Applied Multivariate Statistics with R 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 This book brings the power of multivariate statistics to graduate level practitioners making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays linear algebra univariate bivariate and multivariate normal distributions factor methods linear regression discrimination and classification clustering time series models and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises real data sets and R implementations. The data are interesting real world topics particularly from health and biology related contexts. As an example of the approach the text examines a sample from the Behavior Risk Factor Surveillance System discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.