Handbook and reference guide for students and practitioners of
statistical regression-based analyses in R Handbook of Regression
Analysis with Applications in R, Second Edition is a comprehensive
and up-to-date guide to conducting complex regressions in the R
statistical programming language. The authors' thorough treatment
of "classical" regression analysis in the first edition is
complemented here by their discussion of more advanced topics
including time-to-event survival data and longitudinal and
clustered data. The book further pays particular attention to
methods that have become prominent in the last few decades as
increasingly large data sets have made new techniques and
applications possible. These include: Regularization methods
Smoothing methods Tree-based methods In the new edition of the
Handbook, the data analyst's toolkit is explored and expanded.
Examples are drawn from a wide variety of real-life applications
and data sets. All the utilized R code and data are available via
an author-maintained website. Of interest to undergraduate and
graduate students taking courses in statistics and regression, the
Handbook of Regression Analysis will also be invaluable to
practicing data scientists and statisticians.
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