000			03346cam a2200397 i 4500
999			_c2509 _d2509
001			18155795
003			KWUST
005			20230824134926.0
008			140519t20152015flua b 001 0 eng
010			_a 2014019437
020			_a9781439855676 (hardback)
020			_a1439855676 (hardback)
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA169.B55 2015
082	0	0	_a512/.6202855133 _223
084			_aMAT029000 _2bisacsh
100	1		_aBilder, Christopher R., _eauthor.
245	1	0	_aAnalysis of categorical data with R / _cChristopher R. Bilder, University of Nebraska-Lincoln, Lincoln, Nebraska, USA, Thomas M. Loughin, Simon Fraser University, Surrey, British Columbia, Canada.
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c[2015]
264		4	_c©2015
300			_axiii, 533 pages : _billustrations ; _c26 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	0		_aChapman & Hall/CRC texts in statistical science
504			_aIncludes bibliographical references (pages 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 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"-- _cProvided by publisher.
650		0	_aCategories (Mathematics) _xData processing.
650		0	_aR (Computer program language)
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh
700	1		_aLoughin, Thomas M.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK