3. (10) Based solely on the scatterplot appearance, without reference to the numbers you developed with the regression analysis, comment on the appropriateness of each potential sales price driver.
4. (10) Based on the scatterplots, the regression information, and the information in the background materials, choose the best sales price driver. Explain your choice, including a discussion of why other sales price drivers were not chosen.
5. (10) What is the regression equation that you found for the best sales price driver?
6. (10) Explain what this “best” regression equation tells you (specifically).
7. (10) What is the “best” multiple regression equation for sales price? You might have to create several equations with a combination of variables to find this equation (or, there might not be any useful sales price drivers from this dataset).
8. (8) Assume that the following is the best multiple regression equation (it isn’t!).
Estimated sales price = 41,005 +123.55(sq ft) +2,974(baths) + 957.65(fenced)
Using the following information, what is the expected sales price:
Square feet = 1950
Baths = 1.5
Bedrooms = 3
Garage = 2
Fenced = no
Year built = 1960
9. (8) Assume that a good estimator for sales price is garage size. The equation for this sales price driver is:
Estimated sales price = 241,926.70 + 28,016.98(garage)
Based on this equation, the residual for the first home ($388,000) is $146,073. Explain what this means?
10. (10) List three other variables that you might suggest for predicting sales price of a home. How would you go about collecting the data for each variable (be specific)?
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