Includes bibliographical references (p. 89-90).

1. Introduction -- 2. A formal presentation of the regression assumptions -- The regression surface -- The role of the error term -- Other regression assumptions -- 3. A "weighty" illustration -- 4. The consequences of the regression assumptions being satisfied -- 5. The substantive meaning of regression assumptions -- Drawing dynamic inferences from cross-sectional regressions -- The assumption of the absence of perfect multicollinearity -- The assumption that the error term is uncorrelated with each of the independent variables -- Specification error: using the wrong independent variables -- The assumption that the mean of the error term is zero -- Assumptions about level of measurement -- The assumption of measurement without error -- Random measurement error -- Nonrandom measurement error -- Proxy variables -- The assumptions of linearity and additivity -- The assumption of homoscedasticity and lack of autocorrelation -- The substantive meaning of autocorrelation -- The substantive meaning of homoscedasticity -- The consequences of heteroscedasticity and autocorrelation -- The assumption that the error term is normally distributed -- 6. Conclusion.

Description based on print version record.

Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project.