R programming and Satistic

R2. Set the random number generation seed to the value 1234. Draw a sample of size 11 from Exp(A 0.097) and find the mean get-better time in this sample. Repeat this process for a total of 10000 get-better averages and store these values in the variables gbas. Typically, we would make a histogram of the values, but R has a nice function that will draw a smooth representation of the histogram: plot(density(gbas)). Plot this. We now know that the normal model is not the best fit for this sampling distribution. To convince yourself of this, figure out how to draw a normal distribution (in red) atop the existing plot. You should be able to figure out what the mean and spread of the normal distribution would be if Ho is assumed to be true. Include your code and a sketch of the graph. R2. Set the random number generation seed to the value 1234. Draw a sample of size 11 from Exp(A 0.097) and find the mean get-better time in this sample. Repeat this process for a total of 10000 get-better averages and store these values in the variables gbas. Typically, we would make a histogram of the values, but R has a nice function that will draw a smooth representation of the histogram: plot(density(gbas)). Plot this. We now know that the normal model is not the best fit for this sampling distribution. To convince yourself of this, figure out how to draw a normal distribution (in red) atop the existing plot. You should be able to figure out what the mean and spread of the normal distribution would be if Ho is assumed to be true. Include your code and a sketch of the graph