ABSTRACT
Multiple linear regression is a direct extension of simple linear regression. In
simple linear regression models, only one x predictor variable is present, but in multiple linear regression, there are k predictor values, xi, x2, . . . , xk. For example, a two-variable predictor model is presented in the following equation:
Yi ¼ b0 þ b1xi1 þ b2xi2 þ «i, (4:1)
where b1 is the ith regression slope constant acting on xi1; b2, the ith regression slope constant acting on xi2; xi1, the ith x value in the first x predictor; xi2, the ith x value in the second x predictor; and «i is the ith error term.
