Multiple linear regression
In the previous chapter, we discussed SLR. With SLR, we were able to predict the value of a variable (commonly called the response variable, denoted as y) using another variable (commonly called the explanatory variable, denoted as x). The SLR model is expressed by the following equation where β 0 is the intercept term and β 1 is the slope of the linear model.
y = β 0 + β 1 x + ϵ
While this is a useful model, in many problems, multiple explanatory variables could be used to predict the response variable. For example, if we wanted to predict home prices, we might want to consider many variables, which may include lot size, the number of bedrooms, the number of bathrooms, and overall size. In this situation, we can expand the previous model to include these additional variables. This is called MLR. The MLR model can be expressed with the following equation.
y = β 0 + β 1 x ...
