Booklet of Code and Output for STAC32 Final Exam

Transcription

1 Booklet of Code and Output for STAC32 Final Exam December 8, 2014

2 List of Figures in this document by page: List of Figures 1 Popcorn data MDs by city, with normal quantile plot Reading in the MDs data Output from proc univariate for MDs data Apnea data and dierences (before minus after) Table of binomial distribution with n = 13, p = Data for selecting in SAS SAS code for data and means for the writers data Boxplots for writers data SAS ANOVA for writers data GPA data GPA data: rst regression GPA data: regression without SATM GPA data: regression with only HSGPA and SATV Mystery R code Scatterplot of social worker salaries by experience Regression and residual plot for predicting salary from experience Code and output for Box-Cox transformation of salary Residual plot from regression of transformed salary SAS code for reading and summarizing perch data Obtaining leverages for perch data

3 Brand Trial Unpopped Orville 1 26 Orville 2 35 Orville 3 18 Orville 4 14 Orville 5 8 Orville 6 6 Seaway 1 47 Seaway 2 47 Seaway 3 14 Seaway 4 34 Seaway 5 21 Seaway 6 37 Figure 1: Popcorn data 2

4 R> health=read.table("metrohealth.txt",header=t) R> attach(health) R> head(nummds) [1] R> qqnorm(nummds) R> qqline(nummds) Normal Q Q Plot Theoretical Quantiles Sample Quantiles Figure 2: MDs by city, with normal quantile plot 3

5 Some of the health care data. Values are separated by tabs. Actual lines are very long and begin with a city name (the lines wrap here). SAS code is below. City NumMDs RateMDs NumHospitals NumBeds RateBeds NumMedicare PctChangeMedicare MedicareRate SSBNum SSBRate SSBChange NumRetired SSINum SSIRate SqrtMDs "Holland-Grand Haven, MI" "Louisville, KY-IN" "Battle Creek, MI" data health; infile '/home/ken/metrohealth.txt' firstobs=2 dlm='09'x; input city $ nummds; Figure 3: Reading in the MDs data 4

6 proc univariate; var nummds; The UNIVARIATE Procedure Variable: nummds Moments N 83 Sum Weights 83 Mean Sum Observations Std Deviation Variance Skewness Kurtosis Uncorrected SS Corrected SS Coeff Variation Std Error Mean Basic Statistical Measures Location Variability Mean Std Deviation 1981 Median Variance Mode Range 9267 Interquartile Range 1685 Note: The mode displayed is the smallest of 7 modes with a count of 2. Tests for Location: Mu0=0 Test -Statistic p Value Student's t t Pr > t <.0001 Sign M 41.5 Pr >= M <.0001 Signed Rank S 1743 Pr >= S <.0001 Quantiles (Definition 5) Quantile Estimate 100% Max % % % % Q % Median % Q % 226 5% 200 1% 143 0% Min 143 Extreme Observations ----Lowest Highest--- Value Obs Value Obs The UNIVARIATE Procedure Variable: nummds Extreme Observations ----Lowest Highest--- Value Obs Value Obs Figure 4: Output from proc univariate for MDs data

7 R> apnea=read.table("apnea.txt",header=t) R> attach(apnea) R> diff=before-after R> cbind(apnea,diff) before after diff Figure 5: Apnea data and dierences (before minus after) 6

8 The table below shows the probability of obtaining less than or equal to k successes in a binomial distribution with n = 13, p = 0.5. R> k=0:13 R> p=pbinom(k,13,0.5) R> cbind(k,p) k p [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] [9,] [10,] [11,] [12,] [13,] [14,] Figure 6: Table of binomial distribution with n = 13, p = 0.5 7

9 data mydata; infile '/home/ken/mydata.txt'; input x y g $; proc print; Obs x y g a a a b b a c b b b Figure 7: Data for selecting in SAS data writers; infile 'writers.txt'; input genre $ age; proc means; var age; class genre; The MEANS Procedure Analysis Variable : age N genre Obs N Mean Std Dev Minimum Maximum nonficti novelist poet Figure 8: SAS code for data and means for the writers data 8

10 proc boxplot; plot age*genre / boxstyle=schematic; age novelist poet nonficti genre Figure 9: Boxplots for writers data 9

11 proc anova; class genre; model age=genre; means genre / tukey; The ANOVA Procedure Class Level Information Class Levels Values genre 3 nonficti novelist poet Number of Observations Read 123 Number of Observations Used 123 The ANOVA Procedure Dependent Variable: age Sum of Source DF Squares Mean Square F Value Pr > F Model Error Corrected Total R-Square Coeff Var Root MSE age Mean Source DF Anova SS Mean Square F Value Pr > F genre The ANOVA Procedure Tukey's Studentized Range (HSD) Test for age NOTE: This test controls the Type I experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 120 Error Mean Square Critical Value of Studentized Range Comparisons significant at the 0.05 level are indicated by ***. Difference genre Between Simultaneous 95% Comparison Means Confidence Limits nonficti - novelist nonficti - poet *** novelist - nonficti novelist - poet *** poet - nonficti *** poet - novelist *** 10 Figure 10: SAS ANOVA for writers data

12 R> gpa=read.table("gpa.txt",header=t) R> head(gpa) GPA HSGPA SATV SATM Male HU SS FirstGen White CollegeBound Figure 11: GPA data R> gpa.1=lm(gpa~hsgpa+satv+satm+male,data=gpa) R> summary(gpa.1) Call: lm(formula = GPA ~ HSGPA + SATV + SATM + Male, data = gpa) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) 6.135e e HSGPA 5.069e e e-10 *** SATV 1.174e e ** SATM e e Male 5.534e e Signif. codes: 0 `***' `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: on 214 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 4 and 214 DF, p-value: 1.298e-12 Figure 12: GPA data: rst regression 11

13 R> gpa.2=update(gpa.1,.~.-satm) R> summary(gpa.2) Call: lm(formula = GPA ~ HSGPA + SATV + Male, data = gpa) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) * HSGPA e-10 *** SATV *** Male Signif. codes: 0 `***' `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: on 215 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 3 and 215 DF, p-value: 2.414e-13 Figure 13: GPA data: regression without SATM 12

14 R> gpa.3=update(gpa.2,.~.-male) R> summary(gpa.3) Call: lm(formula = GPA ~ HSGPA + SATV, data = gpa) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) * HSGPA e-10 *** SATV *** --- Signif. codes: 0 `***' `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: on 216 degrees of freedom Multiple R-squared: 0.246, Adjusted R-squared: F-statistic: on 2 and 216 DF, p-value: 5.711e-14 Figure 14: GPA data: regression with only HSGPA and SATV R> my.df=data.frame(hsgpa=3.6,satv=640,satm=670,male=0) R> pp=predict(gpa.3,my.df) R> cbind(my.df,pp) HSGPA SATV SATM Male pp Figure 15: Mystery R code 13

15 R> socwork=read.table("socwork.txt",header=t) R> attach(socwork) R> plot(salary~experience) R> lines(lowess(salary~experience)) e+04 4e+04 6e+04 8e+04 1e+05 experience salary Figure 16: Scatterplot of social worker salaries by experience 14

16 R> esq=experience*experience R> socwork.1=lm(salary~experience+esq) R> r=resid(socwork.1) R> f=fitted(socwork.1) R> plot(r~f) f r Figure 17: Regression and residual plot for predicting salary from experience 15

17 R> library(mass) R> boxcox(salary~experience) 95% log Likelihood λ Figure 18: Code and output for Box-Cox transformation of salary 16

18 R> socwork.2=lm(sal.trans~experience) R> r=resid(socwork.2) R> f=fitted(socwork.2) R> plot(r~f) R> lines(lowess(r~f)) f r Figure 19: Residual plot from regression of transformed salary 17

19 data perch; infile '/home/ken/perch.txt' firstobs=2 expandtabs; input obs weight length width; z=1; proc print; proc means; var weight length width; Obs obs weight length width z The MEANS Procedure Variable N Mean Std Dev Minimum Maximum weight length width Figure 20: SAS code for reading and summarizing perch data

20 proc reg; model z=weight length width / influence; The REG Procedure Model: MODEL1 Dependent Variable: z Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model Error Corrected Total 29 0 Root MSE 0 R-Square. Dependent Mean Adj R-Sq. Coeff Var 0 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept Infty <.0001 weight length width The REG Procedure Model: MODEL1 Dependent Variable: z Output Statistics Hat Diag Cov DFBETAS Obs Residual RStudent H Ratio DFFITS Intercept weight length width Sum of Residuals 0 Sum of Squared Residuals 0 Predicted Residual SS (PRESS) 0 Figure 21: Obtaining leverages for perch data