A marketing manager wants to predict customers 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 (coded as 1) and 15 have not (coded as 0)
a. Develop a logistic regression model to predict the probability of 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 calls 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 significant 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 predict churn.
h. Compare the models in (a), (f), and (g). Evaluate the differences among the models?