Advanced Analytical Theory and Methods: Regression - Data Science and Big Data Analytics - page 252

• Married (true/false)
• Duration as a customer (years)

• Churned_contacts (count)—Number of the customer's contacts that
have churned (count)
• Churned (true/false)—Whether the customer churned

After analyzing the data and fitting a logistic regression model, Age and
Churned_contacts were selected as the best predictor variables. Equation 6.11
provides the estimated model parameters.

6.11

Using the fitted model from Equation 6.11 , Table 6.1 provides the probability of
a customer churning based on the customer's age and the number of churned
contacts. The computed values of are also provided in the table. Recalling the
previous discussion of the logistic function, as the value of y increases, so does the
probability of churning.

Table 6.1 Estimated Churn Probabilities

Customer Age (Years) Churned_Contacts y Prob. of Churning

1 50 1 -4.12 0.016

2 50 3 -3.36 0.034

3 50 6 -2.22 0.098

4 30 1 -0.92 0.285

5 30 3 -0.16 0.460

6 30 6 0.98 0.727

7 20 1 0.68 0.664

8 20 3 1.44 0.808

9 20 6 2.58 0.930

Based on the fitted model, there is a 93% chance that a 20-year-old customer
who has had six contacts churn will also churn. (See the last row of Table 6.1 . )
Examining the sign and values of the estimated coefficients in Equation 6.11 , it
is observed that as the value of Age increases, the value of y decreases. Thus,
the negative Age coefficient indicates that the probability of churning decreases