Introducing Linear Models

The model commonly referred to as a linear model, or lm(), is one of the most flexible and valuable models in all statistics. This flexibility leads the model to also be known as the general linear model to some. This is a bit confusing with the generalized linear model, so we will just use R’s preferred lingo, the linear model. The constraint on a linear model is that the response variable must be normally distributed, but the predictor variable(s) can be continuous or discrete—that is, categorical.