"Allen's book will, no doubt, earn the praises of statistics instructors and students alike...Provides the reader a thorough, yet lucid, journey through a variety of statistical concepts...This gem should be made a part of every prospective statistician's collection. Undergraduates through professional." (Choice, March 1998)

• 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.

"Allen's book will, no doubt, earn the praises of statistics instructors and students alike...Provides the reader a thorough, yet lucid, journey through a variety of statistical concepts...This gem should be made a part of every prospective statistician's collection. Undergraduates through professional." (Choice, March 1998)

• 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.

"Allen's book will, no doubt, earn the praises of statistics instructors and students alike...Provides the reader a thorough, yet lucid, journey through a variety of statistical concepts...This gem should be made a part of every prospective statistician's collection. Undergraduates through professional." (Choice, March 1998)

• 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.