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.
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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.
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Sage Linear Probability, Logit, and Probit Models: Quantitative Applications in the Social Sciences Number 45, 13th printing, 1994. An organization stamp is neatly marked out on the title page; otherwise, the book is in Near Fine or Very Good+ condition. The covers are in great shape. The binding is square and tight. The interior pages are clean and unmarked. The book will be carefully packaged for shipment for protection from the elements. USPS electronic tracking number issued free of charge. 96 pages. Quantitative Applications in the Social Sciences.
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Good. Trade paperback (US). Glued binding. 96 p. Contains: Illustrations. Quantitative Applications in the Social Sciences, 45. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.