Statistical Regression Modeling with R: Longitudinal and Multi-level Modeling

This book provides a concise point of reference for commonly used regression methods, covering linear, nonlinear, logistic, Poisson, and negative-binomial regression, and promoting the applications of R in linear, nonlinear, longitudinal, and multi-level regression. It is valuable for graduate courses and practitioners in data science, statistical analytics, public health, and related fields.

Format: Paperback / softback
Length: 228 pages
Publication date: 10 April 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book serves as a valuable reference for the most commonly employed regression methods. It begins by exploring linear and nonlinear regression for normally distributed data, followed by logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. The book then delves into regression models that handle longitudinal and multi-level data structures. Designed to facilitate the transition from classical to advanced regression modeling, it also contributes to the rapid development of statistics and data science.

To enhance the learning experience, the book includes data and computing programs available in packages like nlme and lme4 for multi-level regression. All the included datasets and the corresponding R program are detailed in Appendix A. This book is particularly valuable for graduate courses on applied regression, as well as for practitioners and researchers in data science, statistical analytics, public health, and related fields.

Weight: 385g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030675851
Edition number: 1st ed. 2021