Refer to the Real estate sales data set in AppendixC. 7. Obtain a random sample of 200 cases from the_ 522 cases in this data set. Using the random sample, build a regression model to predict sales price (Y) as a function of finished square feet (X). The analysis should include an assessment of the degree to which the key regression assumptions are satisfied. If the regression assumptions are not met, include and justify appropriate remedial measures. Use the final mode! to predict sales price for two houses that are about to come on the market: the first has X = 1100 finished square feet and the second has X = 4900 finished square feet. Assess the strengths and weaknesses of the final model.
Appendix C. 7
The city tax assessor was interested in predicting residential home sales prices in a Midwestern city as a function of various characteristics of the home and surrounding property. Data on 522 arms-length transactions were obtained for home sales during the year 2002. Each line of the data set has an identification number and provides information on 12 other variables. The 13 variables are: