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Analyzing Categorical Data (Springer Texts in Statistics) by Jeffrey S. Simonoff (auth.) is a nonfiction available to read on EtoBox.

What is Analyzing Categorical Data (Springer Texts in Statistics) about?

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

Who reads Analyzing Categorical Data (Springer Texts in Statistics)?

Author

Jeffrey S. Simonoff (auth.)

Publisher

Springer-Verlag New York

Published

2003

Language

EN

ISBN

9780387007496

Category

nonfiction

Subjects

Business, Mathematics, Sociology

Updated

2026-03-24