{"product_id":"9781118592557","title":"Matrix Algebra for Linear Models","description":"\u003ch1\u003eMatrix Algebra for Linear Models\u003c\/h1\u003e\u003ch3\u003eMarvin H. J. Gruber\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cb\u003eA self-contained introduction to matrix analysis theory and applications in the field of statistics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComprehensive in scope, \u003ci\u003eMatrix Algebra for Linear Models\u003c\/i\u003e offers a succinct summary of matrix theory and its related applications to statistics, especially linear models. The book provides a unified presentation of the mathematical properties and statistical applications of matrices in order to define and manipulate data.\u003c\/p\u003e \u003cp\u003eWritten for theoretical and applied statisticians, the book utilizes multiple numerical examples to illustrate key ideas, methods, and techniques crucial to understanding matrix algebra’s application in linear models. \u003ci\u003eMatrix Algebra for Linear Models\u003c\/i\u003e expertly balances concepts and methods allowing for a side-by-side presentation of matrix theory and its linear model applications. Including concise summaries on each topic, the book also features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMethods of deriving results from the properties of eigenvalues and the singular value decomposition\u003c\/li\u003e \u003cli\u003eSolutions to matrix optimization problems for obtaining more efficient biased estimators for parameters in linear regression models\u003c\/li\u003e \u003cli\u003eA section on the generalized singular value decomposition\u003c\/li\u003e \u003cli\u003eMultiple chapter exercises with selected answers to enhance understanding of the presented material\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMatrix Algebra for Linear Models\u003c\/i\u003e is an ideal textbook for advanced undergraduate and graduate-level courses on statistics, matrices, and linear algebra. The book is also an excellent reference for statisticians, engineers, economists, and readers interested in the linear statistical model.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e  \u003cp\u003e\u003cb\u003eMARVIN H. J. GRUBER, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Professor Emeritus in the School of Mathematical Sciences at Rochester Institute of Technology. He has authored several books and journal articles in his areas of research interest, which include improving the efficiency of regression estimators. Dr. Gruber is a member of the American Mathematical Society and the American Statistical Association. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e31 December 2013\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781118592557\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e392\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e22.72\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44385466351756,"sku":"9781118592557","price":126.85,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781118592557_e058dfd7-3c15-4155-b41e-c3e52bd65a1b.jpg?v=1780167201","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781118592557","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}