Introduction to SAS and R for Applied Statistical Methods

Introduction to SAS and R for Applied Statistical Methods

Introduction to R for Applied Statistical Methods Larry Winner University of Florida Reading In External Data (Fixed Width) Data are in external ASCII file with each variable assigned to a fixed field, with one line per unit Variable Names are NOT in first row You will need to give the following information: The directory and name of the data file (file=) The width of the field for each variable (width=) The name of each variable (col.names=) 2 Important Notes for R: The Command: c(1,2,3) strings the numbers 1,2,3 into a vector (concatenates them) and c(A,B,C) does same for the characters A,B,C To assign a function or data to a variable name, the command: <- is used as opposed to = y <- c(20,60,40) creates a vector y with 3 rows (cases)

Example Data File File Contains 6 Variables in Fixed Column format Var1 Student Name in Columns 1-30 Var2 College/Year Code in Columns 31-33 Var3 Exam 1 Score in Columns 34-41 Var4 Exam 2 Score in Columns 42-49 Var5 Project 1 Score in Columns 50-57 Var6 Project 2 Score in Columns 58-65 Note Var1 is of Length 30, Var2 3, All Others 8 ***************************************************************** 00000000011111111112222222222333333333334444444444555555555666666

12345678901234567890123456789012345678901234567890123456789012345 ***************************************************************** SMITH ROBERT 7TD 71 83 14 19 WILSON JENNIFER 8RZ 92 89 18 20 NGUYEN QI 9YX 84 79 17 15

Note: The first 4 rows are only there to show the fixed width format, and would not be part of the data file. Assume the following information: File Name: sta666.dat Data File Directory: C:\data R Program Directory: C:\Rmisc In the R program sta666.r, we want to read in this data. We will assign it the name grades666 within the program, and assign names to the variables as well as their field lengths. R Command to Read in Data File grades666 <- read.fwf( file=C:\\data\\sta6166.dat, width=c(30,3,8,8,8,8), col.names=c(Student,Collyear,Exam1,Exam2,Project1,Project2)) attach(grades666) Note that grades666 is now a data frame containing 6 variables. You have told R where the dataset is (C:\data\sta6166.dat, but note you had to use 2 back slashes, you could have also used a single forward slash)

You told R there were 6 variables in fields of widths 30,3,8,8,8,8, respectively You have assigned names to the variables Originally, to R, the exam1 score is: grades666$Exam1 By adding the line: attach(grades666) you can use the shorter label Exam1 instead of grades666$Exam1 in future commands Handling Categorical (Factor) Variables In many applications, we have categorical variables that are coded as 1,2,as opposed to their actual levels (names). In R, you need to inform the program that these are factor levels, not the corresponding numeric levels (so it can differentiate between a 1-Way ANOVA and Simple Regression, for instance). You also may wish to give the levels character names to improve interpreting output.

Example Inoculating Amoebae 5 Methods of Inoculation (Conditions) 1 2 3 4 5 None (Control) Heat at 70C for 10 minutes (Heat) Addition of 10% Formalin (Form) Heat followed by Formalin (HF) Formalin, followed after 1 hour by heat (FH)

n=10 Replicates per Condition (N=50) Y=Yield (10-4) Method is in Columns 1-8 Yield is in Columns 9-16 Data is in C:\data\amoeba.dat Program in C:\Rmisc\amoeba.r 1 1 1 1 1 1 1 1 1 1 2 2 2 2

2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4

4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 265 292 268 251 245

192 228 291 185 247 204 234 197 176 240 190 171 190 222 211 191 207 218 201 192 192

214 206 185 163 221 205 178 167 224 225 171 214 283 277 259 206 179 199 180 146 182

147 182 223 Reading in Data and Declaring Factor amoeba <- read.fwf(file=C:\\amoeba.dat,width=c(8,8), col.names=c(method,yield)) attach(amoeba) method <-factor( method, levels=1:5, labels=c(Control,Heat,Form,HF,FH)) Method was originally to be treated as interval scale (like if it had been a dose) Within the FACTOR command, we change it to being a categorical variable by giving its levels (1,2,3,4,5) and assigning names to those levels (Control, Heat, Form, HF, FH) In Place of levels=1:5 we could have used levels=c(1,2,3,4,5)

Obtaining Summary Statistics Directly obtain summary statistics for variables in R You can simply have the statistic computed You can save the value of statistic to variable for future use Common Statistical Functions (x is the variable): Average: mean(x) Standard Deviation: sd(x) Variance: var(x) Median: median(x) Quantile (0

Obtained by Group: tapply(x,groupvar,mean) where mean can be replaced by sd, var, Program PGA/LPGA Driving Distance/Accuracy pdf("pgalpga1.pdf") pgalpga <- read.fwf(file="C:\\data\\pgalpga2008.dat", width=c(8,8,8), col.names=c("Distance","Accuracy",Gender")) attach(pgalpga) Gender <- factor(Gender, levels=1:2, labels=c("Female","Male")) mean(Distance); mean(Accuracy) sd(Distance); sd(Accuracy) tapply(Distance, Gender, mean); tapply(Accuracy, Gender, mean) tapply(Distance, Gender, sd); tapply(Accuracy, Gender, sd) dev.off Output PGA/LPGA Driving Distance/Accuracy > mean(Distance); mean(Accuracy) [1] 269.5116 [1] 65.23927

> sd(Distance); sd(Accuracy) [1] 22.19603 [1] 5.973796 > > tapply(Distance, Gender, mean); tapply(Accuracy, Gender, mean) Female Male 246.8013 287.6107 Female Male 67.59108 63.36497 > tapply(Distance, Gender, sd); tapply(Accuracy, Gender, sd) Female Male 9.493797 8.554456 Female Male 5.768708 5.461109 Tables for Categorical Data

For categorical variables, we use frequency (and relative frequency) tabulations to describe data Frequency Tabulation: table(x) Relative Frequencies: table(x)/sum(table(x)) For contingency tables (pairs of variables) Frequency Cross-tabulation: table(x,y) Row Marginal totals for x: margin.table(table(x,y),1) Column totals for y: margin.table(table(x,y),2) Relative Freq Cross-tab: table(x,y)/sum(table(x,y)) Extends to more than 2 Dimensions Program UFO Encounters pdf("ufocase.pdf") ufo <- read.fwf(file="C:\\data\\ufocase.dat",

width=c(4,52,16,3,4,4,4), col.names=c("Year","Event","Loc","Effect","Photo","Contact","Abduct")) attach(ufo) Effect <- factor(Effect,levels=0:1,labels=c("No","Yes")) Photo <- factor(Photo,levels=0:1,labels=c("No","Yes")) Contact <- factor(Contact,levels=0:1,labels=c("No","Yes")) Abduct <- factor(Abduct,levels=0:1,labels=c("No","Yes")) table(Effect); table(Photo); table(Contact); table(Abduct); table(Photo,Contact); margin.table(table(Photo,Contact),1); margin.table(table(Photo,Contact),2); table(Photo,Contact)/sum(table(Photo,Contact)); dev.off() Output UFO Encounters > table(Effect); table(Photo); table(Contact); table(Abduct); Effect No Yes 74 129 Photo

No Yes 141 62 Contact No Yes 146 57 Abduct No Yes 182 21 > > table(Photo,Contact); Contact Photo No Yes No 100 41 Yes 46 16 Output UFO Encounters > margin.table(table(Photo,Contact),1); margin.table(table(Photo,Contact),2); Photo No Yes 141 62

Contact No Yes 146 57 > table(Photo,Contact)/sum(table(Photo,Contact)); Contact Photo No Yes No 0.49261084 0.20197044 Yes 0.22660099 0.07881773 Histograms Histograms for Numeric Data (x is the variable): Frequency Histogram (Default # of bins): hist(x) Fixing the number of bins: hist(x, breaks=10) Fixing the Break Points: hist(x, breaks=c(5,10,15,20)) Relative Freq.: hist(x, probability=T) or hist(x,freq=F) Density Curves can be superimposed as well (see example on upcoming slide) Side-by-Side histograms by Group can also be created (see below)

Program Driving Distance Histogram pdf("pgalpga1.pdf") pgalpga <- read.fwf(file="C:\\data\\pgalpga2008.dat", width=c(8,8,8), col.names=c("Distance","Accuracy", "Gender")) attach(pgalpga) Gender <- factor(Gender, levels=1:2, labels=c("Female","Male")) hist(Distance) dev.off Separate by Gender (Same Range) pdf("pgalpga2.pdf") pgalpga <- read.fwf(file="C:\\data\\pgalpga2008.dat", width=c(8,8,8),

col.names=c("Distance","Accuracy", "Gender")) attach(pgalpga) Gender <- factor(Gender, levels=1:2, labels=c("Female","Male")) # Create Distance variables seperately by gender Distance.female <- Distance[Gender=="Female"] Distance.male <- Distance[Gender=="Male"] #There will be 2 rows and 1 column of graphs # Force each axis to have bins from 220 to 320 by 10 (like the original) par(mfrow=c(2,1)) hist(Distance.female,breaks=seq(220,320,10)) hist(Distance.male,breaks=seq(220,320,10)) par(mfrow=c(1,1)) dev.off Program Histogram with Normal Curve pdf("pgalpga3.pdf") pgalpga <- read.fwf(file="C:\\data\\pgalpga2008.dat", width=c(8,8,8), col.names=c("Distance","Accuracy", "Gender")) attach(pgalpga)

Gender <- factor(Gender, levels=1:2, labels=c("Female","Male")) Distance.female <- Distance[Gender=="Female"] Distance.male <- Distance[Gender=="Male"] # Histogram for Male Distances # ylim command "frames" plot area to avoid cutting off curve # Curve command adds a Normal Density with h <- hist(Distance.male,plot=F,breaks=seq(260,320,3)) ylim <- range(0,h$density,dnorm(287.61,287.61,8.55)) hist(Distance.male,freq=F,ylim=ylim) curve(dnorm(x,287.61,8.55),add=T) dev.off Side-by-Side with Normal Curves pdf("pgalpga4.pdf") pgalpga <- read.fwf(file="C:\\data\\pgalpga2008.dat", width=c(8,8,8), col.names=c("Distance","Accuracy", "Gender")) attach(pgalpga) Gender <- factor(Gender, levels=1:2, labels=c("Female","Male")) Distance.female <- Distance[Gender=="Female"] Distance.male <- Distance[Gender=="Male"]

par(mfrow=c(1,2)) # Histogram for Male Distances hm <- hist(Distance.male,plot=F,breaks=seq(260,320,3)) ylim <- range(0,hm$density,dnorm(287.61,287.61,8.55)) hist(Distance.male,freq=F,ylim=ylim) curve(dnorm(x,287.61,8.55),add=T) # Histogram for Female Distances hf <- hist(Distance.female,plot=F,breaks=seq(220,270,2.5)) ylim <- range(0,hf$density,dnorm(246.80,246.80,9.49)) hist(Distance.female,freq=F,ylim=ylim) curve(dnorm(x,246.80,9.49),add=T) dev.off Barplots and Pie Charts Used to show frequencies for categorical variables Barplots Simplest version (single variable): barplot(table(x)) Used to show cross-tabulations: barplot(table(x,y)) Can be structured for side-by-side or stacked (see below) Pie Charts

Simplest version (single variable): pie(table(x)) Used to show cross-tabulations: pie(table(x,y)) Can be used for Categorical or numeric data Barplots UFO Data pdf("ufocase1.pdf") ufo <- read.fwf(file="C:\\data\\ufocase.dat", width=c(4,52,16,3,4,4,4), col.names=c("Year","Event","Loc","Effect","Photo","Contact","Abduct")) attach(ufo) Effect <- factor(Effect,levels=0:1,labels=c("No","Yes")) Photo <- factor(Photo,levels=0:1,labels=c("No","Yes")) Contact <- factor(Contact,levels=0:1,labels=c("No","Yes")) Abduct <- factor(Abduct,levels=0:1,labels=c("No","Yes")) barplot(table(Photo)) dev.off() 4 Plots of a 2-Way Contingency Table pdf("ufocase2.pdf") ufo <- read.fwf(file="C:\\data\\ufocase.dat", width=c(4,52,16,3,4,4,4), col.names=c("Year","Event","Loc","Effect","Photo","Contact","Abduct"))

attach(ufo) Effect <- factor(Effect,levels=0:1,labels=c("No","Yes")) Photo <- factor(Photo,levels=0:1,labels=c("No Photo","Photo")) Contact <- factor(Contact,levels=0:1,labels=c("No Contact","Contact")) Abduct <- factor(Abduct,levels=0:1,labels=c("No","Yes")) pht.cntct <- table(Photo,Contact); # t(x) transposes the table x (interchanges rows/cols) #prop.table(pht.cntct,2) obtains the proportions separately within columns par(mfrow=c(2,2)) barplot(pht.cntct) barplot(t(pht.cntct)) barplot(pht.cntct,beside=T) barplot(prop.table(pht.cntct,2),beside=T) par(mfrow=c(1,1)) dev.off() Pie Chart UFO Data pdf("ufocase3.pdf") ufo <- read.fwf(file="C:\\data\\ufocase.dat", width=c(4,52,16,3,4,4,4), col.names=c("Year","Event","Loc","Effect","Photo","Contact","Abduct"))

attach(ufo) Effect <- factor(Effect,levels=0:1,labels=c("No","Yes")) Photo <- factor(Photo,levels=0:1,labels=c("No Photo","Photo")) Contact <- factor(Contact,levels=0:1,labels=c("No Contact","Contact")) Abduct <- factor(Abduct,levels=0:1,labels=c("No","Yes")) table(Effect); table(Photo); table(Contact); table(Abduct); pie(table(Photo),main="Photo Evidence") dev.off UFO Contact (Column) by Photo (Row) Status pdf("ufocase3.pdf") ufo <- read.fwf(file="C:\\data\\ufocase.dat", width=c(4,52,16,3,4,4,4), col.names=c("Year","Event","Loc","Effect","Photo","Contact","Abduct")) attach(ufo) Effect <- factor(Effect,levels=0:1,labels=c("No","Yes")) Photo <- factor(Photo,levels=0:1,labels=c("No Photo","Photo")) Contact <- factor(Contact,levels=0:1,labels=c("No Contact","Contact")) Abduct <- factor(Abduct,levels=0:1,labels=c("No","Yes")) pht.cntct <- table(Photo,Contact); par(mfrow=c(2,2))

par(mfrow=c(1,2)) pie(pht.cntct["No Photo",],main="No Photo") pie(pht.cntct["Photo",],main="Photo") par(mfrow=c(1,1)) dev.off() Scatterplots Plots of 2 (or more) Quantitative Variables For Basic Plot (default symbol is circle): plot(x,y) Can change plotting symbol to lines or both lines and points (appropriate if time series data) plot(x,y,type=l) for lines or plot(x,y,type=b) for both Can change plotting character to dots: plot(x,y,pch=.) Can add jitter when data are discrete and have ties: plot(jitter(x,3),jitter(y,3)) where the number in second position of jitter command reflects amount of jitter Can add lines to plots: plot(x,y); abline(lm(y~x)); Can do plots by groups of 3rd variable (coplots): coplot(x~y|z) PGA/LPGA Data

pdf("pgalpga5.pdf") pgalpga <- read.fwf(file="C:\\data\\pgalpga2008.dat", width=c(8,8,8), col.names=c("Distance","Accuracy", "Gender")) attach(pgalpga) Gender <- factor(Gender, levels=1:2, labels=c("Female","Male")) par(mfrow=c(2,2)) plot(Accuracy,Distance) plot(jitter(Accuracy,3),jitter(Distance,3),pch=".") plot(Accuracy,Distance); abline(lm(Distance~Accuracy)); coplot(Accuracy~Distance|Gender) dev.off() Comparing 2 Population Means Sampling Designs: Independent/Dependent Distributional Assumptions: Normal/Non-Normal Independent Samples/Normal Distributions Students t-test (Equal and Unequal Variance cases) Independent Samples/Non-Normal Distributions Wilcoxon Rank-Sum Test

Dependent Samples/Normal Distributions Paired t-test Dependent Samples/Non-Normal Distributions Wilcoxon Signed-Rank Test Program -Testing for Accuracy by Gender pdf("pgalpga5.pdf") pgalpga <- read.fwf(file="C:\\data\\pgalpga2008.dat", width=c(8,8,8), col.names=c("Distance","Accuracy", "Gender")) attach(pgalpga) Gender <- factor(Gender, levels=1:2, labels=c("Female","Male")) # Create Distance and Accuracy variables seperately by gender Distance.female <- Distance[Gender=="Female"] Distance.male <- Distance[Gender=="Male"] Accuracy.female <- Accuracy[Gender=="Female"] Accuracy.male <- Accuracy[Gender=="Male"] # 2 ways of getting t-test: As a Model and as 2 Groups of Responses # var.test tests the equal variance assumption

var.test(Accuracy ~ Gender) t.test(Accuracy ~ Gender,var.equal=T) t.test(Accuracy.female,Accuracy.male) wilcox.test(Accuracy ~ Gender) dev.off() Output -Testing for Accuracy by Gender > var.test(Accuracy ~ Gender) F test to compare two variances data: Accuracy by Gender F = 1.1158, num df = 156, denom df = 196, p-value = 0.4664 alternative hypothesis: true ratio of variances is not equal to 1 95 percent confidence interval: 0.8300783 1.5076372 sample estimates: ratio of variances 1.115823 > t.test(Accuracy ~ Gender, var.equal=T) Two Sample t-test data: Accuracy by Gender t = 7.0546, df = 352, p-value = 9.239e-12

alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 3.047924 5.404292 sample estimates: mean in group Female mean in group Male 67.59108 63.36497 Output -Testing for Accuracy by Gender > t.test(Accuracy.female,Accuracy.male) Welch Two Sample t-test data: Accuracy.female and Accuracy.male t = 7.011, df = 326.041, p-value = 1.369e-11 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 3.040267 5.411949 sample estimates: mean of x mean of y 67.59108 63.36497

> wilcox.test(Accuracy ~ Gender) Wilcoxon rank sum test with continuity correction data: Accuracy by Gender W = 22016.5, p-value = 7.417e-12 alternative hypothesis: true mu is not equal to 0 Program Caffeine/Endurance Paired Data pdf("caffeine.pdf") caffeine <- read.fwf("C:\\data\\caffeinesubj.dat", width=c(8,8,8), col.names=c("subject","mg13","mg5")) attach(caffeine) # Plot of 13mg endurance versus 5mg # endurance for the 9 cyclists plot(mg5,mg13) # Conduct Paired t-test to determine if Dose effect exists t.test(mg13, mg5, paired=TRUE) # Conduct Wilcoxon Signed-Rank Test wilcox.test(mg13, mg5, paired=TRUE) dev.off() 1

2 3 4 5 6 7 8 9 37.55 59.30 79.12 58.33 70.54 69.47 46.48 66.35 36.20 42.47 85.15

63.20 52.10 66.20 73.25 44.50 57.17 35.05 Data Output Caffeine/Endurance Paired Data Paired t-test data: mg13 and mg5 t = 0.1205, df = 8, p-value = 0.907 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -8.562982 9.507426 sample estimates: mean of the differences 0.4722222

> > # Conduct Wilcoxon Signed-Rank Test > wilcox.test(mg13, mg5, paired=TRUE) Wilcoxon signed rank test data: mg13 and mg5 V = 28, p-value = 0.5703 alternative hypothesis: true mu is not equal to 0 Pearson Chi-Square Test Used to Test for Association Between 2 or More Categorical Variables Arranges Counts of Outcomes in Contingency Table (CrossTabulation) Null Hypothesis: Distribution of Column (Row) Proportions is Constant Across Rows (Columns) Data Can be Entered Directly into Contingency Table or Table Formed From Raw Data at the Individual Level Case 1 Direct Entry of Contingency Table Diet\Complete 1 Year Atkins Zone

Weight Watchers Ornish Yes 21 26 26 20 No 19 14 14 20 pdf("dietcomp.pdf") # Create Table as string of 8 counts, tell R it's entered by rows # (as opposed to columns) and tell R there are 4 rows (thus 2 columns) diet.table <- matrix(c(21,19,26,14,26,14,20,20),byrow=T,nrow=4) diet.table # Conduct Pearson's chi-square test

chisq.test(diet.table) dev.off() Case 1 Direct Entry of Contingency Table Diet\Complete 1 Year Atkins Zone Weight Watchers Ornish Yes 21 26 26 20 No 19 14 14 20

> diet.table [,1] [,2] [1,] 21 19 [2,] 26 14 [3,] 26 14 [4,] 20 20 > > # Conduct Pearson's chi-square test > chisq.test(diet.table) Pearson's Chi-squared test data: diet.table X-squared = 3.1584, df = 3, p-value = 0.3678

Case 2 Raw Data pdf("rawdiet.pdf") Atkins Atkins Atkins Atkins Zone Zone Zone Zone WW WW WW WW Ornish Ornish

Ornish Ornish C C F F C C F F C C F F C C

F F 160Subjects rawdiet <- read.fwf("C:\\data\\rawdiet.dat",width=c(8,8), col.names=c("diet","comp1yr")) attach(rawdiet) dietcomp <- table(diet,comp1yr) dietcomp # Conditional Distributions of Outcome by Diet (Rows) # The 1 gets row distributions,2 would give columns prop.table(dietcomp,1) # Pearson Chi-Square Test 40 per diet chisq.test(dietcomp) dev.off() Case 2 Raw Data

> dietcomp comp1yr diet C F Atkins 21 19 Ornish 20 20 WW 26 14 Zone 26 14 > # Conditional Distributions of Outcome by Diet (Rows) > prop.table(dietcomp,1) comp1yr diet C F Atkins 0.525 0.475 Ornish

0.500 0.500 WW 0.650 0.350 Zone 0.650 0.350 > # Pearson Chi-Square Test > chisq.test(dietcomp) Pearson's Chi-squared test data: dietcomp X-squared = 3.1584, df = 3, p-value = 0.3678 Comparison of k>2 Means Sampling Designs: Independent Completely Randomized Design Dependent Randomized Block Design Distributional Assumptions: Normal/Non-Normal Independent Samples/Normal Distributions ANOVA F-Test for CRD Independent Samples/Non-Normal Distributions

Kruskal-Wallis Test Dependent Samples/Normal Distributions ANOVA F-Test for RBD Dependent Samples/Non-Normal Distributions Friedmans Test Post-Hoc Comparisons (Normal Data) Bonferronis Method Tukeys Method Program Amoebae Innoculi - F-test, Post-Hoc Tests pdf("amoeba.pdf") amoeba1 <- read.fwf("C:\\data\\entozamoeba.dat", width=c(8,8), col.names=c("Trt", "Yield")) # Create Factor Variable for Trt fTrt <- factor(amoeba1$Trt, levels=1:5) labels=c("None", "H", "F", "HF", "FH")) amoeba <- data.frame(amoeba1, fTrt) attach(amoeba)

# Obtain the 1-Way ANOVA using aov, not lm to obtain Tukeys HSD amoeba1.aov <- aov(Yield~fTrt) summary(amoeba1.aov) # Obtain Tukey's Comparisons among levels of treatment TukeyHSD(amoeba1.aov, "fTrt") # Obtain Bonferroni Comparisons among levels of treatment pairwise.t.test(Yield, fTrt, p.adj="bonf") dev.off() Output 5 Amoebae Innoculi F-Test > amoeba1.aov <- aov(Yield~fTrt) > > summary(amoeba1.aov) Df Sum Sq Mean Sq F value Pr(>F) fTrt 4 19666 4916 5.0444 0.001911 ** Residuals 45 43858 975

--Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > > # Obtain Tukey's Comparisons among levels of treatment > > TukeyHSD(amoeba1.aov, "fTrt") Tukey multiple comparisons of means 95% family-wise confidence level Output Continued on next slide Output 5 Amoebae Innoculi Post-Hoc Tests Fit: aov(Yield ~ fTrt) $fTrt diff H-None -42.9 F-None -49.5 HF-None -29.9 FH-None -56.1 F-H

-6.6 HF-H 13.0 FH-H -13.2 HF-F 19.6 FH-F -6.6 FH-HF -26.2 lwr upr p adj -82.57118 -3.228817 0.0281550 -89.17118 -9.828817 0.0078618 -69.57118 9.771183 0.2208842 -95.77118 -16.428817 0.0019658 -46.27118 33.071183 0.9894377

-26.67118 52.671183 0.8832844 -52.87118 26.471183 0.8774716 -20.07118 59.271183 0.6284290 -46.27118 33.071183 0.9894377 -65.87118 13.471183 0.3444453 pairwise.t.test(Yield, fTrt, p.adj="bonf") Pairwise comparisons using t tests with pooled SD data: Yield and fTrt None H F HF H 0.0360 F 0.0093 1.0000 HF 0.3768 1.0000 1.0000 FH 0.0022 1.0000 1.0000 0.6707 P value adjustment method: bonferroni Program/Output Kruskal-Wallis Test pdf("amoeba.pdf") amoeba1 <- read.fwf("C:\\data\\amoeba1.dat", width=c(8,8), col.names=c("Trt", "Yield")) # Create Treatment Factor Variable

fTrt <- factor((amoeba1$Trt, levels=1:5), labels(fTrt) = c("None", "H", "F", "HF", "FH")) amoeba <- data.frame(amoeba1, fTrt) attach(amoeba) # Conduct the Kruskal-Wallis Test (Rank-Based) kruskal.test(Yield~fTrt) > kruskal.test(Yield~fTrt) Kruskal-Wallis rank sum test data: Yield by fTrt Kruskal-Wallis chi-squared = 12.6894, df = 4, p-value = 0.01290 RBD Program Caffeine and Endurance pdf("caffrbd.pdf") caffrbd <- read.fwf("C:\\data\\caffeinerbd.dat", width=c(1,5,8), col.names=c("subject","dose","enduretime")) attach(caffrbd) # Create Factors from subject and dose subject <- factor(subject, levels=1:9, labels=c("S1","S2","S3","S4","S5","S6","S7","S8","S9"))

dose <- factor(dose, levels=c(0,5,9,13), labels=c("0mg", "5mg", "9mg", "13mg")) # Plot of endurance versus dose separately for the 9 cyclists # The order of vars is: X-Variable, Separate Line Factor, Y-Variable interaction.plot(dose, subject, enduretime) # Obtain Analysis of Variance for RBD caffeine.aov <- aov(enduretime ~ dose + subject) summary(caffeine.aov) # Conduct Tukey's HSD for Doses TukeyHSD(caffeine.aov, "dose") Output Caffeine and Endurance > caffeine.aov <- aov(enduretime ~ dose + subject) > summary(caffeine.aov) Df Sum Sq Mean Sq F value Pr(>F) dose 3 933.1 311.0 5.9168 0.003591 ** subject 8 5558.0

694.7 13.2159 4.174e-07 *** Residuals 24 1261.7 52.6 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > # Conduct Tukey's HSD for Doses > TukeyHSD(caffeine.aov, "dose") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = enduretime $dose diff lwr 5mg-0mg 11.2366667 1.808030 9mg-0mg 12.2411111 2.812474 13mg-0mg 11.7088889 2.280252 9mg-5mg 1.0044444 -8.424192 13mg-5mg 0.4722222 -8.956414 13mg-9mg -0.5322222 -9.960859

~ dose + subject) upr 20.665303 21.669748 21.137526 10.433081 9.900859 8.896414 p adj 0.0153292 0.0076616 0.0110929 0.9909369 0.9990313 0.9986162 Program/Output for Friedmans Test pdf("caffrbd.pdf") caffrbd <- read.fwf("C:\\data\\caffeinerbd.dat", width=c(1,5,8),

col.names=c("subject","dose","enduretime")) attach(caffrbd) subject <- factor(subject, levels=1:9, labels=c("S1","S2","S3","S4","S5","S6","S7","S8","S9")) dose <- factor(dose, levels=c(0,5,9,13), labels=c("0mg", "5mg", "9mg", "13mg")) # Conduct Friedman's Test (DepVar ~ Trt | Block) friedman.test(enduretime ~ dose | subject) > friedman.test(enduretime ~ dose | subject) Friedman rank sum test data: enduretime and dose and subject Friedman chi-squared = 14.2, df = 3, p-value = 0.002645 Multi-Factor ANOVA Used to Measure Main Effects and Interactions among Several Factors Simultaneously Can be Conducted Within CRD or RBD frameworks Can Make Post-Hoc Comparisons among Main Effects of Factor Levels (Assuming Interaction is

Not Present Factors can be Crossed or Nested Crossed Levels of One Factor Observed at all levels of other factor(s) Nested Levels of One Factor Are Different Within Various levels of other factor(s) 2-Way ANOVA (CRD) Lacrosse Helmets pdf("lacrosse.pdf") lacrosse1 <- read.fwf("C:\\data\\lacrosse.dat", width=c(8,8,14), col.names=c("brand", "side", "gadd")) attach(lacrosse1) brand <- factor(brand, levels=1:4, labels=c("SHC", "SCHAF", "SCHUL", "BUL")) side <- factor(side, levels=1:2, labels=c("Front", "Back")) # Descriptive Statistics tapply(gadd, brand, mean) # marginal mean for brand tapply(gadd, side, mean) # marginal mean for side tapply(gadd, list(brand,side), mean) # cell means tapply(gadd, list(brand,side), sd) # cell SDs

# Fit Model with Interaction (A*B says to include main effects and interactions) lacrosse1.aov <- anova(lm(gadd ~ brand*side)) lacrosse1.aov # Plot Means of Front and Back versus Brand interaction.plot(brand,side,gadd) Output Part 1 > tapply(gadd, brand, mean) # marginal SHC SCHAF SCHUL BUL 1070.300 1069.850 1116.650 1359.650 > > tapply(gadd, side, mean) # marginal Front Back 1090.875 1217.350 > > tapply(gadd, list(brand,side), mean) Front

Back SHC 1166.0999 974.4998 SCHAF 1117.5999 1022.1000 SCHUL 857.0001 1376.3000 BUL 1222.8001 1496.5001 > > tapply(gadd, list(brand,side), sd) Front Back SHC 152.3998 72.99994 SCHAF 216.2000 105.09994 SCHUL 151.4998 211.40010 BUL 123.1000 183.99987 mean for brand mean for side

# cell means # cell SDs Output Part 2 > # Fit Model with Interaction > # (A*B says to include main effects and interactions) > > lacrosse1.aov <- anova(lm(gadd ~ brand*side)) > > lacrosse1.aov Analysis of Variance Table Response: gadd Df Sum Sq Mean Sq F value Pr(>F) brand 3 1155478 385159 15.179 9.459e-08 *** side 1 319918 319918 12.608 0.0006816 *** brand:side 3 1632156 544052 21.441 4.988e-10 ***

Residuals 72 1826954 25374 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 2-Way ANOVA (RBD) Fabric Hairiness pdf("hairiness.pdf") hair1 <- read.fwf("C:\\data\\hairiness.dat", width=c(8,8,8,8), col.names=c("twstlvl", "tstspd", "bobbin", "hairiness")) attach(hair1) twstlvl <- factor(twstlvl, levels=1:3,labels=c("373tpm", "563tpm", "665tpm")) tstspd <- factor(tstspd, levels=1:3,labels=c("25mpm", "100mpm", "400mpm")) bobbin <- factor(bobbin, levels=1:6) tapply(hairiness, bobbin, mean) # marginal mean for bobbin tapply(hairiness, list(twstlvl,tstspd), mean) # Trt cell means interaction.plot(tstspd,twstlvl,hairiness) # Interaction Plot # Fit Model with Interaction and bobbin effect hair1.aov <- anova(lm(hairiness ~ twstlvl*tstspd + bobbin)); hair1.aov # Fit Additive Model with bobbin effect (Use aov to get TukeyHSD)

hair2.aov <- aov(hairiness ~ twstlvl + tstspd + bobbin); summary(hair2.aov) # Tukey's HSD For Main Effects TukeyHSD(hair2.aov, "twstlvl") TukeyHSD(hair2.aov, "tstspd") Output Part 1 > tapply(hairiness, bobbin, mean) # marginal mean for bobbin 1 2 3 4 5 6 632.5556 607.5556 608.5556 605.3333 614.4444 614.1111 > tapply(hairiness, list(twstlvl,tstspd), mean) #Trt cell means 25mpm 100mpm 400mpm 373tpm 744.6667 744.5000 775.8333 563tpm 582.3333 584.8333 597.5000 665tpm 492.3333 494.6667 507.1667

> > # Fit Model with Interaction and bobbin effect > hair1.aov <- anova(lm(hairiness ~ twstlvl*tstspd + bobbin)) > hair1.aov Analysis of Variance Table Response: hairiness Df Sum Sq Mean Sq F value Pr(>F) twstlvl 2 611792 305896 1324.6920 < 2.2e-16 tstspd 2 4637 2318 10.0402 0.0002928 bobbin 5 4414 883 3.8231 0.0063403

twstlvl:tstspd 4 826 207 0.8946 0.4761743 Residuals 40 9237 231 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' *** *** ** 0.1 ' ' 1 Output Part 2 > # Fit Additive Model with bobbin effect > hair2.aov <- aov(hairiness ~ twstlvl + tstspd + bobbin) > summary(hair2.aov) Df Sum Sq Mean Sq

F value Pr(>F) twstlvl 2 611792 305896 1337.5107 < 2.2e-16 *** tstspd 2 4637 2318 10.1373 0.0002394 *** bobbin 5 4414 883 3.8601 0.0054948 ** Residuals 44 10063 229 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > # Tukey's HSD For Main Effects >

> TukeyHSD(hair2.aov, "twstlvl") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = hairiness ~ twstlvl + tstspd + bobbin) Output Part 3 Fit: aov(formula = hairiness ~ twstlvl + tstspd + bobbin) $twstlvl diff lwr upr p adj 563tpm-373tpm -166.77778 -179.0046 -154.5509 0 665tpm-373tpm -256.94444 -269.1713 -244.7176 0 665tpm-563tpm -90.16667 -102.3935 -77.9398 0 > TukeyHSD(hair2.aov, "tstspd") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = hairiness ~ twstlvl + tstspd + bobbin)

$tstspd diff lwr upr p adj 100mpm-25mpm 1.555556 -10.671306 13.78242 0.9489254 400mpm-25mpm 20.388889 8.162027 32.61575 0.0005994 400mpm-100mpm 18.833333 6.606472 31.06020 0.0015237 Nested Design Florida Swamp Depths Response=Watlev Nesting Factor=size Nested Factor = swampid pdf("swamp.pdf") swamp <- read.fwf("C:\\data\\swamp1.dat", width=c(8,8,8), col.names=c("size", "swampid", "watlev")) attach(swamp)

size <- factor(size, levels=1:3, labels=c("small", "medium", "large")) swampid <- factor(swampid, levels=1:9) tapply(watlev, size, mean) # Fit the ANOVA with size and swamp nested within size swamp.aov1 <- aov(watlev ~ size/swampid) # This provides ANOVA, not appropriate F-test for size summary(swamp.aov1) swamp.aov2 <- aov(watlev ~ size + Error(swampid)) # This provides appropriate F-test for size summary(swamp.aov2) Output Swamp Depths > tapply(watlev, size, mean) small medium large 52.16667 106.43444 152.76654 > > > # Fit the ANOVA with size and swamp nested within size >

> swamp.aov1 <- aov(watlev ~ size/swampid) > > # This provides ANOVA, not appropriate F-test for size > > summary(swamp.aov1) Df Sum Sq Mean Sq F value Pr(>F) size 2 410724 205362 888.385 < 2.2e-16 *** size:swampid 6 83058 13843 59.884 < 2.2e-16 *** Residuals 234 54092 231 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Output Swamp Depths > swamp.aov2 <- aov(watlev ~ size + Error(swampid)) > > # This provides appropriate F-test for size

> > summary(swamp.aov2) Error: swampid Df Sum Sq Mean Sq F value Pr(>F) size 2 410724 205362 14.835 0.004759 ** Residuals 6 83058 13843 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Error: Within Df Sum Sq Mean Sq F value Pr(>F) Residuals 234 54092 231 Split-Plot Design Wool Shrinkage Response = shrink Whole Plot Factor = trt Subplot Factor = revs Block = runnum pdf("woolfrict.pdf")

wf1 <- read.fwf("C:\\data\\woolfrict.dat", width=c(8,8,8,8), col.names=c("runnum", "trt", "revs", "shrink")) attach(wf1) trt <- factor(trt, levels=1:4, labels=c("Untreated", "AC15sec", "AC4min", "AC15min")) runnum <- factor(runnum, levels=1:4) revs <- factor(revs,levels=seq(200,1400,200),ordered=TRUE) Split-Plot Design Wool Shrinkage # Fit full ANOVA with all terms, F-test for trt is inappropriate woolfrict.sp1 <- aov(shrink ~ trt + runnum + trt:runnum + revs + revs:trt) summary(woolfrict.sp1) # This model uses correct error terms for trt and revs and interaction woolfrict.sp2 <- aov(shrink ~ trt*revs + Error(runnum/trt)) summary(woolfrict.sp2) # Partitions the Revs SS into orthogonal polynomials summary(woolfrict.sp2,split=list(revs=list(linear=1, quadratic=2, cubic=3, quartic=4, quintic=5, sextic=6)))

Output Wool Shrinkage > > # Fit full ANOVA with all terms, F-test for trt is inappropriate > woolfrict.sp1 <- aov(shrink ~ trt + runnum + trt:runnum + revs + revs:trt) > summary(woolfrict.sp1) Df Sum Sq Mean Sq F value Pr(>F) trt 3 3012.5 1004.2 729.1367 < 2.2e-16 *** runnum 3 124.3 41.4 30.0815 1.024e-12 *** revs 6 11051.8 1842.0 1337.4577 < 2.2e-16 *** trt:runnum 9 114.6

12.7 9.2487 3.546e-09 *** trt:revs 18 269.5 15.0 10.8718 4.620e-14 *** Residuals 72 99.2 1.4 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > Output Wool Shrinkage > # This model uses correct error terms for trt and revs and interaction > woolfrict.sp2 <- aov(shrink ~ trt*revs + Error(runnum/trt)) > summary(woolfrict.sp2) Error: runnum

Df Sum Sq Mean Sq F value Pr(>F) Residuals 3 124.286 41.429 Error: runnum:trt Df Sum Sq Mean Sq F value Pr(>F) trt 3 3012.53 1004.18 78.836 8.81e-07 *** Residuals 9 114.64 12.74 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Error: Within Df Sum Sq Mean Sq F value Pr(>F) revs 6 11051.8 1842.0 1337.458 < 2.2e-16 *** trt:revs 18 269.5 15.0 10.872 4.62e-14 *** Residuals 72 99.2

1.4 Output Wool Shrinkage > # Partitions the Revs SS into orthogonal polynomials > summary(woolfrict.sp2,split=list(revs=list(linear=1, quadratic=2, + cubic=3, quartic=4, quintic=5, sextic=6))) Error: runnum Df Sum Sq Mean Sq F value Pr(>F) Residuals 3 124.286 41.429 Error: runnum:trt Df Sum Sq Mean Sq F value Pr(>F) trt 3 3012.53 1004.18 78.836 8.81e-07 *** Residuals 9 114.64 12.74 Error: Within Df Sum Sq Mean Sq F value Pr(>F) revs

6 11051.8 1842.0 1337.4577 < 2.2e-16 *** revs: linear 1 10846.8 10846.8 7875.9309 < 2.2e-16 *** revs: quadratic 1 198.9 198.9 144.4046 < 2.2e-16 *** revs: cubic 1 2.9 2.9 2.0842 0.1532 revs: quartic 1 1.4 1.4 1.0398 0.3113 revs: quintic 1

0.1 0.1 0.0885 0.7669 revs: sextic 1 1.7 1.7 1.1983 0.2773 trt:revs 18 269.5 15.0 10.8718 4.620e-14 *** trt:revs: linear 3 154.4 51.5 37.3624 1.133e-14 *** trt:revs: quadratic 3

104.8 34.9 25.3581 2.646e-11 *** trt:revs: cubic 3 7.4 2.5 1.7944 0.1559 trt:revs: quartic 3 0.8 0.3 0.1861 0.9055 trt:revs: quintic 3 0.9 0.3 0.2099 0.8892

trt:revs: sextic 3 1.3 0.4 0.3199 0.8110 Residuals 72 99.2 1.4 Repeated Measures Design Hair Growth Response = hair Treatment Factor = trt Subject within Treatment Factor = subj Time Factor = time pdf("rogaine.pdf") rogaine <- read.fwf("C:\\data\\rogaine.dat", width=c(8,8,8,8), col.names=c("trt", "subj", "time", "hair")) # Only use values where time>0 rogaine2 <- subset(rogaine,rogaine$time>0)

rogaine1 <- data.frame(trt=factor(rogaine2$trt), subj=factor(rogaine2$subj), time=factor(rogaine2$time), hair=rogaine2$hair) attach(rogaine1) Repeated Measures Design Hair Growth # Give ANOVA with all factors and interactions (inappropriate error for trt) rogaine1.mod1 <- aov(hair ~ trt + trt/subj + time + time:trt) summary(rogaine1.mod1) # Give ANOVA with proper error term for trt rogaine1.mod2 <- aov(hair ~ trt*time + Error(subj)) summary(rogaine1.mod2) Output Hair Growth > # Give ANOVA with all factors and interactions (inappropriate error for trt) > rogaine1.mod1 <- aov(hair ~ trt + trt/subj + time + time:trt) > > summary(rogaine1.mod1)

Df Sum Sq Mean Sq F value Pr(>F) trt 1 8064 8064 27.7389 5.231e-05 *** time 3 2268 756 2.5999 0.08391 . trt:subj 6 55476 9246 31.8027 1.230e-08 *** trt:time 3 2005 668 2.2985 0.11201 Residuals 18

5233 291 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Output Hair Growth > # Give ANOVA with proper error term for trt > rogaine1.mod2 <- aov(hair ~ trt*time + Error(subj)) > > summary(rogaine1.mod2) Error: subj Df Sum Sq Mean Sq F value Pr(>F) trt 1 8064 8064 0.8722 0.3864 Residuals 6 55476 9246 Error: Within Df Sum Sq Mean Sq F value Pr(>F) time 3 2267.6

755.9 2.5999 0.08391 . trt:time 3 2004.8 668.3 2.2985 0.11201 Residuals 18 5233.1 290.7 --Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Linear Regression Response/Dependent Variable Y Explanatory/Predictor/Independent Variable(s) X1,,Xp Predictors can me numeric, qualitative (using dummy variables), polynomials, or crossproducts Methods based on independent and normally distributed errors with constant variance Simple Linear Regression Math Scores / LSD Levels mathlsddat <- read.fwf("C:\\data\\mathlsd.dat", width=c(4,8), col.names=c("lsd","math")) mathlsd <- data.frame(mathlsddat)

attach(mathlsd) # Fit the simple linear regression model with Y = math (score) and X = lsd (concentration) mathlsd.reg <- lm(math ~ lsd) # Plot the data and regression line (Note you enter X first, then Y in plot statement) png(mathlsd1.png) plot(lsd,math) abline(mathlsd.reg) dev.off() # Print out the estimates, standard errors and t-tests, R-Square, and F-test summary(mathlsd.reg) Simple Linear Regression Math Scores / LSD Levels # Print out the ANOVA table (Sums of Squares and degrees of freedom) anova(mathlsd.reg) # Compute the correlation coefficient (r) and test if rho=0 cor(math,lsd) cor.test(math,lsd) # Plot Residuals versus Fitted Values png("mathlsd2.png") plot(predict(mathlsd.reg),residuals(mathlsd.reg))

abline(h=0) dev.off() # Print out standardized, studentized Residuals and Influence Measures round(rstandard(mathlsd.reg),3) round(rstudent(mathlsd.reg),3) influence.measures(mathlsd.reg) Output Math/LSD > summary(mathlsd.reg) Call: lm(formula = math ~ lsd) Residuals: 1 2 3 4 5 6 7 0.3472 -4.1658 7.7170 -9.3995 9.0513 -2.1471 -1.4032 Coefficients: Estimate Std. Error t value Pr(>|t|)

(Intercept) 89.124 7.048 12.646 5.49e-05 *** lsd -9.009 1.503 -5.994 0.00185 ** Residual standard error: 7.126 on 5 degrees of freedom Multiple R-squared: 0.8778, Adjusted R-squared: 0.8534 F-statistic: 35.93 on 1 and 5 DF, p-value: 0.001854 > anova(mathlsd.reg) Analysis of Variance Table Response: math Df Sum Sq Mean Sq F value Pr(>F) lsd 1 1824.30 1824.30 35.928 0.001854 ** Residuals 5 253.88 50.78 Output Math/LSD

> cor(math,lsd) [1] -0.9369285 > cor.test(math,lsd) Pearson's product-moment correlation data: math and lsd t = -5.994, df = 5, p-value = 0.001854 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9908681 -0.6244782 sample estimates: cor -0.9369285 > round(rstandard(mathlsd.reg),3) 1 2 3 4 5 6 7 0.076 -0.664 1.206 -1.430 1.460 -0.352 -0.241

> round(rstudent(mathlsd.reg),3) 1 2 3 4 5 6 7 0.068 -0.622 1.281 -1.663 1.723 -0.319 -0.217 Output Math/LSD > influence.measures(mathlsd.reg) Influence measures of lm(formula = math ~ lsd) : 1 2 3 4 5 6 7

dfb.1_ 0.0805 -0.2900 0.5047 -0.1348 -0.2918 0.0672 0.0694 dfb.lsd -0.0706 0.2033 -0.3230 -0.1358 0.6253 -0.1308 -0.1167 dffit 0.0811

-0.3358 0.6288 -0.6945 0.9752 -0.1921 -0.1541 cov.r 3.783 1.677 0.974 0.642 0.680 2.026 2.295 cook.d 0.00411 0.06424 0.17521 0.17824

0.34114 0.02249 0.01467 hat inf 0.588 * 0.225 0.194 0.149 0.243 0.267 0.335 * Multiple Regression PGA/LPGA Y = Accuracy (pctfrway=Percent Fairways) X1 = Average Distance X2 = Male png("C:\\Rmisc\\pgalpga.png") pgalpga <- read.fwf("C:\\data\\pgalpga.dat",width=c(8,8,8), col.names=c("avedist","pctfrwy","gender")) attach(pgalpga)

# Gender has levels 1=Female, 2=Male. Create Male Dummy variable male <- gender-1 # Fit additive Model (Common slopes) add.model <- lm(pctfrwy ~ avedist + male) # Fit interaction model (Different slopes by gender) int.model <- lm(pctfrwy ~ avedist*male) summary(add.model) summary(int.model) dev.off() PGA/LPGA Output - I > summary(add.model) Call: lm(formula = pctfrwy ~ avedist + male) Residuals: Min 1Q -25.0712 -2.8263 Median 0.4867

3Q 3.3494 Max 12.0275 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 147.26894 7.03492 20.934 < 2e-16 *** avedist -0.32284 0.02846 -11.343 < 2e-16 *** male 8.94888 1.26984 7.047 9.72e-12 *** --Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 4.797 on 351 degrees of freedom Multiple R-squared: 0.3589,

Adjusted R-squared: 0.3552 F-statistic: 98.24 on 2 and 351 DF, p-value: < 2.2e-16 PGA/LPGA Output - II > summary(int.model) Call: lm(formula = pctfrwy ~ avedist * male) Residuals: Min 1Q -23.6777 -2.7462 Median 0.3365 3Q 3.3635 Max 11.0186

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 130.89331 9.92928 13.183 < 2e-16 *** avedist -0.25649 0.04020 -6.380 5.61e-10 *** male 44.03215 15.15809 2.905 0.00391 ** avedist:male -0.13140 0.05657 -2.323 0.02078 * --Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 4.767 on 350 degrees of freedom Multiple R-squared: 0.3686, Adjusted R-squared: 0.3632 F-statistic: 68.11 on 3 and 350 DF, p-value: < 2.2e-16 Obtaining Plot by Gender with Regression Lines png("C:\\Rmisc\\pgalpga.png")

pgalpga <- read.fwf("C:\\data\\pgalpga.dat",width=c(8,8,8), col.names=c("avedist","pctfrwy","gender")) attach(pgalpga) male <- gender-1 # Create measures specifcally by gender ad.female <- avedist[gender == 1]; ad.male <- avedist[gender == 2] pf.female <- pctfrwy[gender == 1]; pf.male <- pctfrwy[gender == 2] # First set up variables to plot but leave blank (type=n). Then add points, # lines, and legend plot(c(ad.female,ad.male),c(pf.female,pf.male), xlab="Average Distance", ylab="Percent Fairways", main="Accuracy vs Distance by Gender", type="n") points(ad.female,pf.female,col="red"); points(ad.male,pf.male,col="blue") abline(lm(pf.female~ad.female),col="red"); abline(lm(pf.male~ad.male),col="blue") legend("topright",c("females","males"),pch=c(1,1),col=c(2,4)) dev.off()

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