Multiple regression is a statistical analysis procedure that expands linear regression by including more than one independent variable in an equation to understand their association with a dependent variable. Multiple regression is one of several [Page 1041]extensions of linear regression and is part of the general linear model statistical family (e.g., analysis of variance, analysis of covariance, t-test, Pearson’s product–moment correlation). Whereas simple linear regression allows researchers to examine the relationship between one predictor variable (i.e., independent, manipulated, explanatory, or input variable) and one outcome variable (i.e., dependent, criterion, or output variable), multiple regression reveals associations between multiple predictor variables and a single outcome variable. This entry describes the uses and advantages of multiple regression, the statistical foundations of multiple regression, how to interpret multiple ...