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. (c) Fit a ridge regression model on the training set, with A chosen by cross-validation. Report the test error obtained. (d) Fit a lasso model on the training set, with A chosen by cross- validation. Report the test error obtained, along with the num- ber of non-zero coefficient estimates. (e) Fit a PCR model on the training set, with A/ chosen by cross- validation. Report the test error obtained, along with the value of A selected by cross-validation. ([) Fit a. PLS model on the training set, with A/ chosen by cross- validation. Report the test error obtained, along with the value of M selected by cross-validation. (g) Comment on the results obtained. How accurately can we pre- dict the number of college applications received? Is there much difference among the test errors resulting from these five ap- proaches