The chapter introduces the concept of extending linear models using R, focusing on three main extensions: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. It begins by reviewing linear models and R, providing an example of analyzing voting data from the 2000 US Presidential election in Georgia. The chapter covers the construction and interpretation of linear models, including the use of dummy variables for qualitative predictors and the interpretation of regression coefficients. It also discusses hypothesis testing, specifically the F-test and t-test, for comparing models and testing specific predictors. The chapter emphasizes the importance of centering variables to simplify interpretation and the need to be cautious when interpreting interactions.The chapter introduces the concept of extending linear models using R, focusing on three main extensions: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. It begins by reviewing linear models and R, providing an example of analyzing voting data from the 2000 US Presidential election in Georgia. The chapter covers the construction and interpretation of linear models, including the use of dummy variables for qualitative predictors and the interpretation of regression coefficients. It also discusses hypothesis testing, specifically the F-test and t-test, for comparing models and testing specific predictors. The chapter emphasizes the importance of centering variables to simplify interpretation and the need to be cautious when interpreting interactions.