The chapter begins with an applied example describing the limitations of bivariate regression and the need to include multiple independent variables in a regression model to explain the dependent variable.The logic of multivariate regression is discussed as it compares to bivariate regression.Running a multivariate regression in the R Commander and interpretation of the results are the main foci of the chapter, with particular attention to the beta coefficients, y-intercept, and adjusted R-squared.Generating the multivariate regression equation from the R Commander output is covered, along with engaging in prediction using this equation.Finally, interpretation of dummy independent variables in a multivariate regression is covered.