So far, we have resolved simple linear regression problems; they study the relation between a dependent variable, y, and an independent variable, x, based on the 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 before, 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 depends on more than one predictor. These models are named multiple linear regression, a straightforward generalization of single predictor models. According to multiple linear regression models, the dependent variable is related to two or...
