Chapter 14, Problem 59PS

A marketing manager wants to predict customer with the risk of churning (switching their service contracts to another company) based on the number of calls the customer makes to the company call center and the number of visits the customer makes to the local service center. Data from a random sample of 30 customers, organized and stored in Churn show that 15 have churned (codes as 1) and 15 have not (codes as 0)

a. Develop a logistic regression model to predict the probability to churn, based on the number of calls the customer makes to the company call center and the number of visits the customer makes to the local service center.

b. Explain the meaning of the regression coefficients in the model in (a).

c. Predict the probability of churn for a customer who called the company call center 10 times and visited the local service center once.

d. At the 0.05 level of significance, is there evidence that a logistic regression model that uses the number of cells the customer makes to the company call center and the number of visits the customer makes to the local service center is a good fitting model?

e. At the 0.05 level of significance, is there evidence that the number of calls the customer makes to the company call center and the number of visits the customer makes to the local service center each make a significance contribution to the logistic model?

f. Develop a logistic regression model that includes only the number of calls the customer makes to the company call center to predict the probability of churn.

g. Develop a logistic regression model that includes only the number of visits the customer makes to the local service center to the predict churn.

h. Compare the model in (a), (f) and (g). Evaluate the differences among the models.