Nast Store has derived the following consumer credit scoring model after years of data collecting and model testing:
Y = (0.20 x EMPLOYMT) + (0.4 x HOMEOWNER) + (0.3 x CARDS)
Where: EMPLOYMT = 1 if employed full-time, 0.5 if employed part-time, and 0 if unemployed
HOMEOWNER = 1 if homeowner, 0 otherwise
CARDS = 1 if presently has 1-5 credit cards, 0 otherwise
Nast determines that a score of at least 0.70 indicates a very good credit risk, and it extends credit to these individuals.
a. If Janice is employed part-time, is a homeowner, and has six credit cards at present, does the model indicate she should receive credit?
b. Janice just got a full-time job and closed two of her credit card accounts. Should she receive credit? Has her creditworthiness increased or decreased, according to the model?
c. Your boss mentions that he just returned from a trade association conference at which one of the speakers recommended that length of time at present residence (regardless of homeownership status) be included in credit scoring models. If the weight turns out to be 0.25, how do you think the variable would be coded (i.e., 0 stands for what, 1 stands for what, etc.)?
d. Suggest other variables that Nast might have left out of the model, and tell how you would code them (i.e.,0,1,2 are assigned to what conditions or variables?)
The problem deal with determining credit scores, for individuals, through consumer modeling.