4. Let's see if we can improve our performance by changing the cutoff. Rather than accepting the above classification of everyone's credit status, let's use the "predicted probability of finding a good applicant" in logistic regression as a basis for selecting the best credit risks first, followed by poorer risk applicants. a. Sort the validation data on "predicted probability of finding a good applicant." b. For each validation case, calculate the actual cost/gain of extending credit. c. Add another column for cumulative net profit. d. How far into the validation data do you go to get maximum net profit? (Often this is specified as a percentile or rounded to deciles.) e. If this logistic regression model is scored to future applicants, what "probability of success" cutoff should be used in extending credit