Using Binary Logistic Regression to Assess Credit Risk
If you are a loan officer at a bank, then you want to be able to identify characteristics that are indicative of people who are likely to default on loans, and use those characteristics to identify good and bad credit risks.
Suppose information on 850 past and prospective customers is contained in bankloan.sav. See the topic Sample Files for more information. The first 700 cases are customers who were previously given loans. Use a random sample of these 700 customers to create a logistic regression model, setting the remaining customers aside to validate the analysis. Then use the model to classify the 150 prospective customers as good or bad credit risks.