6. For this item, you will do a simulation to provide a visualization of the Central Limit Theorem. If you have not already read Section 5.4 (due Monday, July 3), then you are encouraged to do so before completing this part of the activity. (a) You first will need to generate a sampling distribution of sample means. In particular, the following two lines of code will generate 10,000 randomly selected sample means where each sample has size n = 60 and will assign that distribution of 10,000 sample means to the object sampdistr. Enter: > sampdistr = c() #Creates a new empty list named 'sampdistr' > for (i in 1:10000) {temp = sample(population, 60, replace=F); sampdistr = c(sampdistr, mean(temp))} (b) Create a histogram of the sampling distribution (it should be approximately bell-shaped), print it, and include it with your written responses. (No special parameters, like breaks, need to be entered. Use can just use the default system settings.) (c) Compute the mean of the sampling distribution of sample means and assign that value to the object sampdistrmean. (d) Compute the standard deviation of the sampling distribution of sample means and assign that value to the object sampdistrsd. (e) (Written Response) According to the Central Limit Theorem, how should the population mean and the mean of the sampling distribution of sample means be related? How well did the outcomes of your simulation match that theorem? (Be specific referring to actual values in your workspace.) (f) (Written Response) According to the Central Limit Theorem, how should the population standard deviation and the standard deviation of the sampling distribution of sample means be related? How well did the outcomes of your simulation match that theorem? (Be specific referring to actual values in your workspace.)