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Understanding Regression Assumptions
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Understanding Regression Assumptions

Volume: 92
February 1993 | 104 pages | SAGE Publications, Inc
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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.
 
Introduction
 
A Formal Presentation of the Regression Assumptions
 
A `Weighty' Illustration
 
The Consequences of the Regression Assumptions Being Satisfied
 
The Substantive Meaning of Regression Assumptions
 
Conclusion

William D. Berry

Ph.D., University of Minnesota, 1980. Major research and teaching interests include public policy, budgeting, state politics and research methodology. Berry’s primary areas of interest are public policy, American state politics, and research methodology. Throughout his career, his work has focused on explaining why governments make the policy choices they do, and improving the methodologies available for studying policy making. Among his current research projects are analyses of the impact of electoral competition on the policy choices made by state legislators, and the effect of state welfare policy on poverty in the United States. Berry... More About Author

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