000			03032cam a2200325 a 4500
999			_c39 _d39
001			17257090
003			OSt
005			20170209125657.0
008			120416s2012 flua b 001 0 eng
010			_a 2012009661
020			_a9781439806241 (hardback)
040			_aDLC _cDLC _dDLC
042			_apcc
050	0	0	_aQA278.2 _b.T365 2012
082	0	0	_a519.5/3 _223
084			_aMAT029030 _2bisacsh
100	1		_aTang, Wan.
245	1	0	_aApplied categorical and count data analysis / _cWan Tang, Hua He, Xin M. Tu.
260			_aBoca Raton : _bCRC Press, _c2012.
300			_axx, 363 p. : _bill. ; _c25 cm.
490	0		_aChapman & Hall/CRC texts in statistical science series
504			_aIncludes bibliographical references and index.
520			_a"Preface This book focuses on statistical analysis of discrete data, including categorical and count outcomes. Discrete variables are abundant in practice, and knowledge about and ability to analyze such data is important for professionals and practitioners in a wide range of biomedical and psychosocial research areas. Although there are some excellent books on this general subject such as those by Agresti (2002, 2007); Long (1997); Long and Freese (2006), and Stokes et al. (2009), a book that includes models for longitudinal data, real data examples with detailed programming codes, as well as intuitive explanations of the models and their interpretations and di erences thereupon will compliment the repertoire of existing texts. Motivated by the lack of such a text, we decided to write this book ve years ago when preparing a graduate-level biostatistics course on this topic for students within a medical school setting at the University of Rochester. The lecture notes from which this book has evolved have been used for the course over the past ve years. In addition to the classic concepts such as contingency tables and popular topics such as logistic and Poisson regression models, as covered by most available textbooks on categorical data analysis, this book also includes many modern topics. These include models for zero modi ed count outcomes, longitudinal data analysis (both parametric and semi-parametric), reliability analysis, and popular methods for dealing with missing values. More importantly, programming codes are provided for all the examples in the book for the four major software packages, R, SAS, SPSS, and Stata, so that when reading the examples readers can immediately put their knowledge"-- _cSource other than Library of Congress.
650		0	_aRegression analysis.
650		0	_aCategories (Mathematics)
650		7	_aMATHEMATICS / Probability & Statistics / Regression Analysis. _2bisacsh
700	1		_aHe, Hua.
700	1		_aTu, Xin M.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBook