Predicting Customer Churn at QWE - Supplements

Review the case and download the supplementary data spreadsheet. You should be able to open the Case Data page from the Excel file in SPSS and run Binary Logistic Regression (in Regression on the Analyze menu) on the data. You may use other analytic software, such as R, Python, or JMP if you prefer.

1. Is Walls belief about the dependence of churn rates on customer age supported by the data? To get some intuition, try visualizing this dependence. (Hint: There is no need to run any statistical tests to visualize the relationship.)

2. Run a single regression model that best predicts the probability that a customer leaves. What is the predicted probability that Customer 672 will leave between December 2011 and February 2012? Is that high or low? Did that customer actually leave?

Use this link to submit any files related to your analysis of the QWE case that you feel are important as supplementary material. These submissions will not be separately graded from the Turnitin case report.