Analyzing Categorical Data

1. Auflage. Softcover version of original hardcover Auflage 2003
ISBN: 978-1-4419-1837-6
Verlag: Springer

Buch, Englisch, 498 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 1580 g

Reihe: Springer Texts in Statistics

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: [e-mail].

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.

Zielgruppe

Graduate

Autoren/Hrsg.

Simonoff, Jeffrey S.

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

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.