Reference no: EM131093812
Refer to the Real estate sales data set in Appendix C.7 and Case Study 9.3 I. Select a random sample of 300 observations to use as the model-building data set
a. Develop a regression tree for predicting sales price. Justify your choice of number of regions ( tree size), and interpret your model.
b. Assess your model's ability to predict and discuss its usefulness as a tool for predicting sales prices.
c. Compare the performance of your regression tree model with that of the best regression model obtained in Case Study 9.31. Which model is more easily interpreted and why?
Case Study 9.31
Refer to Real estate sales data set in Appendix C7. Residential sales that occurred during the year 2002 were av,1ilable from a city in the midwest. Data on 522 arms-length transactions include sale~ price, style. finished square feet, number of bedrooms, pool. lot size. year built, air conditioning, and whether or not the lot is adjacent to a highway. The city tax assessor was interested in predicting sales price based on the demographic variable information given above. Select a random sample of 300 observations to use in the model-building data set. Develop a best subset model for predicting sales price. Justify your choice of model. Assess your model's ability to predict and discuss its use as a tool for predicting sales price.
Appendix C7
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:
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