Database Reference
In-Depth Information
ratio test can also compare one fitted model to another. For example, consider the
logistic regression model when the categorical variable
Married
is included with
Age
and
Churned_contacts
in the list of input variables. The partial R output
for such a model is provided here:
summary(Churn_logistic2)
Call:
glm(formula = Churned ˜ Age + Married + Churned_contacts,
family = binomial(link = "logit"),
data = churn_input)
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
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 8387.3 on 7999 degrees of freedom
Residual deviance: 5358.3 on 7996 degrees of freedom
The residual deviances from each model can be used to perform a hypothesis test
where against using the base model that includes
the
Age
and
Churned_contacts
variables. The test statistic follows:
Using R, the corresponding p-value is calculated as follows:
pchisq(.9 , 1, lower=FALSE)
[1] 0.3427817
Thus, at a 66% or higher confidence level, the null hypothesis, ,
would not be rejected. Thus, it seems reasonable to exclude the variable
Married
from the logistic regression model.
