Description
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.
Table of Contents
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
