This book is a survey of matrix algebra commonly used in multivariate statistics. It is an uncorrected reprint of an edition of the book that was first published in 1982. The book begins with basic topics in linear algebra, including operations on matrices, determinants, solving linear equations, inverses, rank, eigenvalues, and canonical forms. This is followed by chapters on applications of linear algebra in statistics, particularly in regression analysis. Although this book is still useful, the material is somewhat out of date. Harville (1997) is a more up-to-date reference.
Harville, David A., Matrix Algebra From a Statistician's Perspective . Springer, 1997.
2. Basic Operations.
3. Special Matrices.
5. Inverse Matrices.
7. Canonical Forms.
8. Generalized Inverses.
9. Solving Linear Equations.
10. Partitioned Matrices.
11. Eigenvalues and Eigenvectors.
11A. Appendix to Chapter 11.
13. Applications in Statistics.
14. The Matrix Algebra of Regression Analysis.
15. An Introduction to Linear Statistical Models.