Statistical Regression Modeling with R (PDF) offers a concise point of reference for the most commonly used regression methods. It starts with linear and nonlinear regression for logistic regression for binomially distributed data, normally distributed data, and Poisson regression and negative binomial regression for count data. It then moves 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, in addition, to contribute to the rapid development of statistics and data science. With data and computing programs available to ease readers’ learning experience, Statistical Regression Modeling with R: Longitudinal and Multi-level Modeling encourages the applications of R in linear, longitudinal, nonlinear, and multi-level regression. All included datasets, along with the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This ebook will be valuable in graduate courses on applied regression, in addition for practitioners and researchers in the fields of data science, public health, statistical analytics, and related fields.
NOTE: The product only includes the ebook Statistical Regression Modeling with R in PDF. No access codes are included.
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