Understanding Regression Assumptions

by William D. Berry

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.

104 pages, Paperback

First published February 1, 1993

Reviews

[Stoyan Stoyanov · Rating 5 out of 5]

A truly excellent discussion of regression assumptions and the substantive problems occurring when they are violated. Berry's text is involved and relies on at least rudimentary understanding of mathematical statistics, but he explains his points carefully, with plenty of illustrations. The book accomplishes a lot for all its brevity and Berry goes out of his way to provide examples and calculations in the context of every assumption he examines.