# On Page 204, Bowerman et al. (2005) present a dataset concerning sales prices of houses in a city in Ohio. The variables are:

7.9.16. On Page 204, Bowerman et al. (2005) present a dataset concerning sales prices of houses in a city in Ohio. The variables are: y is the sale price in \$10,000; x1 is the total square footage; x2 is the number of rooms; x3 is the number of bedrooms; and x4 is the age of the house at the time data were collected. The sample size is n = 63. For the reader’s convenien ce the data are in the dataset homesales. Consider the linear model y = α + 4i=1 xiβi + e.

1. (a) Use the Hogg-type adaptive scheme discussed in Section 7.6 on these data; i.e., use the function adaptor. Which score function did it select?
2. (b) Comment on the estimated regression coefficients as to significance and what they mean in terms of the problem.
3. (c) Fit the model using the selected score function in Part (a).
4. (d) Using the Studentized residuals from Part (b), perform a residual analysis which includes at least a residual plot and a q−q plot. Identify all outliers.
5. (e) Based on your analysis in (d), what, if any, other models would you fit to these data?