Introduction.- Elements of R.- Graphical Displays.- Basic Linear Algebra.- The Univariate Normal Distribution.- Bivariate Normal Distribution.- Multivariate Normal Distribution.- Factor Methods.- Multivariate Linear Regression.- Discrimination and Classification.- Clustering.- Time Series Models.- Other Useful Methods.- References.- Appendix.- Selected Solutions.- Index.
Daniel Zelterman, PhD, is Professor in the Department of Biostatistics at Yale University. His research areas include computational statistics, models for discrete valued data, and the design of clinical trials in cancer studies. In his spare time he plays oboe and bassoon in amateur orchestral groups and has backpacked hundreds of miles of the Appalachian Trail.
“This book is so clearly explained with R code throughout that it could be used as a self-learning text for an applied multivariate course and should be assigned as a selflearning adjunct assignment for a graduate level theoretical multivariate course. The real-word examples are page turners and ubiquitous use of color and fancy graphs easily explained make this usually dry topic an exciting one.” (Donna Pauler Ankerst, Biometrics, Vol. 73 (1), March, 2017)“This book demonstrates the process and outcomes for a wide array of multivariate statistical applications using program R. … The chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. This book is strongly recommended for graduate-level statistics practitioners.” (Hemang B. Panchal, Doody’s Book Reviews, December, 2015)
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