Regression Analysis Theory, Methods, and Applications
Any method of fitting equations to data may be called regression. Such equations are valuable for at least two purposes: making predictions and judging the strength of relationships. Because they provide a way of em pirically identifying how a variable i
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Springer
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Stephen Fienberg
Ingram Olkin
Springer Texts in Statistics Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: An Introduction to Times Series and Forecasting Chow and Teicher: Probability Theory: Independence, Interchangeability, Martingales, Third Edition Christensen: Plane Answers to Complex Questions: The Theory of Linear Models, Second Edition Christensen: Linear Models for Multivariate, Time Series, and Spatial Data Christensen: Log-Linear Models and Logistic Regression, Second Edition Creighton: A First Course in Probability Models and Statistical Inference Dean and Voss: Design and Analysis of Experiments du Toit, Steyn, and Stumpf Graphical Exploratory Data Analysis Durrett: Essentials of Stochastic Processes Edwards: Introduction to Graphical Modelling, Second Edition Finkelstein and Levin: Statistics for Lawyers Flury: A First Course in Multivariate Statistics Jobson: Applied Multivariate Data Analysis, Volume I: Regression and Experimental Design Jobson: Applied Multivariate Data Analysis, Volume II: Categorical and Multivariate Methods Kalbfleisch: Probability and Statistical Inference, Volume I: Probability, Second Edition Kalbfleisch: Probability and Statistical Inference, Volume II: Statistical Inference, Second Edition Karr: Probability Keyjitz: Applied Mathematical Demography, Second Edition Kiefer: Introduction to Statistical Inference Kokoska and Nevison: Statistical Tables and Formulae Kulkarni: Modeling, Analysis, Design, and Control of Stochastic Systems Lehmann: Elements of Large-Sample Theory Lehmann: Testing Statistical Hypotheses, Second Edition Lehmann and Casella: Theory of Point Estimation, Second Edition Lindman: Analysis of Variance in Experimental Design Lindsey: Applying Generalized Linear Models Madansky: Prescriptions for Working Statisticians McPherson: Statistics in Scientific Investigation: Its Basis, Application, and Interpretation Mueller: Basic Principles of Structural Equation Modeling: An Introduction to LISREL and EQS (continued after index)
Ashish Sen
Muni Srivastava
Regression Analysis
Theory, Methods, and Applications
With 38 Illustrations
i
Springer
Ashish Sen College of Architecture, Art, and Urban Planning School of Urban Planning and Policy The University of Illinois Chicago, IL 60680 USA
Muni Srivastava Department of Statistics University of Toronto Toronto, Ontario Canada M5S lAl
Editorial Board George Casella Biometrics Unit Cornell University Ithaca, NY 14853-7801 USA
Stephen Fienberg Department of Statistics Carnegie-Mellon U ni versi ty Pittsburgh, PA 15213 USA
Ingram 01kin Department of Statistics Stanford University Stanford, CA 94305 USA
Mathematical Subject Classification: 62Jxx, 62-01 Library of Congress Cataloging-in-Publication Data Se
