Multiple Regression in Practice

by William D. Berry, Stanley Feldman

Overview

The authors provide a systematic treatment of the major problems involved in using regression analysis. They clearly and concisely discuss the consequences of violating the assumptions of the regression model, procedures for detecting violations, and strategies for dealing with these problems.

Product Details

ISBN-13:	9781544342917
Publisher:	SAGE Publications
Publication date:	05/01/1985
Series:	Quantitative Applications in the Social Sciences , #50
Sold by:	Barnes & Noble
Format:	eBook
Pages:	96
File size:	4 MB

About the Author

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’s current research on methodology focuses on the development of techniques for estimating econometric models with binary dependent variables, and methods for studying policy diffusion using geographical information systems. Berry has also been working for several years collecting data on outcomes of all state legislative elections in the U.S. since the 1960s; he has begun to use these data to study the behavior of challengers in state legislative elections, and the role of partisan and incumbent protection goals in determining the nature of redistricting in state legislatures.
Areas of Interest:
Political Behavior: political psychology and socialization; political ideology and values; prejudice and intolerance; public opinion; political participation; political sociology.
Methodology: statistics; econometrics; measurement theory and scaling; analysis of covariance structures; multivariate methods; survey research methods; logic of inquiry and research design.

Table of Contents

The Multiple Regression Model
A Review
Specification Error
Measurement Error
Multicollinearity
Nonlinearity and Nonadditivity
Heteroscedasticity and Autocorrelation
Additional Concerns