
}{}

% 18

\eoce{\qtq{Which is higher\label{which_higher_scatter}} Determine if I or II 
is higher or if they are equal. Explain your reasoning.
\noindent For a regression line, the uncertainty associated with the 
slope estimate, $b_1$, is higher when
\begin{enumerate}
\item[I.] there is a lot of scatter around the regression line or
\item[II.] there is very little scatter around the regression line
\end{enumerate}
}{}

% 19

\eoce{\qt{Over-under, Part I\label{residual_apple_weight}} Suppose we fit a 
regression line to predict the shelf life of an apple based on its weight. 
For a particular apple, we predict the shelf life to be 4.6 days. The 
apple's residual is -0.6 days. Did we over or under estimate the 
shelf-life of the apple? Explain your reasoning.
}{}

% 20

\eoce{\qt{Over-under, Part II\label{residual_sun_cancer}} Suppose we fit a 
regression line to predict the number of incidents of skin cancer per 
1,000 people from the number of sunny days in a year. For a particular 
year, we predict the incidence of skin cancer to be 1.5 per 1,000 people, 
and the residual for this year is 0.5. Did we over or under estimate 
the incidence of skin cancer? Explain your reasoning.
}{}

% 21

\eoce{\qt{Tourism spending\label{tourism_spending_reg_conds}} The Association of 
Turkish Travel Agencies reports the number of foreign tourists 
visiting Turkey and tourist spending by year.
\footfullcite{data:turkeyTourism} Three plots are provided: 
scatterplot showing the relationship between these two variables 
along with the least squares fit, residuals plot, and histogram of 
residuals.
\begin{center}
\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}
\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}
\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}
\end{center}
\begin{parts}
\item Describe the relationship between number of tourists and spending.
\item What are the explanatory and response variables?
\item Why might we want to fit a regression line to these data?
\item Do the data meet the conditions required for fitting a least squares 
line? In addition to the scatterplot, use the residual plot and histogram 
to answer this question. 
\end{parts}
}{}

\D{\newpage}

% 22

\eoce{\qt{Nutrition at Starbucks, Part I\label{starbucks_cals_carbos}} 
The scatterplot below shows the relationship between the number of 
calories and amount of carbohydrates (in grams) Starbucks food menu 
items contain.\footfullcite{data:starbucksCals} Since Starbucks only 
lists the number of calories on the display items, we are interested 
in predicting the amount of carbs a menu item has based on its 
calorie content.
\begin{center}
\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}
\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}
\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}
\end{center}
\begin{parts}
\item Describe the relationship between number of calories and amount 
of carbohydrates (in grams) that Starbucks food menu items contain.
\item In this scenario, what are the explanatory and response 
variables?
\item Why might we want to fit a regression line to these data?
\item Do these data meet the conditions required for fitting a least 
squares line?
\end{parts}
}{}

% 23

\eoce{\qt{The Coast Starlight, Part II\label{coast_starlight_reg}}
Exercise~\ref{coast_starlight_corr_units} introduces data on the Coast Starlight 
Amtrak train that runs from Seattle to Los Angeles. The mean travel 
time from one stop to the next on the Coast Starlight is 129 mins, 
with a standard deviation of 113 minutes. The mean distance traveled 
from one stop to the next is 108 miles with a standard deviation of 
99 miles. The correlation between travel time and distance is 0.636.
\begin{parts}
\item Write the equation of the regression line for predicting travel 
time.
\item Interpret the slope and the intercept in this context.
\item Calculate $R^2$ of the regression line for predicting travel 
time from distance traveled for the Coast Starlight, and interpret 
$R^2$ in the context of the application.
\item The distance between Santa Barbara and Los Angeles is 103 
miles. Use the model to estimate the time it takes for the Starlight 
to travel between these two cities.
\item It actually takes the Coast Starlight about 168 mins to travel 
from Santa Barbara to Los Angeles. Calculate the residual and explain 
the meaning of this residual value.
\item Suppose Amtrak is considering adding a stop to the Coast 
Starlight 500 miles away from Los Angeles. Would it be appropriate to 
use this linear model to predict the travel time from Los Angeles to 
this point? 
\end{parts}
}{}

% 24

\eoce{\qt{Body measurements, Part III\label{body_measurements_shoulder_height_reg}}
Exercise~\ref{body_measurements_shoulder_height_corr_units} introduces 
data on shoulder girth and height of a group of individuals. The 
mean shoulder girth is 107.20 cm with a standard deviation of 
10.37 cm. The mean height is 171.14 cm with a standard deviation 
of 9.41 cm. The correlation between height and shoulder girth is 0.67.
\begin{parts}
\item Write the equation of the regression line for predicting height.
\item Interpret the slope and the intercept in this context.
\item Calculate $R^2$ of the regression line for predicting height 
from shoulder girth, and interpret it in the context of the 
application.
\item A randomly selected student from your class has a shoulder 
girth of 100 cm. Predict the height of this student using the model.
\item The student from part~(d) is 160 cm tall. Calculate the 
residual, and explain what this residual means.
\item A one year old has a shoulder girth of 56 cm. Would it be 
appropriate to use this linear model to predict the height of this 
child?
\end{parts}
}{}

\D{\newpage}

% 25

\eoce{\qt{Murders and poverty, Part I\label{murders_poverty_reg}} The following 
regression output is for predicting annual murders per million from 
percentage living in poverty in a random sample of 20 metropolitan 
areas.\\[2mm]
\begin{minipage}[c]{0.54\textwidth}
{\footnotesize
\begin{tabular}{rrrrr}
    \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
    \hline
(Intercept) & -29.901   & 7.789         & -3.839    & 0.001 \\ 
poverty\%   & 2.559     & 0.390         & 6.562     & 0.000 \\ 
   \hline
\end{tabular} \\
$s = 5.512 \hfill R^2 = 70.52\% \hfill R^2_{adj} = 68.89\%$ 
}
\begin{parts}
\item Write out the linear model.
\item Interpret the intercept.
\item Interpret the slope.
\item Interpret $R^2$.
\item Calculate the correlation coefficient.
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.02\textwidth}
$\:$\\
\end{minipage}
\begin{minipage}[c]{0.41\textwidth}
\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}
\end{minipage}
}{}

% 26

\eoce{\qt{Cats, Part I\label{cat_body_heart_reg}} The following regression output is 
for predicting the heart weight (in g) of cats from their body weight 
(in kg). The coefficients are estimated using a dataset of 144 
domestic cats.\\[2mm]
\begin{minipage}[c]{0.54\textwidth}
{\footnotesize
\begin{tabular}{rrrrr}
    \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
    \hline
(Intercept) & -0.357    & 0.692         & -0.515    & 0.607 \\ 
body wt     & 4.034     & 0.250         & 16.119    & 0.000 \\ 
    \hline
\end{tabular} \\
$s = 1.452 \hfill R^2 = 64.66\% \hfill R^2_{adj} = 64.41\%$ 
}
\begin{parts}
\item Write out the linear model.
\item Interpret the intercept.
\item Interpret the slope.
\item Interpret $R^2$.
\item Calculate the correlation coefficient.
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.02\textwidth}
$\:$\\
\end{minipage}
\begin{minipage}[c]{0.41\textwidth}
\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}
\end{minipage}
}{}


================================================
FILE: ch_regr_simple_linear/TeX/inference_for_linear_regression.tex
================================================
\exercisesheader{}

\noindent%
In the following exercises, visually check the conditions
for fitting a least squares regression line.
However, you do not need to report these conditions in
your solutions.\\[6mm]

% 31

\eoce{\qt{Body measurements, Part IV\label{body_measurements_weight_height_inf}} 
The scatterplot and least squares summary below show the relationship 
between weight measured in kilograms and height measured in centimeters 
of 507 physically active individuals.

\noindent\begin{minipage}[c]{0.4\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.6\textwidth}
{\scriptsize
\begin{center}
\begin{tabular}{rrrrr}
    \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
    \hline
(Intercept) & -105.0113 & 7.5394        & -13.93    & 0.0000 \\ 
height      & 1.0176    & 0.0440        & 23.13     & 0.0000 \\
    \hline
\end{tabular}
\end{center}
}
\end{minipage}
\begin{parts}
\item Describe the relationship between height and weight.
\item Write the equation of the regression line. Interpret the slope 
and intercept in context.
\item Do the data provide strong evidence that an increase in height 
is associated with an increase in weight? State the null and alternative 
hypotheses, report the p-value, and state your conclusion.
\item The correlation coefficient for height and weight is 0.72. 
Calculate $R^2$ and interpret it in context.
\end{parts}
}{}

% 32

\eoce{\qt{Beer and blood alcohol content\label{beer_blood_alcohol_inf}} 
Many people believe that gender, 
weight, drinking habits, and many other factors are much more important 
in predicting blood alcohol content (BAC) than simply considering the 
number of drinks a person consumed. Here we examine data from sixteen 
student volunteers at Ohio State University who each drank a randomly 
assigned number of cans of beer. These students were evenly divided 
between men and women, and they differed in weight and drinking habits. 
Thirty minutes later, a police officer measured their blood alcohol 
content (BAC) in grams of alcohol per deciliter of blood.
\footfullcite{Malkevitc+Lesser:2008} The scatterplot and regression 
table summarize the findings.

\noindent\begin{minipage}[c]{0.4\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.6\textwidth}
{\scriptsize
\begin{center}
\begin{tabular}{rrrrr}
    \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
    \hline
(Intercept) & -0.0127   & 0.0126        & -1.00     & 0.3320 \\ 
beers       & 0.0180    & 0.0024        & 7.48      & 0.0000 \\ 
    \hline
\end{tabular}
\end{center}
}
\end{minipage}
\begin{parts}
\item Describe the relationship between the number of cans of beer 
and BAC.
\item Write the equation of the regression line. Interpret the slope 
and intercept in context.
\item Do the data provide strong evidence that drinking more cans of 
beer is associated with an increase in blood alcohol? State the null 
and alternative hypotheses, report the p-value, and state your 
conclusion.
\item The correlation coefficient for number of cans of beer and BAC 
is 0.89. Calculate $R^2$ and interpret it in context.
\item Suppose we visit a bar, ask people how many drinks they have had, 
and also take their BAC. Do you think the relationship between number 
of drinks and BAC would be as strong as the relationship found in the 
Ohio State study?
\end{parts}
}{}

\D{\newpage}

% 33

\eoce{\qt{Husbands and wives, Part II\label{husbands_wives_height_inf}} The 
scatterplot below summarizes husbands' and wives' heights in a random 
sample of 170 married couples in Britain, where both partners' ages are 
below 65 years. Summary output of the least squares fit for predicting 
wife's height from husband's height is also provided in the table.

\noindent\begin{minipage}[c]{0.4\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.6\textwidth}
{\scriptsize
\begin{center}
\begin{tabular}{rrrrr}
    \hline
                    & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
    \hline
(Intercept)         & 43.5755   & 4.6842        & 9.30      & 0.0000 \\ 
height\_\hspace{0.3mm}husband   & 0.2863    & 0.0686        & 4.17      & 0.0000 \\ 
    \hline
\end{tabular}
\end{center}
}
\end{minipage}
\begin{parts}
\item Is there strong evidence that taller men marry taller women? 
State the hypotheses and include any information used to conduct the test.
\item Write the equation of the regression line for predicting wife's 
height from husband's height.
\item Interpret the slope and intercept in the context of the application.
\item Given that $R^2 = 0.09$, what is the correlation of heights 
in this data set?
\item You meet a married man from Britain who is 5'9" (69 inches). 
What would you predict his wife's height to be? How reliable is this 
prediction?
\item You meet another married man from Britain who is 6'7" (79 inches). 
Would it be wise to use the same linear model to predict his wife's 
height? Why or why not?
\end{parts}
}{}

% 34

\eoce{\qt{Urban homeowners, Part II\label{urban_homeowners_cond}}
Exercise~\ref{urban_homeowners_outlier} gives a scatterplot displaying the 
relationship between the percent of families that own their home and 
the percent of the population living in urban areas. Below is a 
similar scatterplot, excluding District of Columbia, as well as the 
residuals plot. There were 51 cases.

\noindent\begin{minipage}[c]{0.45\textwidth}
{\raggedright\begin{parts}
\item For these data, $R^2=0.28$. What is the correlation? How can 
you tell if it is positive or negative?
\item Examine the residual plot. What do you observe? Is a simple 
least squares fit appropriate for these data?
\end{parts}\vspace{15mm}}
\end{minipage}
\begin{minipage}[c]{0.1\textwidth}
$\:$ \\
\end{minipage}
\begin{minipage}[c]{0.43\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

\D{\newpage}

% 35

\eoce{\qt{Murders and poverty, Part II\label{murders_poverty_inf}}
Exercise~\ref{murders_poverty_reg} presents regression output from a model 
for predicting annual murders per million from percentage living in 
poverty based on a random sample of 20 metropolitan areas. The model 
output is also provided below.
\begin{center}
\begin{tabular}{rrrrr}
    \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
    \hline
(Intercept) & -29.901   & 7.789         & -3.839    & 0.001 \\ 
poverty\%   & 2.559     & 0.390         & 6.562     & 0.000 \\ 
    \hline
\end{tabular}
\[ s = 5.512 \qquad R^2 = 70.52\% \qquad R^2_{adj} = 68.89\% \]
\end{center}
\begin{parts}
\item What are the hypotheses for evaluating whether poverty percentage 
is a significant predictor of murder rate?
\item State the conclusion of the hypothesis test from part (a) in 
context of the data.
\item Calculate a 95\% confidence interval for the slope of poverty 
percentage, and interpret it in context of the data.
\item Do your results from the hypothesis test and the confidence 
interval agree? Explain.
\end{parts}
}{}

% 36

\eoce{\qt{Babies\label{babies_head_gestation_inf}} Is the gestational age 
(time between conception and birth) of a low birth-weight baby useful 
in predicting head circumference at birth? Twenty-five low birth-weight 
babies were studied at a Harvard teaching hospital; the investigators 
calculated the regression of head circumference (measured in centimeters) 
against gestational age (measured in weeks). The estimated regression 
line is
\[ \widehat{head~circumference} = 3.91 + 0.78 \times gestational~age \]
\begin{parts}
\item What is the predicted head circumference for a baby whose 
gestational age is 28 weeks?
\item The standard error for the coefficient of gestational age is 0.
35, which is associated with $df=23$. Does the model provide strong 
evidence that gestational age is significantly associated with head 
circumference?
\end{parts} 
}{}


================================================
FILE: ch_regr_simple_linear/TeX/line_fitting_residuals_and_correlation.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Visualize the residuals\label{visualize_residuals}} 
The scatterplots shown below each have a 
superimposed regression line. If we were to construct a residual plot 
(residuals versus $x$) for each, describe what those plots would look 
like.
\begin{center}
\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}
\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}
\end{center}
}{}

% 2

\eoce{\qt{Trends in the residuals\label{trends_in_residuals}} 
Shown below are two plots of residuals 
remaining after fitting a linear model to two different sets of data. 
Describe important features and determine if a linear model would be 
appropriate for these data. Explain your reasoning.
\begin{center}
\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} 
\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}
\end{center}
}{}

% 3

\eoce{\qt{Identify relationships, Part I\label{identify_relationships_1}} 
For each of the six plots, 
identify the strength of the relationship (e.g. weak, moderate, or 
strong) in the data and whether fitting a linear model would be 
reasonable.
\begin{center}
\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}
\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}
\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}
%
\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}
\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}
\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}
\end{center}
}{}

\D{\newpage}

% 4

\eoce{\qt{Identify relationships, Part II\label{identify_relationships_2}} 
For each of the six plots, 
identify the strength of the relationship (e.g. weak, moderate, or 
strong) in the data and whether fitting a linear model would be 
reasonable.
\begin{center}
\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}
\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}
\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}
%
\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}
\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}
\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}
\end{center}
}{}

% 5

\eoce{\qt{Exams and grades\label{exams_grades_correlation}} 
The two scatterplots below show the 
relationship between final and mid-semester exam grades recorded 
during several years for a Statistics course at a university.
\begin{parts}
\item Based on these graphs, which of the two exams has the strongest 
correlation with the final exam grade? Explain.
\item Can you think of a reason why the correlation between the exam 
you chose in part (a) and the final exam is higher?
\end{parts}
\begin{center}
\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}
\hspace{0.02\textwidth}%
\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}
\end{center}
}{}

\D{\newpage}

% 6

\eoce{\qt{Husbands and wives, Part I\label{husbands_wives_correlation}}
The Great Britain Office of Population Census and Surveys once 
collected data on a random sample of 170 married couples in 
Britain, recording the age (in years) and heights (converted 
here to inches) of the husbands and wives.\footfullcite{Hand:1994} 
The scatterplot on the left shows the wife's age plotted against her 
husband's age, and the plot on the right shows wife's height 
plotted against husband's height.
\begin{center}
\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} 
\hspace{5mm}
\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}
\end{center}
\begin{parts}
\item Describe the relationship between husbands' and wives' ages.
\item Describe the relationship between husbands' and wives' heights.
\item Which plot shows a stronger correlation? Explain your reasoning.
\item Data on heights were originally collected in centimeters, and 
then converted to inches. Does this conversion affect the correlation 
between husbands' and wives' heights?
\end{parts}
}{}

% 7

\eoce{\qt{Match the correlation, Part I\label{match_corr_1}} 
Match each correlation to the corresponding scatterplot.

\noindent%
\begin{minipage}[c]{0.17\textwidth}
\begin{parts}
\item $R = -0.7$
\item $R = 0.45$ 
\item $R = 0.06$
\item $R = 0.92$
\end{parts}\vspace{3mm}
\end{minipage}%
\begin{minipage}[c]{0.83\textwidth}
\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}
\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}
\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}
\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}
\end{minipage}
}{}

% 8

\eoce{\qt{Match the correlation, Part II\label{match_corr_2}} 
Match each correlation to the corresponding scatterplot.

\noindent%
\begin{minipage}[c]{0.17\textwidth}
\begin{parts}
\item $R = 0.49$
\item $R = -0.48$ 
\item $R = -0.03$ 
\item $R = -0.85$
\end{parts}\vspace{3mm}
\end{minipage}%
\begin{minipage}[c]{0.83\textwidth}
\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}
\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}
\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}
\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}
\end{minipage}
}{}

% 9

\eoce{\qt{Speed and height\label{speed_height_gender}} 1,302 UCLA students 
were asked to fill out a survey where they were asked about their height, 
fastest speed they have ever driven, and gender. The scatterplot on the 
left displays the relationship between height and fastest speed, and 
the scatterplot on the right displays the breakdown by gender in 
this relationship.
\begin{center}
\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}
\hspace{0.02\textwidth}%
\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}
\end{center}
\begin{parts}
\item Describe the relationship between height and fastest speed.
\item Why do you think these variables are positively associated?
\item What role does gender play in the relationship between height 
and fastest driving speed?
\end{parts}
}{}

% 10

\eoce{\qt{Guess the correlation\label{guess_correlation}} Eduardo and Rosie 
are both collecting data on number of rainy days in a year and the total 
rainfall for the year. Eduardo records rainfall in inches and Rosie in 
centimeters. How will their correlation coefficients compare?
}{}

% 11

\eoce{\qt{The Coast Starlight, Part I\label{coast_starlight_corr_units}} 
The Coast Starlight Amtrak train runs from Seattle to Los Angeles. 
The scatterplot below displays the distance between each stop 
(in miles) and the amount of time it takes to travel from one stop 
to another (in minutes).\vspace{2mm}

\noindent\begin{minipage}[c]{0.4\textwidth}
{\raggedright\begin{parts}
\item Describe the relationship between distance and travel time.
\item How would the relationship change if travel time was instead measured 
in hours, and distance was instead measured in kilometers?
\item The correlation between travel time (in miles) and distance (in minutes) 
is $r = 0.636$.
Suppose we had instead measured travel time in hours
and measured distance in kilometers (km).
What would be the correlation in these different units?
\end{parts}\vspace{7mm}}
\end{minipage}
\begin{minipage}[c]{0.1\textwidth}
$\:$\\
\end{minipage}
\begin{minipage}[c]{0.485\textwidth}
\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}
\end{minipage}
}{}

% 12

\eoce{\qt{Crawling babies, Part I\label{crawling_babies_corr_units}}  
A study conducted at the University of Denver investigated whether babies 
take longer to learn to crawl in cold months, when they are often bundled 
in clothes that restrict their movement, than in warmer months.
\footfullcite{Benson:1993} Infants born during the study year were split 
into twelve groups, one for each birth month. We consider the average 
crawling age of babies in each group against the average temperature when 
the babies are six months old (that's when babies often begin trying to 
crawl). Temperature is measured in degrees Fahrenheit (\degree F) and age 
is measured in weeks.\vspace{2mm}

\noindent\begin{minipage}[c]{0.4\textwidth}
{\raggedright\begin{parts}
\item Describe the relationship between temperature and crawling age.
\item How would the relationship change if temperature was measured in 
degrees Celsius (\degree C) and age was measured in months?
\item The correlation between temperature in \degree F and age in weeks 
was $r=-0.70$. If we converted the temperature to \degree C and age to 
months, what would the correlation be?
\end{parts}\vspace{3mm}}
\end{minipage}
\begin{minipage}[c]{0.1\textwidth}
$\:$\\
\end{minipage}
\begin{minipage}[c]{0.485\textwidth}
\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}
\end{minipage}
}{}

\D{\newpage}

% 13

\eoce{\qt{Body measurements, Part I\label{body_measurements_shoulder_height_corr_units}} 
Researchers studying anthropometry collected body girth measurements and 
skeletal diameter measurements, as well as age, weight, height and gender 
for 507 physically active individuals.\footfullcite{Heinz:2003} The 
scatterplot below shows the relationship between height and shoulder 
girth (over deltoid muscles), both measured in centimeters.\vspace{3mm}

\noindent%
\begin{minipage}[c]{0.4\textwidth}
{\raggedright\begin{parts}
\item Describe the relationship between shoulder girth and height.
\item How would the relationship change if shoulder girth was measured 
in inches while the units of height remained in centimeters?
\end{parts}\vspace{20mm}}
\end{minipage}
\begin{minipage}[c]{0.1\textwidth}                  
$\:$\\
\end{minipage}
\begin{minipage}[c]{0.485\textwidth}
\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}
\end{minipage}
}{}

% 14

\eoce{\qt{Body measurements, Part II\label{body_measurements_hip_weight_corr_units}} 
The scatterplot below shows the relationship between weight 
measured in kilograms and hip girth measured in centimeters 
from the data described in 
Exercise~\ref{body_measurements_shoulder_height_corr_units}.%
\vspace{3mm}

\noindent%
\begin{minipage}[c]{0.4\textwidth}
{\raggedright\begin{parts}
\item Describe the relationship between hip girth and weight.
\item How would the relationship change if weight was measured in pounds 
while the units for hip girth remained in centimeters?
\end{parts}\vspace{20mm}}
\end{minipage}
\begin{minipage}[c]{0.1\textwidth}
$\:$\\
\end{minipage}
\begin{minipage}[c]{0.485\textwidth}
\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}
\end{minipage}
}{}

% 15

\eoce{\qt{Correlation, Part I\label{corr_husband_wife_age}} What would be the 
correlation between the ages of husbands and wives if men always married 
woman who were
\begin{parts}
\item 3 years younger than themselves? 
\item 2 years older than themselves? 
\item half as old as themselves?
\end{parts}
}{}

% 16

\eoce{\qt{Correlation, Part II\label{corr_men_women_salary}} What would be the 
correlation between the annual salaries of males and females at a company 
if for a certain type of position men always made
\begin{parts}
\item \$5,000 more than women?
\item 25\% more than women?
\item 15\% less than women?
\end{parts}
}{}


================================================
FILE: ch_regr_simple_linear/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 37

\eoce{\qt{True / False\label{tf_correlation}}
Determine if the following statements are true or false.
If false, explain why.
\begin{parts}
\item A correlation coefficient of -0.90 indicates a stronger 
linear relationship than a correlation of 0.5.
\item Correlation is a measure of the association between any 
two variables.
\end{parts}
}{}

% 38

\eoce{\qt{Trees\label{trees_volume_height_diameter}} The scatterplots below 
show the relationship between height, diameter, and volume of timber 
in 31 felled black cherry trees. The diameter of the tree is measured 
4.5 feet above the ground.\footfullcite{data:trees}
\begin{center}
\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}
\hspace{0.07\textwidth}%
\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}
\end{center}
\begin{parts}
\item Describe the relationship between volume and height of these trees.
\item Describe the relationship between volume and diameter of these trees.
\item Suppose you have height and diameter measurements for another black 
cherry tree. Which of these variables would be preferable to use to predict 
the volume of timber in this tree using a simple linear regression model? 
Explain your reasoning.
\end{parts}
}{}

% 39

\eoce{\qt{Husbands and wives, Part III\label{husbands_wives_age_inf}}
Exercise~\ref{husbands_wives_height_inf} presents a scatterplot displaying the 
relationship between husbands' and wives' ages in a random sample of 
170 married couples in Britain, where both partners' ages are below 65 
years. Given below is summary output of the least squares fit for 
predicting wife's age from husband's age.

\noindent\begin{minipage}[c]{0.4\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.6\textwidth}
{\scriptsize
\begin{center}
\begin{tabular}{rrrrr}
  \hline
                & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept)     & 1.5740    & 1.1501        & 1.37      & 0.1730 \\ 
age\_\hspace{0.3mm}husband  & 0.9112    & 0.0259        & 35.25     & 0.0000 \\ 
   \hline
\multicolumn{5}{r}{$df = 168$} \\
\end{tabular}
\end{center}
}
\end{minipage}
\begin{parts}
\item We might wonder, is the age difference between husbands and 
wives consistent across ages? If this were the case, then the slope 
parameter would be $\beta_1 = 1$. Use the information above to evaluate 
if there is strong evidence that the difference in husband and wife ages 
differs for different ages.
\item Write the equation of the regression line for predicting wife's 
age from husband's age.
\item Interpret the slope and intercept in context.
\item Given that $R^2 = 0.88$, what is the correlation of ages  in 
this data set?
\item You meet a married man from Britain who is 55 years old. What 
would you predict his wife's age to be? How reliable is this prediction?
\item You meet another married man from Britain who is 85 years old. 
Would it be wise to use the same linear model to predict his wife's 
age? Explain.
\end{parts}
}{}

% 40

\eoce{\qt{Cats, Part II\label{cat_body_heart_inf}}
Exercise~\ref{cat_body_heart_reg} 
presents regression output from a model for predicting the heart 
weight (in g) of cats from their body weight (in kg). The coefficients 
are estimated using a dataset of 144 domestic cat. The model output 
is also provided below.
\begin{center}
\begin{tabular}{rrrrr}
    \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
    \hline
(Intercept) & -0.357    & 0.692         & -0.515    & 0.607 \\ 
body wt     & 4.034     & 0.250         & 16.119    & 0.000 \\ 
    \hline
\end{tabular}
\[ s = 1.452 \qquad R^2 = 64.66\% \qquad R^2_{adj} = 64.41\% \]
\end{center}
\begin{parts}
\item We see that the point estimate for the slope is positive.
    What are the hypotheses for evaluating whether body weight is
    positively associated with heart weight in cats?
\item State the conclusion of the hypothesis test from part (a) in 
context of the data.
\item Calculate a 95\% confidence interval for the slope of body 
weight, and interpret it in context of the data.
\item Do your results from the hypothesis test and the confidence 
interval agree? Explain.
\end{parts}
}{}

% 41

\eoce{\qt{Nutrition at Starbucks, Part II\label{starbucks_cals_protein}} 
Exercise~\ref{starbucks_cals_carbos} introduced a data set on nutrition 
information on Starbucks food menu items. Based on the scatterplot 
and the residual plot provided, describe the relationship between the 
protein content and calories of these menu items, and determine if a 
simple linear model is appropriate to predict amount of protein from 
the number of calories.
\begin{center}
\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}
\end{center}
}{}

% 42

\eoce{\qt{Helmets and lunches\label{helmet_lunch}}
The scatterplot shows the 
relationship between socioeconomic status measured as the percentage of 
children in a neighborhood receiving reduced-fee lunches at school 
({\tt lunch}) and the percentage of bike riders in the neighborhood 
wearing helmets ({\tt helmet}). The average percentage of children 
receiving reduced-fee lunches is 30.8\% with a standard deviation 
of 26.7\% and the average percentage of bike riders wearing helmets 
is 38.8\% with a standard deviation of 16.9\%.

\noindent\begin{minipage}[c]{0.5\textwidth}
{\raggedright\begin{parts}
\item If the $R^2$ for the least-squares regression line for these 
data is $72\%$, what is the correlation between {\tt lunch} 
and {\tt helmet}?
\item Calculate the slope and intercept for the least-squares regression 
line for these data.
\item Interpret the intercept of the least-squares regression line in 
the context of the application.
\item Interpret the slope of the least-squares regression line in the 
context of the application.
\item What would the value of the residual be for a neighborhood where 
40\% of the children receive reduced-fee lunches and 40\% of the bike 
riders wear helmets? Interpret the meaning of this residual in the context 
of the application.
\end{parts}}
\end{minipage}
\begin{minipage}[c]{0.05\textwidth}
$\:$ \\
\end{minipage}
\begin{minipage}[c]{0.42\textwidth}
\begin{center}
\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} \\
\end{center}
\end{minipage}
}{}

% 43

\eoce{\qt{Match the correlation, Part III\label{match_corr_3}} 
Match each correlation to the corresponding scatterplot.

\noindent%
\begin{minipage}[c]{0.17\textwidth}
\begin{parts}
\item $r = -0.72$
\item $r = 0.07$ 
\item $r = 0.86$ 
\item $r = 0.99$
\end{parts}\vspace{3mm}
\end{minipage}%
\begin{minipage}[c]{0.83\textwidth}
\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}
\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}
\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}
\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}
\end{minipage}
}{}

% 44

\eoce{\qt{Rate my professor\label{rate_my_prof}}
Many college courses conclude by giving students
the opportunity to evaluate the course and the
instructor anonymously.
However, the use of these student evaluations
as an indicator of course quality and teaching
effectiveness is often criticized because these
measures may reflect the influence of
non-teaching related characteristics,
such as the physical appearance of the instructor.
Researchers at University of Texas, Austin
collected data on teaching evaluation score
(higher score means better) and standardized
beauty score (a score of 0 means average, negative
score means below average, and a positive score
means above average) for a sample of 463
professors.\footfullcite{Hamermesh:2005}
The scatterplot below shows the relationship
between these variables, and regression output
is provided for predicting teaching evaluation
score from beauty score.
\begin{center}
\begin{tabular}{rrrrr}
    \hline
            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\ 
  \hline
(Intercept) & 4.010     & 0.0255        & 	157.21  & 0.0000 \\ 
beauty      &  \fbox{\textcolor{white}{{\footnotesize Cell 1}}}  
                        & 0.0322        & 4.13      & 0.0000\vspace{0.8mm} \\ 
   \hline
\end{tabular}
\end{center}
\noindent\begin{minipage}[c]{0.45\textwidth}
{\raggedright\begin{parts}
\item
    Given that the average standardized beauty score
    is -0.0883 and average teaching evaluation score
    is 3.9983, calculate the slope.
    Alternatively, the slope may be computed using just
    the information provided in the model summary table.
\item
    Do these data provide convincing evidence that the
    slope of the relationship between teaching evaluation
    and beauty is positive?
    Explain your reasoning.
\item
    List the conditions required for linear regression
    and check if each one is satisfied for this model
    based on the following diagnostic plots.
\end{parts}}
\end{minipage}
\begin{minipage}[c]{0.07\textwidth}
$\:$ \\
\end{minipage}
\begin{minipage}[c]{0.45\textwidth}
\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} \\
\end{minipage}
\begin{center}
\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}
\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}
\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}
\end{center}
}{}


================================================
FILE: ch_regr_simple_linear/TeX/types_of_outliers_in_linear_regression.tex
================================================
\exercisesheader{}

% 27

\eoce{\qt{Outliers, Part I\label{outliers_1}} Identify the outliers in the 
scatterplots shown below, and determine what type of outliers they are. 
Explain your reasoning.
\begin{center}
\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}
\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}
\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}
\end{center}
}{}

% 28

\eoce{\qt{Outliers, Part II\label{outliers_2}} Identify the outliers in the scatterplots 
shown below and determine what type of outliers they are. Explain 
your reasoning.
\begin{center}
\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}
\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}
\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}
\end{center}
}{}

% 29

\eoce{\qt{Urban homeowners, Part I\label{urban_homeowners_outlier}} The 
scatterplot below shows the percent of families who own their 
home vs. the percent of the population living in urban areas.
\footfullcite{data:urbanOwner} There are 52 observations, each 
corresponding to a state in the US. Puerto Rico and District of 
Columbia are also included.

\noindent\begin{minipage}[c]{0.5\textwidth}
\begin{parts}
\item Describe the relationship between the percent of families who 
own their home and the percent of the population living in urban areas.
\item The outlier at the bottom right corner is District of Columbia, 
where 100\% of the population is considered urban. What type of an outlier 
is this observation?
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.05\textwidth}
$\:$\\
\end{minipage}
\begin{minipage}[c]{0.4\textwidth}
\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}
\end{minipage}
}{}

% 30

\eoce{\qt{Crawling babies, Part II\label{crawling_babies_outlier}} 
Exercise~\ref{crawling_babies_corr_units} introduces 
data on the average monthly temperature during the month babies first 
try to crawl (about 6 months after birth) and the average first 
crawling age for babies born in a given month. A scatterplot of these 
two variables reveals a potential outlying month when the average 
temperature is about 53\degree F and average crawling age is about 
28.5 weeks. Does this point have high leverage? Is it an influential 
point?
}{}


================================================
FILE: ch_regr_simple_linear/figures/brushtail_possum/ReadMe.txt
================================================

https://www.flickr.com/photos/gregthebusker/5653697137/

Photo by Greg Schechter
Creative Commons Attribution 2.0 license


================================================
FILE: ch_regr_simple_linear/figures/elmhurstPlots/elmhurstScatterW2Lines.R
================================================
library(openintro)
d <- elmhurst
d$gift_aid <- d$gift_aid * 1000
d$family_income <- d$family_income * 1000

g <- lm(d$gift_aid ~ d$family_income)
summary(g)

loss <- function(a, b, d) {
  p <- a + b * d$family_income
  sum(abs(d$gift_aid - p))
}
a      <- round(g$coef[1], 2) + seq(-500, 500, 1)
b      <- round(g$coef[2], 3) + seq(-0.01, 0.01, 0.0001)
mins   <- c(a[1], b[1])
theMin <- loss(a[1], b[1], d)
pb     <- txtProgressBar(1, length(a), style=3)
for (i in 1:length(a)) {
  for (j in 1:length(b)) {
    hold <- loss(a[i], b[j], d)
    if (hold < theMin) {
      mins <- c(a[i],b[j])
      theMin <- hold
    }
  }
  setTxtProgressBar(pb, i)
}

BuildElmhurtPlot <- function() {
  plot(d$family_income, d$gift_aid,
      col = COL[1, 2],
      pch = 19,
      xlab = 'Family Income',
      ylab = '', axes=FALSE,
      xlim = c(0, 280e3), 
      ylim = c(0, 35e3))
  AxisInDollars(1, at = (0:8) * 50e3)
  AxisInDollars(2, at = (0:3) * 10e3)
  box()
  par(las = 0)
  mtext("Gift Aid From University", 2, line = 3)
}

myPDF('elmhurstScatterW2Lines.pdf', 5.5, 3.3,
      mar = c(3.1, 4.1, 0.5, 0.5),
      mgp = c(2, 0.6, 0))
BuildElmhurtPlot()
abline(mins[1], mins[2], lty=2, lwd=2)
abline(g, lwd = 2)
dev.off()


myPDF('elmhurstScatterWLSROnly.pdf', 5.5, 3.3,
      mar = c(3.1, 4.1, 0.5, 0.5),
      mgp = c(2, 0.6, 0))
BuildElmhurtPlot()
abline(g, lwd = 2)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/eoce/beer_blood_alcohol_inf/beer_blood_alcohol.txt
================================================
student	beers	BAC
1	5	0.1
2	2	0.03
3	9	0.19
4	8	0.12
5	3	0.04
6	7	0.095
7	3	0.07
8	5	0.06
9	3	0.02
10	5	0.05
11	4	0.07
12	6	0.1
13	5	0.085
14	7	0.09
15	1	0.01
16	4	0.05

================================================
FILE: ch_regr_simple_linear/figures/eoce/beer_blood_alcohol_inf/beer_blood_alcohol_inf.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

beer_data <- read.table("beer_blood_alcohol.txt", h = T, sep = "\t")

# scatterplot of BAC vs. beers --------------------------------------  

pdf("beer_blood_alcohol.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(beer_data$BAC ~ beer_data$beers, 
     xlab = "Cans of beer", ylab = "BAC (grams / deciliter)", 
     pch = 19, col = COL[1])

dev.off()

# model summary -----------------------------------------------------

m_bac <- lm(beer_data$BAC ~ beer_data$beers)

xtable(summary(m_bac))

================================================
FILE: ch_regr_simple_linear/figures/eoce/body_measurements_hip_weight_corr_units/body_measurements_hip_weight.R
================================================
library(openintro)

myPDF("body_measurements_weight_hip_girth.pdf", 5.7, 4.3,
    mar = c(3.8, 3.8, 0.5, 1),
    mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.25,
    cex.axis = 1.25)

plot(bdims$wgt ~ bdims$hip_gi, 
    xlab = "Hip girth (cm)", ylab = "Weight (kg)", 
    pch = 19, col = COL[1,2])

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/body_measurements_shoulder_height_corr_units/body_measurements_shoulder_height.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

data(bdims)

# correlation -------------------------------------------------------

round(cor(crawling_babies$avg_crawling_age, crawling_babies$temperature), 2)

# plot height vs. shoulder girth ------------------------------------

pdf("body_measurements_height_shoulder_girth.pdf", 5.5, 4.3)

par(mar = c(3.8, 3.8, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)

plot(bdims$hgt ~ bdims$sho.gi, 
     xlab = "Shoulder girth (cm)", ylab = "Height (cm)", 
     pch = 19, col = COL[1,2])

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/body_measurements_weight_height_inf/body_measurements_weight_height_inf.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

data(bdims)

# correlation -------------------------------------------------------

round(cor(bdims$hgt, bdims$wgt), 2)

# model -------------------------------------------------------------

m_weight_height <- lm(bdims$wgt ~ bdims$hgt)

xtable(summary(m_weight_height))

# plot weight vs. height --------------------------------------------

pdf("body_measurements_weight_height.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(bdims$wgt ~ bdims$hgt, 
     ylab = "Weight (kg)", xlab = "Height (cm)", 
     pch = 19, col = COL[1,2],
     axes = FALSE, xlim = c(147,199))
axis(1, at = seq(150, 200, 25))
axis(2, at = seq(50, 110, 20))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/cat_body_heart_reg/cat_body_heart_reg.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)
library(MASS)

# load data ---------------------------------------------------------

data(cats)

# model heart weight vs. weight -------------------------------------

m_cats_hwt_bwt <- lm(cats$Hwt ~ cats$Bwt)

xtable(summary(m_cats_hwt_bwt), digits = 3)

round(summary(m_cats_hwt_bwt)$r.squared, 4)
round(summary(m_cats_hwt_bwt)$adj.r.squared, 4)
  
# plot heart weight vs. weight --------------------------------------

pdf("cat_body_heart.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(cats$Hwt ~ cats$Bwt, 
     xlab = "Body weight (kg)", ylab = "Heart weight (g)", 
     pch = 19, col = COL[1,2],
     xlim = c(2,4), ylim = c(5, 20.5), axes = FALSE)
axis(1, at = seq(2, 4, 0.5))
axis(2, at = seq(5, 20, 5))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/coast_starlight_corr_units/coast_starlight.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

coast_starlight <- read.table("coast_starlight.txt", h = T, sep = "\t")

# plot trave time vs. distance --------------------------------------

pdf("coast_starlight.pdf", 5.5, 4.3)

par(mar = c(3.8, 3.8, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)

plot(coast_starlight$travel_time ~ coast_starlight$dist, 
     xlab = "Distance (miles)", ylab = "Travel Time (minutes)", 
     pch = 20, col = COL[1], axes = FALSE)
axis(1, at = seq(0, 400, 100))
axis(2, at = seq(0, 360, 60))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/coast_starlight_corr_units/coast_starlight.txt
================================================
station	distance	hour	minute	travel_time	dist	z_time	z_dist	travel_time_hrs	dist_km
Tacoma	40	10	57	57	40	-0.634081	-0.679118	0.0158333 h	103.6
Olympia	72	11	43	46	32	-0.731123	-0.759681	0.0127778 h	82.8796
Centralia	94	12	6	23	43	-0.934029	-0.648907	0.00638889 h	111.369
Kelso	137	12	52	46	39	-0.731123	-0.689189	0.0127778 h	101.01
Vancouver	176	13	35	43	10	-0.757589	-0.981228	0.0119444 h	25.8999
Portland	186	13	55	20	53	-0.960495	-0.548204	0.00555556 h	137.269
Salem	239	15	45	110	28	-0.166515	-0.799962	0.0305556 h	72.5197
Albany	267	16	17	32	43	-0.854631	-0.648907	0.00888889 h	111.369
Eugene	310	17	7	49	195	-0.704657	0.881784	0.0136111 h	505.048
Sacramento	837	6	30	177	84	0.424558	-0.236024	0.0491667 h	217.559
Emeryville	921	8	30	120	113	-0.0782952	0.0560163	0.0333333 h	292.669
Salinas	1034	12	1	211	352	0.724506	2.46283	0.0586111 h	911.676
SantaBarbara	1286	18	17	376	252	2.18014	1.45579	0.104444 h	652.677
LosAngeles	1389	21	5	168	103	0.345161	-0.0446871	0.0466667 h	266.769
Chico	742	3	33	326	95	1.73904	-0.12525	0.0905556 h	246.049
KlamathFalls	505	22	7	258	237	1.13914	1.30474	0.0716667 h	613.827

================================================
FILE: ch_regr_simple_linear/figures/eoce/crawling_babies_corr_units/crawling_babies.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

crawling_babies <- read.csv("crawling_babies.csv")

# correlation -------------------------------------------------------

round(cor(crawling_babies$avg_crawling_age, crawling_babies$temperature), 2)

# plot trave time vs. distance --------------------------------------

pdf("crawling_babies.pdf", 5.5, 4.3)

par(mar = c(3.5, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)

plot(crawling_babies$avg_crawling_age ~ crawling_babies$temperature, 
     xlab = "Temperature (F)", ylab = "Avg. crawling age (weeks)",
     pch = 19, col = COL[1])

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/crawling_babies_corr_units/crawling_babies.csv
================================================
birth_month,avg_crawling_age,sd,n,temperature
January,29.84,7.08,32,66
February,30.52,6.96,36,73
March,29.7,8.33,23,72
April,31.84,6.21,26,63
May,28.58,8.07,27,52
June,31.44,8.1,29,39
July,33.64,6.91,21,33
August,32.82,7.61,45,30
September,33.83,6.93,38,33
October,33.35,7.29,44,37
November,33.38,7.42,49,48
December,32.32,5.71,44,57

================================================
FILE: ch_regr_simple_linear/figures/eoce/exams_grades_correlation/exam_grades.txt
================================================
semester	sex	exam1	exam2	exam3	course_grade
2000-1	M	84.5	69.5	86.5	76.2564
2000-1	M	80	74	67	75.3882
2000-1	M	56	70	71.5	67.0564
2000-1	M	64	61	67.5	63.4538
2000-1	M	90.5	72.5	75	72.3949
2000-1	M	74	78.5	84.5	71.4128
2000-1	M	60.5	44	58	56.0949
2000-1	M	89	82	88	78.0103
2000-1	F	87.5	86.5	95	82.9026
2000-1	M	91	98	88	89.0846
2000-1	M	79.5	88	56	72.9769
2000-1	M	96	91.5	78	82.5282
2000-1	M	55	79.5	72	69.9872
2000-1	M	80	83.5	70	67.7256
2000-1	F	64.5	83.5	77	80.3051
2000-1	M	73	90	96	74.8744
2000-1	M	83	79	65	71.1615
2000-1	M	72	91	90	76.5615
2000-1	F	57.9	53	47	56.9903
2000-1	M	89	83.5	78.5	80.8026
2000-1	F	69	57	88	69.4308
2000-1	F	91	91	95.5	92.0051
2000-1	M	68	84.5	64	67.9051
2000-1	M	83	89.5	93	79.7872
2000-1	M	72	77.5	67	67.1256
2000-1	F	56	45	61	58.6308
2000-1	M	55	71	72	67.8308
2000-1	F	68.5	64	93	77.6903
2000-1	M	68.5	79.5	50.5	69.9308
2000-1	M	70	71	76	69.6769
2000-1	M	75	81	81.5	68.0923
2000-1	M	68.5	66	58	60.3128
2000-1	M	54	84	88	72.3821
2000-1	M	79.6	67	72	67.4787
2000-1	M	85	61	44.5	66.2364
2000-1	M	82	86.5	92.5	83.3231
2000-1	F	65	83.5	87	77.6436
2000-1	F	76	50	91.5	70.9462
2000-1	M	94.5	67	96	86.0133
2000-1	M	72	67	90	72.0269
2000-1	M	65.5	82.5	79	77.0282
2000-1	M	61.5	80.5	81.5	68.2821
2000-1	M	65	50	43.5	43.2733
2000-1	M	63	82	88	70.1031
2000-1	M	75	66.5	76	65.0846
2000-1	M	69.5	70.5	76	68.6538
2000-1	F	60	95	91	83.0508
2000-1	M	84	88	67	75.0856
2000-1	M	90	99.5	87.5	74.4718
2000-1	F	62.5	85.5	85.5	74.4821
2000-1	M	88.5	86.5	70	77.9526
2000-2	M	74.5	83	62.4	78.7554
2000-2	M	73.5	69.5	74.5	69.0228
2000-2	M	75	60.5	80.9	68.6502
2000-2	M	76.5	76	61	73.6702
2000-2	F	90.5	68	59	69.7842
2000-2	M	88	77.5	90.5	84.1474
2000-2	M	87.2727	58	28	49.1931
2000-2	M	81.9	86	62	73.9116
2000-2	M	65.5	90	61.8	68.5425
2000-2	F	84.5	80.5	72.5	81.2088
2000-2	M	88.9	67	76	80.5554
2000-2	M	70	56	81.5	68.7839
2000-2	M	73	48	55	65.3088
2000-2	M	86	77	82	79.6035
2000-2	M	68	60	59	58.4316
2000-2	F	82	74	81.5	76.6842
2000-2	M	86.4	72	69.5	67.5625
2000-2	M	74	83	79.9	74.573
2000-2	M	92.3	76	97	87.0288
2000-2	M	57	70	56	65.3947
2000-2	M	62.5	41	77.8	59.8863
2000-2	M	75.5	62.5	79.7	73.5435
2000-2	M	67	58	45.8	58.3828
2000-2	M	73	78	77.3	79.2354
2000-2	M	93	75	96	71.3228
2000-2	M	81.5	73	69.5	65.5295
2000-2	M	82.5	83.5	82.5	81.4474
2000-2	F	46.5	77	47	62.8842
2000-2	M	62	71	64	69.4702
2000-2	F	68.5	55.5	52	61.8263
2000-2	M	80	84	48.5	70.0947
2000-2	M	77	77	44	68.0404
2000-2	F	55	94	96	83.5193
2000-2	M	69	74.5	41.5	59.0386
2000-2	F	74	56	69.9	65.0204
2001-1	M	93	76	95.5	85.5
2001-1	M	81	78	79.5	75.7333
2001-1	M	98	87	85.5	86.5833
2001-1	M	92.5	81	74.5	77.2833
2001-1	M	83.5	60.5	78	73.6333
2001-1	M	98	92	90.5	91.8585
2001-1	M	80.5	53.5	67.5	62.9555
2001-1	M	80.9	68	67	72.76
2001-1	M	84.5	46	65.5	60.6167
2001-1	M	93.8	59.5	73.3	70.0067
2001-1	M	98	96.5	98.5	97.5667
2001-1	M	92	78.5	72	73.2833
2001-1	M	91	72	81	75.1167
2001-1	M	80	72	59.5	69.7
2001-1	M	94	84	98.5	88.8667
2001-1	M	90	73	63.5	72.9
2001-1	M	94	82	86.4	80.3267
2001-1	M	86.5	76	85.5	76.2
2001-1	M	87	76	72.5	70.7442
2001-1	M	80.3	88.5	83.5	85.97
2001-1	M	89.5	66.5	78.9	72.5267
2001-1	M	70.5	70.5	58	57
2001-1	M	89.4	84.5	82.5	83.8685
2001-1	F	79.5	56	81	68.25
2001-1	M	93.9	88	71	80.06
2001-1	M	73.5	78.5	72	77.0333
2001-1	M	94.8	84.5	77.5	84.17
2001-1	F	66	62	80.5	70.9333
2001-1	M	96.3	82	76	79.9867
2001-1	M	93.3	93	92.5	89.8033
2001-1	M	87.3	81	78.5	85.7367
2001-1	M	79	81	91	78.1222
2001-1	M	84	81	91	75.0333
2001-1	F	81	81.5	70	74.2167
2001-1	F	84.5	71.5	76	73.9833
2001-1	F	90.625	61.5	59.5	66.6958
2001-1	F	91.5	58	79.5	74.9888
2001-1	M	84	72.5	76	75.7
2001-2	M	73.2	44	50.9	47.9
2001-2	M	70.5	42.8	36.4	48.05
2001-2	F	92.9	88.5	79	86.65
2001-2	M	68.8	52.1	65.4	55.95
2001-2	M	91.3	71	79.5	77.825
2001-2	M	78.3	46.5	63.9	56.725
2001-2	M	71.3	43	67.4	58.375
2001-2	M	74.9	65.4	51.5	56.075
2001-2	M	90.5	71.5	69.5	78.5
2001-2	M	80.3	63	64	64.7
2001-2	F	79	74.5	72.5	68.325
2001-2	M	82.5	62.5	77.5	73.5
2001-2	M	79.7	84.4	77.3	75.55
2001-2	M	79.5	77.9	62	68.35
2001-2	M	79.5	68.7	87	81.75
2001-2	M	93	70.9	67.5	70.425
2001-2	M	88.5	79.5	82	80.5
2001-2	F	86	73	47	66
2001-2	M	93.8	68.5	66.4	74.875
2001-2	F	93.9	84	83.5	88.225
2001-2	M	83.3	83	78	82.575
2001-2	M	75.1	70	61	64.025
2001-2	M	92.9	83	84.5	84.15
2001-2	M	76.5	41.5	48	53.075
2001-2	F	84.1	88.8	75.5	81.55
2001-2	M	87.9	58.2	67	63.525
2001-2	F	79.5	66	67.5	69.125
2001-2	M	82	53.5	52	54.5
2001-2	F	91.4	77	76	76.1
2001-2	M	84.9	69.5	37.3	60.3
2001-2	M	58	61	47.4	57.6
2001-2	M	81.5	57.5	65.8	60.325
2001-2	M	95	68	82.5	76.75
2001-2	M	95.5	80.5	84.3	75.775
2001-2	M	77.5	80.9	75	76.425
2001-2	M	82	46	54	53
2001-2	M	93.8	82.5	80.5	84.65
2002-1	M	89	83.5	92	77.1197
2002-1	M	82	78.5	82	61.7972
2002-1	M	73.5	80	82	73.5563
2002-1	M	73.5	82.8	79.5	69.9459
2002-1	F	79	86.5	97	80.5324
2002-1	M	71.5	71	58	62.4331
2002-1	M	92.5	92	93.5	82.2
2002-1	F	85.5	90.5	85	86.7901
2002-1	M	93	47	70	59.8218
2002-1	M	75.5	66	94	69.0648
2002-1	M	88.5	74	87	78.2085
2002-1	M	87	96	98.5	84.0577
2002-1	F	89.5	77.5	88.9	69.211
2002-1	M	97.5	94.5	76	78.931
2002-1	F	59	73	85.5	58.2606
2002-1	M	81	87	86.5	72.1352
2002-1	M	73.5	57.5	83	61.7713
2002-1	M	90.5	53	78.5	60.7915
2002-1	M	70	62.5	85	64.5225
2002-1	M	98.5	98	90	80.3761
2002-1	M	85.5	67	86	67.6535
2002-1	M	78	58	92.5	65.1831
2002-1	M	86.5	85	85	73.5972
2002-1	F	99.3	96	89	91.4868
2002-1	M	90.5	78.5	79.5	73.0634
2002-1	M	86.5	67.5	85.5	70.0746
2002-1	M	80	94	90.5	88.431
2002-1	M	72	95	78	77.3507
2002-1	M	72.5	69.5	92	73.5648
2002-1	M	77.5	74	82	73.1493
2002-1	M	74	49.5	82	64.669
2002-1	M	79	80.5	80.5	71.9845
2002-1	M	80	82	77	67.7986
2002-1	M	97.4	66.9	83	74.9276
2002-1	M	61.5	64.5	86	63.7651
2002-1	M	86	67	96.8	73.7346
2003-1	F	75.7143	62.6	45.5556	59.5549
2003-1	F	80.7143	38	78.3333	65.2202
2003-1	F	88.2143	75.1	70	72.367
2003-1	M	81.7857	76	68.3333	74.112
2003-1	M	96.0714	68.1	74.4444	70.4423
2003-1	M		58	78.3333	46.7231
2003-1	M	73.5714	62.1	97.7778	69.103
2003-1	M	71.0714	41.5	55.5556	53.9348
2003-1	M	66.4286	59.1	85.5556	68.5149
2003-1	M	85.7143	77.1	83.3333	79.881
2003-1	F	88.9286	72	93.3333	82.3626
2003-1	M	65.7143	60.5	30	48.5152
2003-1	M	92.5	63.5	92.7778	80.3655
2003-1	F	86.0714	77.5	96.6667	80.3527
2003-1	M	75	71.9	88.3333	70.3606
2003-1	M	95.7143	90.1	98.8889	93.3003
2003-1	M	88.9286	52.5	62.5	61.1081
2003-1	M	94.6429	90	90	87.6431
2003-1	M	92.5	80	96.1111	87.9477
2003-1	F	91.0714	39.5	78.8889	67.9143
2003-1	M	92.8571	73.5	92.1111	86.2788
2003-1	F	86.0714	45.5	55.5556	59.8701
2003-1	M	87.1429	81.6	73.8889	76.4304
2003-1	M	74.2857	52.6	47.2222	56.3211
2003-1	M	95.7143	78	84.4444	83.2086
2003-1	M	83.2143	64.6	58.8889	68.0731
2003-1	M	85	78.1	82.2222	70.5416
2003-1	M	85.7143	68.1	72	66.1202
2003-1	F	94.6429	94.5	96.1111	90.719
2003-1	M	90.7143	77.5	78.2222	77.534
2003-1	M	86.4286	70.1	88.3333	74.5553
2003-1	M	98.2143	89.1	71.1111	85.5208
2003-1	F	95	86	78.3333	83.6959
2003-1	M	97.1429	53.5	67.2222	72.1189
2003-1	M	85	63	75	67.1996
2003-1	M	72.8571	48.5	90.5556	66.4035

================================================
FILE: ch_regr_simple_linear/figures/eoce/exams_grades_correlation/exams_grades_correlation.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

exam_data <- read.table("exam_grades.txt", h = T, sep = "\t")

# plot course grade vs. exam 1 --------------------------------------

pdf("exam_grades_1.pdf", 5.5, 4.3)

par(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(exam_data$course_grade ~ exam_data$exam1, 
     pch = 19, col = COL[1,2], 
     xlab = "Exam 1", ylab = "Final Exam", 
     xlim = c(40,100), ylim = c(40,100), axes=FALSE)
axis(1, at = seq(40,100,20))
axis(2, at = seq(40,100,20))
box()

m_course_grade_exam1 = lm(exam_data$course_grade ~ exam_data$exam1)
abline(m_course_grade_exam1, col = COL[2], lwd = 2)

dev.off()

# plot course grade vs. exam 2 --------------------------------------

pdf("exam_grades_2.pdf", 5.5, 4.3)

par(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(exam_data$course_grade ~ exam_data$exam2, 
     pch = 19, col = COL[1,2], 
     xlab = "Exam 2", ylab = "Final Exam", 
     xlim = c(40,100), ylim = c(40,100), axes=FALSE)
axis(1, at = seq(40,100,20))
axis(2, at = seq(40,100,20))
box()

m_course_grade_exam2 = lm(exam_data$course_grade ~ exam_data$exam2)
abline(m_course_grade_exam2, col = COL[2], lwd = 2)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/full_lin_regr_1/prof_evals_beauty.csv
================================================
tenured,profnumber,minority,age,beautyf2upper,beautyflowerdiv,beautyfupperdiv,beautym2upper,beautymlowerdiv,beautymupperdiv,btystdave,btystdf2u,btystdfl,btystdfu,btystdm2u,btystdml,btystdmu,class1,class2,class3,class4,class5,class6,class7,class8,class9,class10,class11,class12,class13,class14,class15,class16,class17,class18,class19,class20,class21,class22,class23,class24,class25,class26,class27,class28,class29,class30,courseevaluation,didevaluation,female,formal,fulldept,lower,multipleclass,nonenglish,onecredit,percentevaluating,profevaluation,students,tenuretrack,blkandwhite,btystdvariance,btystdavepos,btystdaveneg
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,24,1,0,1,0,1,0,0,55.81395,4.7,43,1,0,2.129806,0.201567,0
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,17,0,0,1,0,0,0,0,85,4.6,20,1,0,1.386081,0,-0.826081
1,3,0,51,5,5,2,3,2,3,-0.6603327,-0.1878249,0.4580018,-1.545553,-1.131904,-0.9000649,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,55,0,0,1,0,1,0,0,100,4.1,55,1,0,2.537435,0,-0.660333
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,40,1,0,1,0,1,0,0,86.95652,4.5,46,1,0,1.760577,0,-0.766312
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,42,1,0,1,0,0,0,0,87.5,4.8,48,1,0,1.6931,1.42145,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,182,0,1,1,0,0,0,0,64.53901,4.4,282,1,0,0.9447419,0.50022,0
0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,33,1,0,1,0,1,0,0,80.48781,4.4,41,1,0,0.4898793,0,-0.21435
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,25,1,0,1,0,0,0,0,60.97561,3.4,41,1,0,2.041787,0,-0.346539
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,48,1,0,1,0,0,0,0,80,4.8,60,1,0,2.32433,0.061344,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,16,0,0,1,0,1,0,0,84.21053,4,19,0,0,0.916837,0.452568,0
0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,18,0,0,1,0,1,1,0,72,3.6,25,1,0,3.798652,0.143264,0
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,30,0,0,1,0,0,0,0,88.23529,4.1,34,0,0,0.3504698,0,-0.155023
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,28,0,0,1,0,0,0,0,70,4.1,40,1,0,0.8283713,0.128543,0
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,30,0,0,1,0,0,1,0,83.33334,3.5,36,1,0,4.42171,0,-0.347045
0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.9,23,1,0,1,0,1,0,0,82.14286,2.9,28,1,0,2.113737,0.461939,0
1,16,0,45,3,2,4,7,5,4,-0.1503849,-1.142179,-1.136523,-0.5770065,1.286255,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,23,0,0,1,0,1,0,0,69.69697,4.6,33,1,0,5.230154,0,-0.150385
0,17,0,56,1,3,4,3,2,2,-1.070734,-2.096532,-0.6050149,-0.5770065,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,27,1,0,1,0,1,0,0,65.85366,4.4,41,0,0,1.547655,0,-1.07073
0,18,0,48,6,3,5,4,4,4,-0.1426931,0.2893519,-0.6050149,-0.092733,-0.5273642,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,100,0,0,1,1,0,0,0,74.07407,4,135,0,0,0.7281362,0,-0.142693
1,19,0,46,5,4,4,5,2,6,-0.1563634,-0.1878249,-0.0735065,-0.5770065,0.0771755,-0.9000649,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,25,1,0,1,0,0,0,0,80.64516,4.9,31,1,1,1.565791,0,-0.156363
0,20,0,57,4,5,4,6,5,2,-0.0589354,-0.6650018,0.4580018,-0.5770065,0.6817153,0.8625621,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,17,1,0,0,0,0,0,0,60.71429,3.5,28,0,0,3.413574,0,-0.058935
0,21,0,52,4,6,6,7,2,4,0.1511368,-0.6650018,0.9895102,0.3915404,1.286255,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,19,1,0,1,0,1,0,0,86.36364,3.7,22,0,0,3.940365,0.151137,0
0,22,1,29,4,3,3,2,2,3,-0.937076,-0.6650018,-0.6050149,-1.06128,-1.736444,-0.9000649,-0.6546507,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,19,1,0,1,0,1,1,0,73.07692,3.4,26,1,0,0.9198381,0,-0.937076
1,23,0,62,4,4,3,4,1,2,-0.8214405,-0.6650018,-0.0735065,-1.06128,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,30,0,0,1,0,1,0,0,76.92308,4.3,39,1,0,1.257183,0,-0.821441
1,24,0,64,5,5,4,5,3,3,-0.1994712,-0.1878249,0.4580018,-0.5770065,0.0771755,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,15,0,0,1,0,0,0,0,62.5,4.5,24,1,0,0.8714417,0,-0.199471
0,25,0,34,8,8,9,8,6,8,1.687167,1.243706,2.052527,1.844361,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,20,1,0,1,0,0,0,0,76.92308,4.8,26,1,0,0.454603,1.68717,0
1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4.2,84,0,0,1,0,1,0,0,52.83019,4.4,159,1,0,0.4904639,0,-0.410198
1,27,1,52,7,4,5,4,5,4,0.1233448,0.7665288,-0.0735065,-0.092733,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,13,0,1,1,0,1,1,0,81.25,4.4,16,1,0,1.5706,0.123345,0
1,28,0,73,6,1,3,5,2,1,-0.8059942,0.2893519,-1.668032,-1.06128,0.0771755,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,12,0,1,1,0,1,0,0,70.58823,3.6,17,1,0,3.385376,0,-0.805994
1,29,0,70,2,5,3,2,3,3,-0.8210418,-1.619356,0.4580018,-1.06128,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4.4,47,0,1,1,0,1,0,0,35.07463,4.6,134,1,0,3.45521,0,-0.821042
0,30,0,41,7,3,6,7,4,4,0.3198185,0.7665288,-0.6050149,0.3915404,1.286255,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.3,10,1,0,1,0,1,0,0,83.33334,2.3,12,1,0,2.261486,0.319818,0
0,31,0,63,6,5,4,5,4,2,-0.0985568,0.2893519,0.4580018,-0.5770065,0.0771755,0.2750198,-1.113883,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,28,0,0,1,0,1,0,0,65.11628,4.3,43,0,0,1.890474,0,-0.098557
1,32,0,47,3,1,4,3,2,3,-1.012306,-1.142179,-1.668032,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,10,0,0,1,0,0,0,0,66.66666,4.9,15,1,0,0.7911476,0,-1.01231
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,86,0,0,1,1,0,0,0,34.95935,4.2,246,1,0,1.194346,0.488331,0
0,34,1,47,6,2,6,4,3,5,-0.1719505,0.2893519,-1.136523,0.3915404,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,21,1,0,1,1,1,0,0,100,4.2,21,1,0,1.796693,0,-0.171951
1,35,0,54,4,1,2,4,1,2,-1.167907,-0.6650018,-1.668032,-1.545553,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,15,0,1,1,0,1,0,0,100,4.3,15,1,0,1.161077,0,-1.16791
1,36,1,44,8,6,8,5,5,7,0.9525534,1.243706,0.9895102,1.360087,0.0771755,0.8625621,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,30,1,0,1,0,0,0,0,54.54546,3.9,55,1,0,1.079378,0.952553,0
1,37,1,47,2,2,2,4,2,2,-1.140457,-1.619356,-1.136523,-1.545553,-0.5273642,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,20,0,0,1,0,1,0,0,74.07407,3.9,27,1,0,0.8278397,0,-1.14046
1,38,0,62,5,3,2,3,3,2,-0.8161172,-0.1878249,-0.6050149,-1.545553,-1.131904,-0.3125226,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,12,0,1,1,0,0,0,0,92.30769,4.1,13,1,0,1.413386,0,-0.816117
1,39,0,60,6,4,4,4,2,2,-0.4837456,0.2893519,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,25,0,0,1,0,0,0,0,73.52941,4.8,34,1,0,1.346971,0,-0.483746
0,40,0,37,7,4,8,8,5,5,0.8450468,0.7665288,-0.0735065,1.360087,1.890795,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,15,0,0,1,0,1,0,0,88.23529,3.3,17,1,0,2.546899,0.845047,0
0,41,0,42,3,3,5,4,3,6,-0.3261277,-1.142179,-0.6050149,-0.092733,-0.5273642,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,13,0,0,1,0,0,0,0,92.85714,4.4,14,1,0,1.939639,0,-0.326128
1,42,0,35,2,4,7,6,6,4,0.1865589,-1.619356,-0.0735065,0.8758139,0.6817153,1.450104,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4.2,42,0,0,1,0,1,0,0,82.35294,4.3,51,1,0,5.791667,0.186559,0
0,43,0,39,8,8,8,9,7,9,1.881674,1.243706,2.052527,1.360087,2.495334,2.037647,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3.4,22,1,0,1,1,1,0,0,91.66666,3.3,24,0,0,1.157145,1.88167,0
1,44,0,49,7,6,6,5,6,9,0.9626006,0.7665288,0.9895102,0.3915404,0.0771755,1.450104,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3.9,28,0,1,1,0,1,0,0,62.22222,4,45,1,0,2.682287,0.962601,0
1,45,0,61,4,5,5,6,4,5,0.1534694,-0.6650018,0.4580018,-0.092733,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4.5,22,0,1,1,1,1,0,0,81.48148,4.5,27,1,0,1.129245,0.153469,0
0,46,0,33,7,7,8,7,6,7,1.261046,0.7665288,1.521019,1.360087,1.286255,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4.7,30,0,1,1,0,1,0,0,96.77419,4.9,31,1,0,0.3645249,1.26105,0
1,47,0,58,7,3,6,6,2,4,0.0232144,0.7665288,-0.6050149,0.3915404,0.6817153,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3.5,26,1,1,1,0,1,0,0,76.47059,3.7,34,1,1,2.416721,0.023214,0
1,48,0,56,4,2,4,6,4,3,-0.3460746,-0.6650018,-1.136523,-0.5770065,0.6817153,0.2750198,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3.7,12,1,1,1,0,1,0,0,63.15789,3.9,19,1,0,2.317183,0,-0.346075
0,49,0,50,5,2,3,4,2,3,-0.744618,-0.1878249,-1.136523,-1.06128,-0.5273642,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,22,1,0,1,1,1,0,0,81.48148,4.4,27,0,0,0.6433402,0,-0.744618
1,50,0,52,5,4,3,3,2,2,-0.7447439,-0.1878249,-0.0735065,-1.06128,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,57,0,0,1,0,0,0,0,85.07462,4.4,67,1,0,1.171195,0,-0.744744
0,51,0,33,9,3,7,7,4,5,0.6361284,1.720883,-0.6050149,0.8758139,1.286255,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,10,1,0,1,0,0,0,0,66.66666,4.5,15,1,1,3.466259,0.636128,0
1,52,0,57,6,5,6,5,5,7,0.5434852,0.2893519,0.4580018,0.3915404,0.0771755,0.8625621,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,69,0,0,1,0,0,0,0,89.61039,4.3,77,1,1,0.8222913,0.543485,0
1,53,0,38,9,6,8,7,4,5,0.9825948,1.720883,0.9895102,1.360087,1.286255,0.2750198,0.2638144,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,46,1,1,1,0,1,0,0,70.76923,4.8,65,1,1,1.797135,0.982595,0
0,54,0,34,4,2,1,1,1,1,-1.538843,-0.6650018,-1.136523,-2.029827,-2.340983,-1.487607,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,54,1,0,0,1,0,0,0,59.34066,4.1,91,1,0,1.813754,0,-1.53884
0,55,0,34,7,6,7,6,6,8,1.067531,0.7665288,0.9895102,0.8758139,0.6817153,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,24,0,0,0,0,0,0,0,66.66666,3.5,36,1,0,0.7581155,1.06753,0
0,56,0,32,3,3,7,3,3,3,-0.4143639,-1.142179,-0.6050149,1.360087,-1.131904,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,85,0,1,0,1,0,0,0,34.27419,4,248,1,1,4.297712,0,-0.414364
1,57,0,42,8,5,5,5,6,8,0.7962944,1.243706,0.4580018,-0.092733,0.0771755,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,44,1,0,0,1,0,0,0,70.96774,3.8,62,1,0,2.763981,0.796294,0
1,58,0,43,5,2,3,4,3,3,-0.6466943,-0.1878249,-1.136523,-1.06128,-0.5273642,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,35,1,0,0,1,0,0,0,68.62745,4.1,51,1,0,0.7483485,0,-0.646694
0,59,0,35,3,4,3,5,4,3,-0.4299034,-1.142179,-0.0735065,-1.06128,0.0771755,0.2750198,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3.3,11,0,0,1,0,1,1,0,57.89474,3.7,19,1,1,1.837548,0,-0.429903
1,60,0,62,5,1,5,5,1,4,-0.5924066,-0.1878249,-1.668032,-0.092733,0.0771755,-1.487607,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,22,1,0,1,0,0,0,0,81.48148,3.2,27,1,0,2.977653,0,-0.592407
1,61,0,42,6,3,1,4,1,1,-0.9889295,0.2893519,-0.6050149,-2.029827,-0.5273642,-1.487607,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,45,0,0,1,0,0,0,0,52.32558,4.2,86,1,0,3.667857,0,-0.98893
1,62,0,39,6,5,8,6,4,5,0.5546651,0.2893519,0.4580018,1.360087,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,22,0,0,1,0,0,0,0,75.86207,4.5,29,1,0,0.9073773,0.554665,0
1,63,0,52,7,5,7,6,3,8,0.6851749,0.7665288,0.4580018,0.8758139,0.6817153,-0.3125226,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,64,1,1,1,0,0,0,0,72.72727,3.8,88,1,1,2.004562,0.685175,0
1,64,0,52,9,6,7,6,5,6,0.9755885,1.720883,0.9895102,0.8758139,0.6817153,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,31,1,0,1,0,0,0,0,70.45454,3.7,44,1,1,0.7285256,0.975589,0
0,65,0,52,4,2,3,2,2,1,-1.178738,-0.6650018,-1.136523,-1.06128,-1.736444,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,59,1,0,1,0,0,0,0,78.66666,4.5,75,0,0,0.8237315,0,-1.17874
1,66,0,64,3,2,3,3,2,1,-1.157511,-1.142179,-1.136523,-1.06128,-1.131904,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,9,0,0,1,0,0,0,0,81.81818,3.8,11,1,0,0.2495975,0,-1.15751
1,67,0,50,9,6,7,7,6,8,1.327346,1.720883,0.9895102,0.8758139,1.286255,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,15,0,0,1,0,0,0,0,93.75,4.5,16,1,0,0.5883437,1.32735,0
1,68,0,60,2,1,1,2,2,2,-1.511268,-1.619356,-1.668032,-2.029827,-1.736444,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.2,23,0,0,1,0,0,0,0,71.875,2.4,32,1,0,0.8873489,0,-1.51127
1,69,0,51,7,4,5,6,4,5,0.3034731,0.7665288,-0.0735065,-0.092733,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,47,1,1,0,0,0,0,0,70.14925,3,67,1,0,0.658963,0.303473,0
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0,71,1,50,4,1,5,5,1,4,-0.671936,-0.6650018,-1.668032,-0.092733,0.0771755,-1.487607,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,17,0,0,1,1,0,0,1,70.83334,4.6,24,0,0,2.781287,0,-0.671936
1,72,0,52,6,7,6,6,5,5,0.6683338,0.2893519,1.521019,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,46,0,0,1,0,0,0,0,66.66666,3.5,69,1,0,1.148853,0.668334,0
1,73,0,51,8,6,7,4,6,6,0.7924695,1.243706,0.9895102,0.8758139,-0.5273642,1.450104,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,348,0,1,1,1,0,0,0,60.62718,4.8,574,1,0,2.42865,0.79247,0
1,74,0,38,4,3,4,4,2,3,-0.6548505,-0.6650018,-0.6050149,-0.5770065,-0.5273642,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,44,0,0,1,1,0,0,0,50.57471,4.2,87,1,0,0.0850292,0,-0.65485
1,75,0,47,6,5,7,4,3,6,0.2510546,0.2893519,0.4580018,0.8758139,-0.5273642,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3.8,19,1,1,1,1,1,0,0,79.16666,4,24,1,0,1.58095,0.251055,0
1,76,1,43,4,5,6,4,2,4,-0.2397178,-0.6650018,0.4580018,0.3915404,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,47,1,1,1,1,0,0,0,45.63107,3.7,103,1,0,1.586927,0,-0.239718
0,77,0,38,2,3,4,2,1,3,-1.113346,-1.619356,-0.6050149,-0.5770065,-1.736444,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,33,1,0,1,1,0,0,0,48.52941,4.5,68,0,0,1.54083,0,-1.11335
1,78,0,43,5,4,4,5,4,4,-0.1135935,-0.1878249,-0.0735065,-0.5770065,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,10,0,1,1,1,0,0,0,76.92308,4.7,13,1,0,0.4159773,0,-0.113593
1,79,0,57,3,3,4,3,2,3,-0.8351366,-1.142179,-0.6050149,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,7,0,0,1,0,0,0,0,53.84615,4.3,13,1,0,0.3387237,0,-0.835137
1,80,0,51,5,5,8,6,6,8,0.9005994,-0.1878249,0.4580018,1.360087,0.6817153,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,9,1,0,1,0,0,0,0,81.81818,4,11,1,0,2.490507,0.900599,0
0,81,0,45,2,1,4,4,5,4,-0.620769,-1.619356,-1.668032,-0.5770065,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4.8,12,0,0,1,1,1,0,1,70.58823,4.8,17,0,0,4.485768,0,-0.620769
1,82,0,57,3,2,3,6,2,1,-0.8552412,-1.142179,-1.136523,-1.06128,0.6817153,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,13,0,0,1,1,0,0,0,92.85714,3.5,14,1,1,3.083493,0,-0.855241
0,83,0,47,6,8,6,7,4,9,1.065906,0.2893519,2.052527,0.3915404,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,16,1,0,1,1,0,0,0,76.19048,3.3,21,0,1,3.776171,1.06591,0
1,84,1,54,8,8,5,7,4,9,1.144253,1.243706,2.052527,-0.092733,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3.8,18,1,0,1,1,1,0,0,100,4.2,18,1,1,4.055594,1.14425,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4.7,16,0,0,1,1,1,0,0,94.11765,4.8,17,0,1,0.3413446,1.68598,0
1,86,0,42,10,8,7,8,6,8,1.684802,2.198059,2.052527,0.8758139,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.9,15,0,0,1,1,0,0,0,83.33334,4.9,18,1,1,1.152506,1.6848,0
0,87,0,33,6,6,7,5,5,6,0.6362435,0.2893519,0.9895102,0.8758139,0.0771755,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,85,0,0,0,1,0,0,0,70.83334,4.5,120,1,0,0.673837,0.636243,0
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,11,0,0,0,1,0,0,0,28.94737,3.3,38,1,0,4.449395,0,-1.26998
0,89,1,35,10,7,9,8,6,7,1.681103,2.198059,1.521019,1.844361,1.890795,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,60,1,0,1,1,0,0,0,62.5,3.3,96,1,0,0.6656799,1.6811,0
1,90,0,61,7,3,2,4,1,3,-0.6756103,0.7665288,-0.6050149,-1.545553,-0.5273642,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,27,0,0,1,1,0,0,0,69.23077,3.6,39,1,0,3.523305,0,-0.67561
1,91,0,52,5,4,8,3,3,4,-0.0901815,-0.1878249,-0.0735065,1.360087,-1.131904,-0.3125226,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,61,1,0,0,1,0,0,0,54.95496,4.1,111,1,0,3.258788,0,-0.090181
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,23,1,1,1,0,0,1,0,85.18519,3.7,27,1,1,1.360877,0,-0.145026
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,98,0,0,1,1,0,0,0,74.24242,4.5,132,1,0,3.107088,1.14304,0
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-0.6050149,1.360087,-0.5273642,0.2750198,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,48,1,0,1,0,0,1,0,57.14286,3.5,84,1,0,3.018447,0.332051,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,86,1,0,1,0,1,0,0,68.8,4.1,125,1,0,2.129806,0.201567,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,76,1,0,1,0,1,0,0,60.8,3.9,125,1,0,2.129806,0.201567,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,77,1,0,1,0,1,0,0,62.60163,4.8,123,1,0,2.129806,0.201567,0
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,35,0,0,1,0,0,0,0,87.5,4.3,40,1,0,1.386081,0,-0.826081
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.1,39,0,0,1,0,0,0,0,88.63636,2.8,44,1,0,1.386081,0,-0.826081
1,3,0,51,5,5,2,3,2,3,-0.6603327,-0.1878249,0.4580018,-1.545553,-1.131904,-0.9000649,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.2,111,0,0,1,0,1,0,0,56.92308,3.4,195,1,0,2.537435,0,-0.660333
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,24,1,0,1,0,1,0,0,88.88889,3.8,27,1,0,1.760577,0,-0.766312
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,24,1,0,1,0,0,0,0,96,4.5,25,1,0,1.760577,0,-0.766312
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,17,1,0,1,0,1,0,0,85,4.6,20,1,0,1.760577,0,-0.766312
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1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,15,1,0,1,0,0,0,0,83.33334,4.5,18,1,0,1.760577,0,-0.766312
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,40,1,0,1,0,0,0,0,90.90909,4.6,44,1,0,1.6931,1.42145,0
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,38,1,0,1,0,0,0,0,79.16666,4.6,48,1,0,1.6931,1.42145,0
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,40,1,0,1,0,0,0,0,88.88889,4.9,45,1,0,1.6931,1.42145,0
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0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4.4,49,1,0,1,0,1,0,0,56.32184,4.5,87,1,0,1.6931,1.42145,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,160,0,1,1,0,0,0,0,54.79452,4.6,292,1,0,0.9447419,0.50022,0
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1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,155,0,1,1,0,0,0,0,56.98529,4.8,272,1,0,0.9447419,0.50022,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.8,166,0,1,1,0,0,0,0,58.04196,4.9,286,1,0,0.9447419,0.50022,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,186,0,1,1,0,1,0,0,61.58941,4.5,302,1,0,0.9447419,0.50022,0
0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,29,1,0,1,0,1,0,0,85.29412,4.3,34,1,0,0.4898793,0,-0.21435
0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,37,1,0,1,0,1,0,0,90.2439,4.1,41,1,0,0.4898793,0,-0.21435
0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,29,1,0,1,0,1,0,0,70.7317,4.2,41,1,0,0.4898793,0,-0.21435
0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,28,1,0,1,0,1,0,0,82.35294,3.5,34,1,0,0.4898793,0,-0.21435
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,21,1,0,1,0,0,0,0,95.45454,4.5,22,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,13,1,0,1,0,0,0,0,61.90476,4.4,21,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,16,1,0,1,0,0,0,0,94.11765,4.4,17,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,24,1,0,1,1,0,0,0,80,2.5,30,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,23,1,0,1,0,0,0,0,100,4.3,23,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,20,1,0,1,0,0,0,0,100,4.5,20,1,0,2.041787,0,-0.346539
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4.7,29,1,0,1,0,1,0,0,87.87878,4.8,33,1,0,2.32433,0.061344,0
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,11,1,0,1,0,0,0,0,25,4.4,44,1,0,2.32433,0.061344,0
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,29,1,0,1,0,1,0,0,59.18367,4.7,49,1,0,2.32433,0.061344,0
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,25,1,0,1,0,1,0,0,86.20689,4.4,29,1,0,2.32433,0.061344,0
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,42,1,0,1,0,0,0,0,87.5,4.7,48,1,0,2.32433,0.061344,0
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,34,1,0,1,0,0,0,0,85,4.5,40,1,0,2.32433,0.061344,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,12,0,0,1,0,0,0,0,75,4.3,16,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,14,0,0,1,0,1,0,0,93.33334,4.4,15,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,22,0,0,1,0,1,0,0,95.65218,4.5,23,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,10,0,0,1,1,0,0,0,90.90909,5,11,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,17,0,0,1,1,0,0,0,58.62069,4.9,29,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,16,0,0,1,0,0,0,0,76.19048,4.6,21,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,15,0,0,1,1,0,0,0,83.33334,5,18,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,16,0,0,1,0,1,0,0,84.21053,4.7,19,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,16,0,0,1,1,0,0,0,80,5,20,0,0,0.916837,0.452568,0
0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,30,0,0,1,0,1,1,0,90.90909,3.7,33,1,0,3.798652,0.143264,0
0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,23,0,0,1,0,1,1,0,95.83334,4.3,24,1,0,3.798652,0.143264,0
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4.4,13,0,0,1,0,1,0,0,61.90476,4.2,21,0,0,0.3504698,0,-0.155023
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,24,0,0,1,0,0,0,0,80,4.7,30,0,0,0.3504698,0,-0.155023
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,24,0,0,1,0,0,0,0,96,4.7,25,0,0,0.3504698,0,-0.155023
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,25,0,0,1,0,0,0,0,71.42857,3.5,35,0,0,0.3504698,0,-0.155023
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,18,0,0,1,0,0,0,0,60,4.2,30,1,0,0.8283713,0.128543,0
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,28,0,0,1,0,0,0,0,66.66666,4,42,1,0,0.8283713,0.128543,0
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,25,0,0,1,0,1,0,0,43.85965,4,57,1,0,0.8283713,0.128543,0
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,40,0,0,1,0,1,0,0,70.17544,3.9,57,1,0,0.8283713,0.128543,0
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,40,0,0,1,0,1,0,0,78.43137,4.4,51,1,0,0.8283713,0.128543,0
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,18,0,0,1,0,0,0,0,60,3.8,30,1,0,0.8283713,0.128543,0
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,31,0,0,1,0,1,1,0,83.78378,4.2,37,1,0,4.42171,0,-0.347045
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,15,0,0,1,0,0,1,0,51.72414,3.5,29,1,0,4.42171,0,-0.347045
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,23,0,0,1,0,1,1,0,85.18519,3.6,27,1,0,4.42171,0,-0.347045
0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,34,1,0,1,0,0,0,0,65.38461,3.3,52,1,0,2.113737,0.461939,0
0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.9,21,1,0,1,0,1,0,0,80.76923,3.3,26,1,0,2.113737,0.461939,0
0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,29,1,0,1,0,1,0,0,96.66666,3.2,30,1,0,2.113737,0.461939,0
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0,25,0,34,8,8,9,8,6,8,1.687167,1.243706,2.052527,1.844361,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,33,1,0,1,0,0,0,0,82.5,4.1,40,1,0,0.454603,1.68717,0
1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,71,0,0,1,0,1,0,0,47.01987,4.3,151,1,0,0.4904639,0,-0.410198
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1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,31,0,0,1,1,1,0,0,68.88889,4.3,45,1,0,0.4904639,0,-0.410198
1,27,1,52,7,4,5,4,5,4,0.1233448,0.7665288,-0.0735065,-0.092733,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4.6,23,0,1,1,0,1,1,0,100,4.7,23,1,0,1.5706,0.123345,0
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1,28,0,73,6,1,3,5,2,1,-0.8059942,0.2893519,-1.668032,-1.06128,0.0771755,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,10,0,1,1,0,1,0,0,76.92308,4.4,13,1,0,3.385376,0,-0.805994
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1,29,0,70,2,5,3,2,3,3,-0.8210418,-1.619356,0.4580018,-1.06128,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,5,0,1,1,0,1,0,0,10.41667,4.6,48,1,0,3.45521,0,-0.821042
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0,31,0,63,6,5,4,5,4,2,-0.0985568,0.2893519,0.4580018,-0.5770065,0.0771755,0.2750198,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,11,0,0,1,0,0,0,0,78.57143,4.4,14,0,0,1.890474,0,-0.098557
0,31,0,63,6,5,4,5,4,2,-0.0985568,0.2893519,0.4580018,-0.5770065,0.0771755,0.2750198,-1.113883,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,10,0,0,1,0,1,0,0,66.66666,3.6,15,0,0,1.890474,0,-0.098557
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1,32,0,47,3,1,4,3,2,3,-1.012306,-1.142179,-1.668032,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,10,0,0,1,0,0,0,0,71.42857,4.1,14,1,0,0.7911476,0,-1.01231
1,32,0,47,3,1,4,3,2,3,-1.012306,-1.142179,-1.668032,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,10,0,0,1,0,1,0,0,83.33334,3.2,12,1,0,0.7911476,0,-1.01231
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,154,0,0,1,1,0,0,0,48.73418,3.9,316,1,0,1.194346,0.488331,0
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,12,0,0,1,0,0,0,0,80,4.9,15,1,0,1.194346,0.488331,0
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,14,0,0,1,0,0,0,0,93.33334,4.7,15,1,0,1.194346,0.488331,0
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,27,0,0,1,0,1,0,0,93.10345,4.4,29,1,0,1.194346,0.488331,0
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1,35,0,54,4,1,2,4,1,2,-1.167907,-0.6650018,-1.668032,-1.545553,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4.2,10,0,1,1,0,1,0,0,66.66666,4.1,15,1,0,1.161077,0,-1.16791
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1,36,1,44,8,6,8,5,5,7,0.9525534,1.243706,0.9895102,1.360087,0.0771755,0.8625621,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3.3,35,1,0,1,0,1,0,0,72.91666,3.5,48,1,0,1.079378,0.952553,0
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0,87,0,33,6,6,7,5,5,6,0.6362435,0.2893519,0.9895102,0.8758139,0.0771755,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,67,0,0,0,1,0,0,0,43.22581,4.5,155,1,0,0.673837,0.636243,0
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.9,28,0,0,0,1,0,0,0,40,3.1,70,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,61,0,0,0,1,0,0,0,40.9396,2.8,149,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,49,0,0,0,1,0,0,0,35.76642,3.1,137,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,13,0,0,0,0,0,0,0,44.82759,4.2,29,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,28,0,0,0,1,0,0,0,50.90909,3.4,55,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,67,0,0,0,1,0,0,0,49.26471,3,136,1,0,4.449395,0,-1.26998
0,89,1,35,10,7,9,8,6,7,1.681103,2.198059,1.521019,1.844361,1.890795,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,20,1,0,1,1,0,0,0,33.33333,3.6,60,1,0,0.6656799,1.6811,0
0,89,1,35,10,7,9,8,6,7,1.681103,2.198059,1.521019,1.844361,1.890795,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,43,1,0,1,1,0,0,0,39.81482,3.7,108,1,0,0.6656799,1.6811,0
1,90,0,61,7,3,2,4,1,3,-0.6756103,0.7665288,-0.6050149,-1.545553,-0.5273642,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,13,0,0,1,1,0,0,0,86.66666,4.3,15,1,0,3.523305,0,-0.67561
1,91,0,52,5,4,8,3,3,4,-0.0901815,-0.1878249,-0.0735065,1.360087,-1.131904,-0.3125226,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,14,1,0,0,1,0,0,0,82.35294,4.9,17,1,0,3.258788,0,-0.090181
1,91,0,52,5,4,8,3,3,4,-0.0901815,-0.1878249,-0.0735065,1.360087,-1.131904,-0.3125226,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,19,1,0,0,1,0,0,0,100,4.8,19,1,0,3.258788,0,-0.090181
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,18,1,1,1,0,0,1,0,94.73684,3.9,19,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,11,1,1,1,0,0,1,0,84.61539,4.5,13,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,18,1,1,1,0,0,1,0,94.73684,3.6,19,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,11,1,1,1,0,0,1,0,50,4.4,22,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,7,1,1,1,0,0,1,0,35,3.4,20,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,24,1,1,1,0,0,1,0,88.88889,4.4,27,1,1,1.360877,0,-0.145026
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,111,0,0,1,1,0,0,0,87.40157,4.5,127,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,62,0,0,1,0,0,0,0,72.94118,4.5,85,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,76,0,0,1,1,0,0,0,75.24753,4.6,101,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.2,9,0,0,1,1,0,0,0,42.85714,4.1,21,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,52,0,0,1,0,0,0,0,60.46511,4.5,86,1,0,3.107088,1.14304,0
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-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
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-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
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-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


================================================
FILE: ch_regr_simple_linear/figures/eoce/full_lin_regr_1/rate_my_prof.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

prof_evals_beauty <- read.csv("prof_evals_beauty.csv")

# rename variables for convenience ----------------------------------

beauty <- prof_evals_beauty$btystdave
eval <- prof_evals_beauty$courseevaluation

# model evaluation scores vs. beauty --------------------------------

m_eval_beauty = lm(eval ~ beauty)

xtable(summary(m_eval_beauty))

# scatterplot of evaluation scores vs. beauty -----------------------

pdf("rate_my_prof_eval_beauty.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5, las = 1)

plot(eval ~ beauty, 
     xlab = "Beauty", ylab = "Teaching evaluation", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, at = seq(-1, 2, 1))
axis(2, at = seq(2, 5, 1))
box()

dev.off()

# residuals plot ----------------------------------------------------

pdf("rate_my_prof_residuals.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5, las = 1)

plot(m_eval_beauty$residuals ~ beauty, 
     xlab = "Beauty", ylab = "Residuals", 
     pch = 19, col = COL[1,2], 
     ylim = c(-1.82, 1.82), axes = FALSE)
axis(1, at = seq(-1, 2, 1))
axis(2, at = seq(-1, 1, 1))
box()
abline(h = 0, lty = 3)

dev.off()

# residuals histogram -----------------------------------------------

pdf("rate_my_prof_residuals_hist.pdf", height = 5, width = 5)

par(mar = c(3.9, 3, 0, 0), cex.lab = 1.5, cex.axis = 1.5)

hist(m_eval_beauty$residuals, 
     xlab = "Residuals", ylab = "", main = "",
     col = COL[1],
     xlim = c(-2,2))

dev.off()

# normal probability plot of residuals ------------------------------

pdf("rate_my_prof_residuals_qq.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

qqnorm(m_eval_beauty$residuals, 
       pch = 19, col = COL[1,2],
       main = "", las = 0)
qqline(m_eval_beauty$residuals, col = COL[1])

dev.off()

# order of residuals ---------------------------------------------===

pdf("rate_my_prof_residuals_order.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_eval_beauty$residuals, 
     xlab = "Order of data collection", ylab = "Residuals", main = "",
     pch = 19, col = COL[1,2],
     ylim = c(-1.82, 1.82), axes = FALSE)
axis(1)
axis(2, at = seq(-1, 1, 1))
box()

abline(h = 0, lty = 3)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/full_lin_regr_2/prof_evals_beauty.csv
================================================
tenured,profnumber,minority,age,beautyf2upper,beautyflowerdiv,beautyfupperdiv,beautym2upper,beautymlowerdiv,beautymupperdiv,btystdave,btystdf2u,btystdfl,btystdfu,btystdm2u,btystdml,btystdmu,class1,class2,class3,class4,class5,class6,class7,class8,class9,class10,class11,class12,class13,class14,class15,class16,class17,class18,class19,class20,class21,class22,class23,class24,class25,class26,class27,class28,class29,class30,courseevaluation,didevaluation,female,formal,fulldept,lower,multipleclass,nonenglish,onecredit,percentevaluating,profevaluation,students,tenuretrack,blkandwhite,btystdvariance,btystdavepos,btystdaveneg
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,24,1,0,1,0,1,0,0,55.81395,4.7,43,1,0,2.129806,0.201567,0
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,17,0,0,1,0,0,0,0,85,4.6,20,1,0,1.386081,0,-0.826081
1,3,0,51,5,5,2,3,2,3,-0.6603327,-0.1878249,0.4580018,-1.545553,-1.131904,-0.9000649,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,55,0,0,1,0,1,0,0,100,4.1,55,1,0,2.537435,0,-0.660333
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,40,1,0,1,0,1,0,0,86.95652,4.5,46,1,0,1.760577,0,-0.766312
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,42,1,0,1,0,0,0,0,87.5,4.8,48,1,0,1.6931,1.42145,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,182,0,1,1,0,0,0,0,64.53901,4.4,282,1,0,0.9447419,0.50022,0
0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,33,1,0,1,0,1,0,0,80.48781,4.4,41,1,0,0.4898793,0,-0.21435
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,25,1,0,1,0,0,0,0,60.97561,3.4,41,1,0,2.041787,0,-0.346539
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,48,1,0,1,0,0,0,0,80,4.8,60,1,0,2.32433,0.061344,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,16,0,0,1,0,1,0,0,84.21053,4,19,0,0,0.916837,0.452568,0
0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,18,0,0,1,0,1,1,0,72,3.6,25,1,0,3.798652,0.143264,0
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,30,0,0,1,0,0,0,0,88.23529,4.1,34,0,0,0.3504698,0,-0.155023
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,28,0,0,1,0,0,0,0,70,4.1,40,1,0,0.8283713,0.128543,0
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,30,0,0,1,0,0,1,0,83.33334,3.5,36,1,0,4.42171,0,-0.347045
0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.9,23,1,0,1,0,1,0,0,82.14286,2.9,28,1,0,2.113737,0.461939,0
1,16,0,45,3,2,4,7,5,4,-0.1503849,-1.142179,-1.136523,-0.5770065,1.286255,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,23,0,0,1,0,1,0,0,69.69697,4.6,33,1,0,5.230154,0,-0.150385
0,17,0,56,1,3,4,3,2,2,-1.070734,-2.096532,-0.6050149,-0.5770065,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,27,1,0,1,0,1,0,0,65.85366,4.4,41,0,0,1.547655,0,-1.07073
0,18,0,48,6,3,5,4,4,4,-0.1426931,0.2893519,-0.6050149,-0.092733,-0.5273642,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,100,0,0,1,1,0,0,0,74.07407,4,135,0,0,0.7281362,0,-0.142693
1,19,0,46,5,4,4,5,2,6,-0.1563634,-0.1878249,-0.0735065,-0.5770065,0.0771755,-0.9000649,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,25,1,0,1,0,0,0,0,80.64516,4.9,31,1,1,1.565791,0,-0.156363
0,20,0,57,4,5,4,6,5,2,-0.0589354,-0.6650018,0.4580018,-0.5770065,0.6817153,0.8625621,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,17,1,0,0,0,0,0,0,60.71429,3.5,28,0,0,3.413574,0,-0.058935
0,21,0,52,4,6,6,7,2,4,0.1511368,-0.6650018,0.9895102,0.3915404,1.286255,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,19,1,0,1,0,1,0,0,86.36364,3.7,22,0,0,3.940365,0.151137,0
0,22,1,29,4,3,3,2,2,3,-0.937076,-0.6650018,-0.6050149,-1.06128,-1.736444,-0.9000649,-0.6546507,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,19,1,0,1,0,1,1,0,73.07692,3.4,26,1,0,0.9198381,0,-0.937076
1,23,0,62,4,4,3,4,1,2,-0.8214405,-0.6650018,-0.0735065,-1.06128,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,30,0,0,1,0,1,0,0,76.92308,4.3,39,1,0,1.257183,0,-0.821441
1,24,0,64,5,5,4,5,3,3,-0.1994712,-0.1878249,0.4580018,-0.5770065,0.0771755,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,15,0,0,1,0,0,0,0,62.5,4.5,24,1,0,0.8714417,0,-0.199471
0,25,0,34,8,8,9,8,6,8,1.687167,1.243706,2.052527,1.844361,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,20,1,0,1,0,0,0,0,76.92308,4.8,26,1,0,0.454603,1.68717,0
1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4.2,84,0,0,1,0,1,0,0,52.83019,4.4,159,1,0,0.4904639,0,-0.410198
1,27,1,52,7,4,5,4,5,4,0.1233448,0.7665288,-0.0735065,-0.092733,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,13,0,1,1,0,1,1,0,81.25,4.4,16,1,0,1.5706,0.123345,0
1,28,0,73,6,1,3,5,2,1,-0.8059942,0.2893519,-1.668032,-1.06128,0.0771755,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,12,0,1,1,0,1,0,0,70.58823,3.6,17,1,0,3.385376,0,-0.805994
1,29,0,70,2,5,3,2,3,3,-0.8210418,-1.619356,0.4580018,-1.06128,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4.4,47,0,1,1,0,1,0,0,35.07463,4.6,134,1,0,3.45521,0,-0.821042
0,30,0,41,7,3,6,7,4,4,0.3198185,0.7665288,-0.6050149,0.3915404,1.286255,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.3,10,1,0,1,0,1,0,0,83.33334,2.3,12,1,0,2.261486,0.319818,0
0,31,0,63,6,5,4,5,4,2,-0.0985568,0.2893519,0.4580018,-0.5770065,0.0771755,0.2750198,-1.113883,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,28,0,0,1,0,1,0,0,65.11628,4.3,43,0,0,1.890474,0,-0.098557
1,32,0,47,3,1,4,3,2,3,-1.012306,-1.142179,-1.668032,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,10,0,0,1,0,0,0,0,66.66666,4.9,15,1,0,0.7911476,0,-1.01231
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,86,0,0,1,1,0,0,0,34.95935,4.2,246,1,0,1.194346,0.488331,0
0,34,1,47,6,2,6,4,3,5,-0.1719505,0.2893519,-1.136523,0.3915404,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,21,1,0,1,1,1,0,0,100,4.2,21,1,0,1.796693,0,-0.171951
1,35,0,54,4,1,2,4,1,2,-1.167907,-0.6650018,-1.668032,-1.545553,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,15,0,1,1,0,1,0,0,100,4.3,15,1,0,1.161077,0,-1.16791
1,36,1,44,8,6,8,5,5,7,0.9525534,1.243706,0.9895102,1.360087,0.0771755,0.8625621,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,30,1,0,1,0,0,0,0,54.54546,3.9,55,1,0,1.079378,0.952553,0
1,37,1,47,2,2,2,4,2,2,-1.140457,-1.619356,-1.136523,-1.545553,-0.5273642,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,20,0,0,1,0,1,0,0,74.07407,3.9,27,1,0,0.8278397,0,-1.14046
1,38,0,62,5,3,2,3,3,2,-0.8161172,-0.1878249,-0.6050149,-1.545553,-1.131904,-0.3125226,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,12,0,1,1,0,0,0,0,92.30769,4.1,13,1,0,1.413386,0,-0.816117
1,39,0,60,6,4,4,4,2,2,-0.4837456,0.2893519,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,25,0,0,1,0,0,0,0,73.52941,4.8,34,1,0,1.346971,0,-0.483746
0,40,0,37,7,4,8,8,5,5,0.8450468,0.7665288,-0.0735065,1.360087,1.890795,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,15,0,0,1,0,1,0,0,88.23529,3.3,17,1,0,2.546899,0.845047,0
0,41,0,42,3,3,5,4,3,6,-0.3261277,-1.142179,-0.6050149,-0.092733,-0.5273642,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,13,0,0,1,0,0,0,0,92.85714,4.4,14,1,0,1.939639,0,-0.326128
1,42,0,35,2,4,7,6,6,4,0.1865589,-1.619356,-0.0735065,0.8758139,0.6817153,1.450104,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4.2,42,0,0,1,0,1,0,0,82.35294,4.3,51,1,0,5.791667,0.186559,0
0,43,0,39,8,8,8,9,7,9,1.881674,1.243706,2.052527,1.360087,2.495334,2.037647,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3.4,22,1,0,1,1,1,0,0,91.66666,3.3,24,0,0,1.157145,1.88167,0
1,44,0,49,7,6,6,5,6,9,0.9626006,0.7665288,0.9895102,0.3915404,0.0771755,1.450104,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3.9,28,0,1,1,0,1,0,0,62.22222,4,45,1,0,2.682287,0.962601,0
1,45,0,61,4,5,5,6,4,5,0.1534694,-0.6650018,0.4580018,-0.092733,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4.5,22,0,1,1,1,1,0,0,81.48148,4.5,27,1,0,1.129245,0.153469,0
0,46,0,33,7,7,8,7,6,7,1.261046,0.7665288,1.521019,1.360087,1.286255,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4.7,30,0,1,1,0,1,0,0,96.77419,4.9,31,1,0,0.3645249,1.26105,0
1,47,0,58,7,3,6,6,2,4,0.0232144,0.7665288,-0.6050149,0.3915404,0.6817153,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3.5,26,1,1,1,0,1,0,0,76.47059,3.7,34,1,1,2.416721,0.023214,0
1,48,0,56,4,2,4,6,4,3,-0.3460746,-0.6650018,-1.136523,-0.5770065,0.6817153,0.2750198,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3.7,12,1,1,1,0,1,0,0,63.15789,3.9,19,1,0,2.317183,0,-0.346075
0,49,0,50,5,2,3,4,2,3,-0.744618,-0.1878249,-1.136523,-1.06128,-0.5273642,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,22,1,0,1,1,1,0,0,81.48148,4.4,27,0,0,0.6433402,0,-0.744618
1,50,0,52,5,4,3,3,2,2,-0.7447439,-0.1878249,-0.0735065,-1.06128,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,57,0,0,1,0,0,0,0,85.07462,4.4,67,1,0,1.171195,0,-0.744744
0,51,0,33,9,3,7,7,4,5,0.6361284,1.720883,-0.6050149,0.8758139,1.286255,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,10,1,0,1,0,0,0,0,66.66666,4.5,15,1,1,3.466259,0.636128,0
1,52,0,57,6,5,6,5,5,7,0.5434852,0.2893519,0.4580018,0.3915404,0.0771755,0.8625621,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,69,0,0,1,0,0,0,0,89.61039,4.3,77,1,1,0.8222913,0.543485,0
1,53,0,38,9,6,8,7,4,5,0.9825948,1.720883,0.9895102,1.360087,1.286255,0.2750198,0.2638144,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,46,1,1,1,0,1,0,0,70.76923,4.8,65,1,1,1.797135,0.982595,0
0,54,0,34,4,2,1,1,1,1,-1.538843,-0.6650018,-1.136523,-2.029827,-2.340983,-1.487607,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,54,1,0,0,1,0,0,0,59.34066,4.1,91,1,0,1.813754,0,-1.53884
0,55,0,34,7,6,7,6,6,8,1.067531,0.7665288,0.9895102,0.8758139,0.6817153,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,24,0,0,0,0,0,0,0,66.66666,3.5,36,1,0,0.7581155,1.06753,0
0,56,0,32,3,3,7,3,3,3,-0.4143639,-1.142179,-0.6050149,1.360087,-1.131904,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,85,0,1,0,1,0,0,0,34.27419,4,248,1,1,4.297712,0,-0.414364
1,57,0,42,8,5,5,5,6,8,0.7962944,1.243706,0.4580018,-0.092733,0.0771755,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,44,1,0,0,1,0,0,0,70.96774,3.8,62,1,0,2.763981,0.796294,0
1,58,0,43,5,2,3,4,3,3,-0.6466943,-0.1878249,-1.136523,-1.06128,-0.5273642,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,35,1,0,0,1,0,0,0,68.62745,4.1,51,1,0,0.7483485,0,-0.646694
0,59,0,35,3,4,3,5,4,3,-0.4299034,-1.142179,-0.0735065,-1.06128,0.0771755,0.2750198,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3.3,11,0,0,1,0,1,1,0,57.89474,3.7,19,1,1,1.837548,0,-0.429903
1,60,0,62,5,1,5,5,1,4,-0.5924066,-0.1878249,-1.668032,-0.092733,0.0771755,-1.487607,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,22,1,0,1,0,0,0,0,81.48148,3.2,27,1,0,2.977653,0,-0.592407
1,61,0,42,6,3,1,4,1,1,-0.9889295,0.2893519,-0.6050149,-2.029827,-0.5273642,-1.487607,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,45,0,0,1,0,0,0,0,52.32558,4.2,86,1,0,3.667857,0,-0.98893
1,62,0,39,6,5,8,6,4,5,0.5546651,0.2893519,0.4580018,1.360087,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,22,0,0,1,0,0,0,0,75.86207,4.5,29,1,0,0.9073773,0.554665,0
1,63,0,52,7,5,7,6,3,8,0.6851749,0.7665288,0.4580018,0.8758139,0.6817153,-0.3125226,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,64,1,1,1,0,0,0,0,72.72727,3.8,88,1,1,2.004562,0.685175,0
1,64,0,52,9,6,7,6,5,6,0.9755885,1.720883,0.9895102,0.8758139,0.6817153,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,31,1,0,1,0,0,0,0,70.45454,3.7,44,1,1,0.7285256,0.975589,0
0,65,0,52,4,2,3,2,2,1,-1.178738,-0.6650018,-1.136523,-1.06128,-1.736444,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,59,1,0,1,0,0,0,0,78.66666,4.5,75,0,0,0.8237315,0,-1.17874
1,66,0,64,3,2,3,3,2,1,-1.157511,-1.142179,-1.136523,-1.06128,-1.131904,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,9,0,0,1,0,0,0,0,81.81818,3.8,11,1,0,0.2495975,0,-1.15751
1,67,0,50,9,6,7,7,6,8,1.327346,1.720883,0.9895102,0.8758139,1.286255,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,15,0,0,1,0,0,0,0,93.75,4.5,16,1,0,0.5883437,1.32735,0
1,68,0,60,2,1,1,2,2,2,-1.511268,-1.619356,-1.668032,-2.029827,-1.736444,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.2,23,0,0,1,0,0,0,0,71.875,2.4,32,1,0,0.8873489,0,-1.51127
1,69,0,51,7,4,5,6,4,5,0.3034731,0.7665288,-0.0735065,-0.092733,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,47,1,1,0,0,0,0,0,70.14925,3,67,1,0,0.658963,0.303473,0
0,70,0,43,4,3,4,4,2,4,-0.5783117,-0.6650018,-0.6050149,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,19,0,0,1,1,0,0,0,86.36364,4.5,22,1,0,0.2609582,0,-0.578312
0,71,1,50,4,1,5,5,1,4,-0.671936,-0.6650018,-1.668032,-0.092733,0.0771755,-1.487607,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,17,0,0,1,1,0,0,1,70.83334,4.6,24,0,0,2.781287,0,-0.671936
1,72,0,52,6,7,6,6,5,5,0.6683338,0.2893519,1.521019,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,46,0,0,1,0,0,0,0,66.66666,3.5,69,1,0,1.148853,0.668334,0
1,73,0,51,8,6,7,4,6,6,0.7924695,1.243706,0.9895102,0.8758139,-0.5273642,1.450104,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,348,0,1,1,1,0,0,0,60.62718,4.8,574,1,0,2.42865,0.79247,0
1,74,0,38,4,3,4,4,2,3,-0.6548505,-0.6650018,-0.6050149,-0.5770065,-0.5273642,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,44,0,0,1,1,0,0,0,50.57471,4.2,87,1,0,0.0850292,0,-0.65485
1,75,0,47,6,5,7,4,3,6,0.2510546,0.2893519,0.4580018,0.8758139,-0.5273642,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3.8,19,1,1,1,1,1,0,0,79.16666,4,24,1,0,1.58095,0.251055,0
1,76,1,43,4,5,6,4,2,4,-0.2397178,-0.6650018,0.4580018,0.3915404,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,47,1,1,1,1,0,0,0,45.63107,3.7,103,1,0,1.586927,0,-0.239718
0,77,0,38,2,3,4,2,1,3,-1.113346,-1.619356,-0.6050149,-0.5770065,-1.736444,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,33,1,0,1,1,0,0,0,48.52941,4.5,68,0,0,1.54083,0,-1.11335
1,78,0,43,5,4,4,5,4,4,-0.1135935,-0.1878249,-0.0735065,-0.5770065,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,10,0,1,1,1,0,0,0,76.92308,4.7,13,1,0,0.4159773,0,-0.113593
1,79,0,57,3,3,4,3,2,3,-0.8351366,-1.142179,-0.6050149,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,7,0,0,1,0,0,0,0,53.84615,4.3,13,1,0,0.3387237,0,-0.835137
1,80,0,51,5,5,8,6,6,8,0.9005994,-0.1878249,0.4580018,1.360087,0.6817153,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,9,1,0,1,0,0,0,0,81.81818,4,11,1,0,2.490507,0.900599,0
0,81,0,45,2,1,4,4,5,4,-0.620769,-1.619356,-1.668032,-0.5770065,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4.8,12,0,0,1,1,1,0,1,70.58823,4.8,17,0,0,4.485768,0,-0.620769
1,82,0,57,3,2,3,6,2,1,-0.8552412,-1.142179,-1.136523,-1.06128,0.6817153,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,13,0,0,1,1,0,0,0,92.85714,3.5,14,1,1,3.083493,0,-0.855241
0,83,0,47,6,8,6,7,4,9,1.065906,0.2893519,2.052527,0.3915404,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,16,1,0,1,1,0,0,0,76.19048,3.3,21,0,1,3.776171,1.06591,0
1,84,1,54,8,8,5,7,4,9,1.144253,1.243706,2.052527,-0.092733,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3.8,18,1,0,1,1,1,0,0,100,4.2,18,1,1,4.055594,1.14425,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4.7,16,0,0,1,1,1,0,0,94.11765,4.8,17,0,1,0.3413446,1.68598,0
1,86,0,42,10,8,7,8,6,8,1.684802,2.198059,2.052527,0.8758139,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.9,15,0,0,1,1,0,0,0,83.33334,4.9,18,1,1,1.152506,1.6848,0
0,87,0,33,6,6,7,5,5,6,0.6362435,0.2893519,0.9895102,0.8758139,0.0771755,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,85,0,0,0,1,0,0,0,70.83334,4.5,120,1,0,0.673837,0.636243,0
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,11,0,0,0,1,0,0,0,28.94737,3.3,38,1,0,4.449395,0,-1.26998
0,89,1,35,10,7,9,8,6,7,1.681103,2.198059,1.521019,1.844361,1.890795,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,60,1,0,1,1,0,0,0,62.5,3.3,96,1,0,0.6656799,1.6811,0
1,90,0,61,7,3,2,4,1,3,-0.6756103,0.7665288,-0.6050149,-1.545553,-0.5273642,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,27,0,0,1,1,0,0,0,69.23077,3.6,39,1,0,3.523305,0,-0.67561
1,91,0,52,5,4,8,3,3,4,-0.0901815,-0.1878249,-0.0735065,1.360087,-1.131904,-0.3125226,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,61,1,0,0,1,0,0,0,54.95496,4.1,111,1,0,3.258788,0,-0.090181
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,23,1,1,1,0,0,1,0,85.18519,3.7,27,1,1,1.360877,0,-0.145026
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,98,0,0,1,1,0,0,0,74.24242,4.5,132,1,0,3.107088,1.14304,0
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-0.6050149,1.360087,-0.5273642,0.2750198,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,48,1,0,1,0,0,1,0,57.14286,3.5,84,1,0,3.018447,0.332051,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,86,1,0,1,0,1,0,0,68.8,4.1,125,1,0,2.129806,0.201567,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,76,1,0,1,0,1,0,0,60.8,3.9,125,1,0,2.129806,0.201567,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,77,1,0,1,0,1,0,0,62.60163,4.8,123,1,0,2.129806,0.201567,0
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,35,0,0,1,0,0,0,0,87.5,4.3,40,1,0,1.386081,0,-0.826081
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.1,39,0,0,1,0,0,0,0,88.63636,2.8,44,1,0,1.386081,0,-0.826081
1,3,0,51,5,5,2,3,2,3,-0.6603327,-0.1878249,0.4580018,-1.545553,-1.131904,-0.9000649,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.2,111,0,0,1,0,1,0,0,56.92308,3.4,195,1,0,2.537435,0,-0.660333
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,24,1,0,1,0,1,0,0,88.88889,3.8,27,1,0,1.760577,0,-0.766312
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1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,15,1,0,1,0,0,0,0,83.33334,4.5,18,1,0,1.760577,0,-0.766312
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,40,1,0,1,0,0,0,0,90.90909,4.6,44,1,0,1.6931,1.42145,0
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,38,1,0,1,0,0,0,0,79.16666,4.6,48,1,0,1.6931,1.42145,0
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,40,1,0,1,0,0,0,0,88.88889,4.9,45,1,0,1.6931,1.42145,0
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,52,1,0,1,0,0,0,0,88.13559,4.6,59,1,0,1.6931,1.42145,0
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4.4,49,1,0,1,0,1,0,0,56.32184,4.5,87,1,0,1.6931,1.42145,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,160,0,1,1,0,0,0,0,54.79452,4.6,292,1,0,0.9447419,0.50022,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,79,0,1,1,0,0,0,0,60.76923,4.7,130,1,0,0.9447419,0.50022,0
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0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,29,1,0,1,0,1,0,0,85.29412,4.3,34,1,0,0.4898793,0,-0.21435
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1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,21,1,0,1,0,0,0,0,95.45454,4.5,22,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,13,1,0,1,0,0,0,0,61.90476,4.4,21,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,16,1,0,1,0,0,0,0,94.11765,4.4,17,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,24,1,0,1,1,0,0,0,80,2.5,30,1,0,2.041787,0,-0.346539
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,23,1,0,1,0,0,0,0,100,4.3,23,1,0,2.041787,0,-0.346539
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0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4.7,29,1,0,1,0,1,0,0,87.87878,4.8,33,1,0,2.32433,0.061344,0
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0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,29,1,0,1,0,1,0,0,59.18367,4.7,49,1,0,2.32433,0.061344,0
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0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,34,1,0,1,0,0,0,0,85,4.5,40,1,0,2.32433,0.061344,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,12,0,0,1,0,0,0,0,75,4.3,16,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,14,0,0,1,0,1,0,0,93.33334,4.4,15,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,22,0,0,1,0,1,0,0,95.65218,4.5,23,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,10,0,0,1,1,0,0,0,90.90909,5,11,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,17,0,0,1,1,0,0,0,58.62069,4.9,29,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,16,0,0,1,0,0,0,0,76.19048,4.6,21,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,15,0,0,1,1,0,0,0,83.33334,5,18,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,16,0,0,1,0,1,0,0,84.21053,4.7,19,0,0,0.916837,0.452568,0
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0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,30,0,0,1,0,1,1,0,90.90909,3.7,33,1,0,3.798652,0.143264,0
0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,23,0,0,1,0,1,1,0,95.83334,4.3,24,1,0,3.798652,0.143264,0
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4.4,13,0,0,1,0,1,0,0,61.90476,4.2,21,0,0,0.3504698,0,-0.155023
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1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,18,0,0,1,0,0,0,0,60,4.2,30,1,0,0.8283713,0.128543,0
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1,23,0,62,4,4,3,4,1,2,-0.8214405,-0.6650018,-0.0735065,-1.06128,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,94,0,0,1,1,1,0,0,51.08696,3.7,184,1,0,1.257183,0,-0.821441
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1,24,0,64,5,5,4,5,3,3,-0.1994712,-0.1878249,0.4580018,-0.5770065,0.0771755,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,51,0,0,1,0,0,0,0,75,4.5,68,1,0,0.8714417,0,-0.199471
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0,25,0,34,8,8,9,8,6,8,1.687167,1.243706,2.052527,1.844361,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,33,1,0,1,0,0,0,0,82.5,4.1,40,1,0,0.454603,1.68717,0
1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,71,0,0,1,0,1,0,0,47.01987,4.3,151,1,0,0.4904639,0,-0.410198
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1,27,1,52,7,4,5,4,5,4,0.1233448,0.7665288,-0.0735065,-0.092733,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4.6,23,0,1,1,0,1,1,0,100,4.7,23,1,0,1.5706,0.123345,0
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1,28,0,73,6,1,3,5,2,1,-0.8059942,0.2893519,-1.668032,-1.06128,0.0771755,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,10,0,1,1,0,1,0,0,76.92308,4.4,13,1,0,3.385376,0,-0.805994
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1,29,0,70,2,5,3,2,3,3,-0.8210418,-1.619356,0.4580018,-1.06128,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,5,0,1,1,0,1,0,0,10.41667,4.6,48,1,0,3.45521,0,-0.821042
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0,31,0,63,6,5,4,5,4,2,-0.0985568,0.2893519,0.4580018,-0.5770065,0.0771755,0.2750198,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,11,0,0,1,0,0,0,0,78.57143,4.4,14,0,0,1.890474,0,-0.098557
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1,32,0,47,3,1,4,3,2,3,-1.012306,-1.142179,-1.668032,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,10,0,0,1,0,0,0,0,71.42857,4.1,14,1,0,0.7911476,0,-1.01231
1,32,0,47,3,1,4,3,2,3,-1.012306,-1.142179,-1.668032,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,10,0,0,1,0,1,0,0,83.33334,3.2,12,1,0,0.7911476,0,-1.01231
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,154,0,0,1,1,0,0,0,48.73418,3.9,316,1,0,1.194346,0.488331,0
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,12,0,0,1,0,0,0,0,80,4.9,15,1,0,1.194346,0.488331,0
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,14,0,0,1,0,0,0,0,93.33334,4.7,15,1,0,1.194346,0.488331,0
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,27,0,0,1,0,1,0,0,93.10345,4.4,29,1,0,1.194346,0.488331,0
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1,84,1,54,8,8,5,7,4,9,1.144253,1.243706,2.052527,-0.092733,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4.6,14,1,0,1,1,1,0,0,77.77778,4.8,18,1,1,4.055594,1.14425,0
1,84,1,54,8,8,5,7,4,9,1.144253,1.243706,2.052527,-0.092733,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3.8,16,1,0,1,1,1,0,0,80,3.8,20,1,1,4.055594,1.14425,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,21,0,0,1,1,1,0,0,100,5,21,0,1,0.3413446,1.68598,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4.9,18,0,0,1,1,1,0,0,85.71429,5,21,0,1,0.3413446,1.68598,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4.6,17,0,0,1,1,1,0,0,85,4.9,20,0,1,0.3413446,1.68598,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4.6,19,0,0,1,1,1,0,0,95,4.6,20,0,1,0.3413446,1.68598,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,11,0,0,1,1,1,0,0,84.61539,5,13,0,1,0.3413446,1.68598,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4.6,14,0,0,1,1,1,0,0,87.5,4.8,16,0,1,0.3413446,1.68598,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.9,16,0,0,1,1,0,0,0,94.11765,4.9,17,0,1,0.3413446,1.68598,0
1,86,0,42,10,8,7,8,6,8,1.684802,2.198059,2.052527,0.8758139,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,22,0,0,1,1,0,0,0,91.66666,3.9,24,1,1,1.152506,1.6848,0
1,86,0,42,10,8,7,8,6,8,1.684802,2.198059,2.052527,0.8758139,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,17,0,0,1,0,0,0,0,85,3.9,20,1,1,1.152506,1.6848,0
0,87,0,33,6,6,7,5,5,6,0.6362435,0.2893519,0.9895102,0.8758139,0.0771755,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,67,0,0,0,1,0,0,0,43.22581,4.5,155,1,0,0.673837,0.636243,0
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.9,28,0,0,0,1,0,0,0,40,3.1,70,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,61,0,0,0,1,0,0,0,40.9396,2.8,149,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,49,0,0,0,1,0,0,0,35.76642,3.1,137,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,13,0,0,0,0,0,0,0,44.82759,4.2,29,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,28,0,0,0,1,0,0,0,50.90909,3.4,55,1,0,4.449395,0,-1.26998
1,88,0,62,1,1,1,4,4,1,-1.269975,-2.096532,-1.668032,-2.029827,-0.5273642,0.2750198,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,67,0,0,0,1,0,0,0,49.26471,3,136,1,0,4.449395,0,-1.26998
0,89,1,35,10,7,9,8,6,7,1.681103,2.198059,1.521019,1.844361,1.890795,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,20,1,0,1,1,0,0,0,33.33333,3.6,60,1,0,0.6656799,1.6811,0
0,89,1,35,10,7,9,8,6,7,1.681103,2.198059,1.521019,1.844361,1.890795,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,43,1,0,1,1,0,0,0,39.81482,3.7,108,1,0,0.6656799,1.6811,0
1,90,0,61,7,3,2,4,1,3,-0.6756103,0.7665288,-0.6050149,-1.545553,-0.5273642,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,13,0,0,1,1,0,0,0,86.66666,4.3,15,1,0,3.523305,0,-0.67561
1,91,0,52,5,4,8,3,3,4,-0.0901815,-0.1878249,-0.0735065,1.360087,-1.131904,-0.3125226,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,14,1,0,0,1,0,0,0,82.35294,4.9,17,1,0,3.258788,0,-0.090181
1,91,0,52,5,4,8,3,3,4,-0.0901815,-0.1878249,-0.0735065,1.360087,-1.131904,-0.3125226,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,19,1,0,0,1,0,0,0,100,4.8,19,1,0,3.258788,0,-0.090181
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,18,1,1,1,0,0,1,0,94.73684,3.9,19,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,11,1,1,1,0,0,1,0,84.61539,4.5,13,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,18,1,1,1,0,0,1,0,94.73684,3.6,19,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,11,1,1,1,0,0,1,0,50,4.4,22,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,7,1,1,1,0,0,1,0,35,3.4,20,1,1,1.360877,0,-0.145026
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,24,1,1,1,0,0,1,0,88.88889,4.4,27,1,1,1.360877,0,-0.145026
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,111,0,0,1,1,0,0,0,87.40157,4.5,127,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,62,0,0,1,0,0,0,0,72.94118,4.5,85,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,76,0,0,1,1,0,0,0,75.24753,4.6,101,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.2,9,0,0,1,1,0,0,0,42.85714,4.1,21,1,0,3.107088,1.14304,0
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,52,0,0,1,0,0,0,0,60.46511,4.5,86,1,0,3.107088,1.14304,0
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-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
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-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
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-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


================================================
FILE: ch_regr_simple_linear/figures/eoce/full_lin_regr_2/rate_my_prof.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

prof_evals_beauty <- read.csv("prof_evals_beauty.csv")

# rename variables for convenience ----------------------------------

beauty <- prof_evals_beauty$btystdave
eval <- prof_evals_beauty$courseevaluation

# model evaluation scores vs. beauty --------------------------------

m_eval_beauty = lm(eval ~ beauty)

xtable(summary(m_eval_beauty))

# scatterplot of evaluation scores vs. beauty -----------------------

pdf("rate_my_prof_eval_beauty.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5, las = 1)

plot(eval ~ beauty, 
     xlab = "Beauty", ylab = "Teaching evaluation", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, at = seq(-1, 2, 1))
axis(2, at = seq(2, 5, 1))
box()

dev.off()

# residuals plot ----------------------------------------------------

pdf("rate_my_prof_residuals.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5, las = 1)

plot(m_eval_beauty$residuals ~ beauty, 
     xlab = "Beauty", ylab = "Residuals", 
     pch = 19, col = COL[1,2], 
     ylim = c(-1.82, 1.82), axes = FALSE)
axis(1, at = seq(-1, 2, 1))
axis(2, at = seq(-1, 1, 1))
box()
abline(h = 0, lty = 3)

dev.off()

# residuals histogram -----------------------------------------------

pdf("rate_my_prof_residuals_hist.pdf", height = 5, width = 5)

par(mar = c(3.9, 3, 0, 0), cex.lab = 1.5, cex.axis = 1.5)

hist(m_eval_beauty$residuals, 
     xlab = "Residuals", ylab = "", main = "",
     col = COL[1],
     xlim = c(-2,2))

dev.off()

# normal probability plot of residuals ------------------------------

pdf("rate_my_prof_residuals_qq.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

qqnorm(m_eval_beauty$residuals, 
       pch = 19, col = COL[1,2],
       main = "", las = 0)
qqline(m_eval_beauty$residuals, col = COL[1])

dev.off()

# order of residuals ---------------------------------------------===

pdf("rate_my_prof_residuals_order.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_eval_beauty$residuals, 
     xlab = "Order of data collection", ylab = "Residuals", main = "",
     pch = 19, col = COL[1,2],
     ylim = c(-1.82, 1.82), axes = FALSE)
axis(1)
axis(2, at = seq(-1, 1, 1))
box()

abline(h = 0, lty = 3)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/helmet_lunch/helmet_lunch.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------

lunch <- c(50, 11, 2, 19, 26, 73, 81, 51, 11, 2, 19, 25)
helmet <- c(22.1, 35.9, 57.9, 22.2, 42.4, 5.8, 
            3.6, 21.4, 55.2, 33.3, 32.4, 38.4)

# summary stats -----------------------------------------------------

round(mean(lunch), 1)
round(mean(helmet), 1)

round(sd(lunch), 1)
round(sd(helmet), 1)

cor(lunch, helmet)

# model helmet vs. lunch --------------------------------------------

m_helmet_lunch <- lm(helmet ~ lunch)

summary(m_helmet_lunch)

round(summary(m_helmet_lunch)$r.squared, 2)
  
# plot helmet vs. lunch ---------------------------------------------

myPDF("helmet_lunch.pdf", 5.5, 4.3,
    mar = c(3.7, 5, 0.5, 0.5),
    mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.3,
    cex.axis = 1.5)

plot(helmet ~ lunch, 
    xlab = "Rate of Receiving a Reduced-Fee Lunch",
    ylab = "",
    pch = 19, col = COL[1],
    ylim = c(0, 60), axes = FALSE)
AxisInPercent(1, at = seq(0, 80, 20))
AxisInPercent(2, at = seq(0, 60, 20))
par(las = 0)
mtext("Rate of Wearing a Helmet", 2, 3.8, cex = 1.5)
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_age_inf/husbands_wives.txt
================================================
age_husband	ht_husband	age_wife	ht_wife	age_husb_at_marriage	years_married	age_wife_at_marriage	duration
49	1809	43	1590	25	24	19	>20
25	1841	28	1560	19	6	22	<= 20
40	1659	30	1620	38	2	28	<= 20
52	1779	57	1540	26	26	31	>20
58	1616	52	1420	30	28	24	>20
32	1695	27	1660	23	9	18	<= 20
43	1730	52	1610	33	10	42	<= 20
42	1753		1635	30	12		<= 20
47	1740	43	1580	26	21	22	>20
31	1685	23	1610	26	5	18	<= 20
26	1735	25	1590	23	3	22	<= 20
40	1713	39	1610	23	17	22	<= 20
35	1736	32	1700	31	4	28	<= 20
45	1715		1522	41	4		<= 20
35	1799	35	1680	19	16	19	<= 20
35	1785	33	1680	24	11	22	<= 20
47	1758	43	1630	24	23	20	>20
38	1729	35	1570	27	11	24	<= 20
33	1720	32	1720	28	5	27	<= 20
32	1810	30	1740	22	10	20	<= 20
38	1725	40	1600	31	7	33	<= 20
45	1764		1689	24	21		>20
29	1683	29	1600	25	4	25	<= 20
59	1585	55	1550	23	36	19	>20
26	1684	25	1540	18	8	17	<= 20
50	1674	45	1640	25	25	20	>20
49	1724	44	1640	27	22	22	>20
42	1630	40	1630	28	14	26	<= 20
33	1855	31	1560	22	11	20	<= 20
31	1796		1652	25	6		<= 20
27	1700	25	1580	21	6	19	<= 20
57	1765	51	1570	32	25	26	>20
34	1700	31	1590	28	6	25	<= 20
28	1721	25	1650	23	5	20	<= 20
46	1823		1591				>20
37	1829	35	1670	22	15	20	<= 20
56	1710	55	1600	44	12	43	<= 20
27	1745	23	1610	25	2	21	<= 20
36	1698	35	1610	22	14	21	<= 20
31	1853	28	1670	20	11	17	<= 20
57	1610	52	1510	25	32	20	>20
55	1680	53	1520	21	34	19	>20
47	1809	43	1620	25	22	21	>20
64	1580	61	1530	21	43	18	>20
60	1600		1451	26	34		>20
31	1585	23	1570	28	3	20	<= 20
35	1705	35	1580	25	10	25	<= 20
36	1675	35	1590	22	14	21	<= 20
40	1735	39	1670	23	17	22	<= 20
30	1686	24	1630	27	3	21	<= 20
32	1768	29	1510	21	11	18	<= 20
27	1721		1560	26	1		<= 20
20	1754	21	1660	19	1	20	<= 20
45	1739	39	1610	25	20	19	<= 20
59	1699	52	1440	27	32	20	>20
43	1825	52	1570	25	18	34	<= 20
29	1740	26	1670	24	5	21	<= 20
48	1704		1635	27	21		>20
39	1719		1670	25	14		<= 20
47	1731	48	1730	21	26	22	>20
54	1679	53	1560				>20
43	1755	42	1590	20	23	19	>20
54	1713	50	1600	23	31	19	>20
61	1723	64	1490	26	35	29	>20
27	1783	26	1660	20	7	19	<= 20
51	1585		1504	50	1		<= 20
27	1749	32	1580	24	3	29	<= 20
32	1710	31	1500	31	1	30	<= 20
54	1724	53	1640	20	34	19	>20
37	1620	39	1650	21	16	23	<= 20
55	1764	45	1620	29	26	19	>20
36	1791	33	1550	30	6	27	<= 20
32	1795	32	1640	25	7	25	<= 20
57	1738	55	1560	24	33	22	>20
51	1639		1552	25	26		>20
62	1734		1600	33	29		>20
57	1695		1545	22	35		>20
51	1666	52	1570	24	27	25	>20
50	1745	50	1550	22	28	22	>20
32	1775	32	1600	20	12	20	<= 20
54	1669	54	1660	20	34	20	>20
34	1700	32	1640	22	12	20	<= 20
45	1804	41	1670	27	18	23	<= 20
64	1700	61	1560	24	40	21	>20
55	1664	43	1760	31	24	19	>20
27	1753	28	1640	23	4	24	<= 20
55	1788	51	1600	26	29	22	>20
27	1765		1571				>20
41	1680	41	1550	22	19	22	<= 20
44	1715	41	1570	24	20	21	<= 20
22	1755	21	1590	21	1	20	<= 20
30	1764	28	1650	29	1	27	<= 20
53	1793	47	1690	31	22	25	>20
42	1731	37	1580	23	19	18	<= 20
31	1713	28	1590	28	3	25	<= 20
36	1725	35	1510	26	10	25	<= 20
56	1828	55	1600	30	26	29	>20
46	1735	45	1660	22	24	21	>20
34	1760	34	1700	23	11	23	<= 20
55	1685	51	1530	34	21	30	>20
44	1685	39	1490	27	17	22	<= 20
45	1559	35	1580	34	11	24	<= 20
48	1705	45	1500	28	20	25	<= 20
44	1723	44	1600	41	3	41	<= 20
59	1700	47	1570	39	20	27	<= 20
64	1660	57	1620	32	32	25	>20
34	1681	33	1410	22	12	21	<= 20
37	1803	38	1560	23	14	24	<= 20
54	1866	59	1590	49	5	54	<= 20
49	1884	46	1710	25	24	22	>20
63	1705	60	1580	27	36	24	>20
48	1780	47	1690	22	26	21	>20
64	1801	55	1610	37	27	28	>20
33	1795	45	1660	17	16	29	<= 20
52	1669	47	1610	23	29	18	>20
27	1708	24	1590	26	1	23	<= 20
33	1691	32	1530	21	12	20	<= 20
46	1825	47	1690	23	23	24	>20
54	1760	57	1600	23	31	26	>20
27	1949		1693	25	2		<= 20
50	1685		1580	21	29		>20
42	1806		1636	22	20		<= 20
54	1905	46	1670	32	22	24	>20
49	1739	42	1600	28	21	21	>20
62	1736	63	1570	22	40	23	>20
34	1845	32	1700	24	10	22	<= 20
23	1868	24	1740	19	4	20	<= 20
36	1765	32	1540	27	9	23	<= 20
53	1736		1555	30	23		>20
32	1741		1614	22	10		<= 20
59	1720	56	1530	24	35	21	>20
53	1871	50	1690	25	28	22	>20
55	1720	55	1590	21	34	21	>20
62	1629	58	1610	23	39	19	>20
42	1624	38	1670	22	20	18	<= 20
50	1653	44	1690	35	15	29	<= 20
37	1786	35	1550	21	16	19	<= 20
51	1620	44	1650	30	21	23	>20
25	1695	25	1540	19	6	19	<= 20
54	1674	43	1660	35	19	24	<= 20
34	1864	31	1620	23	11	20	<= 20
43	1643	35	1630	29	14	21	<= 20
43	1705	41	1610	22	21	20	>20
58	1736	50	1540	32	26	24	>20
28	1691	23	1610	23	5	18	<= 20
45	1753	43	1630	21	24	19	>20
47	1680	49	1530	20	27	22	>20
57	1724	59	1520	24	33	26	>20
27	1710		1544	20	7		<= 20
34	1638	38	1570	33	1	37	<= 20
57	1725	42	1580	52	5	37	<= 20
27	1725	21	1550	24	3	18	<= 20
54	1630		1570	34	20		<= 20
24	1810		1521	16	8		<= 20
48	1774	42	1580	30	18	24	<= 20
37	1771	35	1630	28	9	26	<= 20
25	1815	26	1650	20	5	21	<= 20
57	1575	57	1640	20	37	20	>20
40	1729	34	1650	26	14	20	<= 20
61	1749	63	1520	21	40	23	>20
25	1705	23	1620	24	1	22	<= 20
32	1875		1744	22	10		<= 20
37	1784		1647	22	15		<= 20
45	1584		1615	29	16		<= 20
24	1774	23	1680	22	2	21	<= 20
47	1658	46	1670	24	23	23	>20
44	1790	40	1620	24	20	20	<= 20
52	1798	53	1570	25	27	26	>20
45	1824	40	1660	23	22	18	>20
20	1796	22	1550	19	1	21	<= 20
60	1725	60	1590	21	39	21	>20
36	1685	32	1620	25	11	21	<= 20
25	1769	24	1560	18	7	17	<= 20
25	1749	28	1670	21	4	24	<= 20
35	1716	40	1650	17	18	22	<= 20
35	1664		1539	22	13		<= 20
49	1773	48	1470	21	28	20	>20
33	1760	33	1580	20	13	20	<= 20
50	1725	49	1670	23	27	22	>20
63	1645	64	1520	28	35	29	>20
57	1694	55	1620	24	33	22	>20
41	1851	41	1710	23	18	23	<= 20
38	1691	38	1530	20	18	20	<= 20
30	1880	31	1630	22	8	23	<= 20
52	1835	52	1720	30	22	30	>20
51	1730	43	1570	22	29	14	>20
46	1644	51	1560	27	19	32	<= 20
50	1723	47	1650	25	25	22	>20
32	1758		1635	24	8		<= 20
52	1718	32	1590	25	27	5	>20
30	1723	33	1590	22	8	25	<= 20
33	1708		1566	21	12		<= 20
20	1786	18	1590	19	1	17	<= 20
32	1764		1662				>20
51	1675	45	1550	25	26	19	>20
64	1641	64	1570	30	34	30	>20
44	1743	43	1560	25	19	24	<= 20
40	1823	39	1630	23	17	22	<= 20
59	1720	56	1530	24	35	21	>20

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_age_inf/husbands_wives_age_inf.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

hw <- read.table("husbands_wives.txt", h = T, sep = "\t")

# converts heights to inches ----------------------------------------

hw$ht_husband_in <- hw$ht_husband / 25.4
hw$ht_wife_in <- hw$ht_wife / 25.4

# remove cases where wife's age is missing --------------------------

hw <- hw[!is.na(hw$age_wife),]

# model summary -----------------------------------------------------

m_h_w_age <- lm(hw$age_wife ~ hw$age_husband)

xtable(summary(m_h_w_age))

# plot wife vs. husband age -----------------------------------------

pdf("husbands_wives_age.pdf", 5.5, 4.3)

par(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(hw$age_wife ~ hw$age_husband, 
     xlab = "Husband's age (in years)", 
     ylab = "Wife's age (in years)", 
     pch = 19, col = COL[1,2], 
     xlim = c(18, 66), ylim = c(16, 66), axes = FALSE)
axis(1, at = seq(20,60,20))
axis(2, at = seq(20,60,20))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_correlation/husbands_wives.txt
================================================
age_husband	ht_husband	age_wife	ht_wife	age_husb_at_marriage	years_married	age_wife_at_marriage	duration
49	1809	43	1590	25	24	19	>20
25	1841	28	1560	19	6	22	<= 20
40	1659	30	1620	38	2	28	<= 20
52	1779	57	1540	26	26	31	>20
58	1616	52	1420	30	28	24	>20
32	1695	27	1660	23	9	18	<= 20
43	1730	52	1610	33	10	42	<= 20
42	1753		1635	30	12		<= 20
47	1740	43	1580	26	21	22	>20
31	1685	23	1610	26	5	18	<= 20
26	1735	25	1590	23	3	22	<= 20
40	1713	39	1610	23	17	22	<= 20
35	1736	32	1700	31	4	28	<= 20
45	1715		1522	41	4		<= 20
35	1799	35	1680	19	16	19	<= 20
35	1785	33	1680	24	11	22	<= 20
47	1758	43	1630	24	23	20	>20
38	1729	35	1570	27	11	24	<= 20
33	1720	32	1720	28	5	27	<= 20
32	1810	30	1740	22	10	20	<= 20
38	1725	40	1600	31	7	33	<= 20
45	1764		1689	24	21		>20
29	1683	29	1600	25	4	25	<= 20
59	1585	55	1550	23	36	19	>20
26	1684	25	1540	18	8	17	<= 20
50	1674	45	1640	25	25	20	>20
49	1724	44	1640	27	22	22	>20
42	1630	40	1630	28	14	26	<= 20
33	1855	31	1560	22	11	20	<= 20
31	1796		1652	25	6		<= 20
27	1700	25	1580	21	6	19	<= 20
57	1765	51	1570	32	25	26	>20
34	1700	31	1590	28	6	25	<= 20
28	1721	25	1650	23	5	20	<= 20
46	1823		1591				>20
37	1829	35	1670	22	15	20	<= 20
56	1710	55	1600	44	12	43	<= 20
27	1745	23	1610	25	2	21	<= 20
36	1698	35	1610	22	14	21	<= 20
31	1853	28	1670	20	11	17	<= 20
57	1610	52	1510	25	32	20	>20
55	1680	53	1520	21	34	19	>20
47	1809	43	1620	25	22	21	>20
64	1580	61	1530	21	43	18	>20
60	1600		1451	26	34		>20
31	1585	23	1570	28	3	20	<= 20
35	1705	35	1580	25	10	25	<= 20
36	1675	35	1590	22	14	21	<= 20
40	1735	39	1670	23	17	22	<= 20
30	1686	24	1630	27	3	21	<= 20
32	1768	29	1510	21	11	18	<= 20
27	1721		1560	26	1		<= 20
20	1754	21	1660	19	1	20	<= 20
45	1739	39	1610	25	20	19	<= 20
59	1699	52	1440	27	32	20	>20
43	1825	52	1570	25	18	34	<= 20
29	1740	26	1670	24	5	21	<= 20
48	1704		1635	27	21		>20
39	1719		1670	25	14		<= 20
47	1731	48	1730	21	26	22	>20
54	1679	53	1560				>20
43	1755	42	1590	20	23	19	>20
54	1713	50	1600	23	31	19	>20
61	1723	64	1490	26	35	29	>20
27	1783	26	1660	20	7	19	<= 20
51	1585		1504	50	1		<= 20
27	1749	32	1580	24	3	29	<= 20
32	1710	31	1500	31	1	30	<= 20
54	1724	53	1640	20	34	19	>20
37	1620	39	1650	21	16	23	<= 20
55	1764	45	1620	29	26	19	>20
36	1791	33	1550	30	6	27	<= 20
32	1795	32	1640	25	7	25	<= 20
57	1738	55	1560	24	33	22	>20
51	1639		1552	25	26		>20
62	1734		1600	33	29		>20
57	1695		1545	22	35		>20
51	1666	52	1570	24	27	25	>20
50	1745	50	1550	22	28	22	>20
32	1775	32	1600	20	12	20	<= 20
54	1669	54	1660	20	34	20	>20
34	1700	32	1640	22	12	20	<= 20
45	1804	41	1670	27	18	23	<= 20
64	1700	61	1560	24	40	21	>20
55	1664	43	1760	31	24	19	>20
27	1753	28	1640	23	4	24	<= 20
55	1788	51	1600	26	29	22	>20
27	1765		1571				>20
41	1680	41	1550	22	19	22	<= 20
44	1715	41	1570	24	20	21	<= 20
22	1755	21	1590	21	1	20	<= 20
30	1764	28	1650	29	1	27	<= 20
53	1793	47	1690	31	22	25	>20
42	1731	37	1580	23	19	18	<= 20
31	1713	28	1590	28	3	25	<= 20
36	1725	35	1510	26	10	25	<= 20
56	1828	55	1600	30	26	29	>20
46	1735	45	1660	22	24	21	>20
34	1760	34	1700	23	11	23	<= 20
55	1685	51	1530	34	21	30	>20
44	1685	39	1490	27	17	22	<= 20
45	1559	35	1580	34	11	24	<= 20
48	1705	45	1500	28	20	25	<= 20
44	1723	44	1600	41	3	41	<= 20
59	1700	47	1570	39	20	27	<= 20
64	1660	57	1620	32	32	25	>20
34	1681	33	1410	22	12	21	<= 20
37	1803	38	1560	23	14	24	<= 20
54	1866	59	1590	49	5	54	<= 20
49	1884	46	1710	25	24	22	>20
63	1705	60	1580	27	36	24	>20
48	1780	47	1690	22	26	21	>20
64	1801	55	1610	37	27	28	>20
33	1795	45	1660	17	16	29	<= 20
52	1669	47	1610	23	29	18	>20
27	1708	24	1590	26	1	23	<= 20
33	1691	32	1530	21	12	20	<= 20
46	1825	47	1690	23	23	24	>20
54	1760	57	1600	23	31	26	>20
27	1949		1693	25	2		<= 20
50	1685		1580	21	29		>20
42	1806		1636	22	20		<= 20
54	1905	46	1670	32	22	24	>20
49	1739	42	1600	28	21	21	>20
62	1736	63	1570	22	40	23	>20
34	1845	32	1700	24	10	22	<= 20
23	1868	24	1740	19	4	20	<= 20
36	1765	32	1540	27	9	23	<= 20
53	1736		1555	30	23		>20
32	1741		1614	22	10		<= 20
59	1720	56	1530	24	35	21	>20
53	1871	50	1690	25	28	22	>20
55	1720	55	1590	21	34	21	>20
62	1629	58	1610	23	39	19	>20
42	1624	38	1670	22	20	18	<= 20
50	1653	44	1690	35	15	29	<= 20
37	1786	35	1550	21	16	19	<= 20
51	1620	44	1650	30	21	23	>20
25	1695	25	1540	19	6	19	<= 20
54	1674	43	1660	35	19	24	<= 20
34	1864	31	1620	23	11	20	<= 20
43	1643	35	1630	29	14	21	<= 20
43	1705	41	1610	22	21	20	>20
58	1736	50	1540	32	26	24	>20
28	1691	23	1610	23	5	18	<= 20
45	1753	43	1630	21	24	19	>20
47	1680	49	1530	20	27	22	>20
57	1724	59	1520	24	33	26	>20
27	1710		1544	20	7		<= 20
34	1638	38	1570	33	1	37	<= 20
57	1725	42	1580	52	5	37	<= 20
27	1725	21	1550	24	3	18	<= 20
54	1630		1570	34	20		<= 20
24	1810		1521	16	8		<= 20
48	1774	42	1580	30	18	24	<= 20
37	1771	35	1630	28	9	26	<= 20
25	1815	26	1650	20	5	21	<= 20
57	1575	57	1640	20	37	20	>20
40	1729	34	1650	26	14	20	<= 20
61	1749	63	1520	21	40	23	>20
25	1705	23	1620	24	1	22	<= 20
32	1875		1744	22	10		<= 20
37	1784		1647	22	15		<= 20
45	1584		1615	29	16		<= 20
24	1774	23	1680	22	2	21	<= 20
47	1658	46	1670	24	23	23	>20
44	1790	40	1620	24	20	20	<= 20
52	1798	53	1570	25	27	26	>20
45	1824	40	1660	23	22	18	>20
20	1796	22	1550	19	1	21	<= 20
60	1725	60	1590	21	39	21	>20
36	1685	32	1620	25	11	21	<= 20
25	1769	24	1560	18	7	17	<= 20
25	1749	28	1670	21	4	24	<= 20
35	1716	40	1650	17	18	22	<= 20
35	1664		1539	22	13		<= 20
49	1773	48	1470	21	28	20	>20
33	1760	33	1580	20	13	20	<= 20
50	1725	49	1670	23	27	22	>20
63	1645	64	1520	28	35	29	>20
57	1694	55	1620	24	33	22	>20
41	1851	41	1710	23	18	23	<= 20
38	1691	38	1530	20	18	20	<= 20
30	1880	31	1630	22	8	23	<= 20
52	1835	52	1720	30	22	30	>20
51	1730	43	1570	22	29	14	>20
46	1644	51	1560	27	19	32	<= 20
50	1723	47	1650	25	25	22	>20
32	1758		1635	24	8		<= 20
52	1718	32	1590	25	27	5	>20
30	1723	33	1590	22	8	25	<= 20
33	1708		1566	21	12		<= 20
20	1786	18	1590	19	1	17	<= 20
32	1764		1662				>20
51	1675	45	1550	25	26	19	>20
64	1641	64	1570	30	34	30	>20
44	1743	43	1560	25	19	24	<= 20
40	1823	39	1630	23	17	22	<= 20
59	1720	56	1530	24	35	21	>20

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_correlation/husbands_wives_correlation.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

hw <- read.table("husbands_wives.txt", h = T, sep = "\t")

# converts heights to inches ----------------------------------------

hw$ht_husband_in <- hw$ht_husband / 25.4
hw$ht_wife_in <- hw$ht_wife / 25.4

# remove cases where wife's age is missing --------------------------

hw <- hw[!is.na(hw$age_wife),]

# plot wife vs. husband age -----------------------------------------

pdf("husbands_wives_age.pdf", 5.5, 4.3)

par(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(hw$age_wife ~ hw$age_husband, 
     xlab = "Husband's age (in years)", 
     ylab = "Wife's age (in years)", 
     pch = 19, col = COL[1,2], 
     xlim = c(18, 66), ylim = c(16, 66), axes = FALSE)
axis(1, at = seq(20,60,20))
axis(2, at = seq(20,60,20))
box()

dev.off()

# plot wife vs. husband height --------------------------------------

pdf("husbands_wives_height.pdf", 5.5, 4.3)

par(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(hw$ht_wife_in ~ hw$ht_husband_in, 
     xlab = "Husband's height (in inches)", 
     ylab = "Wife's height (in inches)", 
     pch = 19, col = COL[1,2], 
     xlim = c(60, 77), ylim = c(55, 70), axes = FALSE)
axis(1, at = seq(60, 75, 5))
axis(2, at = seq(55, 70, 5))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf/husbands_wives.txt
================================================
age_husband	ht_husband	age_wife	ht_wife	age_husb_at_marriage	years_married	age_wife_at_marriage	duration
49	1809	43	1590	25	24	19	>20
25	1841	28	1560	19	6	22	<= 20
40	1659	30	1620	38	2	28	<= 20
52	1779	57	1540	26	26	31	>20
58	1616	52	1420	30	28	24	>20
32	1695	27	1660	23	9	18	<= 20
43	1730	52	1610	33	10	42	<= 20
42	1753		1635	30	12		<= 20
47	1740	43	1580	26	21	22	>20
31	1685	23	1610	26	5	18	<= 20
26	1735	25	1590	23	3	22	<= 20
40	1713	39	1610	23	17	22	<= 20
35	1736	32	1700	31	4	28	<= 20
45	1715		1522	41	4		<= 20
35	1799	35	1680	19	16	19	<= 20
35	1785	33	1680	24	11	22	<= 20
47	1758	43	1630	24	23	20	>20
38	1729	35	1570	27	11	24	<= 20
33	1720	32	1720	28	5	27	<= 20
32	1810	30	1740	22	10	20	<= 20
38	1725	40	1600	31	7	33	<= 20
45	1764		1689	24	21		>20
29	1683	29	1600	25	4	25	<= 20
59	1585	55	1550	23	36	19	>20
26	1684	25	1540	18	8	17	<= 20
50	1674	45	1640	25	25	20	>20
49	1724	44	1640	27	22	22	>20
42	1630	40	1630	28	14	26	<= 20
33	1855	31	1560	22	11	20	<= 20
31	1796		1652	25	6		<= 20
27	1700	25	1580	21	6	19	<= 20
57	1765	51	1570	32	25	26	>20
34	1700	31	1590	28	6	25	<= 20
28	1721	25	1650	23	5	20	<= 20
46	1823		1591				>20
37	1829	35	1670	22	15	20	<= 20
56	1710	55	1600	44	12	43	<= 20
27	1745	23	1610	25	2	21	<= 20
36	1698	35	1610	22	14	21	<= 20
31	1853	28	1670	20	11	17	<= 20
57	1610	52	1510	25	32	20	>20
55	1680	53	1520	21	34	19	>20
47	1809	43	1620	25	22	21	>20
64	1580	61	1530	21	43	18	>20
60	1600		1451	26	34		>20
31	1585	23	1570	28	3	20	<= 20
35	1705	35	1580	25	10	25	<= 20
36	1675	35	1590	22	14	21	<= 20
40	1735	39	1670	23	17	22	<= 20
30	1686	24	1630	27	3	21	<= 20
32	1768	29	1510	21	11	18	<= 20
27	1721		1560	26	1		<= 20
20	1754	21	1660	19	1	20	<= 20
45	1739	39	1610	25	20	19	<= 20
59	1699	52	1440	27	32	20	>20
43	1825	52	1570	25	18	34	<= 20
29	1740	26	1670	24	5	21	<= 20
48	1704		1635	27	21		>20
39	1719		1670	25	14		<= 20
47	1731	48	1730	21	26	22	>20
54	1679	53	1560				>20
43	1755	42	1590	20	23	19	>20
54	1713	50	1600	23	31	19	>20
61	1723	64	1490	26	35	29	>20
27	1783	26	1660	20	7	19	<= 20
51	1585		1504	50	1		<= 20
27	1749	32	1580	24	3	29	<= 20
32	1710	31	1500	31	1	30	<= 20
54	1724	53	1640	20	34	19	>20
37	1620	39	1650	21	16	23	<= 20
55	1764	45	1620	29	26	19	>20
36	1791	33	1550	30	6	27	<= 20
32	1795	32	1640	25	7	25	<= 20
57	1738	55	1560	24	33	22	>20
51	1639		1552	25	26		>20
62	1734		1600	33	29		>20
57	1695		1545	22	35		>20
51	1666	52	1570	24	27	25	>20
50	1745	50	1550	22	28	22	>20
32	1775	32	1600	20	12	20	<= 20
54	1669	54	1660	20	34	20	>20
34	1700	32	1640	22	12	20	<= 20
45	1804	41	1670	27	18	23	<= 20
64	1700	61	1560	24	40	21	>20
55	1664	43	1760	31	24	19	>20
27	1753	28	1640	23	4	24	<= 20
55	1788	51	1600	26	29	22	>20
27	1765		1571				>20
41	1680	41	1550	22	19	22	<= 20
44	1715	41	1570	24	20	21	<= 20
22	1755	21	1590	21	1	20	<= 20
30	1764	28	1650	29	1	27	<= 20
53	1793	47	1690	31	22	25	>20
42	1731	37	1580	23	19	18	<= 20
31	1713	28	1590	28	3	25	<= 20
36	1725	35	1510	26	10	25	<= 20
56	1828	55	1600	30	26	29	>20
46	1735	45	1660	22	24	21	>20
34	1760	34	1700	23	11	23	<= 20
55	1685	51	1530	34	21	30	>20
44	1685	39	1490	27	17	22	<= 20
45	1559	35	1580	34	11	24	<= 20
48	1705	45	1500	28	20	25	<= 20
44	1723	44	1600	41	3	41	<= 20
59	1700	47	1570	39	20	27	<= 20
64	1660	57	1620	32	32	25	>20
34	1681	33	1410	22	12	21	<= 20
37	1803	38	1560	23	14	24	<= 20
54	1866	59	1590	49	5	54	<= 20
49	1884	46	1710	25	24	22	>20
63	1705	60	1580	27	36	24	>20
48	1780	47	1690	22	26	21	>20
64	1801	55	1610	37	27	28	>20
33	1795	45	1660	17	16	29	<= 20
52	1669	47	1610	23	29	18	>20
27	1708	24	1590	26	1	23	<= 20
33	1691	32	1530	21	12	20	<= 20
46	1825	47	1690	23	23	24	>20
54	1760	57	1600	23	31	26	>20
27	1949		1693	25	2		<= 20
50	1685		1580	21	29		>20
42	1806		1636	22	20		<= 20
54	1905	46	1670	32	22	24	>20
49	1739	42	1600	28	21	21	>20
62	1736	63	1570	22	40	23	>20
34	1845	32	1700	24	10	22	<= 20
23	1868	24	1740	19	4	20	<= 20
36	1765	32	1540	27	9	23	<= 20
53	1736		1555	30	23		>20
32	1741		1614	22	10		<= 20
59	1720	56	1530	24	35	21	>20
53	1871	50	1690	25	28	22	>20
55	1720	55	1590	21	34	21	>20
62	1629	58	1610	23	39	19	>20
42	1624	38	1670	22	20	18	<= 20
50	1653	44	1690	35	15	29	<= 20
37	1786	35	1550	21	16	19	<= 20
51	1620	44	1650	30	21	23	>20
25	1695	25	1540	19	6	19	<= 20
54	1674	43	1660	35	19	24	<= 20
34	1864	31	1620	23	11	20	<= 20
43	1643	35	1630	29	14	21	<= 20
43	1705	41	1610	22	21	20	>20
58	1736	50	1540	32	26	24	>20
28	1691	23	1610	23	5	18	<= 20
45	1753	43	1630	21	24	19	>20
47	1680	49	1530	20	27	22	>20
57	1724	59	1520	24	33	26	>20
27	1710		1544	20	7		<= 20
34	1638	38	1570	33	1	37	<= 20
57	1725	42	1580	52	5	37	<= 20
27	1725	21	1550	24	3	18	<= 20
54	1630		1570	34	20		<= 20
24	1810		1521	16	8		<= 20
48	1774	42	1580	30	18	24	<= 20
37	1771	35	1630	28	9	26	<= 20
25	1815	26	1650	20	5	21	<= 20
57	1575	57	1640	20	37	20	>20
40	1729	34	1650	26	14	20	<= 20
61	1749	63	1520	21	40	23	>20
25	1705	23	1620	24	1	22	<= 20
32	1875		1744	22	10		<= 20
37	1784		1647	22	15		<= 20
45	1584		1615	29	16		<= 20
24	1774	23	1680	22	2	21	<= 20
47	1658	46	1670	24	23	23	>20
44	1790	40	1620	24	20	20	<= 20
52	1798	53	1570	25	27	26	>20
45	1824	40	1660	23	22	18	>20
20	1796	22	1550	19	1	21	<= 20
60	1725	60	1590	21	39	21	>20
36	1685	32	1620	25	11	21	<= 20
25	1769	24	1560	18	7	17	<= 20
25	1749	28	1670	21	4	24	<= 20
35	1716	40	1650	17	18	22	<= 20
35	1664		1539	22	13		<= 20
49	1773	48	1470	21	28	20	>20
33	1760	33	1580	20	13	20	<= 20
50	1725	49	1670	23	27	22	>20
63	1645	64	1520	28	35	29	>20
57	1694	55	1620	24	33	22	>20
41	1851	41	1710	23	18	23	<= 20
38	1691	38	1530	20	18	20	<= 20
30	1880	31	1630	22	8	23	<= 20
52	1835	52	1720	30	22	30	>20
51	1730	43	1570	22	29	14	>20
46	1644	51	1560	27	19	32	<= 20
50	1723	47	1650	25	25	22	>20
32	1758		1635	24	8		<= 20
52	1718	32	1590	25	27	5	>20
30	1723	33	1590	22	8	25	<= 20
33	1708		1566	21	12		<= 20
20	1786	18	1590	19	1	17	<= 20
32	1764		1662				>20
51	1675	45	1550	25	26	19	>20
64	1641	64	1570	30	34	30	>20
44	1743	43	1560	25	19	24	<= 20
40	1823	39	1630	23	17	22	<= 20
59	1720	56	1530	24	35	21	>20

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf/husbands_wives_height_inf.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

hw <- read.table("husbands_wives.txt", h = T, sep = "\t")

# converts heights to inches ----------------------------------------

hw$ht_husband_in <- hw$ht_husband / 25.4
hw$ht_wife_in <- hw$ht_wife / 25.4

# remove cases where wife's age is missing --------------------------

hw <- hw[!is.na(hw$age_wife),]

# model summary -----------------------------------------------------

m_h_w_height <- lm(hw$ht_wife_in ~ hw$ht_husband_in)

xtable(summary(m_h_w_height))

# plot wife vs. husband height --------------------------------------

pdf("husbands_wives_height.pdf", 5.5, 4.3)

par(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(hw$ht_wife_in ~ hw$ht_husband_in, 
     xlab = "Husband's height (in inches)", 
     ylab = "Wife's height (in inches)", 
     pch = 19, col = COL[1,2], 
     xlim = c(60, 77), ylim = c(55, 70), axes = FALSE)
axis(1, at = seq(60, 75, 5))
axis(2, at = seq(55, 70, 5))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf_2s/husbands_wives.txt
================================================
age_husband	ht_husband	age_wife	ht_wife	age_husb_at_marriage	years_married	age_wife_at_marriage	duration
49	1809	43	1590	25	24	19	>20
25	1841	28	1560	19	6	22	<= 20
40	1659	30	1620	38	2	28	<= 20
52	1779	57	1540	26	26	31	>20
58	1616	52	1420	30	28	24	>20
32	1695	27	1660	23	9	18	<= 20
43	1730	52	1610	33	10	42	<= 20
42	1753		1635	30	12		<= 20
47	1740	43	1580	26	21	22	>20
31	1685	23	1610	26	5	18	<= 20
26	1735	25	1590	23	3	22	<= 20
40	1713	39	1610	23	17	22	<= 20
35	1736	32	1700	31	4	28	<= 20
45	1715		1522	41	4		<= 20
35	1799	35	1680	19	16	19	<= 20
35	1785	33	1680	24	11	22	<= 20
47	1758	43	1630	24	23	20	>20
38	1729	35	1570	27	11	24	<= 20
33	1720	32	1720	28	5	27	<= 20
32	1810	30	1740	22	10	20	<= 20
38	1725	40	1600	31	7	33	<= 20
45	1764		1689	24	21		>20
29	1683	29	1600	25	4	25	<= 20
59	1585	55	1550	23	36	19	>20
26	1684	25	1540	18	8	17	<= 20
50	1674	45	1640	25	25	20	>20
49	1724	44	1640	27	22	22	>20
42	1630	40	1630	28	14	26	<= 20
33	1855	31	1560	22	11	20	<= 20
31	1796		1652	25	6		<= 20
27	1700	25	1580	21	6	19	<= 20
57	1765	51	1570	32	25	26	>20
34	1700	31	1590	28	6	25	<= 20
28	1721	25	1650	23	5	20	<= 20
46	1823		1591				>20
37	1829	35	1670	22	15	20	<= 20
56	1710	55	1600	44	12	43	<= 20
27	1745	23	1610	25	2	21	<= 20
36	1698	35	1610	22	14	21	<= 20
31	1853	28	1670	20	11	17	<= 20
57	1610	52	1510	25	32	20	>20
55	1680	53	1520	21	34	19	>20
47	1809	43	1620	25	22	21	>20
64	1580	61	1530	21	43	18	>20
60	1600		1451	26	34		>20
31	1585	23	1570	28	3	20	<= 20
35	1705	35	1580	25	10	25	<= 20
36	1675	35	1590	22	14	21	<= 20
40	1735	39	1670	23	17	22	<= 20
30	1686	24	1630	27	3	21	<= 20
32	1768	29	1510	21	11	18	<= 20
27	1721		1560	26	1		<= 20
20	1754	21	1660	19	1	20	<= 20
45	1739	39	1610	25	20	19	<= 20
59	1699	52	1440	27	32	20	>20
43	1825	52	1570	25	18	34	<= 20
29	1740	26	1670	24	5	21	<= 20
48	1704		1635	27	21		>20
39	1719		1670	25	14		<= 20
47	1731	48	1730	21	26	22	>20
54	1679	53	1560				>20
43	1755	42	1590	20	23	19	>20
54	1713	50	1600	23	31	19	>20
61	1723	64	1490	26	35	29	>20
27	1783	26	1660	20	7	19	<= 20
51	1585		1504	50	1		<= 20
27	1749	32	1580	24	3	29	<= 20
32	1710	31	1500	31	1	30	<= 20
54	1724	53	1640	20	34	19	>20
37	1620	39	1650	21	16	23	<= 20
55	1764	45	1620	29	26	19	>20
36	1791	33	1550	30	6	27	<= 20
32	1795	32	1640	25	7	25	<= 20
57	1738	55	1560	24	33	22	>20
51	1639		1552	25	26		>20
62	1734		1600	33	29		>20
57	1695		1545	22	35		>20
51	1666	52	1570	24	27	25	>20
50	1745	50	1550	22	28	22	>20
32	1775	32	1600	20	12	20	<= 20
54	1669	54	1660	20	34	20	>20
34	1700	32	1640	22	12	20	<= 20
45	1804	41	1670	27	18	23	<= 20
64	1700	61	1560	24	40	21	>20
55	1664	43	1760	31	24	19	>20
27	1753	28	1640	23	4	24	<= 20
55	1788	51	1600	26	29	22	>20
27	1765		1571				>20
41	1680	41	1550	22	19	22	<= 20
44	1715	41	1570	24	20	21	<= 20
22	1755	21	1590	21	1	20	<= 20
30	1764	28	1650	29	1	27	<= 20
53	1793	47	1690	31	22	25	>20
42	1731	37	1580	23	19	18	<= 20
31	1713	28	1590	28	3	25	<= 20
36	1725	35	1510	26	10	25	<= 20
56	1828	55	1600	30	26	29	>20
46	1735	45	1660	22	24	21	>20
34	1760	34	1700	23	11	23	<= 20
55	1685	51	1530	34	21	30	>20
44	1685	39	1490	27	17	22	<= 20
45	1559	35	1580	34	11	24	<= 20
48	1705	45	1500	28	20	25	<= 20
44	1723	44	1600	41	3	41	<= 20
59	1700	47	1570	39	20	27	<= 20
64	1660	57	1620	32	32	25	>20
34	1681	33	1410	22	12	21	<= 20
37	1803	38	1560	23	14	24	<= 20
54	1866	59	1590	49	5	54	<= 20
49	1884	46	1710	25	24	22	>20
63	1705	60	1580	27	36	24	>20
48	1780	47	1690	22	26	21	>20
64	1801	55	1610	37	27	28	>20
33	1795	45	1660	17	16	29	<= 20
52	1669	47	1610	23	29	18	>20
27	1708	24	1590	26	1	23	<= 20
33	1691	32	1530	21	12	20	<= 20
46	1825	47	1690	23	23	24	>20
54	1760	57	1600	23	31	26	>20
27	1949		1693	25	2		<= 20
50	1685		1580	21	29		>20
42	1806		1636	22	20		<= 20
54	1905	46	1670	32	22	24	>20
49	1739	42	1600	28	21	21	>20
62	1736	63	1570	22	40	23	>20
34	1845	32	1700	24	10	22	<= 20
23	1868	24	1740	19	4	20	<= 20
36	1765	32	1540	27	9	23	<= 20
53	1736		1555	30	23		>20
32	1741		1614	22	10		<= 20
59	1720	56	1530	24	35	21	>20
53	1871	50	1690	25	28	22	>20
55	1720	55	1590	21	34	21	>20
62	1629	58	1610	23	39	19	>20
42	1624	38	1670	22	20	18	<= 20
50	1653	44	1690	35	15	29	<= 20
37	1786	35	1550	21	16	19	<= 20
51	1620	44	1650	30	21	23	>20
25	1695	25	1540	19	6	19	<= 20
54	1674	43	1660	35	19	24	<= 20
34	1864	31	1620	23	11	20	<= 20
43	1643	35	1630	29	14	21	<= 20
43	1705	41	1610	22	21	20	>20
58	1736	50	1540	32	26	24	>20
28	1691	23	1610	23	5	18	<= 20
45	1753	43	1630	21	24	19	>20
47	1680	49	1530	20	27	22	>20
57	1724	59	1520	24	33	26	>20
27	1710		1544	20	7		<= 20
34	1638	38	1570	33	1	37	<= 20
57	1725	42	1580	52	5	37	<= 20
27	1725	21	1550	24	3	18	<= 20
54	1630		1570	34	20		<= 20
24	1810		1521	16	8		<= 20
48	1774	42	1580	30	18	24	<= 20
37	1771	35	1630	28	9	26	<= 20
25	1815	26	1650	20	5	21	<= 20
57	1575	57	1640	20	37	20	>20
40	1729	34	1650	26	14	20	<= 20
61	1749	63	1520	21	40	23	>20
25	1705	23	1620	24	1	22	<= 20
32	1875		1744	22	10		<= 20
37	1784		1647	22	15		<= 20
45	1584		1615	29	16		<= 20
24	1774	23	1680	22	2	21	<= 20
47	1658	46	1670	24	23	23	>20
44	1790	40	1620	24	20	20	<= 20
52	1798	53	1570	25	27	26	>20
45	1824	40	1660	23	22	18	>20
20	1796	22	1550	19	1	21	<= 20
60	1725	60	1590	21	39	21	>20
36	1685	32	1620	25	11	21	<= 20
25	1769	24	1560	18	7	17	<= 20
25	1749	28	1670	21	4	24	<= 20
35	1716	40	1650	17	18	22	<= 20
35	1664		1539	22	13		<= 20
49	1773	48	1470	21	28	20	>20
33	1760	33	1580	20	13	20	<= 20
50	1725	49	1670	23	27	22	>20
63	1645	64	1520	28	35	29	>20
57	1694	55	1620	24	33	22	>20
41	1851	41	1710	23	18	23	<= 20
38	1691	38	1530	20	18	20	<= 20
30	1880	31	1630	22	8	23	<= 20
52	1835	52	1720	30	22	30	>20
51	1730	43	1570	22	29	14	>20
46	1644	51	1560	27	19	32	<= 20
50	1723	47	1650	25	25	22	>20
32	1758		1635	24	8		<= 20
52	1718	32	1590	25	27	5	>20
30	1723	33	1590	22	8	25	<= 20
33	1708		1566	21	12		<= 20
20	1786	18	1590	19	1	17	<= 20
32	1764		1662				>20
51	1675	45	1550	25	26	19	>20
64	1641	64	1570	30	34	30	>20
44	1743	43	1560	25	19	24	<= 20
40	1823	39	1630	23	17	22	<= 20
59	1720	56	1530	24	35	21	>20

================================================
FILE: ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf_2s/husbands_wives_height_inf_2s.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

hw <- read.table("husbands_wives.txt", h = T, sep = "\t")

# converts heights to inches ----------------------------------------

hw$ht_husband_in <- hw$ht_husband / 25.4
hw$ht_wife_in <- hw$ht_wife / 25.4

# remove cases where wife's age is missing --------------------------

hw <- hw[!is.na(hw$age_wife),]

# model summary -----------------------------------------------------

m_h_w_height <- lm(hw$ht_wife_in ~ hw$ht_husband_in)

xtable(summary(m_h_w_height))

# plot wife vs. husband height --------------------------------------

pdf("husbands_wives_height_inf_2s.pdf", 5.5, 4.3)

par(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(hw$ht_wife_in ~ hw$ht_husband_in, 
     xlab = "Husband's height (in inches)", 
     ylab = "Wife's height (in inches)", 
     pch = 19, col = COL[1,2], 
     xlim = c(60, 77), ylim = c(55, 70), axes = FALSE)
axis(1, at = seq(60, 75, 5))
axis(2, at = seq(55, 70, 5))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_1.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(9274)

x1 <- seq(0, 6, by = 0.05)

y_u <- (x1-3)^2 - 4 + rnorm(length(x1), mean = 0, sd = 1)
y_lin_pos_strong <- 3*x1 + 10 + rnorm(length(x1), mean = 0, sd = 2)
y_lin_pos_weak <- 3*x1 + 10 + rnorm(length(x1), mean = 0, sd = 20)


x2 <- seq(-8, -2, by = 0.05)

y_n <- -1 * (x2 + 5)^2 + 1 + rnorm(length(x2), mean = 0, sd = 2)
y_lin_neg_strong <- -5 * x2 + 3 + rnorm(length(x2), mean = 0, sd = 2)
y_none <- rnorm(length(x2), mean = 0, sd = 1)

# plot u-shaped -----------------------------------------------------

pdf("identify_relationships_u.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_u ~ x1, xlab = "(a)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot linear positive strong ---------------------------------------

pdf("identify_relationships_lin_pos_strong.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_lin_pos_strong ~ x1, xlab = "(b)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot linear positive weak -----------------------------------------

pdf("identify_relationships_lin_pos_weak.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_lin_pos_weak ~ x1, xlab = "(c)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot n-shaped -----------------------------------------------------

pdf("identify_relationships_n.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_n ~ x2, xlab = "(d)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot n-shaped -----------------------------------------------------

pdf("identify_relationships_lin_neg_strong.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_lin_neg_strong ~ x2, xlab = "(e)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot no relationship ----------------------------------------------

pdf("identify_relationships_none.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_none ~ x2, xlab = "(f)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_2.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(9274)

x <- seq(-3, 4, 0.05)

y_s <-  -0.5 * x^3 + x^2 + x + rnorm(length(x), mean = 0, sd = 2)
y_hockey_stick <-  2 * x^4 + -0.5 * x^3 + x^2 + x + rnorm(length(x), mean = 0, sd = 30)
y_pos_lin_strong <- 3 * x + rnorm(length(x), mean = 0, sd = 2)
y_pos_weak <- 3 * x + rnorm(length(x), mean = 0, sd = 20)
y_pos_weaker <- -3 * x + rnorm(length(x), mean = 0, sd = 10) 
y_neg_lin_weak <- -3 * x + rnorm(length(x), mean = 0, sd = 5) 

# plot s-shaped -----------------------------------------------------

pdf("identify_relationships_s.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_s ~ x, xlab = "(a)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot hockey stick -------------------------------------------------

pdf("identify_relationships_hockey_stick.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_hockey_stick ~ x, xlab = "(b)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot linear positive strong ---------------------------------------

pdf("identify_relationships_pos_lin_strong.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_pos_lin_strong ~ x, xlab = "(c)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot weak positive ------------------------------------------------

pdf("identify_relationships_pos_weak.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_pos_weak ~ x, xlab = "(d)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot weaker positive ----------------------------------------------

pdf("identify_relationships_pos_weaker.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_pos_weaker ~ x, xlab = "(e)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

# plot negative linear ----------------------------------------------

pdf("identify_relationships_neg_lin_weak.pdf", 5.5, 4.3)

par(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(y_neg_lin_weak ~ x, xlab = "(f)", ylab = "",
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/match_corr_1/match_corr_1.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(1234)

x <- seq(0, 6, by = 0.05)

y_1_u <- (x-3)^2 - 4 + rnorm(length(x), mean = 0, sd = 1)
y_2_strong_pos <- 3*x + 10 + rnorm(length(x), mean = 0, sd = 2)
y_3_weak_pos <- 3*x + 10 + rnorm(length(x), mean = 0, sd = 10)
y_4_weak_neg <- -3 * x + rnorm(length(x), mean = 0, sd = 5)

# calculate correlations --------------------------------------------

round(cor(x, y_1_u), 2)
round(cor(x, y_2_strong_pos), 2)
round(cor(x, y_3_weak_pos), 2)
round(cor(x, y_4_weak_neg), 2)

# plot -----------------------------------------------------
width <- 4.5
height <- 3.7
cex.lab <- 2
mgp <- c(1.2,0.7,0)
mar <- c(2.6,1,0.5,1)
pch <- 19
cex <- 1.5
col <- COL[1, 2]

MyPlot <- function(fn, x, y, i) {
  myPDF(fn, width, height,
      mar = mar, mgp = mgp, cex.lab = cex.lab)
  plot(x, y,
      xlab = paste0("(", i, ")"), ylab = "", 
      yaxt = "n", xaxt = "n", 
      pch = pch, col = col, cex = cex)
  dev.off()
}
MyPlot("match_corr_1_u.pdf", x, y_1_u, 1)
MyPlot("match_corr_2_strong_pos.pdf", x, y_2_strong_pos, 2)
MyPlot("match_corr_3_weak_pos.pdf", x, y_3_weak_pos, 3)
MyPlot("match_corr_4_weak_neg.pdf", x, y_4_weak_neg, 4)


================================================
FILE: ch_regr_simple_linear/figures/eoce/match_corr_2/match_corr_2.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(1234)

x <- seq(0, 6, by = 0.05)

y_1_strong_neg_curved <-  -0.5 * x^2 + x + rnorm(length(x), mean = 0, sd = 2)
y_2_weak_pos <-  x + rnorm(length(x), mean = 0, sd = 3)
y_3_n <- -(x-3)^2 - 4 + rnorm(length(x), mean = 0, sd = 0.98)
y_4_weak_neg <- -3 * x + rnorm(length(x), mean = 0, sd = 10)

# calculate correlations --------------------------------------------
# note that these correlations are slightly off from
# those in the textbook due to not having set a seed 
# when the figures were produced (to be fixed for 4th edition)

round(cor(x, y_1_strong_neg_curved), 2)
round(cor(x, y_2_weak_pos), 2)
round(cor(x, y_3_n), 2)
round(cor(x, y_4_weak_neg), 2)

# plot -----------------------------------------------------
width <- 4.5
height <- 3.7
cex.lab <- 2
mgp <- c(1.2,0.7,0)
mar <- c(2.6,1,0.5,1)
pch <- 19
cex <- 1.5
col <- COL[1, 2]

MyPlot <- function(fn, x, y, i) {
  myPDF(fn, width, height,
      mar = mar, mgp = mgp, cex.lab = cex.lab)
  plot(x, y,
      xlab = paste0("(", i, ")"), ylab = "", 
      yaxt = "n", xaxt = "n", 
      pch = pch, col = col, cex = cex)
  dev.off()
}
MyPlot("match_corr_1_strong_neg_curved.pdf", x, y_1_strong_neg_curved, 1)
MyPlot("match_corr_2_weak_pos.pdf", x, y_2_weak_pos, 2)
MyPlot("match_corr_3_n.pdf", x, y_3_n, 3)
MyPlot("match_corr_4_weak_neg.pdf", x, y_4_weak_neg, 4)


================================================
FILE: ch_regr_simple_linear/figures/eoce/match_corr_3/match_corr_2.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(1234)

x <- seq(0, 6, by = 0.05)

y_1_strong_neg_curved <-  -0.5 * x^2 + x + rnorm(length(x), mean = 0, sd = 2)
y_2_weak_pos <-  x + rnorm(length(x), mean = 0, sd = 3)
y_3_n <- -(x-3)^2 - 4 + rnorm(length(x), mean = 0, sd = 0.98)
y_4_weak_neg <- -3 * x + rnorm(length(x), mean = 0, sd = 10)

# calculate correlations --------------------------------------------
# note that these correlations are slightly off from
# those in the textbook due to not having set a seed 
# when the figures were produced (to be fixed for 4th edition)

round(cor(x, y_1_strong_neg_curved), 2)
round(cor(x, y_2_weak_pos), 2)
round(cor(x, y_3_n), 2)
round(cor(x, y_4_weak_neg), 2)

# plot strong negative curved ---------------------------------------

pdf("match_corr_1_strong_neg_curved.pdf", 5.5, 4.3)
par(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)
plot(y_1_strong_neg_curved ~ x, xlab = "(1)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch=19, col=COL[1])
dev.off()

# plot weak positive ------------------------------------------------

pdf("match_corr_2_weak_pos.pdf", 5.5, 4.3)
par(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)
plot(y_2_weak_pos ~ x, xlab = "(2)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch=19, col=COL[1])
dev.off()

# plot n-shaped -----------------------------------------------------

pdf("match_corr_3_n.pdf", 5.5, 4.3)
par(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)
plot(y_3_n ~ x, xlab = "(3)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch=19, col=COL[1])
dev.off()

# plot weak negative ------------------------------------------------

pdf("match_corr_4_weak_neg.pdf", 5.5, 4.3)
par(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)
plot(y_4_weak_neg ~ x, xlab = "(4)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch=19, col=COL[1])
dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/match_corr_3/match_corr_3.R
================================================
library(openintro)

set.seed(1)
n.plots <- 16

n <- 2 * round(runif(n.plots, 25, 75))
b0 <- runif(n.plots, -50, 50)
b1 <- runif(n.plots, -5, 5)
b2 <- runif(n.plots, -0.5, 0.5)
b3 <- runif(n.plots, -0.1, 0.1)
x <- lapply(1:n.plots, function(i) {
  c(runif(n[i] / 2, 0, 10), rnorm(n[i] / 2, 7, 2))
})
s <- runif(n.plots, 0.5, 20)
pow <- 2 * round(runif(n.plots, 0.5, 3) / 2, 1)
y <- lapply(1:n.plots, function(i) {
  noise <- rnorm(n[i], s[i])^pow[i]
  if (any(is.nan(noise))) {
    noise <- rnorm(n[i], s[i])
  }
  b0[i] + b1[i] * x[[i]] + b2[i] * x[[i]]^2 + b3[i] * x[[i]]^3 + noise
})
sapply(x, length)
sapply(y, length)
# par(mfrow = rep(sqrt(n.plots), 2))
tmp <- sapply(1:n.plots, function(i) {
  # plot(x[[i]], y[[i]])
  cor(x[[i]], y[[i]])
})




these <- c(3, 9, 11, 15)
tmp[these]

for (j in 1:length(these)) {
  i <- these[j]
  myPDF(paste0("scatter_", j, ".pdf"),
      4.5, 3.7,
      mar = c(2.6, 1, 0.5, 1),
      mgp = c(1.2, 0.7, 0),
      cex.lab = 2)
  plot(y[[i]] ~ x[[i]],
      xlab = paste0("(", j, ")"), ylab = "", 
      yaxt = "n", xaxt = "n", 
      pch = 19, col = COL[1, 2], cex = 1.5)
  dev.off()
}


================================================
FILE: ch_regr_simple_linear/figures/eoce/murders_poverty_reg/murders.csv
================================================
population,perc_pov,perc_unemp,annual_murders_per_mil
587000,16.5,6.2,11.2
643000,20.5,6.4,13.4
635000,26.3,9.3,40.7
692000,16.5,5.3,5.3
1248000,19.2,7.3,24.8
643000,16.5,5.9,12.7
1964000,20.2,6.4,20.9
1531000,21.3,7.6,35.7
713000,17.2,4.9,8.7
749000,14.3,6.4,9.6
7895000,18.1,6,14.5
762000,23.1,7.4,26.9
2793000,19.1,5.8,15.7
741000,24.7,8.6,36.2
625000,18.6,6.5,18.1
854000,24.9,8.3,28.9
716000,17.9,6.7,14.9
921000,22.4,8.6,25.8
595000,20.2,8.4,21.7
3353000,16.9,6.7,25.7

================================================
FILE: ch_regr_simple_linear/figures/eoce/murders_poverty_reg/murders_poverty.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

murders <- read.csv("murders.csv")

# model murders vs. poverty -----------------------------------------

m_murders_poverty <- lm(murders$annual_murders_per_mil ~ murders$perc_pov)

xtable(summary(m_murders_poverty), digits = 3)

round(summary(m_murders_poverty)$r.squared, 4)
round(summary(m_murders_poverty)$adj.r.squared, 4)
  
# plot murders vs. poverty ------------------------------------------

pdf("murders_poverty.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(murders$annual_murders_per_mil ~ murders$perc_pov, 
     xlab = "Percent in Poverty",
     ylab = "Annual Murders per Million", 
     pch = 19, col = COL[1],
     xlim = c(14, 27), ylim = c(5, 40), axes = FALSE)
AxisInPercent(1, at = seq(14, 26, 4))
axis(2, at = seq(10, 40, 10))
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/outliers_1/outliers_1.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(83629)

x <- seq(1,50,1)

y <- -2 * x + 20 + rnorm(length(x), mean = 0, sd = 10)

x_influential <- c(x[1:49], 200)

y_leverage <- c(y[1:49], -370)

y_outlier <- c(y[1:25], y[26]+100, y[27:50])

# plot influential -------------------------------------------------

pdf("outliers_1_influential.pdf", width = 4, height = 3)

par(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)

plot(y ~ x_influential, 
     pch = 19, col = COL[1,2], 
     xlab = "(a)", ylab = "", 
     xaxt = "n", yaxt = "n")

m_influential = lm(y ~ x_influential)
abline(m_influential, col = COL[2])

dev.off()

# plot leverage ----------------------------------------------------

pdf("outliers_2_leverage.pdf", width = 4, height = 3)

par(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)

plot(y_leverage ~ x_influential, 
     pch = 19, col = COL[1,2], 
     xlab = "(b)", ylab = "", 
     xaxt = "n", yaxt = "n")

m_leverage = lm(y_leverage ~ x_influential)
abline(m_leverage, col = COL[2])

dev.off()

# plot outlier -----------------------------------------------------

pdf("outliers_3_outlier.pdf", width = 4, height = 3)

par(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)

plot(y_outlier ~ x, 
     pch = 19, col = COL[1,2], 
     xlab = "(c)", ylab = "", 
     xaxt = "n", yaxt = "n")

m_outlier = lm(y_outlier ~ x)
abline(m_outlier, col = COL[2])

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/outliers_2/outliers_2.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(83629)

x <- seq(1,50,1)

y <- 3 * x + 3 + rnorm(length(x), mean = 0, sd = 10)

x_influential <- c(x[1:49], -50)

y_influential <- c(y[1:49], -300)

y_outlier <- c(y[1:25], y[26]+100, y[27:50])

# plot influential -------------------------------------------------

pdf("outliers_1_influential.pdf", width = 4, height = 3)

par(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)

plot(y ~ x_influential, 
     pch = 19, col = COL[1,2], 
     xlab = "(a)", ylab = "", 
     xaxt = "n", yaxt = "n")

m_influential = lm(y ~ x_influential)
abline(m_influential, col = COL[2])

dev.off()

# plot another influential ------------------------------------------

pdf("outliers_2_influential.pdf", width = 4, height = 3)

par(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)

plot(y_influential ~ x_influential, 
     pch = 19, col = COL[1,2], 
     xlab = "(b)", ylab = "", 
     xaxt = "n", yaxt = "n")

m_influential = lm(y_influential ~ x_influential)
abline(m_influential, col = COL[2])

dev.off()

# plot outlier -----------------------------------------------------

pdf("outliers_3_outlier.pdf", width = 4, height = 3)

par(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)

plot(y_outlier ~ x, 
     pch = 19, col = COL[1,2], 
     xlab = "(c)", ylab = "", 
     xaxt = "n", yaxt = "n")

m_outlier = lm(y_outlier ~ x)
abline(m_outlier, col = COL[2])

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/rate_my_prof/prof_evals_beauty.csv
================================================
tenured,profnumber,minority,age,beautyf2upper,beautyflowerdiv,beautyfupperdiv,beautym2upper,beautymlowerdiv,beautymupperdiv,btystdave,btystdf2u,btystdfl,btystdfu,btystdm2u,btystdml,btystdmu,class1,class2,class3,class4,class5,class6,class7,class8,class9,class10,class11,class12,class13,class14,class15,class16,class17,class18,class19,class20,class21,class22,class23,class24,class25,class26,class27,class28,class29,class30,courseevaluation,didevaluation,female,formal,fulldept,lower,multipleclass,nonenglish,onecredit,percentevaluating,profevaluation,students,tenuretrack,blkandwhite,btystdvariance,btystdavepos,btystdaveneg
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,24,1,0,1,0,1,0,0,55.81395,4.7,43,1,0,2.129806,0.201567,0
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,17,0,0,1,0,0,0,0,85,4.6,20,1,0,1.386081,0,-0.826081
1,3,0,51,5,5,2,3,2,3,-0.6603327,-0.1878249,0.4580018,-1.545553,-1.131904,-0.9000649,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,55,0,0,1,0,1,0,0,100,4.1,55,1,0,2.537435,0,-0.660333
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,40,1,0,1,0,1,0,0,86.95652,4.5,46,1,0,1.760577,0,-0.766312
0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,42,1,0,1,0,0,0,0,87.5,4.8,48,1,0,1.6931,1.42145,0
1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,182,0,1,1,0,0,0,0,64.53901,4.4,282,1,0,0.9447419,0.50022,0
0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,33,1,0,1,0,1,0,0,80.48781,4.4,41,1,0,0.4898793,0,-0.21435
1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,25,1,0,1,0,0,0,0,60.97561,3.4,41,1,0,2.041787,0,-0.346539
0,9,0,33,5,3,7,5,5,3,0.0613435,-0.1878249,-0.6050149,0.8758139,0.0771755,0.8625621,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,48,1,0,1,0,0,0,0,80,4.8,60,1,0,2.32433,0.061344,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,16,0,0,1,0,1,0,0,84.21053,4,19,0,0,0.916837,0.452568,0
0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,18,0,0,1,0,1,1,0,72,3.6,25,1,0,3.798652,0.143264,0
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,30,0,0,1,0,0,0,0,88.23529,4.1,34,0,0,0.3504698,0,-0.155023
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,28,0,0,1,0,0,0,0,70,4.1,40,1,0,0.8283713,0.128543,0
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,30,0,0,1,0,0,1,0,83.33334,3.5,36,1,0,4.42171,0,-0.347045
0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.9,23,1,0,1,0,1,0,0,82.14286,2.9,28,1,0,2.113737,0.461939,0
1,16,0,45,3,2,4,7,5,4,-0.1503849,-1.142179,-1.136523,-0.5770065,1.286255,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,23,0,0,1,0,1,0,0,69.69697,4.6,33,1,0,5.230154,0,-0.150385
0,17,0,56,1,3,4,3,2,2,-1.070734,-2.096532,-0.6050149,-0.5770065,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,27,1,0,1,0,1,0,0,65.85366,4.4,41,0,0,1.547655,0,-1.07073
0,18,0,48,6,3,5,4,4,4,-0.1426931,0.2893519,-0.6050149,-0.092733,-0.5273642,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,100,0,0,1,1,0,0,0,74.07407,4,135,0,0,0.7281362,0,-0.142693
1,19,0,46,5,4,4,5,2,6,-0.1563634,-0.1878249,-0.0735065,-0.5770065,0.0771755,-0.9000649,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,25,1,0,1,0,0,0,0,80.64516,4.9,31,1,1,1.565791,0,-0.156363
0,20,0,57,4,5,4,6,5,2,-0.0589354,-0.6650018,0.4580018,-0.5770065,0.6817153,0.8625621,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,17,1,0,0,0,0,0,0,60.71429,3.5,28,0,0,3.413574,0,-0.058935
0,21,0,52,4,6,6,7,2,4,0.1511368,-0.6650018,0.9895102,0.3915404,1.286255,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,19,1,0,1,0,1,0,0,86.36364,3.7,22,0,0,3.940365,0.151137,0
0,22,1,29,4,3,3,2,2,3,-0.937076,-0.6650018,-0.6050149,-1.06128,-1.736444,-0.9000649,-0.6546507,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,19,1,0,1,0,1,1,0,73.07692,3.4,26,1,0,0.9198381,0,-0.937076
1,23,0,62,4,4,3,4,1,2,-0.8214405,-0.6650018,-0.0735065,-1.06128,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,30,0,0,1,0,1,0,0,76.92308,4.3,39,1,0,1.257183,0,-0.821441
1,24,0,64,5,5,4,5,3,3,-0.1994712,-0.1878249,0.4580018,-0.5770065,0.0771755,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,15,0,0,1,0,0,0,0,62.5,4.5,24,1,0,0.8714417,0,-0.199471
0,25,0,34,8,8,9,8,6,8,1.687167,1.243706,2.052527,1.844361,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,20,1,0,1,0,0,0,0,76.92308,4.8,26,1,0,0.454603,1.68717,0
1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4.2,84,0,0,1,0,1,0,0,52.83019,4.4,159,1,0,0.4904639,0,-0.410198
1,27,1,52,7,4,5,4,5,4,0.1233448,0.7665288,-0.0735065,-0.092733,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,13,0,1,1,0,1,1,0,81.25,4.4,16,1,0,1.5706,0.123345,0
1,28,0,73,6,1,3,5,2,1,-0.8059942,0.2893519,-1.668032,-1.06128,0.0771755,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,12,0,1,1,0,1,0,0,70.58823,3.6,17,1,0,3.385376,0,-0.805994
1,29,0,70,2,5,3,2,3,3,-0.8210418,-1.619356,0.4580018,-1.06128,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4.4,47,0,1,1,0,1,0,0,35.07463,4.6,134,1,0,3.45521,0,-0.821042
0,30,0,41,7,3,6,7,4,4,0.3198185,0.7665288,-0.6050149,0.3915404,1.286255,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.3,10,1,0,1,0,1,0,0,83.33334,2.3,12,1,0,2.261486,0.319818,0
0,31,0,63,6,5,4,5,4,2,-0.0985568,0.2893519,0.4580018,-0.5770065,0.0771755,0.2750198,-1.113883,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,28,0,0,1,0,1,0,0,65.11628,4.3,43,0,0,1.890474,0,-0.098557
1,32,0,47,3,1,4,3,2,3,-1.012306,-1.142179,-1.668032,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,10,0,0,1,0,0,0,0,66.66666,4.9,15,1,0,0.7911476,0,-1.01231
1,33,0,39,6,5,6,5,6,5,0.4883314,0.2893519,0.4580018,0.3915404,0.0771755,1.450104,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,86,0,0,1,1,0,0,0,34.95935,4.2,246,1,0,1.194346,0.488331,0
0,34,1,47,6,2,6,4,3,5,-0.1719505,0.2893519,-1.136523,0.3915404,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,21,1,0,1,1,1,0,0,100,4.2,21,1,0,1.796693,0,-0.171951
1,35,0,54,4,1,2,4,1,2,-1.167907,-0.6650018,-1.668032,-1.545553,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,15,0,1,1,0,1,0,0,100,4.3,15,1,0,1.161077,0,-1.16791
1,36,1,44,8,6,8,5,5,7,0.9525534,1.243706,0.9895102,1.360087,0.0771755,0.8625621,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,30,1,0,1,0,0,0,0,54.54546,3.9,55,1,0,1.079378,0.952553,0
1,37,1,47,2,2,2,4,2,2,-1.140457,-1.619356,-1.136523,-1.545553,-0.5273642,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,20,0,0,1,0,1,0,0,74.07407,3.9,27,1,0,0.8278397,0,-1.14046
1,38,0,62,5,3,2,3,3,2,-0.8161172,-0.1878249,-0.6050149,-1.545553,-1.131904,-0.3125226,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,12,0,1,1,0,0,0,0,92.30769,4.1,13,1,0,1.413386,0,-0.816117
1,39,0,60,6,4,4,4,2,2,-0.4837456,0.2893519,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,25,0,0,1,0,0,0,0,73.52941,4.8,34,1,0,1.346971,0,-0.483746
0,40,0,37,7,4,8,8,5,5,0.8450468,0.7665288,-0.0735065,1.360087,1.890795,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,15,0,0,1,0,1,0,0,88.23529,3.3,17,1,0,2.546899,0.845047,0
0,41,0,42,3,3,5,4,3,6,-0.3261277,-1.142179,-0.6050149,-0.092733,-0.5273642,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,13,0,0,1,0,0,0,0,92.85714,4.4,14,1,0,1.939639,0,-0.326128
1,42,0,35,2,4,7,6,6,4,0.1865589,-1.619356,-0.0735065,0.8758139,0.6817153,1.450104,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4.2,42,0,0,1,0,1,0,0,82.35294,4.3,51,1,0,5.791667,0.186559,0
0,43,0,39,8,8,8,9,7,9,1.881674,1.243706,2.052527,1.360087,2.495334,2.037647,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3.4,22,1,0,1,1,1,0,0,91.66666,3.3,24,0,0,1.157145,1.88167,0
1,44,0,49,7,6,6,5,6,9,0.9626006,0.7665288,0.9895102,0.3915404,0.0771755,1.450104,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3.9,28,0,1,1,0,1,0,0,62.22222,4,45,1,0,2.682287,0.962601,0
1,45,0,61,4,5,5,6,4,5,0.1534694,-0.6650018,0.4580018,-0.092733,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4.5,22,0,1,1,1,1,0,0,81.48148,4.5,27,1,0,1.129245,0.153469,0
0,46,0,33,7,7,8,7,6,7,1.261046,0.7665288,1.521019,1.360087,1.286255,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4.7,30,0,1,1,0,1,0,0,96.77419,4.9,31,1,0,0.3645249,1.26105,0
1,47,0,58,7,3,6,6,2,4,0.0232144,0.7665288,-0.6050149,0.3915404,0.6817153,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3.5,26,1,1,1,0,1,0,0,76.47059,3.7,34,1,1,2.416721,0.023214,0
1,48,0,56,4,2,4,6,4,3,-0.3460746,-0.6650018,-1.136523,-0.5770065,0.6817153,0.2750198,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3.7,12,1,1,1,0,1,0,0,63.15789,3.9,19,1,0,2.317183,0,-0.346075
0,49,0,50,5,2,3,4,2,3,-0.744618,-0.1878249,-1.136523,-1.06128,-0.5273642,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,22,1,0,1,1,1,0,0,81.48148,4.4,27,0,0,0.6433402,0,-0.744618
1,50,0,52,5,4,3,3,2,2,-0.7447439,-0.1878249,-0.0735065,-1.06128,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,57,0,0,1,0,0,0,0,85.07462,4.4,67,1,0,1.171195,0,-0.744744
0,51,0,33,9,3,7,7,4,5,0.6361284,1.720883,-0.6050149,0.8758139,1.286255,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,10,1,0,1,0,0,0,0,66.66666,4.5,15,1,1,3.466259,0.636128,0
1,52,0,57,6,5,6,5,5,7,0.5434852,0.2893519,0.4580018,0.3915404,0.0771755,0.8625621,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,69,0,0,1,0,0,0,0,89.61039,4.3,77,1,1,0.8222913,0.543485,0
1,53,0,38,9,6,8,7,4,5,0.9825948,1.720883,0.9895102,1.360087,1.286255,0.2750198,0.2638144,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,46,1,1,1,0,1,0,0,70.76923,4.8,65,1,1,1.797135,0.982595,0
0,54,0,34,4,2,1,1,1,1,-1.538843,-0.6650018,-1.136523,-2.029827,-2.340983,-1.487607,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,54,1,0,0,1,0,0,0,59.34066,4.1,91,1,0,1.813754,0,-1.53884
0,55,0,34,7,6,7,6,6,8,1.067531,0.7665288,0.9895102,0.8758139,0.6817153,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,24,0,0,0,0,0,0,0,66.66666,3.5,36,1,0,0.7581155,1.06753,0
0,56,0,32,3,3,7,3,3,3,-0.4143639,-1.142179,-0.6050149,1.360087,-1.131904,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,85,0,1,0,1,0,0,0,34.27419,4,248,1,1,4.297712,0,-0.414364
1,57,0,42,8,5,5,5,6,8,0.7962944,1.243706,0.4580018,-0.092733,0.0771755,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,44,1,0,0,1,0,0,0,70.96774,3.8,62,1,0,2.763981,0.796294,0
1,58,0,43,5,2,3,4,3,3,-0.6466943,-0.1878249,-1.136523,-1.06128,-0.5273642,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,35,1,0,0,1,0,0,0,68.62745,4.1,51,1,0,0.7483485,0,-0.646694
0,59,0,35,3,4,3,5,4,3,-0.4299034,-1.142179,-0.0735065,-1.06128,0.0771755,0.2750198,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3.3,11,0,0,1,0,1,1,0,57.89474,3.7,19,1,1,1.837548,0,-0.429903
1,60,0,62,5,1,5,5,1,4,-0.5924066,-0.1878249,-1.668032,-0.092733,0.0771755,-1.487607,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,22,1,0,1,0,0,0,0,81.48148,3.2,27,1,0,2.977653,0,-0.592407
1,61,0,42,6,3,1,4,1,1,-0.9889295,0.2893519,-0.6050149,-2.029827,-0.5273642,-1.487607,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,45,0,0,1,0,0,0,0,52.32558,4.2,86,1,0,3.667857,0,-0.98893
1,62,0,39,6,5,8,6,4,5,0.5546651,0.2893519,0.4580018,1.360087,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,22,0,0,1,0,0,0,0,75.86207,4.5,29,1,0,0.9073773,0.554665,0
1,63,0,52,7,5,7,6,3,8,0.6851749,0.7665288,0.4580018,0.8758139,0.6817153,-0.3125226,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,64,1,1,1,0,0,0,0,72.72727,3.8,88,1,1,2.004562,0.685175,0
1,64,0,52,9,6,7,6,5,6,0.9755885,1.720883,0.9895102,0.8758139,0.6817153,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,31,1,0,1,0,0,0,0,70.45454,3.7,44,1,1,0.7285256,0.975589,0
0,65,0,52,4,2,3,2,2,1,-1.178738,-0.6650018,-1.136523,-1.06128,-1.736444,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,59,1,0,1,0,0,0,0,78.66666,4.5,75,0,0,0.8237315,0,-1.17874
1,66,0,64,3,2,3,3,2,1,-1.157511,-1.142179,-1.136523,-1.06128,-1.131904,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,9,0,0,1,0,0,0,0,81.81818,3.8,11,1,0,0.2495975,0,-1.15751
1,67,0,50,9,6,7,7,6,8,1.327346,1.720883,0.9895102,0.8758139,1.286255,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,15,0,0,1,0,0,0,0,93.75,4.5,16,1,0,0.5883437,1.32735,0
1,68,0,60,2,1,1,2,2,2,-1.511268,-1.619356,-1.668032,-2.029827,-1.736444,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.2,23,0,0,1,0,0,0,0,71.875,2.4,32,1,0,0.8873489,0,-1.51127
1,69,0,51,7,4,5,6,4,5,0.3034731,0.7665288,-0.0735065,-0.092733,0.6817153,0.2750198,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,47,1,1,0,0,0,0,0,70.14925,3,67,1,0,0.658963,0.303473,0
0,70,0,43,4,3,4,4,2,4,-0.5783117,-0.6650018,-0.6050149,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,19,0,0,1,1,0,0,0,86.36364,4.5,22,1,0,0.2609582,0,-0.578312
0,71,1,50,4,1,5,5,1,4,-0.671936,-0.6650018,-1.668032,-0.092733,0.0771755,-1.487607,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,17,0,0,1,1,0,0,1,70.83334,4.6,24,0,0,2.781287,0,-0.671936
1,72,0,52,6,7,6,6,5,5,0.6683338,0.2893519,1.521019,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,46,0,0,1,0,0,0,0,66.66666,3.5,69,1,0,1.148853,0.668334,0
1,73,0,51,8,6,7,4,6,6,0.7924695,1.243706,0.9895102,0.8758139,-0.5273642,1.450104,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,348,0,1,1,1,0,0,0,60.62718,4.8,574,1,0,2.42865,0.79247,0
1,74,0,38,4,3,4,4,2,3,-0.6548505,-0.6650018,-0.6050149,-0.5770065,-0.5273642,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,44,0,0,1,1,0,0,0,50.57471,4.2,87,1,0,0.0850292,0,-0.65485
1,75,0,47,6,5,7,4,3,6,0.2510546,0.2893519,0.4580018,0.8758139,-0.5273642,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3.8,19,1,1,1,1,1,0,0,79.16666,4,24,1,0,1.58095,0.251055,0
1,76,1,43,4,5,6,4,2,4,-0.2397178,-0.6650018,0.4580018,0.3915404,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,47,1,1,1,1,0,0,0,45.63107,3.7,103,1,0,1.586927,0,-0.239718
0,77,0,38,2,3,4,2,1,3,-1.113346,-1.619356,-0.6050149,-0.5770065,-1.736444,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,33,1,0,1,1,0,0,0,48.52941,4.5,68,0,0,1.54083,0,-1.11335
1,78,0,43,5,4,4,5,4,4,-0.1135935,-0.1878249,-0.0735065,-0.5770065,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,10,0,1,1,1,0,0,0,76.92308,4.7,13,1,0,0.4159773,0,-0.113593
1,79,0,57,3,3,4,3,2,3,-0.8351366,-1.142179,-0.6050149,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,7,0,0,1,0,0,0,0,53.84615,4.3,13,1,0,0.3387237,0,-0.835137
1,80,0,51,5,5,8,6,6,8,0.9005994,-0.1878249,0.4580018,1.360087,0.6817153,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,9,1,0,1,0,0,0,0,81.81818,4,11,1,0,2.490507,0.900599,0
0,81,0,45,2,1,4,4,5,4,-0.620769,-1.619356,-1.668032,-0.5770065,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4.8,12,0,0,1,1,1,0,1,70.58823,4.8,17,0,0,4.485768,0,-0.620769
1,82,0,57,3,2,3,6,2,1,-0.8552412,-1.142179,-1.136523,-1.06128,0.6817153,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,13,0,0,1,1,0,0,0,92.85714,3.5,14,1,1,3.083493,0,-0.855241
0,83,0,47,6,8,6,7,4,9,1.065906,0.2893519,2.052527,0.3915404,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,16,1,0,1,1,0,0,0,76.19048,3.3,21,0,1,3.776171,1.06591,0
1,84,1,54,8,8,5,7,4,9,1.144253,1.243706,2.052527,-0.092733,1.286255,0.2750198,2.100744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3.8,18,1,0,1,1,1,0,0,100,4.2,18,1,1,4.055594,1.14425,0
0,85,0,58,9,8,8,8,6,8,1.685985,1.720883,2.052527,1.360087,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4.7,16,0,0,1,1,1,0,0,94.11765,4.8,17,0,1,0.3413446,1.68598,0
1,86,0,42,10,8,7,8,6,8,1.684802,2.198059,2.052527,0.8758139,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.9,15,0,0,1,1,0,0,0,83.33334,4.9,18,1,1,1.152506,1.6848,0
0,87,0,33,6,6,7,5,5,6,0.6362435,0.2893519,0.9895102,0.8758139,0.0771755,0.8625621,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,85,0,0,0,1,0,0,0,70.83334,4.5,120,1,0,0.673837,0.636243,0
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0,89,1,35,10,7,9,8,6,7,1.681103,2.198059,1.521019,1.844361,1.890795,1.450104,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.4,60,1,0,1,1,0,0,0,62.5,3.3,96,1,0,0.6656799,1.6811,0
1,90,0,61,7,3,2,4,1,3,-0.6756103,0.7665288,-0.6050149,-1.545553,-0.5273642,-1.487607,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,27,0,0,1,1,0,0,0,69.23077,3.6,39,1,0,3.523305,0,-0.67561
1,91,0,52,5,4,8,3,3,4,-0.0901815,-0.1878249,-0.0735065,1.360087,-1.131904,-0.3125226,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,61,1,0,0,1,0,0,0,54.95496,4.1,111,1,0,3.258788,0,-0.090181
0,92,0,60,6,4,6,5,2,3,-0.1450257,0.2893519,-0.0735065,0.3915404,0.0771755,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,23,1,1,1,0,0,1,0,85.18519,3.7,27,1,1,1.360877,0,-0.145026
0,93,0,32,9,6,6,5,7,8,1.143045,1.720883,0.9895102,0.3915404,0.0771755,2.037647,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,98,0,0,1,1,0,0,0,74.24242,4.5,132,1,0,3.107088,1.14304,0
0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-0.6050149,1.360087,-0.5273642,0.2750198,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,48,1,0,1,0,0,1,0,57.14286,3.5,84,1,0,3.018447,0.332051,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,86,1,0,1,0,1,0,0,68.8,4.1,125,1,0,2.129806,0.201567,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,76,1,0,1,0,1,0,0,60.8,3.9,125,1,0,2.129806,0.201567,0
0,1,1,36,6,5,7,6,2,4,0.2015666,0.2893519,0.4580018,0.8758139,0.6817153,-0.9000649,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,77,1,0,1,0,1,0,0,62.60163,4.8,123,1,0,2.129806,0.201567,0
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,35,0,0,1,0,0,0,0,87.5,4.3,40,1,0,1.386081,0,-0.826081
1,2,0,59,2,4,4,3,2,3,-0.8260813,-1.619356,-0.0735065,-0.5770065,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.1,39,0,0,1,0,0,0,0,88.63636,2.8,44,1,0,1.386081,0,-0.826081
1,3,0,51,5,5,2,3,2,3,-0.6603327,-0.1878249,0.4580018,-1.545553,-1.131904,-0.9000649,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.2,111,0,0,1,0,1,0,0,56.92308,3.4,195,1,0,2.537435,0,-0.660333
1,4,0,40,4,2,5,2,3,3,-0.7663125,-0.6650018,-1.136523,-0.092733,-1.736444,-0.3125226,-0.6546507,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,24,1,0,1,0,1,0,0,88.88889,3.8,27,1,0,1.760577,0,-0.766312
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0,5,0,31,9,7,9,6,7,6,1.421445,1.720883,1.521019,1.844361,0.6817153,2.037647,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,40,1,0,1,0,0,0,0,90.90909,4.6,44,1,0,1.6931,1.42145,0
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1,6,0,62,5,6,6,6,5,5,0.5002196,-0.1878249,0.9895102,0.3915404,0.6817153,0.8625621,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,160,0,1,1,0,0,0,0,54.79452,4.6,292,1,0,0.9447419,0.50022,0
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0,7,0,33,5,4,4,4,4,4,-0.2143501,-0.1878249,-0.0735065,-0.5770065,-0.5273642,0.2750198,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,29,1,0,1,0,1,0,0,85.29412,4.3,34,1,0,0.4898793,0,-0.21435
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1,8,0,51,6,4,6,3,2,3,-0.346539,0.2893519,-0.0735065,0.3915404,-1.131904,-0.9000649,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,21,1,0,1,0,0,0,0,95.45454,4.5,22,1,0,2.041787,0,-0.346539
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0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,14,0,0,1,0,1,0,0,93.33334,4.4,15,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,22,0,0,1,0,1,0,0,95.65218,4.5,23,0,0,0.916837,0.452568,0
0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,10,0,0,1,1,0,0,0,90.90909,5,11,0,0,0.916837,0.452568,0
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0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,16,0,0,1,0,0,0,0,76.19048,4.6,21,0,0,0.916837,0.452568,0
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0,10,0,47,6,5,7,6,3,6,0.4525679,0.2893519,0.4580018,0.8758139,0.6817153,-0.3125226,0.723047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,16,0,0,1,0,1,0,0,84.21053,4.7,19,0,0,0.916837,0.452568,0
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0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,30,0,0,1,0,1,1,0,90.90909,3.7,33,1,0,3.798652,0.143264,0
0,11,1,35,4,5,7,7,2,4,0.1432643,-0.6650018,0.4580018,0.8758139,1.286255,-0.9000649,-0.1954181,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,23,0,0,1,0,1,1,0,95.83334,4.3,24,1,0,3.798652,0.143264,0
0,12,0,37,5,4,5,4,3,5,-0.1550228,-0.1878249,-0.0735065,-0.092733,-0.5273642,-0.3125226,0.2638144,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4.4,13,0,0,1,0,1,0,0,61.90476,4.2,21,0,0,0.3504698,0,-0.155023
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1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.8,18,0,0,1,0,0,0,0,60,4.2,30,1,0,0.8283713,0.128543,0
1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,28,0,0,1,0,0,0,0,66.66666,4,42,1,0,0.8283713,0.128543,0
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1,13,0,42,5,4,7,5,4,4,0.1285433,-0.1878249,-0.0735065,0.8758139,0.0771755,0.2750198,-0.1954181,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,40,0,0,1,0,1,0,0,78.43137,4.4,51,1,0,0.8283713,0.128543,0
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1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,31,0,0,1,0,1,1,0,83.78378,4.2,37,1,0,4.42171,0,-0.347045
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,15,0,0,1,0,0,1,0,51.72414,3.5,29,1,0,4.42171,0,-0.347045
1,14,0,49,5,3,3,5,1,7,-0.3470453,-0.1878249,-0.6050149,-1.06128,0.0771755,-1.487607,1.182279,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,23,0,0,1,0,1,1,0,85.18519,3.6,27,1,0,4.42171,0,-0.347045
0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,34,1,0,1,0,0,0,0,65.38461,3.3,52,1,0,2.113737,0.461939,0
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0,15,0,37,7,6,7,4,5,4,0.4619388,0.7665288,0.9895102,0.8758139,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.1,29,1,0,1,0,1,0,0,96.66666,3.2,30,1,0,2.113737,0.461939,0
1,16,0,45,3,2,4,7,5,4,-0.1503849,-1.142179,-1.136523,-0.5770065,1.286255,0.8625621,-0.1954181,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,45,0,0,1,1,1,0,0,25.42373,4.2,177,1,0,5.230154,0,-0.150385
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0,17,0,56,1,3,4,3,2,2,-1.070734,-2.096532,-0.6050149,-0.5770065,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.6,39,1,0,1,0,1,0,0,88.63636,4.8,44,0,0,1.547655,0,-1.07073
0,17,0,56,1,3,4,3,2,2,-1.070734,-2.096532,-0.6050149,-0.5770065,-1.131904,-0.9000649,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,35,1,0,1,0,0,0,0,66.03773,4.3,53,0,0,1.547655,0,-1.07073
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0,18,0,48,6,3,5,4,4,4,-0.1426931,0.2893519,-0.6050149,-0.092733,-0.5273642,0.2750198,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,20,0,0,1,0,0,0,0,60.60606,4.2,33,0,0,0.7281362,0,-0.142693
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1,19,0,46,5,4,4,5,2,6,-0.1563634,-0.1878249,-0.0735065,-0.5770065,0.0771755,-0.9000649,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.9,14,1,0,1,1,0,0,0,93.33334,5,15,1,1,1.565791,0,-0.156363
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1,23,0,62,4,4,3,4,1,2,-0.8214405,-0.6650018,-0.0735065,-1.06128,-0.5273642,-1.487607,-1.113883,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,94,0,0,1,1,1,0,0,51.08696,3.7,184,1,0,1.257183,0,-0.821441
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1,24,0,64,5,5,4,5,3,3,-0.1994712,-0.1878249,0.4580018,-0.5770065,0.0771755,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,51,0,0,1,0,0,0,0,75,4.5,68,1,0,0.8714417,0,-0.199471
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0,25,0,34,8,8,9,8,6,8,1.687167,1.243706,2.052527,1.844361,1.890795,1.450104,1.641512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.9,33,1,0,1,0,0,0,0,82.5,4.1,40,1,0,0.454603,1.68717,0
1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,71,0,0,1,0,1,0,0,47.01987,4.3,151,1,0,0.4904639,0,-0.410198
1,26,0,58,5,4,4,4,2,4,-0.4101976,-0.1878249,-0.0735065,-0.5770065,-0.5273642,-0.9000649,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3.5,36,0,0,1,1,1,0,0,76.59574,3.6,47,1,0,0.4904639,0,-0.410198
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1,27,1,52,7,4,5,4,5,4,0.1233448,0.7665288,-0.0735065,-0.092733,-0.5273642,0.8625621,-0.1954181,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4.6,23,0,1,1,0,1,1,0,100,4.7,23,1,0,1.5706,0.123345,0
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1,28,0,73,6,1,3,5,2,1,-0.8059942,0.2893519,-1.668032,-1.06128,0.0771755,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,10,0,1,1,0,1,0,0,76.92308,4.4,13,1,0,3.385376,0,-0.805994
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1,28,0,73,6,1,3,5,2,1,-0.8059942,0.2893519,-1.668032,-1.06128,0.0771755,-0.9000649,-1.573116,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4.9,13,0,1,1,0,1,0,0,76.47059,4.3,17,1,0,3.385376,0,-0.805994
1,29,0,70,2,5,3,2,3,3,-0.8210418,-1.619356,0.4580018,-1.06128,-1.736444,-0.3125226,-0.6546507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,5,0,1,1,0,1,0,0,10.41667,4.6,48,1,0,3.45521,0,-0.821042
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0,31,0,63,6,5,4,5,4,2,-0.0985568,0.2893519,0.4580018,-0.5770065,0.0771755,0.2750198,-1.113883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.2,11,0,0,1,0,0,0,0,78.57143,4.4,14,0,0,1.890474,0,-0.098557
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================================================
FILE: ch_regr_simple_linear/figures/eoce/rate_my_prof/rate_my_prof.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(xtable)

# load data ---------------------------------------------------------

prof_evals_beauty <- read.csv("prof_evals_beauty.csv")

# rename variables for convenience ----------------------------------

beauty <- prof_evals_beauty$btystdave
eval <- prof_evals_beauty$courseevaluation

# model evaluation scores vs. beauty --------------------------------

m_eval_beauty = lm(eval ~ beauty)

xtable(summary(m_eval_beauty))

# scatterplot of evaluation scores vs. beauty -----------------------

pdf("rate_my_prof_eval_beauty.pdf", 5.5, 4.3)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5, las = 1)

plot(eval ~ beauty, 
     xlab = "Beauty", ylab = "Teaching evaluation", 
     pch = 19, col = COL[1,2], 
     axes = FALSE)
axis(1, at = seq(-1, 2, 1))
axis(2, at = seq(2, 5, 1))
box()

dev.off()

# residuals plot ----------------------------------------------------

pdf("rate_my_prof_residuals.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5, las = 1)

plot(m_eval_beauty$residuals ~ beauty, 
     xlab = "Beauty", ylab = "Residuals", 
     pch = 19, col = COL[1,2], 
     ylim = c(-1.82, 1.82), axes = FALSE)
axis(1, at = seq(-1, 2, 1))
axis(2, at = seq(-1, 1, 1))
box()
abline(h = 0, lty = 3)

dev.off()

# residuals histogram -----------------------------------------------

pdf("rate_my_prof_residuals_hist.pdf", height = 5, width = 5)

par(mar = c(3.9, 3, 0, 0), cex.lab = 1.5, cex.axis = 1.5)

hist(m_eval_beauty$residuals, 
     xlab = "Residuals", ylab = "", main = "",
     col = COL[1],
     xlim = c(-2,2))

dev.off()

# normal probability plot of residuals ------------------------------

pdf("rate_my_prof_residuals_qq.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

qqnorm(m_eval_beauty$residuals, 
       pch = 19, col = COL[1,2],
       main = "", las = 0)
qqline(m_eval_beauty$residuals, col = COL[1])

dev.off()

# order of residuals ---------------------------------------------===

pdf("rate_my_prof_residuals_order.pdf", height = 5, width = 5)

par(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_eval_beauty$residuals, 
     xlab = "Order of data collection", ylab = "Residuals", main = "",
     pch = 19, col = COL[1,2],
     ylim = c(-1.82, 1.82), axes = FALSE)
axis(1)
axis(2, at = seq(-1, 1, 1))
box()

abline(h = 0, lty = 3)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/speed_height_gender/speed_height_gender.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

speed_survey <- read.csv("speed_survey.csv")

# assign colors and plotting characters to gender -------------------

speed_survey$col[speed_survey$gender == "female"] <- COL[4]
speed_survey$col[speed_survey$gender == "male"] <- COL[2]

speed_survey$pch[speed_survey$gender == "female"] <- 4
speed_survey$pch[speed_survey$gender == "male"] <- 19

# plot speed vs. height ---------------------------------------------

pdf("speed_height.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)

plot(speed_survey$speed ~ speed_survey$height, 
     xlab = "Height (in inches)", ylab = "Fastest speed (in mph)", 
     pch = 19, col = COL[1,2], 
     axes = FALSE, ylim = c(0,150))
axis(1, at = seq(50, 80, 10))
axis(2, at = seq(0, 150, 50))
box()

dev.off()

# plot speed vs. height vs. gender ----------------------------------

pdf("speed_height_gender.pdf", 5.5, 4.3)

par(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)

plot(speed_survey$speed ~ speed_survey$height, 
     xlab = "Height (in inches)", ylab = "Fastest speed (in mph)", 
     pch = speed_survey$pch, col = speed_survey$col, 
     axes = FALSE, ylim = c(0,150))
axis(1, at = seq(50, 80, 10))
axis(2, at = seq(0, 150, 50))
box()

legend("bottomright", inset = 0.05, 
       col = c(COL[4],COL[2]), 
       pch = c(4,19), legend = c("female", "male"))

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/speed_height_gender/speed_survey.csv
================================================
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================================================
FILE: ch_regr_simple_linear/figures/eoce/starbucks_cals_carbos/starbucks.csv
================================================
item,calories,fat,carb,fiber,protein,type
8-Grain Roll,350,8,67,5,10,bakery
Apple Bran Muffin,350,9,64,7,6,bakery
Apple Fritter,420,20,59,0,5,bakery
Banana Nut Loaf,490,19,75,4,7,bakery
Birthday Cake Mini Doughnut,130,6,17,0,0,bakery
Blueberry Oat Bar,370,14,47,5,6,bakery
Blueberry Scone,460,22,61,2,7,bakery
Bountiful Blueberry Muffin,370,14,55,0,6,bakery
Butter Croissant ,310,18,32,0,5,bakery
Cheese Danish,420,25,39,0,7,bakery
Chocolate Chunk Cookie,380,17,51,2,4,bakery
Chocolate Cinnamon Bread,320,12,53,3,6,bakery
Chocolate Croissant,300,17,34,2,5,bakery
Chocolate Old-Fashioned Doughnut,420,21,57,2,5,bakery
Chonga Bagel,310,5,52,3,12,bakery
Cinnamon Chip Scone,480,18,70,3,7,bakery
Cranberry Orange Scone,490,18,73,2,8,bakery
Double Chocolate Brownie,410,24,46,3,6,bakery
Double Fudge Mini Doughnut,130,7,16,0,0,bakery
Everything with Cheese Bagel,280,2,56,2,10,bakery
Ginger Molasses Cookie,360,12,58,0,3,bakery
Iced Lemon Pound Cake,490,23,67,0,5,bakery
Mallorca Sweet Bread,420,25,42,0,7,bakery
Maple Oat Pecan Scone ,440,18,59,3,8,bakery
Marble Pound Cake,350,13,54,0,6,bakery
Marshmallow Dream Bar,210,4,43,0,0,bakery
Morning Bun,350,16,45,2,6,bakery
Multigrain Bagel,300,3,60,6,15,bakery
Old-Fashioned Glazed Doughnut,420,21,57,0,4,bakery
Outrageous Oatmeal Cookie,370,14,56,3,5,bakery
Petite Vanilla Bean Scone,140,5,21,0,0,bakery
Plain Bagel,280,1,59,2,9,bakery
Pumpkin Bread,390,14,61,2,6,bakery
Pumpkin Scone ,480,17,78,2,6,bakery
Raspberry Scone,480,25,59,3,8,bakery
Raspberry Swirl Pound Cake,430,16,69,0,4,bakery
Reduced-Fat Banana Chocolate Chip Coffee Cake,400,8,80,4,5,bakery
Reduced-Fat Cinnamon Swirl Coffee Cake,340,9,62,2,4,bakery
Reduced-Fat Very Berry Coffee Cake ,350,10,59,4,7,bakery
Starbucks Classic Coffee Cake,440,19,63,0,6,bakery
Zucchini Walnut Muffin ,490,28,52,2,7,bakery
Cheese & Fruit,480,28,39,6,18,bistro box
Chicken & Hummus,270,8,29,6,16,bistro box
Chicken Lettuce Wraps,360,19,32,4,17,bistro box
Chipotle Chicken Wraps,380,15,35,6,26,bistro box
Protein,380,19,37,5,13,bistro box
Salumi & Cheese,420,26,22,3,25,bistro box
Sesame Noodles,350,11,50,6,15,bistro box
Tuna Salad,380,21,25,5,23,bistro box
Apple Pie,180,7,27,0,2,petite
Birthday Cake Pop,170,9,22,0,0,petite
Brown Sugar Walnut Tart,190,12,24,0,2,petite
Cherry Pie,170,7,24,0,2,petite
Chocolate Crme Whoopie Pie,190,11,23,0,0,petite
Chocolate Hazelnut Tart,180,10,23,0,2,petite
Raspberry Truffle Cake Pop,160,8,24,0,2,petite
Red Velvet Whoopie Pie,190,11,21,0,0,petite
Tiramisu Cake Pop,170,9,22,0,0,petite
Bacon & Gouda Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast
Chicken Sausage Breakfast Wrap,300,10,33,5,14,hot breakfast
Ham & Cheddar Artisan Breakfast Sandwich,350,16,31,0,20,hot breakfast
Sausage & Cheddar Classic Breakfast Sandwich,500,28,41,0,19,hot breakfast
Spinach & Feta Breakfast Wrap,290,10,33,6,19,hot breakfast
Starbucks Perfect Oatmeal,140,2.5,25,4,5,hot breakfast
Turkey Bacon & White Cheddar Classic Breakfast Sandwich,320,7,43,3,18,hot breakfast
Veggie & Monterey Jack Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast
Deluxe Fruit Blend,80,0,20,2,0,salad
Chicken Santa Fe Panini,400,11,47,2,26,sandwich
Egg Salad Sandwich ,460,27,37,5,22,sandwich
Ham & Swiss Panini,360,9,43,2,28,sandwich
Roasted Tomato & Mozzarella Panini,390,18,44,3,15,sandwich
Roasted Vegetable Panini,350,12,48,4,13,sandwich
Tarragon Chicken Salad Sandwich,420,13,46,6,32,sandwich
Turkey & Swiss Sandwich,390,13,36,2,34,sandwich
Greek Yogurt Honey Parfait,300,12,44,0,8,parfait
Peach Raspberry Yogurt Parfait,300,4,57,3,10,parfait
Strawberry & Blueberry Yogurt Parfait,300,3.5,60,3,7,parfait

================================================
FILE: ch_regr_simple_linear/figures/eoce/starbucks_cals_carbos/starbucks_cals_carbos.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

starbucks <- read.csv("starbucks.csv")

# model calories vs. carbos -----------------------------------------

m_carb_cals <- lm(carb ~ calories, data = starbucks)

# plot calories vs. carbos ------------------------------------------

pdf("starbucks_cals_carbos.pdf", 5.5, 4.3)

par(mar = c(3.5, 4, 1, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(carb ~ calories, data = starbucks, 
     pch = 19, col = COL[1,2], 
     xlab = "Calories", ylab = "Carbs (grams)", axes = FALSE)
axis(1)
axis(2, at = seq(20, 80, 20))
box()

abline(m_carb_cals, col = COL[2], lwd = 2)

dev.off()

# plot residuals ----------------------------------------------------

pdf("starbucks_cals_carbos_residuals.pdf", 5.5, 4.3)

par(mar = c(3.5, 4, 1, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(m_carb_cals$residuals ~ starbucks$calories,
     xlab = "Calories", ylab = "Residuals", 
     col = COL[1,2], pch = 19,
     ylim = c(-30, 30), axes = FALSE)
axis(1)
axis(2, at = seq(-20, 20, 20))
box()

abline(h = 0, lty = 2)

dev.off()

# histogram of residuals --------------------------------------------

pdf("starbucks_cals_carbos_residuals_hist.pdf", 5.5, 4.3)

par(mar = c(3.5, 2.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

hist(m_carb_cals$residuals,
     col = COL[1], 
     xlab = "Residuals", ylab = "", main = "", 
     axes = FALSE, xlim = c(-40,40))
axis(1, at = seq(-40, 40, 20))
axis(2)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/starbucks_cals_protein/starbucks.csv
================================================
item,calories,fat,carb,fiber,protein,type
8-Grain Roll,350,8,67,5,10,bakery
Apple Bran Muffin,350,9,64,7,6,bakery
Apple Fritter,420,20,59,0,5,bakery
Banana Nut Loaf,490,19,75,4,7,bakery
Birthday Cake Mini Doughnut,130,6,17,0,0,bakery
Blueberry Oat Bar,370,14,47,5,6,bakery
Blueberry Scone,460,22,61,2,7,bakery
Bountiful Blueberry Muffin,370,14,55,0,6,bakery
Butter Croissant ,310,18,32,0,5,bakery
Cheese Danish,420,25,39,0,7,bakery
Chocolate Chunk Cookie,380,17,51,2,4,bakery
Chocolate Cinnamon Bread,320,12,53,3,6,bakery
Chocolate Croissant,300,17,34,2,5,bakery
Chocolate Old-Fashioned Doughnut,420,21,57,2,5,bakery
Chonga Bagel,310,5,52,3,12,bakery
Cinnamon Chip Scone,480,18,70,3,7,bakery
Cranberry Orange Scone,490,18,73,2,8,bakery
Double Chocolate Brownie,410,24,46,3,6,bakery
Double Fudge Mini Doughnut,130,7,16,0,0,bakery
Everything with Cheese Bagel,280,2,56,2,10,bakery
Ginger Molasses Cookie,360,12,58,0,3,bakery
Iced Lemon Pound Cake,490,23,67,0,5,bakery
Mallorca Sweet Bread,420,25,42,0,7,bakery
Maple Oat Pecan Scone ,440,18,59,3,8,bakery
Marble Pound Cake,350,13,54,0,6,bakery
Marshmallow Dream Bar,210,4,43,0,0,bakery
Morning Bun,350,16,45,2,6,bakery
Multigrain Bagel,300,3,60,6,15,bakery
Old-Fashioned Glazed Doughnut,420,21,57,0,4,bakery
Outrageous Oatmeal Cookie,370,14,56,3,5,bakery
Petite Vanilla Bean Scone,140,5,21,0,0,bakery
Plain Bagel,280,1,59,2,9,bakery
Pumpkin Bread,390,14,61,2,6,bakery
Pumpkin Scone ,480,17,78,2,6,bakery
Raspberry Scone,480,25,59,3,8,bakery
Raspberry Swirl Pound Cake,430,16,69,0,4,bakery
Reduced-Fat Banana Chocolate Chip Coffee Cake,400,8,80,4,5,bakery
Reduced-Fat Cinnamon Swirl Coffee Cake,340,9,62,2,4,bakery
Reduced-Fat Very Berry Coffee Cake ,350,10,59,4,7,bakery
Starbucks Classic Coffee Cake,440,19,63,0,6,bakery
Zucchini Walnut Muffin ,490,28,52,2,7,bakery
Cheese & Fruit,480,28,39,6,18,bistro box
Chicken & Hummus,270,8,29,6,16,bistro box
Chicken Lettuce Wraps,360,19,32,4,17,bistro box
Chipotle Chicken Wraps,380,15,35,6,26,bistro box
Protein,380,19,37,5,13,bistro box
Salumi & Cheese,420,26,22,3,25,bistro box
Sesame Noodles,350,11,50,6,15,bistro box
Tuna Salad,380,21,25,5,23,bistro box
Apple Pie,180,7,27,0,2,petite
Birthday Cake Pop,170,9,22,0,0,petite
Brown Sugar Walnut Tart,190,12,24,0,2,petite
Cherry Pie,170,7,24,0,2,petite
Chocolate Crme Whoopie Pie,190,11,23,0,0,petite
Chocolate Hazelnut Tart,180,10,23,0,2,petite
Raspberry Truffle Cake Pop,160,8,24,0,2,petite
Red Velvet Whoopie Pie,190,11,21,0,0,petite
Tiramisu Cake Pop,170,9,22,0,0,petite
Bacon & Gouda Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast
Chicken Sausage Breakfast Wrap,300,10,33,5,14,hot breakfast
Ham & Cheddar Artisan Breakfast Sandwich,350,16,31,0,20,hot breakfast
Sausage & Cheddar Classic Breakfast Sandwich,500,28,41,0,19,hot breakfast
Spinach & Feta Breakfast Wrap,290,10,33,6,19,hot breakfast
Starbucks Perfect Oatmeal,140,2.5,25,4,5,hot breakfast
Turkey Bacon & White Cheddar Classic Breakfast Sandwich,320,7,43,3,18,hot breakfast
Veggie & Monterey Jack Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast
Deluxe Fruit Blend,80,0,20,2,0,salad
Chicken Santa Fe Panini,400,11,47,2,26,sandwich
Egg Salad Sandwich ,460,27,37,5,22,sandwich
Ham & Swiss Panini,360,9,43,2,28,sandwich
Roasted Tomato & Mozzarella Panini,390,18,44,3,15,sandwich
Roasted Vegetable Panini,350,12,48,4,13,sandwich
Tarragon Chicken Salad Sandwich,420,13,46,6,32,sandwich
Turkey & Swiss Sandwich,390,13,36,2,34,sandwich
Greek Yogurt Honey Parfait,300,12,44,0,8,parfait
Peach Raspberry Yogurt Parfait,300,4,57,3,10,parfait
Strawberry & Blueberry Yogurt Parfait,300,3.5,60,3,7,parfait

================================================
FILE: ch_regr_simple_linear/figures/eoce/starbucks_cals_protein/starbucks_cals_protein.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

starbucks <- read.csv("starbucks.csv")

# lmPlot protein vs. calories ---------------------------------------

myPDF("starbucks_cals_protein.pdf", 5, 4.55)

lmPlot(starbucks$calories, starbucks$protein,
       col = COL[1,2], 
       xlab = "Calories", ylab = "Protein (grams)", 
       lCol = COL[2], lwd = 2, 
       resSymm = TRUE, resAxis = 3, 
       xAxis = 6,
       cex.lab = 1.25, cex.axis = 1.25, mgp = c(2.1, 0.7, 0))

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/tourism_spending_reg_conds/tourism_spending.csv
================================================
year,visitor_count_thousand,tourist_spending
1963,198,7
1964,229,8
1965,361,13
1966,449,12
1967,574,13
1968,602,24
1969,694,36
1970,724,51
1971,926,62
1972,1034,103
1973,1341,171
1974,1110,193
1975,1540,200
1976,1675,180
1977,1661,204
1978,1644,230
1979,1523,280
1980,1288,326
1981,1405,381
1982,1391,370
1983,1625,411
1984,2117,840
1985,2614,1482
1986,2391,1215
1987,2855,1721
1988,4172,2355
1989,4459,2556
1990,5389,2705
1991,5517,2654
1992,7076,3639
1993,6500,3959
1994,6670,4321
1995,7726,4957
1996,8614,5650
1997,9689,7008
1998,9752,7177
1999,7464,5193
2000,10412,7636
2001,11569,8090
2002,13247,8481
2003,14030,9677
2004,17517,12125
2005,21124,13929
2006,19820,12554
2007,23341,13990
2008,26337,16761
2009,27077,15853

================================================
FILE: ch_regr_simple_linear/figures/eoce/tourism_spending_reg_conds/tourism_spending_reg_cond.R
================================================
rm(list = ls())
library(openintro)

tourism$visitor_count <- 1e3 * tourism$visitor_count_tho
tourism$tourist_spending <- 1e6 * tourism$tourist_spending

m_spending_count <- lm(tourist_spending ~ visitor_count,
                       data = tourism)


# plot spending vs. count -------------------------------------------

myPDF(
  "tourism_spending_count.pdf",
  5.5, 4.3,
  mar = c(3.5, 5.5, 1, 0.5),
  mgp = c(2.5, 0.7, 0), 
  cex.lab = 1.5,
  cex.axis = 1.5
)

plot(tourist_spending ~ visitor_count,
     data = tourism, 
     col = COL[1,2], 
     xlab = "Number of Tourists",
     ylab = "",
     pch = 19,
     axes = FALSE)
at <- seq(0, 25e6, 5e6)
axis(1, at = at, labels = paste0(at / 1e6, "m"))
AxisInDollars(2, at = seq(0, 15e9, 5e9))
par(las = 0)
mtext("Spending", 2, 4.2, cex = 1.5)

abline(m_spending_count, col = COL[2], lwd = 2)

dev.off()

# plot residuals ----------------------------------------------------

myPDF(
  "tourism_spending_count_residuals.pdf",
  5.5, 4.3,
  mar = c(3.5, 5.5, 1, 0.5),
  mgp = c(2.5, 0.7, 0), 
  cex.lab = 1.5,
  cex.axis = 1.5
)

plot(
  tourism$visitor_count,
  m_spending_count$residuals,
  xlab = "Number of Tourists",
  ylab = "Residuals", 
  col = COL[1,2], pch = 19,
  ylim = c(-1600e6, 1600e6), axes = FALSE
)
at <- seq(0, 25e6, 5e6)
axis(1, at = at, labels = paste0(at / 1e6, "m"))
axis(2, at = seq(-1e9, 1e9, 1e9), labels = c("-$1b", "$0", "$1b"))

abline(h = 0, lty = 2)

dev.off()

# histogram of residuals --------------------------------------------

myPDF(
  "tourism_spending_count_residuals_hist.pdf",
  5.5, 4.3,
  mar = c(3.7, 4, 1, 0.5),
  mgp = c(2.5, 0.7, 0), 
  cex.lab = 1.5,
  cex.axis = 1.5
)

hist(m_spending_count$residuals,
     col = COL[1], 
     xlab = "Residuals", ylab = "Count", main = "", 
     axes = FALSE, ylim = c(0,20))
axis(1, at = seq(-2e9, 2e9, 1e9), labels = c("-$2b", "-$1b", "$0", "$1b", "$2b"))
axis(2, c(0, 10, 20))
abline(h = 0)

dev.off()


================================================
FILE: ch_regr_simple_linear/figures/eoce/trees_volume_height_diameter/trees_volume_height_diameter.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load data ---------------------------------------------------------

data(trees)

# plot volume vs. height ---------------------------------------------

pdf("trees_volume_height.pdf", 5, 4)

par(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)

plot(trees$Volume ~ trees$Height, 
     xlab = "Height (feet)", ylab = "Volume (cubic feet)", 
     pch = 19, col = COL[1], axes = FALSE,
     xlim = c(60, 90),
     ylim = 1.1 * range(0, trees$Volume))
axis(1, at = seq(60, 90, 10))
axis(2)
box()

dev.off()

# plot volume vs. diameter ---------------------------------------------

pdf("trees_volume_diameter.pdf", 5, 4)

par(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.25, cex.axis = 1.25)

plot(trees$Volume ~ trees$Girth, 
     xlab = "Diameter (inches)", ylab = "Volume (cubic feet)", 
     pch = 19, col = COL[1], axes = FALSE,
     xlim = c(7, 21),
     ylim = 1.1 * range(0, trees$Volume))
axis(1, at = seq(8,20,4))
axis(2)
box()

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/trends_in_residuals/trends_in_residuals.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

set.seed(8313)

x = seq(1:300)
y_fan = rep(NA,300)
for(i in 1:300){
	y_fan[i] = x[i]+rnorm(1)*x[i]
}
y_log = log(x) + rnorm(300, mean = 0, s = 0.5)

# fit models --------------------------------------------------------

m_fan = lm(y_fan ~ x)
m_log = lm(y_log ~ x)

# plot fan residuals ------------------------------------------------

pdf("trends_in_residuals_fan.pdf", 5.5, 2)

par(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(m_fan$res ~ x, xlab = "(a)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

abline(h = 0, lty = 2, lwd = 2)

dev.off()

# plot log residuals ------------------------------------------------

pdf("trends_in_residuals_log.pdf", 5.5, 2)

par(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), 
    cex.lab = 1.75, cex.axis = 1.75)

plot(m_log$res ~ x, xlab = "(b)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

abline(h = 0, lty = 2, lwd = 2)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/urban_homeowners_cond/urban_homeowners_cond.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load packages -----------------------------------------------------
urban_state_data <- read.csv("urban_state_data.csv")

# drop outlier DC ---------------------------------------------------

urban_state_data_noDC <- urban_state_data[urban_state_data$state != "District of Columbia",]

# lmPlot of % urban vs. % owner without DC --------------------------

pdf("urban_homeowners_cond.pdf", 5.5, 6)

lmPlot(urban_state_data_noDC$poppct_urban, 
       urban_state_data_noDC$pct_owner_occupied, 
       col = COL[1,2], 
       xlab = "% Urban population", ylab = "% Who own home", 
       lCol = COL[2], lwd = 2, 
       resSymm = TRUE, 
       resAxis = 3, xAxis = 5, yAxis = 5, 
       cex.lab = 1.5, cex.axis = 1.5)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/eoce/urban_homeowners_cond/urban_state_data.csv
================================================
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
Alabama,"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
Alaska,"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
Arizona,"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
Arkansas,"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
California,"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
Colorado,"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
Connecticut,"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
Delaware,"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
District 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,
Florida,"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
Georgia,"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
Hawaii,"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
Idaho,"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
Illinois,"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
Indiana,"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
Iowa,"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
Kansas,"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
Kentucky,"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
Louisiana,"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
Maine,"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
Maryland,"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
Massachusetts,"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
Michigan,"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
Minnesota,"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
Mississippi,"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
Missouri,"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
Montana,"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
Nebraska,"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
Nevada,"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
New 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
New 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
New 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
New 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
Nortch 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
North 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
Ohio,"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
Oklahoma,"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
Oregon,"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
Pennsylvania,"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
Rhode 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
South 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
South 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
Tennessee,"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
Texas,"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
Utah,"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
Vermont,"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
Virginia,"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
Washington,"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
West 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
Wisconsin,"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
Wyoming,"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
Puerto 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

================================================
FILE: ch_regr_simple_linear/figures/eoce/urban_homeowners_outlier/urban_homeowners_outlier.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# load packages -----------------------------------------------------
urban_state_data <- read.csv("urban_state_data.csv")

# plot with outlier DC ----------------------------------------------

pdf("urban_homeowners_outlier.pdf", 5.5, 4.3)

par(mar = c(4.5, 5, 1.5, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)

plot(urban_state_data$pct_owner_occupied ~ urban_state_data$poppct_urban,
     xlab = 'Percent Urban Population',
     ylab = '', 
     pch = 19, col = COL[1,2], 
     ylim = c(41, 75), axes = FALSE)
AxisInPercent(1, at = seq(40, 100, 20))
AxisInPercent(2, at = seq(45, 75, 10))
box()
par(las = 0)
mtext("Percent Own Their Home", 2, 3.8, cex = 1.5)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/eoce/urban_homeowners_outlier/urban_state_data.csv
================================================
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
Alabama,"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
Alaska,"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
Arizona,"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
Arkansas,"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
California,"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
Colorado,"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
Connecticut,"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
Delaware,"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
District 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,
Florida,"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
Georgia,"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
Hawaii,"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
Idaho,"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
Illinois,"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
Indiana,"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
Iowa,"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
Kansas,"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
Kentucky,"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
Louisiana,"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
Maine,"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
Maryland,"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
Massachusetts,"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
Michigan,"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
Minnesota,"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
Mississippi,"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
Missouri,"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
Montana,"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
Nebraska,"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
Nevada,"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
New 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
New 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
New 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
New 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
Nortch 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
North 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
Ohio,"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
Oklahoma,"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
Oregon,"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
Pennsylvania,"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
Rhode 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
South 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
South 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
Tennessee,"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
Texas,"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
Utah,"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
Vermont,"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
Virginia,"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
Washington,"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
West 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
Wisconsin,"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
Wyoming,"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
Puerto 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

================================================
FILE: ch_regr_simple_linear/figures/eoce/visualize_residuals/visualize_residuals.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# simulate data -----------------------------------------------------

x <- seq(1,100,1)

set.seed(84628)

y_linear <- 3 * x + 5 + rnorm(length(x), mean = 0, sd = 20)
y_fan_back <- 4*x + 5 + rnorm(length(x), mean = 0, sd = sort(x, decreasing = TRUE))

# fit models --------------------------------------------------------

m_linear = lm(y_linear ~ x)
m_fan_back = lm(y_fan_back ~ x)

# plot linear -------------------------------------------------------

pdf("visualize_residuals_linear.pdf", 5.5, 4.3)

par(mar=c(2,1,1,1), las=1, mgp=c(0.9,0.7,0), cex.lab = 1.75, cex.axis = 1.75)

plot(y_linear ~ x, 
     xlab = "(a)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

abline(m_linear, col = COL[2], lwd = 2)

dev.off()

# plot backwards fan shaped -----------------------------------------

pdf("visualize_residuals_fan_back.pdf", 5.5, 4.3)

par(mar=c(2,1,1,1), las=1, mgp=c(0.9,0.7,0), cex.lab = 1.75, cex.axis = 1.75)

plot(y_fan_back ~ x, 
     xlab = "(b)", ylab = "", 
     yaxt = "n", xaxt = "n", 
     pch = 19, col = COL[1])

abline(m_fan_back, col = COL[2], lwd = 2)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/identifyingInfluentialPoints/identifyingInfluentialPoints.R
================================================
library(openintro)
data(COL)

myPDF('identifyingInfluentialPoints.pdf', 7, 2.73,
      mar = c(0.35, 0.654, 0.35, 0.654))
myMat <- rbind(matrix(1:6, 2))
myW <- rep(1, 3)
myH <- c(1, 0.45)
layout(myMat, myW, myH)
set.seed(1)

n <- c(95, 50, 78)
m <- c(-4, 12, 7)
xr <- list(2.16, -0.4, 1.42)
yr <- list(xr[[1]] * m[1], 1, 5.5)
ss <- list(1:(n[1] - 1), 1:(n[2] - 1), 1:(n[3] - 3))
for (i in 1:3) {
  x <- runif(n[i])
  y <- m[i] * x + rnorm(n[i])
  x <- c(x, xr[[i]])
  y <- c(y, yr[[i]])
  linResPlot(x, y,
             col = COL[1, 2],
             subset = ss[[i]],
             yR = ifelse(i == 1, 0.12, 0.44))
}
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/imperfLinearModel/imperfLinearModel.R
================================================
library(openintro)

col <- COL[1, 3]

myPDF('imperfLinearModel.pdf', 5.814, 1.875,
      mfrow = c(1, 3),
      mar = c(2, 2.5, 1, 2),
      mgp = c(1.9, 0.6, 0),
      las = 0)
par(mar = c(2, 2.25, 0.5, 0.8))
these <- simulated_scatter$group == 1
PlotWLine(simulated_scatter$x[these], simulated_scatter$y[these], col = col)
par(mar = c(2, 2.9, 0.5, 0.4))
these <- simulated_scatter$group == 2
PlotWLine(simulated_scatter$x[these], simulated_scatter$y[these], col = col)
par(mar = c(2, 3.3, 0.5, 0))
these <- simulated_scatter$group == 3
PlotWLine(simulated_scatter$x[these], simulated_scatter$y[these], col = col)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/marioKartNewUsed/marioKartNewUsed.R
================================================
library(openintro)
data(COL)
mk      <- mariokart[mariokart$total_pr < 100, ]
mk$cond <- relevel(mk$cond, "used")
cond <- as.numeric(ifelse(mk$cond == "new", 1, 0))

myPDF('marioKartNewUsed.pdf', 4.5, 3.2,
      mar = c(3, 3.5, 0, 0.5),
      mgp = c(1.9, 1.5 ,0))
dotPlot(mk$total_pr, cond,
        vertical = TRUE,
        at = 0:1,
        key = 0:1,
        xlab = "",
        ylab = "",
        axes = FALSE,
        col = COL[1, 3],
        pch = 19,
        cex = 1.3)
at <- -1:2
labels <- c("", "0\n(used)", "1\n(new)", "")
axis(1, at, labels)
par(mgp = c(1.9, 0.6, 0))
AxisInDollars(2, at = seq(30, 70, 10))
par(las = 0)
mtext("Total Price", 2, line = 2.5)
g <- lm(mk$total_pr ~ cond)
abline(g, lwd = 1.5, col = COL[5])
rect(-10, -1000, -0.125, 1000,
     border  =  rgb(1, 1, 1),
     col  =  rgb(1, 1, 1))
rect(10, -1000, 1.125, 1000,
     border  =  rgb(1, 1, 1),
     col  =  rgb(1, 1, 1))
text(0.48, 41.8,
     expression(widehat(price) *" = 42.87 + 10.90     cond_new"),
     cex = 0.8)
points(0.605, 41.5, pch = 4, cex = 0.9)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/notGoodAtAllForALinearModel/notGoodAtAllForALinearModel.R
================================================
library(openintro)
data(COL)

d <- subset(simulated_scatter, group == 5)

myPDF('notGoodAtAllForALinearModel.pdf', 6.4, 2.743,
      mar = c(3, 4, 1, 2))
PlotWLine(d$x, d$y,
          xlab = 'Angle of Incline (Degrees)',
          ylab = 'Distance Traveled (m)',
          axes = FALSE, col = COL[1])
axis(1, at = seq(0, 90, length.out = 7), rep("", 7), tcl = -0.1)
axis(1, at = seq(0, 90, length.out = 4))
axis(2, at = seq(0, 15, 5))
abline(h = 0)
text(mean(d$x), mean(d$y),
     'Best fitting straight line is flat (!)',
     pos = 1,
     col = COL[4])
abline(h = 0)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/outlierPlots/outlierPlots.R
================================================
library(openintro)

pdf('outlierPlots.pdf', 7, 7)
myMat <- rbind(matrix(1:6, 2),
               matrix(7:12, 2))
myW <- rep(1, 3)
myH <- c(0.95, 0.5, 1, 0.45)
layout(myMat, myW, myH)

for(i in 1:6){
  par(mar = c(0.25, 0.5, 1.75, 0.5))
  these <- simulated_scatter$group == 23 + i
  x <- simulated_scatter$x[these]
  y <- simulated_scatter$y[these]
  yR <- c(rep(0.13, 3), 0.5, 0.1, 0.1)
  linResPlot(x, y,
             col = COL[1, 2],
             marRes = c(ifelse(i < 4, 4, 1), 2, 1, 2) / 4,
             yR = yR[i],
             main = paste0("(", i, ")"))
}
dev.off()



================================================
FILE: ch_regr_simple_linear/figures/pValueMidtermUnemp/pValueMidtermUnemp.R
================================================
library(openintro)
data(COL)

myPDF("pValueMidtermUnemp.pdf", 6.325, 2.7,
      mar = c(1.8, 0.5, 0.2, 0.5))
normTail(0, 0.8350,
         L = -0.8897,
         U = 0.8897,
         df = 27,
         col = COL[1])
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/perfLinearModel/perfLinearModel.R
================================================
library(openintro)
data(COL)

these <- simulated_scatter$group == 4
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]

myPDF('perfLinearModel.pdf', 4.5, 3.1,
      mar = c(3, 4, 1, 1),
      mgp = c(1.9, 0.55, 0))
plot(x, y,
     ylim = c(0, max(y)),
     axes = FALSE,
     xlab = 'Number of Target Corporation Stocks to Purchase',
     ylab = '',
     pch = 20,
     cex = 1.7,
     col = COL[1])
buildAxis(1, x, 4, nMax = 4)
AxisInDollars(2, c(-1000, pretty(y, 2)))
abline(5, 64.96, col = COL[5])
par(las = 0)
mtext('Total Cost of the Share Purchase', 2, 2.8)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/posNegCorPlots/CorrelationPlot.R
================================================
CorrelationPlot <- function(x, y, ...) {
  plot(x, y,
       axes = FALSE,
       pch = 20,
       col = COL[1, 2],
       cex = 1.351,
       xlab = '',
       ...)
  box()
  mtext(paste('R =', format(c(round(cor(x,y), 2), 0.01))[1]),
        side = 1,
        line = 1,
        cex = 1.1)
}


================================================
FILE: ch_regr_simple_linear/figures/posNegCorPlots/corForNonLinearPlots.R
================================================
library(openintro)
data(COL)
set.seed(1)
source("CorrelationPlot.R")

n <- 50

myPDF('corForNonLinearPlots.pdf', 6, 2,
      mfrow = c(1, 3),
      mar = c(2.7, rep(0.5, 3)),
      mgp = c(1, 0, 0))
these <- simulated_scatter$group == 17
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y)

these <- simulated_scatter$group == 18
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y)

these <- simulated_scatter$group == 19
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
yR <- range(y) + c(-1, 1) * 0.1 * diff(range(y))
CorrelationPlot(x, y, ylim = yR)

dev.off()

================================================
FILE: ch_regr_simple_linear/figures/posNegCorPlots/posNegCorPlots.R
================================================
library(openintro)
data(COL)
data(possum)
source("CorrelationPlot.R")
set.seed(1)

n <- 50

myPDF('posNegCorPlots.pdf', 6, 3.6,
      mfrow = c(2, 4),
      mar = c(2.7, rep(0.5, 3)),
      mgp = c(1, 0, 0))


# _____ Line 1 _____ #
these <- simulated_scatter$group == 9
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y, xlim = c(-0.2, 4.2), ylim = c(-9, 17))

these <- simulated_scatter$group == 10
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y)

these <- simulated_scatter$group == 11
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y, xlim = c(-0.2, 4.2), ylim = c(-2, 9.6))

these <- simulated_scatter$group == 12
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y, xlim = c(-0.03, 1.03), ylim = c(-.1, 1.1))


# _____ Line 2 _____ #
par(mar = c(2.1,0.5,1.1,0.5))

these <- simulated_scatter$group == 13
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y, xlim = c(-0.2, 4.2), ylim = c(-17, 14))

these <- simulated_scatter$group == 14
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y, xlim = c(-5.2, 5.2), ylim = c(-12, 10))

these <- simulated_scatter$group == 15
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y, xlim = c(-0.03, 1.03), ylim = c(-10, 2))

these <- simulated_scatter$group == 16
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
CorrelationPlot(x, y, xlim = c(-0.03, 1.03), ylim = c(-1.2, .2))

dev.off()


================================================
FILE: ch_regr_simple_linear/figures/sampleLinesAndResPlots/sampleLinesAndResPlots.R
================================================
library(openintro)

GenerateLmPlot <- function(x, y, xlim, ylim1, ylim2.mult) {
  plot(x, y,
       axes = FALSE,
       pch = 20,
       col = COL[1, 2],
       cex = 1.202,
       xlim = xlim,
       ylim = ylim1)
  box()
  g <- lm(y ~ x)
  abline(g, col = COL[5])
  plot(x, g$residuals,
       pch = 20,
       col = COL[1, 2],
       cex = 1.202,
       xlim = xlim,
       axes = FALSE,
       ylim = ylim2.mult * c(-1, 1) * max(abs(g$residuals)))
  box()
  abline(h = 0, col = COL[5], lty = 2)
}

myPDF('sampleLinesAndResPlots.pdf', 5, 2.5,
      mfrow = 2:3,
      mar = rep(0.5, 4))

MyLayOut <- matrix(1:6, 2)
layout(mat = MyLayOut,
       widths = rep(2, 3),
       heights = c(2, 1),
       respect = TRUE)

these <- simulated_scatter$group == 6
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
GenerateLmPlot(x, y,
               xlim = c(-0.03, 1.03),
               ylim1 = c(-10, 1),
               ylim2.mult = 2.5)

these <- simulated_scatter$group == 7
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
GenerateLmPlot(x, y,
               xlim = c(-0.2, 4.2),
               ylim1 = c(-35, 2),
               ylim2.mult = 1.8)

these <- simulated_scatter$group == 8
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
GenerateLmPlot(x, y,
               xlim = c(-0.03, 1.03),
               ylim1 = c(-2, 2),
               ylim2.mult = 1.2)

dev.off()


================================================
FILE: ch_regr_simple_linear/figures/scattHeadLTotalL/scattHeadLTotalL.R
================================================
library(openintro)
data(COL)
data(possum)

myPDF('scattHeadLTotalL.pdf', 6, 4,
      mar = c(3.7, 3.7, 0.5, 0.5),
      mgp = c(2.6, 0.55, 0))
plot(possum$totalL, possum$headL,
     pch = 19,
     col = COL[1, 2],
     cex = 1.2,
     xlab = 'Total Length (cm)',
     ylab = 'Head Length (mm)')
points(89, 94.1, col = COL[4], cex = 1.7)
lines(rep(89, 2), c(0, 93.8), lty = 2, col = COL[4])
lines(c(0, 88.7), rep(94.1, 2), lty = 2, col = COL[4])
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/scattHeadLTotalLLine/scattHeadLTotalLLine.R
================================================
require(openintro)
data(COL)
data(possum)
set.seed(1)

myPDF('scattHeadLTotalLLine.pdf', 5.5, 3.2,
      mar = c(3, 3.2, 0.1, 1),
      mgp = c(1.9, 0.45, 0))
plot(possum$totalL, possum$headL,
     pch = 20,
     col = COL[1, 2],
     cex = 1.7,
     xlab = 'Total Length (cm)',
     ylab = 'Head Length (mm)')
abline(41, 0.59, col = COL[5])
dev.off()


myPDF('scattHeadLTotalLLineResiduals.pdf', 5.5, 3.2,
      mar = c(3, 3.2, 0.1, 1),
      mgp = c(1.9, 0.45, 0))
these <- c(48, 42, 3)
plot(possum$totalL[-these], possum$headL[-these],
     pch = 20,
     col = COL[1, 2],
     cex = 1.7,
     xlab = 'Total Length (cm)',
     ylab = 'Head Length (mm)')
points(possum$totalL[these] + rnorm(3,0,0.02),
       possum$headL[these] + rnorm(3,0,0.02),
       pch = c(3, 4, 2),
       col = COL[4],
       cex = 1.5,
       lwd = 2.5)
abline(41, 0.59, col = COL[5])
for(i in 1:3){
  y2 <- 41 + 0.59 * possum$totalL[these[i]]
  lines(rep(possum$totalL[these[i]], 2),
        c(possum$headL[these[i]], y2),
        lty = 2,
        lwd = 1,
        col = COL[4])
}
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/scattHeadLTotalLResidualPlot/scattHeadLTotalLResidualPlot.R
================================================
require(openintro)
data(COL)
data(possum)

myPDF('scattHeadLTotalLResidualPlot.pdf', 5.5, 2.7,
      mar = c(3, 3, 0.5, 1),
      mgp = c(1.8, 0.6, 0))
these <- c(48, 42, 3)
plot(possum$totalL[-these],
     possum$headL[-these] - (41 + 0.59 * possum$totalL[-these]),
     pch = 19,
     col = COL[1, 2],
     xlab = 'Total Length (cm)',
     ylab = 'Residuals',
     ylim = c(-7, 9))
y.extra <- 0.59 * possum$totalL[these] + rnorm(1,0,0.01)
points(possum$totalL[these] + rnorm(1, 0, 0.01),
       possum$headL[these] - (41 + y.extra),
       pch = c(3, 4, 2),
       col = COL[4],
       cex = 1.3,
       lwd = 2.5)
abline(h = 0, lty = 2)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/scattHeadLTotalLSex/scattHeadLTotalLSex.R
================================================
library(openintro)

myPDF('scattHeadLTotalLSex.pdf', 5, 3,
    mar = c(3.5, 3.5, 0.5, 0.5),
    mgp = c(2.4, 0.55, 0))
plot(possum$totalL, possum$headL,
    pch = ifelse(possum$sex == "m", 1, 3),
    col = ifelse(possum$sex == "m", COL[1, 1], COL[4, 1]),
    lwd = ifelse(possum$sex == "m", 2, 3),
    cex = ifelse(possum$sex == "m", 1.2, 0.7),
    xlab = 'Total Length (cm)',
    ylab = 'Head Length (mm)')
legend("topleft", pch = c(1, 3), col = COL[c(1, 4)], cex = 0.9,
    legend = c("Male", "Female"))
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/scattHeadLTotalLTube/scattHeadLTotalLTube.R
================================================
library(openintro)
data(COL)
data(possum)
data(cars)
myPDF('scattHeadLTotalLTube.pdf', 7.3, 3,
      mar = c(3.2, 3.8, 1, 2),
      mgp = c(2.4, 0.55, 0),
      mfrow = 1:2)
plot(possum$totalL, possum$headL,
     pch = 20,
     col = COL[1, 2],
     cex = 1.7,
     xlab = '',
     ylab = 'Head length (mm)',
     type = "n")
mtext("Total length (cm)", 1, line = 2.1)
g <- lm(headL ~ totalL, possum)
x <- c(0, 200, 200, 0, 0)
y <- 42.71 + c(-5, 0.5729 * 200 - 5, 0.5729 * 200 + 5, 5, -5)
polygon(x, y,
        col = COL[7],
        border = '#00000000')
points(possum$totalL, possum$headL,
       pch = 20,
       col = COL[1, 2],
       cex = 1.7)


set.seed(5)
par(mar = c(3.2, 4.8, 1, 1))
n <- 50
x <- sample(150:420, n, prob = (150:420)^2)
y <- 87 - 0.35 * x + 5.4e-4 * x^2 + rnorm(n, sd = 2)
simulated_scatter <- rbind.data.frame(simulated_scatter,
    data.frame(group = 30, x, y))

plot(x, y,
     pch = 20,
     col = COL[1, 2],
     cex = 1.7,
     xlab = '',
     ylab = 'y',
     type = "n")
mtext("x", 1, line = 2.1)
g <- lm(y ~ x + I(x^2), cars)
x1 <- seq(100, 500, 10)
x2 <- c(x1, rev(x1), 100)
nx1 <- length(x1)
y2 <- g$coef[1] + g$coef[2] * x2 + g$coef[3] * x2^2 +
     2 * sd(g$residuals) * c(rep(-1, nx1), rep(1, nx1), -1)
polygon(x2, y2,
        col = COL[7],
        border = '#00000000')
points(x, y,
       pch = 20,
       col = COL[1, 2],
       cex = 1.7)
dev.off()


================================================
FILE: ch_regr_simple_linear/figures/unemploymentAndChangeInHouse/unemploymentAndChangeInHouse.R
================================================
rm(list=ls())
library(openintro)

d <- midterms_house

myPDF("unemploymentAndChangeInHouse.pdf", 7.2, 4.2,
      mar = c(3.2, 5.3, 0.5, 0.5),
      mgp = c(3.2, 0.55, 0))
th <- !d$year %in% c(1935, 1939)
plot(d$unemp[th], d$house_change[th],
     # col = COL[ifelse(d$party[th] == "Republican", 4, 1)],
     pch = 19,
     xlim = c(3, 12),
     ylim = c(-30, 13),
     axes = FALSE,
     type = 'n',
     xlab = '',
     ylab = paste0("Percent Change in Seats of\n",
                  "President's Party in House of Rep."))
mtext('Unemployment Rate', 1, 2)
abline(h = seq(-100, 100, 10), col = COL[7, 3], lwd = 2)
abline(h = seq(-105, 100, 10), col = COL[7, 3], lwd = 0.7)
abline(v = seq(-100, 100, 4), col = COL[7, 3], lwd = 2)
abline(v = seq(-102, 100, 4), col = COL[7, 3], lwd = 0.7)
repub <- (d$party[th] == "Republican")
points(d$unemp[th], d$house_change[th],
       col = COL[ifelse(repub, 4, 1)],
       pch = ifelse(repub, 17, 19))
AxisInPercent(1, at = seq(0, 20, 4))
AxisInPercent(2, at = seq(-100, 100, 10))
box()
cases <- c(1, 22, 25, 27, 29, 31)
for (i in 1:length(cases)) {
  potus  <- as.character(d$potus[cases[i]])
  potus  <- tail(strsplit(potus, " ")[[1]], 1)
  year   <- d$year[cases[i]]-1
  potus  <- paste0(potus, "\n", year)
  unem   <- d$unemp[cases[i]]
  change <- d$house_change[cases[i]]
  text(unem, change, potus, pos = 3, cex = 0.6)
}

summary(lm(house_change ~ unemp, d))

g <- lm(house_change ~ unemp, d[th,])
summary(g)
abline(g, col = COL[5])
legend('topright',
       bg = "#FFFFFF",
       pch = c(19, 17),
       col = COL[c(1, 4)],
       legend = c("Democrat", "Republican"))
dev.off()

# library(xtable)
# xtable(g)
# acf(g$residual)


================================================
FILE: ch_regr_simple_linear/figures/whatCanGoWrongWithLinearModel/makeTubeAdv.R
================================================
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 <- length(x)
	r <- range(x)
	R <- abs(R)
	R <- r + c(-R,R)*diff(r)
	X <- seq(R[1], R[2], length.out=length.out)
	type <- type[1]
	if(type %in% c('l', 'L', 'lin', 'Lin', 'linear', 'Linear')){
		g <- lm(y ~ x)
		hold <- data.frame(x=X)
		Y <- predict(g, hold)
		S <- sd(g$residuals)
	} else if(type %in% c('q', 'quad', 'Q', 'Quad')){
		x2 <- x^2
		g <- lm(y ~ x + x2)
		hold <- data.frame(x=X, x2=X^2)
		Y <- predict(g, hold)
		S <- sd(g$residuals)
	} else if(type %in% c('r', 'R', 'robust', 'Robust')){
		if(bw[1] == 'default'){
			bw <- bw.nrd0(x)
		}
		Y <- rep(NA, length(X))
		for(i in 1:length(X)){
			if(min(x - X[i]) < 2*bw){
				temp <- dnorm(x-X[i], sd=bw)
				Y[i] <- sum(y*temp)/sum(temp)
			}
		}
		hold <- c()
		for(i in 1:length(y)){
			hold[i] <- Y[which.min(abs(X-x[i]))[1]]
		}
		S <- rep(sd(hold-y), length(Y))
	} else {
		stop('Argument "type" not recognized.\n')
	}
	variance <- variance[1]
	if(variance %in% c('o', 'O', 'other', 'Other')){
		if(bw[1] == 'default'){
			bw <- bw.nrd0(x)
		}
		S <- rep(NA, length(X))
		for(i in 1:length(X)){
			if(min(x - X[i]) < 2*bw){
				temp <- dnorm(x-X[i], sd=bw)
				if(sum(temp) > 2){
					wtdV <- sum(temp*(y-Y[i])^2)/(sum(temp)-1)
					S[i] <- sqrt(wtdV)
				}
			}
		}
		these <- !is.na(Y) & !is.na(S)
		X <- X[these]
		Y <- Y[these]
		S <- S[these]
	} else if(variance %in% c('L', 'l', 'linear', 'Linear')){
		if(bw[1] == 'default'){
			bw <- bw.nrd0(x)
		}
		S <- rep(NA, length(X))
		for(i in 1:length(X)){
			if(min(x - X[i]) < 2*bw){
				temp <- dnorm(x-X[i], sd=bw)
				if(sum(temp) > 2){
					wtdV <- sum(temp*(y-Y[i])^2)/(sum(temp)-1)
					S[i] <- sqrt(wtdV)
				}
			}
		}
		g <- lm(S ~ X)
		S <- predict(g, list(X=X))
		these <- !is.na(Y) & !is.na(S) & (S > 0)
		X <- X[these]
		Y <- Y[these]
		S <- S[these]
	} else if(!(variance %in% c('c', 'C', 'constant', 'Constant'))){
		stop('Did not recognize form of the "variance" argument.\n')
	}
	x <- c(X, rev(X))
	y <- c(Y-Z*S, rev(Y+Z*S))
	if(plotTube){
		polygon(x, y, border=border, col=col, ...)
	}
	invisible(list(x=x, y=y))
}





================================================
FILE: ch_regr_simple_linear/figures/whatCanGoWrongWithLinearModel/whatCanGoWrongWithLinearModel.R
================================================
library(openintro)
source("makeTubeAdv.R")
data(COL)

# load the makeTube function (ch7 folder)
pch <- 20
cex <- 1.75
col <- COL[1, 3]

myPDF('whatCanGoWrongWithLinearModel.pdf', 10, 2.8,
      mar = rep(0.5, 4))
layout(matrix(1:8, 2),
       rep(1, 4),
       c(2, 1))

these <- simulated_scatter$group == 20
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
plot(x, y,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = "#00000000")
box()
makeTube(x, y,
         type = 'quad',
         addLine = FALSE,
         col = COL[7, 3])
points(x, y,
       pch = pch,
       cex = cex,
       col = COL[1, 2])
g <- lm(y ~ x)
abline(g)
yR <- range(g$residuals)
yR <- yR + c(-1, 1) * diff(yR) / 10
plot(x, g$residuals,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = COL[1, 2],
     ylim = yR)
abline(h = 0, lty = 2)
box()

these <- simulated_scatter$group == 21
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
plot(x, y,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = "#00000000")
box()
makeTube(x, y,
         addLine = FALSE,
         col = COL[7, 3])
points(x, y,
       pch = pch,
       cex = cex,
       col = COL[1,2])
g <- lm(y ~ x)
abline(g)
yR <- range(g$residuals)
yR <- yR + c(-1, 1) * diff(yR) / 10
plot(x, g$residuals,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = COL[1, 2],
     ylim = yR)
abline(h = 0, lty = 2)
box()

these <- simulated_scatter$group == 22
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
plot(x, y,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = "#00000000")
box()
makeTubeAdv(x, y,
            type = 'l',
            variance = 'l',
            bw = 0.03,
            Z = 1.7,
            col = COL[7, 3])
points(x, y,
       pch = pch,
       cex = cex,
       col = COL[1, 2])
g <- lm(y ~ x)
abline(g)
yR <- range(g$residuals)
yR <- yR + c(-1, 1) * diff(yR) / 10
plot(x, g$residuals,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = COL[1, 2],
     ylim = yR)
abline(h = 0, lty = 2)
box()

these <- simulated_scatter$group == 23
x <- simulated_scatter$x[these]
y <- simulated_scatter$y[these]
plot(x, y,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = "#00000000")
box()
makeTube(x, y,
         addLine = FALSE,
         col = COL[7, 3])
points(x, y,
       pch = pch,
       cex = cex,
       col = COL[1, 2])
g <- lm(y ~ x)
abline(g)
yR <- range(g$residuals)
yR <- yR + c(-1, 1) * diff(yR) / 10
plot(x, g$residuals,
     axes = FALSE,
     pch = pch,
     cex = cex,
     col = COL[1, 2],
     ylim = yR)
abline(h = 0, lty = 2)
box()

makeTubeAdv(x,y, col = COL[7,3])

dev.off()


================================================
FILE: ch_summarizing_data/TeX/case_study_malaria_vaccine.tex
================================================
\exercisesheader{}

% 25

\eoce{\qt{Side effects of Avandia\label{randomization_avandia}} Rosiglitazone is the 
active ingredient in the controversial type~2 diabetes medicine Avandia and has 
been linked to an increased risk of serious cardiovascular problems such as 
stroke, heart failure, and death. A common alternative treatment is pioglitazone, 
the active ingredient in a diabetes medicine called Actos. In a nationwide 
retrospective observational study of 227,571 Medicare beneficiaries aged  
65 years or older, it was found that 2,593 of the 67,593 patients using 
rosiglitazone and 5,386 of the 159,978 using pioglitazone had serious 
cardiovascular problems. These data are summarized in the contingency 
table below. \footfullcite{Graham:2010}
\begin{center}
\begin{tabular}{ll  cc c} 
                                &   & \multicolumn{2}{c}{\textit{Cardiovascular problems}} \\
\cline{3-4} 
                                    &               & Yes   & No        & Total \\
\cline{2-5}
\multirow{2}{*}{\textit{Treatment}} & Rosiglitazone & 2,593 & 65,000    & 67,593 \\
                                    & Pioglitazone  & 5,386 & 154,592   & 159,978 \\
\cline{2-5}
                                    & Total         & 7,979 & 219,592   & 227,571
\end{tabular}
\end{center}
\begin{parts}
\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.
\begin{subparts}
\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.
\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.
\item The fact that the rate of incidence is higher for the rosiglitazone group proves that rosiglitazone causes serious cardiovascular problems.
\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.
\end{subparts}
\item What proportion of all patients had cardiovascular problems?
\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?
\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).
\end{parts}
\begin{minipage}[c]{0.5\textwidth}
\begin{subparts}
\item What are the claims being tested?
\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?
\item What do the simulation results suggest about the relationship between taking rosiglitazone and having cardiovascular problems in diabetic patients?
\end{subparts}
\end{minipage}
\begin{minipage}[c]{0.5\textwidth}
\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} \\
\end{minipage}
}{}

\D{\newpage}

% 26

\eoce{\qt{Heart transplants\label{randomization_heart_transplants}} The Stanford 
University Heart Transplant Study was conducted to determine whether an 
experimental heart transplant program increased lifespan. Each patient 
entering the program was designated an official heart transplant candidate, 
meaning that he was gravely ill and would most likely benefit from a new heart. 
Some patients got a transplant and some did not. The variable \texttt{transplant} 
indicates which group the patients were in; patients in the treatment group got a 
transplant and those in the control group did not. Of the 34 patients in the 
control group, 30 died. Of the 69 people in the treatment group, 45 died. Another 
variable called \texttt{survived} was used to indicate whether or not the patient 
was alive at the end of the study. \footfullcite{Turnbull+Brown+Hu:1974}
\begin{center}
\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}
\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}
\end{center}
\begin{parts}
\item Based on the mosaic plot, is survival independent of whether or not the 
patient got a transplant? Explain your reasoning.
\item What do the box plots below suggest about the efficacy (effectiveness) of the heart transplant treatment.
\item What proportion of patients in the treatment group and what proportion of 
patients in the control group died?
\item One approach for investigating whether or not the treatment is effective 
is to use a randomization technique.
\begin{subparts}
\item What are the claims being tested?
\item The paragraph below describes the set up for such approach, if we were 
to do it without using statistical software. Fill in the blanks with a number 
or phrase, whichever is appropriate.
\begin{adjustwidth}{2em}{2em}
We write \textit{alive} on \rule{2cm}{0.5pt} cards representing patients who were 
alive at the end of the study, and \textit{dead} on \rule{2cm}{0.5pt} cards 
representing patients who were not. Then, we shuffle these cards and split them 
into two groups: one group of size \rule{2cm}{0.5pt} representing treatment, and 
another group of size \rule{2cm}{0.5pt} representing control. We calculate the 
difference between the proportion of \textit{dead} cards in the treatment and 
control groups (treatment - control) and record this value. We repeat this 100 
times to build a distribution centered at \rule{2cm}{0.5pt}. Lastly, we calculate 
the fraction of simulations where the simulated differences in proportions are 
\rule{2cm}{0.5pt}. If this fraction is low, we conclude that it is unlikely to 
have observed such an outcome by chance and that the null hypothesis should 
be rejected in favor of the alternative.
\end{adjustwidth}
\item What do the simulation results shown below suggest about the effectiveness 
of the transplant program?
\end{subparts}
\end{parts}
\begin{center}
\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}
\end{center}
}{}


================================================
FILE: ch_summarizing_data/TeX/ch_summarizing_data.tex
================================================
\begin{chapterpage}{Summarizing data}
  \chaptertitle{Summarizing data}
  \label{summarizingData}
  \label{ch_summarizing_data}
  \chaptersection{numericalData}
  \chaptersection{categoricalData}
  \chaptersection{caseStudyMalariaVaccine}
\end{chapterpage}
\renewcommand{\chapterfolder}{ch_summarizing_data}

\chapterintro{This chapter focuses on the mechanics
  and construction of summary statistics and graphs.
  We use statistical software for generating
  the summaries and graphs presented in this chapter
  and book.
  However, since this might be your first exposure to these
  concepts, we take our time in this chapter to detail
  how to create them.
  Mastery of the content presented in this chapter
  will be crucial for understanding the methods and
  techniques introduced in rest of the book.}



%%%%%
\section{Examining numerical data}
\label{numericalData}

% 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))
\newcommand{\loanA}{10.90}
\newcommand{\loanB}{9.92}
\newcommand{\loanC}{26.30}
\newcommand{\loanD}{9.92}
\newcommand{\loanY}{9.43}
\newcommand{\loanZ}{6.08}
\newcommand{\loanAvg}{11.57}
\newcommand{\loanVar}{25.52}
\newcommand{\loanSD}{5.05}
\newcommand{\loanN}{50}
\newcommand{\loanMedianBelow}{9.93\%}
\newcommand{\loanMedianAbove}{9.93\%}
\newcommand{\loanMedian}{9.93\%}
\newcommand{\loanQA}{7.96}
\newcommand{\loanQC}{13.72}
\newcommand{\loanIQR}{5.76}
\newcommand{\loanAdev}{-0.67}
\newcommand{\loanBdev}{-1.65}
\newcommand{\loanCdev}{14.73}
\newcommand{\loanDdev}{-1.65}
\newcommand{\loanYdev}{-2.14}
\newcommand{\loanZdev}{-5.49}
\newcommand{\loanSmallestValue}{5.31}
\newcommand{\loanLargestValue}{26.30}


In this section we will explore techniques for
summarizing numerical variables.
For example, consider the \var{loan\us{}amount} variable
from the \data{loan50} data set, which represents the loan
size for all 50 loans in the data set.
This variable is numerical since we can sensibly discuss
the numerical difference of the size of two loans.
On the other hand, area codes and zip codes are not numerical,
but rather they are categorical variables.

Throughout this section and the next, we will apply these
methods using the \data{loan50} and \data{county} data sets,
which were introduced in Section~\ref{dataBasics}.
If you'd like to review the variables from either data set,
see Figures~\ref{loan50DF} and~\ref{countyDF}.


\subsection{Scatterplots for paired data}
\label{scatterPlots}

\index{data!loan50|(}

A \term{scatterplot} provides a case-by-case view of data
for two numerical variables.
In Figure~\ref{multiunitsVsOwnership} on
page~\pageref{multiunitsVsOwnership}, a scatterplot
was used to examine the homeownership rate against
the fraction of housing units that were part of
multi-unit properties
(e.g. apartments) in the \data{county} data set.
Another scatterplot is shown in Figure~\ref{loan50_amt_vs_income},
comparing the total income of a borrower
(\var{total\us{}income}) and the amount they borrowed
(\var{loan\us{}amount}) for the \data{loan50} data set.
In any scatterplot, each point represents a single case.
Since there are \loanN{} cases in \data{loan50},
there are \loanN{} points in Figure~\ref{loan50_amt_vs_income}.

\begin{figure}[h]
  \centering
  \Figure
    [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.]
    {0.8}{loan50_amt_vs_income}
  \caption{A scatterplot of \var{total\us{}income}
      versus \var{loan\us{}amount} for the
      \data{loan50} data set.}
  \label{loan50_amt_vs_income}
\end{figure}

Looking at Figure~\ref{loan50_amt_vs_income},
we see that there are many borrowers with an income below
\$100,000 on the left side of the graph,
while there are a handful of borrowers with income above~\$250,000.

\begin{examplewrap}
\begin{nexample}{Figure~\ref{medianHHIncomePoverty}
    shows a plot of median household income
    against the poverty rate for 3,142 counties.
    What can be said about the relationship between
    these variables?}
  The relationship is evidently \term{nonlinear},
  as highlighted by the dashed line.
  This is different from previous scatterplots we've seen,
  which show relationships that do not show much, if any,
  curvature in the trend.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
  \centering
  \Figure
    [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.]
    {0.8}{medianHHIncomePoverty}
  \caption{A scatterplot of the median household income
      against the poverty rate for the
      \data{county} data set.
      A statistical model has also been fit to the data
      and is shown as a dashed line.}
  \label{medianHHIncomePoverty}
\end{figure}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
What do scatterplots reveal about the data,
and how are they useful?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Answers may vary.
  Scatterplots are helpful in quickly spotting associations
  relating variables,
  whether those associations come in the form of simple
  trends or whether those relationships are more complex.}

\begin{exercisewrap}
\begin{nexercise}
Describe two variables that would have a horseshoe-shaped
association in a scatterplot ($\cap$ or $\frown$).\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Consider the case
  where your vertical axis represents something ``good'' and
  your horizontal axis represents something that is only good
  in moderation.
  Health and water consumption fit this description: we require
  some water to survive, but consume too much and it becomes
  toxic and can kill a person.}



\subsection{Dot plots and the mean}
\label{dotPlot}

Sometimes two variables are one too many:
only one variable may be of interest.
In these cases, a dot plot provides the most basic of displays.
A~\term{dot plot} is a one-variable scatterplot;
an example using the interest rate of \loanN{} loans
is shown in Figure~\ref{loan_int_rate_dot_plot}.
A stacked version of this dot plot is shown in
Figure~\ref{loan_int_rate_dot_plot_stacked}.

\begin{figure}[h]
  \centering
  \Figure
    [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\%.]
    {0.76}{loan_int_rate_dot_plot}
  \caption{A dot plot of \var{interest\us{}rate}
      for the \data{loan50} data set.
      The distribution's mean is shown as a red triangle.}
  \label{loan_int_rate_dot_plot}
\end{figure}

\begin{figure}[h]
  \centering
  \Figures
    [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\%.]
    {0.76}
    {loan_int_rate_dot_plot}
    {loan_int_rate_dot_plot_stacked}
  \caption{A stacked dot plot of
      \var{interest\us{}rate}
      for the \data{loan50} data set.
      The~rates have been rounded to the nearest
      percent in this plot, and the
      distribution's mean is shown as a red triangle.}
  \label{loan_int_rate_dot_plot_stacked}
\end{figure}

\D{\newpage}

The \term{mean}, often called the
\term{average}\index{mean!average}, is a common way
to measure the center of a \mbox{\term{distribution}} of data.
To compute the mean interest rate, we add up all the interest
rates and divide by the number of observations:
\begin{align*}
\bar{x}
    = \frac{\text{\loanA\%} + \text{\loanB\%} + \text{\loanC\%} +
        \cdots + \text{\loanZ\%}}{\loanN{}}
    = \loanAvg{}\%
% library(openintro); loan50$interest_rate[c(1:3, 50)]; mean(loan50$interest_rate)
\end{align*}
The sample mean is often labeled $\bar{x}$.
The letter $x$ is being used as a generic placeholder
for the variable of interest, \var{interest\us{}rate},
and the bar over the $x$ communicates we're looking at the
average interest rate, which for these 50 loans was \loanAvg{}\%.
It is useful to think of the mean as the balancing point
of the distribution, and it's shown as a triangle in Figures~\ref{loan_int_rate_dot_plot}
and~\ref{loan_int_rate_dot_plot_stacked}.

\begin{onebox}{Mean}%
The sample mean can be computed as the sum of the
observed values divided by the number of observations:
\begin{align*}
\bar{x} = \frac{x_1 + x_2 + \cdots + x_n}{n}
\end{align*}
where $x_1$, $x_2$, $\dots$, $x_n$ represent
the $n$ observed values.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
Examine the equation for the mean.
What does $x_1$ correspond to? And $x_2$?
Can you infer a general meaning to what $x_i$
might represent?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{$x_1$ corresponds to the
  interest rate for the first loan in the sample (\loanA\%),
  $x_2$ to the second loan's interest rate (\loanB\%),
  and $x_i$ corresponds to the interest rate for the
  $i^{th}$ loan in the data set.
  For example, if $i = 4$, then we're examining $x_4$,
  which refers to the fourth observation in the data set.}

\begin{exercisewrap}
\begin{nexercise}
What was $n$ in this sample of
loans?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The sample size was $n = 50$.}

The \data{loan50} data set represents a sample from
a larger population of loans made through Lending Club.
We could compute a mean for this population in the same way
as the sample mean.
However, the population mean has a special label: $\mu$.
\index{Greek!mu@mu ($\mu$)}
The symbol $\mu$ is the Greek letter \emph{mu} and represents
the average of all observations in the population.
Sometimes a subscript, such as $_x$,
is used to represent which variable the population mean
refers to, e.g. $\mu_x$.
Often times it is too expensive to measure the
population mean precisely, so we often estimate
$\mu$ using the sample mean, $\bar{x}$.

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{The average interest rate across all loans
    in the population can be estimated using the sample data.
    Based on the sample of 50 loans,
    what would be a reasonable estimate of $\mu_x$,
    the mean interest rate for all loans in the
    full data set?}
  The sample mean, \loanAvg{}\%, provides a rough estimate
  of $\mu_x$.
  While it's not perfect, this is our single best guess
  %\emph{point estimate}\index{point estimate}
  of the average interest rate of all the loans in the
  population under study.

  In Chapter~\ref{foundationsForInference} and beyond,
  we will develop tools to characterize the accuracy
  of \emph{point estimates}\index{point estimate}
  like the sample mean.
  As you might have guessed,
  point estimates based on larger samples tend to be
  more accurate than those based on smaller samples.
\end{nexample}
\end{examplewrap}


\begin{examplewrap}
\begin{nexample}{The mean is useful because it allows us to
    rescale or standardize a metric into something more easily
    interpretable and comparable.
    Provide 2 examples where the mean
    is useful for making comparisons.}

  1. We would like to understand if a new drug is more
  effective at treating asthma attacks than the standard drug.
  A trial of 1500 adults is set up, where 500 receive the new
  drug, and 1000 receive a standard drug in the control
  group:\vspace{-2mm}
  \begin{center}
  \begin{tabular}{l cc}
  %\hline
  &           New drug & Standard drug \\
  \hline
  Number of patients   & 500 & 1000 \\
  Total asthma attacks & 200 & 300 \\
  \hline
  %average attacks
  %per patient    & 0.4 & 0.2 \\
  %\hline
  \end{tabular}
  \end{center}
  Comparing the raw counts of 200 to 300 asthma attacks
  would make it appear that the new drug is better,
  but this is an artifact of the imbalanced group sizes.
  Instead, we should look at the average number of asthma
  attacks per patient in each group:
  \begin{align*}
  & \text{New drug: } 200 / 500 = 0.4 % \\
      %\frac{200}{500} = 0.4 % \\
  && \text{Standard drug: } 300 / 1000 = 0.3
      %\frac{300}{1000} = 0.3
  % &     &&  %\\
  % & = 0.3\text{ asthma attacks per patient}
  %     && = 0.4\text{ asthma attacks per patient}
  \end{align*}
  The standard drug has a lower average number of asthma
  attacks per patient than the average in the treatment group.

  2. Emilio opened a food truck last year where he sells burritos,
  and his business has stabilized over the last 3 months.
  Over that 3 month period, he has made \$11,000 while
  working 625 hours.
  Emilio's average hourly earnings provides
  a useful statistic for evaluating whether his venture is,
  at~least from a financial perspective, worth it:
  \begin{align*}
  \frac{\$11000}{625\text{ hours}} = \$17.60\text{ per hour}
  \end{align*}
  By knowing his average hourly wage,
  Emilio now has put his earnings into a standard unit that
  is easier to compare with many other jobs that he might
  consider.
\end{nexample}
\end{examplewrap}

%{What are some contexts that highlight
%    the value of the mean?}
%  Here are a few scenarios highlighting why the mean can be
%  particularly useful.
%  \begin{itemize}
%  \item If a waitress makes an average of \$3.20 per table,
%      then she can get a reasonable estimate of how much
%      money she will make if she knows she'll turn over
%      about 15 tables in a night:
%      \begin{align*}
%      total &= average \times count
%          = \$3.20 \times 15
%          = \$48.00
%      \begin{align*}
%      The estimate won't be perfect, but it will still
%      be a useful reference of what she can expect.
%  \item For every \$1 played on roulette,
%      a gambler will lose, on average, 2.7 cents.
%      If she plays 1000 games and bets \$1 each time,
%      her expected loss is
%      \begin{align*}
%      total = average \times count
%          = 2.7 \cents \times 1000
%          = \$27
%      \begin{align*}
%  \end{itemize}
%  The average provides us a sensible value to think
%  about scaling gains and losses.

\begin{examplewrap}
\begin{nexample}{Suppose we want to compute the average income
    per person in the US.
    To do so, we might first think to take
    the mean of the per capita incomes across the 3,142 counties
    in the \data{county} data set.
    What would be a better approach?}
    \label{wtdMeanOfIncome}
  The \data{county} data set is special in that each county
  actually represents many individual people.
  If we were to simply average across the \var{income}
  variable, we would be treating counties with 5,000 and
  5,000,000 residents equally in the calculations.
  Instead, we should compute the total income for each county,
  add up all the counties' totals, and then divide by the number
  of people in all the counties.
  If we completed these steps with the \data{county} data,
  we would find that the per capita income for the US is
  \$30,861.
  Had we computed the \emph{simple} mean of per capita income
  across counties, the result would have been just \$26,093!

  This example used what is called a
  \term{weighted mean}\index{mean!weighted mean}.
  For more information
  on this topic, check out the following
  online supplement regarding weighted means
  \oiRedirect{stat_wtd_mean}
      {openintro.org/d?file=stat\_wtd\_mean}.
\end{nexample}
\end{examplewrap}
% 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)

%Example~\ref{wtdMeanOfIncome} used what is called
%a \term{weighted mean}\index{mean!weighted mean},
%which will not be a key topic in this textbook.
%However, we have provided an online supplement on
%weighted means for interested readers under
%\oiRedirect{stat_wtd_mean}
%    {www.openintro.org/d?file=stat\_wtd\_mean}.



\subsection{Histograms and shape}
\label{histogramsAndShape}

Dot plots show the exact value for each observation.
This is useful for small data sets, but they can become
hard to read with larger samples. Rather than showing the
value of each observation, we prefer to think of the value
as belonging to a \emph{bin}.
For example, in the \data{loan50} data set, we created
a table of counts for the number of loans with interest
rates between 5.0\% and 7.5\%, then the number of loans
with rates between 7.5\% and 10.0\%, and so on.
Observations that fall on the boundary of a bin
(e.g. 10.00\%) are allocated to the lower bin.
This tabulation is shown in Figure~\ref{binnedIntRateAmountTable}.
These binned counts are plotted as bars in
Figure~\ref{loan50IntRateHist} into what is called
a \term{histogram}, which resembles a more heavily binned
version of the stacked dot plot shown in
Figure~\ref{loan_int_rate_dot_plot_stacked}.

\begin{figure}[ht]
\centering\small
\begin{tabular}{l ccc ccc ccc}
  \hline
  Interest Rate &
      5.0\% - 7.5\% &
      7.5\% - 10.0\% &
      10.0\% - 12.5\% &
      12.5\% - 15.0\% &
      $\cdots$ &
      25.0\% - 27.5\% \\
  \hline
  Count & 11 & 15 & 8 & 4 & $\cdots$ & 1 \\
  \hline
\end{tabular}
\caption{Counts for the binned
    \var{interest\us{}rate} data.}
\label{binnedIntRateAmountTable}
\end{figure}
% 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))

\begin{figure}[bth]
  \centering
  \Figure
    [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.]
    {0.76}{loan50IntRateHist}
  \caption{A histogram of \var{interest\us{}rate}.
      This distribution is strongly skewed to the right.
      \index{skew!strong}}
  \label{loan50IntRateHist}
\end{figure}

Histograms provide a view of the \term{data density}.
Higher bars represent where the data are relatively more common.
For instance, there are many more loans with rates between
5\%~and~10\% than loans with rates between 20\% and~25\%
in the data set.
The bars make it easy to see how the density of the data
changes relative to the interest rate.

Histograms are especially convenient for understanding the
shape of the data distribution\label{shapeFirstDiscussed}.
Figure~\ref{loan50IntRateHist} suggests that most loans
have rates under 15\%, while only a handful
of loans have rates above 20\%.
When data trail off to the right in this way
and has a longer right \hiddenterm{tail}\index{skew!tail},
the shape is said to be
\termsub{right skewed}{skew!right skewed}.\footnote{Other
  ways to describe data that are right skewed:
  \termni{skewed to the right},
  \termni{skewed to the high end},
  or \termni{skewed to the positive end}.}

Data sets with the reverse characteristic --
a long, thinner tail to the left --
are said to be \termsub{left skewed}{skew!left skewed}.
We also say that such a distribution has a long left tail.
Data sets that show roughly equal trailing off in both
directions are called \term{symmetric}.\index{skew!symmetric}

\begin{onebox}{Long tails to identify skew}
  When data trail off in one direction, the distribution
  has a \term{long tail}. \index{skew!long tail|textbf}
  If a distribution has a long left tail, it is left skewed.
  If a distribution has a long right tail, it is right skewed.
\end{onebox}

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
Take a look at the dot plots in
Figures~\ref{loan_int_rate_dot_plot}
and~\ref{loan_int_rate_dot_plot_stacked}.
Can you see the skew in the data? Is it easier to see the
skew in this histogram or the dot plots?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The skew
  is visible in all three plots, though the flat dot plot
  is the least useful.
  The stacked dot plot and histogram are helpful
  visualizations for identifying skew.}

\begin{exercisewrap}
\begin{nexercise}
Besides the mean (since it was labeled), what can you see
in the dot plots that you cannot see in the
histogram?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The interest rates for individual loans.}

In addition to looking at whether a distribution is skewed
or symmetric, histograms can be used to identify modes.
A \term{mode} is represented by a prominent peak in the
distribution.
There is only one prominent peak in the histogram of
\var{loan\us{}amount}.

A definition of \emph{mode} sometimes
taught in math classes is the value with the
most occurrences in the data set.
However, for many real-world data sets, it is common to have
\emph{no} observations with the same value in a data set,
making this definition impractical in data analysis.

Figure~\ref{singleBiMultiModalPlots} shows histograms that
have one, two, or three prominent peaks.
Such distributions are called
\term{unimodal},
\term{bimodal}, and
\term{multimodal}, respectively.
Any distribution with more than 2~prominent peaks is
called multimodal.
Notice that there was one prominent peak in the unimodal
distribution with a second less prominent peak that was
not counted since it only differs from its neighboring
bins by a few observations.

\begin{figure}[h]
  \centering
  \Figure
    [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.]
    {0.9}{singleBiMultiModalPlots}
  \caption{Counting only prominent peaks, the
      distributions are (left to right) unimodal,
      bimodal, and multimodal.
      Note that we've said the left plot is unimodal
      intentionally.
      This is because we are counting \emph{prominent}
      peaks, not just any peak.}
  \label{singleBiMultiModalPlots}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Figure~\ref{loan50IntRateHist}
    reveals only one prominent mode in the interest rate.
    Is the distribution unimodal, bimodal, or multimodal?}
  Unimodal.
  Remember that \emph{uni} stands for 1 (think \emph{uni}cycles).
  Similarly, \emph{bi} stands for~2 (think \emph{bi}cycles).
  We're hoping a \emph{multicycle} will be invented to complete
  this analogy.
\end{nexample}
\end{examplewrap}

%{Looking back the stacked dot plot in
%    Figure~\ref{loan_int_rate_dot_plot_stacked},
%    it would be reasonable to wonder if the distribution
%    of loan amounts is actually bimodal or even multimodal.
%    In fact, we wondered the same thing -- so we investigated!}
%  What we found is that the bumps evident in the dot plot
%  tend to happen at \$5,000 increments.
%  That is, people made loan requests in round amounts.
%  While that is interesting, we often are more interested
%  in understanding the general shape of a data set rather
%  than characterizing some special property like this,
%  and for this reason, we think the data set is better
%  described as unimodal.
%  However, this example highlights that there isn't
%  always one ``correct'' answer for the number of modes.
%
%  There's a broader lesson to take away
%  from this example:
%  when we plot data in multiple ways,
%  we learn about different properties of the data
%  that no one plot would reveal all on its own.

\begin{exercisewrap}
\begin{nexercise}
Height measurements of young students and adult teachers
at a K-3 elementary school were taken.
How many modes would you expect in this height
data set?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{There might be two height groups visible
  in the data set: one of the students and one of the adults.
  That is, the data are probably bimodal.}

Looking for modes isn't about finding a clear and correct
answer about the number of modes in a distribution,
which is why \emph{prominent}\index{prominent} is not
rigorously defined in this book.
The most important part of this examination is to better
understand your data.



\D{\newpage}

\subsection{Variance and standard deviation}
\label{variability}

The mean was introduced as a method to describe the center of
a data set, and \indexthis{variability}{variability} in the
data is also important.
Here, we introduce two measures of variability:
the variance and the standard deviation.
Both of these are very useful in data analysis,
even though their formulas are a bit tedious to calculate
by hand.
The standard deviation is the easier of the two to comprehend,
and it roughly describes how far away the typical observation
is from the mean.

We 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:
\begin{align*}
x_1^{}-\bar{x} &= \loanA - \loanAvg{} = \loanAdev \hspace{5mm}\text{ } \\
x_2^{}-\bar{x} &= \loanB - \loanAvg{} = \loanBdev \\
x_3^{}-\bar{x} &= \loanC - \loanAvg{} = \loanCdev \\
			&\ \vdots \\
x_{50}^{}-\bar{x} &= \loanZ - \loanAvg{} = \loanZdev
\end{align*}
If we square these deviations and then take an average,
the result is equal to the sample
\term{variance}\label{varianceIsDefined},
denoted by $s_{}^2$:
\begin{align*}
s_{}^2 &= \frac{(\loanAdev)_{}^2 + (\loanBdev)_{}^2 + (\loanCdev)_{}^2 + \cdots + (\loanZdev)_{}^2}{\loanN{}-1} \\
	&= \frac{0.45 + 2.72 + 216.97 + \cdots + 30.14}{49} \\
	&= \loanVar{}
\end{align*}
We divide by $n - 1$, rather than dividing by $n$,
when computing a sample's variance;
there's some mathematical nuance here, but the end result is that
doing this makes this statistic slightly more reliable and useful.

Notice that squaring the deviations does two things.
First, it makes large values relatively much larger,
seen by comparing $(\loanAdev)^2$, $(\loanBdev)^2$, $(\loanCdev)^2$,
and $(\loanZdev)^2$.
Second, it gets rid of any negative signs.

The \term{standard deviation} is defined as the square root of the variance:
\begin{align*}
s = \sqrt{\loanVar{}} = \loanSD{}
\end{align*}
While often omitted, a subscript of $_x$ may be added
to the variance and standard deviation,
i.e. $s_x^2$ and $s_x^{}$, if it is useful as a reminder
that these are the variance and standard deviation of the
observations represented by $x_1^{}$, $x_2^{}$, ..., $x_n^{}$.

\begin{onebox}{Variance and standard deviation}
  The variance is the average squared distance from the mean.
  The standard deviation is the square root of the variance.
  The standard deviation is useful when considering how far
  the data are distributed from the mean.\vspace{3mm}

  The standard deviation represents the typical deviation
  of observations from the mean.
  Usually about 70\% of the data will be within one standard
  deviation of the mean and about 95\% will be within two
  standard deviations.
  However, as seen in Figures~\ref{sdRuleForIntRate}
  and~\ref{severalDiffDistWithSdOf1}, these percentages are
  not strict rules.
\end{onebox}

Like the mean, the population values for variance
and standard deviation have special symbols:
$\sigma_{}^2$ for the variance and $\sigma$ for the
standard deviation.
The symbol $\sigma$\index{Greek!sigma@sigma ($\sigma$)}
is the Greek letter \emph{sigma}.

\begin{figure}[h]
  \centering
  \Figure
    [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.]
    {0.73}{sdRuleForIntRate}
  \caption{For the \var{interest\us{}rate} variable,
      34 of the 50 loans (68\%) had interest rates within
      1~standard deviation of the mean,
      and 48 of the 50 loans (96\%) had rates within
      2~standard deviations.
      Usually about 70\% of the data are within 1~standard
      deviation of the mean and 95\% within 2~standard
      deviations, though this is far from a hard rule.}
  \label{sdRuleForIntRate}
\end{figure}

%\begin{onebox}{How to think about the standard deviation}
%  The standard deviation represents the typical deviation
%  of observations from the mean.
%  Usually about 70\% of the data will be within one standard
%  deviation of the mean and about 95\% will be within two
%  standard deviations.
%  However, as seen in Figures~\ref{sdRuleForIntRate}
%  and~\ref{severalDiffDistWithSdOf1}, these percentages are
%  not strict rules.
%\end{onebox}

\begin{figure}
  \centering
  \Figure
    [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.]
    {0.6}{severalDiffDistWithSdOf1}
  \caption{Three very different population distributions
      with the same mean $\mu=0$ and standard deviation
      $\sigma=1$.}
  \label{severalDiffDistWithSdOf1}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
On page~\pageref{shapeFirstDiscussed}, the concept of
shape of a distribution was introduced.
A good description of the shape of a distribution should
include modality and whether the distribution is symmetric
or skewed to one side.
Using Figure~\ref{severalDiffDistWithSdOf1} as an example,
explain why such a description is
important.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Figure~\ref{severalDiffDistWithSdOf1}
  shows three distributions that look quite different,
  but all have the same mean, variance,
  and standard deviation.
  Using modality, we can distinguish between the
  first plot (bimodal) and the last two (unimodal).
  Using skewness, we can distinguish between the
  last plot (right skewed) and the first two.
  While a picture, like a histogram, tells a more
  complete story, we can use modality and shape
  (symmetry/skew) to characterize basic information
  about a~distribution.}

\begin{examplewrap}
\begin{nexample}{Describe the distribution of the
    \var{interest\us{}rate} variable using
    the histogram in Figure~\ref{loan50IntRateHist}.
    The description should incorporate the center,
    variability, and shape of the distribution,
    and it should also be placed in context.
    Also note any especially unusual cases.}
  The distribution of interest rates is unimodal
  and skewed to the high end.
  Many of the rates fall near the mean at 11.57\%,
  and most fall within one standard deviation (5.05\%)
  of the mean.
  There are a few exceptionally large interest rates
  in the sample that are above 20\%.
\end{nexample}
\end{examplewrap}

In practice, the variance and standard deviation are sometimes
used as a means to an end, where the ``end'' is being able to
accurately estimate the uncertainty associated with a sample
statistic.
For example, in Chapter~\ref{foundationsForInference}
the standard deviation is used in calculations that help us
understand how much a sample mean varies from one sample
to the next.


\D{\newpage}

\subsection{Box plots, quartiles, and the median}

A \term{box plot} summarizes a data set using five
statistics while also plotting unusual observations.
Figure~\ref{loan_int_rate_box_plot_layout} provides
a vertical dot plot alongside a box plot of the
\var{interest\us{}rate} variable from
the \data{loan50} data set.

\begin{figure}[h]
  \centering
  \Figure
    [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\%.]
    {0.86}{loan_int_rate_box_plot_layout}
  \caption{A vertical dot plot, where points have been
      horizontally stacked, next to a labeled box plot
      for the interest rates of the \loanN{} loans.}
  \label{loan_int_rate_box_plot_layout}
\end{figure}

The first step in building a box plot is drawing a dark line
denoting the \term{median}, which splits the data in half.
Figure~\ref{loan_int_rate_box_plot_layout} shows 50\% of the
data falling below the median and other 50\% falling above
the median.
There are \loanN{} loans in the data set
(an even number) so the data are perfectly split into two
groups of~25.
We take the median in this case to be the average of the
two observations closest to the $50^{th}$ percentile,
which happen to be the same value in this data set:
$(\text{\loanMedianAbove{}} + \text{\loanMedianBelow{}}) / 2
  = \text{\loanMedian{}}$.
When there are an odd number of observations,
there will be exactly one observation that splits the data
into two halves, and in such a case that observation
is the median (no average needed).

\begin{onebox}{Median: the number in the middle}
  If the data are ordered from smallest to largest,
  the \term{median} is the observation right in the middle.
  If there are an even number of observations,
  there will be two values in the middle,
  and the median is taken as their average.
\end{onebox}

The second step in building a box plot is drawing
a rectangle to represent the middle 50\% of the data.
The total length of the box, shown vertically in
Figure~\ref{loan_int_rate_box_plot_layout},
is called the \term{interquartile range} (\hiddenterm{IQR},
for short).
It, like the standard deviation, is a measure
of \indexthis{variability}{variability} in data.
The more variable the data, the larger the standard
deviation and~IQR tend to be.
The two boundaries of the box are called the
\term{first quartile} \index{quartile!first quartile}
(the $25^{th}$ \hiddenterm{percentile},
i.e. 25\% of the data fall below this value)
and the \term{third quartile} \index{quartile!third quartile}
(the $75^{th}$ percentile), and these are often labeled $Q_1$
\index{quartile!Q1@Q$_1$} and $Q_3$\index{quartile!Q3@Q$_3$},
respectively.

\begin{onebox}{Interquartile range (IQR)}
  The IQR\index{interquartile range} is the length
  of the box in a box plot.
  It is computed as
  \begin{eqnarray*}
  IQR = Q_3 - Q_1
  \end{eqnarray*}
  where $Q_1$ and $Q_3$ are the $25^{th}$ and $75^{th}$
  percentiles.
\end{onebox}

\begin{exercisewrap}
\begin{nexercise}
What percent of the data fall between $Q_1$ and the median?
What percent is between the median and $Q_3$?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Since
  $Q_1$ and $Q_3$ capture the middle 50\% of the data and
  the median splits the data in the middle, 25\% of the data
  fall between $Q_1$ and the median, and another 25\% falls
  between the median and $Q_3$.}

Extending out from the box, the \term{whiskers} attempt
to capture the data outside of the box.
However, their reach is never allowed to be more than
$1.5\times IQR$.
They capture everything within this reach.
In Figure~\ref{loan_int_rate_box_plot_layout},
the upper whisker does not extend to the last two points,
which is beyond $Q_3 + 1.5\times IQR$,
and so it extends only to the last point below this limit.
The lower whisker stops at the lowest value,
\loanSmallestValue{}\%,
since there is no additional data to reach;
the lower whisker's limit is not shown in the figure because
the plot does not extend down to $Q_1 - 1.5\times IQR$.
In a sense, the box is like the body of the box plot
and the whiskers are like its arms trying to reach the
rest of the data.

Any observation lying beyond the whiskers is labeled with a dot.
The purpose of labeling these points --
instead of extending the whiskers to the minimum
and maximum observed values --
is to help identify any observations that appear to be
unusually distant from the rest of the data.
Unusually distant observations are called
\termsub{outliers}{outlier}.
In this case, it would be reasonable to classify the
interest rates of 24.85\% and \loanLargestValue{}\%
as outliers since they are numerically distant from
most of the data.

\begin{onebox}{Outliers are extreme}
  An \term{outlier} is an observation that appears
  extreme relative to the rest of the data. \vspace{3mm}
  
  Examining data for outliers serves
  many useful purposes, including\vspace{-1mm}
  \begin{enumerate}
  \setlength{\itemsep}{0mm}
  \item Identifying
      \indexthis{strong skew}{skew!strong}
      in the distribution.
  \item Identifying possible data collection or
      data entry errors.
  \item Providing insight into interesting properties
      of the data.\vspace{-1mm}
  \end{enumerate}
\end{onebox}

%The observation \loanLargestValue{}\%, a suspected outlier,
%was found to be an accurate observation.
%What would such an observation suggest about the nature
%of interest rates through Lending Club?\footnote{That
%  occasionally there may be very long emails.}

\begin{exercisewrap}
\begin{nexercise}
Using Figure~\ref{loan_int_rate_box_plot_layout},
estimate the following values for
\var{interest\us{}rate} in the \data{loan50} data set: \\
(a) $Q_1$,
(b) $Q_3$, and
(c) IQR.\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{These
  visual estimates will vary a little from one person
  to the next:
  $Q_1=$ 8\%,
  $Q_3=$ 14\%,
  $\text{IQR} = Q_3 - Q_1 = 6\%$.
  (The true values: $Q_1= \loanQA{}\%$, $Q_3 = \loanQC{}\%$,
  $\text{IQR} = \loanIQR{}\%$.)}

\CalculatorVideos{how to create statistical summaries and box plots}


\D{\newpage}

\subsection{Robust statistics}

How are the \indexthis{sample statistics}{sample statistic}
of the \data{interest\us{}rate} data set affected
by the observation, 26.3\%?
What would have happened if this loan had instead
been only 15\%?
What would happen to these
\indexthis{summary statistics}{summary statistic}
if the observation at 26.3\% had been even larger,
say 35\%? These scenarios are plotted alongside the
original data in Figure~\ref{loan_int_rate_robust_ex},
and sample statistics are computed under each scenario in
Figure~\ref{robustOrNotTable}.

\begin{figure}[ht]
  \centering
  \Figure
    [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.]
    {1}{loan_int_rate_robust_ex}
  \caption{Dot plots of the original interest rate data
      and two modified data sets.}
  \label{loan_int_rate_robust_ex}
\end{figure}

% See `loan_int_rate_robust_ex` figure code for calculations.
\captionsetup{width=135mm}
\begin{figure}[ht]
\centering
\begin{tabular}{l c cc c cc}
% \cline{3-4} \cline{6-7}
& \hspace{0mm} & \multicolumn{2}{c}{\bf robust} &
    \hspace{2mm} & \multicolumn{2}{c}{\bf not robust} \\
\hline
scenario && median & IQR && $\bar{x}$ & $s$ \\ 
\hline
%   & & \multicolumn{2}{c|} & & \multicolumn{2}{c|} \\
original \var{interest\us{}rate} data
    && 9.93\% & 5.76\% && 11.57\% & 5.05\% \\
move 26.3\% $\to$ 15\%
    && 9.93\% & 5.76\% && 11.34\% & 4.61\% \\
move 26.3\% $\to$ 35\%
    && 9.93\% & 5.76\% && 11.74\% & 5.68\% \\
   \hline
\end{tabular}
\caption{A comparison of how the median, IQR,
  mean ($\bar{x}$), and standard deviation ($s$) change
  had an extreme observations from the \var{interest\us{}rate}
  variable been different.}
\label{robustOrNotTable}
\end{figure}
\captionsetup{width=\mycaptionwidth}

\begin{exercisewrap}
\begin{nexercise} \label{interestRateWhichIsMoreRobust}
(a)~Which is more affected by extreme observations,
the mean or median?
Figure~\ref{robustOrNotTable} may be helpful.
(b)~Is the standard deviation or IQR more affected by
extreme observations?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~Mean is affected more.
  (b)~Standard deviation is affected more.
  Complete explanations are provided in the material
  following Guided Practice~\ref{interestRateWhichIsMoreRobust}.}

The median and IQR are called \term{robust statistics} because
extreme observations have little effect on their values:
moving the most extreme value generally has little influence
on these statistics.
On the other hand, the mean and standard deviation
are more heavily influenced by changes in extreme observations,
which can be important in some situations.

\begin{examplewrap}
\begin{nexample}{The median and IQR did not change under the
    three scenarios in Figure~\ref{robustOrNotTable}.
    Why might this be the case?}
  The median and IQR are only sensitive to numbers
  near $Q_1$, the median, and $Q_3$.
  Since values in these regions are stable in the three
  data sets, the median and IQR estimates are also stable.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
The distribution of loan amounts in the \data{loan50} data set
is right skewed, with a few large loans lingering out into the
right tail.
If you were wanting to understand the typical loan size,
should you be more interested in the mean
or median?\footnotemark
\end{nexercise}
\end{exercisewrap}
\footnotetext{Answers will vary!
  If we're looking to simply understand what a typical individual
  loan looks like, the median is probably more useful.
  However, if the goal is to understand something that
  scales well, such as the total amount of money we might
  need to have on hand if we were to offer 1,000 loans,
  then the mean would be more useful.}

\index{data!loan50|)}


\D{\newpage}

\subsection{Transforming data (special topic)}
\label{transformingDataSubsection}

\noindent%
When data are very strongly skewed, we sometimes transform
them so they are easier to model.

\begin{figure}[ht]
  \centering
  \subfigure[]{
    \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.]
        {0.46}
        {county_pop_transformed}
        {county_pop_transformed_i}
    \label{county_pop_transformed_i}
  }
  \subfigure[]{
    \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.]
        {0.46}
        {county_pop_transformed}
        {county_pop_transformed_log}
  \label{county_pop_transformed_log}
  }
  \caption{\subref{county_pop_transformed_i} A histogram of
      the populations of all US counties.
      \subref{county_pop_transformed_log} A histogram of
      log$_{10}$-transformed county populations.
      For this plot, the x-value corresponds to the power
      of 10, e.g. ``4'' on the x-axis corresponds to
      $10^4 =$ 10,000.}
    \label{county_pop_transformed}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Consider the histogram of county populations
    shown in Figure~\ref{county_pop_transformed_i},
    which shows extreme skew\index{skew!extreme}.
    What isn't useful about this plot?}
  Nearly all of the data fall into the left-most bin,
  and the extreme skew obscures many of the potentially
  interesting details in the data.
\end{nexample}
\end{examplewrap}

There are some standard transformations that may be
useful for strongly right skewed data where much of the
data is positive but clustered near zero.
A \term{transformation} is a rescaling of the data
using a function.
For instance, a plot of the logarithm (base 10) of
county populations results in the new histogram in
Figure~\ref{county_pop_transformed_log}.
This data is symmetric, and any potential outliers
appear much less extreme than in the original data set.
By reigning in the outliers and extreme skew,
transformations like this often make it easier to build
statistical models against the data.

Transformations can also be applied to one or both
variables in a scatterplot.
A scatterplot of the population change from 2010 to 2017
against the population in 2010 is shown in Figure~\ref{county_pop_change_v_pop_transform_i}.
In this first scatterplot, it's hard to decipher any
interesting patterns because the population variable
is so strongly skewed.
However, if we apply a log$_{10}$ transformation to
the population variable, as shown in
Figure~\ref{county_pop_change_v_pop_transform_log},
a positive association between the variables is revealed.
In fact, we may be interested in fitting a trend line to
the data when we explore methods around fitting regression
lines in Chapter~\ref{linRegrForTwoVar}.

\begin{figure}
  \centering
  \subfigure[]{
    \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.]
        {0.47}
        {county_pop_change_v_pop_transform}
        {county_pop_change_v_pop_transform_i}
    \label{county_pop_change_v_pop_transform_i}
  }
  \subfigure[]{
    \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\%.]
        {0.47}
        {county_pop_change_v_pop_transform}
        {county_pop_change_v_pop_transform_log}
    \label{county_pop_change_v_pop_transform_log}
  }
  \caption{\subref{county_pop_change_v_pop_transform_i}
      Scatterplot of population change
      against the population before the change.
      \subref{county_pop_change_v_pop_transform_log}~A~scatterplot
      of the same data but where the population
      size has been log-transformed.}
  \label{county_pop_change_v_pop_transform_main}
\end{figure}

Transformations other than the logarithm can be useful, too.
For instance, the square root
($\sqrt{\text{original observation}}$) and inverse
($\frac{1}{\text{original observation}}$) are commonly used
by data scientists.
Common goals in transforming data are to see the data
structure differently, reduce skew, assist in modeling,
or straighten a nonlinear relationship in a scatterplot.

\index{data!county|)}


\D{\newpage}

\subsection{Mapping data (special topic)}

\index{data!county|(}
%\index{intensity map|(}

The \data{county} data set offers many numerical variables
that we could plot using dot plots, scatterplots,
or box plots, but these miss the true nature of the data.
Rather, when we encounter geographic data, we should create
an \term{intensity map}, where colors are used
to show higher and lower values of a variable.
Figures~\ref{countyIntensityMaps1}
and~\ref{countyIntensityMaps2} shows intensity maps for
poverty rate in percent (\var{poverty}),
unemployment rate (\var{unemployment\us{}rate}),
homeownership rate in percent (\var{homeownership}),
and median household income
(\var{median\us{}hh\us{}income}).
The color key indicates which colors correspond to which values.
The intensity maps are not generally very helpful
for getting precise values in any given county,
but they are very helpful for seeing geographic trends
and generating interesting research questions or hypotheses.

\begin{examplewrap}
\begin{nexample}{What interesting features are evident in the
    \var{poverty} and \var{unemployment\us{}rate}
    intensity maps?}\label{map_example_poverty_and_unemployment}
  Poverty rates are evidently higher in a few locations.
  Notably, the deep south shows higher poverty rates,
  as does much of Arizona and New Mexico.
  High poverty rates are evident in the Mississippi
  flood plains a little north of New Orleans and
  also in a large section of Kentucky.

  The unemployment rate follows similar trends,
  and we can see correspondence between the two
  variables. In fact, it makes sense for higher rates
  of unemployment to be closely related to poverty rates.
  One observation that stand out when comparing the two maps:
  the poverty rate is much higher than the unemployment
  rate, meaning while many people may be working,
  they are not making enough to break out of poverty.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
What interesting features are evident in the
\var{median\us{}hh\us{}income} intensity map in
Figure~\ref{countyMedIncomeMap}?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Note: answers will vary.
  There is some correspondence between high earning
  and metropolitan areas, where we can see darker spots
  (higher median household income),
  though there are several exceptions.
  You might look for large cities you are familiar with and
  try to spot them on the map as dark spots.}

\begin{figure}
  \centering
  \subfigure[]{
    \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.]
        {1.00}
        {countyIntensityMaps}
        {countyPovertyMap}
    \label{countyPovertyMap}
  }
  \subfigure[]{
    \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.]
        {1.00}
        {countyIntensityMaps}
        {countyUnemploymentRateMap}
    \label{countyUnemploymentRateMap}
  }
  \caption{\subref{countyPovertyMap} Intensity map of
      poverty rate (percent).
      \subref{countyUnemploymentRateMap}~Map of the
      unemployment rate (percent).}
  \label{countyIntensityMaps1}
\end{figure}

\begin{figure}
  \centering
  \subfigure[]{
    \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.]
        {1.00}
        {countyIntensityMaps}
        {countyHomeownershipMap}
    \label{countyHomeownershipMap}
  }
  \subfigure[]{
    \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.]
        {1.00}
        {countyIntensityMaps}
        {countyMedIncomeMap}
    \label{countyMedIncomeMap}
  }
  \caption{\subref{countyHomeownershipMap} Intensity map
      of homeownership rate (percent).
      \subref{countyMedIncomeMap}~Intensity map of median
      household income (\$1000s).}
\label{countyIntensityMaps2}
\end{figure}

%\index{intensity map|)}
\index{data!county|)}


{\input{ch_summarizing_data/TeX/examining_numerical_data.tex}}




\section{Considering categorical data}
\label{categoricalData}

\index{data!loans|(}

In this section, we will introduce tables and other basic tools
for categorical data that are used throughout this book.
The \data{loan50} data set represents a sample from a larger
loan data set called \data{loans}.
This larger data set contains information on 10,000 loans made
through Lending Club.
We~will examine the relationship between
\var{homeownership}, which for the \data{loans} data can take
a value of \resp{rent}, \resp{mortgage}
(owns but has a mortgage), or \resp{own},
and \var{app\us{}type},
which indicates whether the loan application was made
with a partner or whether it was an individual application.
% library(openintro); dim(loans_full_schema)


\subsection{Contingency tables and bar plots}

\newcommand{\loanapphomeAA}{3496}
\newcommand{\loanapphomeAB}{3839}
\newcommand{\loanapphomeAC}{1170}
\newcommand{\loanapphomeAD}{8505}
\newcommand{\loanapphomeBA}{362}
\newcommand{\loanapphomeBB}{950}
\newcommand{\loanapphomeBC}{183}
\newcommand{\loanapphomeBD}{1495}
\newcommand{\loanapphomeDA}{3858}
\newcommand{\loanapphomeDAPt}{0.3858} % Overall frequency
\newcommand{\loanapphomeDB}{4789}
\newcommand{\loanapphomeDC}{1353}
\newcommand{\loanapphomeDD}{10000}
\newcommand{\loanapphomeN}{\loanapphomeDD{}}

Figure~\ref{loan_home_app_type_totals} summarizes two variables:
\var{app\us{}type}
%\footnote{For those readers already familiar
%  with \emph{joint probabilities}, \resp{joint} in the table
%  refers to a level of the \var{app\us{}type} variable
%  for a joint application.
%  The does not refer to a joint probability!}
and \var{homeownership}.
A table that summarizes data for two categorical variables in
this way is called a \term{contingency table}.
Each value in the table represents the number of times
a particular combination of variable outcomes occurred.
For example, the value \loanapphomeAA{} corresponds to the number of
loans in the data set where the borrower rents their home
and the application type was by an individual.
Row and column totals are also included.
The \term{row totals} \index{contingency table!row totals}
provide the total counts across each row
(e.g. $\loanapphomeAA{} + \loanapphomeAB{} +
  \loanapphomeAC{} = \loanapphomeAD{}$),
and \term{column totals} \index{contingency table!column totals}
are total counts down each column.
We can also create a table that shows only the overall 
percentages or proportions for each combination of categories,
or we can create a table for a single variable,
such as the one shown in Figure~\ref{loan_homeownership_totals}
for the \var{homeownership} variable.

\begin{figure}[ht]
\centering
\begin{tabular}{ll  ccc  rr}
  & & \multicolumn{3}{c}{\bf \var{homeownership}} & \\
  \cline{3-5}
  & & rent & mortgage & own & Total & \hspace{2mm}\  \\ 
  \cline{2-6}
  & individual &
      \loanapphomeAA{} &
      \loanapphomeAB{} &
      \loanapphomeAC{} &
      \loanapphomeAD{} \\
  \raisebox{1.5ex}[0pt]{\var{app\us{}type}} &
  joint &
      \loanapphomeBA{} &
      \loanapphomeBB{} &
	  \loanapphomeBC{} &
	  \loanapphomeBD{} \\
  \cline{2-6}
  & Total &
      \loanapphomeDA{} &
      \loanapphomeDB{} &
      \loanapphomeDC{} &
      \loanapphomeDD{} \\
  \cline{2-6}
\end{tabular}
\caption{A contingency table for
    \var{app\us{}type} and \var{homeownership}.}
\label{loan_home_app_type_totals}
%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)
\end{figure}

\begin{figure}[htb]
\centering
\begin{tabular}{lc}
  \hline
  \var{homeownership} & Count \\
  \hline
  rent & \loanapphomeDA{} \\
  mortgage & \loanapphomeDB{} \\
  own & \loanapphomeDC{} \\
  \hline
  Total & \loanapphomeDD{} \\ 
  \hline
\end{tabular}
\caption{A table summarizing the frequencies of each
    value for the \var{homeownership} variable.}
\label{loan_homeownership_totals}
\end{figure}

A bar plot is a common way to display a single
categorical variable.
The left panel of Figure~\ref{loan_homeownership_bar_plot}
shows a \term{bar plot} for the \var{homeownership} variable.
In the right panel, the counts are converted into proportions,
showing the proportion of observations that are in each level
(e.g. $\loanapphomeDA{} / \loanapphomeDD{} = 0.3858$ for
  \resp{rent}).

\begin{figure}[h]
  \centering
  \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.]
    {0.9}{loan_homeownership_bar_plot}
  \caption{Two bar plots of \var{number}.
      The left panel shows the counts, and the right panel
      shows the proportions in each group.}
  \label{loan_homeownership_bar_plot}
\end{figure}


\D{\newpage}

\subsection{Row and column proportions}

Sometimes it is useful to understand the fractional breakdown
of one variable in another,
and we can modify our contingency table to provide such a view.
Figure~\ref{rowPropAppTypeHomeownership}
shows the
\termsub{row proportions}{contingency table!row proportions}
for Figure~\ref{loan_home_app_type_totals},
which are computed as the counts divided by their row totals.
The value \loanapphomeAA{} at the intersection of
\resp{individual} and \resp{rent} is replaced by
$\loanapphomeAA{}/\loanapphomeAD{} = 0.411$,
i.e. \loanapphomeAA{} divided by its row total,
\loanapphomeAD{}.
So what does 0.411 represent?
It corresponds to the proportion of individual
applicants who rent.

\begin{figure}[h]
\centering
\begin{tabular}{l rrr r}
  \hline
  & rent & mortgage & own & Total \\
  \hline
  individual & 
%      $\loanapphomeAA{}/\loanapphomeAD{} = 0.411$ &
%      $\loanapphomeAB{}/\loanapphomeAD{} = 0.451$ &
%      $\loanapphomeAC{}/\loanapphomeAD{} = 0.138$ &
      0.411 &
      0.451 &
      0.138 &
      1.000 \\
  joint &
%      $\loanapphomeBA{}/\loanapphomeBD{} = 0.242$ &
%      $\loanapphomeBB{}/\loanapphomeBD{} = 0.635$ &
%      $\loanapphomeBC{}/\loanapphomeBD{} = 0.122$ &
      0.242 &
      0.635 &
      0.122 &
      1.000 \\
  \hline
  Total &
%      $\loanapphomeDA{}/\loanapphomeDD{} = 0.386$ &
%      $\loanapphomeDB{}/\loanapphomeDD{} = 0.479$ &
%      $\loanapphomeDC{}/\loanapphomeDD{} = 0.135$ &
      0.386 &
      0.479 &
      0.135 &
      1.000 \\
  \hline
\end{tabular}
\caption{A contingency table with row proportions
    for the \var{app\us{}type} and
    \var{homeownership} variables.
    The row total is off by 0.001 for the
    \resp{joint} row due to a rounding error.}
\label{rowPropAppTypeHomeownership}
\end{figure}

A contingency table of the column proportions is computed in
a similar way, where each
\termsub{column proportion}{contingency table!column proportion}
is computed as the count divided by the corresponding column total.
Figure~\ref{colPropAppTypeHomeownership} shows such a table,
and here the value 0.906 indicates that 90.6\% of renters applied
as individuals for the loan.
This rate is higher compared to loans from people with
mortgages (80.2\%) or who own their home (86.5\%).
Because these rates vary between the three levels of
\var{homeownership} (\resp{rent}, \resp{mortgage}, \resp{own}),
this provides evidence that the \var{app\us{}type} and
\var{homeownership} variables are associated.

\begin{figure}[h]
\centering%\small
\begin{tabular}{l rrr r}
  \hline
  & rent & mortgage & own & Total \\
  \hline
  individual &
%      $\loanapphomeAA{}/\loanapphomeDA{} = 0.906$ &
%      $\loanapphomeAB{}/\loanapphomeDB{} = 0.802$ &
%      $\loanapphomeAC{}/\loanapphomeDC{} = 0.865$ &
%      $\loanapphomeAD{}/\loanapphomeDD{} = 0.851$ \\
      0.906 &
      0.802 &
      0.865 &
      0.851 \\
  joint &
%      $\loanapphomeBA{}/\loanapphomeDA{} = 0.094$ &
%      $\loanapphomeBB{}/\loanapphomeDB{} = 0.198$ &
%      $\loanapphomeBC{}/\loanapphomeDC{} = 0.135$ &
%      $\loanapphomeBD{}/\loanapphomeDD{} = 0.150$ \\
      0.094 &
      0.198 &
      0.135 &
      0.150 \\
  \hline
  Total & 1.000 & 1.000 & 1.000 & 1.000 \\
  \hline
\end{tabular}
\caption{A contingency table with column proportions for the
    \var{app\us{}type} and \var{homeownership}
    variables.
    The total for the last column is off by 0.001 due
    to a rounding error.}
\label{colPropAppTypeHomeownership}
\end{figure}

We 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.

\D{\newpage}

\begin{exercisewrap}
\begin{nexercise}
(a)~What does 0.451 represent in
Figure~\ref{rowPropAppTypeHomeownership}?

(b)~What does 0.802 represent in
Figure~\ref{colPropAppTypeHomeownership}?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~0.451 represents the proportion of individual
  applicants who have a mortgage.
  (b)~0.802 represents the fraction
  of applicants with mortgages who applied as individuals.}

\begin{exercisewrap}
\begin{nexercise}
(a)~What does 0.122 at the intersection of \resp{joint} and
\resp{own} represent in
Figure~\ref{rowPropAppTypeHomeownership}?

(b)~What does 0.135 represent in the
Figure~\ref{colPropAppTypeHomeownership}?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{(a)~0.122 represents the fraction of joint borrowers
  who own their home.
  (b)~0.135 represents the home-owning borrowers
  who had a joint application for the loan.}

\begin{examplewrap}
\begin{nexample}{
    Data scientists use statistics to filter spam from incoming
    email messages.
    By noting specific characteristics of an email,
    a data scientist may be able to classify some emails as spam
    or not spam with high accuracy.
    One such characteristic is whether the email
    contains no numbers, small numbers, or big numbers.
    Another characteristic is the email format, which
    indicates whether or not an email has any HTML content,
    such as bolded text.
    We'll focus on email format and spam status using the
    \data{email} data set, and these variables are summarized
    in a contingency table in
    Figure~\ref{emailSpamHTMLTableTotals}.
    Which would be more helpful to someone hoping to classify
    email as spam or regular email for this table:
    row or column proportions?}
  \label{weighingRowColumnProportions}
  A data scientist would be interested in how the proportion
  of spam changes within each email format.
  This corresponds to column proportions:
  the proportion of spam in plain text emails
  and the proportion of spam in HTML emails.

  If we generate the column proportions, we can see
  that a higher fraction of plain text emails are
  spam ($209/1195 = 17.5\%$)
  than compared to HTML emails ($158/2726 = 5.8\%$).
  This information on its own is insufficient to classify
  an email as spam or not spam, as over 80\% of plain text
  emails are not spam.
  Yet, when we carefully combine this information with many
  other characteristics,
  we stand a reasonable chance of being able to classify
  some emails as spam or not spam with confidence.
\end{nexample}
\end{examplewrap}

\begin{figure}[ht]
\centering
\begin{tabular}{l cc r}
  \hline
  & text & HTML & Total \\ 
  \hline
  spam & 209 & 158 & 367 \\ 
  not spam & 986 & 2568 & 3554 \\ 
  \hline
  Total & 1195 & 2726 & 3921 \\
  \hline
\end{tabular}
\caption{A contingency table for \var{spam} and \var{format}.}
\label{emailSpamHTMLTableTotals}
%library(openintro); library(xtable); data(email); tab <- table(email[,c("spam", "format")])[2:1,]; tab; colSums(tab); rowSums(tab)
\end{figure}

Example~\ref{weighingRowColumnProportions} points out
that row and column proportions are not equivalent.
Before settling on one form for a table,
it is important to consider each to ensure that the
most useful table is constructed.
However, sometimes it simply isn't clear which, if either,
is more useful.

\begin{examplewrap}
\begin{nexample}{Look back to
    Tables~\ref{rowPropAppTypeHomeownership}
    and~\ref{colPropAppTypeHomeownership}.
    Are there any obvious scenarios where one might be more
    useful than the other?}
  None that we thought were obvious!
  What is distinct about \var{app\us{}type}
  and \var{homeownership} vs the email example is that
  these two variables don't have a clear explanatory-response
  variable relationship that we might hypothesize
  (see Section~\ref{explanatoryAndResponse} for these terms).
  Usually it is most useful to ``condition'' on the
  explanatory variable.
  For instance, in the email example, the email format
  was seen as a possible explanatory variable of whether
  the message was spam, so we would find it more interesting
  to compute the relative frequencies (proportions)
  for each email format.
\end{nexample}
\end{examplewrap}

%\Comment{Any risk with the above example that students
%  would think they need not know how to describe (what
%  are effectively) conditional probabilities based
%  on row or column proportions?
%  If so, we could add in an exercise that calls this
%  out and requires them to create such a description.}


\D{\newpage}

\subsection{Using a bar plot with two variables}
\label{bar_plots_subsection}

Contingency tables using row or column proportions
are especially useful for examining how two categorical
variables are related.
Stacked bar plots provide a way to visualize
the information in these tables.

A \termsub{stacked bar plot}{bar plot!stacked bar plot}
\index{bar plot!segmented bar plot}
is a graphical display of contingency table information.
For example, a~stacked bar plot representing
Figure~\ref{colPropAppTypeHomeownership}
is shown in Figure~\ref{loan_app_type_home_seg_bar},
where we have first created a bar plot using the
\var{homeownership} variable and then divided each group
by the levels of \mbox{\var{app\us{}type}}.

One related visualization to the stacked bar plot is the
\termsub{side-by-side bar plot}{bar plot!side-by-side},
where an example is shown in
Figure~\ref{loan_app_type_home_sbs_bar}.

For the last type of bar plot we introduce,
the column proportions for the
\var{app\us{}type} and \var{homeownership} contingency table
have been translated into a standardized stacked bar plot
in Figure~\ref{loan_app_type_home_seg_bar_standardized}.
This type of visualization is helpful in understanding
the fraction of individual or joint loan applications
for borrowers in each level of \var{homeownership}.
Additionally, since the proportions of \resp{joint}
and \resp{individual} vary across the groups,
we can conclude that the two variables are associated.

\newcommand{\loanapptypehomesegbarplotwidth}{0.48\textwidth}
\begin{figure}[h]
  \centering
  \subfigure[]{
    \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.]
        {\loanapptypehomesegbarplotwidth}
        {loan_app_type_home_seg_bar}
        {loan_app_type_home_seg_bar}
    \label{loan_app_type_home_seg_bar}
  }
  \subfigure[]{
    \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).]
        {\loanapptypehomesegbarplotwidth}
        {loan_app_type_home_seg_bar}
        {loan_app_type_home_sbs_bar}
    \label{loan_app_type_home_sbs_bar}
  }
  \subfigure[]{
    \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.]
        {\loanapptypehomesegbarplotwidth}
        {loan_app_type_home_seg_bar}
        {loan_app_type_home_seg_bar_standardized}
    \label{loan_app_type_home_seg_bar_standardized}
  }
%  \subtable{
%    \footnotesize
%    \begin{tabular}{l  ccc  r}
%      \multicolumn{5}{l}{Contingency table summarizing}\\
%      \multicolumn{5}{l}{application type and homeownership:} \\
%      \\
%      & \multicolumn{3}{c}{\bf \var{homeownership}} & \\
%      \cline{2-4}
%      \var{app\us{}type} &
%          rent & mortgage & own & Total \\ 
%      \hline
%      individual &
%          \loanapphomeAA{} &
%          \loanapphomeAB{} &
%          \loanapphomeAC{} &
%          \loanapphomeAD{} \\
%      joint &
%          \loanapphomeBA{} &
%          \loanapphomeBB{} &
%    	  \loanapphomeBC{} &
%    	  \loanapphomeBD{} \\
%      \hline
%      Total &
%          \loanapphomeDA{} &
%          \loanapphomeDB{} &
%          \loanapphomeDC{} &
%          \loanapphomeDD{} \\
%      \hline
%      \ \\
%      \ \\
%      \multicolumn{5}{l}{Version of the table}\\
%      \multicolumn{5}{l}{with column proportions:} \\
%      \\
%      & \multicolumn{3}{c}{\bf \var{homeownership}} & \\
%      \cline{2-4}
%      \var{app\us{}type} &
%          rent & mortgage & own & Total \\ 
%      \hline
%      individual &
%          0.906 &
%          0.802 &
%          0.865 &
%          0.851 \\
%      joint &
%          0.094 &
%          0.198 &
%          0.135 &
%          0.150 \\
%      \hline
%      Total & 1.000 & 1.000 & 1.000 & 1.000 \\
%      \hline
%      \ \\
%    \end{tabular}
%    \label{loan_app_type_home_copied_table}
%  }
  \caption{\subref{loan_app_type_home_seg_bar} Stacked
      bar plot for \var{homeownership},
      where the counts have been further broken down
      by \var{app\us{}type}.
      \subref{loan_app_type_home_sbs_bar}~Side-by-side
      bar plot for \var{homeownership}
      and \var{app\us{}type}.
      \subref{loan_app_type_home_seg_bar_standardized}~Standardized
      version of the stacked bar plot.}
  \label{loan_app_type_home_seg_bar_plot}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Examine the three bar plots in
    Figure~\ref{loan_app_type_home_seg_bar_plot}.
    When is the stacked, side-by-side, or standardized
    stacked bar plot the most useful?}
  The stacked bar plot is most useful when it's reasonable
  to assign one variable as the explanatory variable and
  the other variable as the response, since we are effectively
  grouping by one variable first and then breaking it down by
  the others.

  Side-by-side bar plots are more agnostic in their display
  about which variable, if any, represents the explanatory
  and which the response variable.
  It is also easy to discern the number of cases
  in the six different group combinations.
  However, one downside
  is that it tends to require more horizontal space;
  the narrowness of Figure~\ref{loan_app_type_home_sbs_bar}
  makes the plot feel a bit cramped.
  Additionally, when two groups are of very different sizes,
  as we see in the \resp{own} group relative to either of the
  other two groups,
  it is difficult to discern if there is an association
  between the variables.

  The standardized stacked bar plot is helpful if the primary
  variable in the stacked bar plot is relatively imbalanced,
  e.g. the \resp{own} category has only a third of the
  observations in the \resp{mortgage} category,
  making the simple stacked bar plot less useful for
  checking for an association.
  The major downside of the standardized version
  is that we lose all sense of how many cases each of the
  bars represents.
\end{nexample}
\end{examplewrap}

%Before settling on a particular bar plot, consider each
%carefully.
%It can also be useful to make a couple of the versions,
%which will offer different views and insights into the data
%than if only one bar plot variant is reviewed.

\subsection{Mosaic plots}
\label{mosaic_plots_subsection}

A \term{mosaic plot} is a visualization technique
suitable for contingency tables that resembles
a standardized stacked bar plot with the benefit
that we still see the relative group sizes of the
primary variable as well.

To get started in creating our first mosaic plot,
we'll break a square into columns for each category
of the \var{homeownership} variable,
with the result shown in Figure~\ref{loan_home_mosaic}.
Each column represents a level of \var{homeownership},
and the column widths correspond to the proportion of
loans in each of those categories.
For~instance, there are fewer loans where the borrower
is an owner than where the borrower has a mortgage.
In general, mosaic plots use box \emph{areas}
to represent the number of cases in each category.

\begin{figure}[h]
  \centering
  \subfigure[]{
    \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.]
        {0.36}
        {loan_app_type_home_mosaic_plot}
        {loan_home_mosaic}
    \label{loan_home_mosaic}
  }
  \subfigure[]{
    \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.]
        {0.44}
        {loan_app_type_home_mosaic_plot}
        {loan_app_type_home_mosaic}
    \label{loan_app_type_home_mosaic}
  }
  \caption{\subref{loan_home_mosaic}~The one-variable mosaic
      plot for \var{homeownership}.
      \subref{loan_app_type_home_mosaic}~Two-variable mosaic
      plot for both \var{homeownership}
      and \var{app\us{}type}.}
  \label{loan_app_type_home_mosaic_plot}
\end{figure}

To create a completed mosaic plot, the single-variable
mosaic plot is further divided into pieces in
Figure~\ref{loan_app_type_home_mosaic} using the
\var{app\us{}type} variable.
Each column is split proportional to the
number of loans from individual and joint
borrowers.
For example, the second column represents loans
where the borrower has a mortgage,
and it was divided into individual loans (upper)
and joint loans (lower).
As another example, the bottom segment of the third column
represents loans where the borrower owns their home
and applied jointly, while the upper segment
of this column represents
borrowers who are homeowners and filed individually.
We can again use this plot to see that
the \var{homeownership} and \var{app\us{}type}
variables are associated, since some columns are divided
in different vertical locations than others,
which was the same technique used for checking an
association in the standardized stacked bar plot.

In Figure~\ref{loan_app_type_home_mosaic_plot},
we chose to first split by the homeowner status
of the borrower.
However, we could have instead first split by
the application type, as in
Figure~\ref{loan_app_type_home_mosaic_rev}.
Like with the bar plots, it's common to use
the explanatory variable to represent the
first split in a mosaic plot,
and then for the response to break
up each level of the explanatory variable,
if these labels are reasonable to attach to
the variables under consideration.

\begin{figure}[h]
  \centering
  \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.]
      {0.37}
      {loan_app_type_home_mosaic_plot}
      {loan_app_type_home_mosaic_rev}
  \caption{Mosaic plot where loans are grouped by
      the \var{homeownership} variable after they've
      been divided into the \resp{individual} and
      \resp{joint} application types.}
  \label{loan_app_type_home_mosaic_rev}
\end{figure}

%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}.


\subsection{The only pie chart you will see in this book}

A \term{pie chart} is shown in
Figure~\ref{loan_homeownership_pie_chart} alongside
a bar plot representing the same information.
Pie charts can be useful for giving a high-level overview
to show how a set of cases break down.
However, it is also difficult to decipher details
in a pie chart.
For example, it takes a couple seconds longer to recognize
that there are more loans where the borrower has
a mortgage than rent when looking at the pie chart,
while this detail is very obvious in the bar plot.
While pie charts can be useful, we prefer bar plots
for their ease in comparing groups.
%One benefit of pie charts is that they to make it easier
%to see when a series of groups make up at least 50\%,
%e.g. \Comment{would need to show a pie chart with
%  a large number of categories for this point to make sense}.

\begin{figure}[h]
  \centering
  \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.]
      {}{loan_homeownership_pie_chart}
  \caption{A pie chart and bar plot of \var{homeownership}.}
  \label{loan_homeownership_pie_chart}
\end{figure}

\index{data!loans|)}


\D{\newpage}

\subsection{Comparing numerical data across groups}
\label{comparingAcrossGroups}

\index{data!county|(}

Some of the more interesting investigations can be considered
by examining numerical data across groups.
The methods required here aren't really new:
all that's required is to make a numerical plot for each group
in the same graph.
Here two convenient methods are introduced:
side-by-side box plots and hollow histograms.

We will take a look again at the \data{county} data set
and compare the median household income for counties that
gained population from 2010 to 2017 versus counties that
had no gain.
While we might like to make a causal connection here,
remember that these are observational data and so such
an interpretation would be, at best, half-baked.

\newcommand{\numcountieswithgains}{1454}
\newcommand{\numcountieswithgainsC}{1,454}
\newcommand{\numcountieswithoutgains}{1672}
\newcommand{\numcountieswithoutgainsC}{1,672}

There were \numcountieswithgainsC{} counties where
the population increased from 2010 to 2017, and there
were \numcountieswithoutgainsC{} counties with no gain
(all but one were a loss).
A~random sample of 100 counties from the first group and
50 from the second group are shown in
Figure~\ref{countyIncomeSplitByPopGainTable}
to give a better sense of some of the raw median
income data.

\newcommand{\npgpad}[1]{\hspace{2mm}#1\hspace{1.5mm}\ }
\begin{figure}[h]
\centering
\begin{tabular}{ ccc ccc c ccc }
\multicolumn{10}{c}{\bf Median Income for 150 Counties,
    in \$1000s} \\
\hline
\vspace{-2mm} \\
\multicolumn{6}{c}{\bf Population Gain} &\hspace{5mm}\ &
    \multicolumn{3}{c}{\bf No Population Gain} \\ 
  \cline{1-6} \cline{8-10}
38.2 & 43.6 & 42.2 & 61.5 & 51.1 & 45.7 &&
    \npgpad{48.3} & \npgpad{60.3} & \npgpad{50.7} \\
44.6 & 51.8 & 40.7 & 48.1 & 56.4 & 41.9 && 39.3 & 40.4 & 40.3 \\
40.6 & 63.3 & 52.1 & 60.3 & 49.8 & 51.7 && 57 & 47.2 & 45.9 \\
51.1 & 34.1 & 45.5 & 52.8 & 49.1 & 51 && 42.3 & 41.5 & 46.1 \\
80.8 & 46.3 & 82.2 & 43.6 & 39.7 & 49.4 && 44.9 & 51.7 & 46.4 \\
75.2 & 40.6 & 46.3 & 62.4 & 44.1 & 51.3 && 29.1 & 51.8 & 50.5 \\
51.9 & 34.7 & 54 & 42.9 & 52.2 & 45.1 && 27 & 30.9 & 34.9 \\
61 & 51.4 & 56.5 & 62 & 46 & 46.4 && 40.7 & 51.8 & 61.1 \\
53.8 & 57.6 & 69.2 & 48.4 & 40.5 & 48.6 && 43.4 & 34.7 & 45.7 \\
53.1 & 54.6 & 55 & 46.4 & 39.9 & 56.7 && 33.1 & 21 & 37 \\
63 & 49.1 & 57.2 & 44.1 & 50 & 38.9 && 52 & 31.9 & 45.7 \\
46.6 & 46.5 & 38.9 & 50.9 & 56 & 34.6 && 56.3 & 38.7 & 45.7 \\
74.2 & 63 & 49.6 & 53.7 & 77.5 & 60 && 56.2 & 43 & 21.7 \\
63.2 & 47.6 & 55.9 & 39.1 & 57.8 & 42.6 && 44.5 & 34.5 & 48.9 \\
50.4 & 49 & 45.6 & 39 & 38.8 & 37.1 && 50.9 & 42.1 & 43.2 \\
57.2 & 44.7 & 71.7 & 35.3 & 100.2 &  && 35.4 & 41.3 & 33.6 \\
42.6 & 55.5 & 38.6 & 52.7 & 63 &  && 43.4 & 56.5 &  \\
\cline{1-6} \cline{8-10}
\end{tabular}
\caption{In this table, median household income (in \$1000s)
    from a random sample of 100 counties that had population
    gains are shown on the left.
    Median incomes from a random sample of 50 counties that
    had no population gain are shown on the right.}
\label{countyIncomeSplitByPopGainTable}
\end{figure}

\D{\newpage}

The \term{side-by-side box plot}
\index{box plot!side-by-side box plot}
is a traditional tool for comparing across groups.
An example is shown in the left panel of
Figure~\ref{countyIncomeSplitByPopGain},
where there are two box plots, one for each group,
placed into one plotting window and drawn on the same scale.

\begin{figure}
  \centering
  \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.]
      {1.00}{countyIncomeSplitByPopGain}
  \caption{Side-by-side box plot (left panel)
      and hollow histograms (right panel) for
      \var{med\us{}hh\us{}income},
      where the counties are split by whether there was
      a population gain or there was no gain.}
  \label{countyIncomeSplitByPopGain}
\end{figure}

Another 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}.

\begin{exercisewrap}
\begin{nexercise} \label{comparingPriceByTypeExercise}
Use the plots in Figure~\ref{countyIncomeSplitByPopGain}
to compare the incomes for counties across the two groups.
What do you notice about the approximate center of each group?
What do you notice about the variability between groups?
Is the shape relatively consistent between groups?
How many \emph{prominent} modes are there for each
group?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Answers may vary a little.
  The counties with population gains tend to have higher
  income (median of about \$45,000) versus counties without
  a gain (median of about \$40,000).
  The variability is also slightly larger for the population
  gain group.
  This is evident in the IQR, which is about 50\% bigger
  in the \emph{gain} group.
  Both distributions show slight to moderate right skew
  and are unimodal.
  The box plots indicate there are many observations
  far above the median in each group, though we should
  anticipate that many observations will fall beyond
  the whiskers when examining any data set that
  contain more than a couple hundred data points.}

\begin{exercisewrap}
\begin{nexercise}
What components of each plot in
Figure~\ref{countyIncomeSplitByPopGain}
do you find most useful?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{Answers will vary.
  The side-by-side box plots are especially useful for comparing
  centers and spreads, while the hollow histograms are more useful
  for seeing distribution shape, skew, and potential anomalies.}
\index{data!county|)}



%%___________________________________________
%\section{Exploratory data analysis}
%\label{eda_section}
%
%Over the last two sections, we've learned fundamental
%methods for graphing data.
%In this section, we leverage what we've learned to expand
%into more advanced techniques.
%We'll learn more graphical methods, and importantly,
%examine more complex relationships.
%
%
%\subsection{}


{\input{ch_summarizing_data/TeX/considering_categorical_data.tex}}





%___________________________________________
\section{Case study: malaria vaccine}
\label{caseStudyMalariaVaccine}

\begin{examplewrap}
\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}
While 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}.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
If we don't think the side of the room a person sits on
in class is related to whether the person owns an Apple product,
what assumption are we making about the relationship between
these two variables?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{We would be assuming that these two variables
  are independent.}


\subsection{Variability within data}
\label{variabilityWithinData}

\index{data!malaria vaccine|(}

We consider a study on a new malaria vaccine
called PfSPZ.
In this study, volunteer patients were randomized
into one of two experiment groups:
14 patients received an experimental vaccine
and 6 patients received a placebo vaccine.
Nineteen weeks later, all 20 patients were exposed
to a drug-sensitive malaria parasite strain;
the motivation of using a drug-sensitive strain
of parasite here is for ethical considerations,
allowing any infections to be treated effectively.
The results are summarized in
Figure~\ref{malaria_vaccine_20_exp_summary},
where 9 of the 14 treatment patients remained free
of signs of infection while all of the~6 patients
in the control group patients showed some baseline
signs of infection.

\newcommand{\malariaAA}{5}
\newcommand{\malariaAB}{9}
\newcommand{\malariaAD}{14}
\newcommand{\malariaBA}{6}
\newcommand{\malariaBB}{0}
\newcommand{\malariaBD}{6}
\newcommand{\malariaDA}{11}
\newcommand{\malariaDB}{9}
\newcommand{\malariaDD}{20}
\newcommand{\malariaVIR}{0.357}
\newcommand{\malariaVIRPerc}{35.7\%}
\newcommand{\malariaPIR}{1.000}
\newcommand{\malariaPIRPerc}{100\%}
\newcommand{\malariaIRDiff}{0.643}
\newcommand{\malariaIRDiffPerc}{64.3\%}

\begin{figure}[ht]
\centering
\begin{tabular}{l l cc rr}
  & & \multicolumn{2}{c}{\var{outcome}} \\
  \cline{3-4}
  &  &  {infection} & {no infection} & Total & \hspace{3mm}  \\ 
  \cline{2-5}
  & {vaccine} &
      \malariaAA{} &
      \malariaAB{} &
      \malariaAD{} \\ 
  \raisebox{1.5ex}[0pt]{\var{treatment}}
  & {placebo} & 
      \malariaBA{} &
      \malariaBB{} &
      \malariaBD{} \\ 
  \cline{2-5}
  & Total & 
      \malariaDA{} &
      \malariaDB{} &
      \malariaDD{} \\ 
  \cline{2-5}
\end{tabular}
\caption{Summary results for the malaria vaccine experiment.}
\label{malaria_vaccine_20_exp_summary}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
Is this an observational study or an experiment?
What implications does the study type have on what can
be inferred from the results?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{The
  study is an experiment, as patients were randomly
  assigned an experiment group.
  Since this is an experiment, the results can be used
  to evaluate a causal relationship between the malaria
  vaccine and whether patients showed signs
  of an infection.}

In this study, a smaller proportion of patients
who received the vaccine showed signs of an infection
(\malariaVIRPerc{} versus \malariaPIRPerc{}).
However, the sample is very small,
and it is unclear whether the difference provides
\emph{convincing evidence} that the vaccine is
effective.

\D{\newpage}

\begin{examplewrap}
\begin{nexample}{Data scientists are sometimes called
    upon to evaluate the strength of evidence.
    When looking at the rates of infection for patients
    in the two groups in this study,
    what comes to mind as we try to determine whether
    the data show convincing evidence of a real difference?}
  \label{malaria_vaccine_20_what_is_convincing}
  The observed infection rates
  (\malariaVIRPerc{} for the treatment group versus
  \malariaPIRPerc{} for the control group)
  suggest the vaccine may be effective.
  However, we cannot be sure if the observed difference
  represents the vaccine's efficacy or is just from
  random chance.
  Generally there is a little bit of fluctuation
  in sample data, and we wouldn't expect the sample
  proportions to be \emph{exactly} equal,
  even if the truth was that the infection rates
  were independent of getting the vaccine.
  Additionally, with such small samples,
  perhaps it's common to observe such large differences
  when we randomly split a group due to chance alone!
\end{nexample}
\end{examplewrap}

Example~\ref{malaria_vaccine_20_what_is_convincing}
is a reminder that the observed outcomes in the data
sample may not perfectly reflect the true relationships
between variables since there is \term{random noise}.
While the observed difference in rates of infection
is large, the sample size for the study is small,
making it unclear if this observed difference represents
efficacy of the vaccine or whether it is simply due to
chance.
We label these two competing claims, $H_0$ and $H_A$,
which are spoken as ``H-nought'' and ``H-A'':
\begin{itemize}
\setlength{\itemsep}{0mm}
\item[$H_0$:] \textbf{Independence model.}
    The variables \var{treatment} and \var{outcome}
    are independent.
    They have no relationship, and the observed difference
    between the proportion of patients who developed
    an infection in the two groups, \malariaIRDiffPerc{},
    was due to chance.
\item[$H_A$:] \textbf{Alternative model.}
    The variables are \emph{not} independent.
    The difference in infection rates of
    \malariaIRDiffPerc{}
    was not due to chance,
    and vaccine affected the rate of infection.
\end{itemize}

What would it mean if the independence model,
which says the vaccine had no influence on the
rate of infection, is true?
It would mean 11~patients were going to
develop an infection \emph{no matter which group
they were randomized into},
and 9~patients would not develop an infection
\emph{no matter which group they were randomized
into}.
That~is, if the vaccine did not affect the rate
of infection, the difference in the infection rates
was due to chance alone in how the patients were
randomized.

Now consider the alternative model:
infection rates were influenced by whether a patient
received the vaccine or not.
If this was true, and especially if this influence
was substantial, we would expect to see some difference
in the infection rates of patients in the groups.

We choose between these two competing claims
by assessing if the data conflict so much with
$H_0$ that the independence model cannot be deemed
reasonable.
If this is the case, and the data support $H_A$,
then we will reject the notion of independence
and conclude the vaccine was effective.


\subsection{Simulating the study}
\label{simulatingTheStudy}

We're going to implement
\termsub{simulations}{simulation},
where we will pretend we know that the malaria
vaccine being tested does \emph{not} work.
Ultimately, we want to understand if the large
difference we observed is common in these
simulations.
If it is common, then maybe the difference
we observed was purely due to chance.
If it is very uncommon, then the possibility
that the vaccine was helpful seems more plausible.

Figure~\ref{malaria_vaccine_20_exp_summary}
shows that 11 patients developed infections and 9 did not.
For our simulation, we will suppose the infections
were independent of the vaccine and we were able to
\emph{rewind} back to when the researchers randomized
the patients in the study.
If we happened to randomize the patients differently,
we may get a different result in this hypothetical
world where the vaccine doesn't influence the infection.
Let's complete another \term{randomization} using
a simulation.

\D{\newpage}

In this \term{simulation}, we take 20 notecards to
represent the 20 patients, where we write down ``infection''
on 11 cards and ``no infection'' on 9 cards.
In this hypothetical world, we believe each patient
that got an infection was going to get it regardless
of which group they were in, so let's see what happens
if we randomly assign the patients to the treatment
and control groups again.
We thoroughly shuffle the notecards and deal 14 into
a \resp{vaccine} pile and 6 into a \resp{placebo} pile.
Finally, we tabulate the results, which are shown in
Figure~\ref{malaria_vaccine_20_exp_summary_rand_1}.

\begin{figure}[ht]
\centering
\begin{tabular}{l l cc rr}
  & & \multicolumn{2}{c}{\var{outcome}} \\
  \cline{3-4}
  &  &  {infection} & {no infection} & Total & \hspace{3mm}  \\ 
  \cline{2-5}
  treatment & {vaccine} & 7 & 7 & 14 \\ 
  (simulated) & {placebo} & 4 & 2 & 6 \\ 
  \cline{2-5}
  & Total & 11 & 9 & 20 \\
  \cline{2-5}
\end{tabular}
\caption{Simulation results, where any difference
    in infection rates is purely due to chance.}
\label{malaria_vaccine_20_exp_summary_rand_1}
\end{figure}

\begin{exercisewrap}
\begin{nexercise}
\label{malaria_vaccine_20_exp_summary_rand_1_diff}
What is the difference in infection rates between
the two simulated groups in
Figure~\ref{malaria_vaccine_20_exp_summary_rand_1}?
How does this compare to the observed
\malariaIRDiffPerc{} difference
in the actual data?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\footnotetext{$4 / 6 - 7 / 14 = 0.167$
  or about 16.7\% in favor of the vaccine.
  This difference due to chance is much smaller than the
  difference observed in the actual groups.}


\subsection{Checking for independence}

We computed one possible difference under the
independence model in Guided
Practice~\ref{malaria_vaccine_20_exp_summary_rand_1_diff},
which represents one difference due to chance.
While in this first simulation, we physically dealt
out notecards to represent the patients,
it is more efficient to perform this simulation
using a computer.
Repeating the simulation on a computer, we get another
difference due to chance:
\begin{align*}
\frac{2}{\malariaBD{}} - \frac{9}{\malariaAD{}} = -0.310
\end{align*}
And another:
\begin{align*}
\frac{3}{\malariaBD{}} - \frac{8}{\malariaAD{}} = -0.071
\end{align*}
And so on until we repeat the simulation enough times
that we have a good idea of what represents the
\emph{distribution of differences from chance alone}.
Figure~\ref{malaria_rand_dot_plot} shows a stacked plot
of the differences found from 100 simulations,
where each dot represents a simulated difference between
the infection rates (control rate minus treatment rate).

\begin{figure}[ht]
  \centering
  \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.]
      {0.85}{malaria_rand_dot_plot}
  \caption{A stacked dot plot of differences from
      100 simulations produced under the independence model,
      $H_0$, where in these simulations infections are
      unaffected by the vaccine.
      Two of the 100 simulations had a difference of
      at least \malariaIRDiffPerc{}, the difference observed
      in the study.}
  \label{malaria_rand_dot_plot}
\end{figure}

Note that the distribution of these simulated differences
is centered around 0.
We simulated these differences assuming that the independence
model was true, and under this condition,
we expect the difference to be near zero with some random
fluctuation, where \emph{near} is pretty generous in this
case since the sample sizes are so small in this study.

\begin{examplewrap}
\begin{nexample}{How often would you observe a difference
    of at least \malariaIRDiffPerc{} (\malariaIRDiff{})
    according to Figure~\ref{malaria_rand_dot_plot}?
    Often, sometimes, rarely, or never?}
  It appears that a difference of at least
  \malariaIRDiffPerc{} due to chance alone would only
  happen about 2\% of the time according to
  Figure~\ref{malaria_rand_dot_plot}.
  Such a low probability indicates a rare event.
\end{nexample}
\end{examplewrap}

\D{\newpage}

The difference of \malariaIRDiffPerc{} being
a rare event suggests two possible interpretations
of the results of the study:
\begin{itemize}
  \setlength{\itemsep}{0mm}
  \item[$H_0$] \textbf{Independence model.}
      The vaccine has no effect on infection rate,
      and we just happened to observe a difference
      that would only occur on a rare occasion.
  \item[$H_A$] \textbf{Alternative model.}
      The vaccine has an effect on infection rate,
      and the difference we observed was actually due to
      the vaccine being effective at combatting malaria,
      which explains the large difference
      of~\malariaIRDiffPerc{}.
\end{itemize}
Based on the simulations, we have two options.
(1)~We conclude that the study results do not provide
strong evidence against the independence model.
That is, we do not have sufficiently strong evidence
to conclude the vaccine had an effect in this
clinical setting.
(2)~We conclude the evidence is sufficiently strong
to reject $H_0$ and assert that the vaccine was useful.
When we conduct formal studies, usually we reject the
notion that we just happened to observe a rare
event.\footnote{This reasoning does not generally extend
    to anecdotal observations.
    Each of us observes incredibly rare events every day,
    events we could not possibly hope to predict.
    However, in the non-rigorous setting of anecdotal
    evidence, almost anything may appear to be a rare event,
    so the idea of looking for rare events in day-to-day
    activities is treacherous.
    For example, we might look at the lottery:
    there was only a 1 in 292 million chance that the
    Powerball numbers for the largest jackpot in history
    (January 13th, 2016) would be (04, 08, 19, 27, 34)
    with a Powerball of (10),
    but nonetheless those numbers came up!
    However, no matter what numbers had turned up,
    they would have had the same incredibly rare odds.
    That is, \emph{any set of numbers we could have
    observed would ultimately be incredibly rare}.
    This type of situation is typical of our daily lives:
    each possible event in itself seems incredibly rare,
    but if we consider every alternative, those outcomes
    are also incredibly rare.
    We should be cautious not to misinterpret such
    anecdotal evidence.}
So in this case, we reject the independence model in favor
of the alternative.
That is, we are concluding the data provide strong evidence
that the vaccine provides some protection against malaria
in this clinical setting.

\index{data!malaria vaccine|)}

One field of statistics, statistical inference, is built
on evaluating whether such differences are due to chance.
In statistical inference, data scientists evaluate which
model is most reasonable given the data.
Errors do occur, just like rare events, and we might choose
the wrong model.
While we do not always choose correctly, statistical
inference gives us tools to control and evaluate how
often these errors occur.
In Chapter~\ref{foundationsForInference},
we give a formal introduction to the problem of model
selection.
We spend the next two chapters building a foundation
of probability and theory necessary to make that
discussion rigorous.



{\input{ch_summarizing_data/TeX/case_study_malaria_vaccine.tex}}


================================================
FILE: ch_summarizing_data/TeX/considering_categorical_data.tex
================================================
\exercisesheader{}

% 21

\eoce{\qt{Antibiotic use in children\label{antibiotic_use_children}} The bar plot 
and the pie chart below show the distribution of pre-existing medical 
conditions of children involved in a study on the optimal duration of 
antibiotic use in treatment of tracheitis, which is an upper respiratory 
infection.
\begin{center}
\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}
\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}
\end{center}
\begin{parts}
\item What features are apparent in the bar plot but not in the pie chart?
\item What features are apparent in the pie chart but not in the bar plot?
\item Which graph would you prefer to use for displaying these categorical data?
\end{parts}
}{}

% 22

\eoce{\qt{Views on immigration\label{immigration}} 910 randomly sampled registered 
voters from Tampa, FL were asked if they thought workers who have illegally 
entered the US should be (i) allowed to keep their jobs and apply for 
US citizenship, (ii) allowed to keep their jobs as temporary guest workers 
but not allowed to apply for US citizenship, or (iii) lose their jobs and 
have to leave the country. The results of the survey by political ideology 
are shown below.\footfullcite{survey:immigFL:2012}
\begin{center}
\begin{tabular}{l l c c c c}
                        &                           & \multicolumn{3}{c}{\textit{Political ideology}} \\
\cline{3-5}
                        &                           & Conservative  & Moderate  & Liberal   & Total \\
\cline{2-6}
                        & (i) Apply for citizenship & 57            & 120       & 101       & 278 \\
                        & (ii) Guest worker         & 121           & 113       & 28        & 262 \\
\raisebox{1.5ex}[0pt]{\emph{Response}} & (iii) Leave the country    & 179       & 126       & 45        & 350 \\ 
                        & (iv) Not sure             & 15            & 4         & 1         & 20\\
\cline{2-6}
                        & Total                     & 372           & 363       & 175       & 910
\end{tabular}
\end{center}
\begin{parts}
\item What percent of these Tampa, FL voters identify themselves as conservatives?
\item What percent of these Tampa, FL voters are in favor of the citizenship option?
\item What percent of these Tampa, FL voters identify themselves as conservatives 
and are in favor of the citizenship option?
\item What percent of these Tampa, FL voters who identify themselves as 
conservatives are also in favor of the citizenship option? What percent of 
moderates share this view? What percent of liberals share this view?
\item Do political ideology and views on immigration appear to be independent? 
Explain your reasoning.
\end{parts}
}{}

\D{\newpage}

% 23

\eoce{\qt{Views on the DREAM Act\label{dream_act_mosaic}} A random sample of registered 
voters 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.
The survey also collected information on the political ideology of the respondents. 
Based on the mosaic plot shown below, do views on the DREAM Act and  
political ideology appear to be independent? Explain your reasoning.
\footfullcite{survey:immigFL:2012}
\begin{center}
\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}
\end{center}
}{}

% 24

\eoce{\qt{Raise taxes\label{raise_taxes_mosaic}} A random sample of registered 
voters nationally were asked whether they think it's better to raise taxes 
on the rich or raise taxes on the poor. The survey also collected information 
on the political party affiliation of the respondents. Based on the mosaic 
plot shown below, do views on raising taxes and  
political affiliation appear to be independent? Explain your reasoning.
\footfullcite{survey:raiseTaxes:2015}
\begin{center}
\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}
\end{center}
}{}


================================================
FILE: ch_summarizing_data/TeX/examining_numerical_data.tex
================================================
\exercisesheader{}

% 1

\eoce{\qt{Mammal life spans\label{mammal_life_spans}} Data were collected on life spans (in 
years) and gestation lengths (in days) for 62 mammals. A scatterplot of life span versus 
length of gestation is shown below. \footfullcite{Allison+Cicchetti:1975}

\noindent\begin{minipage}[c]{0.44\textwidth}
\begin{parts}
\item What type of an association is apparent between life span and length of gestation?
\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?
\item Are life span and length of gestation independent? Explain your reasoning.
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.55\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

% 2

\eoce{\qt{Associations\label{association_plots}}
Indicate which of the plots show
(a)~a positive association,
(b)~a negative association, or
(c)~no~association.
Also determine if the positive and negative associations
are linear or nonlinear.
Each part may refer to more than one plot.
\begin{center}
\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}
\end{center}
}{}

% 3

\eoce{\qt{Reproducing bacteria\label{reproducing_bacteria}} Suppose that there is only 
sufficient space and nutrients to support one million bacterial cells in a petri dish. 
You place a few bacterial cells in this petri dish, allow them to reproduce freely, and 
record the number of bacterial cells in the dish over time. Sketch a plot representing 
the relationship between number of bacterial cells and time.
% first exponential
}{}

% 4

\eoce{\qt{Office productivity\label{office_productivity}} Office productivity is relatively low 
when the employees feel no stress about their work or job security. However, high levels 
of stress can also lead to reduced employee productivity. Sketch a plot to represent the 
relationship between stress and productivity.
}{}

% 5

\eoce{\qt{Parameters and statistics\label{parameters_stats}} Identify which value represents 
the sample mean and which value represents the claimed population mean.
\begin{parts}
\item American households spent an average of about \$52 in 2007 on Halloween 
merchandise such as costumes, decorations and candy. To see if this number had changed, 
researchers conducted a new survey in 2008 before industry numbers were reported. The 
survey included 1,500 households and found that average Halloween spending was \$58 per 
household.
\item The average GPA of students in 2001 at a private university was 3.37. A survey 
on a sample of 203 students from this university yielded an average GPA of 3.59 
a decade later.
\end{parts}
}{}

% 6

\eoce{\qt{Sleeping in college\label{college_sleeping}} A recent article in a college newspaper 
stated that college students get an average of 5.5 hrs of sleep each night. A student who 
was skeptical about this value decided to conduct a survey by randomly sampling 25 
students. On average, the sampled students slept 6.25 hours per night. Identify which 
value represents the sample mean and which value represents the claimed population mean.
}{}

\D{\newpage}

% 7

\eoce{\qt{Days off at a mining plant\label{days_off_mining}} Workers at a particular mining 
site receive an average of 35 days paid vacation, which is lower than the national 
average. The manager of this plant is under pressure from a local union to increase the 
amount of paid time off. However, he does not want to give more days off to the workers 
because that would be costly. Instead he decides he should fire 10 employees in such a 
way as to raise the average number of days off that are reported by his employees. In 
order to achieve this goal, should he fire employees who have the most number of days 
off, least number of days off, or those who have about the average number of days off?
}{}

% 8

\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. 
\begin{multicols}{2}
\begin{parts}
\item (1) 3, 5, 6, 7, 9 \\
(2) 3, 5, 6, 7, 20
\item (1) 3, 5, 6, 7, 9 \\
(2) 3, 5, 7, 8, 9
\item (1) 1, 2, 3, 4, 5 \\
(2) 6, 7, 8, 9, 10
\item (1) 0, 10, 50, 60, 100 \\
(2) 0, 100, 500, 600, 1000
\end{parts}
\end{multicols}
}{}

% 9

\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.
\begin{multicols}{2}
\begin{parts}
\item (1) 3, 5, 5, 5, 8, 11, 11, 11, 13 \\
(2) 3, 5, 5, 5, 8, 11, 11, 11, 20 \\
\item (1) -20, 0, 0, 0, 15, 25, 30, 30 \\
(2) -40, 0, 0, 0, 15, 25, 30, 30
\item (1) 0, 2, 4, 6, 8, 10 \\
(2) 20, 22, 24, 26, 28, 30
\item (1) 100, 200, 300, 400, 500 \\
(2) 0, 50, 300, 550, 600
\end{parts}
\end{multicols}
}{}

% 10

\eoce{\qt{Mix-and-match} Describe the distribution in the histograms below and match them to the box plots. \\
\begin{center}
\Figures[Six plots are shown, three histograms labeled a, b, and c, and 3 box plots labeled 1, 2, and 3.

Plot (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.

Plot (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.

Plot (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.

Plot (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.

Plot (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.

Plot (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}
\end{center}
}{}

\D{\newpage}

% 11

\eoce{\qt{Air quality\label{air_quality_durham}} Daily air quality is measured by the air 
quality index (AQI) reported by the Environmental Protection Agency. This index reports 
the pollution level and what associated health effects might be a concern. The index is 
calculated for five major air pollutants regulated by the Clean Air Act and takes values 
from 0 to 300, where a higher value indicates lower air quality. AQI was reported for a 
sample of 91 days in 2011 in Durham, NC. The relative frequency histogram below shows 
the distribution of the AQI values on these days. \footfullcite{data:durhamAQI:2011} \\
\begin{minipage}[c]{0.55\textwidth}
\begin{parts}
\item Estimate the median AQI value of this sample.
\item Would you expect the mean AQI value of this sample to be higher or lower than the 
median? Explain your reasoning.
\item Estimate Q1, Q3, and IQR for the distribution.
\item Would any of the days in this sample be considered to have an unusually low or 
high AQI? Explain your reasoning.
\end{parts}
\end{minipage}
\begin{minipage}[c]{0.45\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
}{}

% 12

\eoce{\qt{Median vs. mean\label{estimate_mean_median_simple}} Estimate the median for the 
400 observations shown in the histogram, and note whether you expect the mean 
to be higher or lower than the median.
\begin{center}
\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.
]{0.6}{eoce/estimate_mean_median_simple}{estimate_mean_median_simple}
\end{center}
}{}

% 13

\eoce{\qt{Histograms vs. box plots\label{hist_vs_box}} Compare the two plots below. What 
characteristics of the distribution are apparent in the histogram and not in the box 
plot? What characteristics are apparent in the box plot but not in the histogram?
\begin{center}
\Figures[Two plots are shown, first a histogram and second a box plot. The data for each plot runs from about 0 to 30.

The 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.

The 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.
]{0.6}{eoce/hist_vs_box}{hist_vs_box}
\end{center}
}{}

% 14

\eoce{\qt{Facebook friends\label{dist_shape_fb_friends}} Facebook data indicate that 
50\% of Facebook users have 100 or more friends, and that the average friend 
count of users is 190. What do these findings suggest about the shape of the 
distribution of number of friends of Facebook users? \footfullcite{Backstrom:2011}
}{}

% 15

\eoce{\qt{Distributions and appropriate statistics, Part I\label{dist_shape_pets_dist_height}} 
For each of the following, state whether you expect the distribution to be 
symmetric, right skewed, or left skewed. Also specify whether the mean or 
median would best represent a typical observation in the data, and whether 
the variability of observations would be best represented using the 
standard deviation or IQR. Explain your reasoning.
\begin{parts}
\item Number of pets per household. 
\item Distance to work, i.e. number of miles between work and home.
\item Heights of adult males.
\end{parts}
}{}

\D{\newpage}

% 16

\eoce{\qt{Distributions and appropriate statistics, Part II\label{dist_shape_housing_alcohol_salary}} 
For each of the following, state whether you expect the distribution to be symmetric, 
right skewed, or left skewed. Also specify whether the mean or median would best 
represent a typical observation in the data, and whether the variability of observations 
would be best represented using the standard deviation or IQR. Explain your reasoning.
\begin{parts}
\item Housing prices in a country where 25\% of the houses cost below \$350,000, 
50\% of the houses cost below \$450,000, 75\% of the houses cost below \$1,000,000 
and there are a meaningful number of houses that cost more than \$6,000,000.
\item Housing prices in a country where 25\% of the houses cost below \$300,000, 
50\% of the houses cost below \$600,000, 75\% of the houses cost below \$900,000 
and very few houses that cost more than \$1,200,000.
\item Number of alcoholic drinks consumed by college students in a given week. 
Assume that most of these students don't drink since they are under 21 years old, 
and only a few drink excessively.
\item Annual salaries of the employees at a Fortune 500 company where only a few 
high level executives earn much higher salaries than all the other employees.
\end{parts}
}{}

% 17

\eoce{\qt{Income at the coffee shop\label{income_coffee_shop}} The first histogram 
below shows the distribution of the yearly incomes of 40 patrons at a college 
coffee shop. Suppose two new people walk into the coffee shop: one making 
\$225,000 and the other \$250,000. The second histogram shows the new income 
distribution. Summary statistics are also provided. \\
\begin{minipage}[c]{0.57\textwidth}
\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}
\end{minipage}
\begin{minipage}[c]{0.4\textwidth}
\begin{center}
\begin{tabular}{rrr}
\hline
        & (1)       & (2) \\ 
\hline
n       & 40        & 42 \\ 
Min.    & 60,680    & 60,680 \\ 
1st Qu. & 63,620    & 63,710 \\ 
Median  & 65,240    & 65,350 \\ 
Mean    & 65,090    & 73,300 \\ 
3rd Qu. & 66,160    & 66,540 \\ 
Max.    & 69,890    & 250,000 \\ 
SD      & 2,122     & 37,321 \\ 
\hline
\end{tabular}
\end{center}
\end{minipage}
\begin{parts}
\item Would the mean or the median best represent what we might think of as a 
typical income for the 42 patrons at this coffee shop? What does this say about 
the robustness of the two measures?
\item Would the standard deviation or the IQR best represent the amount of 
variability in the incomes of the 42 patrons at this coffee shop? What does 
this say about the robustness of the two measures?
\end{parts}
}{}

% 18

\eoce{\qt{Midrange\label{midrange}} The \textit{midrange} of a distribution is defined as 
the average of the maximum and the minimum of that distribution. Is this statistic 
robust to outliers and extreme skew? Explain your reasoning
}{}

\D{\newpage}

% 19

\eoce{\qt{Commute times\label{county_commute_times}} The US census collects data on 
time it takes Americans to commute to work, among many other variables. The 
histogram below shows the distribution of average commute times in 3,142 US 
counties in 2010. Also shown below is a spatial intensity map of the same data.
\begin{center}
\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}
\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}
\end{center}
\begin{parts}
\item Describe the numerical distribution and comment on whether or not a log 
transformation may be advisable for these data.
\item Describe the spatial distribution of commuting times using the map above.
\end{parts} 
}{}

% 20

\eoce{\qt{Hispanic population\label{county_hispanic_pop}} The US census collects 
data on race and ethnicity of Americans, among many other variables. The 
histogram below shows the distribution of the percentage of the population 
that is Hispanic in 3,142 counties in the US in 2010. Also shown is a 
histogram of logs of these values.
\begin{center}
\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}
\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}
\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}
\end{center}
\begin{parts}
\item Describe the numerical distribution and comment on why we might want 
to use log-transformed values in analyzing or modeling these data.
\item What features of the distribution of the Hispanic population in US 
counties are apparent in the map but not in the histogram? What features are 
apparent in the histogram but not the map?
\item Is one visualization more appropriate or helpful than the other? Explain 
your reasoning.
\end{parts} 
}{}


================================================
FILE: ch_summarizing_data/TeX/review_exercises.tex
================================================
\reviewexercisesheader{}

% 27

\eoce{\qt{Make-up exam\label{makeup_exam}} In a class of 25 students, 24 of them took an exam 
in class and 1 student took a make-up exam the following day. The professor graded the 
first batch of 24 exams and found an average score of 74 points with a standard 
deviation of 8.9 points. The student who took the make-up the following day scored 64 
points on the exam.
\begin{parts}
\item Does the new student's score increase or decrease the average score?
\item What is the new average?
\item Does the new student's score increase or decrease the standard deviation of the 
scores?
\end{parts}
}{}

% 28

\eoce{\qt{Infant mortality\label{infant_mortality}} The infant mortality rate is defined as 
the number of infant deaths per 1,000 live births. This rate is often used as an 
indicator of the level of health in a country. The relative frequency histogram below 
shows the distribution of estimated infant death rates for 224 countries for which such 
data were available in 2014. 
\footfullcite{data:ciaFactbook}

\noindent\begin{minipage}[c]{0.43\textwidth}
\begin{parts}
\item Estimate Q1, the median, and Q3 from the histogram.
\item Would you expect the mean of this data set to be smaller or larger than the 
median? Explain your reasoning.
\end{parts} \vfill \
\end{minipage}
\begin{minipage}[c]{0.52\textwidth}
\hfill%
\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}
\end{minipage}
}{}

% 29

\eoce{\qt{TV watchers\label{dist_shape_TV_watchers}} Students in an AP Statistics class 
were asked how many hours of television they watch per week (including online 
streaming). This sample yielded an average of 4.71 hours, with a standard 
deviation of 4.18 hours. Is the distribution of number of hours students watch 
television weekly symmetric? If not, what shape would you expect this distribution 
to have? Explain your reasoning.
}{}

% 30

\eoce{\qt{A new statistic\label{new_stat}} The statistic $\frac{\bar{x}}{median}$ can 
be used as a measure of skewness. Suppose we have a distribution where all 
observations are greater than 0, $x_i > 0$. What is the expected shape of 
the distribution under the following conditions? Explain your reasoning.
\begin{parts}
\item $\frac{\bar{x}}{median} = 1$
\item $\frac{\bar{x}}{median} < 1$
\item $\frac{\bar{x}}{median} > 1$
\end{parts}
}{}

% 31

\eoce{\qt{Oscar winners\label{oscar_winners}} The first Oscar awards for best actor 
and best actress were given out in 1929. The histograms below show the age 
distribution for all of the best actor and best actress winners from 1929 to 
2018. Summary statistics for these distributions are also provided. Compare the 
distributions of ages of best actor and actress winners.\footfullcite{data:oscars} \\
\begin{minipage}[c]{0.72\textwidth}
\begin{center}
\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}
\end{center}
\end{minipage}
\begin{minipage}[c]{0.27\textwidth}
{\small
\begin{tabular}{l c}
\hline
        & Best Actress  \\
\hline
Mean    & 36.2      \\
SD      & 11.9      \\
n       & 92        \\  
        & \\
        & \\
        & \\
        & \\
        & \\
\hline
        & Best Actor \\
\hline
Mean    & 43.8 \\
SD      & 8.83 \\
n       & 92
\end{tabular}
}
\end{minipage}
}{}

% 32

\eoce{\qt{Exam scores\label{dist_shape_exam_scores}} The average on a history exam 
(scored out of 100 points) was 85, with a standard deviation of 15. Is the 
distribution of the scores on this exam symmetric? If not, what shape would 
you expect this distribution to have? Explain your reasoning.
}{}

% 33

\eoce{\qt{Stats scores\label{stats_scores_box}} Below are the final exam scores of twenty 
introductory statistics students.
\begin{center}
57, 66, 69, 71, 72, 73, 74, 77, 78, 78, 79, 79, 81, 81, 82, 83, 83, 88, 89, 94
\end{center}
Create a box plot of the distribution of these scores. The five number summary provided below may be useful.
\begin{center}
\renewcommand\arraystretch{1.5}
\begin{tabular}{ccccc}
Min & Q1    & Q2 (Median)   & Q3    & Max \\
\hline
57  & 72.5  & 78.5          & 82.5  & 94 \\
\end{tabular}
\end{center}
}{}

% 34

\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.
\begin{center}
\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}
\end{center}
\begin{parts}
\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?
\item What may be the reason for the bimodal distribution? Explain.
\item Compare the distribution of marathon times for men and women based on the box plot shown below.
\begin{center}
\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}
\end{center}
\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.
\end{parts}
\begin{center}
\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} \\
\end{center}
}{}


================================================
FILE: ch_summarizing_data/figures/boxPlotLayoutNumVar/boxPlotLayoutNumVar.R
================================================
require(openintro)
data(email50)
data(COL)
d   <- email50$num_char

myPDF("boxPlotLayoutNumVar.pdf", 5.5, 3.8,
      mar = c(0, 4, 0, 1),
      mgp = c(2.8, 0.7, 0))
boxPlot(d,
        ylab = 'Number of Characters (in thousands)',
        xlim = c(0.3, 3),
        axes = FALSE,
        ylim = range(d))
axis(2)

arrows(2,0, 1.35, min(d) - 0.5, length = 0.08)
text(2, 0, 'lower whisker', pos = 4)

arrows(2, quantile(d, 0.25) + sd(d) / 7,
       1.35, quantile(d, 0.25),
       length = 0.08)
text(2, quantile(d, 0.25) + sd(d)/6.5,
     expression(Q[1]~~'(first quartile)'), pos = 4)

m <- median(d)
arrows(2, m + sd(d) / 5, 1.35, m, length = 0.08)
text(2,m + sd(d) / 4.7, 'median', pos = 4)

q <- quantile(d, 0.75)
arrows(2, q + sd(d) / 4, 1.35, q, length = 0.08)
text(2, q + sd(d) / 3.8,
     expression(Q[3]~~'(third quartile)'), pos = 4)

arrows(2, rev(sort(d))[4] - sd(d) / 7,
       1.35, rev(sort(d))[4], length = 0.08)
text(2, rev(sort(d))[4] - sd(d) / 6.8,
     'upper whisker', pos = 4)

y <- quantile(d, 0.75) + 1.5 * IQR(d)
arrows(2, y - 0.1 * sd(d),
       1.35, y, length = 0.08)
lines(c(0.72, 1.28), rep(y, 2),
      lty = 3, col = '#00000066')
text(2, y - 0.1 * sd(d),
     'max whisker reach', pos = 4)

m <- rev(tail(sort(d), 5))
s <- m[1] - 0.3 * sd(m)
arrows(2, s, 1.1, m[1] - 0.2, length = 0.08)
arrows(2, s, 1.1, m[2] + 0.3, length = 0.08)
arrows(2, s, 1.1, m[3] + 0.35, length = 0.08)
text(2, s, 'suspected outliers', pos = 4)

set.seed(5)
pt.jitter <- 0.08
points(rep(0.4, 50) + runif(50, -pt.jitter, pt.jitter),
       d,
       col = rep(COL[1, 3], 25),
       pch = 19)
# points(rep(0.4, 25) + runif(25, -pt.jitter, pt.jitter),
#        rev(sort(d))[1:25],
#        col = rep(COL[1, 3], 25),
#        cex = 0.8)
# points(rep(0.4, 25) + runif(25, -pt.jitter, pt.jitter),
#        sort(d)[1:25],
#        col = rep(COL[4,3], 25),
#        pch = 19,
#        cex = 0.8)
dev.off()

sort(d)[25:26]
quantile(d, c(0.25, 0.5, 0.75))
tail(sort(d), 4)


myPDF("boxPlotNumVarSmall.pdf", 1.5, 2.5,
      mar = c(0, 4.1, 0, 0),
      mgp = c(2.3, 0.45, 0),
      tcl = -0.2)
boxPlot(d,
        ylab = '',
        axes = FALSE,
        xlim = c(0.5, 1.45),
        ylim = range(d) + sd(d) * c(-1,1) * 0.2)
axis(2, cex = 1.1)
par(las = 0)
mtext("Number of Characters\n(in thousands)", 2,
      line = 2,
      cex = 1.1)
dev.off()


================================================
FILE: ch_summarizing_data/figures/carsPriceVsWeight/carsPriceVsWeight.R
================================================
library(openintro)
data(cars)
data(COL)

myPDF("carsPriceVsWeight.pdf", 6, 3.7,
      mar = c(3.6, 3.6, 1, 1),
      mgp = c(2.5, 0.7, 0))
plot(cars$weight, cars$price, 
     xlab = 'Weight (Pounds)', ylab = 'Price ($1000s)', 
     pch = 19, cex = 1.3, col = COL[1, 2], 
     ylim = c(0, max(cars$price)))
g <- lm(price ~ weight + I(weight^2),
        cars,
        weights = 1/weight^2)
w <- seq(1000, 5000, 100)
lines(w,
      predict(g, data.frame(weight = w)),
      lty = 2,
      col = COL[5, 3])
dev.off()


================================================
FILE: ch_summarizing_data/figures/countyIncomeSplitByPopGain/countyIncomeSplitByPopGain.R
================================================
library(openintro)
data(countyComplete)
data(COL)

cc  <- county
pop <- sign(cc$pop2017 - cc$pop2010 - 0.5)
sum(is.na(pop))
pov <- cc$median_hh_income

set.seed(1)
these <- sample(sum(pop == -1, na.rm = TRUE), 50)
sampL <- round(pov[pop == -1][these] / 1000, 1)
these <- sample(sum(pop == 1, na.rm = TRUE), 100)
sampG <- round(pov[pop == 1][these] / 1000, 1)
M  <- matrix(c(sampG, rep("", 2), sampL, rep("", 1)), 17)
DB <- 6
for(i in 1:nrow(M)){
  for(j in 1:ncol(M)){
    cat(M[i,j])
    if (j == DB) {
      cat(" && ")
    } else if (j == ncol(M)) {
      cat(" \\\\")
    } else {
      cat(" & ")
    }
  }
  cat("\n")
}
pop[pop == 1] <- "Gain"
pop[pop == -1] <- "No Gain"


myPDF("countyIncomeSplitByPopGain.pdf", 7.5, 4,
      mar = c(3.6, 4.6, 1, 0.5),
      mgp = c(2.4, 0.7, 0),
      mfrow = 1:2)
boxPlot(pov, pop,
        axes = FALSE,
        xlim = c(0.5, 2.5),
        xlab = 'Change in Population',
        ylab = '',
        lcol = "#00000000",
        col = "#00000000")
axis(1, at = 1:2, c("Gain", "No Gain"))
AxisInDollars(2, at = pretty(pov))
par(las = 0)
mtext("Median Household Income", 2, 3.6)
par(las = 1)
boxPlot(pov[pop == "Gain"],
        lcol = COL[1],
        col = COL[1,3],
        add = 1)
boxPlot(pov[pop == "No Gain"],
        lcol = COL[4],
        col = COL[4,3],
        add = 2)

par(mar = c(3.6, 0.5, 1, 1))

xlim <- range(pov[pop == 'No Gain'], na.rm = TRUE)
histPlot(pov[pop == 'No Gain'],
         breaks = 50,
         col = '#ffffff00',
         border = COL[4],
         probability = TRUE,
         xlim = xlim,
         xlab = 'Median Household Income',
         ylab = '',
         hollow = TRUE,
         axes = FALSE,
         lty = 3,
         lwd = 4)
histPlot(pov[pop == 'No Gain'],
         breaks = 50,
         col = '#ffffff00',
         border = COL[4],
         probability = TRUE,
         add = TRUE,
         hollow = TRUE,
         lty = 3,
         lwd = 2)
histPlot(pov[pop == 'No Gain'],
         breaks = 50,
         col = '#ffffff00',
         border = COL[4],
         probability = TRUE,
         add = TRUE,
         hollow = TRUE,
         lty = 3,
         lwd = 1)
histPlot(pov[pop == 'Gain'],
         breaks = 50,
         col = '#ffffff00',
         border = COL[1],
         probability = TRUE,
         add = TRUE,
         hollow = TRUE,
         lty = 1,
         lwd = 2)
AxisInDollars(1, at = pretty(xlim))
legend('topright',
       col = COL[c(1,4)],
       lty = c(1,3),
       lwd = c(2,2.8),
       legend = c('Gain', 'No Gain'))
legend('topright',
       col = c(rgb(0,0,0,0), COL[4]),
       lty = c(1, 3),
       lwd = c(2,1.4),
       legend = c('Gain', 'No Gain'),
       bg = rgb(0,0,0,0),
       box.col = rgb(0,0,0,0),
       text.col = rgb(0,0,0,0))
legend('topright',
       col = c(rgb(0,0,0,0), COL[4]),
       lty = c(1, 3),
       lwd = c(2,0.7),
       legend = c('Gain', 'No Gain'),
       bg = rgb(0,0,0,0),
       box.col = rgb(0,0,0,0),
       text.col = rgb(0,0,0,0))
dev.off()



================================================
FILE: ch_summarizing_data/figures/countyIntensityMaps/countyIntensityMaps.R
================================================
library(openintro)
source("countyMap.R")

myPDF("countyPovertyMap.pdf", 7.8, 4.5)
val <- county$poverty
val[val > 25] <- 25
countyMap(val, county_complete$FIPS, "red", gtlt=">",
    label = "Poverty")
dev.off()

myPDF("countyPopChangeMap.pdf", 7.8, 4.5)
val <- county$pop_change
val[val > 18] <- 18
countyMap(val, county_complete$FIPS, "ye", gtlt=">",
    label = "Population Change")
dev.off()

myPDF("countyUnemploymentRateMap.pdf", 7.8, 4.5)
val <- county$unemployment_rate
val[val > 7] <- 7
countyMap(val, county_complete$FIPS, "ye", gtlt=">",
    label = "Unemployment Rate")
dev.off()

myPDF("countyHomeownershipMap.pdf", 7.8, 4.5)
val <- county$homeownership
val[val < 55] <- 55
countyMap(val, county_complete$FIPS, "bg", gtlt="<",
    label = "Homeownership Rate")
dev.off()

myPDF("countyMedIncomeMap.pdf", 7.8, 4.5)
val <- county$median_hh_income / 1000
val[val > 75] <- 75
countyMap(val, county_complete$FIPS, "green", gtlt=">",
    label = "Median Household Income", unit = "dollars")
dev.off()


================================================
FILE: ch_summarizing_data/figures/countyIntensityMaps/countyMap.R
================================================
library(maps)
countyMap <- function(values, FIPS,
                      col = c("red", "green", "blue"),
                      varTrans = I,
                      gtlt = "",
                      label = "",
                      units = c("percent", "dollars"),
                      ...){
  if(missing(FIPS)){
    stop("Must provide the county FIPS")
  }
  
  # _____ Drop NAs _____ #
  values[is.na(values)] <- median(values, na.rm = TRUE)
  
  # _____ Scale Values _____ #
  MI  <- min(values)
  MA  <- max(values)
  Leg <- seq(MI, MA, length.out = 50)
  if(identical(varTrans, log)){
    VAL    <- varTrans(values+0.1)
    valCol <- varTrans(values+0.1)
    LegCol <- varTrans(Leg+0.1)
  } else {
    VAL    <- varTrans(values)
    valCol <- varTrans(values)
    LegCol <- varTrans(Leg)
  }
  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01
  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01
  
  val.000 <- 0.500*(1-valCol)
  val.114 <- 0.557*(1-valCol)
  val.200 <- 0.600*(1-valCol)
  val.298 <- 0.649*(1-valCol)
  val.318 <- 0.659*(1-valCol)
  val.337 <- 0.669*(1-valCol)
  val.447 <- 0.724*(1-valCol)
  val.608 <- 0.804*(1-valCol)
  val.741 <- 0.871*(1-valCol)
  val.863 <- 0.932*(1-valCol)
  val.941 <- 0.971*(1-valCol)
  val.957 <- 0.979*(1-valCol)
  
  Leg.000 <- 0.500*(1-LegCol)
  Leg.114 <- 0.557*(1-LegCol)
  Leg.200 <- 0.600*(1-LegCol)
  Leg.298 <- 0.649*(1-LegCol)
  Leg.318 <- 0.659*(1-LegCol)
  Leg.337 <- 0.669*(1-LegCol)
  Leg.447 <- 0.724*(1-LegCol)
  Leg.608 <- 0.804*(1-LegCol)
  Leg.741 <- 0.871*(1-LegCol)
  Leg.863 <- 0.932*(1-LegCol)
  Leg.941 <- 0.971*(1-LegCol)
  Leg.957 <- 0.979*(1-LegCol)
  
  if(col[1] == "red"){
    col <- rgb(val.941, val.318, val.200)
    COL <- rgb(Leg.941, Leg.318, Leg.200)
  } else if(col[1] == "green"){
    col <- rgb(val.298, val.941, val.114)
    COL <- rgb(Leg.298, Leg.941, Leg.114)
    # col <- rgb(val.298, val.447, val.114)
    # COL <- rgb(Leg.298, Leg.447, Leg.114)
  } else if(col[1] == "bg"){
    col <- rgb(val.337, val.741, val.957)
    COL <- rgb(Leg.337, Leg.741, Leg.957)
  } else if(col[1] == "ye"){
    col <- rgb(val.957, val.863, val.000)
    COL <- rgb(Leg.957, Leg.863, Leg.000)
  } else {
    col <- rgb(val.06, val.06, val.10)
    COL <- rgb(Leg.06, Leg.06, Leg.10)
  }

  # _____ Remove These _____ #
  data(county.fips)
  col <- col[match(county.fips$fips, FIPS)]
  plot(0,0,type = "n", axes = FALSE, xlab = "", ylab = "")
  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))
  map("county", col = col, fill = TRUE, resolution = 0,
    lty = 0, projection = "polyconic", mar = rep(0.1,4), add = TRUE, ...)
  
  x1 <- 0.305
  x2 <- 0.335
  for(i in 1:50){
    y1 <- i/50 * 0.25 + 0.48
    y2 <- (i-1)/50 * 0.25 + 0.48
    rect(x1, y1, x2, y2, border = "#00000000", col = COL[i])
  }
  
  
  VR    <- range(VAL)
  VR[3] <- VR[2]
  VR[2] <- mean(VR[c(1,3)])
  
  VR1    <- c()
  VR1[1] <- values[which.min(abs(VAL - VR[1]))]
  VR1[2] <- values[which.min(abs(VAL - VR[2]))]
  VR1[2] <- values[which.min(abs(VAL - VR[3]))]
  
  VR  <- round(VR)
  units <- match.arg(units)
  if (units == "percent") {
    VR <- paste0(VR, "%")
  } else if (units == "dollars") {
    VR <- paste0("$", VR)
  }
  if(gtlt %in% c(">", "><")){
    VR[3] <- paste0(">", VR[3])
  }
  if(gtlt %in% c("<", "><")){
    VR[1] <- paste0("<", VR[1])
  }
  text(0.335, 0.49, VR[1], pos = 4, cex = 0.9)
  text(0.335, 0.605, VR[2], pos = 4, cex = 0.9)
  text(0.335, 0.72, VR[3], pos = 4, cex = 0.9)
  par(srt = 90)
  text(0.395, 0.615, label, pos = 1)
}







================================================
FILE: ch_summarizing_data/figures/county_pop_change_v_pop_transform/county_pop_change_v_pop_transform.R
================================================
library(openintro)
data(COL)

x <- county$pop2010
y <- county$pop_change
cex <- 0.5
col <- COL[1, 4]
col.shell <- COL[1, 2]

myPDF("county_pop_change_v_pop_transform_i.pdf",
      4.5,
      3.3,
      mar = c(3, 3.9, 0.5, 1.2),
      mgp = c(2.8, 0.5, 0))
plot(x, y, type = "n",
     xlab = "",
     ylab = "Population Change",
     axes = FALSE)
abline(h = pretty(y), v = pretty(x), col = COL[7, 3])
points(x, y, pch = 19, cex = cex, col = col)
AxisInPercent(2, at = pretty(y))
at <- pretty(x)
axis(1, at, paste0(at / 1e6, "m"))
box()
points(x, y, cex = cex, col = col.shell)
mtext("Population Before Change (m = millions)", 1, 1.9)
dev.off()


myPDF("county_pop_change_v_pop_transform_log.pdf",
      4.5,
      3.3,
      mar = c(3, 4, 0.5, 1.2),
      mgp = c(1.8, 0.5, 0))
x. <- log(x, 10)
plot(x., y, type = "n",
     xlab = expression(log[10] * "(Population Before Change)"),
     ylab = "",
     axes = FALSE)
abline(h = pretty(y), v = pretty(x.), col = COL[7, 3])
points(x., y, pch = 19, cex = cex, col = col)
points(x., y, cex = cex, col = col.shell)
axis(1)
AxisInPercent(2, at = pretty(y))
par(las = 0)
mtext("Population Change", 2, 2.9)
box()
dev.off()


================================================
FILE: ch_summarizing_data/figures/county_pop_transformed/county_pop_transformed.R
================================================
library(openintro)
data(COL)

d <- county$pop2017
mean(d, na.rm = TRUE)
median(d, na.rm = TRUE)

myPDF("county_pop_transformed_i.pdf",
      4,
      3,
      mar = c(3.4, 4, 0.5, 0.5),
      mgp = c(2.1, 0.5, 0))
hist(d,
     breaks = 25,
     main = "",
     xlab = "Population (m = millions)",
     ylab = "",
     axes = FALSE,
     col = COL[1])
axis(1, at = pretty(d), paste0(pretty(d / 1e6), "m"))
axis(2, seq(0, 3000, 500))
par(las = 0)
mtext("Frequency", 2, 2.9)
dev.off()

myPDF("county_pop_transformed_log.pdf",
      4,
      3,
      mar = c(3.4, 3.7, 0.5, 0.5),
      mgp = c(2.2, 0.5, 0))
expr <- expression(log[10]*"(Population)")
hist(log(d, 10),
     main = "",
     breaks = 15,
     xlab = expr,
     axes = FALSE,
     ylab = "",
     col = COL[1])
axis(1)
axis(2, seq(0, 1000, 500))
par(las = 0)
mtext("Frequency", 2, 2.6)
dev.off()


================================================
FILE: ch_summarizing_data/figures/discRandDotPlot/discRandDotPlot.R
================================================
library(openintro)
data(COL)

set.seed(8535)

gender  <- c(rep('male', 24), rep('female', 24))
outcome <- c(rep(c('promoted', 'not promoted'), c(21, 3)),
             rep(c('promoted', 'not promoted'), c(14, 10)))

nsim    <- 100
n       <- length(gender)
group   <- gender
var1    <- outcome
success <- "promoted"
sim     <- matrix(NA, nrow = n, ncol = nsim)
n1      <- 24
n2      <- 24

statistic <- function(var1, group) {
  t1 <- var1 == success & group == levels(as.factor(group))[1]
  t2 <- var1 == success & group == levels(as.factor(group))[2]
  return(sum(t1) / n1 - sum(t2) / n2)
}

for (i in 1:nsim) {
  sim[,i] <- sample(group, replace = FALSE)
}


sim_dist <- apply(sim, 2, statistic, var1 = outcome)
diffs    <- sim_dist
pval     <- sum(diffs >= 0.29) / nsim
values   <- table(sim_dist)


X <- c()
Y <- c()
for (i in 1:length(diffs)) {
  x   <- diffs[i]
  rec <- sum(sim_dist == x)
  X   <- append(X, rep(x, rec))
  Y   <- append(Y, 1:rec)
}


myPDF('discRandDotPlot.pdf', 6, 3.5,
      mar = c(3.4, 0.5, 0.5, 0.5),
      mgp = c(2.35, 0.6, 0))
plot(X, Y,
     xlim = range(diffs) + c(-1, 1) * sd(diffs) / 4,
     xlab = "Difference in promotion rates",
     axes = FALSE,
     ylim = c(0, max(Y)),
     col = COL[1],
     pch = 20)
at <- seq(-0.4, 0.4, 0.1)
labels <- c(-0.4, "", -0.2, "", 0, "", 0.2, "", 0.4)
axis(1, at, labels)
abline(h = 0)
dev.off()


================================================
FILE: ch_summarizing_data/figures/email50LinesCharacters/email50LinesCharacters.R
================================================
library(openintro)
data(email50)
data(COL)

myPDF("email50LinesCharacters.pdf",
      6,
      3.3,
      mar = c(3, 3.9, 0.5, 1.2),
      mgp = c(2.8, 0.5, 0))
plot(email50$num_char,
     email50$line_breaks,
     pch = 19,
     cex = 1.3,
     col = COL[1, 4],
     xlab = "",
     ylab = "Number of Lines")
points(email50$num_char,
       email50$line_breaks,
       cex = 1.3,
       col = COL[1])
mtext("Number of Characters (in thousands)", 1, 1.9)
dev.off()


================================================
FILE: ch_summarizing_data/figures/email50LinesCharactersMod/email50LinesCharactersMod.R
================================================
library(openintro)
data(email50)
data(COL)

myPDF("email50LinesCharactersMod.pdf",
      4.5,
      3.3,
      mar = c(3, 3.9, 0.5, 1.2),
      mgp = c(2.8, 0.5, 0))
plot(email50$num_char,
     email50$line_breaks,
     pch = 19,
     cex = 1.3,
     col = COL[1,4],
     xlab = "",
     ylab = "line_breaks",
     axes = FALSE)
axis(2)
at <- seq(0, 60, 10)
labels <- seq(0, 60, 10)
axis(1, at, labels)
box()
points(email50$num_char,
       email50$line_breaks,
       cex = 1.3,
       col = COL[1])
mtext("num_char", 1, 1.9)
dev.off()


myPDF("email50LinesCharactersModLog.pdf",
      4.5,
      3.3,
      mar = c(3, 2.9, 0.5, 1.2),
      mgp = c(1.8, 0.5, 0))
plot(log(email50$num_char),
     log(email50$line_breaks),
     pch = 19,
     cex = 1.3,
     col = COL[1,4],
     xlab = "",
     ylab = expression(log[e](line_breaks)))
points(log(email50$num_char),
       log(email50$line_breaks),
       cex = 1.3,
       col = COL[1])
mtext(expression(log[e](num_char)), 1, 1.9)
dev.off()


================================================
FILE: ch_summarizing_data/figures/email50NumCharDotPlotRobustEx/email50NumCharDotPlotRobustEx.R
================================================
library(openintro)
data(email50)
data(COL)

p1 <- email50$num_char
p2 <- p1[-which.max(p1)]
p3 <- p1
p3[which.max(p1)] <- 150

myPDF("email50NumCharDotPlotRobustEx.pdf",
      7.04,
      1.43,
      mar = c(2.6, 0.1, 0.3, 0),
      mgp = c(1.45, 0.25, 0),
      cex.lab = 0.85)


dotPlot(p1,
        at = 3,
        xlab = 'Number of Characters (in thousands)',
        ylab = '',
        pch = 20,
        col = COL[1,3],
        cex = 1,
        ylim = c(0.5, 3.5),
        xlim = c(-35, 151),
        axes = FALSE)
at <- seq(0, 150, 50)
axis(1, at, cex.axis = 0.8)
text(0, 3, 'Original', pos = 2, cex = 0.8)

dotPlot(p2,
        at = 2,
        add = TRUE,
        pch = 20,
        col = COL[1, 3],
        cex = 1)
text(0, 2,
     'Drop 64,401',
     pos = 2,
     cex = 0.8)

dotPlot(p3,
        at = 1,
        add = TRUE,
        pch = 20,
        col = COL[1, 3],
        cex = 1)
text(0, 1,
     '64,401 to 150,000',
     pos = 2,
     cex = 0.8)

dev.off()


# _____ Summary Statistics _____ #
GetSummaries <- function(p) {
  temp <- round(quantile(p, c(0.25, 0.5, 0.75)), 3)
  hold <- temp[3] - temp[1]
  names(hold) <- NULL
  return(c(temp,
           IQR = hold,
           mean = mean(p),
           sd = sd(p)))
}
GetSummaries(p1)
GetSummaries(p2)
GetSummaries(p3)


================================================
FILE: ch_summarizing_data/figures/email50NumCharHist/email50NumCharHist.R
================================================
library(openintro)
data(email50)
data(COL)

H <- hist(email50$num_char,
          breaks = 12,
          plot = FALSE)
counts <- rbind(H$counts)
from   <- head(H$breaks, -1)
to     <- tail(H$breaks, -1)
colnames(counts) <- paste(from, 'to', to)
require(xtable)
xtable(counts)

myPDF("email50NumCharHist.pdf",
      6.05, 3.1,
      mar = c(3.5, 3.5, 0.5, 1),
      mgp = c(2.4, 0.7, 0))
histPlot(email50$num_char,
         breaks = 12,
         xlab = 'Number of Characters (in thousands)',
         ylab = "Frequency",
         ylim = c(0, 20),
         col = COL[1],
         border = COL[5])
dev.off()


================================================
FILE: ch_summarizing_data/figures/emailCharactersDotPlot/emailCharactersDotPlot.R
================================================
library(openintro)
data(email50)
data(COL)



myPDF("emailCharactersDotPlot.pdf",
      7.5,
      1.25,
      mar = c(3.6, 1, 0, 1),
      mgp = c(2.5, 0.7, 0),
      tcl = -0.4)
d <- email50$num_char
dotPlot(d,
        xlab = 'Number of Characters (in thousands)',
        ylab = '',
        pch = 20,
        col = COL[1, 2],
        cex = 1.5,
        ylim = c(0.95, 1.05),
        axes = FALSE)
axis(1, at = seq(0, 70, 10))
M <- mean(d)
polygon(M + c(-2, 2, 0) * 1.5,
        c(0.95, 0.95, 0.98),
        border = COL[4],
        col = COL[4])
dev.off()



set.seed(10)
myPDF("emailCharactersDotPlotStacked.pdf",
      5,
      2,
      mar = c(3.6, 1, 0.5, 1),
      mgp = c(2.5, 0.7, 0))
round.to <- 2
binned <- round.to * round(d / round.to)
tab <- table(binned)
cex    <- 1
plot(0,
     type = "n",
     xlab = paste("Number of Characters",
                  "(in thousands, with rounding)"),
     ylab = "",
     axes = FALSE,
     xlim = c(0, 75),
     ylim = c(-1, max(tab)))
for (i in 1:length(binned)) {
  points(rep(as.numeric(names(tab[i])), tab[i]),
         1:tab[i] - 0.4,
         pch = 19,
         col = COL[1],
         cex = cex)
}
abline(h = 0)
at <- seq(0, 70, 10)
axis(1, at)
polygon(M + c(-1.7, 1.7, 0) * 2.5,
        c(-1.7, -1.7, -0.1),
        border = COL[4],
        col = COL[4])
dev.off()

M
sd(d)


================================================
FILE: ch_summarizing_data/figures/emailNumberBarPlot/emailNumberBarPlot.R
================================================
require(openintro)
data(email)
data(COL)

myPDF('emailNumberBarPlot.pdf',
      7,
      3,
      mar = c(3.6, 4.5, 1, 1.5),
      mgp = c(3.4, 0.7, 0),
      mfrow = 1:2)
t <- table(email$number)
barplot(t,
        axes = TRUE,
        xlab = '',
        ylab = 'count',
        main = '',
        ylim = c(0,2700),
        col = COL[1])
abline(h = 0)
mtext("number", 1, 2.4)

par(mar = c(3.6, 4.7, 1, 1))
barplot(t / sum(t),
        axes = FALSE,
        xlab = 'number',
        ylab = '',
        main = '',
        ylim = c(0, 2700) / sum(t),
        col = COL[1])
at <- seq(0, 0.6, 0.2)
axis(2, at)
par(las = 0)
mtext('proportion', side = 2, line = 2.7)
mtext("number", 1, 2.4)
abline(h = 0)
dev.off()

table(email$number, email$spam)


================================================
FILE: ch_summarizing_data/figures/emailNumberPieChart/emailNumberPieChart.R
================================================
library(openintro)
data(email)
data(COL)

myPDF("emailNumberPieChart.pdf",
      7.5,
      4,
      mar = c(0, 2, 0, 0.5),
      mgp = c(2.4, 0.5, 0))
layout(matrix(1:2, 1), c(1, 1.1))
tab <- table(email$number)
pie(tab, col = COL[c(3, 1, 2)], radius = 0.75)

par(mar = c(3.6, 5.2, 1, 1))
barplot(tab,
        axes = FALSE,
        xlab = 'number',
        ylab = '',
        main = '',
        col = COL[c(3, 1, 2)])
axis(2)
abline(h = 0)
dev.off()


================================================
FILE: ch_summarizing_data/figures/emailSpamNumberMosaicPlot/emailSpamNumberMosaicPlot.R
================================================
library(openintro)
data(email)
email$spam <- ifelse(email$spam == 0, "not spam", "spam")
tab <- table(email[,c('spam', 'number')])
tab  <- t(tab)

rp <- prop.table(tab, 1)
cp <- prop.table(tab, 2)

myPDF("emailNumberMosaic.pdf",
      2.625,
      2.25,
      mar = rep(1, 4) / 4)
mosaicplot(rowSums(tab),
           main = '',
           xlab = '',
           ylab = '',
           off = 4,
           col = COL[c(2,1,4)])
dev.off()
colnames(tab)[1] <- "not\nspam"

email$spam[email$spam == "not spam"] <- "not    \nspam"
myPDF("emailSpamNumberMosaic.pdf",
      3,
      2.25,
      mar = c(0.25, 2, 1, 1))
MosaicPlot(number ~ spam, email,
           col = COL[c(2, 1, 4)],
           off = 0.02)
dev.off()

myPDF("emailSpamNumberMosaicRev.pdf",
      3,
      2.25,
      mar = rep(1, 4) / 4)
colnames(tab)[1] <- "not spam"
mosaicplot(t(tab),
           main = '',
           xlab = '',
           ylab = '',
           col = COL[c(2, 1, 4)])
dev.off()



================================================
FILE: ch_summarizing_data/figures/emailSpamNumberSegBar/emailSpamNumberSegBar.R
================================================
library(openintro)
data(email)
data(COL)

tab <- table(email[,c('spam', 'number')])[2:1, ]
row.names(tab) <- c("spam", "not spam")
tab <- t(tab)

rp <- prop.table(tab, 1)
cp <- prop.table(tab, 2)

myPDF("emailSpamNumberSegBar.pdf",
      4.5,
      3.5,
      mar = c(2, 3, 0.5, 0.5),
      mgp = c(2.2, 0.6, 0))
barplot(apply(tab, 1, sum),
        col = COL[3])
tabTemp <- tab[,1]
names(tabTemp) <- NULL
barplot(tabTemp,
        col = COL[1],
        add = TRUE,
        axes = FALSE)
abline(h = 0)
legend("topright",
       fill = COL[c(3,1)],
       legend = c("not spam", "spam"))
dev.off()

myPDF("emailSpamNumberSegBarSta.pdf",
      4.5,
      3.5,
      mar = c(2, 2.5, 0.5, 0.5),
      mgp = c(2.2, 0.6, 0))
barplot(apply(tab, 1, sum) / apply(tab, 1, sum), col = COL[3])
tabTemp <- rp[, 1]
names(tabTemp) <- NULL
barplot(tabTemp,
        col = COL[1],
        add = TRUE,
        axes = FALSE)
abline(h = 0)
dev.off()



================================================
FILE: ch_summarizing_data/figures/eoce/air_quality_durham/air_quality_durham.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
pm25_durham = read.csv("pm25_2011_durham.csv", 
                       na.strings = ".", stringsAsFactors = FALSE)

# calculate sample size ---------------------------------------------
n = pm25_durham %>%
  filter(!is.na(DAILY_AQI_VALUE)) %>%
  nrow() # n = 91

# histogram parameters ----------------------------------------------
histo = hist(pm25_durham$DAILY_AQI_VALUE, plot = FALSE)
breaks = histo$breaks
width = breaks[2] - breaks[1]
counts = histo$counts
rel_freqs = round(counts / n, 2)

five_perc = n * 0.05

# relative frequency histogram --------------------------------------
pdf("air_quality_durham_rel_freq_hist.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4))
axis(1)
axis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))
abline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4), add = TRUE)
dev.off()

# relative frequency histogram - solution ---------------------------
pdf("air_quality_durham_rel_freq_hist_soln.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4 + 1))
axis(1)
axis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))
abline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])
hist(pm25_durham$DAILY_AQI_VALUE, 
     main = "", xlab = "Daily AQI", ylab = "",
     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4), add = TRUE)
text(x = breaks[-1] - width/2, y = counts + 1, 
     labels = paste(rel_freqs),
     col = COL[4], cex = 1)
dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/air_quality_durham/pm25_2011_durham.csv
================================================
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
1/3/11,37-063-0015,1,5.9,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
1/6/11,37-063-0015,1,10.4,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
1/9/11,37-063-0015,1,5.6,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
1/10/11,37-063-0015,1,6.2,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
1/18/11,37-063-0015,1,9.4,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
1/21/11,37-063-0015,1,5,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
1/24/11,37-063-0015,1,11.5,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
1/27/11,37-063-0015,1,9.8,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
1/30/11,37-063-0015,1,12.5,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
2/2/11,37-063-0015,1,5.5,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
2/5/11,37-063-0015,1,5.3,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
2/8/11,37-063-0015,1,5,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
2/11/11,37-063-0015,1,11.3,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
2/14/11,37-063-0015,1,5.9,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
2/17/11,37-063-0015,1,17.2,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
2/20/11,37-063-0015,1,5.3,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
2/23/11,37-063-0015,1,7.5,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
2/26/11,37-063-0015,1,7.6,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
3/1/11,37-063-0015,1,3.7,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
3/4/11,37-063-0015,1,8.9,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
3/7/11,37-063-0015,1,4.5,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
3/10/11,37-063-0015,1,2.7,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
3/13/11,37-063-0015,1,10.5,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
3/16/11,37-063-0015,1,6.1,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
3/19/11,37-063-0015,1,8.3,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
3/22/11,37-063-0015,1,13.8,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
3/25/11,37-063-0015,1,9.1,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
3/28/11,37-063-0015,1,10.6,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
3/31/11,37-063-0015,1,4.8,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
4/3/11,37-063-0015,1,6.1,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
4/6/11,37-063-0015,1,5.6,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
4/9/11,37-063-0015,1,9.1,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
4/12/11,37-063-0015,1,7.2,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
4/16/11,37-063-0015,1,6.6,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
4/18/11,37-063-0015,1,8.6,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
4/21/11,37-063-0015,1,8.6,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
4/24/11,37-063-0015,1,11,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
4/27/11,37-063-0015,1,5.6,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
4/30/11,37-063-0015,1,6.2,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
5/3/11,37-063-0015,1,8.5,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
5/6/11,37-063-0015,1,9.3,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
5/9/11,37-063-0015,1,8.8,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
5/11/11,37-063-0015,1,18.6,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
5/12/11,37-063-0015,1,20,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
5/15/11,37-063-0015,1,8,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
5/18/11,37-063-0015,1,6.3,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
5/21/11,37-063-0015,1,10.8,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
5/27/11,37-063-0015,1,6.8,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
5/30/11,37-063-0015,1,14.9,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
5/31/11,37-063-0015,1,22.5,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
6/2/11,37-063-0015,1,16.9,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
6/5/11,37-063-0015,1,16.8,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
6/8/11,37-063-0015,1,21.3,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
6/11/11,37-063-0015,1,14.2,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
6/14/11,37-063-0015,1,10.8,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
6/17/11,37-063-0015,1,12,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
6/20/11,37-063-0015,1,8.4,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
6/23/11,37-063-0015,1,4.3,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
6/26/11,37-063-0015,1,14.1,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
6/29/11,37-063-0015,1,8.4,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
7/2/11,37-063-0015,1,17.3,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
7/5/11,37-063-0015,1,10,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
7/8/11,37-063-0015,1,12.4,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
7/14/11,37-063-0015,1,9.2,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
7/17/11,37-063-0015,1,7.9,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
7/18/11,37-063-0015,1,9.6,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
7/21/11,37-063-0015,1,18,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
7/23/11,37-063-0015,1,17.2,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
7/26/11,37-063-0015,1,10,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
7/29/11,37-063-0015,1,14.3,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
8/1/11,37-063-0015,1,10.7,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
8/4/11,37-063-0015,1,16.2,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
8/7/11,37-063-0015,1,10.1,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
8/10/11,37-063-0015,1,8.8,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
8/13/11,37-063-0015,1,15.2,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
8/16/11,37-063-0015,1,10.1,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
8/19/11,37-063-0015,1,13.7,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
8/22/11,37-063-0015,1,8.4,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
8/25/11,37-063-0015,1,6.6,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
8/28/11,37-063-0015,1,15.2,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
8/31/11,37-063-0015,1,8.7,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
9/3/11,37-063-0015,1,15.8,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
9/6/11,37-063-0015,1,3.8,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
9/9/11,37-063-0015,1,10.7,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
9/12/11,37-063-0015,1,11.7,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
9/15/11,37-063-0015,1,13.2,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
9/18/11,37-063-0015,1,2.9,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
9/21/11,37-063-0015,1,4.6,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
9/24/11,37-063-0015,1,5.6,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
9/27/11,37-063-0015,1,8.2,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
9/30/11,37-063-0015,1,5.7,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
1/1/11,37-063-0015,3,16.7125,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
1/2/11,37-063-0015,3,3.754166667,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
1/3/11,37-063-0015,3,4.855555556,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
1/4/11,37-063-0015,3,8.6875,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
1/5/11,37-063-0015,3,10.18333333,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
1/6/11,37-063-0015,3,8.495833333,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
1/7/11,37-063-0015,3,5.991666667,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
1/8/11,37-063-0015,3,5.320833333,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
1/9/11,37-063-0015,3,6.9125,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
1/10/11,37-063-0015,3,6.604166667,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
1/11/11,37-063-0015,3,5.804166667,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
1/12/11,37-063-0015,3,7.808333333,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
1/13/11,37-063-0015,3,9.095833333,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
1/14/11,37-063-0015,3,10.45416667,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
1/15/11,37-063-0015,3,11.92916667,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
1/16/11,37-063-0015,3,14.01666667,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
1/17/11,37-063-0015,3,12.98333333,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
1/18/11,37-063-0015,3,8.579166667,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
1/19/11,37-063-0015,3,7.195833333,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
1/20/11,37-063-0015,3,6.9375,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
1/21/11,37-063-0015,3,4.9125,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
1/22/11,37-063-0015,3,7.183333333,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
1/23/11,37-063-0015,3,14.22916667,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
1/24/11,37-063-0015,3,10.61904762,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
1/25/11,37-063-0015,3,13.15833333,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
1/26/11,37-063-0015,3,3.95,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
1/27/11,37-063-0015,3,10.58333333,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
1/28/11,37-063-0015,3,12.18333333,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
1/29/11,37-063-0015,3,9.420833333,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
1/30/11,37-063-0015,3,14.25833333,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
1/31/11,37-063-0015,3,13.80833333,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
2/1/11,37-063-0015,3,10.25238095,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
2/2/11,37-063-0015,3,6.129166667,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
2/3/11,37-063-0015,3,6.7875,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
2/4/11,37-063-0015,3,7.604166667,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
2/5/11,37-063-0015,3,4.320833333,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
2/6/11,37-063-0015,3,8.225,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
2/7/11,37-063-0015,3,10.31666667,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
2/8/11,37-063-0015,3,6.833333333,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
2/9/11,37-063-0015,3,5.6125,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
2/10/11,37-063-0015,3,7.25,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
2/11/11,37-063-0015,3,11.30833333,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
2/12/11,37-063-0015,3,8.595833333,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
2/13/11,37-063-0015,3,5.2625,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
2/14/11,37-063-0015,3,7.25,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
2/15/11,37-063-0015,3,7.070833333,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
2/16/11,37-063-0015,3,11.10416667,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
2/17/11,37-063-0015,3,21.9125,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
2/18/11,37-063-0015,3,17.39166667,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
2/19/11,37-063-0015,3,2.683333333,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
2/20/11,37-063-0015,3,5.8875,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
2/21/11,37-063-0015,3,7.485714286,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
2/22/11,37-063-0015,3,8.186363636,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
2/23/11,37-063-0015,3,7.770833333,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
2/24/11,37-063-0015,3,10.55833333,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
2/25/11,37-063-0015,3,7.416666667,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
2/26/11,37-063-0015,3,8.770833333,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
2/27/11,37-063-0015,3,15.825,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
2/28/11,37-063-0015,3,10.32380952,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
3/1/11,37-063-0015,3,3.5125,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
3/2/11,37-063-0015,3,8.079166667,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
3/3/11,37-063-0015,3,4.595833333,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
3/4/11,37-063-0015,3,7.416666667,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
3/5/11,37-063-0015,3,5.041666667,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
3/6/11,37-063-0015,3,1.870833333,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
3/7/11,37-063-0015,3,4.6875,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
3/8/11,37-063-0015,3,4.470833333,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
3/9/11,37-063-0015,3,5.904166667,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
3/10/11,37-063-0015,3,2.3875,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
3/11/11,37-063-0015,3,4.395833333,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
3/12/11,37-063-0015,3,8.408333333,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
3/13/11,37-063-0015,3,11.71666667,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
3/14/11,37-063-0015,3,8.875,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
3/15/11,37-063-0015,3,8.416666667,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
3/16/11,37-063-0015,3,6.279166667,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
3/17/11,37-063-0015,3,5.491666667,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
3/18/11,37-063-0015,3,12.34166667,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
3/19/11,37-063-0015,3,7.575,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
3/20/11,37-063-0015,3,6.166666667,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
3/21/11,37-063-0015,3,9.225,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
3/22/11,37-063-0015,3,11.16363636,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
3/23/11,37-063-0015,3,9.745454545,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
3/24/11,37-063-0015,3,3.9625,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
3/25/11,37-063-0015,3,7.483333333,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
3/26/11,37-063-0015,3,6.354166667,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
3/27/11,37-063-0015,3,6.320833333,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
3/28/11,37-063-0015,3,9.5625,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
3/29/11,37-063-0015,3,11.50416667,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
3/30/11,37-063-0015,3,5.7,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
3/31/11,37-063-0015,3,2.891666667,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
4/1/11,37-063-0015,3,9.195833333,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
4/2/11,37-063-0015,3,7.733333333,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
4/3/11,37-063-0015,3,5.570833333,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
4/4/11,37-063-0015,3,7.454166667,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
4/5/11,37-063-0015,3,3.566666667,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
4/6/11,37-063-0015,3,5.520833333,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
4/7/11,37-063-0015,3,7.783333333,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
4/8/11,37-063-0015,3,16.52083333,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
4/9/11,37-063-0015,3,7.883333333,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
4/10/11,37-063-0015,3,5.645833333,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
4/11/11,37-063-0015,3,12.15833333,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
4/12/11,37-063-0015,3,6.129166667,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
4/13/11,37-063-0015,3,4.266666667,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
4/14/11,37-063-0015,3,9.8625,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
4/15/11,37-063-0015,3,8.891666667,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
4/16/11,37-063-0015,3,4.9875,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
4/17/11,37-063-0015,3,4.983333333,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
4/18/11,37-063-0015,3,9.775,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
4/19/11,37-063-0015,3,15.72916667,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
4/20/11,37-063-0015,3,11,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
4/21/11,37-063-0015,3,8.641666667,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
4/22/11,37-063-0015,3,5.8625,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
4/23/11,37-063-0015,3,8.85,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
4/24/11,37-063-0015,3,12.40833333,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
4/25/11,37-063-0015,3,10.1125,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
4/26/11,37-063-0015,3,4.220833333,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
4/27/11,37-063-0015,3,5.514285714,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
4/28/11,37-063-0015,3,6.6375,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
4/29/11,37-063-0015,3,5.904166667,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
4/30/11,37-063-0015,3,7.429166667,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
5/1/11,37-063-0015,3,9.325,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
5/2/11,37-063-0015,3,9.129166667,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
5/3/11,37-063-0015,3,8.104166667,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
5/4/11,37-063-0015,3,3.45,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
5/5/11,37-063-0015,3,5.541666667,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
5/6/11,37-063-0015,3,9.116666667,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
5/7/11,37-063-0015,3,8.679166667,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
5/8/11,37-063-0015,3,7.570833333,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
5/9/11,37-063-0015,3,8.645833333,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
5/10/11,37-063-0015,3,11.79166667,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
5/11/11,37-063-0015,3,16.47916667,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
5/12/11,37-063-0015,3,16.37083333,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
5/13/11,37-063-0015,3,11.47083333,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
5/14/11,37-063-0015,3,9.3875,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
5/15/11,37-063-0015,3,5.691666667,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
5/16/11,37-063-0015,3,4.429166667,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
5/17/11,37-063-0015,3,5.366666667,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
5/18/11,37-063-0015,3,5.170833333,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
5/21/11,37-063-0015,3,9.9375,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
5/22/11,37-063-0015,3,13.2625,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
5/23/11,37-063-0015,3,14.3875,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
5/24/11,37-063-0015,3,10.94166667,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
5/25/11,37-063-0015,3,8.961904762,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
5/26/11,37-063-0015,3,16.26666667,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
5/27/11,37-063-0015,3,3.995238095,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
5/28/11,37-063-0015,3,6.579166667,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
5/29/11,37-063-0015,3,11.26666667,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
5/30/11,37-063-0015,3,13.23333333,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
5/31/11,37-063-0015,3,19.67916667,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
6/1/11,37-063-0015,3,28.65,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
6/2/11,37-063-0015,3,15.675,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
6/3/11,37-063-0015,3,7.979166667,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
6/4/11,37-063-0015,3,14.50833333,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
6/7/11,37-063-0015,3,19.48333333,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
6/8/11,37-063-0015,3,23.2625,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
6/9/11,37-063-0015,3,23.37083333,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
6/10/11,37-063-0015,3,20.39166667,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
6/11/11,37-063-0015,3,14.49583333,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
6/12/11,37-063-0015,3,15.61666667,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
6/15/11,37-063-0015,3,10.1625,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
6/16/11,37-063-0015,3,14.79166667,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
6/17/11,37-063-0015,3,12.15,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
6/18/11,37-063-0015,3,12.39583333,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
6/19/11,37-063-0015,3,4.454545455,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
6/20/11,37-063-0015,3,9.2125,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
6/21/11,37-063-0015,3,42.44583333,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
6/22/11,37-063-0015,3,8.245833333,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
6/23/11,37-063-0015,3,4.825,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
6/24/11,37-063-0015,3,9.716666667,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
6/25/11,37-063-0015,3,11.20416667,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
6/26/11,37-063-0015,3,15.7125,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
6/27/11,37-063-0015,3,15.2,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
6/28/11,37-063-0015,3,9.85,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
6/29/11,37-063-0015,3,8.379166667,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
6/30/11,37-063-0015,3,12.5125,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
7/1/11,37-063-0015,3,16.475,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
7/2/11,37-063-0015,3,18.1875,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
7/3/11,37-063-0015,3,23.37916667,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
7/4/11,37-063-0015,3,19.64583333,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
7/5/11,37-063-0015,3,12.95833333,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
7/6/11,37-063-0015,3,19.87727273,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
7/7/11,37-063-0015,3,11.35833333,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
7/8/11,37-063-0015,3,11.95416667,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
7/9/11,37-063-0015,3,8.570833333,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
7/10/11,37-063-0015,3,17.77916667,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
7/11/11,37-063-0015,3,20.425,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
7/12/11,37-063-0015,3,18.9625,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
7/13/11,37-063-0015,3,18.22083333,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
7/14/11,37-063-0015,3,9.9,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
7/15/11,37-063-0015,3,5.266666667,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
7/16/11,37-063-0015,3,6.266666667,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
7/17/11,37-063-0015,3,7.05,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
7/18/11,37-063-0015,3,10.12916667,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
7/19/11,37-063-0015,3,21.9,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
7/20/11,37-063-0015,3,19.525,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
7/21/11,37-063-0015,3,18.91666667,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
7/22/11,37-063-0015,3,21.9375,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
7/23/11,37-063-0015,3,17.40416667,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
7/24/11,37-063-0015,3,13.30416667,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
7/25/11,37-063-0015,3,9.558333333,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
7/26/11,37-063-0015,3,10.93181818,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
7/27/11,37-063-0015,3,14.6,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
7/28/11,37-063-0015,3,18.75416667,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
7/29/11,37-063-0015,3,14.9,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
7/30/11,37-063-0015,3,19.44166667,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
7/31/11,37-063-0015,3,7.1375,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
8/1/11,37-063-0015,3,9.475,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
8/2/11,37-063-0015,3,15.2875,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
8/3/11,37-063-0015,3,19.225,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
8/4/11,37-063-0015,3,17.52083333,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
8/5/11,37-063-0015,3,15.625,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
8/6/11,37-063-0015,3,8.879166667,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
8/7/11,37-063-0015,3,13.99166667,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
8/8/11,37-063-0015,3,12.27727273,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
8/9/11,37-063-0015,3,9.370833333,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
8/10/11,37-063-0015,3,9.38,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
8/11/11,37-063-0015,3,12.19583333,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
8/12/11,37-063-0015,3,19.375,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
8/13/11,37-063-0015,3,15.075,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
8/14/11,37-063-0015,3,6.225,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
8/15/11,37-063-0015,3,7.8625,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
8/16/11,37-063-0015,3,12.025,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
8/17/11,37-063-0015,3,12.8,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
8/18/11,37-063-0015,3,13.99583333,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
8/19/11,37-063-0015,3,14.75454545,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
8/20/11,37-063-0015,3,12.20416667,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
8/21/11,37-063-0015,3,12.07083333,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
8/22/11,37-063-0015,3,8.283333333,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
8/23/11,37-063-0015,3,8.716666667,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
8/24/11,37-063-0015,3,9.663636364,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
8/25/11,37-063-0015,3,9.220833333,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
8/26/11,37-063-0015,3,8.695833333,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
8/27/11,37-063-0015,3,4.4375,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
8/28/11,37-063-0015,3,15.875,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
8/29/11,37-063-0015,3,13.91666667,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
8/30/11,37-063-0015,3,9.9625,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
8/31/11,37-063-0015,3,8.920833333,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
9/1/11,37-063-0015,3,11.32083333,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
9/2/11,37-063-0015,3,17.72083333,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
9/3/11,37-063-0015,3,16.54166667,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
9/4/11,37-063-0015,3,13.375,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
9/5/11,37-063-0015,3,11.91666667,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
9/6/11,37-063-0015,3,6.391666667,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
9/7/11,37-063-0015,3,5.941666667,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
9/8/11,37-063-0015,3,14.42916667,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
9/9/11,37-063-0015,3,14.83809524,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
9/10/11,37-063-0015,3,11.44166667,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
9/11/11,37-063-0015,3,9.333333333,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
9/12/11,37-063-0015,3,12.28333333,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
9/13/11,37-063-0015,3,14.55416667,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
9/14/11,37-063-0015,3,14.39166667,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
9/15/11,37-063-0015,3,13.4125,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
9/17/11,37-063-0015,3,5.391666667,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
9/18/11,37-063-0015,3,3.333333333,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
9/19/11,37-063-0015,3,5.35,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
9/20/11,37-063-0015,3,7.620833333,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
9/21/11,37-063-0015,3,4.880952381,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
9/22/11,37-063-0015,3,6.152380952,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
9/23/11,37-063-0015,3,5.1,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
9/24/11,37-063-0015,3,7.070833333,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
9/25/11,37-063-0015,3,3.683333333,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
9/26/11,37-063-0015,3,5.120833333,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
9/27/11,37-063-0015,3,9.870833333,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
9/28/11,37-063-0015,3,7.375,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
9/29/11,37-063-0015,3,8.533333333,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
9/30/11,37-063-0015,3,7.195833333,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
10/1/11,37-063-0015,3,2.145833333,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
10/2/11,37-063-0015,3,4.8875,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
10/3/11,37-063-0015,3,5.329166667,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
10/4/11,37-063-0015,3,6.033333333,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
10/5/11,37-063-0015,3,8.304166667,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
10/6/11,37-063-0015,3,9.7875,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
10/7/11,37-063-0015,3,7.325,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
10/8/11,37-063-0015,3,7.35,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
10/9/11,37-063-0015,3,5.775,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
10/10/11,37-063-0015,3,9.020833333,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
10/11/11,37-063-0015,3,10.58636364,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
10/12/11,37-063-0015,3,7.208333333,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
10/13/11,37-063-0015,3,6.2,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
10/14/11,37-063-0015,3,7.366666667,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
10/15/11,37-063-0015,3,7.15,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
10/16/11,37-063-0015,3,5.820833333,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
10/17/11,37-063-0015,3,11.775,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
10/18/11,37-063-0015,3,11.45238095,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
10/19/11,37-063-0015,3,1.5625,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
10/20/11,37-063-0015,3,4.6875,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
10/21/11,37-063-0015,3,6.641666667,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
10/22/11,37-063-0015,3,7.166666667,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
10/23/11,37-063-0015,3,9.904166667,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
10/24/11,37-063-0015,3,12.24583333,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
10/25/11,37-063-0015,3,10.27083333,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
10/26/11,37-063-0015,3,12.9625,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
10/27/11,37-063-0015,3,12.44166667,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
10/28/11,37-063-0015,3,1.645833333,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
10/29/11,37-063-0015,3,2.108333333,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
10/30/11,37-063-0015,3,9.079166667,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
10/31/11,37-063-0015,3,6.483333333,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
11/1/11,37-063-0015,3,7.7625,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
11/2/11,37-063-0015,3,9.508333333,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
11/3/11,37-063-0015,3,11.55416667,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
11/4/11,37-063-0015,3,8.425,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
11/5/11,37-063-0015,3,5.1625,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
11/6/11,37-063-0015,3,5.983333333,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
11/7/11,37-063-0015,3,6.841666667,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
11/8/11,37-063-0015,3,9.458333333,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
11/9/11,37-063-0015,3,8.616666667,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
11/10/11,37-063-0015,3,7.115,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
11/11/11,37-063-0015,3,6.475,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
11/12/11,37-063-0015,3,8.9125,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
11/13/11,37-063-0015,3,9.204166667,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
11/14/11,37-063-0015,3,9.370833333,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
11/15/11,37-063-0015,3,8.975,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
11/16/11,37-063-0015,3,10.07916667,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
11/17/11,37-063-0015,3,3.408333333,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
11/18/11,37-063-0015,3,5.879166667,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
11/19/11,37-063-0015,3,11.85,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
11/20/11,37-063-0015,3,13.17083333,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
11/21/11,37-063-0015,3,8.421052632,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
11/22/11,37-063-0015,3,14.00416667,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
11/23/11,37-063-0015,3,2.25,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
11/24/11,37-063-0015,3,6.575,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
11/25/11,37-063-0015,3,8.775,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
11/26/11,37-063-0015,3,9.8375,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
11/27/11,37-063-0015,3,6.395833333,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
11/28/11,37-063-0015,3,3.883333333,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
11/29/11,37-063-0015,3,2.175,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
11/30/11,37-063-0015,3,4.208333333,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
12/1/11,37-063-0015,3,6.15,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
12/2/11,37-063-0015,3,10.625,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
12/3/11,37-063-0015,3,9.533333333,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
12/4/11,37-063-0015,3,10.95416667,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
12/5/11,37-063-0015,3,9.2,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
12/6/11,37-063-0015,3,4.25,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
12/7/11,37-063-0015,3,1.9375,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
12/8/11,37-063-0015,3,5.558333333,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
12/9/11,37-063-0015,3,10.625,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
12/10/11,37-063-0015,3,9.554166667,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
12/11/11,37-063-0015,3,7.245833333,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
12/12/11,37-063-0015,3,8.633333333,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
12/13/11,37-063-0015,3,11.54583333,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
12/14/11,37-063-0015,3,10.37368421,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
12/15/11,37-063-0015,3,10.6125,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
12/16/11,37-063-0015,3,7.466666667,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
12/17/11,37-063-0015,3,7.541666667,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
12/18/11,37-063-0015,3,10.8375,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
12/19/11,37-063-0015,3,12.025,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
12/20/11,37-063-0015,3,15.22916667,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
12/21/11,37-063-0015,3,8.275,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
12/22/11,37-063-0015,3,7.366666667,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
12/23/11,37-063-0015,3,3.15,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
12/24/11,37-063-0015,3,7.929166667,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
12/25/11,37-063-0015,3,10.7875,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
12/26/11,37-063-0015,3,7.329166667,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
12/27/11,37-063-0015,3,4.120833333,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
12/28/11,37-063-0015,3,4.283333333,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
12/29/11,37-063-0015,3,8.4,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
12/30/11,37-063-0015,3,10.15833333,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
12/31/11,37-063-0015,3,8.616666667,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

================================================
FILE: ch_summarizing_data/figures/eoce/antibiotic_use_children/antibiotic_use_children.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# create data -------------------------------------------------------
conditions = c(rep("Prematurity", 33),
               rep("Neuromuscular", 10),
               rep("Cardiovascular", 16),
               rep("Genetic/metabolic", 6),
               rep("Respiratory", 13),
               rep("Trauma", 10),
               rep("Gastrointestinal", 2),
               rep("Immunocompromised", 2)
               )

# summary table -----------------------------------------------------
summary_table = sort(table(conditions))/sum(table(conditions))

# barplot -----------------------------------------------------------
pdf("antibiotic_use_children_bar.pdf", height = 3, width = 6)
par(mar = c(3.7, 11.3, 0, 0.5), las = 1, mgp = c(2.5, 1, 0),
    cex.lab = 1.25, cex.axis = 1.25)
barplot(summary_table, ylab = "", xlab = "Relative frequency", 
        col = COL[1], horiz = TRUE)
dev.off()

# pie chart ---------------------------------------------------------
pdf("antibiotic_use_children_pie.pdf", height = 3, width = 6)
par(mar=c(0, 2.8, 0, 6), las = 1)
pie(summary_table, 
    col = c(COL[1,1], COL[1,4], COL[2,1], COL[2,4], 
            COL[3,1], COL[3,4], COL[4,1], COL[4,4]), 
    cex = 1, clockwise = FALSE,
    labels = names(summary_table))
dev.off()


================================================
FILE: ch_summarizing_data/figures/eoce/association_plots/association_plots.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# set seed ----------------------------------------------------------
set.seed = 2306

# create x ----------------------------------------------------------
x = seq(0, 10, 0.1)

# create y_poslin: positive linear with x ---------------------------
y_poslin = x * runif(1, min = 0, max = 4) + 
  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - 
  runif(1, min = 0, max = 3)

# create y_neglin: negative linear with x ---------------------------
y_neglin = x * -runif(1, min = 0, max = 4) + 
  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - 
  runif(1, min = 0, max = 5)

# create y_poscur: curved positive with x ---------------------------
y_poscur = x^2 + rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4))

# create y_none: no association with x ------------------------------
y_none = x + rnorm(length(x), mean = 0, sd = runif(1, min = 30, max = 40))

# plot the associations --------------------------------------------- 
Plot <- function(x, y, i) {
  plot(y ~ x,
      xlab = paste0("(", i, ")"),
      ylab = "",
      col = COL[1, 2],
      cex = 1.5)
}

pdf("association_plots.pdf", 10, 2.5)
par(mar = c(2.4, 0.5, 0.5, 0.5), las = 1, mgp = c(0.9, 0.5, 0), 
    cex.lab = 1.75, pch = 19, mfrow = c(1, 4), 
    yaxt = "n", xaxt = "n")
Plot(x, y_poslin, 1)
Plot(x, y_none, 2)
Plot(x, y_poscur, 3)
Plot(x, y_neglin, 4)
dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/cleveland_sacramento/cleveland_sacramento.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# take a sample -----------------------------------------------------
cle_sac = cle_sac[!is.na(cle_sac$personal_income),]

set.seed(8957)
sac = sample(cle_sac$personal_income[cle_sac$city == "Sacramento"], 17)
cle = sample(cle_sac$personal_income[cle_sac$city == "Cleveland"], 21)

# plot of total personal income in Cle and Sac ----------------------
pdf("cleveland_sacramento_hist.pdf", height = 5, width = 7)

par(mar = c(3.7, 2, 1,1), las = 1, mgp = c(2.5, 0.7, 0), 
    mfrow = c(2,1), cex.lab = 1.25)

histPlot(cle, xlim = c(0, 180000), ylim = c(0,10),
         ylab = "", xlab = "", col = COL[1], breaks = 8, axes = FALSE)
axis(1, at = seq(0,180000,45000))
axis(2, at = seq(0,10,5))
text(x = 120000, y = 8, labels = "Cleveland, OH", pos = 4, cex = 1.25)

histPlot(sac, xlim = c(0,180000), ylim = c(0,10), 
         ylab = "", xlab = "Total personal income", col = COL[1], breaks = 8,
         axes = FALSE)
axis(1, at = seq(0,180000,45000))
axis(2, at = seq(0,10,5))
text(x = 120000, y = 8, labels = "Sacramento, CA", pos = 4, cex = 1.25)

dev.off()

# summary stats -----------------------------------------------------
mean(cle, na.rm = TRUE)
sd(cle, na.rm = TRUE)
length(cle)

mean(sac, na.rm = TRUE)
sd(sac, na.rm = TRUE)
length(sac)


================================================
FILE: ch_summarizing_data/figures/eoce/county_commute_times/countyMap.R
================================================
library(maps)
countyMap <- function(values, FIPS,
                      col = c("red", "green", "blue"),
                      varTrans = I,
                      gtlt = "",
                      ...){
  if(missing(FIPS)){
    stop("Must provide the county FIPS")
  }
  
  # _____ Drop NAs _____ #
  FIPS   <- FIPS[!is.na(values)]
  values <- values[!is.na(values)]
  
  # _____ Scale Values _____ #
  MI  <- min(values)
  MA  <- max(values)
  Leg <- seq(MI, MA, length.out = 50)
  if(identical(varTrans, log)){
    VAL    <- varTrans(values+0.1)
    valCol <- varTrans(values+0.1)
    LegCol <- varTrans(Leg+0.1)
  } else {
    VAL    <- varTrans(values)
    valCol <- varTrans(values)
    LegCol <- varTrans(Leg)
  }
  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01
  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01
  
  val.000 <- 0.500*(1-valCol)
  val.114 <- 0.557*(1-valCol)
  val.200 <- 0.600*(1-valCol)
  val.298 <- 0.649*(1-valCol)
  val.318 <- 0.659*(1-valCol)
  val.337 <- 0.669*(1-valCol)
  val.447 <- 0.724*(1-valCol)
  val.608 <- 0.804*(1-valCol)
  val.741 <- 0.871*(1-valCol)
  val.863 <- 0.932*(1-valCol)
  val.941 <- 0.971*(1-valCol)
  val.957 <- 0.979*(1-valCol)
  
  Leg.000 <- 0.500*(1-LegCol)
  Leg.114 <- 0.557*(1-LegCol)
  Leg.200 <- 0.600*(1-LegCol)
  Leg.298 <- 0.649*(1-LegCol)
  Leg.318 <- 0.659*(1-LegCol)
  Leg.337 <- 0.669*(1-LegCol)
  Leg.447 <- 0.724*(1-LegCol)
  Leg.608 <- 0.804*(1-LegCol)
  Leg.741 <- 0.871*(1-LegCol)
  Leg.863 <- 0.932*(1-LegCol)
  Leg.941 <- 0.971*(1-LegCol)
  Leg.957 <- 0.979*(1-LegCol)
  
  if(col[1] == "red"){
    col <- rgb(val.941, val.318, val.200)
    COL <- rgb(Leg.941, Leg.318, Leg.200)
  } else if(col[1] == "green"){
    col <- rgb(val.298, val.447, val.114)
    COL <- rgb(Leg.298, Leg.447, Leg.114)
  } else if(col[1] == "bg"){
    col <- rgb(val.337, val.608, val.741)
    COL <- rgb(Leg.337, Leg.608, Leg.741)
  } else if(col[1] == "ye"){
    col <- rgb(val.957, val.863, val.000)
    COL <- rgb(Leg.957, Leg.863, Leg.000)
  } else {
    col <- rgb(val.06, val.06, val.10)
    COL <- rgb(Leg.06, Leg.06, Leg.10)
  }

  # _____ Remove These _____ #
  data(county.fips)
  col <- col[match(county.fips$fips, FIPS)]
  plot(0,0,type = "n", axes = FALSE, xlab = "", ylab = "")
  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))
  map("county", col = col, fill = TRUE, resolution = 0,
    lty = 0, projection = "polyconic", mar = rep(0.1,4), add = TRUE, ...)
  
  x1 <- 0.335
  x2 <- 0.365
  for(i in 1:50){
    y1 <- i/50 * 0.25 + 0.5
    y2 <- (i-1)/50 * 0.25 + 0.5
    rect(x1, y1, x2, y2, border = "#00000000", col = COL[i])
  }
  
  VR    <- range(VAL)
  VR[3] <- VR[2]
  VR[2] <- mean(VR[c(1,3)])
  
  VR1    <- c()
  VR1[1] <- values[which.min(abs(VAL - VR[1]))]
  VR1[2] <- values[which.min(abs(VAL - VR[2]))]
  VR1[2] <- values[which.min(abs(VAL - VR[3]))]
  
  VR    <- round(VR)
  if(gtlt %in% c(">", "><")){
    VR[3] <- paste(">", VR[3], sep = "")
  }
  if(gtlt %in% c("<", "><")){
    VR[1] <- paste("<", VR[1], sep = "")
  }
  text(0.365, 0.51, VR[1], pos = 4)
  text(0.365, 0.625, VR[2], pos = 4)
  text(0.365, 0.74, VR[3], pos = 4)
}







================================================
FILE: ch_summarizing_data/figures/eoce/county_commute_times/county_commute_times.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load mapproj package for map functions ----------------------------
library(mapproj)

# load data ---------------------------------------------------------
data(countyComplete)

# histogram of travel to work time ----------------------------------
pdf("county_commute_times_hist.pdf", 7.5, 4)

par(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(county_complete$mean_work_travel_2010, breaks = 40, 
         xlab = "Mean work travel (in min)", ylab = "", 
         col = COL[1], axes = FALSE)
axis(1)
axis(2, at = seq(0, 200, 100))

dev.off()

# source custom code for county maps --------------------------------
source("countyMap.R")

# map of travel to work time ----------------------------------------

pdf("county_commute_times_map.pdf", 7.5, 4)

val <- county_complete$mean_work_travel_2010
val[val >= 33] <- 33
countyMap(val, county_complete$FIPS, "green", gtlt = ">")

dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/county_hispanic_pop/countyMap.R
================================================
library(maps)
countyMap <- function(values, FIPS,
                      col = c("red", "green", "blue"),
                      varTrans = I,
                      gtlt = "",
                      ...){
  if(missing(FIPS)){
    stop("Must provide the county FIPS")
  }
  
  # _____ Drop NAs _____ #
  FIPS   <- FIPS[!is.na(values)]
  values <- values[!is.na(values)]
  
  # _____ Scale Values _____ #
  MI  <- min(values)
  MA  <- max(values)
  Leg <- seq(MI, MA, length.out = 50)
  if(identical(varTrans, log)){
    VAL    <- varTrans(values+0.1)
    valCol <- varTrans(values+0.1)
    LegCol <- varTrans(Leg+0.1)
  } else {
    VAL    <- varTrans(values)
    valCol <- varTrans(values)
    LegCol <- varTrans(Leg)
  }
  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01
  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01
  
  val.000 <- 0.500*(1-valCol)
  val.114 <- 0.557*(1-valCol)
  val.200 <- 0.600*(1-valCol)
  val.298 <- 0.649*(1-valCol)
  val.318 <- 0.659*(1-valCol)
  val.337 <- 0.669*(1-valCol)
  val.447 <- 0.724*(1-valCol)
  val.608 <- 0.804*(1-valCol)
  val.741 <- 0.871*(1-valCol)
  val.863 <- 0.932*(1-valCol)
  val.941 <- 0.971*(1-valCol)
  val.957 <- 0.979*(1-valCol)
  
  Leg.000 <- 0.500*(1-LegCol)
  Leg.114 <- 0.557*(1-LegCol)
  Leg.200 <- 0.600*(1-LegCol)
  Leg.298 <- 0.649*(1-LegCol)
  Leg.318 <- 0.659*(1-LegCol)
  Leg.337 <- 0.669*(1-LegCol)
  Leg.447 <- 0.724*(1-LegCol)
  Leg.608 <- 0.804*(1-LegCol)
  Leg.741 <- 0.871*(1-LegCol)
  Leg.863 <- 0.932*(1-LegCol)
  Leg.941 <- 0.971*(1-LegCol)
  Leg.957 <- 0.979*(1-LegCol)
  
  if(col[1] == "red"){
    col <- rgb(val.941, val.318, val.200)
    COL <- rgb(Leg.941, Leg.318, Leg.200)
  } else if(col[1] == "green"){
    col <- rgb(val.298, val.447, val.114)
    COL <- rgb(Leg.298, Leg.447, Leg.114)
  } else if(col[1] == "bg"){
    col <- rgb(val.337, val.608, val.741)
    COL <- rgb(Leg.337, Leg.608, Leg.741)
  } else if(col[1] == "ye"){
    col <- rgb(val.957, val.863, val.000)
    COL <- rgb(Leg.957, Leg.863, Leg.000)
  } else {
    col <- rgb(val.06, val.06, val.10)
    COL <- rgb(Leg.06, Leg.06, Leg.10)
  }

  # _____ Remove These _____ #
  data(county.fips)
  col <- col[match(county.fips$fips, FIPS)]
  plot(0,0,type = "n", axes = FALSE, xlab = "", ylab = "")
  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))
  map("county", col = col, fill = TRUE, resolution = 0,
    lty = 0, projection = "polyconic", mar = rep(0.1,4), add = TRUE, ...)
  
  x1 <- 0.335
  x2 <- 0.365
  for(i in 1:50){
    y1 <- i/50 * 0.25 + 0.5
    y2 <- (i-1)/50 * 0.25 + 0.5
    rect(x1, y1, x2, y2, border = "#00000000", col = COL[i])
  }
  
  VR    <- range(VAL)
  VR[3] <- VR[2]
  VR[2] <- mean(VR[c(1,3)])
  
  VR1    <- c()
  VR1[1] <- values[which.min(abs(VAL - VR[1]))]
  VR1[2] <- values[which.min(abs(VAL - VR[2]))]
  VR1[2] <- values[which.min(abs(VAL - VR[3]))]
  
  VR    <- round(VR)
  if(gtlt %in% c(">", "><")){
    VR[3] <- paste(">", VR[3], sep = "")
  }
  if(gtlt %in% c("<", "><")){
    VR[1] <- paste("<", VR[1], sep = "")
  }
  text(0.365, 0.51, VR[1], pos = 4)
  text(0.365, 0.625, VR[2], pos = 4)
  text(0.365, 0.74, VR[3], pos = 4)
}







================================================
FILE: ch_summarizing_data/figures/eoce/county_hispanic_pop/county_hispanic_pop.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load mapproj package for map functions ----------------------------
library(mapproj)

# load data ---------------------------------------------------------
data(county_complete)

# histogram of hispanic % -------------------------------------------
pdf("county_hispanic_pop_hist.pdf", 7.5, 4)

par(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(county_complete$hispanic_2010, breaks = 25, 
         xlab = "Percent Hispanic", ylab = "", 
         col = COL[1], axes = FALSE)
AxisInPercent(1, at = seq(0, 100, 20))
axis(2)

dev.off()

# log of histogram of hispanic % ------------------------------------
pdf("county_hispanic_pop_log_hist.pdf", 7.5, 4)

par(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(log(county_complete$hispanic_2010), breaks = 25, 
         xlab = "log(Percent Hispanic)", ylab = "", 
         col = COL[1])

dev.off()

# source custom code for county maps --------------------------------
source("countyMap.R")

# map of travel to work time ----------------------------------------

pdf("county_hispanic_pop_map.pdf", 7.5, 4)

val <- county_complete$hispanic_2010
val[val >= 40] <- 40
countyMap(val, county_complete$FIPS, "bg", gtlt=">")

dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/dream_act_mosaic/dream_act_mosaic.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# create data -------------------------------------------------------

ideology = c(rep("Conservative", 372), rep("Moderate", 363), rep("Liberal", 175))
ideology = factor(ideology, levels = c("Conservative", "Moderate", "Liberal"))
dream = c(rep("Support", 186), rep("Not support", 151), rep("Not sure", 35), 
          rep("Support", 174), rep("Not support", 161), rep("Not sure", 28),
          rep("Support", 114), rep("Not support", 52), rep("Not sure", 9)
)
dream = factor(dream, levels = c("Support", "Not support", "Not sure"))


# mosaicplot --------------------------------------------------------

pdf("dream_act_mosaic.pdf", 7, 3)
par(mar=c(0.5,0,0.25,0.5), las=1, mgp=c(4,1,0))

mosaicplot(ideology ~ dream, main = "", cex.axis = 1.1, 
           xlab = "", ylab = "", color = COL[1])

dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/estimate_mean_median_simple/estimate_mean_median_simple.R
================================================
# load packages -----------------------------------------------------
library(openintro)

# create data -------------------------------------------------------
set.seed(9823)
x <- 100 * rbeta(400, 12, 3)

# plot --------------------------------------------------------------
myPDF("estimate_mean_median_simple.pdf", 6, 2,
      mar = c(1.7, 2.2, 0.2, 0.4), cex = 1.1)
h <- hist(
    x,
    col = COL[1],
    xlab = "",
    ylab = "",
    main = "",
    axes = FALSE)
axis(1)
at <- pretty(par("yaxp")[1:2])
axis(2)
abline(h = at, col = COL[6, 2], lty = 2)
hist(x, col = COL[1, 2], add = TRUE)
dev.off()


================================================
FILE: ch_summarizing_data/figures/eoce/hist_box_match/hist_box_match.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# generate data -----------------------------------------------------
set.seed(7365)

sym = rnorm(1000, mean = 60, sd = 3)
uni = runif(1000, min = 0, max = 100)
rs = rgamma(1000, shape = 3, rate = 2)

# histograms and box plots ------------------------------------------
pdf("hist_box_match.pdf", width = 10, height = 3)
par(mar=c(4, 3.6, 0, 0), las = 1, mgp = c(2.7, 0.7, 0), 
    mfrow = c(1,6), 
    cex.lab = 1.5, cex.axis = 1.5)

histPlot(sym, xlab = "(a)", ylab = "", col = COL[1], axes = FALSE)
axis(1, seq(50,70,10))

histPlot(uni, xlab = "(b)", ylab = "", col = COL[1], axes = FALSE)
axis(1, seq(0,100,50))

histPlot(rs, xlab = "(c)", ylab = "", col = COL[1], axes = FALSE)
axis(1, seq(0,6,2))

boxPlot(rs, xlab = "(1)", ylab = "", col = COL[1,3])
boxPlot(sym, xlab = "(2)", ylab = "", col = COL[1,3])
boxPlot(uni, xlab = "(3)", ylab = "", col = COL[1,3])

dev.off()


================================================
FILE: ch_summarizing_data/figures/eoce/hist_vs_box/hist_vs_box.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# generate data -----------------------------------------------------
set.seed(12345)
bimod = c(rnorm(300, mean = 5, sd = 1), 
          rnorm(300, mean = 12, sd = 1), 
          runif(25, min = 13, max = 28))

# histogram and box plot --------------------------------------------
pdf("hist_vs_box.pdf", height = 2.2, width = 8)
par(mar = c(2, 2.8, 0.2, 0.5), las = 1, mgp = c(2.9, 0.7, 0),
    cex.axis = 1.5, cex.lab = 1.5)
layout(matrix(1:2, 1), 2:1)
histPlot(bimod, xlab = "", ylab = "", yaxt = "n", col = COL[1])
par(mar = c(2, 2.8, 0.2, 0))
boxPlot(bimod, col = COL[1,2], xlim = c(0.4, 1.6))
dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/income_coffee_shop/income_coffee_shop.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# generate data -----------------------------------------------------
set.seed(956)

sal_symmetric = rnorm(40, mean = 65000, sd = 2000)
sal_skewed = c(sal_symmetric, 225000, 250000)

options(scipen=2)

# histograms --------------------------------------------------------
pdf("income_coffee_shop.pdf", 5.5, 4.3)
par(mar = c(3.6, 2, 0.5, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    mfrow = c(2,1), cex.lab = 1.5, cex.axis = 1)

histPlot(sal_symmetric, xlim = c(60000, 70000), 
         xlab = "(1)", ylim = c(0,12), col = COL[1], 
         axes = FALSE, ylab = "")
AxisInDollars(1, at = seq(0, 1000000, 2500))
axis(2, at = seq(0,12,4))

histPlot(sal_skewed, xlab = "(2)", ylim = c(0,12), 
         breaks = seq(0, 260000, by = 1000), col = COL[1], 
         axes = FALSE, xlim = c(60000,260000), ylab = "")
AxisInDollars(1, at = seq(60000, 260000, 50000))
axis(2, at = seq(0,12,4))

dev.off()

# summary stats -----------------------------------------------------
library(xtable)

summary_table = as.data.frame(cbind(summary(sal_symmetric), summary(sal_skewed)))
names(summary_table) = c("(1)","(2)")
summary_table = rbind(c(length(sal_symmetric), length(sal_skewed)), 
                      summary_table, c(sd(sal_symmetric), sd(sal_skewed)))
rownames(summary_table)[1] = "n"
rownames(summary_table)[dim(summary_table)[1]] = "SD"

xtable(summary_table, digits = 0)

================================================
FILE: ch_summarizing_data/figures/eoce/infant_mortality_rel_freq/infant_mortality.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(dplyr)

# load data ---------------------------------------------------------
load("factbook.rda")
# this dataset will also be available in the cia_factbook package
# with the same name

# calculate # of countries with life exp. & internet data -----------
cia_factbook %>%
  subset(!is.na(infant_mortality_rate)) %>%
  nrow() # n = 224

# histogram parameters ----------------------------------------------
histo = hist(cia_factbook$infant_mortality_rate, plot = FALSE)
breaks = histo$breaks
width = breaks[2] - breaks[1]
counts = histo$counts
n = sum(counts)
rel_freqs = round(counts / n, 2)

five_perc = n * 0.05

# rel. freq. histogram of infant mortality --------------------------
pdf("infant_mortality_rel_freq_hist.pdf", 5.5, 3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(cia_factbook$infant_mortality_rate, 
     main = "",
     xlab = "Infant Mortality (per 1000 Live Births)",
     ylab = "Fraction of Countries",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))
axis(1)
axis(2, at = seq(0, 8 * five_perc, 2 * five_perc),
     labels = seq(0, 0.4, 0.1))
axis(2, at = seq(five_perc, 7 * five_perc, 2 * five_perc),
     labels = rep("", 4), tcl = -0.25)
abline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])
hist(cia_factbook$infant_mortality_rate, 
     main = "", xlab = "", ylab = "",
     col = COL[1], axes = FALSE, add = TRUE)
dev.off()

# rel. freq. histogram of infant mortality  - solution --------------
summary(cia_factbook$infant_mortality_rate)

pdf("infant_mortality_rel_freq_hist_soln.pdf", 6, 3.2)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
hist(cia_factbook$infant_mortality_rate, 
     main = "",
     xlab = "Infant Mortality (per 1000 Live Births)",
     ylab = "Fraction of Countries",
     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))
axis(1)
axis(2, at = seq(0, five_perc*8, five_perc), label = c(0, NA, 0.1, NA, 0.2, NA, 0.3, NA, 0.4))
abline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])
hist(cia_factbook$infant_mortality_rate, 
     main = "", xlab = "", ylab = "",
     col = COL[1], axes = FALSE, add = TRUE)
text(x = breaks[-1] - width/2, y = counts + 5, 
     labels = paste(rel_freqs),
     col = COL[4], cex = 1)
dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/mammal_life_spans/mammal_life_spans.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(mammals)

# calculate # of countries with life exp. & internet data -----------
nrow(mammals) # n = 62

# scatterplot of gpa vs. study hours --------------------------------
pdf("mammal_life_spans_scatterplot.pdf", 5.5, 4.3)
par(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
plot(mammals$LifeSpan ~ mammals$Gestation, 
     xlab = "Gestation (days)", ylab = "Life Span (years)", 
     pch = 20, col = COL[1], axes = FALSE)
axis(1, at = seq(0, 600, 100), labels = c(0, NA, 200, NA, 400, NA, 600))
axis(2, at = seq(0, 100, 25))
box()
dev.off()


================================================
FILE: ch_summarizing_data/figures/eoce/marathon_winners/marathon_winners.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# load data ---------------------------------------------------------
data(marathon)

# histogram and box plot of marathon finishing times of winners -----
pdf("marathon_winners_hist_box.pdf", height = 2.2, width = 7)

par(mar = c(2, 2.8, 0.5, 5), las = 1, mgp = c(2.9, 0.7, 0),
    cex.axis = 1.5, cex.lab = 1.5)
layout(matrix(1:2, 1), 2:1)
histPlot(marathon$Time, col = COL[1], 
         xlab = "Marathon times", ylab = "", yaxt = "n", 
         axes = FALSE)
axis(1, at = seq(2, 3.2, 0.4))
axis(2, at = seq(0, 20, 10))

par(mar = c(2, 2.8, 0.5, 0))
boxPlot(marathon$Time, col = COL[1,2], ylim = c(2, 3.2),
        ylab = "Marathon times",
        axes = FALSE)
axis(2, at = seq(2, 3.2, 0.4))

dev.off()

# finishing times vs. gender ----------------------------------------
pdf("marathon_winners_gender_box.pdf", height = 1.5, width = 7)

par(mar = c(2, 5.1, 0, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    mfrow = c(1,1), cex.lab = 1.5, cex.axis = 1.5)
boxPlot(marathon$Time, horiz = TRUE, fact = marathon$Gender, 
        xlim = c(2,3.2), ylim = c(0.5, 2.5),
        axes = FALSE, col = COL[1,2])
axis(1, at = seq(2,3.2,0.4))
axis(2, at = c(1,2), labels = c("Women", "Men"))

dev.off()

# times series by gender --------------------------------------------
pdf("marathon_winners_time_series.pdf", height = 3, width = 9)

par(mar = c(2, 4, 0.5, 1.3), las = 1, mgp = c(2.7, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
plot(marathon$Time[marathon$Gender == "m"] ~ marathon$Year[marathon$Gender == "m"],
     xlab = "Year", ylab = "Marathon times", 
     pch = 19, col = COL[1], ylim = c(2, 3.2), axes = FALSE)

points(marathon$Time[marathon$Gender == "f"] ~ marathon$Year[marathon$Gender == "f"],
       xlab = "Year", pch = 4, lwd = 1.7, col = COL[2])
axis(1)
axis(2, at = seq(2, 3.2, 0.4))
legend("topright", inset = 0, pch = c(4, 19), col = c(COL[2], COL[1]), 
       legend = c("Women", "Men"))

dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/office_productivity/office_productivity.R
================================================
# set seed ------------------------------------------------
set.seed(2406)

# sketch --------------------------------------------------
pdf("office_productivity_sketch.pdf", 5.5, 3)
par(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), 
    mfrow = c(1,1), cex.lab = 1.5)
curve(rev(dgamma(x, 2.5,1/2)), 0, 14, 
      xlab = "stress", ylab = "productivity", lwd = 2, axes = FALSE)
box()
dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/oscar_winners/oscar_winners.R
================================================
# load packages -----------------------------------------------------
library(openintro)
library(forcats)

# load data ---------------------------------------------------------
data(oscars)

# plot of oscar winner women and men ages ---------------------------
myPDF("oscars_winners_hist.pdf", 5, 3.15)
oscars %>%
  ggplot(aes(x = age)) +
    geom_histogram(binwidth = 10, fill = COL[1,1], color = COL[5,1], size = 0.3) +
    facet_wrap(~fct_rev(award), ncol = 1) +
    theme_minimal() +
    theme(strip.text = element_text(hjust = 0)) +
    labs(x = "Age (in years)", y = "")
dev.off()

# summary stats -----------------------------------------------------
oscars %>%
  group_by(award) %>%
  summarise(
    mean = mean(age),
    sd = sd(age),
    n = n()
  )


================================================
FILE: ch_summarizing_data/figures/eoce/raise_taxes_mosaic/raise_taxes_mosaic.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# create data -------------------------------------------------------
# based on http://www.publicpolicypolling.com/pdf/2015/PPP_Release_National_30215.pdf

n = 691

n_dem = round(n * 0.40)
n_rep = round(n * 0.34)
n_indep = 691 - (n_dem + n_rep)

party =  c(rep("Democrat", n_dem), rep("Republican", n_rep), rep("Indep / Other", n_indep))
party = factor(party, levels = c("Democrat", "Republican", "Indep / Other"))
taxes = c(rep("Raise taxes on the rich", round(n_dem * 0.91)), 
          rep("Raise taxes on the poor", round(n_dem * 0.04)), 
          rep("Not sure", round(n_dem * 0.05)),
          rep("Raise taxes on the rich", round(n_rep * 0.47)), 
          rep("Raise taxes on the poor", round(n_rep * 0.10)), 
          rep("Not sure", round(n_rep * 0.43)),
          rep("Raise taxes on the rich", round(n_indep * 0.49)), 
          rep("Raise taxes on the poor", round(n_indep * 0.11)), 
          rep("Not sure", round(n_indep * 0.40)) 
)
taxes = factor(taxes, levels = c("Raise taxes on the rich", "Raise taxes on the poor", "Not sure"))


# mosaicplot --------------------------------------------------------

pdf("raise_taxes_mosaic.pdf", 7, 3)
par(mar=c(0.5,0,0.2,0.5), las=1, mgp=c(4,1,0))

mosaicplot(party ~ taxes, main = "", cex.axis = 1.1, 
           xlab = "", ylab = "", color = COL[1])

dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/randomization_avandia/randomization_avandia.R
================================================
# load openintro package for colors -----------------------
library(openintro)

# create data ---------------------------------------------
gr <- c(rep("rosig", 67593), rep("piog",159978))
out <- c(rep(c("y", "n"), c(2593, 67593-2593)), 
         rep(c("y", "n"), c(5386, 159978-5386)))

set.seed(13)
N <- 10^2
rand_dist <- rep(NA, N)
for(i in 1:N){
  rand_group <- sample(gr)
  rand_dist[i] <- sum(out[rand_group == "rosig"] == "y")
}

# plot randomization distribution -----------------------------------
pdf("randomization_avandia.pdf", 6, 4)
par(mar = c(4,2.7,0,0), las = 1 , mgp = c(2.7, 0.9, 0), 
    cex.lab = 1.5, cex.axis = 1.5)
histPlot(rand_dist, main="", 
         xlab = "Simulated rosiglitazone cardiovascular events", ylab="", 
         col = COL[1], axes = FALSE)
axis(1, at = seq(2250, 2550, 100))
axis(2, at = (0:4)*N/20, labels = c(0, NA, 2, NA, 4)/20)
abline(h = 0)
dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/randomization_heart_transplants/randomization_heart_transplants.R
================================================
library(openintro)
heartTr <- heart_transplant

# mosaic plot -------------------------------------------------------
pdf("randomization_heart_transplants_mosaic.pdf", 5.5, 4.3)
par(mar = c(0, 0, 0, 0), las = 1, mgp = c(2.7, 0.9, 0))
mosaicplot(transplant ~ survived, data = heartTr, 
           main = "", xlab = "", ylab = "", color = COL[1],
           cex.axis = 1.25)
dev.off()

# box plot ----------------------------------------------------------
pdf("randomization_heart_transplants_box.pdf", 5.5, 4.3)
par(mar = c(2, 4.8, 0, 0), las = 1, mgp = c(3.5, 0.7, 0), 
    cex.lab = 1.5, cex.axis = 1.25)
boxPlot(heartTr$survtime, fact = heartTr$transplant, 
        ylab = "Survival Time (days)", col = COL[1,2])
dev.off()

# randomization -----------------------------------------------------
load("inference.RData")

diffs = inference(heartTr$survived, heartTr$transplant, 
                  success = "dead", order = c("treatment","control"), 
                  est = "proportion", type = "ht", method = "simulation", 
                  nsim = 100, null = 0, alternative = "twosided", simdist = TRUE,
                  seed = 95632)

# plot randomization distribution -----------------------------------
pdf("randomization_heart_transplants_rando.pdf", height = 3, width = 7)

par(mar = c(3.6, 2.2, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), 
    cex.axis = 1.25, cex.lab = 1.5)

values  <- table(diffs)
plot(diffs, type = "n", xlim = c(-0.25, 0.25), 
     xlab = "simulated differences in proportions", 
     ylab = "", axes = FALSE, ylim = c(0, max(values)))
axis(1, at = seq(-0.25, 0.25, 0.05), 
     labels = c(-0.25, NA,-0.15, NA,-0.05, NA, 0.05, NA, 0.15, NA, 0.25))
for(i in 1:length(diffs)){
  x   <- diffs[i]
  rec <- sum(diffs == x)
  points(rep(x, rec), 1:rec, pch = 20, cex = 0.8, col = COL[1])
}

dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/reproducing_bacteria/reproducing_bacteria.R
================================================
# set seed ------------------------------------------------
set.seed(2406)

# sketch --------------------------------------------------
pdf("reproducing_bacteria_sketch.pdf", 5.5, 3)
par(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), 
    mfrow = c(1,1), cex.lab = 1.5)
curve(-1*dexp(x, rate = 4), lwd = 2,
      xlab = "time", ylab = "number of bacteria cells", axes = FALSE)
box()
dev.off()

================================================
FILE: ch_summarizing_data/figures/eoce/stats_scores_box/stats_scores_box.R
================================================
# load openintro package for colors ---------------------------------
library(openintro)

# data --------------------------------------------------------------
stats_scores = c(79, 83, 57, 82, 94, 83, 72, 74, 73, 71, 66, 89, 78, 
                 81, 78, 81, 88, 69, 77, 79)

# summary -----------------------------------------------------------
summary(stats_scores)

# scatterplot of gpa vs. study hours --------------------------------
pdf("stats_scores_boxplot.pdf", 5.5, 2)
par(mar = c(3, 0.5, 0.5, 0.5), las = 1, mgp = c(1.75, 0.7, 0), 
    cex.axis = 1.5, cex.lab = 1.5)
boxplot(stats_scores, horizontal = TRUE, col = COL[1], xlab = "Scores")
dev.off()

================================================
FILE: ch_summarizing_data/figures/histMLBSalaries/histMLBSalaries.R
================================================
library(openintro)
data(MLB)
data(COL)

myPDF("histMLBSalariesReg.pdf",
      4,
      3,
      mar = c(3.4, 2.4, 0.5, 0.5),
      mgp = c(2.1, 0.5, 0))
hist(MLB$salary/1000,
     breaks = 15,
     main = "",
     xlab = "Salary (millions of dollars)",
     ylab = "",
     axes = FALSE,
     col = COL[1])
axis(1, seq(0, 40, 10))
axis(2, c(0, 500))
axis(2,
     seq(100, 400, 100),
     rep("", 4),
     tcl = -0.2)
dev.off()

myPDF("histMLBSalariesLog.pdf",
      4,
      3,
      mar = c(3.4, 2.4, 0.5, 0.5),
      mgp = c(2.2, 0.5, 0))
expr <- expression(log[e]*"(Salary), where Salary is in millions USD")
hist(log(MLB$salary/1000),
     main = "",
     breaks = 15,
     xlab = expr,
     axes = FALSE,
     ylab = "",
     col = COL[1])
axis(1)
axis(2, seq(0, 300, 100))
dev.off()


================================================
FILE: ch_summarizing_data/figures/loan50IncomeHist/loan50IncomeHist.R
================================================
library(openintro)
data(email50)
data(COL)

x <- loan50$total_income
H <- hist(x,
          breaks = 12,
          plot = FALSE)
counts <- rbind(H$counts)
from   <- head(H$breaks, -1)
to     <- tail(H$breaks, -1)
colnames(counts) <- paste(from, 'to', to)
require(xtable)
xtable(counts)

myPDF("loan50IncomeHist.pdf",
      6.05, 3.1,
      mar = c(3.5, 3.5, 0.5, 1),
      mgp = c(2.4, 0.7, 0))
histPlot(x,
         breaks = seq(0, 350e3, 25e3),
         # breaks = seq(0, 40000, 5000),
         xlab = 'Total Income',
         ylab = "Frequency",
         # ylim = c(0, 20),
         col = COL[1],
         border = COL[5],
         axes = FALSE)
bin <- table(round(x / 2000) * 2000)
for (i in 1:length(bin)) {
  # points(rep(as.numeric(names(bin)[i]), bin[i]), 1:(bin[i]), cex = 2)
}
axis(2)
AxisInDollars(1, pretty(x))
dev.off()


================================================
FILE: ch_summarizing_data/figures/loan50IntRateHist/loan50IntRateHist.R
================================================
library(openintro)
data(email50)
data(COL)

x <- loan50$interest_rate
H <- hist(x,
          breaks = 12,
          plot = FALSE)
counts <- rbind(H$counts)
from   <- head(H$breaks, -1)
to     <- tail(H$breaks, -1)
colnames(counts) <- paste(from, 'to', to)
require(xtable)
xtable(counts)

myPDF("loan50IntRateHist.pdf",
      6.05, 3.1,
      mar = c(3.5, 3.5, 0.5, 1),
      mgp = c(2.4, 0.7, 0))
histPlot(x,
         breaks = seq(5, 27.5, 2.5),
         # breaks = seq(0, 350e3, 25e3),
         # breaks = seq(0, 350e3, 25e3),
         # breaks = seq(0, 40000, 5000),
         xlab = 'Interest Rate',
         ylab = "Frequency",
         # ylim = c(0, 20),
         col = COL[1],
         border = COL[5],
         axes = FALSE)
bin <- table(round(x / 2000) * 2000)
for (i in 1:length(bin)) {
  # points(rep(as.numeric(names(bin)[i]), bin[i]), 1:(bin[i]), cex = 2)
}
axis(2)
AxisInPercent(1, pretty(x))
dev.off()


================================================
FILE: ch_summarizing_data/figures/loan50LoanAmountHist/loan50LoanAmountHist.R
================================================
library(openintro)
data(email50)
data(COL)

x <- loan50$loan_amount
H <- hist(x,
          breaks = 12,
          plot = FALSE)
counts <- rbind(H$counts)
from   <- head(H$breaks, -1)
to     <- tail(H$breaks, -1)
colnames(counts) <- paste(from, 'to', to)
require(xtable)
xtable(counts)

myPDF("loan50LoanAmountHist.pdf",
      6.05, 3.1,
      mar = c(3.5, 3.5, 0.5, 1),
      mgp = c(2.4, 0.7, 0))
histPlot(x,
         breaks = seq(0, 40000, 5000),
         xlab = 'Loan Amount',
         ylab = "Frequency",
         # ylim = c(0, 20),
         col = COL[1],
         border = COL[5],
         axes = FALSE)
bin <- table(round(x / 2000) * 2000)
for (i in 1:length(bin)) {
  # points(rep(as.numeric(names(bin)[i]), bin[i]), 1:(bin[i]), cex = 2)
}
axis(2)
AxisInDollars(1, pretty(x))
dev.off()


================================================
FILE: ch_summarizing_data/figures/loan50_amt_vs_income/loan50_amt_vs_income.R
================================================
library(openintro)
data(loan50)
data(COL)

d <- loan50

myPDF("loan50_amt_vs_income.pdf",
      6, 3.5,
      mar = c(3.4, 4.1, 0.5, 0.5),
      mgp = c(2.1, 0.5, 0),
      xaxs = "i", yaxs = "i")
x <- d$total_income
y <- d$loan_amount
plot(x, y, type = "n",
     xlim = c(0, 1.05 * max(x)),
     ylim = c(0, 1.05 * max(y)),
     xlab = "Total Income",
     ylab = "",
     axes = FALSE)
abline(h = pretty(c(0, y)), v = pretty(c(0, x)), col = COL[7, 3])
points(x, y, pch = 19, col = COL[1, 2])
AxisInDollars(1, pretty(c(0, x)))
AxisInDollars(2, pretty(c(0, y)))
mtext("Loan Amount", 2, 3, las = 0)

x. <- seq(min(x), max(x), length.out = 300)
m <- lm(y ~ log(x))
y. <- predict(m, newdata = data.frame(x = x.))
# lines(x., y., lty = 2, col = COL[5, 3])
dev.off()

# library(ggplot2); qplot(x, y, geom = c("point", "smooth"))


================================================
FILE: ch_summarizing_data/figures/loan50_amt_vs_interest/loan50_amt_vs_interest.R
================================================
library(openintro)
data(loan50)
data(COL)

d <- loan50

myPDF("loan50_amt_vs_interest.pdf",
      6, 3.5,
      mar = c(3.4, 4.1, 0.5, 0.5),
      mgp = c(2.1, 0.5, 0),
      xaxs = "i", yaxs = "i")
x <- d$loan_amount
y <- d$interest_rate
plot(x, y,
     xlim = c(0, 1.05 * max(x)),
     ylim = c(0, 1.05 * max(y)),
     xlab = "Loan Amount",
     ylab = "",
     axes = FALSE,
     pch = 19,
     col = COL[1, 2])
AxisInDollars(1, pretty(c(0, x)))
AxisInPercent(2, pretty(c(0, y)))
mtext("Interest Rate", 2, 3, las = 0)
dev.off()



================================================
FILE: ch_summarizing_data/figures/loan_amount_dot_plot/loan_amount_dot_plot.R
================================================
library(openintro)

myPDF("loan_amount_dot_plot.pdf",
      5.5,
      1.25,
      mar = c(3.6, 1, 0, 1),
      mgp = c(2.5, 0.7, 0),
      tcl = -0.4)
d <- loan50$loan_amount
dotPlot(d,
        at = 1.007,
        xlab = 'Loan Amount',
        ylab = '',
        pch = 20,
        col = COL[1, 3],
        cex = 3, # 1.5,
        xlim = c(0, 1.05 * max(d)),
        ylim = c(0.95, 1.05),
        axes = FALSE)
abline(h = 0.983)
AxisInDollars(1, pretty(c(0, d)))
M <- mean(d)
polygon(M + c(-1, 1, 0) * 1500,
        c(0.95, 0.95, 0.98),
        border = COL[4],
        col = COL[4])
dev.off()



set.seed(10)
myPDF("loan_amount_dot_plot_stacked.pdf",
      5.5,
      2.5,
      mar = c(3.6, 1, 0.5, 1),
      mgp = c(2.5, 0.7, 0))
round.to <- 2000
binned <- round.to * round(d / round.to)
tab <- table(binned)
cex    <- 1
plot(0,
     type = "n",
     xlab = "Loan Amount, Rounded to Nearest $1000",
     ylab = "",
     axes = FALSE,
     xlim = c(0, 1.05 * max(d)),
     ylim = c(-1, 1.5 * max(tab)))
for (i in 1:length(tab)) {
  points(rep(as.numeric(names(tab[i])), tab[i]),
         1.5 * (1:tab[i]) - 0.4,
         pch = 19,
         col = COL[1],
         cex = 2 * cex)
}
abline(h = 0)
AxisInDollars(1, pretty(c(0, d)))
polygon(M + c(-1, 1, 0) * 1500,
        c(-1.2, -1.2, -0.1),
        border = COL[4],
        col = COL[4])
dev.off()

M
sd(d)


================================================
FILE: ch_summarizing_data/figures/loan_app_type_home_mosaic_plot/loan_app_type_home_mosaic_plot.R
================================================
if ("loans_full_schema" %in% ls()) {
  rm(loans_full_schema)
}
library(openintro)

# There are some levels for the factor variables below that don't
# have any observations, so they create zeros and break the visuals.
# The next lines address that while ensuring a consistent order of
# the levels for the plots.
application_type_order <- c("individual", "joint")
loans_full_schema$application_type <- factor(
  as.character(loans_full_schema$application_type),
  levels = application_type_order
)
homeownership_order <- c("rent", "mortgage", "own")
loans_full_schema$homeownership <- factor(
  tolower(as.character(loans_full_schema$homeownership)),
  levels = homeownership_order
)

tab <- table(loans_full_schema[,c('application_type', 'homeownership')])
tab  <- t(tab)

rp <- prop.table(tab, 1)
cp <- prop.table(tab, 2)

myPDF("loan_home_mosaic.pdf",
      2.625,
      2.25,
      mar = rep(1, 4) / 4)
mosaicplot(rowSums(tab),
           main = '',
           xlab = '',
           ylab = '',
           off = 4,
           col = COL[c(2,1,4)])
dev.off()
# colnames(tab)[1] <- "not\nspam"

myPDF("loan_app_type_home_mosaic.pdf",
      3,
      2.25,
      mar = c(0.25, 2, 1, 1))
levels(loans_full_schema$application_type)[1] <- "indiv."
MosaicPlot(homeownership ~ application_type, loans_full_schema,
           col = COL[c(2, 1, 4)],
           off = 0.02)
dev.off()

myPDF("loan_app_type_home_mosaic_rev.pdf",
      3 / 1.2,
      2.25 / 1.5,
      mar = rep(1, 4) / 4)
# colnames(tab)[1] <- "not spam"
mosaicplot(t(tab),
           main = '',
           xlab = '',
           ylab = '',
           col = COL[c(2, 1, 4)])
dev.off()



================================================
FILE: ch_summarizing_data/figures/loan_app_type_home_seg_bar/loan_app_type_home_seg_bar.R
================================================
library(openintro)

tab <- table(loans_full_schema[, c("application_type", "homeownership")])
tab <- tab[
    c("individual", "joint"),
    c("RENT", "MORTGAGE", "OWN")]
tab <- t(tab)
rownames(tab) <- tolower(rownames(tab))

rp <- prop.table(tab, 1)
cp <- prop.table(tab, 2)

myPDF("loan_app_type_home_seg_bar.pdf",
    4.5, 3.5,
    mar = c(2, 4, 0.5, 0.5),
    mgp = c(2.2, 0.6, 0))
ylim <- c(0, max(apply(tab, 1, sum)))
barplot(apply(tab, 1, sum),
    col = COL[3],
    ylim = ylim)
tabTemp <- tab[,1]
names(tabTemp) <- NULL
barplot(tabTemp,
    col = COL[1],
    add = TRUE,
    axes = FALSE)
abline(h = 0)
legend("topright",
    fill = COL[c(3,1)],
    legend = c("joint", "individual"))
par(las = 0)
mtext("Frequency", 2, 2.9)
dev.off()

myPDF("loan_app_type_home_sbs_bar.pdf",
    4.5, 3.5,
    mar = c(2, 4, 0.5, 0.5),
    mgp = c(2.2, 0.6, 0))
barplot(t(tab),
    ylim = ylim,
    col = COL[c(1, 3)], beside = TRUE)
abline(h = 0)
legend("topright",
    fill = COL[c(3,1)],
    legend = c("joint", "individual"))
par(las = 0)
mtext("Frequency", 2, 2.9)
dev.off()

myPDF("loan_app_type_home_seg_bar_standardized.pdf",
    5, 3.5,
    mar = c(2, 4, 0.5, 0.5),
    mgp = c(2.2, 0.6, 0))
barplot(apply(tab, 1, sum) / apply(tab, 1, sum), col = COL[3])
tabTemp <- rp[, 1]
names(tabTemp) <- NULL
barplot(tabTemp,
    col = COL[1],
    add = TRUE,
    axes = FALSE)
legend(2.65, 0.3,
    fill = COL[c(3,1)],
    legend = c("joint", "individual"),
    bg = "white")
abline(h = 0)
par(las = 0)
mtext("Proportion", 2, 2.9)
dev.off()



================================================
FILE: ch_summarizing_data/figures/loan_homeownership_bar_plot/loan_homeownership_bar_plot.R
================================================
require(openintro)

x <- loans_full_schema$homeownership

myPDF('loan_homeownership_bar_plot.pdf',
      7,
      3,
      mar = c(3.6, 4.2, 1, 1.5),
      mgp = c(3.2, 0.55, 0),
      mfrow = 1:2)
t <- table(x)
names(t) <- tolower(names(t))
barplot(t,
        axes = TRUE,
        xlab = '',
        ylab = 'Frequency',
        main = '',
        # ylim = c(0,2700),
        col = COL[1])
abline(h = 0)
mtext("Homeownership", 1, 2.4)

par(mar = c(3.6, 4.7, 1, 1))
barplot(t / sum(t),
        axes = FALSE,
        xlab = '',
        ylab = '',
        main = '',
        # ylim = c(0, 2700) / sum(t),
        col = COL[1])
# at <- seq(0, 0.6, 0.2)
axis(2)
# AxisInPercent(2, at = seq(0, 40, 10))
par(las = 0)
mtext('Proportion', side = 2, line = 2.7)
mtext("Homeownership", 1, 2.4)
abline(h = 0)
dev.off()

table(x)


================================================
FILE: ch_summarizing_data/figures/loan_homeownership_pie_chart/loan_homeownership_pie_chart.R
================================================
library(openintro)
data(email)
data(COL)

tab <- table(loans_full_schema$homeownership)

myPDF("loan_homeownership_pie_chart.pdf",
      7.5,
      4,
      mar = c(0, 2, 0, 0.5),
      mgp = c(2.4, 0.5, 0))
layout(matrix(1:2, 1), c(1, 1.1))
pie(tab, col = COL[c(2, 1, 4)], radius = 0.75)

par(mar = c(3.6, 5.2, 1, 1))
barplot(tab,
        axes = FALSE,
        xlab = 'Homeownership',
        ylab = '',
        main = '',
        col = COL[c(2, 1, 4)])
axis(2) #, at = seq(0, 4000, 1000), labels = c(0, paste0(1:4, "k")))
abline(h = 0)
par(las = 0)
mtext("Frequency", 2, line = 2.9)
dev.off()


================================================
FILE: ch_summarizing_data/figures/loan_int_rate_box_plot_layout/loan_int_rate_box_plot_layout.R
================================================
require(openintro)
data(COL)
d <- loan50$interest_rate
the.seed <- 2

myPDF("loan_int_rate_box_plot_layout.pdf", 5.5, 3.8,
      mar = c(0, 4, 0, 1),
      mgp = c(2.8, 0.55, 0))
boxPlot(d,
        ylab = 'Interest Rate',
        xlim = c(0.3, 3),
        axes = FALSE,
        ylim = range(d) + sd(d) * c(-1,1) * 0.2)
AxisInPercent(2, c(0, pretty(d)))

arrows(2, min(d) + 1, 1.35, min(d), length = 0.08)
text(2, min(d) + 1, 'lower whisker', pos = 4)

arrows(2, quantile(d, 0.25) + sd(d) / 7,
       1.35, quantile(d, 0.25),
       length = 0.08)
text(2, quantile(d, 0.25) + sd(d)/6.5,
     expression(Q[1]~~'(first quartile)'), pos = 4)

m <- median(d)
arrows(2, m + sd(d) / 5, 1.35, m, length = 0.08)
text(2,m + sd(d) / 4.7, 'median', pos = 4)

q <- quantile(d, 0.75)
arrows(2, q + sd(d) / 4, 1.35, q, length = 0.08)
text(2, q + sd(d) / 3.8,
     expression(Q[3]~~'(third quartile)'), pos = 4)

arrows(2, rev(sort(d))[3] - sd(d) / 4,
       1.35, rev(sort(d))[3], length = 0.08)
text(2, rev(sort(d))[3] - sd(d) / 3.8,
     'upper whisker', pos = 4)

y <- quantile(d, 0.75) + 1.5 * IQR(d)
arrows(2, y - 0.1 * sd(d),
       1.35, y, length = 0.08)
lines(c(0.72, 1.28), rep(y, 2),
      lty = 3, col = '#00000066')
text(2, y - 0.1 * sd(d),
     'max whisker reach', pos = 4)

m <- rev(tail(sort(d), 5))
s <- m[1] - 0.3 * sd(m)
arrows(2, s, 1.1, m[1] - 0.2, length = 0.08)
arrows(2, s, 1.1, m[2] + 0.3, length = 0.08)
text(2, s, 'suspected outliers', pos = 4)

set.seed(the.seed)
pt.jitter <- 0.05
nco <- 50
cutoffs <- seq(0.9 * min(d), max(d), length.out = nco)
for (i in 2:nco) {
  these <- which(cutoffs[i - 1] < d & d <= cutoffs[i])
  lt <- length(these)
  if (lt == 0) {
    next
  }
  x <- pt.jitter * (1:lt)
  x <- x - mean(x)
  points(rep(0.4, lt) + x, d[these],
      col = rep(COL[1, 3], 25), pch = 19)
}

dev.off()

sort(d)[25:26]
quantile(d, c(0.25, 0.5, 0.75))
tail(sort(d), 4)


myPDF("loan_int_rate_box_plot_layout_small.pdf", 1.5, 2.5,
      mar = c(0, 4.1, 0, 0),
      mgp = c(2.3, 0.45, 0),
      tcl = -0.2)
boxPlot(d,
        ylab = '',
        axes = FALSE,
        xlim = c(0.5, 1.45),
        ylim = range(d) + sd(d) * c(-1,1) * 0.2)
AxisInPercent(2, c(0, pretty(d)), cex = 1.1)
par(las = 0)
mtext("Interest Rate", 2,
      line = 2.5,
      cex = 1.1)
dev.off()


================================================
FILE: ch_summarizing_data/figures/loan_int_rate_dot_plot/loan_int_rate_dot_plot.R
================================================
library(openintro)

d <- loan50$interest_rate
xlim <- c(0.9 * min(d), 1.05 * max(d))

myPDF("loan_int_rate_dot_plot.pdf",
      5.5,
      1.25,
      mar = c(3.6, 1, 0, 1),
      mgp = c(2.5, 0.7, 0),
      tcl = -0.4)
dotPlot(d,
        at = 1.007,
        xlab = 'Interest Rate',
        ylab = '',
        pch = 20,
        col = COL[1, 3],
        cex = 3, # 1.5,
        xlim = xlim,
        ylim = c(0.95, 1.05),
        axes = FALSE)
abline(h = 0.983)
AxisInPercent(1, pretty(c(0, d)))
M <- mean(d)
polygon(M + c(-1, 1, 0) * 1,
        c(0.95, 0.95, 0.98),
        border = COL[4],
        col = COL[4])
dev.off()



set.seed(10)
myPDF("loan_int_rate_dot_plot_stacked.pdf",
      5.5,
      2.5,
      mar = c(3.6, 1, 0.5, 1),
      mgp = c(2.5, 0.7, 0))
round.to <- 1
binned <- round.to * round(d / round.to)
tab <- table(binned)
cex    <- 1
plot(0,
     type = "n",
     xlab = "Interest Rate, Rounded to Nearest Percent",
     ylab = "",
     axes = FALSE,
     xlim = xlim,
     ylim = c(-1, 1.5 * max(tab)))
for (i in 1:length(tab)) {
  points(rep(as.numeric(names(tab[i])), tab[i]),
         1.5 * (1:tab[i]) - 0.4,
         pch = 19,
         col = COL[1],
         cex = 2 * cex)
}
abline(h = 0)
AxisInPercent(1, pretty(c(0, d)))
polygon(M + c(-1, 1, 0) * 1,
        c(-1.2, -1.2, -0.1),
        border = COL[4],
        col = COL[4])
dev.off()

M
sd(d)


================================================
FILE: ch_summarizing_data/figures/loan_int_rate_robust_ex/loan_int_rate_robust_ex.R
================================================
library(openintro)
data(COL)

set.seed(16)

RetrieveOffsets <- function(d, jitter = 0.1, num_buckets = 70) {
  cutoffs <- seq(0.9 * min(d), max(d), length.out = num_buckets)
  x <- rep(NA, length(d))
  for (i in 2:num_buckets) {
    these <- which(cutoffs[i - 1] < d & d <= cutoffs[i])
    lt <- length(these)
    if (lt == 0) {
      next
    }
    x[these] <- jitter * ((1:lt) - (lt + 1) / 2)
  }
  return(x)
}


p1 <- loan50$interest_rate
y1 <- 3 + RetrieveOffsets(p1)
p2 <- p1
p2[which.max(p2)] <- 15
y2 <- 2 + RetrieveOffsets(p2, num_buckets = 50)
p3 <- p1
p3[which.max(p1)] <- 35
y3 <- 1 + RetrieveOffsets(p3)
n1 <- length(p1)

myPDF("loan_int_rate_robust_ex.pdf",
      7.04,
      1.7,
      mar = c(2.45, 0, 0, 0),
      mgp = c(1.35, 0.25, 0),
      cex.lab = 0.85)

plot(p1, y1,
    xlab = 'Interest Rate',
    ylab = '',
    pch = 20,
    col = COL[1,3],
    xlim = c(1, max(p1, p2, p3)),
    ylim = c(0.6, 3.4),
    axes = FALSE)
points(max(p1), y1[which.max(p1)],
    col = COL[4])

at <- seq(5, 100, 5)
AxisInPercent(1, at, cex.axis = 0.8)
text(5, 3, 'Original', pos = 2, cex = 0.8)

# y2 <- rep(2, n1) + rnorm(n1, sd = jitter)
y2[p2 == 15] <- 2.15
points(p2, y2,
    pch = 20, col = COL[1, 3])
points(15, y2[p2 == 15],
    col = COL[4])
text(5, 2,
     '26.3% to 15%',
     pos = 2,
     cex = 0.8)

# y3 <- rep(1, n1) + rnorm(n1, sd = jitter)
points(p3, y3,
    pch = 20, col = COL[1, 3])
points(35, y3[p3 == 35],
    col = COL[4])
text(5, 1,
     '26.3% to 35%',
     pos = 2,
     cex = 0.8)

dev.off()


# _____ Summary Statistics _____ #
GetSummaries <- function(p) {
  temp <- round(quantile(p, c(0.25, 0.5, 0.75)), 3)
  hold <- temp[3] - temp[1]
  names(hold) <- NULL
  return(c(temp,
           IQR = hold,
           mean = mean(p),
           sd = sd(p)))
}
GetSummaries(p1)
GetSummaries(p2)
GetSummaries(p3)


================================================
FILE: ch_summarizing_data/figures/malaria_rand_dot_plot/malaria_rand_dot_plot.R
================================================
library(openintro)
library(dplyr)

set.seed(3)

exp_gp  <- rep(c("vaccine", "placebo"), c(14, 6))
outcome <- c(rep(c('infection', 'no infection'), c(5, 9)),
             rep(c('infection', 'no infection'), c(6, 0)))

nsim    <- 100
n       <- length(exp_gp)
success <- "infection"

SimulateTable <- function(exp_gp, outcome, ...) {
  table(sample(exp_gp), outcome)
}
# SimulateTable(exp_gp, outcome)

sim_tables <-
    lapply(1:nsim, SimulateTable,
        exp_gp = exp_gp,
        outcome = outcome)
result <- sim_tables %>%
    lapply(function(x) {
    	  x[1, 1] / sum(x[1, ]) - x[2, 1] / sum(x[2, ])
    	}) %>%
    	unlist()
sim_tables[1:5]
result[1:5]

pval <- sum(result >= 0.64) / nsim
values <- table(result)
diffs <- unique(result)

X <- c()
Y <- c()
for (i in 1:length(diffs)) {
  x   <- diffs[i]
  rec <- sum(result == x)
  X   <- append(X, rep(x, rec))
  Y   <- append(Y, 1:rec)
}


myPDF('malaria_rand_dot_plot.pdf', 6, 3.5,
      mar = c(3.4, 0.5, 0.5, 0.5),
      mgp = c(2.35, 0.6, 0))
plot(X, Y,
     xlim = range(diffs) + c(-1, 1) * sd(diffs) / 4,
     xlab = "Difference in Infection Rates",
     axes = FALSE,
     ylim = c(0, max(Y)),
     col = COL[1],
     pch = 20)
# at <- seq(-0.4, 0.4, 0.1)
# labels <- c(-0.4, "", -0.2, "", 0, "", 0.2, "", 0.4)
axis(1) #, at, labels)
abline(h = 0)
dev.off()


================================================
FILE: ch_summarizing_data/figures/medianHHIncomePoverty/medianHHIncomePoverty.R
================================================
library(openintro)
library(splines)

ind <- 1088

myPDF("medianHHIncomePoverty.pdf", 6, 3.5,
      mar = c(3, 4.7, 0.5, 1),
      mgp = c(2.4, 0.5, 0))
x <- county$poverty
y <- county$median_hh_income
plot(x, y, type = "n",
     xlim = c(0, max(x, na.rm = TRUE)),
     ylim = c(0, max(y, na.rm = TRUE)),
     xlab = "",
     ylab = "",
     axes = FALSE)
abline(h = pretty(c(0, y)), v = pretty(c(0, x)), col = COL[7, 3])
points(x, y, pch = 20, cex = 0.7, col = COL[1, 3])
AxisInPercent(1, pretty(c(0, x)))
AxisInDollars(2, pretty(c(0, y)))
box()
points(x, y, pch = ".", col = COL[5, 4])
mtext("Poverty Rate (Percent)", 1, 1.9)
par(las = 0)
mtext("Median Household Income", 2, 3.5)
t1 <- x[ind]
t2 <- y[ind]
# lines(c(t1, t1), c(-1e5, t2), lty = 2, col = COL[4])
# lines(c(-1e5, t1), c(t2, t2), lty = 2, col = COL[4])
# points(t1, t2, col = COL[4])
my_exp <- 1.2
(m <- lm(y ~ I(1 / x^my_exp) + I(x^0.3)))
(m <- lm(y ~ x + I(x^2) + I(x^3)))
x. <- seq(0.1, 100, 0.1)
y. <- m$coef[1] + m$coef[2] / x.^my_exp + m$coef[3] * x.^0.3
y. <- m$coef[1] + m$coef[2] * x. + m$coef[3] * x.^2 + m$coef[4] * x.^3
i <- 350
m. <- (y.[i] - y.[i-1]) / 0.1
b. <- y.[i] - m. * i / 10
y.[i:1000] <- m. * x.[i:1000] + b.
y. <- y.[x. > 1.8]
x. <- x.[x. > 1.8]
lines(x., y., lwd = 1.5, col = COL[7, 1])
lines(x., y., lty = 2, lwd = 1.5, col = COL[5])

dev.off()

county[ind, ]


================================================
FILE: ch_summarizing_data/figures/sdAsRuleForEmailNumChar/sdAsRuleForEmailNumChar.R
================================================
library(openintro)
data(email50)
data(COL)
d <- email50$num_char
mean(d)
sd(d)

myPDF("sdAsRuleForEmailNumChar.pdf", 6, 1.5,
      mar = c(3.5, 0, 0, 0),
      mgp = c(2.2, 0.7, 0))
expr <- expression(paste("Number of Characters (in thousands), ",
                         bar(x),
                         " = 11,600, ",
                         s[x],
                         " = 13,130"))
dotPlot(d,
        col = COL[1,2],
        pch = 20,
        cex = 2,
        xlim = range(d) + sd(d) / 2 * c(-1, 1),
        axes = FALSE,
        xlab = expr,
        type = 'n')
m <- round(mean(d), 1)
s <- round(sd(d), 1)
abline(v = m, col = COL[7])
col <- '#0000000D'
border <- '#00000000'
rect(m - s, -5, m + s, 5,
     col = col, border = border)
rect(m - 2 * s, -5, m + 2 * s, 5,
     col = col, border = border)
rect(m - 3 * s, -5, m + 3 * s, 5,
     col = col, border = border)
rect(m - 4 * s, -5, m + 4 * s, 5,
     col = col, border = border)
dotPlot(d,
        col = COL[1, 2],
        pch = 20,
        cex = 2,
        add = TRUE,
        axes = FALSE)
dotPlot(d,
        col = 1,
        pch = ".",
        add = TRUE,
        axes = FALSE)
axis(1,
     at = m + s * (-3:7),
     labels = format(m + s * (-3:7)))
dev.off()

sum(d > m - s & d < m + s) / length(d)
sum(d > m - 2 * s & d < m + 2 * s) / length(d)


================================================
FILE: ch_summarizing_data/figures/sdRuleForIncome/sdRuleForIncome.R
================================================
library(openintro)
data(email50)
data(COL)
d <- loan50$total_income
mean(d)
sd(d)

myPDF("sdRuleForIncome.pdf", 6.3, 1.5,
      mar = c(3.5, 1.3, 0, 1.3),
      mgp = c(2.2, 0.7, 0))
expr <- expression(paste("Loan Amount, ",
                         bar(x),
                         " = $105,221, ",
                         s[x],
                         " = $68,142"))
dotPlot(d,
        col = COL[1,2],
        pch = 20,
        cex = 2,
        xlim = range(d) + sd(d) / 2 * c(-1, 1),
        axes = FALSE,
        xlab = expr,
        type = 'n')
m <- round(mean(d), -3)
s <- round(sd(d), -3)
abline(v = m, col = COL[7])
col <- '#0000000D'
border <- '#00000000'
rect(m - s, -5, m + s, 5,
     col = col, border = border)
rect(m - 2 * s, -5, m + 2 * s, 5,
     col = col, border = border)
rect(m - 3 * s, -5, m + 3 * s, 5,
     col = col, border = border)
rect(m - 4 * s, -5, m + 4 * s, 5,
     col = col, border = border)
dotPlot(d,
        col = COL[1, 2],
        pch = 20,
        cex = 3,
        add = TRUE,
        axes = FALSE)
dotPlot(d,
        col = 1,
        pch = ".",
        add = TRUE,
        axes = FALSE)
AxisInDollars(1, m + s * (-7:7))
dev.off()

sum(d > m - s & d < m + s) / length(d)
sum(d > m - 2 * s & d < m + 2 * s) / length(d)


================================================
FILE: ch_summarizing_data/figures/sdRuleForIntRate/sdRuleForIntRate.R
================================================
library(openintro)
data(email50)
data(COL)
d <- loan50$interest_rate
mean(d)
sd(d)

myPDF("sdRuleForIntRate.pdf", 6.3, 1.5,
      mar = c(3.5, 1.3, 0, 1.3),
      mgp = c(2.2, 0.7, 0))
expr <- expression(paste("Interest Rate, ",
                         bar(x),
                         " = 11.57%, ",
                         s[x],
                         " = 5.05%"))
dotPlot(d,
        col = COL[1,2],
        pch = 20,
        cex = 2,
        xlim = range(d) + sd(d) / 2 * c(-1, 1),
        axes = FALSE,
        xlab = expr,
        type = 'n')
m <- round(mean(d), 1)
s <- round(sd(d), 1)
abline(v = m, col = COL[7])
col <- '#0000000D'
border <- '#00000000'
rect(m - s, -5, m + s, 5,
     col = col, border = border)
rect(m - 2 * s, -5, m + 2 * s, 5,
     col = col, border = border)
rect(m - 3 * s, -5, m + 3 * s, 5,
     col = col, border = border)
rect(m - 4 * s, -5, m + 4 * s, 5,
     col = col, border = border)
dotPlot(d,
        col = COL[1, 2],
        pch = 20,
        cex = 3,
        add = TRUE,
        axes = FALSE)
dotPlot(d,
        col = 1,
        pch = ".",
        add = TRUE,
        axes = FALSE)
AxisInPercent(1, m + s * (-7:7))
dev.off()

sum(d > m - s & d < m + s) / length(d)
sum(d > m - 2 * s & d < m + 2 * s) / length(d)


================================================
FILE: ch_summarizing_data/figures/sdRuleForLoanAmount/sdRuleForLoanAmount.R
================================================
library(openintro)
data(email50)
data(COL)
d <- loan50$loan_amount
mean(d)
sd(d)

myPDF("sdRuleForLoanAmount.pdf", 6.3, 1.5,
      mar = c(3.5, 1.3, 0, 1.3),
      mgp = c(2.2, 0.7, 0))
expr <- expression(paste("Loan Amount, ",
                         bar(x),
                         " = $17,083, ",
                         s[x],
                         " = $10,455"))
dotPlot(d,
        col = COL[1,2],
        pch = 20,
        cex = 2,
        xlim = range(d) + sd(d) / 2 * c(-1, 1),
        axes = FALSE,
        xlab = expr,
        type = 'n')
m <- round(mean(d), -2)
s <- round(sd(d), -2)
abline(v = m, col = COL[7])
col <- '#0000000D'
border <- '#00000000'
rect(m - s, -5, m + s, 5,
     col = col, border = border)
rect(m - 2 * s, -5, m + 2 * s, 5,
     col = col, border = border)
rect(m - 3 * s, -5, m + 3 * s, 5,
     col = col, border = border)
rect(m - 4 * s, -5, m + 4 * s, 5,
     col = col, border = border)
dotPlot(d,
        col = COL[1, 2],
        pch = 20,
        cex = 2,
        add = TRUE,
        axes = FALSE)
dotPlot(d,
        col = 1,
        pch = ".",
        add = TRUE,
        axes = FALSE)
AxisInDollars(1, m + s * (-7:7))
dev.off()

sum(d > m - s & d < m + s) / length(d)
sum(d > m - 2 * s & d < m + 2 * s) / length(d)


================================================
FILE: ch_summarizing_data/figures/severalDiffDistWithSdOf1/severalDiffDistWithSdOf1.R
================================================
library(openintro)
data(COL)
pdf("severalDiffDistWithSdOf1.pdf", 5.2, 3.8)


x1 <- rep(0:1, c(10,10))
x1 <- (x1-mean(x1))/sd(x1)
x2 <- qnorm(seq(0.0025,0.9975, 0.00049))
x2 <- (x2-mean(x2))/sd(x2)
x3 <- qchisq(seq(0.01,0.98, 0.0005), 4)
x3 <- (x3-mean(x3))/sd(x3)

drawSDs <- function(m = 0, s = 1) {
  abline(v = m, col = '#00000033')
  rect(m - s, -5, m + s, 500,
       col = '#00000025',
       border = '#00000000')
  rect(m + s, -5, m + 2 * s, 500,
       col = '#00000015', border = '#00000000')
  rect(m - s, -5, m - 2 * s, 500,
       col = '#00000015', border = '#00000000')
  rect(m + 2 * s, -5, m + 3 * s, 500,
       col = '#0000000B', border = '#00000000')
  rect(m - 2 * s, -5, m - 3 * s, 500,
       col = '#0000000B', border = '#00000000')
  rect(m + 4 * s, -5, m + 3 * s, 500,
       col = '#00000008', border = '#00000000')
  rect(m - 4 * s, -5, m - 3 * s, 500,
       col = '#00000008', border = '#00000000')
}

xR <- c(-1, 1) * max(c(x1, x2, x3))
par(mfrow = c(3, 1),
    mar = c(3, 1, 0, 1),
    mgp = c(2.7, 1, 0))
histPlot(x1,
         breaks = c(-1.05, -0.95, 0.95, 1.05),
         xlim = xR,
         ylim = c(0, 5.6),
         axes = FALSE,
         xlab = '',
         border = TRUE,
         lty = 1,
         probability = TRUE)
drawSDs()
histPlot(x1,
         breaks = c(-1.05, -0.95, 0.95, 1.05),
         add = TRUE,
         probability = TRUE,
         col = COL[1],
         ylim = c(0, 0.75))
axis(1, at = -4:4, cex.axis = 1.5)
par(mar = c(3,1,0,1),
    mgp = c(2.7,1,0))
histPlot(x2,
         breaks = 25,
         xlim = xR,
         axes = FALSE,
         xlab = '',
         border = TRUE,
         lty = 1,
         probability = TRUE,
         ylim = c(0, 0.43))
drawSDs()
histPlot(x2,
         breaks = 25,
         add = TRUE,
         probability = TRUE,
         col = COL[1])
axis(1, at = -4:4, cex.axis = 1.5)
par(mar = c(2.1,1,0,1),
    mgp = c(2.7,1,0))
histPlot(x3,
         breaks = 25,
         xlim = xR,
         axes = FALSE,
         xlab = '',
         border = TRUE,
         lty = 1,
         probability = TRUE,
         ylim = c(0, 0.5))
drawSDs()
histPlot(x3,
         breaks = 25,
         add = TRUE,
         probability = TRUE,
         col = COL[1])
axis(1, at = -4:4, cex.axis = 1.5)
dev.off()


================================================
FILE: ch_summarizing_data/figures/singleBiMultiModalPlots/singleBiMultiModalPlots.R
================================================
library(openintro)
data(COL)

myPDF("singleBiMultiModalPlots.pdf", 6.5, 2)

set.seed(51)
x1 <- rchisq(65, 6)
x2 <- c(rchisq(22, 5.8),
        rnorm(40, 16.5, 2))
x3 <- c(rchisq(25, 3),
        rnorm(35, 11.7),
        rnorm(42, 18, 1.5))

par(mfrow=c(1, 3),
    mar=c(1.9, 2, 1, 2),
    mgp=c(2.4, 0.7, 0))

HistPlot1 <- function(x, COL = COL) {
  histPlot(x, axes=FALSE, xlab='', ylab='', col=COL[1],
      ylim = c(0, 20))
  abline(h = 0)
  axis(1, at = seq(-20, 50, 5))
}

HistPlot1(x1, COL)
axis(2)

HistPlot1(x2, COL)
axis(2)

HistPlot1(x3, COL)
axis(2)

dev.off()


================================================
FILE: ch_summarizing_data/figures/total_income_dot_plot/total_income_dot_plot.R
================================================
library(openintro)

d <- loan50$total_income


myPDF("total_income_dot_plot.pdf",
      5.5,
      1.25,
      mar = c(3.6, 1, 0, 1),
      mgp = c(2.5, 0.7, 0),
      tcl = -0.4)
dotPlot(d,
        at = 1.007,
        xlab = 'Loan Amount',
        ylab = '',
        pch = 20,
        col = COL[1, 3],
        cex = 2.25, # 1.5,
        xlim = c(0, 1.05 * max(d)),
        ylim = c(0.95, 1.05),
        axes = FALSE)
abline(h = 0.983)
AxisInDollars(1, pretty(c(0, d)))
M <- mean(d)
polygon(M + c(-1, 1, 0) * 15000,
        c(0.95, 0.95, 0.98),
        border = COL[4],
        col = COL[4])
dev.off()



set.seed(10)
myPDF("total_income_dot_plot_stacked.pdf",
      5.5,
      2.25,
      mar = c(3.6, 1, 0.5, 1),
      mgp = c(2.5, 0.7, 0))
round.to <- 10000
binned <- round.to * round(d / round.to)
tab <- table(binned)
cex    <- 1
plot(0,
     type = "n",
     xlab = "Loan Amount, Rounded to Nearest $1000",
     ylab = "",
     axes = FALSE,
     xlim = c(0, 1.05 * max(d)),
     ylim = c(-1, 1.5 * max(tab)))
for (i in 1:length(tab)) {
  points(rep(as.numeric(names(tab[i])), tab[i]),
         1.5 * (1:tab[i]) - 0.4,
         pch = 19,
         col = COL[1],
         cex = 1.5 * cex)
}
abline(h = 0)
AxisInDollars(1, pretty(c(0, d)))
polygon(M + c(-1, 1, 0) * 15000,
        c(-1.2, -1.2, -0.1),
        border = COL[4],
        col = COL[4])
dev.off()

M
sd(d)


================================================
FILE: eoce.bib
================================================
% Chp 1 - Data Collection

% migraine_and_acupuncture
@article{Allais:2011,
  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}},
  author={Allais, G. and Romoli, M. and Rolando, S. and Airola, G. and Castagnoli Gabellari, I. and Allais, R. and Benedetto, C.},
  journal={Neurological Sci.},
  volume={32},
  number={1},
  pages={173--175},
  year={2011},
  publisher={Springer},
}

% sinusitis_and_antibiotics
@article{Garbutt:2012,
  title={\oiRedirect{textbook-amoxicillin_acute_rhinosinusitis_2012}{Amoxicillin for Acute Rhinosinusitis: A Randomized Controlled Trial}},
  author={Garbutt, J.M. and Banister, C. and Spitznagel, E. and Piccirillo, J.F.},
  journal={JAMA: The Journal of the American Medical Association},
  volume={307},
  number={7},
  pages={685--692},
  year={2012},
  publisher={American Medical Association}
}

% study_components_airpoll
@article{Ritz+Yu+Chapa+Fruin:2000,
  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}},
  author={Ritz, B. and Yu, F. and Chapa, G. and Fruin, S.},
  journal={Epidemiology},
  volume={11},
  number={5},
  pages={502--511},
  year={2000},
}

% study_components_buteyko
@article{McDowan:2003,
  title={{Health Education: Does the Buteyko Institute Method make a difference?}},
  author={McGowan, J.},
  journal={Thorax},
  volume={58},
  year={2003}
}

% study_components_cheaters
@article{Bucciol:2011,
  title={\oiRedirect{textbook-luck-cheating}{Luck or cheating? A field experiment on honesty with children}},
  author={Bucciol, Alessandro and Piovesan, Marco},
  journal={Journal of Economic Psychology},
  volume={32},
  number={1},
  pages={73--78},
  year={2011},
  publisher={Elsevier}
}

% study_components_stealers
@article{Piff:2012,
  title={Higher social class predicts increased unethical behavior},
  author={Piff, P.K. and Stancato, D.M. and C{\^o}t{\'e}, S. and Mendoza-Denton, R. and Keltner, D.},
  journal={Proceedings of the National Academy of Sciences},
  year={2012},
  publisher={National Acad Sciences}
}

% fisher_irises
@article{Fisher:1936,
  title={\oiRedirect{textbook-taxonomy_multiple_measurements_1936}{The Use of Multiple Measurements in Taxonomic Problems}},
  author={Fisher, R.A},
  journal={Annals of Eugenics},
  volume={7},
  pages={179-188},
  year={1936}
}

@misc{irisPic,
  note={Photo by rtclauss on Flickr, \oiRedirect{textbook-iris_picture}{Iris}.}
}

% smoking_habits_UK_datamatrix
@misc{data:smoking,
  note = {National STEM Centre, \oiRedirect{textbook-Stats4Schools_smoking}{Large Datasets from stats4schools}.}
}

% airports
@misc{data:usairports,
  note = {Federal Aviation Administration, \oiRedirect{textbook-FAA_airports}{www.faa.gov/airports/airport\_safety/airportdata\_5010}.}
}

@Manual{data:unvotes,
  title = {unvotes: United Nations General Assembly Voting Data},
  author = {David Robinson},
  year = {2017},
  note = {R package version 0.2.0}, url = {https://CRAN.R-project.org/package=unvotes}
}

% eat_well_feel_better
@article{conner2017let,
  title={Let them eat fruit! The effect of fruit and vegetable consumption on psychological well-being in young adults: A randomized controlled trial},
  author={Conner, Tamlin S and Brookie, Kate L and Carr, Anitra C and Mainvil, Louise A and Vissers, Margreet CM},
  journal={PloS one},
  volume={12},
  number={2},
  pages={e0171206},
  year={2017},
  publisher={Public Library of Science}
}

% screen time
@article{orben2018screens,
  title={\oiRedirect{textbook-screens_orben_2018}{Screens, Teens and Psychological Well-Being: Evidence from three time-use diary studies}},
  author={Orben, Amy and Baukney-Przybylski, AK},
  journal={Psychological Science},
  year={2018},
  publisher={SAGE Publications}
}

% gender pay gap medicine
@article{LoSassoMedicineGenderPayGap,
  title={\oiRedirect{textbook-LoSassoMedicineGenderPayGap}{The \$16,819 Pay Gap For Newly Trained Physicians: The Unexplained Trend Of Men Earning More Than Women}},
  author={Lo Sasso AT and Richards MR and Chou CF and Gerber SE},
  journal={Health Affairs},
  year={2011},
  volume={30},
  number={2}
}

% stanford open policing
@article{pierson2017large,
  title={\oiRedirect{textbook-police_pierson_2017}{A large-scale analysis of racial disparities in police stops across the United States}},
  author={Pierson, Emma and Simoiu, Camelia and Overgoor, Jan and Corbett-Davies, Sam and Ramachandran, Vignesh and Phillips, Cheryl and Goel, Sharad},
  journal={arXiv preprint arXiv:1706.05678},
  year={2017}
}

% space launches
@misc{data:spacelaunches,
  note = {JSR Launch Vehicle Database, \oiRedirect{textbook-space-launches-data}{A comprehensive list of suborbital space launches, 2019 Feb 10 Edition}.}
}

% Torque on a rusty bolt
@misc{youtube:torque_on_rusty_bolt,
    note = {Project Farm on YouTube, \oiRedirect{textbook-torque_on_rusty_bolt}{youtu.be/xUEob2oAKVs}, April 16, 2018.}
}

% vegetarianism
@misc{webpage:vegetarianism,
    note = {Gallup Poll, \oiRedirect{textbook-gallup-vegetarianism-2018}{Snapshot: Few Americans Vegetarian or Vegan}, August 1, 2018.}
}

% NOAA 1948 and 2018 data
@misc{webpage:noaa_1948_2018,
    note = {NOAA, \oiRedirect{textbook-noaa_1948_2018}{www.ncdc.noaa.gov/cdo-web/datasets}, April 24, 2019.}
}
Retrieved on 2019-04-24.
  \url{https://www.ncdc.noaa.gov/cdo-web/datasets}

% Raising the minimum wage
@misc{webpage:rasmussen-2019-raise-minimum-wage,
    note = {Rasmussen Reports survey, \oiRedirect{rasmussen-2019-raise-minimum-wage}{Most Favor Minimum Wage of \$10.50 Or Higher}, April 16, 2019.}
}

% gss data
@misc{data:gss,
  note = {National Opinion Research Center, \oiRedirect{textbook-gss-data}{General Social Survey, 2018}.}
}



@misc{data:ciaFactbook,
  note = {CIA Factbook, \oiRedirect{textbook-cia_factbook}{Country Comparisons, 2014}.}
}

@misc{data:ITU:2012,
  note = {ITU World Telecommunication/ICT Indicators database, \oiRedirect{textbook-telecommunication_ICT_2012}{World Telecommunication/ICT Indicators Database, 2012}}
}

@article{Hepler:2013,
  title={\oiRedirect{textbook-dispositional-attitude}{Attitudes without objects - Evidence for a dispositional attitude, its measurement, and its consequences}},
  author={Hepler, Justin and Albarrac{\'\i}n, Dolores},
  journal={Journal of personality and social psychology},
  volume={104},
  number={6},
  pages={1060},
  year={2013},
  publisher={American Psychological Association}
}

@article{news:smokingDementia,
  author={Rabin, R.C.}, 
  title = {\oiRedirect{textbook-nytimes_smoking_dementia}{Risks: Smokers Found More Prone to Dementia}},
  journal={New York Times},
   MONTH = {October 29},
   YEAR    = {2010}
}

@article{news:bullySleep,
  author={Parker-Pope, T.},
  title = {\oiRedirect{textbook-school_bully_sleepy_2011}{The School Bully Is Sleepy}},
  journal={New York Times},
   MONTH = {June 2},
   YEAR    = {2011}
}

@article{Orr:2009,
  title={\oiRedirect{textbook-shyness_FB_usage_2009}{The influence of shyness on the use of Facebook in an undergraduate sample}},
  author={Orr, E.S. and Sisic, M. and Ross, C. and Simmering, M.G. and Arseneault, J.M. and Orr, R.R.},
  journal={CyberPsychology \& Behavior},
  volume={12},
  number={3},
  pages={337--340},
  year={2009},
  publisher={Mary Ann Liebert, Inc.}
}

@article{Audera:2001,
  title={\oiRedirect{textbook-vitamin_C_cold_treatment_2001}{Mega-dose vitamin C in treatment of the common cold: a randomised controlled trial}},
  author={Audera, C. and Patulny, R.V. and Sander, B.H. and Douglas, R.M. and others},
  journal={Medical Journal of Australia},
  volume={175},
  number={7},
  pages={359--362},
  year={2001},
  publisher={AUSTRALASIAN MEDICAL PUBLISHING COMPANY LTD}
}

@article{Nieman:2009,
  title={\oiRedirect{textbook-chia_seeds_2009}{Chia seed does not promote weight loss or alter disease risk factors in overweight adults}},
  author={Nieman, D.C. and Cayea, E.J. and Austin, M.D. and Henson, D.A. and McAnulty, S.R. and Jin, F.},
  journal={Nutrition Research},
  volume={29},
  number={6},
  pages={414--418},
  year={2009},
  publisher={Elsevier}
}

@article{Suldo:2014,
  title={\oiRedirect{textbook-middle-school-satisfaction}{Increasing middle school students' life satisfaction: Efficacy of a positive psychology group intervention}},
  author={Suldo, Shannon M and Savage, Jessica A and Mercer, Sterett H},
  journal={Journal of happiness studies},
  volume={15},
  number={1},
  pages={19--42},
  year={2014},
  publisher={Springer}
}

% Chp 2 - Summarizing data

@misc{data:acs:2012,
  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.}
}

@misc{data:MLB:2014,
  note = {\oiRedirect{textbook-mlb2014-espn}{ESPN: MLB Team Stats - 2014}}
}

@article{Harris:2012,
  title={\oiRedirect{textbook-cereal-facts-2012}{Cereal FACTS 2012: Limited progress in the nutrition quality and marketing of children's cereals}},
  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},
  journal={Rudd Center for Food Policy \& Obesity.},
  volume={12},
  year={2012}
}

@article{Allison+Cicchetti:1975,
  title={\oiRedirect{textbook-mammal_sleep_1975}{Sleep in mammals: ecological and constitutional correlates}},
  author={Allison, T. and Cicchetti, D.V.},
  journal={Arch. Hydrobiol},
  volume={75},
  pages={442},
  year={1975}
}

@misc{data:ciaFactBookInfMort:2012,
  note = {CIA Factbook, \oiRedirect{textbook-cia_infant_mortality_2012}{Country Comparison: Infant Mortality Rate, 2012}}
}

@misc{data:durhamAQI:2011,
  note = {US Environmental Protection Agency, \oiRedirect{textbook-airdata_2011}{AirData, 2011.}}
}

@article{Backstrom:2011,
  title={\oiRedirect{textbook-anatomy-of-facebook}{Anatomy of Facebook}},
  author={Backstrom, Lars},
  journal={Facebook Data Team’s Notes},
  year={2011}
}

@misc{survey:immigFL:2012,
  note = {SurveyUSA, \oiRedirect{textbook-SurveyUSA_18927}{News Poll \#18927}, data collected Jan 27-29, 2012}
}

@misc{survey:raiseTaxes:2015,
  note = {Public Policy Polling, \oiRedirect{textbook-PPP_30215}{Americans on College Degrees, Classic Literature, the Seasons, and More}, data collected Feb 20-22, 2015}
}

@article{Graham:2010,
  title={Risk of acute myocardial infarction, stroke, heart failure, and death in elderly Medicare patients treated with rosiglitazone or pioglitazone},
  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.},
  journal = {JAMA},
  volume={304},
  number={4},
  pages={411},
  issn={0098-7484},
  year={2010},
  publisher={Am Med Assoc}
}

@article{Turnbull+Brown+Hu:1974,
  title={\oiRedirect{textbook-heart_transplant_1974}{Survivorship of Heart Transplant Data}},
  author={Turnbull, B. and Brown, B. and Hu, M.},
  journal={Journal of the American Statistical Association},
  volume={69},
  pages={74-80},
  year={1974}
}

% Chp 3 - Probability

@misc{data:BRFSS2010,
  note={Office of Surveillance, Epidemiology, and Laboratory Services Behavioral Risk Factor Surveillance System, {\oiRedirect{textbook-BRFSS_2010}{BRFSS 2010 Survey Data}}.}
}

@misc{rouletteWheelPic,
  note={Photo by H\r{a}kan Dahlstr\"{o}m on Flickr, \oiRedirect{textbook-flickr_roulette_wheel}{Roulette wheel}.}
}

@misc{indepSwing,
  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.}
}

@misc{pew_cyber_bully_2018,
  note={Pew Research Center, \oiRedirect{pew_cyber_bully_2018}{A Majority of Teens Have Experienced Some Form of Cyberbullying}. September 27, 2018.}
}

@misc{poorLang,
  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}.}
}

@misc{eduSex,
  note={U.S. Census Bureau, 2010 American Community Survey 1-Year Estimates, \oiRedirect{textbook-acs_educational_2010}{Educational Attainment}.}
}

@article{Mizan:2011,
  title={\oiRedirect{textbook-tardiness_asthma_2011}{Absence, Extended Absence, and Repeat Tardiness Related to Asthma Status among Elementary School Children}},
  author={Mizan, S.S. and Shendell, D.G. and Rhoads, G.G.},
  journal={Journal of Asthma},
  volume={48},
  number={3},
  pages={228-234},
  year={2011},
  publisher={Informa Healthcare}
}

@misc{globalWarming,
  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.}
}

@misc{burgers,
  note={SurveyUSA, \oiRedirect{textbook-SurveyUSA_17718}{Results of SurveyUSA News Poll \#17718},  data collected on December 2, 2010.}
}

@article{Laeng:2007,
  title={\oiRedirect{textbook-eye_color_pref_2010}{Why do blue-eyed men prefer women with the same eye color?}},
  author={Laeng, B. and Mathisen, R. and Johnsen, J.A.},
  journal={Behavioral Ecology and Sociobiology},
  volume={61},
  number={3},
  pages={371--384},
  year={2007},
  publisher={Springer}
}

@misc{ciaFactBookHIV:2012,
  note = {Source: CIA Factbook, \oiRedirect{textbook-cia_hiv_2012}{Country Comparison: HIV/AIDS - Adult Prevalence Rate}.}
}

@misc{data:scott,
  note = {New York Times, \oiRedirect{textbook-nytimes_wi_exit_polls_2012}{Wisconsin recall exit polls}}
}

@misc{webpage:alcohol,
    note = {SAMHSA, Office of Applied Studies, \oiRedirect{textbook-SAMHSA_2007_8}{National Survey on Drug Use and Health, 2007 and 2008}.}
}

@Book{cats,
    title = {Modern Applied Statistics with S},
    author = {W. N. Venables and B. D. Ripley},
    publisher = {Springer},
    edition = {Fourth Edition},
    address = {New York},
    year = {2002},
    note = {\oiRedirect{textbook-modern_applied_stat_with_s}{www.stats.ox.ac.uk/pub/MASS4}},
}

@misc{acsIncome2005-2009,
  note={U.S. Census Bureau, \oiRedirect{textbook-acd2005_9}{2005-2009 American Community Survey}}
}

$ Chp 4 - Distributions

@conference{Johnson+Murray:2010,
  title={\oiRedirect{textbook-rural_auto_speeds_2010}{Empirical Analysis of Truck and Automobile Speeds on Rural Interstates: Impact of Posted Speed Limits}},
  author={Johnson, S. and Murray, D.},
  booktitle={Transportation Research Board 89th Annual Meeting},
  year={2010}
}

@misc{marWomenACS,
  note={U.S. Census Bureau, 2010 American Community Survey, \oiRedirect{textbook-acs_marriage_2010}{Marital Status}.}
}

@misc{surveysPew,
note={Pew Research Center, \oiRedirect{textbook-pew_Representativeness_Surveys_2012}{Assessing the Representativeness of Public Opinion Surveys}, May 15, 2012.}
}

@misc{dreidelPic,
  note={\oiRedirect{textbook-flickr_dreidelPic}{Photo by Staccabees on Flickr}.}
}

@misc{webpage:spiders,
    note = {Gallup Poll, \oiRedirect{textbook-frightens_youth_2005}{What Frightens America's Youth?}, March 29, 2005.}
}

@misc{data:nsfg:2010,
    note = {Centers for Disease Control and Prevention, \oiRedirect{textbook-ntnl_survey_family_growth_2010}{National Survey of Family Growth, 2010.}
    }
}

@misc{data:povertycps:2013,
  note = {United States Census Bureau. {\oiRedirect{textbook-CPS_2013_poverty}{2013 Current Population Survey}}.Historical Poverty Tables - People. Web.}
}

@misc{data:hispaniccps:2012,
  note = {United States Census Bureau.{\oiRedirect{textbook-CPS_2012_hispanic}{2012 Current Population Survey}}.The Hispanic Population in the United States: 2012. Web.}
}

@misc{data:pewsocialnetwork:2014,
  note = {Pew Research Center, Washington, D.C. {\oiRedirect{textbook-pew_socialnetwork}{Social Networking Fact Sheet}}, accessed on May 9, 2015.}
}

% Chp 5 - Foundations for inference

@article{Heinz:2003,
  title={\oiRedirect{textbook-body_dim_2003}{Exploring relationships in body dimensions}},
  author={Heinz, G. and Peterson, L.J. and Johnson, R.W. and Kerk, C.J.},
  journal={Journal of Statistics Education},
  volume={11},
  number={2},
  year={2003}
}

@misc{data:pewdiagnosis:2013,
  note = {Pew Research Center, Washington, D.C. {\oiRedirect{textbook-The_Diagnosis_Difference}{The Diagnosis Difference}}, November 26, 2013.}
}

@misc{data:pewtwitternews:2013,
  note = {Pew Research Center, Washington, D.C. {\oiRedirect{textbook-twitter_news_consumers_2013}{Twitter News Consumers: Young, Mobile and Educated}}, November 4, 2013.}
}

@misc{data:gss:2010,
  note = {National Opinion Research Center, \oiRedirect{textbook-gss_2010}{General Social Survey, 2010}.}
}

@misc{data:pewwomenleaders:2014,
  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.}
}

@misc{data:yawn,
  note = {MythBusters, \oiRedirect{textbook-mythbusters_s3e28}{Season 3, Episode 28.}}
}

@misc{data:egypt,
  note={Gallup Politics, \oiRedirect{textbook-americans_views_of_egypt_2011}{Americans' Views of Egypt Sharply More Negative}, data collected February 2-5, 2011.}
}

@misc{web:art,
  title={\oiRedirect{textbook-2008_Assisted_Reproductive_Technology_Report}{2008 Assisted Reproductive Technology Report}},
  author ={CDC},
}

@misc{webpage:spam,
  note = {Rockbridge, \oiRedirect{textbook-spam_report_2009}{2009 National Technology Readiness Survey SPAM Report}.}
}

@misc{webpage:horrormovies,
  note = {FiveThirtyEight, \oiRedirect{textbook-fivethirtyeight-scary-movies}{Scary Movies Are The Best Investment In Hollywood}.}
}

% Chp 6 - Inference for proportions



@misc{data:govt_shuthown,
  note={Survey USA, \oiRedirect{textbook-SurveyUSA_24568}{News Poll \#24568}, data collected on April 21, 2019.}
}

@article{news:youngAmericans1,
  author={Vaughn, A.}, 
  title = {\oiRedirect{textbook-young_americans_2011}{Poll finds young adults optimistic, but not about money}},
  journal={Los Angeles Times},
   MONTH = {November 3},
   YEAR    = {2011}
}

@article{news:youngAmericans2,
  author={Demos.org}, 
  title = {\oiRedirect{textbook-young_americans_2011_extra}{The State of Young America: The Poll}},
     MONTH = {November 2},
   YEAR    = {2011}
}



@misc{data:healthcare2010,
  note = {Gallup, \oiRedirect{textbook-healthcare_split_2012}{Americans Issue Split Decision on Healthcare Ruling}, data collected June 28, 2012.}
}

@misc{data:july4,
  note={Survey USA, \oiRedirect{textbook-SurveyUSA_19333}{News Poll \#19333}, data collected on June 27, 2012.}
}

@misc{data:elderlyDriving,
  note={Marist Poll, \oiRedirect{textbook-drivers_at_65_2011}{Road Rules: Re-Testing Drivers at Age 65?}, March 4, 2011}
}

@misc{data:suffering,
  note={Gallup World, \oiRedirect{textbook-1_in_10_suffering_2011}{More Than One in 10 ``Suffering" Worldwide}, data collected throughout 2011.}
}

@misc{data:studyAbroad,
  note={studentPOLL, \oiRedirect{textbook-Interests_in_Study_Abroad_2008}{College-Bound Students' Interests in Study Abroad and Other International Learning Activities}, January 2008}
}

@article{news:publicOption,
  author={Balz, D. and Cohen, J.}, 
  title = {\oiRedirect{textbook-healthcare_public_option_2009}{Most support public option for health insurance, poll finds}},
  journal={The Washington Post},
   MONTH = {October 20},
   YEAR    = {2009}
}

@misc{data:KFF2019_nat_health_plan,
  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.}
}

@misc{data:civilWar,
  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.}
}

@misc{data:mobileBrowse,
  note={Pew Internet, \oiRedirect{textbook-cell_internet_use_2012}{Cell Internet Use 2012}, data collected between March 15 - April 13, 2012.}
}

@article{news:mobileBrowseChinese,
  author={Chang, S.}, 
  title = {The Chinese Love to Use Feature Phone to Access the Internet},
  journal={M.I.C Gadget},
   MONTH = {March 23},
   YEAR    = {2012}
}

@misc{data:collegeWorthIt,
  note={Pew Research Center Publications, \oiRedirect{textbook-college_worth_it_2011}{Is College Worth It?}, data collected between March 15-29, 2011.}
}

@article{Ellis:2001,
  title={{\oiRedirect{textbook-color_pref_2001}{Color preferences according to gender and sexual orientation}}},
  author={L Ellis and C Ficek},
  journal={Personality and Individual Differences},
  volume={31},
  number={8},
  pages={1375-1379},
  year={2001},
  publisher={Elsevier}
}

@misc{data:dailyShow,
  note={The Pew Research Center, \oiRedirect{textbook-americans_news_2010}{Americans Spending More Time Following the News}, data collected June 8-28, 2010.}
}

@misc{data:sleepCAandOR,
  note={CDC, \oiRedirect{textbook-Perceived_Insufficient_Rest_or_Sleep_Among_Adults}{Perceived Insufficient Rest or Sleep Among Adults --- United States, 2008}}
}

@misc{data:prop19_and_offshoreDrill,
  note = {Survey USA, \oiRedirect{textbook-SurveyUSA_16804}{Election Poll \#16804}, data collected July 8-11, 2010.}
}

@article{news:fullBodyScan,
  author={Condon, S.}, 
  title = {\oiRedirect{textbook-airport_scanners_2010}{Poll: 4 in 5 Support Full-Body Airport Scanners}},
  journal={CBS News},
   MONTH = {November 15},
   YEAR    = {2010}
}

@misc{data:sleepTransport,
  note={National Sleep Foundation, \oiRedirect{textbook-trans_workers_sleep_2012}{2012 Sleep in America Poll: Transportation Workers' Sleep}, 2012}
}

@article{Schmidt:2011,
  title={\oiRedirect{textbook-prenatal_vitamins_autism_2011}{Prenatal vitamins, one-carbon metabolism gene variants, and risk for autism}},
  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.},
  journal={Epidemiology},
  volume={22},
  number={4},
  pages={476},
  year={2011}
}

@article{news:prenatalVitAutism,
  author={Rabin, R.C.}, 
  title = {\oiRedirect{textbook-nytimes_prenatal_vitamins_autism_2011}{Patterns: Prenatal Vitamins May Ward Off Autism}},
  journal={New York Times},
   MONTH = {June 13},
   YEAR    = {2011}
}

@article{Lockman:2007,
  title={\oiRedirect{textbook-antiretroviral_therapy_2007}{Response to antiretroviral therapy after a single, peripartum dose of nevirapine}},
  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},
  journal={Obstetrical \& gynecological survey},
  volume={62},
  number={6},
  pages={361},
  year={2007}
}

@misc{data:employmentDiabetes,
  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.}
}

@misc{CreationismGallup,
  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}}
}

@article{Teng:2004,
  title={Forage and bed sites characteristics of Indian muntjac (Muntiacus muntjak) in Hainan Island, China},
  author={Teng, Liwei and Liu, Zhensheng and SONG, Yan-Ling and Zeng, Zhigao},
  journal={Ecological Research},
  volume={19},
  number={6},
  pages={675--681},
  year={2004},
  publisher={Wiley Online Library}
}

@article{Lucas:2011,
  title={\oiRedirect{textbook-coffee_caffeine_depression_2011}{Coffee, caffeine, and risk of depression among women}},
  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.},
  journal={Archives of internal medicine},
  volume={171},
  number={17},
  pages={1571},
  year={2011},
  publisher={Am Med Assoc}
}

@article{news:coffeeDepression,
  author={O'Connor, A.}, 
  title = {\oiRedirect{textbook-coffee_depression_2011}{Coffee Drinking Linked to Less Depression in Women}},
  journal={New York Times},
   MONTH = {September 26},
   YEAR    = {2011}
}

@misc{data:anes:2012,
  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].}
}

@misc{photo:barkingDeer,
  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}.}
}

% Chp 7 - Inference for means

@misc{data:prius,
  note = {Fuelecomy.gov, \oiRedirect{textbook-toyota_prius_2012_mpg}{Shared MPG Estimates: Toyota Prius 2012}.}
}

@book{Graybill:1994,
  title={Regression Analysis: Concepts and Applications},
  author={Graybill, F.A. and Iyer, H.K.},
  year={1994},
  publisher={Duxbury Press},
  pages={511--516}
}

@misc{data:oscars,
  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}.}
}

@article{Scanlon:1993,
  title={\oiRedirect{textbook-Friday13_1993}{Is Friday the 13th Bad For Your Health?}},
  author={Scanlon, T.J. and Luben, R.N. and Scanlon and F.L., Singleton, N.},
  journal={BMJ},
  volume={307},
  pages={1584-1586},
  year={1993}
}

@Book{ggplot2,
    author = {Wickham, H.},
    title = {\oiRedirect{textbook-ggplot2_book}{ggplot2: elegant graphics for data analysis}},
    publisher = {Springer New York},
    year = {2009}
}

@misc{data:chickwts,
  note = {\oiRedirect{textbook-feed_and_chicken_weights}{Chicken Weights by Feed Type}, from the \texttt{datasets} package in R.}
}

@misc{data:epaMPG,
  note = {U.S. Department of Energy, \oiRedirect{textbook-fuel_economy_data_2012}{Fuel Economy Data, 2012 Datafile}.}
}

@article{Oldham:2011,
  title={\oiRedirect{textbook-playing_computer_games_2011}{Playing a computer game during lunch affects fullness, memory for lunch, and later snack intake}},
  author={Oldham-Cooper, R.E. and Hardman, C.A. and Nicoll, C.E. and Rogers, P.J. and Brunstrom, J.M.},
  journal={The American Journal of Clinical Nutrition},
  volume={93},
  number={2},
  pages={308},
  year={2011},
  publisher={Am Soc Nutrition}
}

@misc{data:prison,
  note = {\oiRedirect{textbook-prison_isolation_exp}{Prison isolation experiment, stat.duke.edu/resources/datasets/prison-isolation}.}
}

@misc{data:china,
  note = {UNC Carolina Population Center, \oiRedirect{textbook-china_health_nut_survey_2006}{China Health and Nutrition Survey, 2006}.}
}

@article{Mortada:2000,
  title={Study of lead exposure from automobile exhaust as a risk for nephrotoxicity among traffic policemen.},
  author={Mortada, WI and Sobh, MA and El-Defrawy, MM and Farahat, SE},
  journal={American journal of nephrology},
  volume={21},
  number={4},
  pages={274--279},
  year={2000}
}

% Chp 8 - Simple linear regression

@book{Hand:1994,
  title={{A handbook of small data sets}},
  author={Hand, D.J.},
  year={1994},
  publisher={Chapman \& Hall/CRC}
}

@misc{data:trees,
  note = {Source: R Dataset, \oiRedirect{textbook-R_datasets_trees}{stat.ethz.ch/R-manual/R-patched/library/datasets/html/trees.html}}
}

@article{Benson:1993,
  title={\oiRedirect{textbook-birth_season_locomotion_1993}{Season of birth and onset of locomotion: Theoretical and methodological implications}},
  author={Benson, J.B.},
  journal={Infant behavior and development},
  volume={16},
  number={1},
  pages={69-81},
  issn={0163-6383},
  year={1993},
  publisher={Elsevier}
}

@misc{data:turkeyTourism,
  note = {Association of Turkish Travel Agencies, \oiRedirect{textbook-turkey_tourist_spending}{Foreign Visitors Figure \& Tourist Spendings By Years}}
}

@misc{data:starbucksCals,
  note={Source: Starbucks.com, collected on March 10, 2011, \\ \oiRedirect{textbook-starbucks_com_menu_nutrition}{www.starbucks.com/menu/nutrition}}
}

@misc{data:urbanOwner,
  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}.}
}

@book{Malkevitc+Lesser:2008,
  title={{For All Practical Purposes: Mathematical Literacy in Today's World}},
  author={Malkevitch, J. and Lesser, L.M.},
  year={2008},
  publisher={WH Freeman \& Co}
}

@article{Hamermesh:2005,
  title={Beauty in the classroom: Instructors’ pulchritude and putative pedagogical productivity},
  author={Hamermesh, Daniel S and Parker, Amy},
  journal={Economics of Education Review},
  volume={24},
  number={4},
  pages={369--376},
  year={2005},
  publisher={Elsevier}
}

%%%%%%%%%%%%%
% not sure which chapter, move later
%%%%%%%%%%%%%

@Book{data:quine,
    title = {Modern Applied Statistics with S},
    author = {W. N. Venables and B. D. Ripley},
    publisher = {Springer},
    edition = {Fourth Edition},
    address = {New York},
    year = {2002},
    note = {\href{http://www.stats.ox.ac.uk/pub/MASS4}{Data can also be found in the R MASS package}},
}

@misc{data:babies,
  note = {Child Health and Development Studies, \href{http://www.ma.hw.ac.uk/~stan/aod/library}{Baby weights data set}}
}

@article{King_Suamani_2018,
  title={\oiRedirect{textbook-King_Suamani_2018}{A Trial of a Triple-Drug Treatment for Lymphatic Filariasis}},
  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},
  journal={New England Journal of Medicine},
  volume={379},
  pages={1801-1810},
  year={2018}
}

@misc{bostonchildrenshospital:chickenpox,
  note={Boston Children's Hospital, \oiRedirect{textbook-bostonchildrenshospital_chickenpox_vaccine}{Chickenpox summary page}, referenced April 29, 2021.}
}

%%%%%%%%%%%%%
% used in textbook (not in eoce)
%%%%%%%%%%%%%

@misc{data:facebookPrivacy,
  note={Survey USA, \oiRedirect{textbook-SurveyUSA_17960}{News Poll \#17960}, data collected February 16-17, 2011.}
}



================================================
FILE: extraTeX/data/data.tex
================================================
\chapter{Data sets within the text}
\label{appendix_data}
\label{data_appendix}

%A foundational principle that supports quality statistical
%analysis is well-organized data.

\index{data|(}

Each data set within the text is described in this appendix,
and there is a corresponding page for each of these data sets at
\oiRedirect{data}
    {\color{black}\textbf{openintro.org/data}}.
This page also includes additional data sets that can be
used for honing your skills.
Each data set has its own page with the following information:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item
    List of the data set's variables.
\item
    CSV download.
\item
    R object file download.
\end{itemize}

%\vspace{10mm}

\newcommand{\datawrap}[1]{#1 $\to$}
\newcommand{\seedataappendix}[1]{This data set
    is described in Data Appendix~\ref{#1}.}
\newcommand{\seedataappendixplural}[1]{These data sets
    are described in Data Appendix~\ref{#1}.}
\newcommand{\madeup}{This example was made up.}



\section{\nameref{ch_intro_to_data}}
\label{ch_intro_to_data_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{basicExampleOfStentsAndStrokes}]
    \datawrap{\datalink{stent30}, \datalink{stent365}}
    The stent data is split across two data sets,
    one for days 0-30 results and one for days 0-365
    results. \\
    Chimowitz MI, Lynn MJ, Derdeyn CP, et al. 2011.
    Stenting versus Aggressive Medical Therapy for
    Intracranial Arterial Stenosis.
    New England Journal of Medicine 365:993-1003.
    \oiRedirect{textbook-nejm_stent_study}
        {www.nejm.org/doi/full/10.1056/NEJMoa1105335}. \\
    NY Times article:
    \oiRedirect{textbook-nytimes_stent_study}
        {www.nytimes.com/2011/09/08/health/research/08stent.html}.

\item[\ref{dataBasics}]
    \datawrap{\datalink{loan50},
        \datalink{loans\_full\_schema}}
    This data comes from Lending Club
    (\oiRedirect{lendingclub-info-download-data}
        {lendingclub.com}),
    which provides a large set of data on the people who
    received loans through their platform.
    The data used in the textbook comes from a sample
    of the loans made in Q1 (Jan, Feb, March) 2018.
\item[\ref{dataBasics}]
    \datawrap{\datalink{county}, \datalink{county\_complete}}
    These data come from several government sources.
    For those variables included in the
    county data set, only the most recent data is reported,
    as of what was available in late 2018.
    Data prior to 2011 is all from
    \oiRedirect{census_gov}{census.gov},
    where the specific Quick Facts page providing the data
    is no longer available.
    The more recent data comes from
    \oiRedirect
        {ers_usda_gov-data_products-county_level_data_sets}
        {USDA (ers.usda.gov)},
    \oiRedirect
        {bls_gov-lau}
        {Bureau of Labor Statistics (bls.gov/lau)},
    \oiRedirect
        {census_gov-did-www-saipe}
        {SAIPE (census.gov/did/www/saipe)},
    and
    \oiRedirect
        {census_gov-programs_surveys-acs}
        {American Community Survey
            (census.gov/programs-surveys/acs)}.

\item[\ref{section_obs_data_sampling}]
    \datawrap{Nurses' Health Study}
    For more information on this data set, see \\
    \oiRedirect{textbook-channing_nurse_study}
        {www.channing.harvard.edu/nhs}

\item[\ref{experimentsSection}]
    The study we had in mind when discussing the
    simple randomization (no blocking) study was \\
    Anturane Reinfarction Trial Research Group. 1980.
    \emph{Sulfinpyrazone in the prevention of sudden
    death after myocardial infarction.}
    New England Journal of Medicine 302(5):250-256.
\end{itemize}






\section{\nameref{ch_summarizing_data}}
\label{ch_summarizing_data_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{numericalData}]
    \datawrap{\datalink{loan50}, \datalink{county}}
    \seedataappendixplural{ch_intro_to_data_data}

\item[\ref{categoricalData}]
    \datawrap{\datalink{loan50}, \datalink{county}}
    \seedataappendixplural{ch_intro_to_data_data}

\item[\ref{caseStudyMalariaVaccine}]
    \datawrap{\datalink{malaria}}
    Lyke et al. 2017.
    PfSPZ vaccine induces strain-transcending T cells
    and durable protection against heterologous controlled
    human malaria infection.
    PNAS 114(10):2711-2716.
    \oiRedirect{lyke-ishizuka-2017}
        {www.pnas.org/content/114/10/2711}
\end{itemize}









\section{\nameref{ch_probability}}
\label{ch_probability_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{basicsOfProbability}]
    \datawrap{\datalink{loan50}, \datalink{county}}
    \seedataappendixplural{ch_intro_to_data_data}
\item[\ref{basicsOfProbability}]
    \datawrap{\datalink{playing\_cards}}
    Data set describing the 52 cards in a standard deck.

\item[\ref{conditionalProbabilitySection}]
    \datawrap{\datalink{family\_college}}
    Simulated data based on real population summaries at \\
    \oiRedirect{textbook-student_parent_college_2001}
        {nces.ed.gov/pubs2001/2001126.pdf}.
\item[\ref{conditionalProbabilitySection}]
    \datawrap{\datalink{smallpox}}
    Fenner F. 1988.
    Smallpox and Its Eradication
    (History of International Public Health, No. 6).
    Geneva: World Health Organization. ISBN 92-4-156110-6.

\item[\ref{conditionalProbabilitySection}]
    \datawrap{Mammogram screening, probabilities}
    The probabilities reported were obtained using studies
    reported at
    \oiRedirect{textbook-breastCancerDotOrg_20090831b}
        {www.breastcancer.org}
    and \oiRedirect{textbook-ncbi_nih_breast_cancer}
        {www.ncbi.nlm.nih.gov/pmc/articles/PMC1173421}. 

\item[\ref{conditionalProbabilitySection}]
    \datawrap{Jose campus visits, probabilities}
    \madeup{}

\item[\ref{smallPop}]
    No data sets were described in this section.

\item[\ref{randomVariablesSection}]
    \datawrap{Course material purchases and probabilities}
    \madeup{}

\item[\ref{randomVariablesSection}]
    \datawrap{Auctions for TV and toaster}
    \madeup{}

\item[\ref{randomVariablesSection}]
    \datawrap{\datalink{stocks\_18}}
    Monthly returns for Caterpillar, Exxon Mobil Corp,
    and Google for November 2015 to October 2018.

\item[\ref{contDist}]
    \datawrap{\datalink{fcid}}
    This sample can be considered a simple random sample
    from the US population.
    It relies on the USDA Food Commodity Intake Database.

\end{itemize}




\section{\nameref{ch_distributions}}
\label{ch_distributions_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{normalDist}]
    \datawrap{SAT and ACT score distributions}
    The SAT score data comes from the 2018 distribution,
    which is provided at \\
    {\small
    \oiRedirect{textbook-collegeboard_sat_2018_score_distribution}
        {reports.collegeboard.org/pdf/2018-total-group-sat-suite-assessments-annual-report.pdf}} \\
    The ACT score data is available at \\
    {\footnotesize
    \oiRedirect{textbook-act_2018_score_distribution}
        {act.org/content/dam/act/unsecured/documents/cccr2018/P\_99\_999999\_N\_S\_N00\_ACT-GCPR\_National.pdf}} \\
    We also acknowledge that the actual ACT score distribution
    is \emph{not} nearly normal.
    However, since the topic is very accessible,
    we decided to keep the context and examples.
\item[\ref{normalDist}]
    \datawrap{Male heights}
    The distribution is based on the
    USDA Food Commodity Intake Database.
\item[\ref{normalDist}]
    \datawrap{\datalink{possum}}
    The distribution parameters are based on a sample
    of possums from Australia and New Guinea.
    The original source of this data is as follows.
    Lindenmayer DB, et al. 1995.
    \emph{Morphological variation among columns of the
        mountain brushtail possum, Trichosurus caninus
        Ogilby (Phalangeridae: Marsupiala)}.
    Australian Journal of Zoology 43: 449-458.

%\item[\ref{assessingNormal}]
%    \datawrap{\datalink{male\_heights\_fcid}}
%    This sample can be considered a simple random sample
%    from the US population.
%    It relies on the USDA Food Commodity Intake Database.
%\item[\ref{assessingNormal}]
%    \datawrap{\datalink{simulated\_normal}}
%    These data were simulated from a standard normal distribution.
%    This data set includes three different data sets.
%\item[\ref{assessingNormal}]
%    \datawrap{\datalink{nba\_players\_19}}
%    Summary information from the NBA players for the
%    2018-2019 season.
%    Data were retrieved from
%    \oiRedirect{data-nba_players_19}{www.nba.com/players}.
%\item[\ref{assessingNormal}]
%    \datawrap{\datalink{poker}}
%    Poker winnings (and losses) for 50 days by a professional
%    poker player, which represents their first 50 days trying
%    to play for a living.
%    Anonymity has been requested by the player.
%\item[\ref{assessingNormal}]
%    \datawrap{\datalink{simulated\_dist}}
%    Simulated data sets,
%    not necessarily drawn from a normal distribution.
%    This data set includes six different data sets.

\item[\ref{geomDist}]
    \datawrap{Exceeding insurance deductible}
    These statistics were made up but are possible
    values one might observe for low-deductible plans.

\item[\ref{binomialModel}]
    \datawrap{Exceeding insurance deductible}
    These statistics were made up but are possible
    values one might observe for low-deductible plans.
\item[\ref{binomialModel}]
    \datawrap{Smoking friends}
    Unfortunately, we don't currently have additional
    information on the source for the 30\% statistic,
    so don't consider this one as fact since we cannot
    verify it was from a reputable source.    
\item[\ref{binomialModel}]
    \datawrap{US smoking rate}
    The 15\% smoking rate in the US figure is close to
    the value from the Centers for Disease Control and
    Prevention website, which reports a value of 14\%
    as of the 2017 estimate: \\
    \oiRedirect{cdc_gov-tobacco-data_statistics}
        {cdc.gov/tobacco/data\_statistics/fact\_sheets/adult\_data/cig\_smoking/index.htm}

\item[\ref{negativeBinomial}]
    \datawrap{Football kicker}
    \madeup{}
\item[\ref{negativeBinomial}]
    \datawrap{Heart attack admissions}
    This example was made up, though the heart attack
    admissions are realistic for some hospitals.

\item[\ref{poisson}]
    \datawrap{\datalink{ami\_occurrences}}
    This is a simulated data set but resembles actual
    AMI data for New York City based on typical AMI
    incidence rates.
\end{itemize}








\section{\nameref{ch_foundations_for_inf}}
\label{ch_foundations_for_inf_data}

\begin{itemize}
\item[\ref{pointEstimates}]
    \datawrap{\datalink{pew\_energy\_2018}}
    The actual data has more observations than were referenced
    in this chapter.
    That is, we used a subsample since it helped smooth some
    of the examples to have a bit more variability.
    The \data{pew\us{}energy\us{}2018} data set represents
    the full data set for each of the different energy source
    questions, which covers solar, wind, offshore drilling,
    hydrolic fracturing, and nuclear energy.
    The statistics used to construct the data are from
    the following page:
    \begin{center}
    \oiRedirect{textbook-pew_2018_poll_on_solar_and_wind_expansion}
        {{\small{www.pewinternet.org/2018/05/14/majorities-see-government-efforts-to-protect-the-environment-as-insufficient/}}}
    \end{center}
    
\item[\ref{confidenceIntervals}]
    \datawrap{\datalink{pew\_energy\_2018}}
    See the details for this data set above
    in the Section~\ref{pointEstimates} data section.
\item[\ref{confidenceIntervals}]
    \datawrap{\datalink{ebola\_survey}}
    In New York City on October 23rd, 2014, a doctor who had
    recently been treating Ebola patients in Guinea went to
    the hospital with a slight fever and was subsequently
    diagnosed with Ebola.
    Soon thereafter, an NBC~4 New York/The Wall Street
    Journal/Marist Poll found that
    82\% of New Yorkers favored a
    ``mandatory 21-day quarantine for anyone who has come
    in contact with an Ebola patient''.
    This poll included responses of 1,042
    New York adults between Oct 26th and~28th, 2014.
    \oiRedirect{textbook-maristpoll_ebola_201410}
        {Poll ID NY141026 on maristpoll.marist.edu}.

\item[\ref{hypothesisTesting}]
    \datawrap{\datalink{pew\_energy\_2018}}
    See the details for this data set above
    in the Section~\ref{pointEstimates} data section.
\item[\ref{hypothesisTesting}]
    \datawrap{Rosling questions}
    We noted much smaller samples than the Roslings'
    describe in their book,
    \oiRedirect{amazon_factfulness}{Factfulness},
    The samples we describe are similar but not
    the same as the actual rates.
    The approximate rates for the correct answers for the
    two questions for (sometimes different) populations
    discussed in the book, as reported in
    \oiRedirect{amazon_factfulness}{Factfulness},
    are
    \begin{itemize}
    \item
        80\% of the world's 1 year olds have been vaccinated
        against some disease:
        13\% get this correct (17\% in the US).
        \oiRedirect{gapm-io-q9}{gapm.io/q9}
    \item
        Number of children in the world in 2100:
        9\% correct.
        \oiRedirect{gapm-io-q5}{gapm.io/q5}
    \end{itemize}
    Here are a few more questions and a rough percent
    of people who get them correct:
    \begin{itemize}
    \item
        In all low-income countries across the world today,
        how many girls finish primary school: 20\%, 40\%, or 60\%?
        Answer: 60\%.
        About 7\% of people get this question correct.
        \oiRedirect{gapm-io-q1}{gapm.io/q1}
    \item
        What is the life expectancy of the world today:
        50 years, 60 years, or 70 years?
        Answer: 70 years.
        In the US, about 43\% of people get this question correct.
        \oiRedirect{gapm-io-q4}{gapm.io/q4}
%    \item
%        How many of the world's 1 year old children today
%        have been vaccinated against some disease:
%        20\%, 50\%, or 80\%?
%        Answer: 80\%.
%        About 13\% of people get this question correct.
%        \oiRedirect{gapm-io-q9}{gapm.io/q9}
    \item
        In 1996, tigers, giant pandas, and black rhinos
        were all listed as endangered.
        How many of these three species are more
        critically endangered today:
        two of them,
        one of them,
        none of them?
        Answer: none of them.
        About 7\% of people get this question correct.
        \oiRedirect{gapm-io-q11}{gapm.io/q11}
    \item
        How many people in the world have some access
        to electricity? 20\%, 50\%, 80\%.
        Answer: 80\%.
        About 22\% of people get this correct.
        \oiRedirect{gapm-io-q12}{gapm.io/q12}
    \end{itemize}
    For more information, check out the book,
    \oiRedirect{amazon_factfulness}{Factfulness}.
\item[\ref{hypothesisTesting}]
    \datawrap{\datalink{pew\_energy\_2018}}
    See the details for this data set above
    in the Section~\ref{pointEstimates} data section.
\item[\ref{hypothesisTesting}]
    \datawrap{\datalink{nuclear\_survey}}
    A simple random sample of 1,028 US adults in March 2013
    found that 56\% of US adults support nuclear arms
    reduction. \\
    \oiRedirect{textbook-nuclear_arms_reduction_201303}
        {www.gallup.com/poll/161198/favor-russian-nuclear-arms-reductions.aspx}
\item[\ref{hypothesisTesting}]
    \datawrap{Car manufacturing}
    \madeup{}
\item[\ref{hypothesisTesting}]
    \datawrap{\datalink{stent30}, \datalink{stent365}}
    \seedataappendixplural{ch_intro_to_data_data}

\end{itemize}





\D{\newpage}

\section{\nameref{ch_inference_for_props}}
\label{ch_inference_for_props_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{singleProportion}]
    \datawrap{Payday loans}
    The statistics come from the following source: \\
    {\footnotesize\oiRedirect{pew-payday-loans-2017}
        {pewtrusts.org/-/media/assets/2017/04/payday-loan-customers-want-more-protections-methodology.pdf}}
\item[\ref{singleProportion}]
    \datawrap{Tire factory}
    \madeup{}

\item[\ref{differenceOfTwoProportions}]
    \datawrap{\datalink{cpr}}
    B$\ddot{\text{o}}$ttiger et al.
    \emph{Efficacy and safety of thrombolytic therapy after
        initially unsuccessful cardiopulmonary resuscitation:
        a prospective clinical trial}.
        The Lancet, 2001.
\item[\ref{differenceOfTwoProportions}]
    \datawrap{\datalink{fish\_oil\_18}}
    Manson JE, et al. 2018.
    \emph{Marine n-3 Fatty Acids and Prevention of
    Cardiovascular Disease and Cancer.}
    NEJMoa1811403.
\item[\ref{differenceOfTwoProportions}]
    \datawrap{\datalink{mammogram}}
    \oiRedirect{textbook-90k_mammogram_study_2014}
        {Miller AB. 2014.
            \emph{Twenty five year follow-up for breast cancer
            incidence and mortality of the Canadian National
            Breast Screening Study: randomised screening trial}.
            BMJ 2014;348:g366.}
\item[\ref{differenceOfTwoProportions}]
    \datawrap{\datalink{drone\_blades}}
    The quality control data set for quadcopter drone blades
    is a made-up data set for an example.
    We provide the simulated data in the
    \data{drone\us{}blades} data set.

\item[\ref{oneWayChiSquare}]
    \datawrap{\datalink{jury}}
    The jury data set for examining discrimination
    is a made-up data set an example.
    We provide the simulated data in the \data{jury} data set.
\item[\ref{oneWayChiSquare}]
    \datawrap{\datalink{sp500\_1950\_2018}}
    Data is sourced from
    \oiRedirect{yahoo_finance}
        {finance.yahoo.com}.

\item[\ref{twoWayTablesAndChiSquare}]
    \datawrap{\datalink{ask}}
    Minson JA, Ruedy NE, Schweitzer ME.
    \emph{There is such a thing as a stupid question:
    Question disclosure in strategic communication}. \\
    {\small\oiRedirect{minson_ruedy_data_source}
        {opim.wharton.upenn.edu/DPlab/papers/workingPapers/}}\\
    {\small\oiRedirect{minson_ruedy_data_source}
        {Minson\_working\_Ask\%20(the\%20Right\%20Way)\%20and\%20You\%20Shall\%20Receive.pdf}}

\item[\ref{twoWayTablesAndChiSquare}]
    \datawrap{\datalink{diabetes2}}
    Zeitler P, et al. 2012.
    \emph{A Clinical Trial to Maintain Glycemic Control
    in Youth with Type~2 Diabetes}.
    N Engl J Med.

\end{itemize}






\section{\nameref{ch_inference_for_means}}
\label{ch_inference_for_means_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{oneSampleMeansWithTDistribution}]
    \datawrap{Risso's dolphins}
    Endo T and Haraguchi K. 2009.
    \emph{High mercury levels in hair samples from
    residents of Taiji, a Japanese whaling town}.
    Marine Pollution Bulletin 60(5):743-747.

    Taiji was featured in the movie
    \emph{The Cove}, and it is a significant source of dolphin
    and whale meat in Japan.
    Thousands of dolphins pass through the Taiji area annually,
    and we assumes these 19 dolphins reasonably represent
    a simple random sample from those dolphins.
\item[\ref{oneSampleMeansWithTDistribution}]
    \datawrap{Croaker white fish}
    \oiRedirect{textbook-fda_mercury_in_fish_2010}
        {fda.gov/food/foodborneillnesscontaminants/metals/ucm115644.htm}
\item[\ref{oneSampleMeansWithTDistribution}]
    \datawrap{\datalink{run17}}
    \oiRedirect{textbook-cherryblossom_org}{www.cherryblossom.org}

\item[\ref{pairedData}]
    \datawrap{\datalink{textbooks},
        \datalink{ucla\_textbooks\_f18}}
    Data were collected by OpenIntro staff in 2010 and again
    in 2018.
    For the 2018 sample, we sampled 201 UCLA courses.
    Of those, 68 required books that could be
    found on Amazon.
    The websites where information was retrieved: \\
    \oiRedirect{ucla_class_schedule}
        {sa.ucla.edu/ro/public/soc},
    \oiRedirect{ucla_verbacompare}{ucla.verbacompare.com},
    and \oiRedirect{amazon}{amazon.com}.

\item[\ref{differenceOfTwoMeans}]
    \datawrap{\datalink{stem\_cells}}
    \oiRedirect{textbook-menard_stem_cells_2005}
        {Menard C, et al. 2005.
            Transplantation of cardiac-committed mouse
            embryonic
            stem cells to infarcted sheep myocardium:
            a preclinical study.
            The Lancet: 366:9490, p1005-1012.}
\item[\ref{differenceOfTwoMeans}]
    \datawrap{\datalink{ncbirths}}
    Birth records released by North Carolina in 2004.
    Unfortunately, we don't currently have additional
    information on the source for this data set.
\item[\ref{differenceOfTwoMeans}]
    \datawrap{Exam versions}
    \madeup{}

\item[\ref{PowerForDifferenceOfTwoMeans}]
    \datawrap{Blood pressure statistics}
    The blood pressure standard deviation for patients
    with blood pressure ranging from 140 to 180 mmHg
    is guessed and may be a little
    (but likely not dramatically)
    imprecise from what we'd observe in actual data.

\item[\ref{anovaAndRegrWithCategoricalVariables}]
    \datawrap{\datalink{toy\_anova}}
    Data used for Figure~\ref{toyANOVA},
    where this data was made up.
\item[\ref{anovaAndRegrWithCategoricalVariables}]
    \datawrap{\datalink{mlb\_players\_18}}
    Data were retrieved from
    \oiRedirect{mlb-stats}{mlb.mlb.com/stats}.
    Only players with at least 100 at bats were considered
    during the analysis.
\item[\ref{anovaAndRegrWithCategoricalVariables}]
    \datawrap{\datalink{classdata}}
    \madeup{}

\end{itemize}






\section{\nameref{ch_regr_simple_linear}}
\label{ch_regr_simple_linear_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{fitting_line_to_data_section}]
    \datawrap{\datalink{simulated\_scatter}}
    Fake data used for the first three plots.
    The perfect linear plot uses group~4 data,
    where \var{group} variable in the data set
    (Figure~\ref{perfLinearModel}).
    The group of 3 imperfect linear plots use groups~1-3
    (Figure~\ref{imperfLinearModel}).
    The sinusoidal curve uses group~5 data
    (Figure~\ref{notGoodAtAllForALinearModel}).
    The group of 3 scatterplots with residual plots use groups~6-8
    (Figure~\ref{sampleLinesAndResPlots}).
    The correlation plots uses groups~9-19 data
    (Figures~\ref{posNegCorPlots} and~\ref{corForNonLinearPlots}).
\item[\ref{fitting_line_to_data_section}]
    \datawrap{\datalink{possum}}
    \seedataappendix{ch_distributions_data}

\item[\ref{fittingALineByLSR}]
    \datawrap{\datalink{elmhurst}}
    These data were sampled from a table of data for all
    freshman from the 2011 class at Elmhurst College that
    accompanied an article titled
    \emph{What Students Really Pay to Go to College}
    published online by \emph{The~Chronicle of Higher Education}:
    \oiRedirect{textbook-chronicle_elmhurst_article}
        {chronicle.com/article/What-Students-Really-Pay-to-Go/131435}.
\item[\ref{fittingALineByLSR}]
    \datawrap{\datalink{simulated\_scatter}}
    The plots for things that can go wrong uses groups 20-23
    (Figure~\ref{whatCanGoWrongWithLinearModel}).
\item[\ref{fittingALineByLSR}]
    \datawrap{\datalink{mariokart}}
    Auction data from Ebay (ebay.com) for the game Mario Kart
    for the Nintendo Wii.
    This data set was collected in early October, 2009.

\item[\ref{typesOfOutliersInLinearRegression}]
    \datawrap{\datalink{simulated\_scatter}}
    The plots for types of outliers uses groups 24-29
    (Figure~\ref{outlierPlots}).

\item[\ref{inferenceForLinearRegression}]
    \datawrap{\datalink{midterms\_house}}
    Data was retrieved from Wikipedia.

\end{itemize}







\section{\nameref{ch_regr_mult_and_log}}
\label{ch_regr_mult_and_log_data}

\begin{itemize}
\setlength{\itemsep}{0mm}
\item[\ref{introductionToMultipleRegression}]
    \datawrap{\datalink{loans\_full\_schema}}
    \seedataappendix{ch_intro_to_data_data}

\item[\ref{model_selection_section}]
    \datawrap{\datalink{loans\_full\_schema}}
    \seedataappendix{ch_intro_to_data_data}

\item[\ref{multipleRegressionModelAssumptions}]
    \datawrap{\datalink{loans\_full\_schema}}
    \seedataappendix{ch_intro_to_data_data}

\item[\ref{mario_kart_case_study}]
    \datawrap{\datalink{mariokart}}
    \seedataappendix{ch_regr_simple_linear_data}

\item[\ref{logisticRegression}]
    \datawrap{\datalink{resume}}
    Bertrand M, Mullainathan S. 2004.
    \emph{Are Emily and Greg More Employable than Lakisha and Jamal?
    A Field Experiment on Labor Market Discrimination}.
    The American Economic Review 94:4 (991-1013).
    \oiRedirect{resume-data-2004}
        {www.nber.org/papers/w9873}

    We did omit discussion of some structure in
    the data for the analysis presented:
    the experiment design included blocking,
    where typically four resumes were sent to each job:
    one for each inferred race/sex combination
    (as inferred based on the first name).
    We did not worry about this blocking aspect,
    since accounting for the blocking would
    \emph{reduce} the standard error without notably
    changing the point estimates for the
    \var{race} and \var{sex} variables
    versus the analysis performed in the section.
    That is, the most interesting conclusions in the
    study are unaffected even when completing a more
    sophisticated analysis.

%\item[\ref{logisticRegression}]
%    \datawrap{\datalink{research\_reply}}
%    Milkman KL, Akinola M, Chugh D. 2015.
%    What Happens Before?
%    A Field Experiment Exploring How Pay and
%    Representation Differentially Shape Bias
%    on the Pathway Into Organizations.
%    Journal of Applied Psychology, 100:6, p1678-1712.
%
%    This study highlights results where fictional students
%    contacted faculty members.
%    The outcome of interest was whether the faculty member
%    would reply, and the variables of interest were the
%    race and sex of the prospective student as well as
%    demographics of the faculty member who received the message.
%    The authors have made the data set publicly available,
%    and we've put it into a CSV file that is friendly
%    for downloading through the \data{research\_reply} data set.
%    \Comment{Either get this data set in a sharable form
%      or remove this reference.}

\end{itemize}

\index{data|)}


================================================
FILE: extraTeX/eoceSolutions/eoceSolutions.tex
================================================
\chapter{Exercise solutions}
\label{eoceSolutions}





%_______________
\eocesolch{Introduction to data}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~Treatment: $10/43 = 0.23 \rightarrow 23\%$. \\
(b)~Control: $2/46 = 0.04 \rightarrow 4\%$. 
(c)~A higher percentage of patients in the treatment group were pain 
free 24 hours after receiving acupuncture. 
(d)~It is possible that the observed difference between the two group 
percentages is due to chance.}

% 3

\eocesol{(a)~``Is there an association between air pollution exposure and preterm births?"
(b)~143,196 births in Southern California between 1989 and 1993.
(c)~Measurements of carbon monoxide, nitrogen dioxide, ozone, and particulate 
matter less than 10$\mu g/m^3$ (PM$_{10}$) collected at air-quality-monitoring 
stations as well as length of gestation.
Continuous numerical variables. }

% 5

\eocesol{(a)~``Does explicitly telling children not to cheat affect their likelihood to 
cheat?".
(b)~160 children between the ages of 5 and 15.
(c)~Four variables: (1) age (numerical, continuous), (2) sex (categorical), 
(3) whether they were an only child or not (categorical), (4) whether they 
cheated or not (categorical).}

% 7

\eocesol{Explanatory: acupuncture or not.
Response: if the patient was pain free or not.}

% 9

\eocesol{(a)~$50 \times 3 = 150$. 
(b)~Four continuous numerical variables: sepal length, sepal width, petal length, and petal width. 
(c)~One categorical variable, species, with three levels: \emph{setosa}, \emph{versicolor}, and \emph{virginica}.}

% 11

\eocesol{(a)~Airport ownership status (public/private),
    airport usage status (public/private),
    latitude,
    and longitude.
(b)~Airport ownership status: categorical, not ordinal.
    Airport usage status: categorical, not ordinal.
    Latitude: numerical, continuous.
    Longitude: numerical, continuous.}

% 13

\eocesol{(a)~Population: all births, sample: 143,196 births between 1989 and 1993 in 
Southern California. 
(b)~If births in this time span at the geography can be considered to be 
representative of all births, then the results are generalizable to the 
population of Southern California. However, since the study is observational 
the findings cannot be used to establish causal relationships.}

% 15

\eocesol{(a)~Population: all asthma patients aged 18-69 who rely on medication for 
asthma treatment. Sample: 600 such patients.
(b)~If the patients in this sample, who are likely not randomly sampled, can 
be considered to be representative of all asthma patients aged 18-69 who rely 
on medication for asthma treatment, then the results are generalizable to the 
population defined above. Additionally, since the study is experimental, the 
findings can be used to establish causal relationships.}

% 17

\eocesol{(a)~Observation.
(b)~Variable.
(c)~Sample statistic (mean).
(d)~Population parameter (mean).}

% 19

\eocesol{(a)~Observational.
(b)~Use stratified sampling to randomly sample a fixed number of students, 
say 10, from each section for a total sample size of 40 students.}

% 21

\eocesol{(a)~Positive, non-linear, somewhat strong. Countries in which a higher 
percentage of the population have access to the internet also tend to have 
higher average life expectancies, however rise in life expectancy trails 
off before around 80 years old.
(b)~Observational.
(c)~Wealth: countries with individuals who can widely afford the internet 
can probably also afford basic medical care. (Note: Answers may vary.)}

% 23

\eocesol{(a)~Simple random sampling is okay. In~fact, it's rare for simple random 
sampling to not be a reasonable sampling method! 
(b)~The student opinions may vary by field of study, so the stratifying 
by this variable makes sense and would be reasonable. 
(c)~Students of similar ages are probably going to have more similar 
opinions, and we want clusters to be diverse with respect to the outcome 
of interest, so this would \textbf{not} be a good approach. (Additional 
thought: the clusters in this case may also have very different numbers 
of people, which can also create unexpected sample sizes.)}

\end{multicols}
\newpage
\begin{multicols}{2}

% 25

\eocesol{(a)~The cases are 200 randomly sampled men and women.
(b)~The response variable is attitude towards a fictional microwave oven.
(c)~The explanatory variable is dispositional attitude.
(d)~Yes, the cases are sampled randomly.
(e)~This is an observational study since there is no random assignment to 
treatments.
(f)~No, we cannot establish a causal link between the explanatory and response 
variables since the study is observational.
(g)~Yes, the results of the study can be generalized to the population at 
large since the sample is random.}

% 27

\eocesol{(a)~Simple random sample. Non-response bias, if only those people who have 
strong opinions about the survey responds his sample may not be representative 
of the population.
(b)~Convenience sample. His sample may not be 
representative of the population since it consists only of his friends. It is 
also possible that the study will have non-response bias if some choose to not 
bring back the survey.
(c)~Convenience sample. This will have a similar issues to handing out surveys 
to friends.
(d)~Multi-stage sampling. If the classes are similar to each other with 
respect to student composition this approach should not introduce bias, 
other than potential non-response bias.}

% 29

\eocesol{(a)~Exam performance.
(b)~Light level: fluorescent overhead lighting, yellow overhead lighting, no overhead 
lighting (only desk lamps).
(c)~Sex: man, woman.}

% 31

\eocesol{(a)~Experiment.
(b)~Light level (overhead lighting, yellow overhead lighting, no overhead lighting) and 
noise level (no noise, construction noise, and human chatter noise).
(c)~Since the researchers want to ensure equal gender representation, sex will be a blocking variable.}

% 33

\eocesol{Need randomization and blinding. One possible outline: 
(1)~Prepare two cups for each 
participant, one containing regular Coke and the other containing Diet Coke. Make sure 
the cups are identical and contain equal amounts of soda. Label the cups A (regular) and 
B (diet). (Be sure to randomize A and B for each trial!)
(2)~Give each participant the 
two cups, one cup at a time, in random order, and ask the participant to record a value 
that indicates how much she liked the beverage.  Be sure that neither the participant nor 
the person handing out the cups knows the identity of the beverage to make this a double-
blind experiment. (Answers may vary.)}

% 35

\eocesol{(a)~Observational study.
(b)~Dog: Lucy. Cat: Luna.
(c)~Oliver and Lily.
(d)~Positive, as the popularity of a name for dogs increases, so does the 
popularity of that name for cats. }

% 37

\eocesol{(a)~Experiment.
(b)~Treatment: 25 grams of chia seeds twice a day, control: placebo. 
(c)~Yes, gender.
(d)~Yes, single blind since the patients were blinded to the treatment 
they received.
(e)~Since this is an experiment, we can make a causal statement. However, since the 
sample is not random, the causal statement cannot be generalized to the population at 
large.}

% 39

\eocesol{(a)~Non-responders may have a different response to this question, e.g. 
parents who returned the surveys likely don't have difficulty spending time 
with their children.
(b)~It is unlikely that the women who were reached at the same address 3 years 
later are a random sample. These missing responders are probably renters 
(as opposed to homeowners) which means that they might be in a lower socio-
economic status than the respondents.
(c)~There is no control group in this study, this is an observational study, 
and there may be confounding variables, e.g. these people may go running 
because they are generally healthier and/or do other exercises.}

% 41

\eocesol{(a)~Randomized controlled experiment.
(b)~Explanatory: treatment group (categorical, with 3 levels). Response variable: 
Psychological well-being.
(c)~No, because the participants were volunteers.
(d)~Yes, because it was an experiment.
(e)~The statement should say ``evidence'' instead of ``proof''.}

% 43

\eocesol{(a)~Categorical, non-ordinal: County, State, Driver's race. Numerical, discrete: No. of stops per year. Numerical, continuous: \% searched, \% drivers arrested.
(b)~All categorical, non-ordinal.
(c)~Response: whether the car was searched or not.
    Explanatory: race of the driver.}



%_______________
\end{multicols}



%_______________
\eocesolch{Summarizing data}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~Positive association: mammals with longer gestation periods tend to live longer as 
well.
(b)~Association would still be positive.
(c)~No, they are not independent. See part~(a).}

% 3

\eocesol{The graph below shows a ramp up period.
There may also be a period of exponential growth at the start
before the size of the petri dish becomes a factor in slowing growth. \\
\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}}

% 5

\eocesol{(a)~Population mean, $\mu_{2007} = 52$; sample mean, $\bar{x}_{2008} = 58$.
(b)~Population mean, $\mu_{2001} = 3.37$; sample mean, $\bar{x}_{2012} = 3.59$.}

% 7

\eocesol{Any 10 employees whose average number of days off is between the minimum and the mean 
number of days off for the entire workforce at this plant.}

% 9

\eocesol{(a)~Dist~2 has a higher mean since $20 > 13$, and a higher standard deviation 
since 20 is further from the rest of the data than 13.
(b)~Dist~1 has a higher mean since $-20 > -40$, and Dist~2 has a 
higher standard deviation since -40 is farther away from the rest of the data than -20.
(c)~Dist~2 has a higher mean since all values in this distribution are higher 
than those in Dist~1, but both distribution have the same standard deviation 
since they are equally variable around their respective means.
(d)~Both distributions have the same mean since they're both centered at 300, but 
Dist~2 has a higher standard deviation since the observations are farther from 
the mean than in Dist~1.}

% 11

\eocesol{(a)~About 30.
(b)~Since the distribution is right skewed the mean is higher than the median.
(c)~Q1: between 15 and 20, Q3: between 35 and 40, IQR: about 20.
(d)~Values that are considered to be unusually low or high lie more than 1.5$\times$IQR 
away from the quartiles. Upper fence: Q3 + 1.5 $\times$ IQR =  $37.5 + 1.5 \times 20 = 67.5$; 
Lower fence: Q1 - 1.5 $\times$ IQR =  $17.5 - 1.5 \times 20 =  -12.5$; The lowest AQI 
recorded is not lower than 5 and the highest AQI recorded is not higher than 65, which 
are both within the fences. Therefore none of the days in this sample would be considered 
to have an unusually low or high AQI.}

% 13

\eocesol{The histogram shows that the distribution is bimodal, which is not apparent in the box 
plot. The box plot makes it easy to identify more precise values of observations outside 
of the whiskers.}

% 15

\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.
(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.
(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.}

% 17

\eocesol{(a)~The median is a much better measure of the typical amount earned by these 42 
people. The mean is much higher than the income of 40 of the 42 people. This is 
because 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 
outliers.
(b)~The IQR  is a much better measure of variability in the amounts earned by nearly 
all of the 42 people. The standard deviation gets affected greatly by the two high 
salaries, but the IQR is robust to these extreme observations.}

% 19

\eocesol{(a)~The distribution is unimodal and symmetric with a mean of about 25 minutes 
and a standard deviation of about 5 minutes. There does not appear to be any 
counties with unusually high or low mean travel times. Since the distribution 
is already unimodal and symmetric, a log transformation is not necessary.
(b)~Answers will vary. There are pockets of longer travel time around DC, 
Southeastern NY, Chicago, Minneapolis, Los Angeles, and many other big cities. 
There is also a large section of shorter average commute times that overlap 
with farmland in the Midwest. Many farmers' homes are adjacent to their 
farmland, so their commute would be brief, which may explain why the 
average commute time for these counties is relatively low.}

% 21

\eocesol{(a)~We see the order of the categories and the relative frequencies in the bar plot.
(b)~There are no features that are apparent in the pie chart but not in the bar plot.
(c)~We usually prefer to use a bar plot as we can also see the relative frequencies of the categories in this graph.}

% 23

\eocesol{The vertical locations at which the ideological groups break into the Yes, No, 
and Not Sure categories differ, which indicates that likelihood of supporting 
the DREAM act varies by political ideology. This suggests that the two variables 
may be dependent.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 25

\eocesol{(a)~(i) False. Instead of comparing counts, we should compare percentages of people in each group who suffered cardiovascular problems.
(ii)~True.
(iii)~False. Association does not imply causation. We cannot infer a causal 
relationship based on an observational study. The difference from part~(ii) 
is subtle.
(iv)~True. \\
(b)~Proportion of all patients who had cardiovascular problems: $\frac{7,979}{227,571} \approx 0.035$ \\
(c)~The expected number of heart attacks in the rosiglitazone group, if having 
cardiovascular problems and treatment were independent, can be calculated as the 
number of patients in that group multiplied by the overall cardiovascular problem 
rate in the study: $67,593 * \frac{7,979}{227,571} \approx 2370$. \\
(d)~(i)~$H_0$: The treatment and cardiovascular problems are independent. They have 
no relationship, and the difference in incidence rates between the rosiglitazone and 
pioglitazone groups is due to chance.
$H_A$: The treatment and cardiovascular problems are not independent. The difference 
in the incidence rates between the rosiglitazone and pioglitazone groups is not due 
to chance and rosiglitazone is associated with an increased risk of serious 
cardiovascular problems.
(ii)~A higher number of patients with cardiovascular problems than expected under 
the assumption of independence would provide support for the alternative hypothesis 
as this would suggest that rosiglitazone increases the risk of such problems.
(iii)~In the actual study, we observed 2,593 cardiovascular events in the 
rosiglitazone group. In the 1,000 simulations under the independence model, we 
observed somewhat less than 2,593 in every single simulation, which suggests that 
the actual results did not come from the independence model. That is, the variables 
do not appear to be independent, and we reject the independence model in favor of 
the alternative. The study's results provide convincing evidence that rosiglitazone 
is associated with an increased risk of cardiovascular problems.}

% 27

\eocesol{(a)~Decrease: the new score is smaller than the mean of the 24 previous scores.
(b)~Calculate a weighted mean. Use a weight of 24 for the old mean and 1 for the new 
mean: $(24\times 74 + 1\times64)/(24+1) = 73.6$.
%There are other ways to solve this 
%exercise that do not use a weighted mean.
(c)~The new score is more than 1 standard deviation away from the previous mean, so 
increase.}

% 29

\eocesol{No, we would expect this distribution to be right skewed. There are two reasons 
for this: (1)~there is a natural boundary at 0 (it is not possible to watch less 
than 0 hours of TV), (2)~the standard deviation of the distribution is very large 
compared to the mean.}

% 31

\eocesol{The distribution of ages of best actress winners are right skewed with a 
median around 30 years. The distribution of ages of best actor winners 
is also right skewed, though less so, with a median around 40 years. The 
difference between the peaks of these distributions suggest that best actress 
winners are typically younger than best actor winners. The ages of best actress 
winners are more variable than the ages of best actor winners. There are 
potential outliers on the higher end of both of the distributions. }

% 33

\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}}



%_______________
\end{multicols}



%_______________
\eocesolch{Probability}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~False. These are independent trials.
(b)~False. There are red face cards.
(c)~True. A card cannot be both a face card and an ace.}

% 3

\eocesol{(a)~10 tosses. Fewer tosses mean more variability in the sample fraction of heads, 
meaning there's a better chance of getting at least 60\% heads.
(b)~100 tosses. More flips means the observed proportion of heads would often be 
closer to the average, 0.50, and therefore also above 0.40.
(c)~100 tosses. With more flips, the observed proportion of heads would often be 
closer to the average, 0.50.
(d)~10 tosses. Fewer flips would increase variability in the fraction of tosses 
that are heads.}

% 5

\eocesol{(a)~$0.5^{10}$ = 0.00098.
(b)~$0.5^{10}$ = 0.00098.
(c)~$P$(at least one tails) = $1 - P$(no tails) = $1 - (0.5^{10}) \approx 1 - 0.001 = 0.999$.}

% 7

\eocesol{(a)~No, there are voters who are both independent and swing voters. \\
(b)\\
\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} \\
(c)~Each Independent voter is either a swing voter or not. Since 35\% of voters 
are Independents and 11\% are both Independent and swing voters, the other 24\% 
must not be swing voters.
(d)~0.47.
(e)~0.53.
(f)~P(Independent) $\times$ P(swing) = $0.35\times0.23 = 0.08$, which 
does not equal P(Independent and swing) = 0.11, so the events are dependent.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 9

\eocesol{(a)~If the class is not graded on a curve, they are independent. If graded on a 
curve, then neither independent nor disjoint -- unless the instructor will only give 
one A, which is a situation we will ignore in parts~(b) and~(c).
(b)~They are probably not independent: if you study together, your study habits 
would be related, which suggests your course performances are also related.
(c)~No. See the answer to part~(a) when the course is not graded on a curve. More 
generally: if two things are unrelated (independent), then one occurring does not 
preclude the other from occurring.}

% 11

\eocesol{(a)~$0.16 + 0.09 = 0.25$.
(b)~$0.17 + 0.09 = 0.26$.
(c)~Assuming that the education level of the husband and wife are independent: 
$0.25 \times 0.26 = 0.065$. You might also notice we actually made a second 
assumption: that the decision to get married is unrelated to education level.
(d)~The husband/wife independence assumption is probably not reasonable, because 
people often marry another person with a comparable level of education. We will 
leave it to you to think about whether the second assumption noted in part~(c) is 
reasonable.}

% 13

\eocesol{(a)~No, but we could if A and B are independent.
(b-i)~0.21.
(b-ii)~0.79.
(b-iii)~0.3. 
(c)~No, because 0.1 $\ne$ 0.21, where 0.21 was the value computed under 
independence from part~(a).
(d)~0.143.}

% 15

\eocesol{(a)~No, 0.18 of respondents fall into this combination.
(b)~$0.60 + 0.20 - 0.18 = 0.62$.
(c)~$0.18 / 0.20 = 0.9$.
(d)~$0.11 / 0.33 \approx 0.33$.
(e)~No, otherwise the answers to (c) and (d) would be the same.
(f)~$0.06 / 0.34 \approx 0.18$.}

% 17

\eocesol{(a)~No. There are 6~females who like Five Guys Burgers.
(b)~$162 / 248 = 0.65$.
(c)~$181 / 252 = 0.72$.
(d)~Under the assumption of a dating choices being independent of 
hamburger preference, which on the surface seems reasonable: 
$0.65 \times 0.72 = 0.468$.
(e)~$(252 + 6 - 1)/500 = 0.514$.}

% 19

\eocesol{(a) \\

\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}
(b)~0.84}

% 21

\eocesol{0.0714. Even when a patient tests positive for lupus, there is only a 7.14\% 
chance that he actually has lupus. House may be right. \\

\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}}

% 23

\eocesol{(a)~0.3.
(b)~0.3.
(c)~0.3.
(d)~$0.3\times0.3=0.09$.
(e)~Yes, the population that is being sampled from is identical in each draw.}

% 25

\eocesol{(a)~$2 / 9 \approx 0.22$.
(b)~$3 / 9 \approx 0.33$.
(c)~$\frac{3}{10} \times \frac{2}{9} \approx 0.067$.
(d)~No, e.g. in this exercise, removing one chip meaningfully 
changes the probability of what might be drawn next.}

% 27

\eocesol{$P(^1$leggings, $^2$jeans, $^3$jeans$) = \frac{5}{24} \times \frac{7}{23} \times \frac{6}{22} = 0.0173$. 
However, the person with leggings could have come 2nd or 3rd, and these each 
have this same probability, so $3 \times 0.0173 = 0.0519$.}

% 29

\eocesol{(a)~13.
(b)~No, these 27 students are not a random sample from the university's student 
population. For example, it might be argued that the proportion of smokers among 
students who go to the gym at 9 am on a Saturday morning would be lower than the 
proportion of smokers in the university as a whole.}

% 31

\eocesol{(a)~E(X) = 3.59. SD(X) = 9.64.
(b)~E(X) = -1.41. SD(X) = 9.64.
(c)~No, the expected net profit is negative, so on average you expect to lose money.}

% 33

\eocesol{5\% increase in value.}

% 35

\eocesol{E = -0.0526. SD = 0.9986.}

% 37

\eocesol{Approximate answers are OK. \\
(a)~$(29+32)/144 = 0.42$.
(b)~$21/144 = 0.15$.
(c)~$(26+12+15)/144 = 0.37$.}

% 39

\eocesol{(a)~Invalid. Sum is greater than~1.
(b)~Valid. Probabilities are between 0 and 1, and they sum to 1. In this class, 
every student gets a~C.
(c)~Invalid. Sum is less than~1.
(d)~Invalid. There is a negative probability.
(e)~Valid. Probabilities are between 0 and 1, and they sum to~1.
(f)~Invalid. There is a negative probability.}

% 41

\eocesol{0.8247. \\
\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}}

% 43

\eocesol{(a)~E = \$3.90. SD = \$0.34. \\
(b)~E = \$27.30. SD = \$0.89.}

% 45

\eocesol{$Var\left(\frac{X_1 + X_2}{2}\right)$ \\
  $= Var\left(\frac{X_1}{2} + \frac{X_2}{2}\right)$ \\
  $= \frac{Var(X_1)}{2^2} + \frac{Var(X_2)}{2^2}$ \\
  $= \frac{\sigma^2}{4} + \frac{\sigma^2}{4}$ \\
  $= \sigma^2 / 2$ \\}

% 47

\eocesol{$Var\left(\frac{X_1 + X_2 + \dots + X_n}{n}\right)$ \\
  $= Var\left(\frac{X_1}{n} + \frac{X_2}{n} + \dots +
      \frac{X_n}{n}\right)$ \\
  $= \frac{Var(X_1)}{n^2} + \frac{Var(X_2)}{n^2} + \dots +
      \frac{Var(X_n)}{n^2}$ \\
  $= \frac{\sigma^2}{n^2} + \frac{\sigma^2}{n^2} + \dots +
      \frac{\sigma^2}{n^2}$ (there are $n$ of these terms) \\
  $= n \frac{\sigma^2}{n^2}$ \\
  $= \sigma^2 / n$}



%_______________
\end{multicols}



%_______________
\eocesolch{Distributions of random variables}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~8.85\%.
(b)~6.94\%.
(c)~58.86\%.
(d)~4.56\%. \\
\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}
\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}
\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}
\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}}

% 3

\eocesol{(a)~Verbal: $N(\mu = 151, \sigma = 7)$, Quant: $N(\mu = 153, \sigma = 7.67)$.
(b)~$Z_{VR} = 1.29$, $Z_{QR} = 0.52$. \\
\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} \\
(c)~She scored 1.29 standard deviations above the mean on the Verbal 
Reasoning section and 0.52 standard deviations above the mean on the 
Quantitative Reasoning section.
(d)~She did better on the Verbal Reasoning section since her Z-score on that 
section was higher.
(e)~$Perc_{VR} = 0.9007 \approx 90\%$, $Perc_{QR} = 0.6990 \approx 70\%$.
(f)~$100\% - 90\% = 10\%$ did better than her on VR, and $100\% - 70\% = 30\%$
 did better than her on QR.
(g)~We cannot compare the raw scores since they are on different scales. 
Comparing her percentile scores is more appropriate when comparing her 
performance to others.
(h)~Answer to part (b) would not change as Z-scores can be calculated for 
distributions that are not normal. However, we could not answer parts~(d)-(f) 
since we cannot use the normal probability table to calculate probabilities 
and percentiles without a normal model.}

% 5

\eocesol{(a)~$Z = 0.84$, which corresponds to approximately 159 on QR.
(b)~$Z = -0.52$, which corresponds to approximately 147 on VR.}

% 7

\eocesol{(a)~$Z = 1.2$, $P(Z > 1.2) = 0.1151$. \\
(b)~$Z= -1.28 \to 70.6\degree$F or colder.}

% 9

\eocesol{(a)~$N(25, 2.78)$.
(b)~$Z = 1.08$, $P(Z > 1.08) = 0.1401$.
(c)~The answers are very close because only the units were changed. (The only 
reason why they differ at all because 28\degree C is 
82.4\degree F, not precisely 83\degree F.)
(d)~Since $IQR = Q3 - Q1$, we first need to find $Q3$ and $Q1$ and take the 
difference between the two. Remember that $Q3$ is the $75^{th}$ and $Q1$ is 
the $25^{th}$ percentile of a distribution. Q1 = 23.13, Q3 = 26.86, IQR = 26.
86 - 23.13 = 3.73.}

% 11

\eocesol{(a)~No. The cards are not independent. For example, if the first card is an 
ace of clubs, that implies the second card cannot be an ace of clubs. 
Additionally, there are many possible categories, which would need to be 
simplified.
(b)~No. There are six events under consideration. The Bernoulli distribution 
allows for only two events or categories. Note that rolling a die could be a 
Bernoulli trial if we simplify to two events, e.g. rolling a 6 and not rolling 
a 6, though specifying such details would be necessary.}

% 13

\eocesol{(a)~$0.875^2\times 0.125 = 0.096$.
(b)~$\mu=8$, $\sigma=7.48$.}

% 15

\eocesol{If ${p}$ is the probability of a success, then the mean of a Bernoulli random variable $X$ is given by \\
$\mu = E[X] = P(X = 0) \times 0 + P(X = 1) \times 1$ \\
$= (1 - p) \times 0 + p\times 1 = 0 + p = p$}

% 17

\eocesol{(a)~Binomial conditions are met: 
(1)~Independent trials: In a random sample, whether or not one 18-20 year 
old has consumed alcohol does not depend on whether or not another one has.
(2)~Fixed number of trials: $n = 10$.
(3)~Only two outcomes at each trial: Consumed or did not consume alcohol.
(4)~Probability of a success is the same for each trial: $p = 0.697$.
(b)~0.203.
(c)~0.203.
(d)~0.167.
(e)~0.997.}

% 19

\eocesol{(a)~$\mu = 35$, $\sigma = 3.24$
(b)~$Z = \frac{45 - 35}{3.24} = 3.09$. 45 is more than 3 standard 
deviations away from the mean, we can assume that it is an unusual 
observation. Therefore yes, we would be surprised.
(c)~Using the normal approximation, 0.0010. With 0.5 correction, 0.0017.}

% 21

\eocesol{(a)~$1-0.75^3 = 0.5781$.
(b)~0.1406.
(c)~0.4219.
(d)~$1-0.25^3=0.9844$.}

% 23

\eocesol{(a)~Geometric distribution: 0.109.
(b)~Binomial: 0.219.
(c)~Binomial: 0.137.
(d)~$1-0.875^6=0.551$.
(e)~Geometric: 0.084.
(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.}

% 25

\eocesol{(a)~$\stackrel{Anna}{1/5}\times\stackrel{Ben}{1/4}\times\stackrel{Carl}{1/3}\times\stackrel{Damian}{1/2}\times\stackrel{Eddy}{1/1} = 1/5!=1/120$.
(b)~Since the probabilities must add to 1, there must be $5!=120$ possible orderings.
(c)~$8!=\text{40,320}$.}

% 27

\eocesol{(a)~Geometric, 0.0804.
(b)~Binomial, 0.0322.
(c)~Negative binomial, 0.0193.}

% 29

\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.
(b)~0.1838.
(c)~${3 \choose 1} = 3$.
(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.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 31

\eocesol{(a)~Poisson with $\lambda=75$.
(b)~$\mu=\lambda=75$, $\sigma=\sqrt{\lambda} = 8.66$.
(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.
(d)~Using Poisson with $\lambda = 75$: 0.0402.}

% 33

\eocesol{(a)~$\frac{\lambda^k \times e^{-\lambda}}{k!}
      = \frac{6.5^5 \times e^{-6.5}}{5!}
      = 0.1454$ \\
(b)~The probability will come to
    $0.0015 + 0.0098 + 0.0318 = 0.0431$
    (0.0430 if no rounding error). \\
(c)~The number of people per car is $11.7 / 6.5 = 1.8$,
    meaning people are coming in small clusters.
    That is, if one person arrives, there's a chance
    that they brought one or more other people in their
    vehicle.
    This means individuals (the people) are not independent,
    even if the car arrivals are independent,
    and this breaks a core assumption for the Poisson
    distribution.
    That is, the number of people visiting between
    2pm and 3pm would not follow a Poisson distribution.}

% 35

\eocesol{0 wins (-\$3): 0.1458. 1 win (-\$1): 0.3936. 2 wins (+\$1): 0.3543. 
3 wins (+\$3): 0.1063.}

% 37

\eocesol{Want to find the probability that there will be 1,787 or more enrollees.
Using the normal approximation, with $\mu = np = 2,500 \times 0.7 = 1750$ and 
$\sigma = \sqrt{np(1-p)} = \sqrt{2,500 \times 0.7 \times 0.3} \approx 23$, 
$Z = 1.61$, and $P(Z > 1.61) = 0.0537$. With a 0.5 correction: 0.0559.}

% 39

\eocesol{(a)~$Z=0.67$.
(b)~$\mu=\$1650$, $x=\$1800$.
(c)~$0.67 = \frac{1800-1650}{\sigma} \to \sigma=\$223.88$.}

% 41

\eocesol{(a)~$(1-0.471)^2\times0.471 = 0.1318$.
(b)~$0.471^3 = 0.1045$.
(c)~$\mu = 1/0.471 = 2.12$, $\sigma=\sqrt{2.38} = 1.54$.
(d)~$\mu = 1/0.30 = 3.33$, $\sigma=2.79$.
(e)~When $p$ is smaller, the event is rarer, meaning the expected number of 
trials before a success and the standard deviation of the waiting time are 
higher.}

% 43

\eocesol{$Z = 1.56$, $P(Z > 1.56) = 0.0594$, i.e. 6\%.}

% 45

\eocesol{(a)~$Z = 0.73$, $P(Z > 0.73) = 0.2327$.
(b)~If you are bidding on only one auction and set a low maximum bid price, 
someone will probably outbid you. If you set a high maximum bid price, you 
may win the auction but pay more than is necessary. If bidding on more than 
one auction, and you set your maximum bid price very low, you probably won't 
win any of the auctions. However, if the maximum bid price is even modestly 
high, you are likely to win multiple auctions.
(c)~An answer roughly equal to the 10th percentile would be reasonable. 
Regrettably, no percentile cutoff point guarantees beyond any possible event 
that you win at least one auction. However, you may pick a higher percentile 
if you want to be more sure of winning an auction.
(d)~Answers will vary a little but should correspond to the answer in 
part~(c). We use the 10$^{th}$ percentile: $Z = -1.28 \to \$69.80$.}

% 47

\eocesol{(a)~$Z = 3.5$, upper tail is 0.0002.
    (More precise value: 0.000233, but we'll use
    0.0002 for the calculations here.) \\
(b)~$0.0002 \times 2000 = 0.4$.
    We would expect about 0.4 10 year olds
    who are 76 inches or taller to show up. \\
(c)~${{2000}\choose{0}} (0.0002)^0 (1 - 0.0002)^{2000}
    = 0.67029$. \\
(d)~$\frac{0.4^0 \times e^{-0.4}}{0!}
    = \frac{1 \times e^{-0.4}}{1}
    = 0.67032$.}



%_______________
\end{multicols}



%_______________
\eocesolch{Foundations for inference}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~Mean. Each student reports a numerical value: a number of hours.
(b)~Mean. Each student reports a number, which is a percentage, and we can 
average over these percentages.
(c)~Proportion. Each student reports Yes or No, so this is a categorical 
variable and we use a proportion.
(d)~Mean. Each student reports a number, which is a percentage like in part~(b).
(e)~Proportion. Each student reports whether or not s/he expects to get a job, 
so this is a categorical variable and we use a proportion.}

% 3

\eocesol{(a)~The sample is from all computer chips manufactured
    at the factory during the week of production.
    We might be tempted to generalize the population
    to represent all weeks, but we should exercise
    caution here since the rate of defects may change
    over time.
(b)~The fraction of computer chips manufactured
    at the factory during the week of production
    that had defects.
(c)~Estimate the parameter using the data:
    $\hat{p} = \frac{27}{212} = 0.127$.
(d)~\emph{Standard error} (or $SE$).
(e)~Compute the $SE$ using
    $\hat{p} = 0.127$ in place of $p$:
    $SE
      \approx \sqrt{\frac{\hat{p}(1 - \hat{p})}{n}}
      = \sqrt{\frac{0.127(1 - 0.127)}{212}}
      = 0.023$.
(f)~The standard error is the
    standard deviation of $\hat{p}$.
    A value of 0.10 would be about one standard
    error away from the observed value, which would
    not represent a very uncommon deviation.
    (Usually beyond about 2 standard errors
    is a good rule of thumb.)
    The engineer should not be surprised.
(g)~Recomputed standard error using $p = 0.1$:
    $SE = \sqrt{\frac{0.1(1 - 0.1)}{212}}
      = 0.021$.
    This value isn't very different,
    which is typical when the standard error
    is computed using relatively similar
    proportions (and even sometimes when
    those proportions are quite different!).}

% 5

\eocesol{(a)~Sampling distribution.
(b)~If the population proportion is in the 5-30\% range,
    the success-failure condition would be satisfied and
    the sampling distribution would be symmetric.
(c)~We use the formula for the standard error:
    $SE
      = \sqrt{\frac{p (1 - p)}{n}}
      = \sqrt{\frac{0.08 (1 - 0.08)}{800}}
      = 0.0096$.
(d)~Standard error.
(e)~The distribution will tend to be more variable
    when we have fewer observations per sample.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 7

\eocesol{Recall that the general formula is $point~estimate \pm z^{\star} \times SE$.
First, identify the three different values. The point estimate is 45\%, 
$z^{\star} = 1.96$ for a 95\% confidence level, and $SE = 1.2\%$. Then, plug the 
values into the formula:
$ 45\% \pm 1.96 \times 1.2\% \quad\to\quad (42.6\%, 47.4\%) $
We are 95\% confident that the proportion of US adults who live with one or more 
chronic conditions is between 42.6\% and 47.4\%.}

% 9

\eocesol{(a)~False. Confidence intervals provide a range of plausible values, and 
sometimes the truth is missed. A 95\% confidence interval ``misses'' about 5\% 
of the time.
(b)~True. Notice that the description focuses on the true population value.
(c)~True. If we examine the 95\% confidence interval computed in
Exercise~\ref{chronic_illness_intro}, we can see that 50\% is not included in this interval.
This 
means that in a hypothesis test, we would reject the null hypothesis that the 
proportion is~0.5.
(d)~False. The standard error describes the uncertainty in the overall estimate 
from natural fluctuations due to randomness, not the uncertainty corresponding 
to individuals' responses.}

% 11

\eocesol{(a)~False. The point estimate is always in the confidence interval,
and this is a non-sensical use of a confidence interval with a point estimate
(because the point estimate is, by design, listed within the confidence interval).
(b)~True.
(c)~False. The confidence interval is not about a sample mean.
(d)~False. To be more confident that we capture the parameter, we need a wider 
interval. Think about needing a bigger net to be more sure of catching a fish in 
a murky lake.
(e)~True. Optional explanation: This is true since the normal model was used to 
model the sample mean. The margin of error is half the width of the interval, 
and the sample mean is the midpoint of the interval.
(f)~False. In the calculation of the standard error, we divide the standard 
deviation by the square root of the sample size. To cut the SE (or margin of 
error) in half, we would need to sample $2^2 = 4$ times the number of people in 
the initial sample.}

% 13

\eocesol{(a)~The visitors are from a simple random sample,
    so independence is satisfied.
    The success-failure condition is also satisfied,
    with both 64 and $752 - 64 = 688$ above 10.
    Therefore, we can use a normal distribution to
    model $\hat{p}$ and construct a confidence interval.
(b)~The sample proportion is $\hat{p} = \frac{64}{752} = 0.085$.
    The standard error is
    {\footnotesize\begin{align*}
    SE
      &= \sqrt{\frac{p (1 - p)}{n}}
      \approx \sqrt{\frac{\hat{p} (1 - \hat{p})}{n}} \\
      &= \sqrt{\frac{0.085 (1 - 0.085)}{752}}
      = 0.010
    \end{align*}}%
(c)~For a 90\% confidence interval,
    use $z^{\star} = 1.6449$.
    The confidence interval is
    $0.085 \pm 1.6449 \times 0.010 \to
    (0.0683, 0.1017)$.
    We are 90\% confident that 6.83\% to 10.17\%
    of first-time site visitors will register using
    the new design.}

% 15

\eocesol{(a)~$H_0: p = 0.5$
    (Neither a majority nor minority of students' grades improved)
  $H_A: p \neq 0.5$
    (Either a majority or a minority of students' grades improved) \\
(b)~$H_0: \mu = 15$
    (The average amount of company time each employee spends not 
    working is 15 minutes for March Madness.)
  $H_A: \mu \neq 15$
    (The average amount of company time each employee spends not 
    working is different than 15 minutes for March Madness.)}

% 17

\eocesol{(1)~The hypotheses should be about the
    population proportion ($p$), not the sample proportion.
(2)~The null hypothesis should have an equal sign.
(3)~The alternative hypothesis should have a not-equals
    sign, and
(4)~it should reference the null value, $p_0 = 0.6$,
    not the observed sample proportion.
The correct way to set up these hypotheses is:
$H_0: p = 0.6$ and
$H_A: p \neq 0.6$.}

% 19

\eocesol{(a)~This claim is reasonable, since the entire interval
    lies above 50\%.
(b)~The value of 70\% lies outside of the interval,
    so we have convincing evidence that the researcher's
    conjecture is wrong.
(c)~A~90\% confidence interval will be narrower than a
    95\%~confidence interval.
    Even without calculating the interval,
    we can tell that 70\% would not fall in the interval,
    and we would reject the researcher's conjecture based
    on a 90\% confidence level as well.}

% 21

\eocesol{(i)~Set up hypotheses. $H_0$: $p = 0.5$, $H_A$: $p \neq 0.5$.
  We will use a significance level of $\alpha = 0.05$.
(ii)~Check conditions: simple random sample gets us independence,
  and the success-failure conditions is satisfied since
  $0.5 \times 1000 = 500$ for each group is at least~10.
(iii)~Next, we calculate:
  $SE = \sqrt{0.5 (1 - 0.5) / 1000} = 0.016$.
  $Z = \frac{0.42 - 0.5}{0.016} = -5$,
  which has a one-tail area of about 0.0000003,
  so the p-value is twice this one-tail area at
  0.0000006.
(iv)~Make a conclusion:
  Because the p-value is less than $\alpha = 0.05$,
  we reject the null hypothesis and conclude that the
  fraction of US adults who believe raising the minimum
  wage will help the economy is not 50\%.
  Because the observed value is less than 50\% and we
  have rejected the null hypothesis, we can conclude
  that this belief is held by fewer than 50\% of US adults.
(For reference, the survey also explores support for
changing the minimum wage, which is a different
question than if it will help the economy.)}

% 23

\eocesol{If the p-value is 0.05, this means the test statistic would be
either $Z = -1.96$ or $Z = 1.96$.
We'll show the calculations for $Z = 1.96$.
Standard error:
$SE = \sqrt{0.3 (1 - 0.3) / 90} = 0.048$.
Finally, set up the test statistic formula and solve
for $\hat{p}$:
$1.96 = \frac{\hat{p} - 0.3}{0.048}
  \to \hat{p} = 0.394$
Alternatively, if $Z = -1.96$ was used: $\hat{p} = 0.206$.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 25

\eocesol{(a)~$H_0$: Anti-depressants do not affect the symptoms
    of Fibromyalgia.
    $H_A$: Anti-depressants do affect the symptoms of
    Fibromyalgia (either helping or harming).
(b)~Concluding that anti-depressants either help or worsen
    Fibromyalgia symptoms when they actually do neither.
(c)~Concluding that anti-depressants do not affect
    Fibromyalgia symptoms when they actually do.}

% 27

\eocesol{(a)~We are 95\% confident that Americans spend an average
    of 1.38 to 1.92 hours per day relaxing or pursuing
    activities they enjoy.
(b)~Their confidence level must be higher as the width
    of the confidence interval increases as the confidence
    level increases.
(c)~The new margin of error will be smaller,
    since as the sample size increases,
    the standard error decreases,
    which will decrease the margin of error.}

% 29

\eocesol{(a)~$H_0$: The restaurant meets food safety and sanitation regulations.
$H_A$: The restaurant does not meet food safety and sanitation regulations.
(b)~The food safety inspector concludes that the restaurant does not meet food 
safety and sanitation regulations and shuts down the restaurant when the 
restaurant is actually safe.
(c)~The food safety inspector concludes that the restaurant meets food safety 
and sanitation regulations and the restaurant stays open when the restaurant is 
actually not safe.
(d)~A Type~1 Error may be more problematic for the restaurant owner since his 
restaurant gets shut down even though it meets the food safety and sanitation 
regulations.
(e)~A Type~2 Error may be more problematic for diners since the restaurant 
deemed safe by the inspector is actually not.
(f)~Strong evidence. Diners would rather a restaurant that meet the regulations get
shut down than a restaurant that doesn't meet the regulations not get shut down.}

% 31

\eocesol{(a)~$H_0: p_{unemp} = p_{underemp}$: The proportions of unemployed and 
underemployed people who are having relationship problems are equal.
$H_A: p_{unemp} \ne p{underemp}$: The proportions of unemployed and 
underemployed people who are having relationship problems are different.
(b)~If in fact the two population proportions are equal, the probability of 
observing at least a 2\% difference between the sample proportions is 
approximately 0.35. Since this is a high probability we fail to reject the null 
hypothesis. The data do not provide convincing evidence that the proportion of 
of unemployed and underemployed people who are having relationship problems are 
different.}

% 33

\eocesol{Because 130 is inside the confidence interval,
we do not have convincing evidence that the true
average is any different than what the nutrition
label suggests.}

% 35

\eocesol{True. If the sample size gets ever larger, then the
standard error will become ever smaller.
Eventually, when the sample size is large enough and
the standard error is tiny, we can find statistically
significant yet very small differences between the
null value and point estimate (assuming they are not
exactly equal).}

% 37

\eocesol{(a)~In effect, we're checking whether
    men are paid more than women (or vice-versa),
    and we'd expect these outcomes with either
    chance under the null hypothesis:
    \begin{align*}
    &H_0: p = 0.5
    &&H_A: p \neq 0.5
    \end{align*}
    We'll use $p$ to represent the fraction of cases
    where men are paid more than women. \\
(b)~Below is the completion of the hypothesis test.
  \begin{itemize}
  \item
    There isn't a good way to check independence here
    since the jobs are not a simple random sample.
    However, independence doesn't seem unreasonable,
    since the individuals in each job are different from
    each other.
    The success-failure condition is met since we check
    it using the null proportion:
    $p_0 n = (1 - p_0) n = 10.5$ is greater than 10.
  \item
    We can compute the sample proportion, $SE$, and
    test statistic:
    \begin{align*}
    \hat{p} &= 19 / 21 = 0.905 \\
    SE &= \sqrt{\frac{0.5 \times (1 - 0.5)}{21}} = 0.109 \\
    Z &= \frac{0.905 - 0.5}{0.109} = 3.72
    \end{align*}
    The test statistic $Z$ corresponds to an upper tail
    area of about 0.0001, so the p-value is 2 times this
    value: 0.0002.
  \item
    Because the p-value is smaller than 0.05, we reject
    the notion that all these gender pay disparities are
    due to chance.
    Because we observe that men are paid more in a higher proportion
    of cases and we have rejected $H_0$, we can conclude that
    men are being paid higher amounts in ways not explainable
    by chance alone.
  \end{itemize}
  If you're curious for more info around this topic,
  including a discussion about adjusting for additional
  factors that affect pay,
  please see the following video by Healthcare Triage:
  \oiRedirect{textbook-yt_healthcare_triage_gender_pay_gap}
      {youtu.be/aVhgKSULNQA}.}



%_______________
\end{multicols}


\newpage


%_______________
\eocesolch{Inference for categorical data}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~False. Doesn't satisfy success-failure condition.
(b)~True. The success-failure condition is not satisfied. In most samples we 
would expect $\hat{p}$ to be close to 0.08, the true population proportion. 
While $\hat{p}$ can be much above 0.08, it is bound below by 0, suggesting it 
would take on a right skewed shape. Plotting the sampling distribution would 
confirm this suspicion.
(c)~False. $SE_{\hat{p}} = 0.0243$, and $\hat{p} = 0.12$ is only 
$\frac{0.12 - 0.08}{0.0243} = 1.65$ SEs away from the mean, which would not 
be considered unusual.
(d)~True. $\hat{p}=0.12$ is 2.32 standard errors away from the mean, which is 
often considered unusual.
(e)~False. Decreases the SE by a factor of $1/\sqrt{2}$.}

% 3

\eocesol{(a)~True. See the reasoning of 6.1(b).
(b)~True. We take the square root of the sample size in the SE formula.
(c)~True. The independence and success-failure conditions are satisfied.
(d)~True. The independence and success-failure conditions are satisfied.}

% 5

\eocesol{(a)~False. A confidence interval is constructed to estimate the population 
proportion, not the sample proportion.
(b)~True. 95\% CI: $82\%\ \pm\ 2\%$.
(c)~True. By the definition of the confidence level.
(d)~True. Quadrupling the sample size decreases the SE and ME by a factor 
of $1/\sqrt{4}$.
(e)~True. The 95\% CI is entirely above 50\%.}

% 7

\eocesol{With a random sample, independence is 
satisfied. The success-failure condition is also satisfied. 
$ME = z^{\star} \sqrt{ \frac{\hat{p} (1-\hat{p})} {n} } 
= 1.96 \sqrt{ \frac{0.56 \times  0.44}{600} }= 0.0397 \approx 4\%$}

% 9

\eocesol{(a)~No. The sample only represents students who took the SAT, and this was 
also an online survey.
(b)~(0.5289, 0.5711). We are 90\% confident that 53\% to 57\% of high school 
seniors who took the SAT are fairly certain that they will participate in a 
study abroad program in college.
(c)~90\% of such random samples would produce a 90\% confidence interval 
that includes the true proportion.
(d)~Yes. The interval lies entirely above 50\%.}

% 11

\eocesol{(a)~We want to check for a majority (or minority),
    so we use the following hypotheses:
    \begin{align*}
    &H_0: p = 0.5
    &&H_A: p \neq 0.5
    \end{align*}
    We have a sample proportion of $\hat{p} = 0.55$
    and a sample size of $n = 617$ independents. \\
    Since this is a random sample, independence
    is satisfied.
    The success-failure condition is also satisfied:
    $617 \times 0.5$ and $617 \times (1 - 0.5)$
    are both at least 10 (we use the null proportion
    $p_0 = 0.5$ for this check in a one-proportion
    hypothesis test). \\
    Therefore, we can model $\hat{p}$ using a
    normal distribution with a standard error of
    \begin{align*}
    SE = \sqrt{\frac{p(1 - p)}{n}}
      = 0.02
    \end{align*}
    (We use the null proportion $p_0 = 0.5$
    to compute the standard error for a
    one-proportion hypothesis test.)
    Next, we compute the test statistic:
    \begin{align*}
    Z = \frac{0.55 - 0.5}{0.02} = 2.5
    \end{align*}
    This yields a one-tail area of 0.0062,
    and a p-value of $2 \times 0.0062 = 0.0124$. \\
    Because the p-value is smaller than 0.05,
    we reject the null hypothesis.
    We have strong evidence that the support
    is different from 0.5, and since the data
    provide a point estimate above 0.5,
    we have strong evidence to support this
    claim by the TV pundit. \\
(b)~No.
    Generally we expect a hypothesis test
    and a confidence interval to align,
    so we would expect the confidence interval
    to show a range of plausible values
    entirely above 0.5.
    However, if the confidence level is
    misaligned (e.g. a 99\% confidence level
    and a $\alpha = 0.05$ significance level),
    then this is no longer generally true.}

% 13

\eocesol{(a)~$H_0: p = 0.5$. $H_A: p \neq 0.5$.
Independence (random sample) is satisfied,
as is the success-failure conditions (using $p_0 = 0.5$,
we expect 40 successes and 40 failures).
$Z = 2.91$ $\to$ the one tail area is 0.0018,
so the p-value is 0.0036.
Since the p-value $< 0.05$, we reject the null hypothesis.
Since we rejected $H_0$ and the point estimate suggests people are
better than random guessing,
we can conclude the rate of correctly identifying a 
soda for these people is significantly better than
just by random guessing.
(b)~If in fact people cannot tell the difference between diet and regular 
soda and they were randomly guessing, the probability of getting
a random sample of 
80 people where 53 or more identify a soda correctly
(or 53 or more identify a soda incorrectly)
would be 0.0036.}

% 15

\eocesol{Because a sample proportion ($\hat{p} = 0.55$) is available,
we use this for the sample size calculations.
The margin of error for a 90\% confidence interval is
$1.6449 \times SE = 1.6449 \times \sqrt{\frac{p(1 - p)}{n}}$.
We want this to be less than 0.01, where we use
$\hat{p}$ in place of $p$:
\begin{align*}
1.6449 \times \sqrt{\frac{0.55(1 - 0.55)}{n}} \leq 0.01 \\
1.6449^2 \frac{0.55(1 - 0.55)}{0.01^2} \leq n
\end{align*}
From this, we get that $n$ must be at least 6697.}

% 17

\eocesol{This is not a randomized experiment, and it is unclear whether people would 
be affected by the behavior of their peers. That is, independence may not 
hold. Additionally, there are only 5 interventions under the provocative 
scenario, so the success-failure condition does not hold. Even if we consider 
a hypothesis test where we pool the proportions, the success-failure 
condition will not be satisfied. Since one condition is questionable and the 
other is not satisfied, the difference in sample proportions will not follow 
a nearly normal distribution.}

% 19

\eocesol{(a)~False. The entire confidence interval is above 0.
(b)~True.
(c)~True.
(d)~True.
(e)~False. It is simply the negated and reordered values: (-0.06,-0.02).}

% 21

\eocesol{(a)~Standard error:
    \begin{align*}
    SE
      = \sqrt{\frac{0.79(1 - 0.79)}{347} +
          \frac{0.55(1 - 0.55)}{617}}
      = 0.03
    \end{align*}
    Using $z^{\star} = 1.96$, we get:
    \begin{align*}
    0.79 - 0.55 \pm 1.96 \times 0.03
      \to (0.181, 0.299)
    \end{align*}
    We are 95\% confident that the proportion
    of Democrats who support the plan is 18.1\%
    to 29.9\% higher than the proportion of
    Independents who support the plan.
(b)~True.}

% 23

\eocesol{(a)~College grads: 23.7\%. Non-college grads: 33.7\%.
(b)~Let $p_{CG}$ and $p_{NCG}$ represent the proportion of college graduates 
and non-college graduates who responded ``do not know". 
$H_0: p_{CG} = p_{NCG}$. $H_A: p_{CG} \ne p_{NCG}$. Independence is satisfied 
(random sample), and the success-failure 
condition, which we would check using the pooled proportion 
($\hat{p}_{\textit{pool}} = 235/827 = 0.284$), is also satisfied. $Z = -3.18$ $\to$ 
p-value = 0.0014. Since the p-value is very small, we reject $H_0$. The data 
provide strong evidence that the proportion of college graduates who do not 
have an opinion on this issue is different than that of non-college 
graduates. The data also indicate that fewer college grads say they ``do not 
know'' than non-college grads (i.e. the data indicate the direction after we 
reject $H_0$).}

% 25

\eocesol{(a)~College grads: 35.2\%. Non-college grads: 33.9\%.
(b)~Let $p_{CG}$ and $p_{NCG}$ represent the proportion
of college graduates 
and non-college grads who support offshore drilling.
$H_0: p_{CG} = p_{NCG}$. 
$H_A: p_{CG} \ne p_{NCG}$. Independence is satisfied (random sample),
and the success-failure condition, which we would check 
using the pooled proportion ($\hat{p}_{\textit{pool}} = 286/827 = 0.346$), is also 
satisfied. $Z = 0.39$ $\to$ p-value $=0.6966$. Since the p-value 
$> \alpha$ (0.05), we fail to reject $H_0$. The data do not provide strong 
evidence of a difference between the proportions of college graduates
and non-college graduates who support off-shore drilling in California.}

% 27

\eocesol{Subscript $_C$ means control group. Subscript $_T$ means truck drivers. 
$H_0: p_C = p_T$. $H_A: p _C \ne p_T$. Independence is satisfied (random 
samples), as is the success-failure condition, which 
we would check using the pooled proportion ($\hat{p}_{\textit{pool}} = 70/495 = 0.141$). 
$Z = -1.65$ $\to$ p-value $ = 0.0989$. Since the p-value is high (default to alpha = 0.05), we fail to 
reject $H_0$. The data do not provide strong evidence that the rates of sleep 
deprivation are different for non-transportation workers and truck drivers.}

% 29

\eocesol{(a)~Summary of the study:
\begin{center}\scriptsize
\begin{tabular}{l l c c c}
        &           & \multicolumn{2}{c}{\textit{Virol. failure}}   &       \\
\cline{3-4}
        &           & Yes       & No        & Total \\
\cline{2-5}
\multirow{2}{*}{\textit{Treatment}}     & Nevaripine    & 26   & 94 & 120   \\
        & Lopinavir & 10            & 110   & 120   \\
\cline{2-5}
        & Total     & 36            & 204   & 240
\end{tabular}
\end{center}
(b)~$H_0: p_N = p_L$. There is no difference in virologic failure rates between 
the Nevaripine and Lopinavir groups. $H_A: p_N \ne p_L$. There is some 
difference in virologic failure rates between the Nevaripine and Lopinavir 
groups.
(c)~Random assignment was used, so the observations in each group are 
independent. If the patients in the study are representative of those in the 
general population (something impossible to check with the given information), 
then we can also confidently generalize the findings to the population. The 
success-failure condition, which we would check using the pooled proportion 
($\hat{p}_{pool} = 36/240 = 0.15$), is satisfied. $Z = 2.89$ $\to$ p-value 
$=0.0039$.
Since the p-value is low, we reject $H_0$.
There is strong evidence of a difference in virologic failure rates between
the Nevaripine and Lopinavir groups.
Treatment and virologic failure do not appear to be independent.}

% 31

\eocesol{(a)~False. The chi-square distribution has one parameter called degrees of 
freedom.
(b)~True.
(c)~True.
(d)~False. As the degrees of freedom increases, the shape of the chi-square 
distribution becomes more symmetric.}

% 33

\eocesol{(a)~$H_0$: The distribution of the format of the book used by the students 
follows the professor's predictions. $H_A$: The distribution of the format of 
the book used by the students does not follow the professor's predictions.
(b)~$E_{hard~copy} = 126 \times  0.60 = 75.6$. 
$E_{print} = 126 \times  0.25 = 31.5$.
$E_{online} = 126 \times  0.15 = 18.9$.
(c)~Independence:  The sample is not random. However, if the professor has 
reason to believe that the proportions are stable from one term to the next 
and students are not affecting each other's study habits, independence is 
probably reasonable. Sample size: All expected counts are at least 5.
(d)~$\chi^2 = 2.32$, $df=2$, p-value = 0.313.
(e)~Since the p-value is large, we fail to reject $H_0$. The data do not 
provide strong evidence indicating the professor's predictions were 
statistically inaccurate.}

% 35

\eocesol{(a)~Two-way table:
\begin{center}\scriptsize
\begin{tabular}{l l c c c}
& \multicolumn{2}{c}{\textit{Quit}} &       \\
\cline{2-3}
\textit{Treatment}      & Yes       & No        & Total \\
\hline
Patch + support group   & 40            & 110   & 150   \\
Only patch          & 30            & 120   & 150   \\
\cline{1-4}
Total               & 70            & 230   & 300 \\
\cline{1-4}
\end{tabular}
\end{center}
(b-i)~$E_{row_1, col_1} = \frac{(row~1~total)\times(col~1~total)}{table~total} = 35$. 
This is lower than the observed value. \\
(b-ii)~$E_{row_2, col_2} = \frac{(row~2~total)\times(col~2~total)}{table~total} = 115$. 
This is lower than the observed value.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 37

\eocesol{$H_0$: The opinion of college grads and non-grads is not different on 
the topic of drilling for oil and natural gas off the coast of 
California. $H_A$: Opinions regarding the drilling for oil and natural 
gas off the coast of California has an association with earning a 
college degree.
\begin{align*}
&E_{row~1, col~1} = 151.5 && E_{row~1, col~2} = 134.5 \\
&E_{row~2, col~1} = 162.1 && E_{row~2, col~2} = 143.9 \\
&E_{row~3, col~1} = 124.5 && E_{row~3, col~2} = 110.5
\end{align*}
Independence: The samples are both random, unrelated, and from less 
than 10\% of the population, so independence between observations is 
reasonable. Sample size: All expected counts are at least 5.
$\chi^2 = 11.47$, $df = 2$ $\to$ p-value = 0.003.
Since the p-value $< \alpha$, we reject $H_0$.  There is strong 
evidence that there is an association between support for off-shore 
drilling and having a college degree.}

% 39

\eocesol{No. The samples at the beginning and at the end of the semester are not 
independent since the survey is conducted on the same students.}

% 41

\eocesol{(a)~$H_0$:~The age of Los Angeles residents is independent of shipping carrier 
preference variable. $H_A$:~The age of Los Angeles residents is associated with 
the shipping carrier preference variable. 
(b)~The conditions are not satisfied since some expected counts are below~5.}

% 43

\eocesol{(a)~Independence is satisfied (random sample),
    as is the success-failure condition (40 smokers, 160 
    non-smokers).
    The 95\% CI: (0.145, 0.255).
    We are 95\% confident that 14.5\% 
    to 25.5\% of all students at this university smoke.
(b)~We want $z^{\star}SE$ to be no larger than 0.02 for
    a 95\% confidence level.
    We use $z^{\star}=1.96$ and plug in the point estimate
    $\hat{p}=0.2$ within the SE formula:
    $1.96\sqrt{0.2(1-0.2)/n} \leq 0.02$.
    The sample size $n$ should be at least 1,537.}

% 45

\eocesol{(a)~Proportion of graduates from this university who found a job within one 
year of graduating. $\hat{p} = 348/400 = 0.87$.
(b)~This is a random sample, so the 
observations are independent.
Success-failure condition is satisfied: 348 
successes, 52 failures, both well above~10.
(c)~(0.8371, 0.9029). We are 95\% confident that approximately 84\% to 90\% 
of graduates from this university found a job within one year of completing 
their undergraduate degree.
(d)~95\% of such random samples would produce a 95\% confidence interval 
that includes the true proportion of students at this university who found a 
job within one year of graduating from college.
(e)~(0.8267, 0.9133). Similar interpretation as before.
(f)~99\% CI is wider, as we are more confident that the true proportion is 
within the interval and so need to cover a wider range.}

% 47

\eocesol{Use a chi-squared goodness of fit test.
$H_0$: Each option is equally likely.
$H_A$: Some options are preferred over others.
Total sample size: 99.
Expected counts: (1/3) * 99 = 33 for each option. These are all above 5, so 
conditions are satisfied.
$df = 3 - 1 = 2$ and 
$\chi^2 = \frac{(43 - 33)^2}{33} + \frac{(21 - 33)^2}{33} + \frac{(35 - 33)^2}{33} = 7.52 
\rightarrow$ p-value $= 0.023$. Since 
the p-value is less than 5\%, we reject $H_0$. The data provide convincing 
evidence that some options are preferred over others.}

% 49

\eocesol{(a)~$H_0: p = 0.38$. $H_A: p \ne 0.38$. Independence (random sample)
and the success-failure condition are satisfied. $Z=-20.5$ 
$\to$ p-value $\approx 0$. Since the p-value is very small, we reject $H_0$. 
The data provide strong evidence that the proportion of Americans who only 
use their cell phones to access the internet is different than the Chinese 
proportion of 38\%, and the data indicate that the proportion is lower in 
the US.
(b)~If in fact 38\% of Americans used their cell phones as a primary access 
point to the internet, the probability of obtaining a random sample of 2,254 
Americans where 17\% or less or 59\% or more use their only their cell 
phones to access the internet would be approximately 0.
(c)~(0.1545, 0.1855). We are 95\% confident that approximately 15.5\% to 
18.6\% of all Americans primarily use their cell phones to browse the 
internet.}



%_______________
\end{multicols}



%_______________
\eocesolch{Inference for numerical data}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~$df=6-1=5$, $t_{5}^{\star} = 2.02$ (column with two tails of 0.10, 
row with $df=5$).
(b)~$df=21-1=20$, $t_{20}^{\star} = 2.53$ (column with two tails of 0.02, 
row with $df=20$).
(c)~$df=28$, $t_{28}^{\star} = 2.05$.
(d)~$df=11$, $t_{11}^{\star} = 3.11$.}

% 3

\eocesol{(a)~0.085, do not reject $H_0$.
(b)~0.003, reject $H_0$.
(c)~0.438, do not reject $H_0$.
(d)~0.042, reject $H_0$.}

% 5

\eocesol{The mean is the midpoint: $\bar{x} = 20$. Identify the margin of error: 
$ME = 1.015$, then use $t^{\star}_{35} = 2.03$ and $SE=s/\sqrt{n}$ in the 
formula for margin of error to identify $s = 3$.\\[6mm]}

% 7

\eocesol{(a)~$H_0$: $\mu = 8$ (New Yorkers sleep 8 hrs per night on average.) 
$H_A$: $\mu \neq 8$ (New Yorkers sleep less or more than 8 hrs per
night on average.)
(b)~Independence: The sample is random.
The min/max suggest there are no concerning outliers.
$T = -1.75$. $df=25-1=24$.
(c)~ p-value $= 0.093$.
If in fact the true population mean of the 
amount New Yorkers sleep per night was 8 hours,
the probability of getting a 
random sample of 25 New Yorkers where the average
amount of sleep is 7.73 hours
per night or less (or 8.27 hours or more) is 0.093.
(d)~Since p-value $>$ 0.05, do not reject $H_0$.
The data do not provide strong evidence that
New Yorkers sleep more or less than 8 hours per night
on average.
(e)~No, since the p-value is smaller than $1 - 0.90 = 0.10$.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 9

\eocesol{$T$ is either -2.09 or 2.09.
Then $\bar{x}$ is one of the following:
\begin{align*}
-2.09 &= \frac{\bar{x} - 60}{\frac{8}{\sqrt{20}}} \ \rightarrow \ \bar{x} = 56.26 \\
2.09 &= \frac{\bar{x} - 60}{\frac{8}{\sqrt{20}}} \ \rightarrow \ \bar{x} = 63.74
\end{align*}}

% 11

\eocesol{(a)~We will conduct a 1-sample $t$-test.
    $H_0$: $\mu = 5$. $H_A$: $\mu \neq 5$.
    We'll use $\alpha = 0.05$.
    This is a random sample, so the observations are independent.
    To proceed, we assume the distribution of years of piano
    lessons is approximately normal.
    $SE = 2.2 / \sqrt{20} = 0.4919$.
    The test statistic is $T = (4.6 - 5) / SE = -0.81$.
    $df = 20 - 1 = 19$.
    The one-tail area is about 0.21, so
    the p-value is about 0.42, which is bigger than
    $\alpha = 0.05$ and we do not reject $H_0$.
    That is, we do not have sufficiently strong evidence
    to reject the notion that the average is 5 years. \\
(b)~Using $SE = 0.4919$ and $t_{df = 19}^{\star} = 2.093$,
    the confidence interval is (3.57, 5.63).
    We are 95\% confident that the average number of
    years a child takes piano lessons in this city is
    3.57 to 5.63 years. \\
(c)~They agree, since we did not reject the null hypothesis
    and the null value of 5 was in the $t$-interval.}

% 13

\eocesol{If the sample is large, then the margin of error will be about 
$1.96 \times 100 / \sqrt{n}$. We want this value to be less than 10, which 
leads to $n \geq 384.16$, meaning we need a sample size of at least 385 (round 
up for sample size calculations!).}

% 15

\eocesol{Paired, data are recorded in the same cities at two
different time points. 
The air quality in a city at one point is not independent
of the air quality in the same city at another time point.}

% 17

\eocesol{(a)~Since it's the same students at the beginning and the end of the semester, 
there is a pairing between the datasets, for a given student their beginning 
and end of semester grades are dependent.
(b)~Since the subjects were sampled randomly, each observation in the men's 
group does not have a special correspondence with exactly one observation in 
the other (women's) group.
(c)~Since it's the same subjects at the beginning and the end of the study, 
there is a pairing between the datasets, for a subject student their beginning 
and end of semester artery thickness are dependent.
(d)~Since it's the same subjects at the beginning and the end of the study, 
there is a pairing between the datasets, for a subject student their beginning 
and end of semester weights are dependent.}

% 19

\eocesol{(a)~For each observation in one data set,
    there is exactly one specially corresponding
    observation in the other data set for the
    same geographic location.
    The data are paired.
(b)~$H_0: \mu_{\text{diff}} = 0$
    (There is no difference in average number
    of days exceeding 90\textdegree{}F in 1948
    and 2018 for NOAA stations.)
    $H_A: \mu_{\text{diff}} \neq 0$
    (There is a difference.)
(c)~Locations were randomly sampled, so independence
    is reasonable.
    The sample size is at least 30, so we're just looking
    for particularly extreme outliers:
    none are present (the observation off left in the
    histogram would be considered a clear outlier,
    but not a particularly extreme one).
    Therefore, the conditions are satisfied.
(d)~$SE = 17.2 / \sqrt{197} = 1.23$.
    $T = \frac{2.9 - 0}{1.23} = 2.36$
    with degrees of freedom $df = 197 - 1 = 196$.
    This leads to a one-tail area of 0.0096
    and a p-value of about 0.019.
(e)~Since the p-value is less than 0.05,
    we reject $H_0$.
    The data provide strong evidence that
    NOAA stations observed more 90\textdegree{}F
    days in 2018 than in 1948.
(f)~Type~1 Error, since we may have incorrectly
    rejected $H_0$.
    This error would mean that NOAA stations
    did not actually observe a decrease, but the
    sample we took just so happened to make it
    appear that this was the case.
(g)~No, since we rejected $H_0$,
    which had a null value of 0.}

% 21

\eocesol{(a)~$SE = 1.23$ and $t^{\star} = 1.65$.
    $2.9 \pm 1.65 \times 1.23 \to (0.87, 4.93)$. \\
(b)~We are 90\% confident that there was an
    increase of 0.87 to 4.93 in the average number
    of days that hit 90\textdegree{}F in 2018
    relative to 1948 for NOAA stations. \\
(c)~Yes, since the interval lies entirely above~0.}

% 23

\eocesol{(a)~These data are paired. For example, the Friday the 13th in say, September 
1991, would probably be more similar to the Friday the 6th in September 1991 
than to Friday the 6th in another month or year. \\
(b)~Let $\mu_{\textit{diff}} = \mu_{sixth} - \mu_{thirteenth}$. $H_0: \mu_{\textit{diff}} = 0$. 
$H_A: \mu_{\textit{diff}} \ne 0$. \\
(c)~Independence: The months selected are not random. However, if we think 
these dates are roughly equivalent to a simple random sample of all such Friday 
6th/13th date pairs, then independence is reasonable.
To proceed, we must make this strong assumption,
though we should note this assumption in any reported results.
Normality: With fewer than 10 observations,
we would need to see clear outliers to be concerned.
There is a borderline outlier on the right of the histogram of the differences,
so we would want to report this in formal analysis results. \\
(d)~$T = 4.93$ for $df = 10 - 1 = 9$ $\to$ p-value = 0.001. \\
(e)~Since p-value $<$ 0.05, reject $H_0$. The data provide strong evidence that 
the average number of cars at the intersection is higher on Friday the 
6$^{\text{th}}$ than on Friday the 13$^{\text{th}}$. (We should exercise caution
about generalizing the interpretation to all intersections or roads.) \\
(f)~If the average number of cars passing the intersection actually was the 
same on Friday the 6$^{\text{th}}$ and $13^{th}$, then the probability that we 
would observe a test statistic so far from zero is less than 0.01. \\
(g)~We might have made a Type~1 Error, i.e. incorrectly rejected the null 
hypothesis.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 25

\eocesol{(a)~$H_0: \mu_{diff} = 0$. $H_A: \mu_{diff} \ne 0$.
    $T=-2.71$. $df=5$.
    p-value $= 0.042$.
    Since p-value $<$ 0.05, reject $H_0$.
    The data provide strong evidence that the average
    number of traffic accident related emergency room
    admissions are different between Friday the 6$^{\text{th}}$
    and Friday the 13$^{\text{th}}$.
    Furthermore, the data indicate that the direction of that
    difference is that accidents are lower on Friday the
    $6^{th}$ relative to Friday the 13$^{\text{th}}$. \\
(b)~(-6.49, -0.17). \\
(c)~This is an observational study, not an experiment,
    so we cannot so easily infer a causal intervention
    implied by this statement.
    It is true that there is a difference.
    However, for example, this does not mean that
    a responsible adult going out on Friday the $13^{th}$
    has a higher chance of harm than on any other night.}

% 27

\eocesol{(a)~Chicken fed linseed weighed an average of 218.75 grams
while those fed horsebean weighed an average of 160.20 grams.
Both distributions are relatively symmetric with no apparent
outliers.
There is more variability in the weights of chicken fed linseed. \\
(b)~$H_0: \mu_{ls} = \mu_{hb}$. $H_A: \mu_{ls} \ne \mu_{hb}$. \\
We leave the conditions to you to consider. \\
$T=3.02$, $df = min(11, 9) = 9$ $\to$ p-value $= 0.014$.
Since p-value $<$ 0.05, reject $H_0$.
The data provide strong evidence that there is a
significant difference between the average weights of 
chickens that were fed linseed and horsebean. \\
(c)~Type~1 Error, since we rejected $H_0$. \\
(d)~Yes, since p-value $>$ 0.01, we would not have rejected~$H_0$.}

% 29

\eocesol{$H_0: \mu_C = \mu_S$. $H_A: \mu_C \ne \mu_S$.
$T = 3.27$, $df=11$ $\to$ p-value $= 0.007$.
Since p-value $< 0.05$, reject $H_0$.
The data provide strong evidence that the average weight
of chickens that were fed casein is different than the
average weight of chickens that were fed soybean
(with weights from casein being higher).
Since this is a randomized experiment, the observed 
difference can be attributed to the diet.}

% 31

\eocesol{Let $\mu_{diff} = \mu_{pre} - \mu_{post}$.
$H_0: \mu_{diff} = 0$:
Treatment has no effect.
$H_A: \mu_{diff} \neq 0$:
Treatment has an effect on P.D.T. scores, either positive or negative.
Conditions:
The subjects are randomly assigned to treatments, so independence within
and between groups is satisfied. 
All three sample sizes are smaller than 30, so we look for clear outliers.
There is a borderline outlier in the first treatment group.
Since it is borderline, we will proceed,
but we should report this caveat with any results.
For all three groups: $df=13$.
$T_1 = 1.89 \to$ p-value = 0.081,
$T_2 = 1.35 \to$ p-value = 0.200),
$T_3 = -1.40 \to$ (p-value = 0.185).
We do not reject the null hypothesis for any of these groups.
As earlier noted, there is some uncertainty about if
the method applied is reasonable for the first group.}

% 33

\eocesol{Difference we care about: 40. Single tail of 90\%: $1.28 \times SE$. 
Rejection region bounds: $\pm 1.96 \times SE$ (if 5\% significance level). 
Setting $3.24 \times SE = 40$, subbing in $SE = \sqrt{\frac{94^2}{n} + 
\frac{94^2}{n}}$, and solving for the sample size $n$ gives 116 plots of 
land for each fertilizer.}

% 35

\eocesol{Alternative.}

% 37

\eocesol{$H_0$: $\mu_1 = \mu_2 = \cdots = \mu_6$. $H_A$: The average weight varies 
across some (or all) groups. Independence: Chicks are randomly assigned to 
feed types (presumably kept separate from one another), therefore 
independence of observations is reasonable. Approx. normal: the distributions 
of weights within each feed type appear to be fairly symmetric. Constant 
variance: Based on the side-by-side box plots, the constant variance 
assumption appears to be reasonable. There are differences in the actual 
computed standard deviations, but these might be due to chance as these are 
quite small samples. $F_{5,65} = 15.36$ and the p-value is approximately 0. 
With such a small p-value, we reject $H_0$. The data provide convincing 
evidence that the average weight of chicks varies across some (or all) feed 
supplement groups.}

% 39

\eocesol{(a)~$H_0$: The population mean of MET for each group is equal to the others. 
$H_A$: At least one pair of means is different.
(b)~Independence: We don't have any information on how the data were collected, 
so we cannot assess independence. To proceed, we must assume the subjects in each 
group are independent. In practice, we would inquire for more details. 
Normality: The data are bound below by zero and the standard deviations are larger 
than the means, indicating very strong skew. However, since the sample sizes are 
extremely large, even extreme skew is acceptable. Constant variance: This condition 
is sufficiently met, as the standard deviations are reasonably consistent across groups.
(c)~See below, with the last column omitted:\\[-2mm]
\begin{adjustwidth}{-4em}{-4em}
{\tiny
\begin{center}
\renewcommand{\arraystretch}{1.25}
\begin{tabular}{lrrrr}
  \hline
            & Df    & Sum Sq        & Mean Sq   & F value \\ 
  \hline
coffee      & {\textcolor{oiB}{{\scriptsize 4}}}     & {\textcolor{oiB}{{\scriptsize 10508}}}       & {\textcolor{oiB}{{\scriptsize 2627}}}             & {\textcolor{oiB}{{\scriptsize 5.2}}} \\ 
Residuals       & {\textcolor{oiB}{{\scriptsize 50734}}} & 25564819     & {\textcolor{oiB}{{\scriptsize  504}}}         &  \\ 
   \hline
Total           & {\textcolor{oiB}{{\scriptsize 50738}}} & 25575327 \\
\hline
\end{tabular}
\end{center}
}
\end{adjustwidth} \vspace{1mm}
(d)~Since p-value is very small, reject $H_0$. The data provide convincing evidence 
that the average MET differs between at least one pair of groups.}

% 41

\eocesol{(a)~$H_0$: Average GPA is the same for all majors. $H_A$: At least one pair of means are different.
(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.
(c)~The total degrees of freedom is $195 + 2 = 197$, so the sample size is $197+1=198$.}

% 43

\eocesol{(a)~False. As the number of groups increases, so does the number of comparisons and hence the modified significance level decreases.
(b)~True.
(c)~True.
(d)~False. We need observations to be independent regardless of sample size.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 45

\eocesol{(a)~$H_0$: Average score difference is the same for all treatments. $H_A$: At 
least one pair of means are different.
(b)~We should check conditions. If we look back to the earlier exercise, we 
will see that the patients were randomized, so independence is satisfied. 
There are some minor concerns about skew, especially with the third group, 
though this may be acceptable. The standard deviations across the groups are 
reasonably similar. Since the p-value is less than 0.05, reject $H_0$. The 
data provide convincing evidence of a difference between the average 
reduction in score among treatments.
(c)~We determined that at least two means are different in part (b), so we 
now conduct $K = 3\times2/2 = 3$ pairwise $t$-tests that each use $\alpha = 0.05/3 
 =  0.0167$ for a significance level. Use the following hypotheses for each 
pairwise test. $H_0$: The two means are equal. $H_A$: The two means are 
different. The sample sizes are equal and we use the pooled SD, so we can 
compute $SE = 3.7$ with the pooled $df = 39$.
Looking at the largest difference, Trmt 1 vs Trmt 3:
$Z = \frac{6.21 - (-3.21)}{3.7} = 2.52$ on $df = 39$ yields a p-value of 0.015.
Because this is smaller than $0.05 / 3 = 1.67$,
we have strong evidence to that this particular pair of groups are different.
When doing similar calculations for Trmt 1 vs 2 or 2 vs 3, we do not
find any statistically significant difference.
(Note that we get a different result if not using the pooled result.)}

% 47

\eocesol{$H_0: \mu_{T} = \mu_{C}$. $H_A: \mu_{T} \ne \mu_{C}$. $T=2.24$, $df=21$ $\to$ 
p-value $= 0.036$. Since p-value $<$ 0.05, reject $H_0$.
The data provide 
strong evidence that the average food consumption by the patients in the 
treatment and control groups are different. Furthermore, the data indicate 
patients in the distracted eating (treatment) group consume more food than 
patients in the control group.}

% 49

\eocesol{False. While it is true that paired analysis requires equal sample sizes, 
only having the equal sample sizes isn't, on its own, sufficient for doing 
a paired test. Paired tests require that there be a special correspondence 
between each pair of observations in the two groups.}

% 51

\eocesol{(a)~We are building a distribution of sample statistics, in this case the sample 
mean. Such a distribution is called a sampling distribution.
(b)~Because we are dealing with the distribution of sample means, we need to 
check to see if the Central Limit Theorem applies. Our sample size is greater 
than 30, and we are told that random sampling is employed. With these conditions 
met, we expect that the distribution of the sample mean will be nearly normal 
and therefore symmetric.
(c)~Because we are dealing with a sampling distribution, we measure its 
variability with the standard error. $SE = 18.2 / \sqrt{45} = 2.713$.
(d)~The sample means will be more variable with the smaller sample size.}

% 53

\eocesol{(a)~We should set 1.0\% equal to 2.8 standard errors:
    $2.8 \times SE_{desired} = 1.0\%$
    (see Example~\ref{sample_size_for_80_percent_power}
    on page~\pageref{sample_size_for_80_percent_power}
    for details).
    This means the standard error should be about $SE = 0.36\%$
    to achieve the desired statistical power. \\
(b)~The margin of error was
    $0.5 \times (2.6\% - (-0.2\%)) = 1.4\%$,
    so the standard error in the experiment must have been
    $1.96 \times SE_{original} = 1.4\%$
    $\to$
    $SE_{original} = 0.71\%$. \\
(c)~The standard error decreases with the square root of the
    sample size, so we should increase the sample size by
    a factor of $1.97^2 = 3.88$. \\
(d)~The team should run an experiment 3.88 times larger,
    so they should have a random sample of 3.88\% of their
    users in each of the experiment arms in the new experiment.}

% 55

\eocesol{Independence: it is a random sample,
so we can assume that the students in this
sample are independent of each other with 
respect to number of exclusive relationships
they have been in.
Notice that there are no students who have
had no exclusive relationships in the 
sample, which suggests some student responses
are likely missing
(perhaps only positive values were reported).
The sample size is at least 30, and there are
no particularly extreme outliers, so the normality
condition is reasonable.
90\% CI: (2.97, 3.43).
We are 90\% confident that undergraduate students
have been in 2.97 to 3.43 
exclusive relationships, on average.}

% 57

\eocesol{The hypotheses should be about the population mean ($\mu$),
not the sample mean. 
The null hypothesis should have an equal sign and the
alternative hypothesis 
should be about the null hypothesized value, not the observed
sample mean. 
Correction:
\begin{align*}
H_0&: \mu = 10~hours \\
H_A&: \mu \neq 10~hours
\end{align*}
A two-sided test allows us to consider the possibility
that the data show us something that we would find surprising.}



%_______________
\end{multicols}


\newpage

%_______________
\eocesolch{Introduction to linear regression}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~The residual plot will show randomly distributed residuals around 0. 
The variance is also approximately constant.
(b)~The residuals will show a fan shape, with higher variability for 
smaller $x$. There will also be many points on the right above the line. 
There is trouble with the model being fit here.}

% 3

\eocesol{(a)~Strong relationship, but a straight line would not fit the data.
(b)~Strong relationship, and a linear fit would be reasonable.
(c)~Weak relationship, and trying a linear fit would be reasonable.
(d)~Moderate relationship, but a straight line would not fit the data.
(e)~Strong relationship, and a linear fit would be reasonable.
(f)~Weak relationship, and trying a linear fit would be reasonable.}

% 5

\eocesol{(a)~Exam 2 since there is less of a scatter in the plot of final 
exam grade versus exam 2. Notice that the relationship between 
Exam 1 and the Final Exam appears to be slightly nonlinear.
(b)~Exam 2 and the final are relatively close to each other 
chronologically, or Exam 2 may be cumulative so has greater 
similarities in material to the final exam. Answers may vary.}

% 7

\eocesol{(a)~$r = -0.7$ $\rightarrow$ (4).
(b)~$r = 0.45$ $\rightarrow$ (3).
(c)~$r = 0.06$ $\rightarrow$ (1).
(d)~$r = 0.92$ $\rightarrow$ (2).}

% 9

\eocesol{(a)~The relationship is positive, weak, and possibly linear. However, 
there do appear to be some anomalous observations along the left where 
several students have the same height that is notably far from the 
cloud of the other points. Additionally, there are many students who 
appear not to have driven a car, and they are represented by a set of 
points along the bottom of the scatterplot.
(b)~There is no obvious explanation why simply being tall should lead 
a person to drive faster. However, one confounding factor is gender. 
Males tend to be taller than females on average, and personal 
experiences (anecdotal) may suggest they drive faster. If we were to 
follow-up on this suspicion, we would find that sociological studies 
confirm this suspicion.
(c)~Males are taller on average and they drive faster. The gender 
variable is indeed an important confounding variable.}

% 11

\eocesol{(a)~There is a somewhat weak, positive, possibly linear relationship 
between the distance traveled and travel time. There is clustering 
near the lower left corner that we should take special note of.
(b)~Changing the units will not change the form, direction or strength 
of the relationship between the two variables. If longer distances 
measured in miles are associated with longer travel time measured in 
minutes, longer distances measured in kilometers will be associated 
with longer travel time measured in hours.
(c)~Changing units doesn't affect correlation: $r = 0.636$.}

% 13

\eocesol{(a)~There is a moderate, positive, and linear relationship between 
shoulder girth and height.
(b)~Changing the units, even if just for one of the variables, will 
not change the form, direction or strength of the relationship between 
the two variables.}

% 15

\eocesol{In each part, we can write the husband ages as a linear function of 
the wife ages. \\
(a)~$age_{H} = age_{W} + 3$. \\
(b)~$age_{H} = age_{W} - 2$. \\
(c)~$age_{H} = 2 \times age_{W}$. \\
Since the slopes are positive and these are perfect linear 
relationships, the correlation will be exactly 1 in all three parts. 
An alternative way to gain insight into this solution is to create a 
mock data set, e.g. 5 women aged 26, 27, 28, 29, and 30, then find the 
husband ages for each wife in each part and create a scatterplot.}

% 17

\eocesol{Correlation: no units. Intercept: kg. Slope: kg/cm.}

% 19

\eocesol{Over-estimate. Since the residual is calculated as 
$observed\ -\ predicted$, a negative residual means that the 
predicted value is higher than the observed value.}

% 21

\eocesol{(a)~There is a positive, very strong, linear association between the 
number of tourists and spending.
(b)~Explanatory: number of tourists (in thousands). Response: 
spending (in millions of US dollars).
(c)~We can predict spending for a given number of tourists using a 
regression line. This may be useful information for determining how 
much the country may want to spend in advertising abroad, or to 
forecast expected revenues from tourism.
(d)~Even though the relationship appears linear in the scatterplot, 
the residual plot actually shows a nonlinear relationship. This is 
not a contradiction: residual plots can show divergences from 
linearity that can be difficult to see in a scatterplot. A simple 
linear model is inadequate for modeling these data. It is also 
important to consider that these data are observed sequentially, 
which means there may be a hidden structure not evident in the 
current plots but that is important to consider.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 23

\eocesol{(a)~First calculate the slope: 
$b_1 = R\times s_y/s_x = 0.636 \times 113 / 99 = 0.726$. 
Next, make use of the fact that the regression line passes through 
the point $(\bar{x},\bar{y})$: $\bar{y} = b_0 + b_1 \times \bar{x}$. 
Plug in $\bar{x}$, $\bar{y}$, and $b_1$, and solve for $b_0$: 51. 
Solution: $\widehat{travel~time} = 51 + 0.726 \times distance$.
(b)~$b_1$: For each additional mile in distance, the model predicts 
an additional 0.726 minutes in travel time. $b_0$: When the distance 
traveled is 0 miles, the travel time is expected to be 51 minutes. It 
does not make sense to have a travel distance of 0 miles in this 
context. Here, the $y$-intercept serves only to adjust the height of 
the line and is meaningless by itself.
(c)~$R^2 = 0.636^2 = 0.40$. About 40\% of the variability in travel 
time is accounted for by the model, i.e. explained by the distance 
traveled.
(d)~$\widehat{travel~time} = 51 + 0.726 \times distance 
= 51 + 0.726 \times 103 \approx 126$ minutes. (Note: we should be 
cautious in our predictions with this model since we have not yet 
evaluated whether it is a well-fit model.)
(e)~$e_i = y_i - \hat{y}_i = 168 - 126 = 42$ minutes. A positive 
residual means that the model underestimates the travel time.
(f)~No, this calculation would require extrapolation.}

% 25

\eocesol{(a)~$\widehat{murder} = -29.901 + 2.559 \times poverty\%$.
(b)~Expected murder rate in metropolitan areas with no poverty is -29.
901 per million. This is obviously not a meaningful value, it just 
serves to adjust the height of the regression line.
(c)~For each additional percentage increase in poverty, we expect 
murders per million to be higher on average by 2.559.
(d)~Poverty level explains 70.52\% of the variability in murder rates 
in metropolitan areas.
(e)~$\sqrt{0.7052} = 0.8398$.}

% 27

\eocesol{(a)~There is an outlier in the bottom right. Since it is far from the 
center of the data, it is a point with high leverage. It is also an 
influential point since, without that observation, the regression 
line would have a very different slope. \\
(b)~There is an outlier in the bottom right. Since it is far from the 
center of the data, it is a point with high leverage. However, it 
does not appear to be affecting the line much, so it is not an 
influential point. \\
(c)~The observation is in the center of the data (in the x-axis 
direction), so this point does \emph{not} have high leverage. This 
means the point won't have much effect on the slope of the line and 
so is not an influential point.}

% 29

\eocesol{(a)~There is a negative, moderate-to-strong, somewhat linear 
relationship between percent of families who own their home and the 
percent of the population living in urban areas in 2010. There is one 
outlier: a state where 100\% of the population is urban. The 
variability in the percent of homeownership also increases as we move 
from left to right in the plot.
(b)~The outlier is located in the bottom right corner, horizontally 
far from the center of the other points, so it is a point with high 
leverage. It is an influential point since excluding this point from 
the analysis would greatly affect the slope of the regression line.}

% 31

\eocesol{(a)~The relationship is positive, moderate-to-strong, and linear. 
There are a few outliers but no points that appear to be influential. \\
(b)~$\widehat{weight} = -105.0113 + 1.0176 \times height$. \\
Slope: For each additional centimeter in height, the model
predicts the average weight to be 1.0176 additional kilograms
(about 2.2 pounds).   \\
Intercept: People who are 0 centimeters tall are expected to weigh -
105.0113 kilograms. This is obviously not possible. Here, the $y$-
intercept serves only to adjust the height of the line and is 
meaningless by itself. \\
(c)~$H_0$: The true slope coefficient of height is zero 
($\beta_1 = 0$). \\
$H_A$: The true slope coefficient of height is 
different than zero ($\beta_1 \neq 0$). \\
The p-value for the two-sided alternative hypothesis
($\beta_1 \ne 0$) is incredibly small, so we reject $H_0$.
The data provide convincing evidence that height and 
weight are positively correlated.
The true slope parameter is indeed greater than~0. \\
(d)~$R^2 = 0.72^2 = 0.52$. Approximately 52\% of the variability in 
weight can be explained by the height of individuals.}

% 33

\eocesol{(a)~$H_0$: $\beta_1 = 0$. $H_A$: $\beta_1 \neq 0$.
The p-value, as reported in the table, is incredibly small
and is smaller than 0.05, so we reject $H_0$.
The data provide convincing evidence that wives' and husbands' 
heights are positively correlated. \\
(b)~$\widehat{height}_{W} = 43.5755 + 0.2863 \times height_{H}$. \\
(c)~Slope: For each additional inch in husband's height, the average 
wife's height is expected to be an additional 0.2863 inches on 
average. Intercept: Men who are 0 inches tall are expected to have 
wives who are, on average, 43.5755 inches tall. The intercept here is 
meaningless, and it serves only to adjust the height of the line. \\
(d)~The slope is positive, so $r$ must also be positive. 
$r = \sqrt{0.09} = 0.30$. \\
(e)~63.33. Since $R^2$ is low, the prediction based on this 
regression model is not very reliable. \\
(f)~No, we should avoid extrapolating.}

% 35

\eocesol{(a)~$H_0: \beta_1 = 0; H_A: \beta_1 \ne 0$
(b)~The p-value for this test is approximately 0, therefore we reject 
$H_0$. The data provide convincing evidence that poverty percentage 
is a significant predictor of murder rate.
(c)~$n = 20, df = 18, T^*_{18} =  2.10$; $2.559 \pm 2.10 \times 0.390 
= (1.74, 3.378)$; For each percentage point poverty is higher, murder 
rate is expected to be higher on average by 1.74 to 3.378 per million.
(d)~Yes, we rejected $H_0$ and the confidence interval does not 
include 0.}

% 37

\eocesol{(a)~True.
(b)~False, correlation is a measure of the linear association 
between any two numerical variables.}

% 39

\eocesol{(a)~The point estimate and standard error are $b_1 = 0.9112$ and 
$SE = 0.0259$. We can compute a T-score: $T = (0.9112 - 1)/0.0259 = -3.43$. 
Using $df=168$, the p-value is about 0.001,
which is less than $\alpha = 0.05$.
That is, 
the data provide strong evidence that the average difference between 
husbands' and wives' ages has actually changed over time.
(b)~$\widehat{age}_W = 1.5740 + 0.9112 \times age_{H}$.
(c)~Slope: For each additional year in husband's age, the model predicts 
an additional 0.9112 years in wife's age. This means that wives' ages 
tend to be lower for later ages, suggesting the average gap of husband 
and wife age is larger for older people.
Intercept: Men who are 0 years old are expected to have wives who are on 
average 1.5740 years old. The intercept here is meaningless and serves only 
to adjust the height of the line.
(d)~$R = \sqrt{0.88} = 0.94$. The regression of wives' ages on husbands' 
ages has a positive slope, so the correlation coefficient will be positive.
(e)~$\widehat{age}_W = 1.5740 + 0.9112 \times 55 = 51.69$.
Since $R^2$ is pretty high, the prediction based on this regression model 
is reliable.
(f)~No, we shouldn't use the same model to predict an 85 year old man's 
wife's age. This would require extrapolation. The scatterplot from an 
earlier exercise shows that husbands in this data set are approximately 
20 to 65 years old. The regression model may not be reasonable outside 
of this range.}

% 41

\eocesol{There is an upwards trend. However, the variability is higher for 
higher calorie counts, and it looks like there might be two clusters 
of observations above and below the line on the right, so we should 
be cautious about fitting a linear model to these data.}

% 43

\eocesol{(a)~$r = -0.72 \to (2)$
(b)~$r = 0.07 \to (4)$
(c)~$r = 0.86 \to (1)$
(d)~$r = 0.99 \to (3)$}



%_______________
\end{multicols}



%_______________
\eocesolch{Multiple and logistic regression}



%_______________
\begin{multicols}{2}

% 1

\eocesol{(a)~$\widehat{baby\_\hspace{0.3mm}weight} = 123.05 - 8.94 \times smoke$
(b)~The estimated body weight of babies born to smoking mothers is 8.94 
ounces lower than babies born to non-smoking mothers.  
Smoker: $123.05 - 8.94 \times 1 = 114.11$ ounces. 
Non-smoker: $123.05 - 8.94 \times 0 = 123.05$ ounces.
(c)~$H_0$: $\beta_1 = 0$. $H_A$: $\beta_1 \ne 0$. $T= -8.65$, and the p-value is 
approximately 0. Since the p-value is very small, we reject $H_0$. The data 
provide strong evidence that the true slope parameter is different than 0 and 
that there is an association between birth weight and smoking. Furthermore, 
having rejected $H_0$, we can conclude that smoking is associated with lower 
birth weights.}

% 3

\eocesol{(a)~$\widehat{baby\_weight} = -80.41 + 0.44 \times gestation 
- 3.33 \times parity - 0.01 \times age + 1.15 \times height 
+ 0.05 \times weight - 8.40 \times smoke$.
(b)~$\beta_{gestation}$: The model predicts a 0.44 ounce increase in the birth 
weight of the baby for each additional day of pregnancy, all else held constant.
$\beta_{age}$: The model predicts a 0.01 ounce decrease in the birth weight of 
the baby for each additional year in mother's age, all else held constant. 
(c)~Parity might be correlated with one of the other variables in the model, 
which complicates model estimation.
(d)~$\widehat{baby\_\hspace{0.3mm}weight} = 120.58$.
$e = 120 - 120.58 = -0.58$. The model over-predicts this baby's birth weight.
(e)~$R^2 = 0.2504$. $R_{adj}^2 = 0.2468$.}

% 5

\eocesol{(a)~(-0.32, 0.16). We are 95\% confident that male students on average have GPAs 
0.32 points lower to 0.16 points higher than females when controlling for the 
other variables in the model.
(b)~Yes, since the p-value is larger than 0.05 in all cases (not including the 
intercept).}

% 7

\eocesol{Remove age.}

% 9

\eocesol{Based on the p-value alone, either gestation or smoke should be added to the 
model first. However, since the adjusted $R^2$ for the model with gestation is 
higher, it would be preferable to add gestation in the first step of the forward-
selection algorithm. (Other explanations are possible. For instance, it would be 
reasonable to only use the adjusted $R^2$.)}

% 11

\eocesol{She should use p-value selection since she is interested in finding out about 
significant predictors, not just optimizing predictions.}

% 13

\eocesol{Nearly normal residuals:
With so many observations in the data set,
we look for particularly extreme outliers
in the histogram and do not see any.
variability of residuals: The scatterplot of the residuals versus the fitted 
values does not show any overall structure. However, values that have very low 
or very high fitted values appear to also have somewhat larger outliers. In 
addition, the residuals do appear to have constant variability between the two 
parity and smoking status groups, though these items are relatively minor. \\ 
Independent residuals: The scatterplot of residuals versus the order of data 
collection shows a random scatter, suggesting that there is no apparent 
structures related to the order the data were collected. \\ Linear relationships 
between the response variable and numerical explanatory variables: The residuals 
vs. height and weight of mother are randomly distributed around 0. The residuals 
vs. length of gestation plot also does not show any clear or strong remaining 
structures, with the possible exception of very short or long gestations. The 
rest of the residuals do appear to be randomly distributed around 0. \\All 
concerns raised here are relatively mild. There are some outliers, but there is 
so much data that the influence of such observations will be minor.}

\end{multicols}
\newpage
\begin{multicols}{2}

% 15

\eocesol{(a)~There are a few potential outliers, e.g. on the left in the 
\var{total\_\hspace{0.3mm}length} variable, but nothing that will be of serious 
concern in a data set this large.
(b)~When coefficient estimates are sensitive to which variables are included in 
the model, this typically indicates that some variables are collinear. For 
example, a possum's gender may be related to its head length, which would 
explain why the coefficient (and p-value) for \var{sex\_\hspace{0.3mm}male} 
changed when we removed the \var{head\_\hspace{0.3mm}length} variable. Likewise, 
a possum's skull width is likely to be related to its head length, probably even 
much more closely related than the head length was to gender.}

% 17

\eocesol{(a)~The logistic model relating $\hat{p}_i$ to the predictors may be written as 
$\log\left( \frac{\hat{p}_i}{1 - \hat{p}_i} \right) 
= 33.5095 - 1.4207\times sex\_male_i - 0.2787 \times skull\_width_i 
+ 0.5687 \times total\_length_i - 1.8057 \times tail\_length_i$. 
Only \var{total\_\hspace{0.3mm}length} has a positive association with a possum 
being from Victoria.
(b)~$\hat{p} = 0.0062$. While the probability is very near zero, we have not run 
diagnostics on the model. We might also be a little skeptical that the model 
will remain accurate for a possum found in a US zoo. For example, perhaps the 
zoo selected a possum with specific characteristics but only looked in one 
region. On the other hand, it is encouraging that the possum was caught in the 
wild. (Answers regarding the reliability of the model probability will vary.)}

% 19

\eocesol{(a)~False.
    When predictors are collinear, it means they are correlated,
    and the inclusion of one variable can have a substantial
    influence on the point estimate (and standard error) of
    another.
(b)~True.
(c)~False.
    This would only be the case if the data was from
    an experiment and $x_1$ was one of the variables set by
    the researchers.
    (Multiple regression can be useful for forming hypotheses
    about causal relationships, but it offers zero guarantees.)
(d)~False.
    We should check normality like we would for inference
    for a single mean:
    we look for particularly extreme outliers if $n \geq 30$
    or for clear outliers if $n < 30$.}

% 21

\eocesol{(a)~\resp{exclaim\us{}subj} should be removed,
    since it's removal reduces AIC the most
    (and the resulting model has lower AIC
    than the None Dropped model).
(b)~Removing any variable will increase AIC,
    so we should not remove any variables from
    this set.}

% 23

\eocesol{(a)~The equation is:
    \begin{align*}
    \log\left(\frac{p_i}{1 - p_i}\right)
      &= -0.8124 \\
        &\quad- 2.6351 \times \resp{to\us{}multiple} \\
        &\quad + 1.6272 \times \resp{winner} \\
        &\quad- 1.5881 \times \resp{format} \\
        &\quad - 3.0467 \times \resp{re\us{}subj}
    \end{align*}
(b)~First find $\log\left(\frac{p}{1 - p}\right)$,
    then solve for $p$:
    \begin{align*}
    &\log\left(\frac{p}{1 - p}\right) \\
      &\quad= -0.8124
          - 2.6351 \times 0
          + 1.6272 \times 1 \\
        &\qquad- 1.5881 \times 0
          - 3.0467 \times 0 \\
      &\quad= 0.8148 \\
    &\frac{p}{1 - p} = e^{0.8148}
      \quad\to\quad p = 0.693
    \end{align*}
(c)~It should probably be pretty high, since it could
    be very disruptive to the person using the email
    service if they are missing emails that aren't spam.
    Even only a 90\% chance that a message is spam is
    probably enough to warrant keeping it in the inbox.
    Maybe a probability of 99\% would be a reasonable
    cutoff.
    As for other ideas to make it even better,
    it may be worth building a second model that tries
    to classify the importance of an email message.
    If we have both the spam model and the importance
    model, we now have a better way to think about
    cost-benefit tradeoffs.
    For instance, perhaps we would be willing to
    have a lower probability-of-spam threshold for
    messages we were confident were not important,
    and perhaps we want an even higher probability
    threshold (e.g. 99.99\%) for emails we are
    pretty sure are important.}



%_______________
\end{multicols}


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\index{probability sample|see{sample}}
\index{df|see{degrees of freedom (df)}}

================================================
FILE: extraTeX/preamble/copyright.tex
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\chapter*{}
\vfill

\noindent%
Copyright $\copyright$ 2019. Fourth Edition. \\
Updated: \versiondate. \\

\noindent%
This book may be downloaded as a free PDF at
\oiRedirect{os}
    {\color{black}\textbf{openintro.org/book/os}}.
This textbook is also available under a
\oiRedirect{license}{Creative Commons license},
with the source files hosted on
\oiRedirect{os_source}{Github}. \\

\printlocation

%\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}.



================================================
FILE: extraTeX/preamble/copyright_derivative.tex
================================================
\chapter*{}
\vfill

% We encourage you to leave this page entirely intact.

\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} \\

\noindent For license and attribution guidance, see \urlwofont{https://github.com/OpenIntroOrg/openintro-statistics/blob/master/LICENSE}


================================================
FILE: extraTeX/preamble/preface.tex
================================================
\chapter*{{\color{oiB}Preface}}
%\chaptertext{}
%\sectiontext{}

\noindent%
OpenIntro Statistics covers a first course in statistics,
providing a rigorous introduction to applied statistics
that is clear, concise, and accessible.
This book was written with the undergraduate level in mind,
but it's also popular in high schools and graduate courses.
\vspace{3mm}

We hope readers will take away three ideas from
this book in addition to forming a foundation of statistical
thinking and methods.\vspace{-1mm}
\begin{itemize}
\setlength{\itemsep}{0mm}
\item
    Statistics is an applied field with a wide range
    of practical applications.
\item
    You don't have to be a math guru to learn
    from real, interesting data.
\item
    Data are messy, and statistical tools are imperfect.
    But, when you understand the strengths and weaknesses of
    these tools, you can use them to learn about the world.
\end{itemize}


%\subsection*{Is this a data science book?}
%
%\noindent%
%Short answer: yes.
%Long answer: it depends what you mean by \term{data science},
%since two types of data scientists have emerged.
%\vspace{3mm}
%
%\noindent%
%Type~A data scientists focus on \emph{analysis},
%such as exploratory data analysis, inference,
%model building, and other related topics.
%Type~B data scientists focus on \emph{building},
%typically in the form of machine learning models
%or other systems.
%As you might expect, these two types share many skills,
%though their main focuses differ.
%This book focuses on skills most commonly used by
%Type~A data scientists.
%For more thoughts, please check out the following page:
%\begin{center}
%\oiRedirect{data_science_types}{{\color{red}BROKEN}}
%\end{center}
%\vspace{3mm}
%
%\noindent%


\subsection*{{\color{oiB}Textbook overview}}

\noindent%
The chapters of this book are as follows:%\vspace{2mm}
\begin{description}
\setlength{\itemsep}{0mm}
\item[1. Introduction to data.]
    Data structures, variables,
    and basic data collection techniques.
\item[2. Summarizing data.]
    Data summaries, graphics,
    and a teaser of inference using randomization.
\item[3. Probability.]
    Basic principles of probability.
    %This chapter is not required for the later chapters.
\item[4. Distributions of random variables.]
    The normal model and other key distributions.
\item[5. Foundations for inference.]
    %Introduction to uncertainty in point estimates,
    %confidence intervals, and hypothesis tests.
    General ideas for statistical inference in the context
    of estimating the population proportion.
\item[6. Inference for categorical data.]
    Inference for proportions and tables using the normal
    and chi-square distributions.
\item[7. Inference for numerical data.]
    Inference for one or two sample means using the
    \mbox{$t$-distribution},
    statistical power for comparing two groups,
    and also comparisons of many
    means using ANOVA.
\item[8. Introduction to linear regression.]
    Regression for a numerical outcome with one predictor variable.
    Most of this chapter could be covered after
    Chapter~\ref{introductionToData}.
\item[9. Multiple and logistic regression.]
    Regression for numerical and categorical data
    using many predictors. %for an accelerated course.
\end{description}


%\newpage

\noindent%
\emph{OpenIntro Statistics} supports flexibility
in choosing and ordering topics.
If the main goal is to reach multiple regression
(Chapter~\ref{ch_regr_mult_and_log})
as quickly as possible, then the following are the
ideal prerequisites:
\begin{itemize}
\setlength{\itemsep}{0mm}
\item Chapter~\ref{ch_intro_to_data},
    Sections~\ref{numericalData},
    and Section~\ref{categoricalData} for a solid
    introduction to data structures and statistical
    summaries that are used throughout the book.
\item Section~\ref{normalDist}
    for a solid understanding of the normal distribution.
\item Chapter~\ref{ch_foundations_for_inf}
    to establish the core set of inference tools.
%\item Section~\ref{oneSampleMeansWithTDistribution}
%    and Chapter~\ref{ch_regr_simple_linear}
%    provide required for multiple regression with a numerical
%    outcome.
%    For the remaining chapters, they could be tackled in
%    almost any order, with the exception that
%    
%    
%    come before Chapter~\ref{ch_regr_mult_and_log}.
\item Section~\ref{oneSampleMeansWithTDistribution}
    to give a foundation for the $t$-distribution
\item Chapter~\ref{ch_regr_simple_linear}
    for establishing ideas and principles for single
    predictor regression.
%    introduce the 
%    which introduces the $t$-distribution, should come before
%    Section~\ref{oneSampleMeansWithTDistribution}
%Chapters~\ref{ch_inference_for_props}-\ref{ch_regr_mult_and_log},
%    could be tackled in
%    almost any order, with the exception that
%    Section~\ref{oneSampleMeansWithTDistribution}
%    and Chapter~\ref{ch_regr_simple_linear}
%    come before Chapter~\ref{ch_regr_mult_and_log}.
%\item Sections~\ref{ch_inference_for_props}
%    and~\ref{} are recommended before logistic regression.
\end{itemize}
%One conspicuously missing topic from the list above is the
%chapter on Probability.
%While useful for a deeper understanding of the calculations,
%especially for anyone looking to take a second course in
%statistics, it is not required reading when the focus is on
%applied data analysis.


\subsection*{{\color{oiB}Examples and exercises}}
%, and appendices}

\noindent%
Examples are provided to establish an understanding of how
to apply methods

\begin{examplewrap}
\begin{nexample}{This is an example.
    When a question is asked here, where can the answer be found?}
  The answer can be found here, in the solution section
  of the example!
\end{nexample}
\end{examplewrap}

\noindent%
When we think the reader should be ready to try determining
the solution to an example, we frame it as Guided Practice.

\begin{exercisewrap}
\begin{nexercise}
The reader may check or learn the answer to any Guided Practice
problem by reviewing the full solution in a footnote.\footnotemark{}
%Readers are strongly encouraged to attempt these practice problems.
\end{nexercise}
\end{exercisewrap}
\footnotetext{Guided Practice problems are intended to stretch
  your thinking, and you can check yourself by reviewing the
  footnote solution for any Guided Practice.}

\noindent%
Exercises are also provided at the end of each section
as well as review exercises at the end of each chapter.
Solutions are given for odd-numbered exercises in
Appendix~\ref{eoceSolutions}.
%Probability tables for the normal, $t$,
%and chi-square distributions are in
%Appendix~\ref{distributionTables}.


\subsection*{{\color{oiB}Additional resources}}

Video overviews, slides, statistical software labs,
data sets used in the textbook,
and much more are readily available at\\[-5mm]
\begin{center}
\oiRedirect{os}
    {\color{black}\textbf{openintro.org/os}}
\end{center}
%Data sets for this textbook are available on the website
%and in a companion R package.\footnote{Diez DM,
%    Barr CD, \c{C}etinkaya-Rundel M. 2015.
%    \texttt{openintro}: OpenIntro data sets and supplement
%    functions.
%    \oiRedirect{textbook-github_openintro}
%        {github.com/OpenIntroOrg/openintro-r-package}.}
%All of these resources are free and may be used with
%or without this textbook as a companion.
We also have improved the ability to access data in this book
through the addition of Appendix~\ref{appendix_data},
which provides additional information for each of the data sets
used in the main text and is new in the Fourth Edition.
Online guides to each of these data sets are also provided at
\oiRedirect{data}
    {\color{black}\textbf{openintro.org/data}}
and through a
\oiRedirect{textbook-github_openintro}
    {companion R~package}.
% Official:
% http://www.openintro.org/package/openintro
% Currently redirect it to:
% http://openintrostat.github.io/openintro-r-package/
\vspace{3mm}

\noindent%
We appreciate all feedback as well as reports of any
typos through the website.
A short-link to report a new typo or review known typos is
\oiRedirect{os_typos}
    {\color{black}\textbf{openintro.org/os/typos}}. \vspace{3mm}

\noindent%
For those focused on statistics at the high school level,
consider
\oiRedirect{textbook-books}
    {\emph{Advanced High School Statistics}},
which is a version of \emph{OpenIntro Statistics} that has
been heavily customized by \oiRedirect{people}{Leah Dorazio}
for high school courses and
AP\textsuperscript{\textregistered} Statistics.


\subsection*{{\color{oiB}Acknowledgements}}
This project would not be possible without the passion and
dedication of many more people beyond those on the author list.
The authors would like to thank the
\oiRedirect{textbook-openintro_about}{OpenIntro Staff}
for their involvement and ongoing contributions.
We~are also very grateful to the hundreds of students
and instructors who have provided us with valuable feedback
since we first started posting book content in~2009. \vspace{3mm}

\noindent%
We also want to thank the many teachers who helped review
this edition, including
Laura Acion,
\oiRedirect{matthew_e_aiello-lammens}
    {Matthew E. Aiello-Lammens},
\oiRedirect{jonathan_akin}{Jonathan Akin},
Stacey C. Behrensmeyer,
Juan Gomez,
Jo Hardin,
\oiRedirect{nicholas_horton}{Nicholas Horton},
\oiRedirect{danish_khan}{Danish Khan},
\oiRedirect{peter_hm_klaren}{Peter H.M. Klaren},
Jesse Mostipak,
Jon C. New,
Mario Orsi,
Steve Phelps,
and David Rockoff.
We appreciate all of their feedback, which helped
us tune the text in significant ways and greatly
improved this book.


================================================
FILE: extraTeX/preamble/review_copy.tex
================================================
\chapter*{Feedback Instructions}
%\chaptertext{}
%\sectiontext{}

This is a review copy of an unfinished version of the
Fourth Edition of OpenIntro Statistics.
Please read these next few pages before reviewing this book.


\subsection*{What *not* to watch for}

\noindent%
There are several components that you should ignore.
\begin{enumerate}
\setlength{\itemsep}{0mm}
\item
    \textbf{End-of-section/chapter exercises
    and odd-numbered solutions will be included
    in the final version.}
    The newer exercises are not yet ready for sharing,
    so we've omitted exercises from this review copy
    to avoid any confusion.
\item
    \Comment{This is comment text that we are using to
    call out items and that you can consider as FYIs.}
    There's a big dot in the margin that makes it easy
    to spot these notes.
\item
    There are plenty of formatting issues,
    e.g. awkward page breaks or footnotes on the wrong page.
    These issues will be fixed during final textbook formatting.
\item
    There are some broken references such as
    ``Figure~\ref{}'' or ``Section~\ref{}''.
    Any such references will be fixed before
    the Fourth Edition is released.
\end{enumerate}


\subsection*{We will send a survey for you to complete}

\noindent%
We will send you a survey by December 31st.
Responding to this survey by January 7th will be most
helpful to us, which is when we will be starting to
incorporate significant amounts of feedback.


\subsection*{Sending feedback as you read}

\noindent%
If you are browsing through the book and think,
``Hey, they should add / do / change / etc [thing]'',
send a note to \url{os4@openintro.org}
or via
\href{http://www.openintro.org/os4}{\texttt{openintro.org/os4}}
\vspace{3mm}

\noindent%
Below are specific topics where you may
want to voice your thoughts:
\begin{enumerate}
\item
    If you are reading an example
    or case study and think that there's an
    interesting comment that might be made on
    confounding variables or on what a multivariate
    analysis would be like, please let us know.
    We'll be adding such comments and discussion
    during January and February.
\item
    If you read the new
    \emph{Foundations for Inference} chapter,
    what do you think about it?
    Do you like, dislike, or not care that we
    now introduce inference using proportions
    before means?
\item
    We have also reversed the ordering of the two chapters
    covering inference for proportions / means.
    Do we move too quickly or too slowly in spots
    for either section?
    Which spots require more explanation or examples?
\item
    The new case study for logistic regression
    covers a sensitive yet important topic:
    racial discrimination.
    If you read this section, do you think the
    topic was presented and discussed in an
    appropriately respectful and responsible way?

    We will also be getting a thorough
    review by subject-matter experts for this section.
%\item
%    In newer examples, we more strongly suggest software
%    over using tables for finding tail areas.
%    We are planning to do further changes around wording
%    in existing examples and would like feedback on this
%    direction.
%\item
%    The 3rd Edition launched with only black-and-white
%    paperbacks, and a year after launch we made
%    full color hardcovers available.
%    How important is it to you that we offer
%    (1) full-color books available and/or
%    (2) hardcover textbooks available?
%    (Our tentative plan is to launch with
%    a black-and-white paperback and also
%    a full-color paperback, where the expected
%    prices are \$20 and \$35, respectively.)
%\item
    
\end{enumerate}


\subsection*{Some of the changes already implemented}

\noindent%
The following sections contained notable updates
in content or examples:
\begin{itemize}
%\setlength{\itemsep}{0mm}
\item 1.2,
\item 1.3.4,
\item all of Chapter~\ref{ch_summarizing_data},
\item some loan data examples in 3.1,
\item stock return examples in 3.4,
\item (nothing notable in Chapter~\ref{ch_distributions}]),
\item all of Chapter~\ref{ch_foundations_for_inf},
\item 6.1.2,
\item 6.1.3,
\item 6.3.5,
\item 6.4,
\item 7.1.5,
\item 7.2,
\item 7.5 (updated MLB data),
\item 8.4 (updated election data),
\item 9.4
\end{itemize}


\noindent%
Here are some special callouts for changes made:
\begin{description}
\item[Stylistic.]
    Each section now starts at the top of a page.
    Section, subsection, term boxes, tip boxes,
    examples, and guided practice
    all have updated appearances.
    
    There are some bugs with spacing here and there,
    e.g. with sections and the horizontal lines,
    that we are still working out.
    
    Video and slide icons / links have also been removed,
    since these will be presented in a different way
    in the Fourth Edition.
\item[Graphics and statistical summaries get their own chapter.]
    The first chapter of the Third Edition has been
    broken into two chapters in the Fourth Edition.
\item[Inference: proportions before means.]
    We introduce inference using proportion before means
    in the Fourth Edition.
    The inference of proportions chapter also now
    precedes the inference for means chapter.
\item[Simulation and randomization.]
    Two sections in the inference for proportions
    in small sample situations have been removed
    and will become online extras in about April.
    The randomization case study section near the start of the
    textbook was retained with a new case study.
\item[Lots of new examples.]
    We have replaced or updated many older or less interesting
    data sets with new case studies to make the book more
    engaging for both students and teachers.
    (A few lingering instances remain that will be resolved
    before the Fourth Edition is complete.)
    If any data sets strike you as outdated or uninteresting,
    please send a note.
\item
    
\end{description}


\subsection*{Changes in progress or that will be completed}

\noindent%
For reference, we will go to print in April.

\noindent%
Below are tentative changes, and we welcome
feedback and suggestions on these plans.
\begin{enumerate}
%\item
%    As earlier mentioned, exercises will be moved to the
%    end of sections, and there will be some new exercises
%    in the new edition.
\item
    \textbf{We are moving all data references into an
    appendix and out of footnotes in the text}
    (you can observe in the book that many footnotes
    for references have disappeared).
    Our goals with this change are to
    \begin{enumerate}
    \item
        simplify reading for the large majority of readers, and
    \item
        provide a place where we can provide a complete
        list of all data sets in the text.
    \end{enumerate}
    The appendix will also include links (in the PDF)
    to pages dedicated to each data set
    and a CSV download link.
\item
    \textbf{We are tentatively planning to place exercises
    at the end of each section.}
    We would also include a handful of exercises at the
    end of each chapter that would be more comprehensive.
\item
    Create a couple lead-in pages for each chapter that
    stand out more strongly.
    Designs have been drawn up but are not yet implemented
    in the \LaTeX{} source files.
\item
    Replace the Mario Kart auction data in
    Chapter~9 with a new data set that is to-be-determined.
\item
    We are cutting out the condition that the
    \emph{sample size needs to be $\leq 10\%$ of the
    population size}.
    It will be mentioned briefly as a consideration
    but no longer included as a condition.
%    We've received several cases of feedback that this
%    is confusing (often asked: why is collecting more data bad?),
%    or that it is not practically relevant except
%    in very rare cases.
%    If you are concerned about this change,
%    please let us know.
\item
    The discussion of statistical vs practical significance
    is not in the new \emph{Foundations for Inference} chapter.
    However, it will be added back into the book before the
    Fourth Edition is released in a location to-be-determined.
\item
    We will be completing a thorough review of the inference
    chapters to ensure they read well in their new order.
    Most especially, we want to be confident the 2-prop
    description is reasonable since it is no longer preceded
    by the 2-mean scenario.
\item
    We may add a new section on graphics
    that would follow the sections on summarizing numerical
    and categorical data.
\item
    We may include some basics on R code at the end
    of some sections.
    If this is of particular interest to you,
    please let us know.
\item
    We may include some blank pages in the Fourth Edition
    launch if we plan to add specific types of new content.
    This strategy would allow us to add extra (non-critical)
    content later without affecting page numbering of
    textbooks already purchased or downloaded.
\item
    You'll also find several comments throughout the book
    that callout additional items.
\end{enumerate}









================================================
FILE: extraTeX/preamble/title.tex
================================================

\title{\huge OpenIntro Statistics\vspace{1.5mm} \\
    \Large Fourth Edition}
\author{David Diez \\
\small\emph{Data Scientist}\\
\small\emph{OpenIntro} \\[6mm]
Mine \c{C}etinkaya-Rundel \\
\small\emph{Associate Professor of the Practice,
    Duke University} \\
\small\emph{Professional Educator, RStudio} \\[6mm]
Christopher D Barr \\
\small\emph{Investment Analyst} \\
\small\emph{Varadero Capital} \\
}


================================================
FILE: extraTeX/preamble/title_derivative.tex
================================================

% For attribution guidelines, please see
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\title{\huge [New Title Goes Here] \\[2mm]
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OpenIntro Statistics \\
Fourth Edition}
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\normalsize Mine \c{C}etinkaya-Rundel \\[8mm]
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\small [Briefly Describe Contribution] \\[2mm]
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\small [Briefly Describe Contribution] \\[2mm]
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% 5 Section Coloring
% 6 Utilities
% 7 
% 8 Figures and Captions
% 9 Examples and Exercises
% 10 Special Boxes

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\raggedbottom


\newcommand{\stdspace}[0]{3mm}
\newcommand{\stdvspace}[0]{\vspace{\stdspace{}}}
\newcommand{\stdaddvspace}[0]{\addvspace{\stdspace{}}}




%-------------------------------------------------------------
% 1 Page Parameters
% 1.1
\setlength\paperheight{11in}
\setlength\paperwidth{8.5in}
\newcommand{\officialtextheight}{9.7in}
\newcommand{\officialtextwidth}{6in}
%\setlength\paperheight{10in}
%\setlength\paperwidth{8in}
%\newcommand{\officialtextheight}{8.7in}
%\newcommand{\officialtextwidth}{6in}
\newcommand{\officialvoffset}{-0.6in}
\setlength\textheight{\officialtextheight}
\setlength\textwidth{\officialtextwidth}
\setlength\voffset{\officialvoffset}
\renewcommand{\baselinestretch}{1.0}
% 1.2 Margin Size
\setlength\hoffset{0.25in}
% 1.2.1 Even
\setlength\oddsidemargin{0in}
\setlength\evensidemargin{0in}
% 1.2.2 Slightly offset
%\setlength\oddsidemargin{0.08in}
%\setlength\evensidemargin{-0.08in}
% 1.2.3 Significant offset
% WARNING: The chapter pages will show partially hidden page numbers.
%\setlength\oddsidemargin{0.2in}
%\setlength\evensidemargin{-0.2in}
% 1.3 PDF Parameters
%\setlength\paperheight{11in}
%\setlength\textheight{8.25in}
%\setlength\paperwidth{8.5in}
%\setlength\textwidth{5.45in}
%\setlength\voffset{-10mm}
%\setlength\oddsidemargin{0.75in}
%\setlength\evensidemargin{0.75in}
% 1.4 Margin Spacing
\setlength{\marginparsep}{5mm}
\setlength{\marginparwidth}{20mm}
% 1.5 Page Header
\pagestyle{fancy}
\renewcommand{\headrulewidth}{0pt}
\fancyhead[RO,LE]{\thepage}
\fancyhead[RE]{\leftmark}
\fancyhead[LO]{\rightmark}
\fancyfoot[c]{}
\fancyheadoffset[RO,LE]{0.9in}



% Tablet Version
%\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}




%-------------------------------------------------------------
% 2 Special Commands for Editions
\newcommand{\referrer}{os4_pdf}
\newcommand{\vspaceB}[1]{}
\newcommand{\hspaceB}[1]{}
\newcommand{\textB}[1]{}
\newcommand{\textC}[1]{}
\newcommand{\D}[1]{#1}



%-------------------------------------------------------------
% 3 Content Modifications
\newcommand{\APVersion}[2]{#2}
\newcommand{\MultipleRegression}[2]{#1}
\newcommand{\MultipleRegressionChapter}[2]{#1}
\newcommand{\SimulationAndRandomization}[1]{#1}
\newcommand{\ANOVASection}[2]{#1}
\newcommand{\GLMSection}[2]{#1}




%-------------------------------------------------------------
% 4 Counters and Parameters
% 4.1 Counters
\newcounter{alwaysOne}
\setcounter{alwaysOne}{1}
\newcounter{alwaysTwo}
\setcounter{alwaysTwo}{2}
\newcounter{alwaysThree}
\setcounter{alwaysThree}{3}
\newcounter{alwaysFour}
\setcounter{alwaysFour}{4}
\newcounter{withinChNum}[chapter]
\setcounter{withinChNum}{0}
\newcounter{eoce}[chapter]
\renewcommand{\theeoce}
    {\arabic{chapter}.\arabic{eoce}}
\newcounter{eocesolch}
\setcounter{eocesolch}{0}
\newcounter{eocesol}[eocesolch]
\renewcommand{\theeocesol}
     {\arabic{eocesolch}.\arabic{eocesol}}
\newcounter{eoceNeedSolution}[chapter]
\renewcommand{\theeoceNeedSolution}
    {\arabic{chapter}.\arabic{eoceNeedSolution}}
\newcounter{eoceReplace}[chapter]
\renewcommand{\theeoceReplace}
    {\arabic{chapter}.\arabic{eoceReplace}}
\newcounter{eoceFF}[chapter]
\renewcommand{\theeoceFF}
    {\arabic{chapter}.\arabic{eoceFF}}
% 4.2 Parameters
\newlength{\exampleAboveBar}
\newlength{\exampleBelowBar}
\setlength{\exampleAboveBar}{-3.15mm}
\setlength{\exampleBelowBar}{-1.15mm}
\newlength{\nexampleAboveBar}
\newlength{\nexampleBelowBar}
\setlength{\nexampleAboveBar}{-1mm}
\setlength{\nexampleBelowBar}{-1mm}
% 4.3 Chapter Declarations
\newcommand\includechapter[2]{
  \setcounter{chapter}{#1}
  \addtocounter{chapter}{-1}
  \normalsize
  \include{#2/TeX/#2}
  \newpage\input{#2/TeX/review_exercises}
  }



%-------------------------------------------------------------
% 5 Section Headers
%
% See headers.tex file for main chapters.
\newcommand{\chapterpagepaddingtopinner}[0]{35mm} % 45mm
\newcommand{\chapterpagepaddingbottominner}[0]{25mm}
\newcommand{\chapterXfontsize}[0]{92}
\newcommand{\chaptertitlefontsize}[0]{30}


%-------------------------------------------------------------
% 6 Utilities
% 6.1 Helpful Editing Commands
\newcommand\Add[1]{\marginpar[{\Huge\color{oiR}$\bullet$}]{\Huge\color{oiR}$\bullet$}{\color{oiB}#1}}
\newcommand\Cut[1]{\marginpar[{\Huge\color{oiR}$\bullet$}]{\Huge\color{oiR}$\bullet$}{\color{oiGC}#1}}
%\newcommand\Comment[1]{\marginpar[{\Huge\color{oiR}$\bullet$}]{\Huge\color{oiR}$\bullet$} {\color{oiG}{[#1]}}}
\newcommand{\note}[1]{\Comment{#1}}
% 6.2 Special Symbols
\newcommand{\degree}{\ensuremath{^\circ}}
\newcommand{\R}{\textbf{\textsf{R}}}
% 6.3 Text Commands (Terms, Data, Variable, Response)
\newcommand{\term}[1]{\textbf{#1}\index{#1|textbf}}
\newcommand{\termsub}[2]{\textbf{#1}\index{#2|textbf}}
\newcommand{\termni}[1]{\textbf{#1}}
\newcommand{\hiddenterm}[1]{#1\index{#1|textbf}}
\newcommand{\indexthis}[2]{#1\index{#2}}
\newcommand{\termO}[1]{\textbf{\color{termOColor}#1}}
\newenvironment{data}[1]{\texttt{#1}}{}
\newcommand{\datalink}[1]{\index{#1|textbf}\texttt{\oiRedirect{data_#1}{#1}}}
\newenvironment{var}[1]{\texttt{#1}}{}
\newenvironment{resp}[1]{\texttt{#1}}{}
\newcommand{\lmlevel}[1]{:~\emph{#1}}{}
\newenvironment{calctext}[1]{{\color{oiB}\texttt{#1}}}{}
\newenvironment{calctextmath}[1]{{\color{oiB}\mathtt{#1}}}{}
\newenvironment{calcbutton}[1]{{\color{oiB}\texttt{#1}}}{}
\newcommand{\codeindent}{\hspace{5mm}}
% 6.4 Highlighting
\newenvironment{highlight}{\textbf}{}
\newcommand{\highlightO}[1]{\textbf{\color{oiB}#1}}
\newcommand{\highlightT}[1]{\emph{\color{oiR}#1}}
% 6.5 Lengths
\setlength{\parindent}{0.3in}
% 6.6 Hyperreferences
\newcommand{\urlwofont}[1]{\urlstyle{same}\url{#1}}
\newcommand{\oiRedirect}[2]{\href{http://www.openintro.org/redirect.php?go=#1&referrer=\referrer}{#2}}
\newcommand{\videoicon}[1][4.5mm]{\includegraphics[height=#1]{extraTeX/icons/video_camera.png}~}
\newcommand{\CalculatorVideos}[1]{}%{\begin{tipBox}{\tipBoxTitle[\videoicon]{Calculator videos}
%Videos covering #1 using TI and Casio graphing calculators are available at \mbox{\oiRedirect{textbook-openintro_videos}{openintro.org/videos}}.}
%\end{tipBox}}
\newcommand{\videohref}[2][4.5mm]{\oiRedirect{#2}{\raisebox{-0.3mm}[0pt]{\includegraphics[height=#1]{extraTeX/icons/video_camera.png}}}}
\newcommand{\slideshref}[2][4.5mm]{\oiRedirect{#2}{\raisebox{-0.3mm}[0pt]{\includegraphics[height=#1]{extraTeX/icons/slides.png}}}}
\newcommand{\videomarginhref}[2][4mm]{\oiRedirect{#2}{\raisebox{-3mm}[0pt]{\includegraphics[height=#1]{extraTeX/icons/video_camera.png}}}}
\newcommand{\sectionvideohref}[2][6mm]{\oiRedirect{#2}{\raisebox{-0.5mm}[0pt]{\includegraphics[height=#1]{extraTeX/icons/video_camera.png}}}}
\newcommand{\sectionslideshref}[2][6mm]{\oiRedirect{#2}{\raisebox{-0.5mm}[0pt]{\includegraphics[height=#1]{extraTeX/icons/slides.png}}}}
\newcommand{\MarginVideo}[1]{\marginpar[{\videomarginhref{#1}}]{{\videomarginhref{#1}}}}
% 6.7 Helper commands
\newcommand{\us}[0]{\_\hspace{0.3mm}}
%\newcommand{\quadplus}[0]{\quad + \quad}
\newcommand{\indfunc}[2]{\var{#1}_{\resp{#2}}}


%-------------------------------------------------------------
% 7 



%-------------------------------------------------------------
% 8 Figures and Captions
% 8.1 & 8.2 Table & Figure Numbering
% Thanks @Herbert on StackExchange for helping clean up this style code!
% http://tex.stackexchange.com/questions/176978/latex-numbering-in-counters-appears-to-have-changed/177045?noredirect=1#comment409945_177045
\makeatletter
\let\c@table\c@figure
\makeatother
% 8.3 Caption Width
\newlength{\mycaptionwidth}
\setlength{\mycaptionwidth}{0.825\textwidth}
\captionsetup{width=\mycaptionwidth}
\newcommand{\Figure}[3][]{\includegraphics[width=#2\textwidth]{\chapterfolder/figures/#3/#3}}
\newcommand{\Figures}[4][]{\includegraphics[width=#2\textwidth]{\chapterfolder/figures/#3/#4}}
\newcommand{\Figuress}[4][]{\includegraphics[width=#2]{\chapterfolder/figures/#3/#4}}
\newcommand{\FigureFullPath}[3][]{\includegraphics[width=#2\textwidth]{#3}}
\newcommand{\chapterfolder}{}




%-------------------------------------------------------------
% 9 Examples and Exercises
% 9.1 Exercises, within the text
% 9.1.1 Exercise Environment
\newcommand{\excolor}[1]{{\color{excolor}#1}}
\newenvironment{exercise}
{
\begin{itemize}\item[\color{oiB}$\bigodot$]\refstepcounter{equation}\noindent\normalsize\textbf{\color{oiB}Guided Practice \theexercise}%\hspace{3mm}

}
{\normalsize

\stdaddvspace{}
\end{itemize}}
% 9.1.2 Exercise Fine Tuning
\newcommand\theexercise{\thechapter.\arabic{equation}}
% 9.2 Examples
% 9.2.1 Example Environment
\newcommand\theexample{\thechapter.\arabic{equation}}
\newenvironment{example}[1]
{\begin{itemize}
\item[\color{oiB}\Large$\CIRCLE$]\refstepcounter{equation}\noindent\textbf{\color{oiB}Example \theexample}

#1\vspace{\exampleAboveBar}

{\color{examplegray}\rule{20mm}{0.1mm}}

\vspace{\exampleBelowBar}

\normalsize}{

\end{itemize}
\stdaddvspace{}
}
% 9.2.2 Wrappers
%\reversemarginpar
\def\warningsymbol{\protect\marginsymbolhelper}
\def\marginsymbolhelper{\tabto*{0mm} {\dbend} \tabto*{0mm}}
\newcommand{\exampleicon}[1]{\vspace{#1} \raggedleft\includegraphics[width=5mm]{extraTeX/icons/example.png}\hspace{2mm}\ }
\newenvironment{gpewrapper}[1]{\addvspace{4mm}

\noindent\hspace{-12.45mm}\begin{minipage}[c]{\textwidth+8mm}
\begin{minipage}[c]{8.4mm}
\hspace{0.5mm}\includegraphics[width=5mm]{extraTeX/icons/#1.png}
\end{minipage}\begin{minipage}[c]{\textwidth-0.45mm}\begin{mdframed}[%
    topline=false,
    rightline=false,
    bottomline=false,
    linewidth=0.5mm,
    linecolor=oiB]}{\end{mdframed}\end{minipage}\end{minipage}

    \addvspace{4mm}}
\newenvironment{examplewrap}
    {\begin{gpewrapper}{example}}
    {\end{gpewrapper}}
\newenvironment{exercisewrap}
    {\begin{gpewrapper}{guided_practice}}
    {\end{gpewrapper}}
% 9.2.3 Example Title
\newcommand{\exampletitle}[1]{\textbf{\color{oiB}\small\fontfamily{phv}%
\selectfont{\MakeUppercase{Example~#1}}} \\[1mm]}
\newcommand{\exercisetitle}[1]{\textbf{\color{oiB}\small\fontfamily{phv}%
\selectfont{\MakeUppercase{Guided Practice~#1}}} \\[1mm]}
% 9.2.4 NEW Example and Guided Practice Environment
\newcommand{\exspace}{\stdvspace{}}
\newenvironment{nexample}[1]{\addvspace{6mm}

\refstepcounter{equation}\exampletitle{\theexample}
#1

\addvspace{\nexampleAboveBar}

{\color{examplegray}\rule{20mm}{0.1mm}}

\addvspace{\nexampleBelowBar}

\setlength{\parskip}{2mm}}{}
\newenvironment{nexercise}{\addvspace{6mm}

\refstepcounter{equation}\exercisetitle{\theexample}}{}

% 9.3 EOCEs: End of Chapter Exercises
% 9.3.1 Environment
\newenvironment{eoce}[2][]
{\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

\addvspace{4mm}

}
%{\em #2 } $\:$ \\ }
{}
% 9.3.2 EOCE Solutions
\newcommand{\eocesolch}[1]{
\refstepcounter{eocesolch}\noindent\textbf{\color{oiB}\arabic{eocesolch}\hspace{2mm}#1}

\addvspace{2mm}

}
{
\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}}}

\addvspace{1mm}

}
% 9.3.3 EOCE Utilities
\newcommand{\qt}[2][.]{{\fontfamily{phv}\selectfont\textcolor{oiB}{\textbf{#2#1}}}}
\newcommand{\qtq}[1]{{\fontfamily{phv}\selectfont\textcolor{oiB}{\textbf{#1?}}}}
\newcommand{\ec}[1]{\textcolor{oiB}{\footnotesize{~(#1)}}}% 9.3.4 EOCE Roman Parts
\newenvironment{romanparts}{
\begin{enumerate}[I.]
\setlength{\itemsep}{0mm}
}{\end{enumerate}}
% 9.3.5 EOCE Parts
\newenvironment{parts}{
%\vspace{-0.25cm}
\begin{enumerate}[(a)]
\setlength{\itemsep}{0mm}}
{\end{enumerate}}
% 9.3.6 EOCE Subparts
\newenvironment{subparts}{
\begin{enumerate}[i.]
\setlength{\itemsep}{0mm}}
{\end{enumerate}}
% 9.3.7 EOCE hyp environment
\newenvironment{hyp}{
\begin{itemize}
\setlength{\itemsep}{0mm}
}
{\end{itemize}
}
% 9.3.8 cond environment
\newenvironment{cond}{
\begin{enumerate}[1.]
\setlength{\itemsep}{0mm}
}
{\end{enumerate}
}
% 9.3.9 Exercise fixes required.
\newcommand{\eoceNeedSolution}[1][]
{\textbf{\refstepcounter{eoceNeedSolution} \color{red}ADD SOLUTION. #1}}
\newcommand{\eoceReplace}[1][]
{\textbf{\refstepcounter{eoceReplace} \color{red}REPLACE THIS EXERCISE. #1}}
\newcommand{\eoceFF}[1][]
{\textbf{\refstepcounter{eoceFF} \color{red}FINAL FORMATTING.}}





%-------------------------------------------------------------
% 10 Special Boxes
% 10.1.1 Term Box
\newcommand\tBoxTitleBuffer{\\[1.5mm]}
\newenvironment{tBoxTitle}[2][\tBoxTitleBuffer]{\textbf{\color{oiB}#2} #1
}{}
\newenvironment{termBox}[1]{
\addvspace{4mm}
\noindent{\color{oiB}\framebox[\textwidth][c]{\framebox[\textwidth-3mm][l]{ \\
	\vspace{0.5cm} \\
	\begin{minipage}[b]{\textwidth-3mm}
		\begin{minipage}[t]{2mm}
			\hspace{2mm}
		\end{minipage}
		\begin{minipage}[b]{\textwidth-10mm}
			\color{black}\ \\[-0.7mm]
			#1
			
			\vspace{1mm}
		\end{minipage}
	\end{minipage}}}}
}{

\addvspace{1mm}}
% 10.2 Tip Box
\newenvironment{tipBoxTitle}[2][TIP:\ ]{\textbf{\color{oiB}#1#2}\\[0.3mm]}{}
\newenvironment{tipBox}[1]{
\addvspace{4mm}
\noindent{\color{oiB}\framebox[\textwidth][l]{ \\
	\vspace{5mm} \\
	\begin{minipage}[b]{\textwidth-4mm}
		\begin{minipage}[t]{2mm}
			\hspace{2mm}
		\end{minipage}
		\begin{minipage}[b]{\textwidth-8mm}
			\color{black}\ \\[-0.7mm]
			#1
			
			\vspace{1mm}
		\end{minipage}
	\end{minipage}}}
}{

\addvspace{1mm}}
% 10.3 Caution Box
\newenvironment{caution}[2]{
\addvspace{4mm}
\noindent{\color{oiB}\framebox[\textwidth][l]{ \\
	\vspace{5mm} \\
	\begin{minipage}[b]{\textwidth-4mm}
		\begin{minipage}[t]{2mm}
			\hspace{2mm}
		\end{minipage}
		\begin{minipage}[b]{\textwidth-8mm}
			\textbf{\color{oiB}Caution: #1} \\[1mm]
			\color{black}#2
		\end{minipage}
	\end{minipage}}}
}{

\addvspace{1mm}}
% 10.4 One Box
\newenvironment{onebox}[1]{
\addvspace{4mm}
\noindent\begin{minipage}{\textwidth}
\noindent\rule{\textwidth}{0.3pt}\vspace{-6mm}
\begin{mdframed}[%
    topline=false,
    rightline=false,
    leftline=false,
    bottomline=false,
    backgroundcolor=grayBackground]
\textbf{\color{oiB}\small\fontfamily{phv}%
\selectfont{\MakeUppercase{#1}}} \\[1mm]}{
\end{mdframed}\vspace{-4.2mm}
\rule{\textwidth}{0.3pt}
\end{minipage}

\addvspace{4mm}}





================================================
FILE: extraTeX/style/style_appendices.tex
================================================
\newcommand{\clearpageforsection}{\addvspace{8mm}

}
\fancyhead[LO]{}

%% 5.1 Chapter
\titleformat{\chapter}[display]
{\color{oiB}\normalfont\huge\bfseries\raggedright}{\chaptertitlename\
\thechapter\\[5mm]{\Huge#1\vspace{-15mm}}}{20pt}{\Huge}
% 5.2 Section
\titleformat{\section}
{\color{oiB}\normalfont\Large\bfseries}
{\color{oiB}\thesection$\quad$#1}{1em}{}
% 5.3 Subsection
\titleformat{\subsection}
{\color{oiB}\normalfont\large\bfseries}
{\color{oiB}\thesubsection$\quad$#1}{1em}{}


================================================
FILE: extraTeX/style/style_simple.tex
================================================
% 1 Page Parameters
% 2 Special Commands for Editions
% 3 Content Modifications
% 4 Counters and Parameters
% 5 Section Coloring
% 6 Utilities
% 7 
% 8 Figures and Captions
% 9 Examples and Exercises
% 10 Special Boxes

%\renewcommand\chapter{\if@openright\cleardoublepage\else\clearpage\fi
%                    \thispagestyle{fancy}%
%                    \global\@topnum\z@
%                    \@afterindentfalse
%                    \secdef\@chapter\@schapter}
\fancypagestyle{plain}{%
\fancyhf{} % clear all header and footer fields
\fancyhead[RO,RE]{\thepage} %RO=right odd, RE=right even
\renewcommand{\headrulewidth}{0pt}
\renewcommand{\footrulewidth}{0pt}}
\raggedbottom


\newcommand{\stdspace}[0]{3mm}
\newcommand{\stdvspace}[0]{\vspace{\stdspace{}}}
\newcommand{\stdaddvspace}[0]{\addvspace{\stdspace{}}}




%-------------------------------------------------------------
% 1 Page Parameters
% 1.1
\setlength\paperheight{11in}
\setlength\paperwidth{8.5in}
\newcommand{\officialtextheight}{9.7in}
\newcommand{\officialtextwidth}{6in}
%\setlength\paperheight{10in}
%\setlength\paperwidth{8in}
%\newcommand{\officialtextheight}{8.7in}
%\newcommand{\officialtextwidth}{6in}
\newcommand{\officialvoffset}{-0.6in}
\setlength\textheight{\officialtextheight}
\setlength\textwidth{\officialtextwidth}
\setlength\voffset{\officialvoffset}
\renewcommand{\baselinestretch}{1.0}
% 1.2 Margin Size
\setlength\hoffset{0.25in}
% 1.2.1 Even
\setlength\oddsidemargin{0in}
\setlength\evensidemargin{0in}
% 1.2.2 Slightly offset
%\setlength\oddsidemargin{0.08in}
%\setlength\evensidemargin{-0.08in}
% 1.2.3 Significant offset
% WARNING: The chapter pages will show partially hidden page numbers.
%\setlength\oddsidemargin{0.2in}
%\setlength\evensidemargin{-0.2in}
% 1.3 PDF Parameters
%\setlength\paperheight{11in}
%\setlength\textheight{8.25in}
%\setlength\paperwidth{8.5in}
%\setlength\textwidth{5.45in}
%\setlength\voffset{-10mm}
%\setlength\oddsidemargin{0.75in}
%\setlength\evensidemargin{0.75in}
% 1.4 Margin Spacing
\setlength{\marginparsep}{5mm}
\setlength{\marginparwidth}{20mm}
% 1.5 Page Header
\pagestyle{fancy}
\renewcommand{\headrulewidth}{0pt}
\fancyhead[RO,LE]{\thepage}
\fancyhead[RE]{\leftmark}
\fancyhead[LO]{\rightmark}
\fancyfoot[c]{}
\fancyheadoffset[RO,LE]{0.9in}



% Tablet Version
%\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}




%-------------------------------------------------------------
% 2 Special Commands for Editions
\newcommand{\referrer}{os4_pdf}
\newcommand{\vspaceB}[1]{}
\newcommand{\hspaceB}[1]{}
\newcommand{\textB}[1]{}
\newcommand{\textC}[1]{}
\newcommand{\D}[1]{#1}



%-------------------------------------------------------------
% 3 Content Modifications
\newcommand{\APVersion}[2]{#2}
\newcommand{\MultipleRegression}[2]{#1}
\newcommand{\MultipleRegressionChapter}[2]{#1}
\newcommand{\SimulationAndRandomization}[1]{#1}
\newcommand{\ANOVASection}[2]{#1}
\newcommand{\GLMSection}[2]{#1}




%-------------------------------------------------------------
% 4 Counters and Parameters
% 4.1 Counters
\newcounter{alwaysOne}
\setcounter{alwaysOne}{1}
\newcounter{alwaysTwo}
\setcounter{alwaysTwo}{2}
\newcounter{alwaysThree}
\setcounter{alwaysThree}{3}
\newcounter{alwaysFour}
\setcounter{alwaysFour}{4}
\newcounter{withinChNum}[chapter]
\setcounter{withinChNum}{0}
\newcounter{eoce}[chapter]
\renewcommand{\theeoce}
    {\arabic{chapter}.\arabic{eoce}}
\newcounter{eocesolch}
\setcounter{eocesolch}{0}
\newcounter{eocesol}[eocesolch]
\renewcommand{\theeocesol}
     {\arabic{eocesolch}.\arabic{eocesol}}
\newcounter{eoceNeedSolution}[chapter]
\renewcommand{\theeoceNeedSolution}
    {\arabic{chapter}.\arabic{eoceNeedSolution}}
\newcounter{eoceReplace}[chapter]
\renewcommand{\theeoceReplace}
    {\arabic{chapter}.\arabic{eoceReplace}}
\newcounter{eoceFF}[chapter]
\renewcommand{\theeoceFF}
    {\arabic{chapter}.\arabic{eoceFF}}
% 4.2 Parameters
\newlength{\exampleAboveBar}
\newlength{\exampleBelowBar}
\setlength{\exampleAboveBar}{-3.15mm}
\setlength{\exampleBelowBar}{-1.15mm}
\newlength{\nexampleAboveBar}
\newlength{\nexampleBelowBar}
\setlength{\nexampleAboveBar}{-1mm}
\setlength{\nexampleBelowBar}{-1mm}
% 4.3 Chapter Declarations
\newcommand\includechapter[2]{
  \setcounter{chapter}{#1}
  \addtocounter{chapter}{-1}
  \normalsize
  \include{#2/TeX/#2}
  \newpage\input{#2/TeX/review_exercises}
  }



%-------------------------------------------------------------
% 5 Section Headers
%
% See headers.tex file for main chapters.
\newcommand{\chapterpagepaddingtopinner}[0]{35mm} % 45mm
\newcommand{\chapterpagepaddingbottominner}[0]{25mm}
\newcommand{\chapterXfontsize}[0]{92}
\newcommand{\chaptertitlefontsize}[0]{30}


%-------------------------------------------------------------
% 6 Utilities
% 6.1 Helpful Editing Commands
\newcommand\Add[1]{\marginpar[{\Huge\color{oiR}$\bullet$}]{\Huge\color{oiR}$\bullet$}{\color{oiB}#1}}
\newcommand\Cut[1]{\marginpar[{\Huge\color{oiR}$\bullet$}]{\Huge\color{oiR}$\bullet$}{\color{oiGC}#1}}
%\newcommand\Comment[1]{\marginpar[{\Huge\color{oiR}$\bullet$}]{\Huge\color{oiR}$\bullet$} {\color{oiG}{[#1]}}}
\newcommand{\note}[1]{\Comment{#1}}
% 6.2 Special Symbols
\newcommand{\degree}{\ensuremath{^\circ}}
\newcommand{\R}{\textbf{\textsf{R}}}
% 6.3 Text Commands (Terms, Data, Variable, Response)
\newcommand{\term}[1]{\textbf{#1}\index{#1|textbf}}
\newcommand{\termsub}[2]{\textbf{#1}\index{#2|textbf}}
\newcommand{\termni}[1]{\textbf{#1}}
\newcommand{\hiddenterm}[1]{#1\index{#1|textbf}}
\newcommand{\indexthis}[2]{#1\index{#2}}
\newcommand{\termO}[1]{\textbf{\color{termOColor}#1}}
\newenvironment{data}[1]{\texttt{#1}}{}
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% 7 



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================================================
FILE: extraTeX/tables/TeX/chiSquareTable.tex
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\section{Chi-Square Probability Table}
\label{chiSquareProbabilityTable}

A \term{chi-square probability table} may be used
to find tail areas of a chi-square distribution.
The \term{chi-square table} is partially shown in
Figure~\ref{chiSquareProbabilityTableShort},
and the complete table may be found on
page~\pageref{fullChiSqTable}.
When using a chi-square table, we examine a particular
row for distributions
with different degrees of freedom, and we identify a range for
the area (e.g. 0.025 to 0.05).
Note that the chi-square table provides upper tail values,
which is different than the normal and $t$-distribution tables.

\begin{figure}[h]
\centering
\begin{tabular}{r | rrrr | rrrr |}
  \hline
Upper tail & 0.3 & 0.2 & 0.1 & 0.05 & 0.02 & 0.01 & 0.005 & 0.001 \\ 
  \hline
%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 \\ 
df \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 \\ 
  \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 \\ 
  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 \\ 
  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 \\ 
  \hline
  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 \\ 
  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 \\ 
  \hline
\end{tabular}
\caption{A section of the chi-square table. A complete table is in Appendix~\ref{chiSquareProbabilityTable}.}
\label{chiSquareProbabilityTableShort}
\end{figure}

\begin{examplewrap}
\begin{nexample}{Figure~\ref{app_chiSquareAreaAbove6Point25WithDF3}
    shows a chi-square distribution with 3 degrees of freedom
    and an upper shaded tail starting at 6.25.
    Use Figure~\ref{chiSquareProbabilityTableShort}
    to estimate the shaded area.}
  This distribution has three degrees of freedom,
  so only the row with 3 degrees of freedom (df) is relevant.
  This row has been italicized in the table.
  Next, we see that the value -- 6.25 -- falls in the column
  with upper tail area 0.1.
  That is, the shaded upper tail of
  Figure~\ref{app_chiSquareAreaAbove6Point25WithDF3}
  has area 0.1.

  This example was unusual, in that we observed the
  \emph{exact} value in the table.
  In the next examples, we encounter situations where
  we cannot precisely estimate the tail area and must
  instead provide a range of values.
\end{nexample}
\end{examplewrap}

\begin{figure}
\centering
\subfigure[]{
\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}
\label{app_chiSquareAreaAbove6Point25WithDF3}
}
\subfigure[]{
\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}
\label{app_chiSquareAreaAbove4Point3WithDF2}
}
\subfigure[]{
\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}
\label{app_chiSquareAreaAbove5Point1WithDF5}
}
\subfigure[]{
\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}
\label{app_chiSquareAreaAbove11Point7WithDF7}
}
%\subfigure[]{
%\includegraphics[width=0.475\textwidth]{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove10WithDF4/chiSquareAreaAbove10WithDF4}
%\label{app_chiSquareAreaAbove10WithDF4}
%}
%\subfigure[]{
%\includegraphics[width=0.475\textwidth]{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove9Point21WithDF3/chiSquareAreaAbove9Point21WithDF3}
%\label{app_chiSquareAreaAbove9Point21WithDF3}
%}
\caption{
\textbf{\subref{app_chiSquareAreaAbove6Point25WithDF3}}~Chi-square distribution with 3~degrees of freedom, area above 6.25 shaded.
\textbf{\subref{app_chiSquareAreaAbove4Point3WithDF2}}~2~degrees of freedom, area above 4.3 shaded.
\textbf{\subref{app_chiSquareAreaAbove5Point1WithDF5}}~5~degrees of freedom, area above 5.1 shaded.
\textbf{\subref{app_chiSquareAreaAbove11Point7WithDF7}}~7~degrees of freedom, area above 11.7 shaded.
%\textbf{\subref{app_chiSquareAreaAbove10WithDF4}}~4~degrees of freedom, area above 10 shaded.
%\textbf{\subref{app_chiSquareAreaAbove9Point21WithDF3}}~3~degrees of freedom, area above 9.21 shaded.
}
\label{arrayOfFigureAreasForChiSquareDistributionChiSqAppendix}
\end{figure}

\begin{examplewrap}
\begin{nexample}{
    Figure~\ref{app_chiSquareAreaAbove4Point3WithDF2}
    shows the upper tail of a chi-square distribution
    with 2~degrees of freedom.
    The area above value 4.3 has been shaded;
    find this tail area.}
  The cutoff 4.3 falls between the second and third columns
  in the 2~degrees of freedom row.
  Because these columns correspond to tail areas of 0.2 and 0.1,
  we can be certain that the area shaded in
  Figure~\ref{app_chiSquareAreaAbove4Point3WithDF2}
  is between 0.1 and 0.2.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\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.}
Looking 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.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{Figure~\ref{app_chiSquareAreaAbove11Point7WithDF7}
    shows a cutoff of 11.7 on a chi-square distribution with
    7~degrees of freedom.
    Find the area of the upper tail.}
  The value 11.7 falls between 9.80 and 12.02 in the 7 df row.
  Thus, the area is between 0.1 and 0.2.
\end{nexample}
\end{examplewrap}

%\begin{exercisewrap}
%\begin{nexercise}
%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
%\end{nexercise}
%\end{exercisewrap}
%\footnotetext{The area is between 0.02 and 0.05.}
%
%\begin{exercisewrap}
%\begin{nexercise}
%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
%\end{nexercise}
%\end{exercisewrap}
%\footnotetext{Between 0.02 and 0.05.}

%\begin{figure}[hhh]
%\centering
%\includegraphics[height=1.5in]{extraTeX/tables/figures/chiSquareTail/chiSquareTail}
%\caption{Areas in the chi-square table always refer to the right tail.}
%\end{figure}

\begin{center}
\begin{tabular}{r | rrrr | rrrr |}
  \hline
Upper tail & 0.3 & 0.2 & 0.1 & 0.05 & 0.02 & 0.01 & 0.005 & 0.001 \\ 
  \hline
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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  \hline
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  \hline
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  \hline
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  \hline
  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 \\ 
  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 \\ 
  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 \\ 
  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 \\ 
  \hline
\end{tabular}
\label{fullChiSqTable}
\end{center}


================================================
FILE: extraTeX/tables/TeX/tTable.tex
================================================
\section{$\pmb{t}$-Probability Table}
\label{tDistributionTable}

A \termsub{$\pmb{t}$-probability table}
    {t-probability table@$t$-probability table}
may be used to
find tail areas of a $t$-distribution using a T-score,
or vice-versa.
Such a table lists T-scores and the corresponding percentiles.
A partial
\termsub{$\pmb{t}$-table}{t-table@$t$-table}
is shown in Figure~\ref{tTableSample},
and the complete table starts on page~\pageref{tTableFirstPage}.
Each row in the $t$-table represents a $t$-distribution with
different degrees of freedom.
The columns correspond to tail probabilities.
For instance, if we know we are working with the
$t$-distribution with $df=18$, we can examine row 18,
which is highlighted in Figure~\ref{tTableSample}.
If we want the value in this row that identifies the T-score
(cutoff) for an upper tail of 10\%, we can look in the column
where \emph{one tail} is 0.100.
This cutoff is 1.33.
If we had wanted the cutoff for the lower 10\%, we would
use -1.33.
Just like the normal distribution,
all $t$-distributions are symmetric.

\begin{figure}[hht]
\centering
\begin{tabular}{r | rrr rr}
one 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  \\
two tails & 0.200 & 0.100 & 0.050 & 0.020 & 0.010 \\
\hline
{$df$} \hfill 1  &  {\normalsize  3.08} & {\normalsize  6.31} & {\normalsize 12.71} & {\normalsize 31.82} & {\normalsize 63.66}  \\ 
2  &  {\normalsize  1.89} & {\normalsize  2.92} & {\normalsize  4.30} & {\normalsize  6.96} & {\normalsize  9.92}  \\ 
3  &  {\normalsize  1.64} & {\normalsize  2.35} & {\normalsize  3.18} & {\normalsize  4.54} & {\normalsize  5.84}  \\ 
$\vdots$ & $\vdots$ &$\vdots$ &$\vdots$ &$\vdots$ & \\
17  &  {\normalsize  1.33} & {\normalsize  1.74} & {\normalsize  2.11} & {\normalsize  2.57} & {\normalsize  2.90}  \\ 
\highlightO{18}  &  \highlightO{\normalsize  1.33} & \highlightO{\normalsize  1.73} & \highlightO{\normalsize  2.10} & \highlightO{\normalsize  2.55} & \highlightO{\normalsize  2.88}  \\ 
19  &  {\normalsize  1.33} & {\normalsize  1.73} & {\normalsize  2.09} & {\normalsize  2.54} & {\normalsize  2.86}  \\ 
20  &  {\normalsize  1.33} & {\normalsize  1.72} & {\normalsize  2.09} & {\normalsize  2.53} & {\normalsize  2.85}  \\ 
$\vdots$ & $\vdots$ &$\vdots$ &$\vdots$ &$\vdots$ & \\
400  &  {\normalsize  1.28} & {\normalsize  1.65} & {\normalsize  1.97} & {\normalsize  2.34} & {\normalsize  2.59}  \\ 
500  &  {\normalsize  1.28} & {\normalsize  1.65} & {\normalsize  1.96} & {\normalsize  2.33} & {\normalsize  2.59}  \\ 
$\infty$  &  {\normalsize  1.28} & {\normalsize  1.64} & {\normalsize  1.96} & {\normalsize  2.33} & {\normalsize  2.58}  \\ 
\end{tabular}
\caption{An abbreviated look at the $t$-table.
    Each row represents a different $t$-distribution.
    The columns describe the cutoffs for specific tail areas.
    The row with $df=18$ has been \highlightO{highlighted}.}
\label{tTableSample}
\end{figure}

\begin{examplewrap}
\begin{nexample}{What proportion of the $t$-distribution with
    18 degrees of freedom falls below -2.10?}
  Just like a normal probability problem, we first draw the
  picture and shade the area below -2.10:
  \begin{center}
  \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}
  \end{center}
  To find this area, we first identify the appropriate row:
  $df = 18$.
  Then we identify the column containing the absolute value
  of -2.10;
  it~is the third column.
  Because we are looking for just one tail, we examine the
  top line of the table, which shows that a one tail area
  for a value in the third row corresponds to 0.025.
  That is, 2.5\% of the distribution falls below -2.10.

  In the next example we encounter a case where the exact
  T-score is not listed in the table.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{A $t$-distribution with 20 degrees of freedom
    is shown in the left panel of
    Figure~\ref{tDistAppendixTwoEx}.
    Estimate the proportion of the distribution falling
    above~1.65.}
  We identify the row in the $t$-table using the degrees
  of freedom: $df=20$.
  Then we look for 1.65; it is not listed.
  It falls between the first and second columns.
  Since these values bound 1.65, their tail areas will
  bound the tail area corresponding to 1.65.
  We identify the one tail area of the first and
  second columns, 0.050 and 0.10, and we conclude that
  between 5\% and 10\% of the distribution is more than
  1.65 standard deviations above the mean.
  If we like, we can identify the precise area using
  statistical software: 0.0573.
\end{nexample}
\end{examplewrap}

\begin{figure}[h]
\centering
\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}
\caption{Left: The $t$-distribution with 20 degrees of freedom,
    with the area above 1.65 shaded.
    Right: The $t$-distribution with 475 degrees of freedom,
    with the area further than 2 units from 0 shaded.}
\label{tDistAppendixTwoEx}
\end{figure}

\begin{examplewrap}
\begin{nexample}{A $t$-distribution with 475 degrees of freedom
    is shown in the right panel of
    Figure~\ref{tDistAppendixTwoEx}.
    Estimate the proportion of the distribution falling more
    than 2 units from the mean (above or below).}
  As before, first identify the appropriate row: $df=475$.
  This row does not exist!
  When this happens, we use the next smaller row, which in
  this case is $df = 400$.
  Next, find the columns that capture 2.00;
  because $1.97 < 3 < 2.34$, we use the third and fourth columns.
  Finally, we find bounds for the tail areas by looking at
  the two tail values: 0.02 and 0.05.
  We use the two tail values because we are looking for two
  symmetric tails in the $t$-distribution.
\end{nexample}
\end{examplewrap}

\begin{exercisewrap}
\begin{nexercise}
What proportion of the $t$-distribution with 19 degrees of freedom falls above -1.79 units?\footnotemark{}
\end{nexercise}
\end{exercisewrap}
\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.}

\begin{examplewrap}
\begin{nexample}{Find the value of $t_{18}^{\star}$
    using the $t$-table, where $t_{18}^{\star}$
    is the cutoff for the $t$-distribution with
    18 degrees of freedom where 95\% of the distribution
    lies between -$t_{18}^{\star}$ and +$t_{18}^{\star}$.}
  For a 95\% confidence interval, we want to find
  the cutoff $t^{\star}_{18}$ such that 95\% of the
  $t$-distribution is between -$t^{\star}_{18}$
  and $t^{\star}_{18}$;
  this is the same as where the two tails have a total
  area of 0.05.
  We look in the $t$-table on page~\pageref{tTableSample},
  find the column with area totaling 0.05 in the two tails
  (third column), and then the row with 18 degrees of
  freedom: $t^{\star}_{18} = 2.10$.
\end{nexample}
\end{examplewrap}


\newpage

\begin{center}
\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}
\end{center}

\begin{center}
\begin{tabular}{r | rrr rr}
\hline
one 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  \\
two tails & 0.200 & 0.100 & 0.050 & 0.020 & 0.010 \\
\hline
{df} \hfill 1  &  {\normalsize  3.08} & {\normalsize  6.31} & {\normalsize 12.71} & {\normalsize 31.82} & {\normalsize 63.66}  \\ 
2  &  {\normalsize  1.89} & {\normalsize  2.92} & {\normalsize  4.30} & {\normalsize  6.96} & {\normalsize  9.92}  \\ 
3  &  {\normalsize  1.64} & {\normalsize  2.35} & {\normalsize  3.18} & {\normalsize  4.54} & {\normalsize  5.84}  \\ 
4  &  {\normalsize  1.53} & {\normalsize  2.13} & {\normalsize  2.78} & {\normalsize  3.75} & {\normalsize  4.60}  \\ 
5  &  {\normalsize  1.48} & {\normalsize  2.02} & {\normalsize  2.57} & {\normalsize  3.36} & {\normalsize  4.03}  \\ 
\hline
6  &  {\normalsize  1.44} & {\normalsize  1.94} & {\normalsize  2.45} & {\normalsize  3.14} & {\normalsize  3.71}  \\ 
7  &  {\normalsize  1.41} & {\normalsize  1.89} & {\normalsize  2.36} & {\normalsize  3.00} & {\normalsize  3.50}  \\ 
8  &  {\normalsize  1.40} & {\normalsize  1.86} & {\normalsize  2.31} & {\normalsize  2.90} & {\normalsize  3.36}  \\ 
9  &  {\normalsize  1.38} & {\normalsize  1.83} & {\normalsize  2.26} & {\normalsize  2.82} & {\normalsize  3.25}  \\ 
10  &  {\normalsize  1.37} & {\normalsize  1.81} & {\normalsize  2.23} & {\normalsize  2.76} & {\normalsize  3.17}  \\ 
\hline
\hline
11  &  {\normalsize  1.36} & {\normalsize  1.80} & {\normalsize  2.20} & {\normalsize  2.72} & {\normalsize  3.11}  \\ 
12  &  {\normalsize  1.36} & {\normalsize  1.78} & {\normalsize  2.18} & {\normalsize  2.68} & {\normalsize  3.05}  \\ 
13  &  {\normalsize  1.35} & {\normalsize  1.77} & {\normalsize  2.16} & {\normalsize  2.65} & {\normalsize  3.01}  \\ 
14  &  {\normalsize  1.35} & {\normalsize  1.76} & {\normalsize  2.14} & {\normalsize  2.62} & {\normalsize  2.98}  \\ 
15  &  {\normalsize  1.34} & {\normalsize  1.75} & {\normalsize  2.13} & {\normalsize  2.60} & {\normalsize  2.95}  \\ 
\hline
16  &  {\normalsize  1.34} & {\normalsize  1.75} & {\normalsize  2.12} & {\normalsize  2.58} & {\normalsize  2.92}  \\ 
17  &  {\normalsize  1.33} & {\normalsize  1.74} & {\normalsize  2.11} & {\normalsize  2.57} & {\normalsize  2.90}  \\ 
18  &  {\normalsize  1.33} & {\normalsize  1.73} & {\normalsize  2.10} & {\normalsize  2.55} & {\normalsize  2.88}  \\ 
19  &  {\normalsize  1.33} & {\normalsize  1.73} & {\normalsize  2.09} & {\normalsize  2.54} & {\normalsize  2.86}  \\ 
20  &  {\normalsize  1.33} & {\normalsize  1.72} & {\normalsize  2.09} & {\normalsize  2.53} & {\normalsize  2.85}  \\ 
\hline
\hline
21  &  {\normalsize  1.32} & {\normalsize  1.72} & {\normalsize  2.08} & {\normalsize  2.52} & {\normalsize  2.83}  \\ 
22  &  {\normalsize  1.32} & {\normalsize  1.72} & {\normalsize  2.07} & {\normalsize  2.51} & {\normalsize  2.82}  \\ 
23  &  {\normalsize  1.32} & {\normalsize  1.71} & {\normalsize  2.07} & {\normalsize  2.50} & {\normalsize  2.81}  \\ 
24  &  {\normalsize  1.32} & {\normalsize  1.71} & {\normalsize  2.06} & {\normalsize  2.49} & {\normalsize  2.80}  \\ 
25  &  {\normalsize  1.32} & {\normalsize  1.71} & {\normalsize  2.06} & {\normalsize  2.49} & {\normalsize  2.79}  \\ 
\hline
26  &  {\normalsize  1.31} & {\normalsize  1.71} & {\normalsize  2.06} & {\normalsize  2.48} & {\normalsize  2.78}  \\ 
27  &  {\normalsize  1.31} & {\normalsize  1.70} & {\normalsize  2.05} & {\normalsize  2.47} & {\normalsize  2.77}  \\ 
28  &  {\normalsize  1.31} & {\normalsize  1.70} & {\normalsize  2.05} & {\normalsize  2.47} & {\normalsize  2.76}  \\ 
29  &  {\normalsize  1.31} & {\normalsize  1.70} & {\normalsize  2.05} & {\normalsize  2.46} & {\normalsize  2.76}  \\ 
30  &  {\normalsize  1.31} & {\normalsize  1.70} & {\normalsize  2.04} & {\normalsize  2.46} & {\normalsize  2.75}  \\ 
\hline
\end{tabular}
\label{tTableFirstPage}
\end{center}


\newpage

\begin{center}
\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}
\end{center}

\begin{center}
\begin{tabular}{r | rrr rr}
\hline
one 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  \\
two tails & 0.200 & 0.100 & 0.050 & 0.020 & 0.010 \\
\hline
{df} \hfill 31  &  {\normalsize  1.31} & {\normalsize  1.70} & {\normalsize  2.04} & {\normalsize  2.45} & {\normalsize  2.74}  \\ 
32  &  {\normalsize  1.31} & {\normalsize  1.69} & {\normalsize  2.04} & {\normalsize  2.45} & {\normalsize  2.74}  \\ 
33  &  {\normalsize  1.31} & {\normalsize  1.69} & {\normalsize  2.03} & {\normalsize  2.44} & {\normalsize  2.73}  \\ 
34  &  {\normalsize  1.31} & {\normalsize  1.69} & {\normalsize  2.03} & {\normalsize  2.44} & {\normalsize  2.73}  \\ 
35  &  {\normalsize  1.31} & {\normalsize  1.69} & {\normalsize  2.03} & {\normalsize  2.44} & {\normalsize  2.72}  \\ 
\hline
36  &  {\normalsize  1.31} & {\normalsize  1.69} & {\normalsize  2.03} & {\normalsize  2.43} & {\normalsize  2.72}  \\ 
37  &  {\normalsize  1.30} & {\normalsize  1.69} & {\normalsize  2.03} & {\normalsize  2.43} & {\normalsize  2.72}  \\ 
38  &  {\normalsize  1.30} & {\normalsize  1.69} & {\normalsize  2.02} & {\normalsize  2.43} & {\normalsize  2.71}  \\ 
39  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.02} & {\normalsize  2.43} & {\normalsize  2.71}  \\ 
40  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.02} & {\normalsize  2.42} & {\normalsize  2.70}  \\ 
\hline
\hline
41  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.02} & {\normalsize  2.42} & {\normalsize  2.70}  \\ 
42  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.02} & {\normalsize  2.42} & {\normalsize  2.70}  \\ 
43  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.02} & {\normalsize  2.42} & {\normalsize  2.70}  \\ 
44  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.02} & {\normalsize  2.41} & {\normalsize  2.69}  \\ 
45  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.01} & {\normalsize  2.41} & {\normalsize  2.69}  \\ 
\hline
46  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.01} & {\normalsize  2.41} & {\normalsize  2.69}  \\ 
47  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.01} & {\normalsize  2.41} & {\normalsize  2.68}  \\ 
48  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.01} & {\normalsize  2.41} & {\normalsize  2.68}  \\ 
49  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.01} & {\normalsize  2.40} & {\normalsize  2.68}  \\ 
50  &  {\normalsize  1.30} & {\normalsize  1.68} & {\normalsize  2.01} & {\normalsize  2.40} & {\normalsize  2.68}  \\ 
\hline
\hline
%55  &  {\normalsize  1.30} & {\normalsize  1.67} & {\normalsize  2.00} & {\normalsize  2.40} & {\normalsize  2.67}  \\ 
60  &  {\normalsize  1.30} & {\normalsize  1.67} & {\normalsize  2.00} & {\normalsize  2.39} & {\normalsize  2.66}  \\ 
%65  &  {\normalsize  1.29} & {\normalsize  1.67} & {\normalsize  2.00} & {\normalsize  2.39} & {\normalsize  2.65}  \\ 
70  &  {\normalsize  1.29} & {\normalsize  1.67} & {\normalsize  1.99} & {\normalsize  2.38} & {\normalsize  2.65}  \\ 
%75  &  {\normalsize  1.29} & {\normalsize  1.67} & {\normalsize  1.99} & {\normalsize  2.38} & {\normalsize  2.64}  \\ 
%\hline
80  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.99} & {\normalsize  2.37} & {\normalsize  2.64}  \\ 
%85  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.99} & {\normalsize  2.37} & {\normalsize  2.63}  \\ 
90  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.99} & {\normalsize  2.37} & {\normalsize  2.63}  \\ 
%95  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.99} & {\normalsize  2.37} & {\normalsize  2.63}  \\ 
100  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.98} & {\normalsize  2.36} & {\normalsize  2.63}  \\ 
\hline
%\hline
%120  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.98} & {\normalsize  2.36} & {\normalsize  2.62}  \\ 
%140  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.98} & {\normalsize  2.35} & {\normalsize  2.61}  \\ 
150  &  {\normalsize  1.29} & {\normalsize  1.66} & {\normalsize  1.98} & {\normalsize  2.35} & {\normalsize  2.61}  \\ 
%160  &  {\normalsize  1.29} & {\normalsize  1.65} & {\normalsize  1.97} & {\normalsize  2.35} & {\normalsize  2.61}  \\ 
%180  &  {\normalsize  1.29} & {\normalsize  1.65} & {\normalsize  1.97} & {\normalsize  2.35} & {\normalsize  2.60}  \\ 
200  &  {\normalsize  1.29} & {\normalsize  1.65} & {\normalsize  1.97} & {\normalsize  2.35} & {\normalsize  2.60}  \\ 
%\hline
300  &  {\normalsize  1.28} & {\normalsize  1.65} & {\normalsize  1.97} & {\normalsize  2.34} & {\normalsize  2.59}  \\ 
400  &  {\normalsize  1.28} & {\normalsize  1.65} & {\normalsize  1.97} & {\normalsize  2.34} & {\normalsize  2.59}  \\ 
500  &  {\normalsize  1.28} & {\normalsize  1.65} & {\normalsize  1.96} & {\normalsize  2.33} & {\normalsize  2.59}  \\ 
\hline
\hline
$\infty$  &  {\normalsize  1.28} & {\normalsize  1.645} & {\normalsize  1.96} & {\normalsize  2.33} & {\normalsize  2.58}  \\ 
\hline
\end{tabular}
\end{center}



================================================
FILE: extraTeX/tables/TeX/zTable.tex
================================================
\chapter{Distribution tables}
\label{distributionTables}

\section{Normal Probability Table}
\label{normalProbabilityTable}

A \term{normal probability table} may be used to
find percentiles of a normal distribution using a Z-score,
or vice-versa.
Such a table lists Z-scores and the corresponding percentiles.
An abbreviated probability table is provided in
Figure~\ref{zTableShort} that we'll use for the examples
in this appendix.
A~full table may be found on page~\pageref{normTableSide1}.

\begin{figure}[h]
\centering
\begin{tabular}{c | rrrrr | rrrrr |}
  \cline{2-11}
&&&& \multicolumn{4}{c}{Second decimal place of $Z$} &&& \\
  \cline{2-11}
$Z$ & \highlightT{0.00} & 0.01 & 0.02 & 0.03 &
    \highlightO{0.04} & 0.05 & 0.06 & 0.07 & 0.08 & 0.09 \\
  \hline
  \hline
0.0 & \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} \\
  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} \\
  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} \\
%  May comment out 0.0-0.2 to make extra space. Then insert the following line:
%  $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ \\
  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} \\
  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} \\
  \hline
  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} \\
  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} \\
  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} \\
\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} \\
  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} \\
  \hline
  \hline
  \highlightT{1.0} & \highlightT{\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} \\
  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} \\
  $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &
      $\vdots$ &   $\vdots$ &   $\vdots$ &   $\vdots$ &
      $\vdots$ &   $\vdots$ &   $\vdots$ \\
   \hline
\end{tabular}
\caption{A section of the normal probability table.
    The percentile for a normal random variable with $Z=1.00$
    has been \highlightT{highlighted}, and the percentile
    closest to 0.8000 has also been \highlightO{highlighted}.}
\label{zTableShort}
\end{figure}

When using a normal probability table to find a percentile
for $Z$ (rounded to two decimals),
identify the proper row in the normal probability
table up through the first decimal, and then determine the
column representing the second decimal value.
The intersection of this row and column is the percentile
of the observation.
For instance, the percentile of $Z = 0.45$ is shown in row
$0.4$ and column $0.05$ in Figure~\ref{zTableShort}:
0.6736, or the $67.36^{th}$ percentile.

\begin{figure}[h]
  \centering
  \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}
  \caption{The area to the left of $Z$ represents the
      percentile of the observation.}
\end{figure}

\begin{examplewrap}
\begin{nexample}{
    SAT scores follow a normal distribution, $N(1100, 200)$.
    Ann earned a score of 1300 on her SAT with
    a corresponding Z-score of $Z = 1$.
    She would like to know what percentile she falls in among
    all SAT test-takers.}
  Ann's \term{percentile} is the percentage of people who
  earned a lower SAT score than her.
  We shade the area representing those individuals in the
  following graph:
  \begin{center}
  \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}
  \end{center}
  The total area under the normal curve is always equal to~1,
  and the proportion of people who scored below Ann on the SAT
  is equal to the \emph{area} shaded in the graph.
  We find this area by looking in row $1.0$ and column $0.00$
  in the normal probability table:~0.8413.
  In other words, Ann is in the $84^{th}$ percentile of
  SAT takers.
\end{nexample}
\end{examplewrap}

\begin{examplewrap}
\begin{nexample}{How do we find an upper tail area?}
  The normal probability table \emph{always} gives the area
  to the left.
  This means that if we want the area to the right,
  we first find the lower tail and then subtract it from~1.
  For instance, 84.13\% of SAT takers scored below Ann,
  which means 15.87\% of test takers scored higher than Ann.
\end{nexample}
\end{examplewrap}

We can also find the Z-score associated with a percentile.
For example, to identify $Z$ for the $80^{th}$ percentile,
we look for the value closest to 0.8000 in the middle portion
of the table: 0.7995.
We determine the Z-score for the $80^{th}$ percentile by
combining the row and column Z values: 0.84.

\begin{examplewrap}
\begin{nexample}{Find the SAT score for the $80^{th}$ percentile.}
  We look for the are to the value in the table closest to 0.8000.
  The closest value is 0.7995, which corresponds to $Z = 0.84$,
  where 0.8 comes from the row value and 0.04 comes from the
  column value.
  Next, we set up the equation for the Z-score and the unknown
  value $x$ as follows, and then we solve for $x$:
  \begin{align*}
  Z = 0.84 = \frac{x - 1100}{200}
  \quad\to\quad x = 1268
  \end{align*}
  The College Board scales scores to increments of 10,
  so the $80^{th}$ percentile is 1270.
  (Reporting 1268 would have been perfectly okay for our purposes.)
\end{nexample}
\end{examplewrap}

%\noindent%
%Remember: to find the area to the right, calculate 1 minus the area to the left.\vspace{1mm}
%\begin{center}
%\includegraphics[width=0.55\textwidth]{extraTeX/tables/figures/normalTails/subtractingArea/subtractingArea}\vspace{3mm}
%\end{center}
For additional details about working with the normal distribution and the normal probability table, see Section~\ref{normalDist}, which starts on page~\pageref{normalDist}.

\begin{table}[p]
\begin{center}{\small
\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} \\
\begin{tabular}{| rrrrr | rrrrr | c}
  \cline{1-10}
&&& \multicolumn{4}{c}{Second decimal place of $Z$} &&& \\
  \cline{1-10}
0.09 &  0.08 &  0.07 &  0.06 &  0.05 &  0.04 &  0.03 &  0.02 &  0.01 &  0.00 & $Z$  \\
  \hline
  \hline
\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} & $-3.4$ \\
  \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} & $-3.3$ \\
  \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} & $-3.2$ \\
  \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} & $-3.1$ \\
  \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} & $-3.0$ \\
    \hline
    \hline
  \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} & $-2.9$ \\
  \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} & $-2.8$ \\
  \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} & $-2.7$ \\
  \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} & $-2.6$ \\
  \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} & $-2.5$ \\
    \hline
  \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} & $-2.4$ \\
  \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} & $-2.3$ \\
  \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} & $-2.2$ \\
  \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} & $-2.1$ \\
  \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} & $-2.0$ \\
    \hline
    \hline
  \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} & $-1.9$ \\
  \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} & $-1.8$ \\
  \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} & $-1.7$ \\
  \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} & $-1.6$ \\
  \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} & $-1.5$ \\
    \hline
  \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} & $-1.4$ \\
  \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} & $-1.3$ \\
  \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} & $-1.2$ \\
  \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} & $-1.1$ \\
  \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} & $-1.0$ \\
    \hline
    \hline
  \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} & $-0.9$ \\
  \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} & $-0.8$ \\
  \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} & $-0.7$ \\
  \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} & $-0.6$ \\
  \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} & $-0.5$ \\
    \hline
  \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} & $-0.4$ \\
  \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} & $-0.3$ \\
  \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} & $-0.2$ \\
  \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} & $-0.1$ \\
  \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} & $-0.0$ \\
    \hline
\multicolumn{11}{l}{{\normalsize$^*$For $Z \leq -3.50$, the probability is less than or equal to $0.0002$.}}
\end{tabular}}
\label{normTableSide1}
\end{center}
\end{table}

\begin{table}[p]
\begin{center}{\small
\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} \\
\begin{tabular}{c | rrrrr | rrrrr |}
  \cline{2-11}
&&&& \multicolumn{4}{c}{Second decimal place of $Z$} &&& \\
  \cline{2-11}
$Z$ & 0.00 & 0.01 & 0.02 & 0.03 & 0.04 & 0.05 & 0.06 & 0.07 & 0.08 & 0.09 \\
  \hline
  \hline
0.0 & \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} \\
  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} \\
  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} \\
  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} \\
  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} \\
  \hline
  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} \\
  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} \\
  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} \\
  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} \\
  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} \\
  \hline
  \hline
  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} \\
  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} \\
  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} \\
  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} \\
  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} \\
  \hline
  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} \\
  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} \\
  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} \\
  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} \\
  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} \\
  \hline
  \hline
  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} \\
  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} \\
  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} \\
  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} \\
  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} \\
  \hline
  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} \\
  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} \\
  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} \\
  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} \\
  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} \\
  \hline
  \hline
  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} \\
  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} \\
  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} \\
  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} \\
  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} \\
   \hline
\multicolumn{11}{l}{{\normalsize$^*$For $Z \geq 3.50$, the probability is greater than or equal to $0.9998$.}}
\end{tabular}}
\end{center}
\end{table}


================================================
FILE: extraTeX/tables/code/chiSquareProbTable.R
================================================
library(xtable)

DF    <- c(seq(0.5, 3, 0.5), 4:20, 25, 30, 40, 50)
tails <- c(0.3, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.001)

cst <- matrix(NA, length(DF), length(tails))
for (i in 1:nrow(cst)) {
  for (j in 1:ncol(cst)) {
    cst[i,j] <- round(qchisq(1-tails[j], DF[i]), 2)
  }
}
colnames(cst) <- tails
row.names(cst) <- DF

xtable(cst)


================================================
FILE: extraTeX/tables/code/normalProbTable.R
================================================
library(xtable)


# _____ Negative Z Table _____ #
z <- matrix(NA, 39, 10)
for (i in 1:39) {
  for (j in 1:9) {
    z[i,j] <- -((39 - i) / 10 + (10 - j) / 100) + 0.01
  }
}
Z <- matrix(NA, 39, 10)
for(i in 1:39){
  for(j in 1:9){
    hold <- format(c(round(pnorm(z[i, j]), 4), 0.1234))[1]
    Z[i,j] <- paste('scriptsize{', hold, '}', sep='')
  }
  hold <- format(c(z[i, 9], 0.1))[1]
  Z[i,10] <- paste('$', hold, '$', sep='')
}
tmp  <- c(round(pnorm(seq(-3.89, -0.09, 0.1)), 4), 0.0001)
hold <- as.character(format(tmp)[1:39])
rownames(Z) <- paste('scriptsize{', hold, '}', sep='')
colnames(Z) <- format(seq(0.08, -0.01, -0.01))
xtable(Z[5:39, ])


# _____ Positive Z Table _____ #
z <- matrix(NA, 39, 10)
for (i in 1:39) {
  for (j in 1:10) {
    z[i,j] <- (i - 1) / 10 + (j - 1) / 100
  }
}
Z <- matrix(NA, 39, 10)
for (i in 1:39) {
  for (j in 1:10) {
    hold <- format(c(round(pnorm(z[i,j]), 4), 0.1234))[1]
    Z[i,j] <- paste('scriptsize{', hold, '}', sep='')
  }
}
hold <- as.character(format(seq(0, 3.8, 0.1)))
rownames(Z) <- hold
colnames(Z) <- format(seq(0, 0.09, 0.01))
xtable(Z[1:35, ])


================================================
FILE: extraTeX/tables/figures/chiSquareTail/chiSquareTail.R
================================================
library(openintro)
data(COL)

myPDF('chiSquareTail.pdf', 3.5, 2.1,
      mar = c(2, 1, 0.5, 1),
      mgp = c(3, 0.8, 0))
X <- seq(0, 25, 0.05)
Y <- dchisq(X, 3.5)

plot(X, Y, type = 'l', axes = FALSE, xlim = c(0, 15))
axis(1)
these <- which(X > 5.79)
polygon(c(X[these[1]], X[these], X[rev(these)[1]]),
        c(0, Y[these], 0), col = COL[1])
lines(X, Y)
abline(h = 0)
dev.off()


================================================
FILE: extraTeX/tables/figures/normalTails/normalTails.R
================================================
library(openintro)
data(COL)

GeneratePlot <- function(X, Y, label, start = -10, end = 10) {
  plot(X, Y,
       type = 'l',
       axes = FALSE,
       xlim = c(-3.4, 3.4))
  axis(1,
       at = c(-5, 0, 5),
       label = c(-5, label, 5),
       cex.axis = 0.7,
       tick = FALSE)
  these <- which(start < X & X < end)
  polygon(c(X[these[1]], X[these],X[rev(these)[1]]),
          c(0, Y[these], 0),
          col = COL[1])
  lines(X, Y)
  abline(h = 0)
  lines(c(0, 0),
        dnorm(0) * c(0.01, 0.99),
        col = COL[1],
        lty = 3)
}

X <- seq(-4, 4, 0.01)
Y <- dnorm(X)

myPDF('normalTails.pdf', 4.5, 1.3,
      mar = c(1.3, 1, 0.5, 1),
      mgp = c(3, -0.2, 0),
      mfrow = 1:2)
GeneratePlot(X, Y, "Negative Z", -10, -0.801)
GeneratePlot(X, Y, "Positive Z", -10, 0.801)
dev.off()



myPDF('normalTailLeft.pdf', 2.75, 1.05,
      mar = c(0.9, 1, 0.1, 3.05),
      mgp = c(3, -0.2, 0))
GeneratePlot(X, Y, "Negative Z", -10, -0.801)
dev.off()


myPDF('normalTailRight.pdf', 2.75, 1.05,
      mar = c(0.9, 2.9, 0.1, 1),
      mgp = c(3, -0.2, 0))
GeneratePlot(X, Y, "Positive Z", -10, 0.801)
dev.off()


================================================
FILE: extraTeX/tables/figures/normalTails/subtractingArea/subtractingArea.R
================================================
library(openintro)
data(COL)

AddShadedPlot <- function(x, y, offset,
                          shade.start = -8,
                          shade.until = 8) {
  lines(x + offset, y)
  lines(x + offset, rep(0, length(x)))
  these <- which(shade.start <= x & x <= shade.until)
  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,
          c(0, y[these], 0),
          col = COL[1])
  lines(x + offset, y)
}
AddText <- function(x, text) {
  text(x, 0.549283, text, cex = 1.69238)
}

pdf('subtractingArea.pdf', 8, 1.67)
par(las = 1,
    mar = rep(0, 4),
    mgp = c(3, 0, 0))
X <- seq(-3.2, 3.2, 0.01)
Y <- dnorm(X)

plot(X, Y,
     type = 'l',
     axes = FALSE,
     xlim = c(-3.4, 16 + 3.4),
     ylim = c(0, 0.622))

AddShadedPlot(X, Y, 0)
AddText(0, format(c(1, 0.0001), scientific = FALSE)[1])

AddShadedPlot(X, Y, 8, -8, 0.43)
AddText(8, format(0.6664, scientific = FALSE)[1])

AddShadedPlot(X, Y, 16, 0.43, 8)
AddText(16, format(0.3336, scientific = FALSE)[1])

lines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)
lines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)

text(12, 0.549283,
     ' = ',
     cex = 1.69238)
segments(c(11, 11), c(0.17, 0.23), c(13, 13), lwd = 3)
dev.off()


================================================
FILE: extraTeX/tables/figures/tTails/tTails.R
================================================
library(openintro)
data(COL)

myPDF("tTails.pdf", 6, 1.6,
      mfrow = c(1, 3),
      mar = c(3.3, 0.5, 0.5, 0.5))
normTail(L = -1.2, df = 8, col = COL[1])
mtext("One Tail", 1, line = 2.1, cex = 0.75)
normTail(U = 1.2, df = 8, col = COL[1])
mtext("One Tail", 1, line = 2.1, cex = 0.75)
normTail(L = -1.2, U = 1.2, df = 8, col = COL[1])
mtext("Two Tails", 1, line = 2.1, cex = 0.75)
dev.off()


================================================
FILE: fullminipage.sty
================================================
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%%
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%%
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%% 
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%% 
%% Copyright 2012 Christian Schneider <software(at)chschneider(dot)eu>
%% 
%% This file is part of fullminipage.
%% 
%% fullminipage is free software: you can redistribute it and/or modify
%% it under the terms of the GNU General Public License version 3 as
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%% 
%% fullminipage is distributed in the hope that it will be useful,
%% but WITHOUT ANY WARRANTY; without even the implied warranty of
%% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%% GNU General Public License for more details.
%% 
%% You should have received a copy of the GNU General Public License
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%% 
%% WARNING: THIS IS ALPHA SOFTWARE AND MAY CONTAIN SERIOUS BUGS!
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================================================
FILE: main.tex
================================================
\documentclass[10pt,openany]%,oneside]
{book}
\newcommand{\versiondate}[0]{Dec 30th, 2024}

\usepackage{
  amsmath, calc,
  caption, changepage,
  endnotes, enumerate,
  epstopdf,
  fancyhdr,
  fix-cm,
  fncychap,
  footmisc,
  fullminipage,
  geometry, graphicx,
  ifthen, lscape,
  makeidx, manfnt,
  marginnote,
  mdframed,
  multicol, multirow,
  setspace, soul,
  tabto,
  textcomp,
  %tocloft,
  varioref, verbatim,
  wasysym, wrapfig
}
\usepackage{subfigure}
\usepackage[explicit]{titlesec}
%\usepackage[usenames,dvipsnames]{color}  
%\newcommand{\href}[2]{#2} \newcommand{\url}[1]{#1} \newcommand{\urlstyle}[1]{}
\include{extraTeX/style/colorsV1}
\newcommand{\printlocation}[0]{}

\newcommand{\chapterpagepadding}[0]{7mm}
\newcommand{\chapterpagepaddingleftright}[0]{\chapterpagepadding{}}
\newcommand{\chapterpagepaddingleftinner}[0]{25mm}
\newcommand{\chapterpagepaddingrightinner}[0]{30mm}

% _____ (1) PDF _____ %
\usepackage[bookmarksnumbered, colorlinks = false, pdfborder = {0 0 0}, urlcolor = oiGB, colorlinks=true, linkcolor = oiGB, citecolor = oiGB, backref = true]{hyperref}

% _____ (2) PDF -- screenreader _____ %
% !!!!!
% 0. Uncomment out the following package:
%      \usepackage{pdfcomment}
% 1. Use the `style_simple` instead of `style`.
% 2. Use the `headers_simple` instead of `headers`.
% 3. Adjust the TOC depth to 3.
% !!!!!

% _____ (3) B&W Paperback _____ %
%\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}

% _____ (4) Hardcover _____ %
%\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}
% !!!!!
% Also must \include{extraTeX/style/hardcover} below.
% !!!!!
% \renewcommand{\printlocation}[0]{\noindent Printed in China. \\}

% _____ (5) Color Paperback _____ %
%\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}




\usepackage[style=authortitle-ibid, maxnames=2,natbib=true,sortcites=true,block=space,backend=bibtex]{biblatex}
\bibliography{eoce.bib}

\makeindex
\include{extraTeX/style/style}
%\include{extraTeX/style/style_simple}
%\include{extraTeX/style/tablet}
%\include{extraTeX/style/video}
% The following style file supports an 8.25 x 11 paper size.
%\include{extraTeX/style/hardcover}
\include{extraTeX/preamble/title}%_derivative}
\date{}
\renewcommand\contentsname{Table of Contents}
\setcounter{tocdepth}{1} % standard version
%\setcounter{tocdepth}{3} % screen reader version
%\renewcommand{\cftchapfont}{\scshape}
%\renewcommand{\cftsecfont}{\bfseries}

\begin{document}
%\include{extraTeX/preamble/review_copy}
\renewcommand{\thepage}{}
\maketitle
\include{extraTeX/preamble/copyright}%_derivative}
\renewcommand{\thepage}{\arabic{page}}
\tableofcontents
\include{extraTeX/preamble/preface}
\normalsize

\begingroup
\include{extraTeX/style/headers}
%\include{extraTeX/style/headers_simple}
\includechapter{1}{ch_intro_to_data}
\includechapter{2}{ch_summarizing_data}
\includechapter{3}{ch_probability}
\includechapter{4}{ch_distributions}
\includechapter{5}{ch_foundations_for_inf}
\includechapter{6}{ch_inference_for_props}
\includechapter{7}{ch_inference_for_means}
\includechapter{8}{ch_regr_simple_linear}
\includechapter{9}{ch_regr_mult_and_log}
\endgroup

\begingroup
\include{extraTeX/style/style_appendices}
\appendix{}
\addtocontents{toc}{\protect\setcounter{tocdepth}{0}}\include{extraTeX/eoceSolutions/eoceSolutions}
\include{extraTeX/data/data}
\include{extraTeX/tables/TeX/zTable}
\include{extraTeX/tables/TeX/tTable}
\include{extraTeX/tables/TeX/chiSquareTable}
\endgroup

\include{extraTeX/index/index}
\printindex

\end{document}

================================================
FILE: openintro-statistics.Rproj
================================================
Version: 1.0

RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default

EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8

RnwWeave: Sweave
LaTeX: pdfLaTeX