https://docs.google.com/spreadsheets/d/1DcXS96HY0qXiM4ntanyRb1fmCywtbxag58dFzNd6Tsc/edit?usp=sharing

Finance 1:The National Bank of Fort Worth, Texas, wants to examine methods for predicting sub-par payment performance on loans. It has data on unsecured consumer loans made over a 3-day period in October 2016 with a final maturity of 2 years. The data, which have been transformed to provide confidentiality, include the following.

PASTDUE: Coded as 1 if the loan payment is past due and 0 if the loan is settled (i.e., currently paid in full).

CBSCORE: Credit score generated by the CSC Credit Reporting Agency. Values range from 400 to 804, with higher values indicating a better credit rating.

DEBT: This is a debt ratio calculated by taking required monthly payments on all debt and dividing it by the gross monthly income of the applicant and co-applicant. This ratio represents the amount of the applicant's income that will go toward repayment of debt. Values range between 0 and 99.

GROSSINC: Gross monthly income of the applicant and co-applicant (Measured in hundreds of U.S. dollars).

LOANAMT: Loan amount in USD (Measured in thousands of U.S. dollars).

You have been asked to examine the feasibility of predicting past-due loan payment.

THE RAW DATA FOR THIS QUESTION ARE **NOT** AVAILABLE TO YOU. Use the output below to answer the following questions.

Logistic Regression Model Dependent Variable: PASTDUE Dependent Variable Information Variable Value Coding Frequency Percent PASTDUE 1 150 43.10 (Event) 198 56.90 Total (N Used) 348 100.00 Global Test for All Slopes: BETA = 0 Test Chi-Square df 0-value Likelihood Ratio 63.060 4