Multiple regressions refer to designs in which all independent variables are continuous. Its inclusion is justified by its similarity to the two-factor unbalanced design and the analysis of covariance; this contributes to completeness without requiring many new concepts. Multiple regressions are introduced by showing it as a version of the models already developed earlier in the text. The terms in the multiple regression models represent the deviations due to particular values of the independent variables. The multiple regression models are often presented in a modified but equivalent form in the output from computer programs. In the multiple regression models the individual selected has an average value for professional motivation and obtained a grade on the exam that expected from his or her professional motivation value. Most computer packages that provide multiple regression offer forward stepwise procedures. The result is usually expressed in terms of increments in multiple R-squared.