Linear Algebra and Linear Models comprises a concise and rigorous introduction to linear
algebra required for statistics followed by the basic aspects of the theory of linear
estimation and hypothesis testing. The emphasis is on the approach using generalized inverses.
Topics such as the multivariate normal distribution and distribution of quadratic forms are
included. For this third edition the material has been reorganised to develop the linear
algebra in the first six chapters to serve as a first course on linear algebra that is
especially suitable for students of statistics or for those looking for a matrix theoretic
approach to the subject. Other key features include: coverage of topics such as rank additivity
inequalities for eigenvalues and singular values a new chapter on linear mixed models over
seventy additional problems on rank: the matrix rank is an important and rich topic with
connections to many aspects of linear algebra such as generalized inverses idempotent matrices
and partitioned matrices. This text is aimed primarily at advanced undergraduate and first-year
graduate students taking courses in linear algebra linear models multivariate analysis and
design of experiments. A wealth of exercises complete with hints and solutions help to
consolidate understanding. Researchers in mathematics and statistics will also find the book a
useful source of results and problems.
