Linear models, normally presented in a highly theoretical and mathematical style, are brought down to earth in this comprehensive textbook. Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building mean models, regression models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets. The author includes both applied and theoretical discussions of the multivariate normal distribution, quadratic forms, maximum likelihood estimation, less than full rank models, and general mixed models. The mean model is used to bring all of these topics together in a coherent presentation of linear model theory.
Publisher: Elsevier Science Publishing Co Inc
Number of pages: 228
Weight: 580 g
Dimensions: 229 x 152 x 19 mm
"At the theorectical level, this book deals with the general linear model: the usual results on the distribution of linear functions of the observations and of quadratic forms are all derived in the general case." --MATHEMATICAL REVIEWS "This text presents the linear model (i.e., the analysis of variance and regression theory) from a sophisticated matrix algebra formulation. The book would be most suitable for graduate students of statistics who are already familiar with both linear algebra and the linear model." --JOURNAL OF MATHEMATICL PSYCHOLOGY
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