Beyond the Straight Line: Choosing Between OLS, Interaction Terms, and Tweedie Regression

This piece compares Ordinary Least Squares (OLS), OLS with interaction terms, and Tweedie regression for modeling skewed data with many zero values, using an insurance claims dataset as an example. It demonstrates that standard OLS is a poor fit because its assumptions of linearity and normally distributed errors are violated by the zero-inflated nature of the claims data. While adding interaction terms can capture more complex relationships, it fails to solve the fundamental problem of the data's underlying distribution. Tweedie regression is presented as the appropriate solution, as it is specifically designed for non-negative data that has a large spike at zero and a right-skewed distribution for positive values.