Applied Categorical Data Analysis / Edition 1by Chap T. Le, Le
Pub. Date: 09/09/1998
With a succinct, unified approach to applied categorical data analysis and an emphasis on applications, this book is immensely useful to researchers and students in the biomedical disciplines and to anyone concerned with statistical analysis. This self-contained volume provides up-to-date
The nonstatistician's quick reference to applied categorical data analysis
With a succinct, unified approach to applied categorical data analysis and an emphasis on applications, this book is immensely useful to researchers and students in the biomedical disciplines and to anyone concerned with statistical analysis. This self-contained volume provides up-to-date coverage of all major methodologies in this area of applied statistics and acquaints the reader with statistical thinking as expressed through a variety of modern-day topics and techniques. Applied Categorical Data Analysis introduces a number of new research areas, including the Mantel-Haenszel method, Kappa statistics, ordinal risks, odds ratio estimates, goodness-of-fit, and various regression models for categorical data.
Chap T. Le, author of Health and Numbers and Applied Survival Analysis, presents his information in a user-friendly format and an accessible style while purposefully keeping the mathematics to a level appropriate for students in applied fields. Well supplemented with helpful graphs and tables, Applied Categorical Data Analysis:
* Covers both basic and advanced topics
* Employs many real-life examples from biomedicine, epidemiology, and public health
* Presents case studies in meticulous detail
* Provides end-of-chapter exercise sets and solutions
* Incorporates samples of computer programs (most notably in SAS).
Applied Categorical Data Analysis is an important resource for graduate students and professionals who need a compact reference and guide to both the fundamentals and applications of the major methods in the field.
Table of Contents
Two-way Contingency Tables.
Logistic Regression Models.
Methods for Matched Data.
Methods for Count Data.
Transition from Categorical to Survival Data.
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