Refer to the Real estate sales data set in Appendix C.7 and Case Study 16,50. Carry a one-way analysis of covariance of this data set, where the response of interest is sales price (variable 2), the single factor is number of bedrooms (variable 4), and the possible covariates are finished square feet (variable 3) and lot size (variable 12). Recode the number of bedroom into four categories: 0-2, 3, 4, and greater than or equal to 5. The analysis should consider transformations of the response variable and the covariate~. Document steps taken in your analysis, and justify your conclusions.
Case Study 16.50
Refer to the Real estate sales data set in Appendix C.7. Carry out a one-way analysis of variance of this data set, where the response of interest is sales price (variable 2) and the single factor is number of bedrooms (variable 4). Recode the number of bedrooms into four categories: 0-2, 3, 4, and greater than or equal to 5. The analysis should consider transformations of the response variable. Document steps taken in your analysis, and justify your conclusions.
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:
