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AuthorSimonoff, Jeffrey S. author
TitleAnalyzing Categorical Data [electronic resource] / by Jeffrey S. Simonoff
ImprintNew York, NY : Springer New York : Imprint: Springer, 2003
Connect tohttp://dx.doi.org/10.1007/978-0-387-21727-7
Descript XVI, 498 p. online resource

SUMMARY

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


CONTENT

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


Social sciences Probabilities Statistics Social Sciences Methodology of the Social Sciences Probability Theory and Stochastic Processes Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science Behavorial Science Education Public Policy and Law



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