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.
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Professor William Moser is a Professor of Chemical Engineering at the Worcester Polytechnic Institute. He is also a member of the Center for Inorganic Membrane Studies at WPI. Professor Moser invented both the Cylindrical Internal Reflectance Reactors (CIR-REACTORS) and Optical Fiber coupled CIR-Reactors (OFCIR-REACTORS), which are now commercial products used for reaction monitoring in homogeneous and heterogeneous catalysis. He was recently issued a patent on remote infrared sensing using optical fiber cables connected to high pressure reactors as well as normal laboratory glassware reactors. Dr. Moser is a Fellow of the New York Academy of Sciences, and is a co-founder of the Organic Reactions Catalysis Society. He is a past Chairman of the ACS Petroleum Division. He is the editor of several books on homogeneous and heterogeneous catalysis, and has a variety of publications and patents in the catalytic and materials science fields.
"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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