Statistical Regression Modeling with R

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

Autorentext

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.

Zusammenfassung
"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)

Inhalt

1. 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.