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Analysis of categorical data with R / Christopher R. Bilder and Thomas M. Loughin.

By: Bilder, Christopher R.
Contributor(s): Loughin, Thomas M.
Material type: TextTextSeries: Chapman & Hall/CRC texts in statistical science series.Publisher: Boca Raton : CRC Press, c2015Description: xiii, 533 p. : illustrations ; 26 cm.ISBN: 9781439855676 (hardback).Subject(s): Categories (Mathematics) -- Data processing | R (Computer program language) | MATHEMATICS / Probability & Statistics / GeneralDDC classification: 512.6202855133
Contents:
1. Analyzing a Binary Response, Part 1: Introduction-- 2. Analyzing a Binary Response, Part 2: Regression Models-- 3. Analyzing a Multicategory Response-- 4. Analyzing a count response-- 5. Model selection and evaluation-- 6. Additional topics-- A. An introduction to R-- B. Likelihood methods-- Bibliography-- Index.
Summary: "We live in a categorical world! From a positive or negative disease diagnosis to choosing all items that apply in a survey, outcomes are frequently organized into categories so that people can more easily make sense of them. However, analyzing data from categorical responses requires specialized techniques beyond those learned in a first or second course in Statistics. We o er this book to help students and researchers learn how to properly analyze categorical data. Unlike other texts on similar topics, our book is a modern account using the vastly popular R software. We use R not only as a data analysis method but also as a learning tool. For example, we use data simulation to help readers understand the underlying assumptions of a procedure and then to evaluate that procedure's performance. We also provide numerous graphical demonstrations of the features and properties of various analysis methods. The focus of this book is on the analysis of data, rather than on the mathematical development of methods. We o er numerous examples from a wide rage of disciplines medicine, psychology, sports, ecology, and others and provide extensive R code and output as we work through the examples. We give detailed advice and guidelines regarding which procedures to use and why to use them. While we treat likelihood methods as a tool, they are not used blindly. For example, we write out likelihood functions and explain how they are maximized. We describe where Wald, likelihood ratio, and score procedures come from. However, except in Appendix B, where we give a general introduction to likelihood methods, we do not frequently emphasize calculus or carry out mathematical analysis in the text. The use of calculus is mostly from a conceptual focus, rather than a mathematical one"--
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
512.6202855133 B595 (Browse shelf) Available 135714
Total holds: 0

Includes bibliographical references (pages 513-523) and index.

1. Analyzing a Binary Response, Part 1: Introduction--
2. Analyzing a Binary Response, Part 2: Regression Models--
3. Analyzing a Multicategory Response--
4. Analyzing a count response--
5. Model selection and evaluation--
6. Additional topics--
A. An introduction to R--
B. Likelihood methods--
Bibliography--
Index.

"We live in a categorical world! From a positive or negative disease diagnosis to choosing all items that apply in a survey, outcomes are frequently organized into categories so that people can more easily make sense of them. However, analyzing data from categorical responses requires specialized techniques beyond those learned in a first or second course in Statistics. We o er this book to help students and researchers learn how to properly analyze categorical data. Unlike other texts on similar topics, our book is a modern account using the vastly popular R software. We use R not only as a data analysis method but also as a learning tool. For example, we use data simulation to help readers understand the underlying assumptions of a procedure and then to evaluate that procedure's performance. We also provide numerous graphical demonstrations of the features and properties of various analysis methods. The focus of this book is on the analysis of data, rather than on the mathematical development of methods. We o er numerous examples from a wide rage of disciplines medicine, psychology, sports, ecology, and others and provide extensive R code and output as we work through the examples. We give detailed advice and guidelines regarding which procedures to use and why to use them. While we treat likelihood methods as a tool, they are not used blindly. For example, we write out likelihood functions and explain how they are maximized. We describe where Wald, likelihood ratio, and score procedures come from. However, except in Appendix B, where we give a general introduction to likelihood methods, we do not frequently emphasize calculus or carry out mathematical analysis in the text. The use of calculus is mostly from a conceptual focus, rather than a mathematical one"--

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