國立高雄大學圖資館 |

Language: English

Back

Analysis of categorical data with R /

Bilder, Christopher R.

Analysis of categorical data with R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Analysis of categorical data with R /Christopher R. Bilder, Thomas M. Loughin.
Author:	Bilder, Christopher R.
other author:	Loughin, Thomas M.
Published:	Boca Raton :CRC Press,c2015.
Description:	xiii, 533 p. :ill. ;27 cm.
Subject:	Categories (Mathematics)Data processing.
ISBN:	9781439855676 (hbk.) :

Analysis of categorical data with R /
Bilder, Christopher R.

Analysis of categorical data with R /Christopher R. Bilder, Thomas M. Loughin. - Boca Raton :CRC Press,c2015. - xiii, 533 p. :ill. ;27 cm. - Texts in statistical science.

Includes bibliographical references (p. 513-523) and 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 rst 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"--

ISBN: 9781439855676 (hbk.) :NT$2239

LCCN: 2014019437Subjects--Topical Terms:

671269
Categories (Mathematics)
--Data processing.

LC Class. No.: QA169 / .B55 2015

Dewey Class. No.: 512/.6202855133

Analysis of categorical data with R /
LDR:02554nam a2200241 a 4500 001 427293
003 DLC
005 20140602085710.0
008 140917s2015 flua b 001 0 eng
010 $a 2014019437
020 $a 9781439855676 (hbk.) : $c NT$2239
020 $a 1439855676 (hbk.)
035 $a 2014019437
040 $a DLC $b eng $c DLC
042 $a pcc
050 0 0 $a QA169 $b .B55 2015
082 0 0 $a 512/.6202855133 $2 23
084 $a MAT029000 $2 bisacsh
100 1 $a Bilder, Christopher R. $3 671267
245 1 0 $a Analysis of categorical data with R / $c Christopher R. Bilder, Thomas M. Loughin.
260 $a Boca Raton : $b CRC Press, $c c2015.
300 $a xiii, 533 p. : $b ill. ; $c 27 cm.
490 0 $a Texts in statistical science
504 $a Includes bibliographical references (p. 513-523) and index.
520 $a "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 rst 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"-- $c Provided by publisher.
650 0 $a Categories (Mathematics) $x Data processing. $3 671269
650 0 $a R (Computer program language) $3 210846
650 7 $a MATHEMATICS / Probability & Statistics / General. $2 bisacsh $3 603543
700 1 $a Loughin, Thomas M. $3 671268