Simulation can be used to illustrate a number of results from statistics that are difficule to understand with nonsimulation arguments. One is the famous central limit theorem, which says that if you sample enough values from any population distribution and then average these values, the resulting average will be approximately normally distributed. Confirm this by using eRISK with the following population distributions (run a separate simulation for each): (a) discrete with possible values 1 and 2 and probabalies 0.2 and 0.8 ; (b) exponental with mean 1 (use the RISKEXPON function with the single argument 1 ); (c) triangular with minimum, most likely, and maximum values equal to 1,9 , and 10 . (Note that each of these distnbutions is very skewed.) Run each simulation with 10 values in each average, and run 1000 iterations to simulate 1000 averages, What are the means of these averages? if needed, round your answers to three decinal digits. Run each simulation with 30 values in each average, and run 1000 iterations to simulate 1000 averages. Are the histograms based on 10 valoes quahtatively different from those based on 30 ? Please provide steps and answers