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Applied Categorical and Count Data Analysis
Contributor(s): Tang, Wan (Author), He, Hua (Author), Tu, Xin M. (Author)

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ISBN: 1439806241     ISBN-13: 9781439806241
Publisher: CRC Press
OUR PRICE: $131.25  

Binding Type: Hardcover - See All Available Formats & Editions
Published: June 2012
* Out of Print *

Annotation:

In addition to traditional topics like logistic and Poisson regression models, this book covers modern statistical analysis subjects, such as models for count variables, longitudinal data analysis, reliability analysis, and methods for dealing with missing values. It pays special attention to small samples by including exact methods for inference where possible. The authors analyze dependent outcomes from clustered and longitudinal study designs using the generalized linear mixed-effects model and weighted generalized estimating equations. They also include SAS, SPSS, and R codes for implementation of the methods discussed.

Click for more in this series: Chapman & Hall/CRC Texts in Statistical Science

Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - Regression Analysis
Dewey: 519.53
LCCN: 2012009661
Series: Chapman & Hall/CRC Texts in Statistical Science
Physical Information: 1.07" H x 6.37" W x 9.42" L (1.51 lbs) 384 pages
Features: Bibliography, Illustrated, Index, Table of Contents
 
Descriptions, Reviews, Etc.
Publisher Description:

Developed from the authors' graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments.

The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies.

Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.

 
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