So far, we have resolved simple linear regression problems that study the relation between a dependent variable, y, and an independent variable, x, based on the following regression equation:
In this equation, the explanatory variable is represented by x and the response variable is represented by y. To solve this problem, the least squares method was used. In this method, we can find the best fit by minimizing the sum of squares of the vertical distances from each data point on the line. As mentioned previously, we don't find that a variable depends solely on another very often. Usually, we find that the response variable depends on at least two predictors. In practice, we will have to create models with a response variable that depend on more than one predictor. These models are...
