The modeling workflow

First, some words about the terminology. Statisticians call the Y variable the response, the outcome, or the dependent variable. The X variables are often called the predictors, the explanatory variables, or the independent variables. Some of the predictors are of our main interest, other predictors are added just because they are potential confounders. Continuous predictors are sometimes called covariates.

The GLM is a generalization of linear regression. GLM (also referred to as glm in R, from the stats package) allows the predictors to be related to the response variable via a link function, and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.

Whatever regression model you use, the main question is, "in what form can we add continuous predictors to the model?" If the relationship between the response and the predictor does not meet the model assumptions, you can transform the variable in some way. For example, a...