000			02952nam a22003497a 4500
001			sulb0077669
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005			20230320113951.0
008			230320s2021 sz a b 001 0 eng d
020			_z9783030675820
040			_aYDX _beng _erda _cYDX _dGW5XE _dOCLCO _dEBLCP _dOCLCF _dN$T _dUKAHL _dBD-SySUS
082	0	4	_a519.536 _222 _bCHS
100	1		_aChen, Ding-Geng, _eauthor. _960845
245	1	0	_aStatistical regression modeling with R : _blongitudinal and multi-level modeling / _cDing-Geng (Din) Chen, Jenny K. Chen.
264		1	_aCham : _bSpringer, _c[2021]
300			_axvii, 228 p. _bill. ; _c26 cm.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_a1. Linear Regression -- 2. Introduction to Multi-Level Regression -- 3. Two-Level Multi-Level Modeling -- 4. Higher-Level Multi-Level Modeling -- 5. Longitudinal Data Analysis -- 6. Nonlinear Regression Modeling -- 7. Nonlinear Mixed-Effects Modeling -- 8. Generalized Linear Model -- 9. Generalized Multi-Level Model for Dichotomous Outcome -- 10. Generalized Multi-Level Model for Counts Outcome.
520			_aThis book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.
588	0		_aOnline resource; title from PDF title page (SpringerLink, viewed April 16, 2021).
650		0	_aRegression analysis. _960846
650		0	_aR (Computer program language) _960847
650		7	_aR (Computer program language) _2fast _0(OCoLC)fst01086207 _960848
650		7	_aRegression analysis. _2fast _0(OCoLC)fst01432090 _960849
700	1		_aChen, Jenny K., _eauthor. _960850
776	0	8	_iPrint version: _z3030675823 _z9783030675820 _w(OCoLC)1226762505
856	4	0	_3Springer Complete eBooks _uhttps://link.springer.com/10.1007/978-3-030-67583-7
942			_2ddc _cBK
999			_c84516 _d84516