X <- read.csv("Spock.csv", header = TRUE)     # replace by your own path here

attach(X)
lm1 <- lm(PERCENT ~ JUDGE)
summary(lm1)

 mean.Spock <-  mean(X[X[,2]=="SPOCK'S",1])
 mean.Others <- mean(X[X[,2]!="SPOCK'S",1])

 n.Spock <- sum(X[,2]=="SPOCK'S")
 n.Others <- sum(X[,2]!="SPOCK'S")


 center <- mean.Others - mean.Spock

 # Residual standard error: 6.914 on 39 degrees of freedom 


 # the following will do the comparison based on ANOVA's s_p

 half.width <- qt(0.975, df= 39)*6.914*sqrt(1/n.Spock + 1/n.Others)
 CI <- c(center-half.width, center + half.width)
 print( paste("95% C.I. for difference Spock vs Others", CI ))

 t.test(X[X[,2]=="SPOCK'S",1], X[X[,2]!="SPOCK'S",1])


 # are the other judges different? 

  boxplot(PERCENT ~ JUDGE)

 lm.other <-  lm(X[X[,2]!="SPOCK'S",1] ~  X[X[,2]!="SPOCK'S",2])
 summary(lm.other)
  n.B <- sum(X[,2]=="B")
  n.A <- sum(X[,2]=="A")
  sp <- 7.321
  sp*sqrt(1/n.B + 1/n.A)   


 # compare the result with p. 130

detach(X)