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| REGRESSION ANALYSIS FOR SOCIAL SCIENCES
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To order this title, and for more information, click here
By
Alexander von Eye, Michigan State University, East Lansing, U.S.A.
Christof Schuster, University of Michigan, Ann Arbor, U.S.A.
Description
Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method
having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear
regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate
and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students
and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the
book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text.
Audience
Academics, researchers, and students in the social sciences including psychology and sociology.
Contents
Preface.
Introduction.
Simple Linear Regression.
Multiple Linear Regression.
Categorical Predictors.
Outlier Analysis.
Residual Analysis.
Polynomial Regression.
Multicollinearity.
Multiple Curvilinear Regression.
Interaction Terms in Regression.
Robust Regression.
Symmetric
Regression.
Variable Selection Techniques.
Regression for Longitudinal Data.
Piecewise Regression.
Dichotomous Criterion Variables.
Computational
Issues.
Elements of Matrix Algebra.
Basics of Differentiation.
Basics of Vector Differentiation.
Polynomials.
Data Sets.
References.
Index.
| Bibliographic details |
Paperback, 386 pages, publication date: JUN-1998
ISBN-13: 978-0-12-724955-1
ISBN-10: 0-12-724955-9
Imprint: ACADEMIC PRESS
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| Price and Ordering |
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USD 115
GBP 73
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Last update: 4 Sep 2009
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