Nast stores has derived the following consumer credit-scoring model after years of data collect Y=(0.20 x Employment) + (0.4 x Homeowner) + (0.3 x Cards)

Employment = 1 if employed part-time, and 0 if unemployed

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. (each letter below is a separate question, answer a-d)

PLEASE SHOW ALL WORK

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 credit worthiness increased or decreased, according to 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 include 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 associated 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?).