Having explained how to build a regression model with multiple variables and having touched on the theme of its utilization and interpretation, we start from this paragraph to explore how to improve it. As a first step, we will work on its fit with present data. In the following chapters, devoted to model selection and validation, we will concentrate on how to make it really generalizable—that is, capable of correctly predicting on new, previously unseen data.
As we previously reasoned, the beta coefficients in a linear regression represent the link between a unit change in the predictors and the response variations. The assumptions at the core of such a model are of a constant and unidirectional relationship between each predictor and the target. It is the linear relationship assumption, having the characteristics of a line where direction and fluctuation are determined by the angular coefficient (hence the name linear regression, hinting at the operation of regressing...
