001435702 000__ 03566cam\a2200553\i\4500
001435702 001__ 1435702
001435702 003__ OCoLC
001435702 005__ 20230309003949.0
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001435702 020__ $$a9783030675837$$q(electronic bk.)
001435702 020__ $$a3030675831$$q(electronic bk.)
001435702 020__ $$z9783030675820
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001435702 0247_ $$a10.1007/978-3-030-67583-7$$2doi
001435702 035__ $$aSP(OCoLC)1245776480
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001435702 049__ $$aISEA
001435702 050_4 $$aQA278.2
001435702 08204 $$a519.5/36$$223
001435702 1001_ $$aChen, Ding-Geng,$$eauthor.
001435702 24510 $$aStatistical regression modeling with R :$$blongitudinal and multi-level modeling /$$cDing-Geng (Din) Chen, Jenny K. Chen.
001435702 264_1 $$aCham :$$bSpringer,$$c[2021]
001435702 300__ $$a1 online resource
001435702 336__ $$atext$$btxt$$2rdacontent
001435702 337__ $$acomputer$$bc$$2rdamedia
001435702 338__ $$aonline resource$$bcr$$2rdacarrier
001435702 4901_ $$aEmerging topics in statistics and biostatistics,$$x2524-7735
001435702 504__ $$aIncludes bibliographical references and index.
001435702 5050_ $$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.
001435702 506__ $$aAccess limited to authorized users.
001435702 520__ $$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.
001435702 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 16, 2021).
001435702 650_0 $$aRegression analysis.
001435702 650_0 $$aR (Computer program language)
001435702 650_6 $$aAnalyse de régression.
001435702 650_6 $$aR (Langage de programmation)
001435702 655_7 $$aLlibres electrònics.$$2thub
001435702 655_0 $$aElectronic books.
001435702 7001_ $$aChen, Jenny K.,$$eauthor.
001435702 77608 $$iPrint version:$$z3030675823$$z9783030675820$$w(OCoLC)1226762505
001435702 830_0 $$aEmerging topics in statistics and biostatistics.$$x2524-7735
001435702 852__ $$bebk
001435702 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-67583-7$$zOnline Access$$91397441.1
001435702 909CO $$ooai:library.usi.edu:1435702$$pGLOBAL_SET
001435702 980__ $$aBIB
001435702 980__ $$aEBOOK
001435702 982__ $$aEbook
001435702 983__ $$aOnline
001435702 994__ $$a92$$bISE