Statistical Regression Modeling with R

Beschreibung

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.
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.

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 and non-normal data and the application of R Features data and computer programs so that readers can replicate and implement newly learned methods

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Ding-Geng (Din) Chen

Themen in »Statistical Regression Modeling with R«

linear regression logistic regression poisson regression generalized linear model nonlinear regression mixed-effects models multilevel modeling R programming modeling with R longitudinal data analysis

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“This is a great book and teachers, researchers and students interested in the subject can fruitfully use this manuscript benefiting from this comprehensive arsenal of information in multi-level regression analysis especially due to the practical examples offered.” (Vasile Lucian Boiculese, ISCB News, iscb.info, June, 2022)
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“This is an outstanding book on statistical regression modeling using R. The reader is guided step-by-step to an in-depth understanding of most commonly used regression modeling analyses through explanations, practical examples, datasets, and R packages. I highly recommend this book to all students and scholars interested in regression modeling and more advanced longitudinal and multi-level modeling. For researchers it is an invaluable source of knowledge.” (Prof. Claudio Robazza, Ph.D., University of Chieti-Pescara)
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