This book is designed for the graduate-level practitioner who needs to use these multivariate statistics without time spent on mathematical derivations. Multivariate statistics, itself, is a mature field with many different methods. The premise forms from the reality of most scientific investigations, where there is more than one variable of interest. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distribution, factor methods, linear regression, discrimination and classification, clustering, time series models and a few additional methods. Chapters include R code throughout, exercises, and real data sets. The data cover intriguing topics, particularly from health or biology-related contexts. As an example of the approach, this book uses the sample average of the multivariate distribution to estimate the population mean. At the same time, it avoids getting bogged down in the optimal properties of such an estimator when sampled from a normal parent population. Readers will be empowered to analyze their data within particular discipline-specific questions. Those with backgrounds in statistics will learn new methods, but will also have the chance to review some topics they already know. R enables the analysis within the book and the future work the book will inspire. Prior experience with R is not necessary.