Scott William Menard - Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences)

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title	:	Applied Logistic Regression Analysis Sage University Papers Series. Quantitative Applications in the Social Sciences ; No. 07-106
author	:	Menard, Scott W.
publisher	:	Sage Publications, Inc.
isbn10 | asin	:	0803957572
print isbn13	:	9780803957572
ebook isbn13	:	9780585180823
language	:	English
subject	Regression analysis, Logistic distribution.
publication date	:	1995
lcc	:	QA278.2.M46 1995eb
ddc	:	519.5/36
subject	:	Regression analysis, Logistic distribution.

Applied Logistic Regression Analysis

SAGE UNIVERSITY PAPERS Series: Quantitative Applications in the Social Sciences Series Editor: Michael S. Lewis-Beck, University of Iowa Editorial Consultants Richard A. Berk, Sociology, University of California, Los Angeles William D. Berry, Political Science, Florida State University Kenneth A. Bollen, Sociology, University of North Carolina, Chapel Hill Linda B. Bourque, Public Health, University of California, Los Angeles Jacques A. Hagenaars, Social Sciences, Tilburg University Sally Jackson, Communications, University of Arizona Richard M. Jaeger, Education, University of North Carolina, Greensboro Gary King, Department of Government, Harvard University Roger E. Kirk, Psychology, Baylor University Helena Chmura Kraemer, Psychiatry and Behavioral Sciences, Stanford University Peter Marsden, Sociology, Harvard University Helmut Norpoth, Political Science, SUNY, Stony Brook Frank L. Schmidt, Management and Organization, University of Iowa Herbert Weisberg, Political Science, The Ohio State University Publisher Sara Miller McCune, Sage Publications, Inc. INSTRUCTIONS TO POTENTIAL CONTRIBUTORS For guidelines on submission of a monograph proposal to this series, please write Michael S. Lewis-Beck, Editor Sage QASS Series Department of Political Science University of Iowa Iowa City, IA 52242

Page i Series/Number 07-106 Applied Logistic Regression Analysis Scott Menard Institute of Behavioral Science University of Colorado, Boulder SAGE PUBLICATIONS International Educational and Professional Publisher Thousand Oaks London New Delhi

Page ii Copyright 1995 by Sage Publications, Inc. All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher. For information address: SAGE Publications, Inc. 2455 Teller Road Thousand Oaks, California 91320 E-mail: order@sagepub.com SAGE Publications Ltd. 6 Bonhill Street London EC2A 4PU United Kingdom SAGE Publications India Pvt. Ltd. M-32 Market Greater Kailash I New Delhi 110 048 India Printed in the United States of America Library of Congress Cataloging-in-Publication Data Menard, Scott W. Applied logistic regression analysis / Scott Menard. p. cm. (Sage university papers series. Quantitative applications in the social sciences; no. 07-106) Includes bibliographical references (pp. 96-97). ISBN 0-8039-5757-2 (pbk.: acid-free) 1. Regression analysis. 2. Logistic distribution. I. Title. II. Series. QA278.2.M46 1995 519.5'36dc20 95-9071 99 10 9 8 7 6 5 Sage Project Editor: Susan McElroy When citing a university paper, please use the proper form. Remember to cite the current Sage University Paper series title and include the paper number. One of the following formats can be adapted (depending on the style manual used): (1) MENARD, S. (1995) Applied Logistic Regression Analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-106. Thousand Oaks, CA: Sage. OR (2) Menard, S. (1995). Applied logistic regression analysis (Sage University Paper series on Quantitative Applications in the Social Sciences, series no. 07-106). Thousand Oaks, CA: Sage.

Page iii Contents Series Editor's Introduction v 1. Linear Regression and the Logistic Regression Model 1 Regression Assumptions 4 Nonlinear Relationships and Variable Transformations 10 Probabilities, Odds, Odds Ratios, and the Logit Transformation for Dichotomous Dependent Variables 12 Logistic Regression: A First Look

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