Description
Statistical Regression Modeling with R (PDF) serves as an essential reference guide that delves into the most prevalent and effective regression methodologies used today. This comprehensive resource begins by exploring the foundational concepts of linear and nonlinear regression, progressing to specialized techniques such as logistic regression tailored for binomially distributed data, as well as Poisson and negative binomial regression designed for count data analysis.
As the content unfolds, readers are introduced to advanced regression frameworks that are particularly adept at handling longitudinal and multi-level data structures. This structured approach effectively bridges the gap between classical techniques and cutting-edge regression modeling practices, ensuring readers are well-equipped for the rapid advancements in statistics and data science.
Statistical Regression Modeling with R: Longitudinal and Multi-level Modeling not only emphasizes the application of R in a variety of contexts—ranging from linear and nonlinear regression analyses to more complex longitudinal and multi-level models—but also enhances the learning experience with available datasets and computing programs. Appendices, particularly Appendix A, detail all datasets and provide specific instructions on utilizing R packages such as nlme and lme4 for multi-level regression applications.
This ebook is an invaluable resource for graduate-level courses focused on applied regression and serves as a vital tool for professionals and researchers across diverse fields, including data science, public health, statistical analytics, and more.
NOTE: The product exclusively includes the ebook Statistical Regression Modeling with R in PDF format. Access codes are not part of this offering.
Reviews
There are no reviews yet.