Remember that in simulation the process is to generate a large number of random numbers that are uniform over (0,1) and then set that equal to the inverse of the cdf of the distribution you are modeling and return that value (in this case age at death of a newborn). In this case, we will set the random number value equal to F0(x) and solve for x. First, create a vector of 10,000 random values in R that are uniform (0,1). A quick google search should be able to find the command that will do that for you. Then, from those random numbers, create a vector of ages at death that is the same length that correspond to the random numbers via the inverse transform method described. You can solve on paper how to get the inverse of the random number corresponding to F0(x), then let R solve for age at death for each of the 10,000 newborns. R makes this easy because you can simply apply your calculation to the entire random number vector. Generate a different random number vector for each of the three mortality assumptions and create a histogram of the results for each of the three. Put an appropriate title on each of your three histograms, create appropriate x and y labels and have your bars colored red. In your histogram command set breaks=20 and xlim=c(0,150) for all three mortality assumptions. This creates 20 bins (breaks) of the ages at death and the range shown on your graph will be from 0 to 150 years. To submit your results for this problem, copy and paste your script and all three histograms into a word document to be scanned and uploaded along with the rest of this homework assignment to the course webpage. In addition, submit your answers to these questions. a.) Did your first histogram look as you would have expected it to and why? b.) How did your histogram simulating with a CFOM=2% campare to your first histogram and is that what you expected? c.) How did the histogram with a CFOM=5% compare with the one with 2% and is that what you would have expected?