Statistical Regression Modeling with R

1. Front Matter

   Pages i-xvii

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2. Linear Regression

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 1-26

3. Introduction to Multi-Level Modeling

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 27-44

4. Two-Level Multi-Level Modeling

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 45-70

5. Higher-Level Multi-Level Modeling

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 71-89

6. Longitudinal Data Analysis

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 91-113

7. Nonlinear Regression Modeling

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 115-129

8. Nonlinear Mixed-Effects Modeling

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 131-142

9. The Generalized Linear Model

   • Ding-Geng (Din) Chen, Jenny K. Chen

   Pages 143-163

10. Generalized Multi-Level Model for Dichotomous Outcome

    • Ding-Geng (Din) Chen, Jenny K. Chen

    Pages 165-180

11. Generalized Multi-Level Model for Counts Outcome

    • Ding-Geng (Din) Chen, Jenny K. Chen

    Pages 181-196

12. Back Matter

    Pages 197-228

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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.

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

“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) “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)

• School of Social Work and Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA

  Ding-Geng (Din) Chen

• Department of Statistics, Cornell University, Ithaca, USA

  Jenny K. Chen

Dr. Ding-Geng Chen is a fellow of the American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina at Chapel Hill. He was a professor in biostatistics at the University of Rochester and the Karl E. Peace Endowed Eminent Scholar Chair in biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceutical organizations and government agencies with extensive expertise in Monte Carlo simulations, clinical trial biostatistics, and public health statistics. Dr. Chen has more than 200 professional publications, and he has coauthored/coedited 31 books on clinical trial methodology, meta-analysis, data sciences, Monte Carlo simulation-based statistical modeling, and public health applications. He has been invited nationally and internationally to give speeches on his research.

Ms. Jenny K. Chen graduated with a master's degree from the Department of Statistics and Data Science at Cornell University. She is currently working as a financial analyst at Morgan Stanley (Midtown New York Office) for their Wealth Management division. Previously, Jenny worked as a product manager for Google, where she led a team of data scientists to develop several prediction algorithms for the 2019 NCAA March Madness Basketball Tournament. She has published several research papers in statistical modeling and data analytics.

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