GLM VI+: Logistic Regression
Channel: Caspar van Lissa
Date: 2023-11-14

When our dependent variable is binary (coded as 0 or 1), we can use logistic regression. Instead of predicting the raw scores, we predict a transformation of the dependent variable. Specifically, we model the log odds of the probability of Y being one category (e.g., 1) versus the other category (e.g., 0). This results in an s-shaped regression curve bounded by 0 and 1. Logistic regression assumes a Bernoulli error distribution, instead of a normal error distribution, which accounts for the fact that observed outcomes can only take the values 0 or 1.