000			05678cam a22007098i 4500
001			ocn953576519
003			OCoLC
005			20200827121525.0
006			m o d
007			cr |||||||||||
008			160712t20162016cau ob 001 0 eng
010			_a 2016032215
040			_aDLC _beng _erda _epn _cDLC _dOCLCO _dN$T _dOCLCF _dYDX _dEBLCP _dCSAIL _dIDB _dOCLCQ _dNJR _dOCLCQ _dUUM _dJSTOR _dCOF _dU3W _dOCLCQ _dG3B _dIGB _dSTF _dDEGRU _dOCLCQ
019			_a957616014 _a958350436 _a958447112
020			_a9780520965492 _q(electronic bk.)
020			_a0520965493 _q(electronic bk.)
020			_z9780520289291 _q(pbk. ; _qalk. paper)
020			_z0520289293 _q(pbk. ; _qalk. paper)
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029	1		_aCHVBK _b374449597
035			_a(OCoLC)953576519 _z(OCoLC)957616014 _z(OCoLC)958350436 _z(OCoLC)958447112
037			_a22573/ctv1wqdp5 _bJSTOR
042			_apcc
050	1	0	_aHA31.3
072		7	_aMAT _x003000 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
072		7	_aSOC _x027000 _2bisacsh
082	0	0	_a519.5/36 _223
049			_aMAIN
100	1		_aHoffmann, John P. _q(John Patrick), _d1962- _eauthor. _0http://id.loc.gov/authorities/names/n2003008271
245	1	0	_aRegression models for categorical, count, and related variables : _ban applied approach / _cJohn P. Hoffmann.
263			_a1608
264		1	_aOakland, California : _bUniversity of California Press, _c[2016]
264		4	_c�2016
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aReview of linear regression models -- Categorical data and generalized linear models -- Logistic and probit regression models -- Ordered logistic and probit regression models -- Multinomial logistic and probit regression models -- Poisson and negative binomial regression models -- Event history models -- Regression models for longitudinal data -- Multilevel regression models -- Principal components and factor analysis -- Appendix A : SAS, SPSS, and R code for examples in chapters -- Appendix B : using simulations to examine assumptions of OLS regression -- Appendix C : working with missing data.
520			_a"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented under the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book"--Provided by publisher.
588	0		_aPrint version record and CIP data provided by publisher; resource not viewed.
590			_aeBooks on EBSCOhost _bEBSCO eBook Subscription Academic Collection - Worldwide
650		0	_aRegression analysis _xMathematical models. _0http://id.loc.gov/authorities/subjects/sh2009006876
650		0	_aRegression analysis _xComputer programs. _0http://id.loc.gov/authorities/subjects/sh85112393
650		0	_aSocial sciences _xStatistical methods. _0http://id.loc.gov/authorities/subjects/sh85124018
650		7	_aMATHEMATICS _xApplied. _2bisacsh
650		7	_aMATHEMATICS _xProbability & Statistics _xGeneral. _2bisacsh
650		7	_aSOCIAL SCIENCE _xStatistics. _2bisacsh
650		7	_aRegression analysis _xComputer programs. _2fast _0(OCoLC)fst01093273
650		7	_aRegression analysis _xMathematical models. _2fast _0(OCoLC)fst01093277
650		7	_aSocial sciences _xStatistical methods. _2fast _0(OCoLC)fst01122983
655		4	_aElectronic books.
776	0	8	_iPrint version: _aHoffmann, John P. (John Patrick), 1962- _tRegression models for categorical, count, and related variables. _dOakland, California : University of California Press, [2016] _z9780520289291 _w(DLC) 2016030975
856	4	0	_uhttps://libproxy.firstcity.edu.my:8443/login?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1293234
938			_aDe Gruyter _bDEGR _n9780520965492
938			_aEBL - Ebook Library _bEBLB _nEBL4700075
938			_aEBSCOhost _bEBSC _n1293234
938			_aYBP Library Services _bYANK _n13128039
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