Database Reference
In-Depth Information
(Intercept) 3.415201 0.163734 20.858 <2e-16 ***
Age -0.156643 0.004088 -38.320 <2e-16 ***
Married 0.066432 0.068302 0.973 0.331
Cust_years 0.017857 0.030497 0.586 0.558
Churned_contacts 0.382324 0.027313 13.998 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' '
1
As in the linear regression case, there are tests to determine if the coefficients
are significantly different from zero. Such significant coefficients correspond to
small values of
Pr(>|z|)
, which denote the p-value for the hypothesis test to
determine if the estimated model parameter is significantly different from zero.
Rerunning this analysis without the
Cust_years
variable, which had the largest
corresponding p-value, yields the following:
Churn_logistic2 <- glm (Churned˜Age + Married +
Churned_contacts,
data=churn_input, family=binomial(link="logit"))
summary(Churn_logistic2)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.472062 0.132107 26.282 <2e-16 ***
Age -0.156635 0.004088 -38.318 <2e-16 ***
Married 0.066430 0.068299 0.973 0.331
Churned_contacts 0.381909 0.027302 13.988 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' '
1
Because the p-value for the
Married
coefficient remains quite large, the
Married
variable is dropped from the model. The following R code provides the third and
final model, which includes only the
Age
and
Churned_contacts
variables:
Churn_logistic3 <- glm (Churned˜Age + Churned_contacts,
data=churn_input, family=binomial(link="logit"))
summary(Churn_logistic3)
