Logistic regression with glm()
Linear regression and logistic regression are special cases of a broader type of models called generalized linear models ("GLMs"). A linear regression makes the assumption that the residuals follow a Gaussian (normal) distribution. By contrast, a logistic regression assumes that residuals follow a binomial distribution.
Here, you'll model how the length of relationship with a customer affects churn.
churn is available.
This exercise is part of the course
Introduction to Regression in R
Exercise instructions
- Fit a logistic regression of
has_churnedversustime_since_first_purchaseusing thechurndataset. Assign tomdl_churn_vs_relationship.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit a logistic regression of churn vs. length of relationship using the churn dataset
mdl_churn_vs_relationship <- ___
# See the result
mdl_churn_vs_relationship