S-PLUS : Copyright (c) 1988, 1996 MathSoft, Inc.
S : Copyright AT&T.
Version 3.4 Release 1 for Sun SPARC, SunOS 5.3 : 1996 
Working data will be in .Data 
> dat <- read.table("data", header = T)
> #
> #    Read in the data.  Columns are named by the words 
> #    read off line 1 because of the header=T bit.
> #
> attach(dat)
> postscript("xyplot.ps",onefile=F,horizontal=F)
> plot(Transfers,Broken,xlab="Number of Transfers",ylab="Number Broken")
> dev.off()
Starting to make postscript file.
Finished postscript file, executing command "lpr -h xyplot.ps &".
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> postscript("xrootyplot.ps",onefile=F,horizontal=F)
> plot(Transfers, sqrt(Broken),xlab="Number of Transfers",ylab="Square Root Transform")
> dev.off()
Starting to make postscript file.
Finished postscript file, executing command "lpr -h xrootyplot.ps &".
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> linfit <- lm( Broken ~ Transfers, data=dat)
> #
> #   Regress Number Broken on Number of Transfers
> #
> summary(linfit)

Call: lm(formula = Broken ~ Transfers, data = dat)
Residuals:
  Min   1Q Median  3Q Max 
 -2.2 -1.2    0.3 0.8 1.8

Coefficients:
              Value Std. Error t value Pr(>|t|) 
(Intercept) 10.2000  0.6633    15.3771  0.0000 
  Transfers  4.0000  0.4690     8.5280  0.0000 

Residual standard error: 1.483 on 8 degrees of freedom
Multiple R-Squared: 0.9009 
F-statistic: 72.73 on 1 and 8 degrees of freedom, the p-value is 2.749e-05 

Correlation of Coefficients:
          (Intercept) 
Transfers -0.7071    
> #
> #   Print out a summary of the regression results.
> #
> rootlinfit <- lm( sqrt(Broken) ~ Transfers, data=dat)
> #
> #   Regress Square Root of Number Broken on Number of Transfers
> #
> summary(rootlinfit)

Call: lm(formula = sqrt(Broken) ~ Transfers, data = dat)
Residuals:
     Min      1Q  Median     3Q   Max 
 -0.3722 -0.1263 0.01059 0.1392 0.274

Coefficients:
              Value Std. Error t value Pr(>|t|) 
(Intercept)  3.2006  0.1010    31.6789  0.0000 
  Transfers  0.5254  0.0714     7.3538  0.0001 

Residual standard error: 0.2259 on 8 degrees of freedom
Multiple R-Squared: 0.8711 
F-statistic: 54.08 on 1 and 8 degrees of freedom, the p-value is 7.965e-05 

Correlation of Coefficients:
          (Intercept) 
Transfers -0.7071    
> #
> #   Print out a summary of the regression results.
> #
> glmfit <- glm( Broken ~ Transfers, data=dat, family=poisson)
> # 
> #   This fits the model that log(E(Y)) is a linear function
> #   of Dose and that the variance is equal to the mean
> #
> summary(glmfit)

Call: glm(formula = Broken ~ Transfers, family = poisson, data = dat)
Deviance Residuals:
        Min         1Q      Median        3Q       Max 
 -0.8105292 -0.2389281 -0.02029464 0.3299091 0.6074179

Coefficients:
                Value Std. Error   t value 
(Intercept) 2.3529495  0.1317376 17.860883
  Transfers 0.2638422  0.0792345  3.329891

(Dispersion Parameter for Poisson family taken to be 1 )

    Null Deviance: 12.56868 on 9 degrees of freedom

Residual Deviance: 1.813176 on 8 degrees of freedom

Number of Fisher Scoring Iterations: 3 

Correlation of Coefficients:
          (Intercept) 
Transfers -0.770864  
> postscript("points_plus_curve.ps",onefile=F,horizontal=F)
> plot(Transfers,Broken,xlab="Number of Transfers",ylab="Number Broken",ylim=c(7,24))
> d <- seq(0,3.1,length=200)
> etalin <- coef(linfit)[1] + d*coef(linfit)[2]
> lines(d,etalin)
> etarootlin <- coef(rootlinfit)[1] + d*coef(rootlinfit)[2]
> lines(d,etarootlin^2,lty=2)
> etaglm <- coef(glmfit)[1] + d*coef(glmfit)[2]
> p  <- exp(etaglm)
> lines(d,p,lty=3)
> legend(0,20,lty=1:3,legend=c("OLS","OLS on Root Y","GLM"))
> dev.off()
Starting to make postscript file.
Finished postscript file, executing command "lpr -h points_plus_curve.ps &".
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> q() # end-of-file