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Analyzing Categorical Data

  • Jeffrey S. Simonoff

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Jeffrey S. Simonoff
    Pages 1-5
  3. Jeffrey S. Simonoff
    Pages 7-28
  4. Jeffrey S. Simonoff
    Pages 29-54
  5. Jeffrey S. Simonoff
    Pages 55-123
  6. Jeffrey S. Simonoff
    Pages 125-196
  7. Jeffrey S. Simonoff
    Pages 197-245
  8. Jeffrey S. Simonoff
    Pages 247-308
  9. Jeffrey S. Simonoff
    Pages 309-364
  10. Jeffrey S. Simonoff
    Pages 365-426
  11. Jeffrey S. Simonoff
    Pages 427-449
  12. Back Matter
    Pages 459-498

About this book

Introduction

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.

Keywords

Analysis Estimator Excel SAS Statistical Method best fit data analysis linear optimization mathematical statistics modeling sets

Authors and affiliations

  • Jeffrey S. Simonoff
    • 1
  1. 1.Leonard N. Stern School of BusinessNew York UniversityNew YorkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-21727-7
  • Copyright Information Springer-Verlag New York 2003
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-1837-6
  • Online ISBN 978-0-387-21727-7
  • Series Print ISSN 1431-875X
  • Buy this book on publisher's site