A refresher on regression models

It is best if we start with a brief refresher on regression models in general to ensure a common understanding. Let’s begin with the following regression equation:

Let's break down the notation in this equation:

• Y is the dependent variable, representing the process you are trying to explain or predict.
• is the intercept, which is the value of the dependent variable if all of the independent variables are 0.
• , known as beta, represent the coefficients applied to the independent variables. These are computed by the regression algorithm and represent the strength and direction of the relationship between the independent and dependent variables.
• are the independent or explanatory variables used to explain or predict the dependent variable.
• is the error term.

Now that we’ve aligned on a common understanding of the regression equation and terms, let’s shift our focus to...