The origins and uses of regression analysis -- Basic matrix algebra: Manipulating vectors -- The mean and variance of a variable -- Regression models and linear functions -- Errors of prediction and least-squares estimation -- Least-squares regression and covariance -- Covariance and linear independence -- Separating explained and error variance -- Transforming variables to standard form -- Regression analysis with standardized variables -- Populations, samples, and sampling distributions -- Sampling distributions and test statistics -- Testing hypotheses using the t test -- The t test for the simple regression coefficient -- More matrix algebra: Manipulating matrices -- The multiple regression model -- Normal equations and partial regression coefficients -- Partial regression and residualized variables -- The coefficient of determination in multiple regression -- Standard errors of partial regression coefficients -- The incremental contributions of variables -- Testing simple hypotheses using the F test -- Testing compound hypotheses using the F test -- Testing hypotheses in nested regression models -- Testing for interaction in multiple regression -- Nonlinear relationships and variable transformations -- Regression analysis with dummy variables -- One-way analysis of variance using the regression model -- Two-way analysis of variance using the regression model -- Testing for interaction in analysis of variance -- Analysis of covariance using the regression model -- Interpreting interaction in analysis of covariance -- Structural equation models and path analysis -- Computing direct and total effects of variables -- Model specification in regression analysis -- Influential cases in regression analysis -- The problem of multicollinearity -- Assumptions of ordinary least-squares estimation -- Beyond ordinary regression analysis.