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＜電子ブック＞
Analyzing Categorical Data / by Jeffrey S. Simonoff
(Springer Texts in Statistics. ISSN:21974136)

版	1st ed. 2003.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2003
本文言語	英語
大きさ	XVI, 498 p : online resource
著者標目	*Simonoff, Jeffrey S author SpringerLink (Online service)
件　名	LCSH:Sociology -- Methodology  全ての件名で検索 LCSH:Probabilities LCSH:Statistics  LCSH:Social sciences -- Statistical methods  全ての件名で検索 FREE:Sociological Methods FREE:Probability Theory FREE:Statistical Theory and Methods FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
一般注記	1 Introduction -- 2 Gaussian-Based Data Analysis -- 3 Gaussian-Based Model Building -- 4 Categorical Data and Goodness-of-Fit -- 5 Regression Models for Count Data -- 6 Analyzing Two-Way Tables -- 7 Tables with More Structure -- 8 Multidimensional Contingency Tables -- 9 Regression Models for Binary Data -- 10 Regression Models for Multiple Category Response Data -- A Some Basics of Matrix Algebra -- References Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories; and regression models for two-category (binary) and multiple-category target variables, such as logistic and proportional odds models. All methods are illustrated with analyses of real data examples, many from recent subject area journal articles. These analyses are highlighted in the text, and are more detailed than is typical, providing discussion of the context and background of the problem, model checking, and scientific implications. More than 200 exercises are provided, many also based on recent subject area literature. Data sets and computer code are available at a web site devoted to the text. Adopters of this book may request a solutions manual from: textbook@springer-ny.com. Jeffrey S. Simonoff is Professor of Statistics at New York University. He is author of Smoothing Methods in Statistics and coauthor of A Casebook for a First Course in Statistics and Data Analysis, as well as numerous articles in scholarly journals. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute HTTP:URL=https://doi.org/10.1007/978-0-387-21727-7

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