Summary
Contents
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. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption, for example, lack of measurement error, absence of specification error, linearity, homoscedasticity and lack of autocorrelation.
Conclusion
Conclusion
Although most social scientists can recite the formal definitions of the various regression assumptions, many have little appreciation of the substantive meanings of these assumptions. And unless the meanings of these assumptions are understood, regression analysis almost inevitably will be a rigid exercise in which a handful of independent ...