Profitable banks are ones that make good decisions on loan applications. Credit scoring is the statistical technique that helps banks make that decision. However, many branches overturn credit scoring recommendations, whereas other banks do not use the technique. In an attempt to determine the factors that affect loan decisions, a statistics practitioner surveyed 100 banks and recorded the percentage of bad loans (any loan that is not completely repaid), the average size of the loan, and whether a scorecard is used, and if so, whether scorecard recommendations are overturned more than 10% of the time. These results are stored in columns 1 (percentage good loans), 2 (average loan), and 3 (code 1 = no scorecard, 2 = scorecard overturned more than 10% of the time, and 3 = scorecard overturned less than 10% of the time).

a. Create indicator variables to represent the codes.

b. Perform a regression analysis.

c. How well does the model fit the data?

d. Is multicollinearity a problem?

e. Interpret and test the coefficients. What does this tell you?

f. Predict with 95% confidence the percentage of bad loans for a bank whose average loan is $10,000 and that does not use a scorecard.