Decision analyst Sandy Baron has taken a job with an up-and-coming consulting firm in San Francisco. As part of the move, Sandy will purchase a house in the area. There are two houses that are especially attractive, but their prices seem high. To study the situation a bit more, Sandy obtained data on 30 recent real estate transactions involving properties roughly similar to the two candidates. The data are shown in Table 10.12. The Attractiveness Index measure is a score based on several aspects of a property’s character, including overall condition and features (e.g., swimming pool, view, seclusion).
a. Run a regression analysis on the data in Table 10.12 with SalePrice as the response variable and House Size, Lot Size, and Attractiveness as explanatory variables. What are the coefficients for the three explanatory variables? Write out the expression for the conditional expected Sale Price given House Size, Lot Size, and Attractiveness.

b. Create a CDF based on the residuals from the regression in part a.
c. The two properties that Sandy is considering are as follows:

What is the expected Sale Price for each of these houses accordingto the regression analysis from part a?
d. Create a probability distribution of the Sale Price for each house inpart c. Where does the List Price for each house fall in its respec-tive distribution? What advice can you give Sandy for negotiatingon these houses?