(SMSS 13.7 & 13.8 combined, modified)

(Data file: house.selling.price in smss R package)

1. Using the house.selling.price data, run and report regression results modeling y = selling price (in dollars) in terms of size of home (in square feet) and whether the home is new (1 = yes; 0 = no). (In other words, price is the outcome variable and size and new are the explanatory variables.)

2. Report and interpret the prediction equation, and form separate equations relating selling price to size for new and for not new homes. In particular, for each variable; discuss statistical significance and interpret the meaning of the coefficient.

3. Find the predicted selling price for a home of 3000 square feet that is (i) new, (ii) not new.

4. Fit another model, this time with an interaction term allowing interaction between size and new, and report the regression results

5. Report the lines relating the predicted selling price to the size for homes that are (i) new, (ii) not new.

6. Find the predicted selling price for a home of 3000 square feet that is (i) new, (ii) not new.

7. Find the predicted selling price for a home of 1500 square feet that is (i) new, (ii) not new. Comparing to (F), explain how the difference in predicted selling prices changes as the size of home increases.

8. Do you think the model with interaction or the one without it represents the relationship of size and new to the outcome price? What makes you prefer one model over another?