4. Basic Linear Models

Linear models answer the question of how one quantitative outcome, say ToT, decreases or increases, when a condition changes. This chapter introduces the three basic ways how such conditions can enter a linear model. The most basic LM, the grand mean model, does not account for any conditions and produces just a single estimate: the grand mean in the population. In linear regression models, a metric predictor (e.g. age) is linked to the outcome by a linear function, with a slope and an intercept. A very common type of question is how an outcome changes by a set of discrete conditions, such as two different designs. Factorial model uses a mechanism called dummy variables to make categorical variables fit into the linear term. With ordinal factor models, dummy variables are put to a good use.