# Note: We'll have to set the sample size first, e.g. by uncommenting:
# n <- 100
# Set the random seed
set.seed(1234567)
# set true parameters: intercept & slope
b0<-1; b1<-0.5
# initialize b1hat to store 10000 results:
b1hat <- numeric(10000)
# Draw a sample of x, fixed over replications:
x <- rnorm(n,4,1)
# repeat r times:
for(j in 1:10000) {
# Draw a sample of u (standardized chi-squared[1]):
u <- ( rchisq(n,1)-1 ) / sqrt(2)
# Draw a sample of y:
y <- b0 + b1*x + u
# regress y on x and store slope estimate at position j
bhat <- coef( lm(y~x) )
b1hat[j] <- bhat["x"]
}