Multivariate statistical methods have evolved from the pioneering work of Fisher, Pearson, Hotelling, and others, motivated by practical problems in biological and other sciences. In the past fifty years, the field has grown rapidly, largely due to the availability of computers that make the calculations feasible. This book gives a comprehensive and self-contained introduction, carefully balancing mathematical theory and practical applications. Unique features of A First Course in Multivariate Statistics include the presentation of the EM algorithm for maximum likelihood estimation with incomplete data, resampling-based methods of testing, a brief introduction to the theory of elliptical distributions, and a comparison of linear and quadratic classification rules. Examples from biology, anthropology, chemistry, and other areas are worked out in detail.

The book contains a wealth of exercises, ranging from easy to advanced, making it an ideal text for the classroom. Many graphical illustrations help the student develop an intuitive understanding of the mathematical concepts.