The data if any needed for this assignment is posted on eLearn. If after giving some thought, you have problems doing this assignment, do not hesitate to email or meet with me. You can

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Individual Assignment on Logistic Regression

The data if any needed for this assignment is posted on eLearn. If after giving some thought, you have problems doing this assignment, do not hesitate to email or meet with me. You can discuss with each other, but please finish and write up the answers independently. Please submit a soft copy of your homework (typed answers in this word document and attach the SPSS output file to eLearn).

The National Bank of Fort Worth, Texas wants to examine methods for predicting sub-par payment performance on loans. They have data on unsecured consumer loans made over a 3-day period in October 2013 with a final maturity of 2 years. There are a total of 348 observations in the sample. The data, which have been transformed to provide confidentiality, include the following:

PAST DUE: Coded as 1 if the loan payment is past due and zero otherwise

CBSCORE: Score generated by the CSC Credit reporting agency from 400 to 839 with higher values indicating better credit rating

DEBT: Debt ratio calculated by taking required monthly payments on all debt and dividing it by gross monthly income of applicant and co-applicant. This ratio represents the amount of the applicant’s income that will go towards repayment of debt

GROSS INC: Gross monthly income of applicant and co-applicant

LOAN AMT: Loan Amount

You have been asked to examine the feasibility of predicting past-due loan payment. Report your results to the bank in a two-part report. The report should include an executive summary with a brief non-technical description of your results (less than 1-page) and an accompanying technical report with the details of your analysis. The data are in an excel file posted on eLearn.

For the report, you should consider the following: Use of logistic to analyze the data; appropriate variables which are useful in predicting performance; the hit-rate in the estimation sample and how it compares with appropriate benchmark criteria.

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• Logistic Regression
• Title
• Active Dataset
• Case Processing Summary
• Dependent Variable Encoding
• Block 0: Beginning Block
• Title
• Classification Table
• Variables in the Equation
• Variables not
  

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