Clothing retailer. Since the average purchase amount Purchase12 was such a good predictor, the manager would like you to consider another explanatory variable: the average purchase amount from the previous 12 months. Create the new variable
and add it to the final model obtained in Example 28.24 (page 28-49).
(a) What is R2 for this model? How does this value compare with R2 in Example 28.24?
(b) What is the value of the individual t statistic for this new explanatory variable? How much did the individual t statistics change from their previous values?
(c) Would you recommend this model over the model in Example 28.24? Explain.
Example 28.24:
Create the variable Purchase12sq, the square of Purchase12, to allow some curvature in the model. Previous explorations also revealed that the dollar amount spent depends on how recent the customer visited the store, so an interaction term
was created to incorporate this relationship into the model. The output for the multiple regression model using the three explanatory variables Purchase12, Purchase12sq, and IntRecency12 is shown in Figure 28.19. This model does a great job for the manager by explaining almost 94% of the variation in the purchase amounts.
Figure 28.19:
