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Alan Agresti
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Categorical Data Analysis (Wiley Series in Probability and Statistics) 3rd Edition, Kindle Edition

by Alan Agresti (Author) Format: Kindle Edition
4.8 out of 5 stars 66 ratings

Praise for the Second Edition

"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."
—Statistics in Medicine

"It is a total delight reading this book."
—Pharmaceutical Research

"If you do any analysis of categorical data, this is an essential desktop reference."
—Technometrics

The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.

Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:

  • An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models
  • Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis
  • New sections introducing the Bayesian approach for methods in that chapter
  • More than 100 analyses of data sets and over 600 exercises
  • Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources
  • A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions

Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.

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Product description

From the Publisher

ALAN AGRESTI is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has presented short courses on categorical data methods in thirty countries. He is the author of five other books, including An Introduction to Categorical Data Analysis, Second Edition and Analysis of Ordinal Categorical Data, Second Edition, both published by Wiley.

From the Inside Flap

Praise for the Second Edition

"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."
―Statistics in Medicine

"It is a total delight reading this book."
―Pharmaceutical Research

"If you do any analysis of categorical data, this is an essential desktop reference."
―Technometrics

The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.

Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:

  • An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models
  • Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis
  • New sections introducing the Bayesian approach for methods in that chapter
  • More than 100 analyses of data sets and over 600 exercises
  • Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources
  • A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions

Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.

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  • Customer Reviews:
    4.8 out of 5 stars 66 ratings

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Alan Agresti

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4.8 out of 5 stars
66 global ratings

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  • POM
    4.0 out of 5 stars 統計知識のブラッシュアップ
    Reviewed in Japan on 1 January 2016
    Verified Purchase
    統計学の知識をアップデートすることはめんどくさいところですが、過去の方法と最近の方法を体系的に説明しているところはすごく便利です
    Report
  • Un Nowen
    5.0 out of 5 stars Five Stars
    Reviewed in the United Kingdom on 2 March 2015
    Verified Purchase
    A nicely written, readable text covering covering the majority of the field of categorical analysis.
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  • Rachel
    5.0 out of 5 stars Love it.
    Reviewed in Germany on 4 September 2024
    Verified Purchase
    I have experience with linear regression and some education in statistics. I found this book explained the material from the basics (mathematical statistics) to application to troubleshooting in a way I was comfortable with. It is not a simple topic, but presenting it along the lines of how traditional statistics, and linear regression, is taught helped me to frame the material along lines I already knew.
    Report
  • Gioacchino
    5.0 out of 5 stars ottimo
    Reviewed in Italy on 2 May 2014
    Verified Purchase
    libro arrivato in perfette condizioni, nessun danno in nella confezione e nessun danno nella copertina. Puntuale e preciso. Consigliato, venditore di massima serietà
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  • Amazon Customer
    5.0 out of 5 stars Should be on the shelf of any practicing statistician
    Reviewed in the United States on 1 April 2014
    Verified Purchase
    I had the second version of this book and it was the primary text for my Generalized Linear Model (GLM) course. I recently purchased the new version after reading about some of the differences between the two. I'll discuss my thoughts shortly.

    First off, this book is another must have for those who will not only practice statistical modeling of categorical data, but who also need to be able understand the mathematical underpinnings of the GLM and the Generalized Linear Mixed Model (GLMM). Chapter 4, in particular, reviews the components of the GLM - systematic and random components, and the link function - and then goes on to explain what how these three differ depending on the type of your response ("y"): continuous data, binary data, count data, and so fourth. The next few chapters then go into detail on more of the application of the GLM for each of these type of data, followed by chapters discussing different models for GLMM - when your response variable is correlated. Finally, the book reviews asymptotic theory (chapter 16) which is vital for understanding how sample size affects the results for these models. For me, this book is does a better job at explaining these kind of topics (particularly, the Delta Method) than Casella, which is also an important book. Despite the emphasis on theory, there are applied examples and problems and the author does provide code for R, SAS, and Stata on his website.

    The main differences between this version and the second version are threefold. First, there are two additional chapters: one chapter devoted to other ways to model binary data and one based on other model approaches to classifying and clustering data. For the former, Agresti expanded coverage on probit regression, which is like a logistic regression but using a different distribution assumption. It also discusses topics that have become of interest in the last ten to fifteen years such as high dimensional analysis. The other new chapter discusses methods such as decision trees and linear discriminant analysis. Next, some chapters also include a discussion on bayesian approaches to the GLM, including the alternative approach to modeling binary data that I mentioned. Finally, the problem sets have been partly changed. I feel like the classification and clustering topics are better covered in a book like Johnson's Multivariate Data Analysis, and much of the new material feels more like an overview than a rigorous discussion of the math like other parts of the book. On the positive side, I thought that the section on probit regression was good because it provided insight on its importance in latent modeling.

    Despite my critiques this is still an excellent book to own. If you already own the second edition, I can't recommend purchasing this book but if you don't then you definitely should.
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