Chapter 1 introduces readers to the elementary concepts of the General Linear Model (GLM). The GLM is the most widely used model in applied statistics. It states that an observed variable can be explained from a number of predictors that each carry their individual weight. The weights are estimated from the data. The predictors are either metric or categorical. The error terms quantify model-data discrepancies and can be conceptualized as a convolution of unconsidered factors that impact the dependent variable, model imperfections, and measurement error of variables. The model is linear in the model parameters because none of the weights, that is, none of the model parameters is raised to a power different than 1. The model can, however, be non-linear in the independent variables (e.g., raising continuous predictors to a power different than 1 results in polynomial curvilinear regression)