##### RUN LINES 5-21 TO GENERATE ONE SIMULATED DATA SET FOR EXERCISE 5 #####

##### (Based on Kery 2010) #################################################

n.groups <- 3
n.sample <- 100
n <- n.groups * n.sample
x <- rep(1:n.groups, rep(n.sample, n.groups))
pop <- factor(x, labels = c("U","Z","G"))
hours <- runif(n,4.5,7.0)
mm <- mean(hours)
hours <- hours-mm
Xmat <- model.matrix(~pop*hours)
beta.vec <- c(-2,1,2,5,-2,-7)
lin.pred <- Xmat[,]%*%beta.vec
lambda <- exp(lin.pred)
C <- rpois(n=n, lambda=lambda)
ticks <- as.data.frame(cbind(hours,pop,C))
ticks$pop <- factor(ticks$pop)
levels(ticks$pop) <- c("U","Z","G")
ticks