Using R, generate 30 observations from a standard normal distribution and store the values in x. Generate 20 observations from a chi-squared distribution with one degree of freedom and store them in z. Compute y=4(z-1), so x and y contain data sampled from distributions having identical means. Apply the permutation test based on means with the function permg. Repeat this 200 times an determine how often the function rejects.

What do the results indicate about controlling the probability of a type I error with the permutation test when testing the hypothesis of equal means? What does this suggest about computing a confidence interval for the difference between the means based on the permutation test?