000			02134nam a2200337| 4500
001			84033
005			20220110140038.0
010			_a978-3-319-23805-0 _dcompra
090			_a84033
100			_a20190128d2015 k||y0pory50 ba
101	0		_aeng
102			_aUS
200	1		_aModeling binary correlated responses using SAS, SPSS and R _bDocumento electrn̤ico _fJeffrey R. Wilson, Kent A. Lorenz
210			_aCham _cSpringer International Publishing _d2015
215			_aXXIII, 264 p. 26 il.
225	2		_aICSA book series in statistics
300			_aColocaȯ̂: Online
303			_aStatistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects.  Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.
410	1		_x2199-0980 _v9
606			_91379 _aEstats̕tica matemt̀ica
606			_911537 _aMťodos estats̕ticos
606			_93627 _aEstats̕tica
680			_aQA276
700			_aWilson _bJeffrey R. _964555
701			_aLorenz _bKent A.
801		0	_gRPC _aPT
856			_uhttps://doi.org/10.1007/978-3-319-23805-0
942			_2lcc _cF _n0