# First load PASWR library that has specialized functions.
# library(PASWR)


# Example 6.4
# Set parameters
set.seed(2009)
alpha=2
beta=5
m=500 
#Set up graphing window as in text
par(mfrow=c(2,2))
n=5
MEANSweibull=array(0,m)
for (i in 1:m) {MEANSweibull[i]<-mean(rweibull(n,alpha,beta))}
# Plot histogram of simulated sampling distribution
hist(MEANSweibull,prob=T,xlab="xbar",xlim=c(1,9),nclass="scott",main=paste("n=",n))

n=10
MEANSweibull=array(0,m)
for (i in 1:m) {MEANSweibull[i]<-mean(rweibull(n,alpha,beta))}
# Plot histogram of simulated sampling distribution
hist(MEANSweibull,prob=T,xlab="xbar",xlim=c(1,9),nclass="scott",main=paste("n=",n))

n=20
MEANSweibull=array(0,m)
for (i in 1:m) {MEANSweibull[i]<-mean(rweibull(n,alpha,beta))}
# Plot histogram of simulated sampling distribution
hist(MEANSweibull,prob=T,xlab="xbar",xlim=c(1,9),nclass="scott",main=paste("n=",n))

n=30
MEANSweibull=array(0,m)
for (i in 1:m) {MEANSweibull[i]<-mean(rweibull(n,alpha,beta))}
# Plot histogram of simulated sampling distribution
hist(MEANSweibull,prob=T,xlab="xbar",xlim=c(1,9),nclass="scott",main=paste("n=",n))