| 020 | $a9783030675837$9978-3-030-67583-7 |
| 082 | $a519.5$223 |
| 099 | $aOnline resource: Springer |
| 100 | $aChen, Ding-Geng (Din).$eauthor.$4aut$4http://id.loc.gov/vocabulary/relators/aut |
| 245 | $aStatistical Regression Modeling with R$h[EBook] :$bLongitudinal and Multi-level Modeling /$cby Ding-Geng (Din) Chen, Jenny K. Chen. |
| 250 | $a1st ed. 2021. |
| 260 | $aCham :$bSpringer International Publishing :$bImprint: Springer,$c2021. |
| 300 | $aXVII, 228 p. 45 illus.$bonline resource. |
| 336 | $atext$btxt$2rdacontent |
| 337 | $acomputer$bc$2rdamedia |
| 338 | $aonline resource$bcr$2rdacarrier |
| 440 | $aEmerging Topics in Statistics and Biostatistics ,$x2524-7743 |
| 505 | $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. |
| 533 | $nMode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| 538 | $aOnline access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| 700 | $aChen, Jenny K.$eauthor.$4aut$4http://id.loc.gov/vocabulary/relators/aut |
| 710 | $aSpringerLink (Online service) |
| 830 | $aEmerging Topics in Statistics and Biostatistics ,$x2524-7743 |
| 856 | $uhttps://doi.org/10.1007/978-3-030-67583-7 |
