000			04401nam a22003855i 4500
001			286277
003			MX-SnUAN
005			20160429154451.0
007			cr nn 008mamaa
008			150903s2010 xxu| o |||| 0|eng d
020			_a9781441917423 _99781441917423
024	7		_a10.1007/9781441917423 _2doi
035			_avtls000338392
039		9	_a201509030324 _bVLOAD _c201404300344 _dVLOAD _y201402060909 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aKleinbaum, David G. _eautor _9301886
245	1	0	_aLogistic Regression : _bA Self-Learning Text / _cby David G. Kleinbaum, Mitchel Klein.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_axiv, 616 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aStatistics for Biology and Health, _x1431-8776
500			_aSpringer eBooks
505	0		_ato Logistic Regression -- Important Special Cases of the Logistic Model -- Computing the Odds Ratio in Logistic Regression -- Maximum Likelihood Techniques: An Overview -- Statistical Inferences Using Maximum Likelihood Techniques -- Modeling Strategy Guidelines -- Modeling Strategy for Assessing Interaction and Confounding -- Additional Modeling Strategy Issues -- Assessing Goodness of Fit for Logistic Regression -- Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves -- Analysis of Matched Data Using Logistic Regression -- Polytomous Logistic Regression -- Ordinal Logistic Regression -- Logistic Regression for Correlated Data: GEE -- GEE Examples -- Other Approaches for Analysis of Correlated Data.
520			_aThis very popular textbook is now in its third edition. Whether students or working professionals, readers appreciate its unique "lecture book" format. They often say the book reads like they are listening to an outstanding lecturer. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams. Like previous editions, this textbook provides a highly readable description of fundamental and more advanced concepts and methods of logistic regression. It is suitable for researchers and statisticians in medical and other life sciences as well as academicians teaching second-level regression methods courses. The new chapters are: • Additional Modeling Strategy Issues, including strategy with several exposures, screening variables, collinearity, influential observations and multiple-testing • Assessing Goodness to Fit for Logistic Regression • Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves The Computer Appendix provides step-by-step instructions for using STATA (version 10.0), SAS (version 9.2), and SPSS (version 16) for procedures described in the main text. David Kleinbaum is Professor of Epidemiology at Emory University Rollins School of Public Health in Atlanta, Georgia. Dr. Kleinbaum is internationally known for his innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has taught more than 200 courses worldwide. The recipient of numerous teaching awards, he received the first Association of Schools of Public Health Pfizer Award for Distinguished Career Teaching in 2005. Mitchel Klein is Research Assistant Professor with a joint appointment in the Environmental and Occupational Health Department and the Epidemiology Department at Emory University Rollins School of Public Health. He has successfully designed and taught epidemiologic methods physicians at Emory’s Master of Science in Clinical Research Program. Dr. Klein is co-author with Dr. Kleinbaum of the second edition of Survival Analysis-A Self-Learning Text.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aKlein, Mitchel. _eautor _9301887
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781441917416
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4419-1742-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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