Logistic regression with 2 explanatory variables
To include multiple explanatory variables in logistic regression models, the syntax is the same as for linear regressions. The only change is the same as in the simple case: you run a generalized linear model with a binomial error family.
Here you'll fit a model of churn status with both of the explanatory variables from the dataset: the length of customer relationship and the recency of purchase.
churn is available.
This exercise is part of the course
Intermediate Regression in R
Exercise instructions
- Fit a logistic regression of churn status,
has_churnedversus length of customer relationship,time_since_first_purchaseand recency of purchase,time_since_last_purchase, and an interaction between the explanatory variables.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit a logistic regression of churn status vs. length of relationship, recency, and an interaction
mdl_churn_vs_both_inter <- ___
# See the result
mdl_churn_vs_both_inter