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May 28, 2014

Bank Revenues Case - Multiple Regression

The Bank Revenues analytics case study.  The complete collection of analytics cases is available from Collection: Analytics Case Study Library.

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Key ideas:

The log transformation, stepwise regression, regression assumptions, residuals, Cook’s D, interpreting model coefficients, singularity, Prediction Profiler, inverse transformations.

Background:

A bank wants to understand how customer banking habits contribute to revenues and profitability. The bank has customer age and bank account information, e.g., whether the customer has a savings account, whether the customer has received bank loans, and other indicators of account activity.

The Task:

We want to build a model that allows the bank to predict profitability for a given customer. A surrogate for customer profitability available in our data set is the Total Revenue a customer generates through their accounts and transactions. The resulting model will be used to forecast bank revenues and guide the bank in future marketing campaigns.

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From Building Better Models with JMP® Pro, Chapter 4, SAS Press (2015). Grayson, Gardner and Stephens. Used with permission.

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