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Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise "limited" dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.
About the Authors:
John H. Aldrich is Pfizer-Pratt University Professor of Political Science at Duke University. He is author of Why Parties: A Second Look (2011), coeditor of Positive Changes in Political Science (2007), and author of Why Parties (1995) and Before the Convention (1980). He is a past president of both the Southern Political Science Association and the Midwest Political Science Association and is serving as president of the American Political Science Association. In 2001 he was elected a fellow in the American Academy of Arts and Sciences.
Title: Linear Probability Logit & Probit Models
Publisher: Sage Publications, Inc
Publication Date: 1984
Binding: Paperback
Condition: Fair
Dust Jacket Condition: No Jacket
Seller: books4less (Versandantiquariat Petra Gros GmbH & Co. KG), Welling, Germany
Broschiert. Condition: Gut. 95 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten Bibliothek und kann die entsprechenden Kennzeichnungen aufweisen (Rückenschild, Instituts-Stempel.); der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 135. Seller Inventory # 2177143
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Trade Paperback. Condition: Very Good. First. Sage University Paper in the series: Quanitative Applications in the Social Sciences. Series editors: John L. Sullivan, University of Minnesota and Richard G. Niemi, University of Rochester. Seller Inventory # 056390
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