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010   $a3-030-67583-1
024 7 $a10.1007/978-3-030-67583-7
035   $a(CKB)4100000011881237
035   $a(MiAaPQ)EBC6543709
035   $a(Au-PeEL)EBL6543709
035   $a(OCoLC)1245776480
035   $a(DE-He213)978-3-030-67583-7
035   $a(PPN)25529347X
035   $a(EXLCZ)994100000011881237
100   $a20210408d2021      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aStatistical Regression Modeling with R$b[electronic resource] $eLongitudinal and Multi-level Modeling /$fby Ding-Geng (Din) Chen, Jenny K. Chen
205   $a1st ed. 2021.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021.
215   $a1 online resource (239 pages)
225 1 $aEmerging Topics in Statistics and Biostatistics,$x2524-7743
311   $a3-030-67582-3 
327   $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.
330   $aThis book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.
410  0$aEmerging Topics in Statistics and Biostatistics,$x2524-7743
606   $aStatistics
606   $aProgramming languages (Electronic computers)
606   $aStatistical Theory and Methods
606   $aApplied Statistics
606   $aProgramming Language
606   $aAnàlisi de regressió$2thub
606   $aR (Llenguatge de programació)$2thub
608   $aLlibres electrònics$2thub
615  0$aStatistics.
615  0$aProgramming languages (Electronic computers).
615 14$aStatistical Theory and Methods.
615 24$aApplied Statistics.
615 24$aProgramming Language.
615  7$aAnàlisi de regressió
615  7$aR (Llenguatge de programació)
676   $a519.536
700   $aChen$b Ding-Geng (Din)$0767993
702   $aChen$b Jenny K.
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a996466552203316
996   $aStatistical regression modeling with R$91907223
997   $aUNISA