Formulas useful for linear regression analysis and related matrix theory. It's only formulas but we like them
The book contains very useful formulas for linear regression analysis including related matrix theory. As the authors mention in the preface of the book, this work is neither a textbook nor a handbook but still very useful for those who work in the field of linear regression and need a brief refreshment on formulas and tools. Hence, the advantage of this book is that it collects together some important amount of formulas in the field of linear regression. However, some basics concepts on linear algebra, statistics and regression are needed before using this book. The contents is divided into 26 sections, covering with formulas and short statements the following topics and concepts: the model matrix, fitted values, residuals, regression coefficients, decompositions of sum of squares, distributions, best linear predictors, testing hypotheses, regression diagnostics, BLUE estimators, OLSE estimators, best linear unbiased predictors, mixed models, multivariate linear models, principal components, discriminant analysis, factor analysis, linear sufficiency, admissibility, canonical correlations, rank rules, inverses of matrices, projectors, eigenvalues, matrix decompositions, Löwner ordering, some matrix inequalities, Kronecker products and rules of calculus for matrix derivatives. Also a short list of notations and an index are provided.
scientific article; zbMATH DE number 6090405