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Applied categorical and count data analysis

By: Tang, Wan.
Contributor(s): He, Hua | M. Tu, Xin.
Series: Texts in statistical science series. Publisher: Boca Raton CRC Press 2012Description: xx, 363 p.ISBN: 9781439806241.Subject(s): Regression analysis | Categories (Mathematics) | MathematicsDDC classification: 519.53 Summary: Developed from the authorsH 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.(http://www.crcpress.com/product/isbn/9781439806241)
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Books Vikram Sarabhai Library
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Non-fiction 519.53 T2A7 (Browse shelf) Available 177093

Includes bibliographical references and index.

Developed from the authorsH 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.(http://www.crcpress.com/product/isbn/9781439806241)

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