Please use R to do the following questions. Thank you so much. If you can, please do all the questions with code and your anlysis.

In this exercise, we will predict the number of applications received using the other variables in the College data set. (a) Split the data set into a training set and a test set. (b) Fit a linear model using least squares on the training set, and report the test error obtained (MSE). (c) Fit a ridge regression model on the training set, with chosen by cross-validation. Report the test error obtained. (d) Fit a lasso model on the training set, with chosen by crossvalidation. Report the test error obtained (mse), along with the number of non-zero coefficient estimates. (e) Comment on the results obtained. How accurately can we predict the number of college applications received? Is there much difference among the three test errors resulting from these three approaches? Is ridge or lasso regression recommending a more parsimonious model?