Sebastian Sauer takes us through two of the most important assumptions of linear models:

Additivity and linearity as the second most important assumptions in linear models

We assume that \(y\) is a linear function of the predictors. If y is not a linear function of the predictors, we cannot expect the model to deliver correct insights (predictions, causal coefficients). Let’s check an example.

Read on to understand what this means, as well as the most important assumption.