) In this exercise, we will predict the number of applications received using the other variables in the College data set in the ISLR package. ** be sure to look closely at this data, you may want to consider the multi-scale nature of the problem, and perhaps use a transformation on some of the variables.**

(a) Split the data set into a training set and a test set. Fit a linear model using least squares on the training set, and report the test error obtained.

(b) Fit a ridge regression model on the training set, with chosen by cross[1]validation. Report the test error obtained.

(d) Fit a lasso model on the training set, with chosen by cross-validation. Report the test error obtained, along with the number of non-zero coefficient estimates.

(e) Fit a PCR model on the training set. Report the test error obtained, along with justification for the choice of "k".

(f) Fit a PLS model on the training set. Report the test error obtained, along with justification for the choice of "k".

(g) Comment on the results obtained. How accurately can we predict the number of college applications received? Is there much difference among the test errors resulting from these five approaches?