000			01745nam a22002057a 4500
005			20240206133527.0
008			240206b |||||||| |||| 00| 0 eng d
020			_a9783030675851
082			_a519.536 _bCHE
100			_aChen, Ding-Geng _915045
245			_aStatistical regression modeling with R: _blongitudinal and multi-level modeling
260			_bSpringer _aSwitzerland _c2021
300			_a228 p.
365			_aEURO _b79.99
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.
650			_aRegression analysis _915047
650			_aR (Computer program language) _913319
700			_aChen, Jenny K. _915046
942			_cBK _2ddc
999			_c5662 _d5662