This realistic modeling project requires much more timethan a typical exercise. Table

Chapter 0, Problem 28.54

(choose chapter or problem)

This realistic modeling project requires much more timethan a typical exercise. Table 28.16 shows catalog-spending data for the first 9 of200 randomly selected individuals from a very large (over 20,000 households)data base. 17 We are interested in developing a model to predict spending ratio.There are no missing values in the data set, but there are some incorrect entriesthat must be identified and removed before completing the analysis. Income iscoded as an ordinal value, ranging from 1 to 12. Age can be regarded asquantitative, and any value less than 18 is invalid. Length of residence (LOR) is avalue ranging from zero to someones age. LOR should not be higher than age. Allof the catalog variables are represented by indicator variables, either theconsumer bought and the variable is coded as 1 or the consumer didnt buy andthe variable is coded as 0. The other variables can be viewed as indexes formeasuring assets, liquidity, and spending. Find a multiple regression model forpredicting the amount of money that consumers will spend on catalog shopping,as measured by spending ratio. Your goal is to identify the best model you can.Remember to check the conditions for inference as you evaluate yourmodels.