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Understanding Regression Assumptions

Understanding Regression Assumptions by William D. Berry
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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 auto-correlation.

SAGE Publications; February 1993
104 pages; ISBN 9781506315829
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Title: Understanding Regression Assumptions
Author: William D. Berry