Since we already covered the exponential distribution in class, we'll simulate from a different distribution: the Poisson distribution. Note that the Poisson distribution is a discrete distribution: a Poisson random variable takes on possible values 0, 1, 2, . . ., rather than taking on any positive real number as with the exponential distribution. The Poisson distribution is indexed by a paremeter , which is equal to the mean of the distribution. For n = 10, 50, 200 and B = 1000, run the following simulation exercise. Draw a sample of size n from the Poisson distribution with = 5 (rpois(n, lambda=5) in R). Do this B times, each time computing the sample mean and standard error, and the endpoints of 1 the 95% CI from the lecture notes (the one that adds and subtracts two times the standard error). a.) Report a histogram of the sample mean over the B simulations (3 histograms, one for each sample size n). b.) Report a histogram of the lower endpoint of the 95% CI, and another histogram of the upper endpoint of the 95% CI (6 total histograms). c.) For each n, report the proportion of the B CIs that cont