| 书目名称 | Statistical Regression Modeling with R | | 副标题 | Longitudinal and Mul | | 编辑 | Ding-Geng (Din) Chen,Jenny K. Chen | | 视频video | | | 概述 | Compiles commonly used regression methods that are essential for graduate students, applied data science, and related.Offers a step-by-step implementation linear and multilevel regressions with normal | | 丛书名称 | Emerging Topics in Statistics and Biostatistics | | 图书封面 |  | | 描述 | This 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.. | | 出版日期 | Textbook 2021 | | 关键词 | linear regression; logistic regression; poisson regression; generalized linear model; nonlinear regressi | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-67583-7 | | isbn_softcover | 978-3-030-67585-1 | | isbn_ebook | 978-3-030-67583-7Series ISSN 2524-7735 Series E-ISSN 2524-7743 | | issn_series | 2524-7735 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 16:04:37
