ABSTRACT
Certainly, the most popular of statistical models over the last 100 years of statistical practice has been the general linear model (GLM). The GLM links a dependent, or outcome variable to one or more independent variables, and can take the form of such popular tools as analysis of variance (ANOVA) and regression. Given its popularity and utility, and the fact that it serves as the foundation for many other models, including the multilevel models featured in this book, we will start with a brief review of the linear model, particularly focusing on regression. This review will include a short technical discussion of linear regression models, followed by a description of how they can be estimated using the R language and environment. The technical aspects of this discussion are purposefully not highly detailed, as we focus on the model from a conceptual perspective.
