logit, probit, and neural network analysis of loan acquisition/regression?

Assignment What’s due:

Submit a logit, probit, and neural network analysis of loan acquisition behavior before the live class in week 10. Suggested length is five pages, but should not exceed ten pages, single- spaced, 12-point font.

This is a group assignment; each student should upload a copy of the assignment to the Learning Management System. The paper must be a Microsoft Word document. You should also submit the Excel spreadsheet with the prediction models and sensitivity analyses. Name the file HW4_Team# where # is your team number. Be sure to include the names of everyone on the team on the first page of the paper. Late assignments will not be accepted. Failure to follow directions will be penalized.

Outline and grading criteria:

1. Perform a logit and probit analysis of the variables that affect whether a customer takes out a loan. Consider only main effects. Which variables are significant? How do the significant variables influence the likelihood of taking out a loan? Copy screen snapshots of your analysis in R to your report. (20%) 


2. Add moderating effects (interactions of variables). Which interactions make sense conceptually? Which interactions are statistically significant? How do you interpret the coefficients on these variables? Copy screen snapshots of your analysis in R to your report. (20%) 


3. Create a final regression model with the variables that you feel are important (both main effects and interaction terms). Create a spreadsheet prediction of the model. Which variables have the greatest influence on the customers’ loan behavior (combined main effects and interaction effects)? Perform a sensitivity analysis as seen earlier in the semester. Copy screen snapshots of your analysis in R to your report. (20%) 


4. Perform a neural network analysis of the variables found to be significant in the logit and probit analysis above. Copy screen snapshots of your final neural network model in R to your report. (20%) 


5. Create a prediction model of the neural network. Using the prediction model, perform a sensitivity analysis for the neural network model similar to the logit and probit sensitivity analysis. (20%) 


Justify your answers. Provide a snapshot of output from your analysis in your final paper.

Homework #4 1

SCM 651: Business Analytics

Universal Bank Data Fields

ID
Personal Loan Age Experience Income
Zip code Family
CCAvg Education Mortgage Securities CDAccount Online CreditCard

unique identifier
did the customer accept the personal load offered (1=Yes, 0=No)
customer’s age
number of years of profession experience
annual income of the customer ($000)
home address zip code
family size of customer
average spending on credit cards per month ($000)
education level (1) undergraduate, (2) graduate, (3) advanced/professional value of house mortgage ($000)
does the customer have a securities account with the bank? (1=Yes, 0=No) does the customer have a certificate of deposit with the bank? (1=Yes, 0=No) does the customer use Internet banking facilities (1=Yes, 0=No)
does the customer use a credit card issued by Universal Bank? (1=Yes, 0=No)

Homework #4 2