# Normalverteilungsplots fuer Beispiele 3.1 und 3.2;
# siehe Abschnitt 6.3:

dd <- read.table("Michelson.dat", header=T)
X <- dd$velocity
n <- length(X)
NQ <- qnorm((1:n)/(n+1))
par(mai=c(0.5,0.5,0.05,0.05))
qqplot(NQ,X,pch=19,xlab="",ylab="")

dd <- read.table("SIDS.txt",header=T)
attach(dd)
# Jetzt gibt es die Variable Weight an Stelle von dd$Weight.
n <- length(Weight)
NQ <- qnorm((1:n)/(n+1))
par(mfrow=c(2,2))
par(mai=c(0.05,0.05,0.05,0.05))
qqplot(NQ,rnorm(n),pch=19,xlab="",ylab="",xaxt="n",yaxt="n")
qqplot(NQ,Weight,pch=19,xlab="",ylab="",xaxt="n",yaxt="n")
qqplot(NQ,rnorm(n),pch=19,xlab="",ylab="",xaxt="n",yaxt="n")
qqplot(NQ,rnorm(n),pch=19,xlab="",ylab="",xaxt="n",yaxt="n")


par(mfrow=c(2,2))
par(mai=c(0.4,0.4,0.1,0.1))
nv <- c(20,40,100,500)
for (i in 1:4)
{
	n <- nv[i]
	NQ <- qnorm((1:n)/(n+1))
	X <- rchisq(n,df=7)
	qqplot(NQ,X,pch=19,xlab="",ylab="")
}

par(mai=c(0.4,0.4,0.1,0.1))
n <- 1000
NQ <- qnorm((1:n)/(n+1))
Chi2Q <- qchisq((1:n)/(n+1),df=7)
qqplot(NQ,Chi2Q,type="l",lwd=2,xlab="",ylab="")