The file Toyota Corolla.csv contains data on used cars (Toyota Corolla) on sale during late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications. The goal is to predict the price of a used Toyota Corolla based on its specifications. Split the data into training (60%), and validation (40%) datasets. a. Run a multiple linear regression with the outcome variable Price and predictor variables Age_08_04, KM, Fuel Type, HP, Automatic, Doors, Quarterly Tax, Mfr Guarantee, Guarantee Period, Airco, Automatic airco, CD Player, Powered Windows, Sport Model, and Tow Bar. (1 point) b. What appear to be the three or four most important car specifications (i.e. independent variables) for predicting the car's price? (1 point) c. Use stepwise regression with the three options (backward, forward, both) to reduce the remaining predictors as follows: Run stepwise on the training set. Choose the top model from each stepwise run. Then use each of these models separately to predict the validation set. Finally, describe the best model by interpreting the results. (3 points)