Predicting Prices of Used Cars. The file 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,
lIP, and other specifications. The goal is to predict the price of a used Toyota Corolla
based on its specifications. (The example in Section 6.3 is a subset of this dataset.)
Data preprocessing. Split the data into training (50%), validation (30%), and
test (20%) datasets. Run a multiple linear regression with the output variable Price
and input variables: Age_08_04, KM, FueLType, lIP, Automatic, Doors, Quarterly_
Tax, Mfg_Guarantee, Guarantee.Period, Airco, Automatic.Airco, CD.Player,
Powered-Windows, Sport.Model, and Tow..Bar.
II- What appear to be the three or four most important car specifications for predicting
the car’s price? Use the Prediction Pro filer to explore the relationship between
Price and these variables.
b. Use the Stepwise platform to develop a reduced predictive model for Price. What
are the predictors in your reduced model?
c. Using metrics you consider useful, assess the performance of the reduced model
in predicting prices.