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    Artikelzustand
    Akzeptabel: Buch mit deutlichen Gebrauchsspuren. Der Einband kann einige Beschädigungen aufweisen, ...
    Release Year
    1999
    Book Title
    Linear Models in Statistics
    ISBN
    9780471315643
    Kategorie

    Über dieses Produkt

    Product Identifiers

    Publisher
    Wiley & Sons, Incorporated, John
    ISBN-10
    0471315648
    ISBN-13
    9780471315643
    eBay Product ID (ePID)
    526736

    Product Key Features

    Number of Pages
    600 Pages
    Language
    English
    Publication Name
    Linear Models in Statistics
    Subject
    Probability & Statistics / General
    Publication Year
    1999
    Type
    Textbook
    Subject Area
    Mathematics
    Author
    Alvin C. Rencher
    Series
    Wiley Series in Probability and Statistics Ser.
    Format
    Hardcover

    Dimensions

    Item Height
    1.3 in
    Item Weight
    33.7 Oz
    Item Length
    9.3 in
    Item Width
    6.5 in

    Additional Product Features

    Intended Audience
    Scholarly & Professional
    LCCN
    99-030176
    Reviews
    "Rencher...offers a textbook for a one-semester advanced undergraduate or beginning graduate course.... He includes more material than can actually squeeze into one semester...a good idea in statistics." (SciTech Book News, Vol. 24, No. 4, December 2000) "An excellent book. Highly recommended. Upper-division undergraduate and graduate students; professionals." (Choice, Vol. 38, No. 7, March 2001) "I would recommend the book to anyone as a reference book for the topics covered.... The book should also be a strong candidate for any M.S. course in linear models because of the numerous exercises with solutions and clear writing style." (Technometrics, Vol. 42, No. 4, May 2001) "Rencher's textbook is certainly of interest for students and instructors looking for a mathematical introduction to linear statistical models." (Statistics & Decisions, Volume 19, No 2, 2001) "...courses that go by the name "linear models" cover a combination of linear model theory, regression diagnostic, analysis of variance and more complex models that use linear models as a stepping stone. This book is appropriate for such courses...the collection of exercises adds to the book's value as a textbook." (Journal of the American Statistical Association, September 2001) "Gives a solid theoretical foundation to standard topics..." (American Mathematical Monthly, November 2001)
    Dewey Edition
    22
    Series Volume Number
    346
    Illustrated
    Yes
    Dewey Decimal
    519.535
    Table Of Content
    Matrix Algebra.Random Vectors and Matrices.Multivariate Normal Distribution.Distribution of Quadratic Forms in y.Simple Linear Regression.Multiple Regression: Estimation.Multiple Regression: Tests of Hypotheses and Confidence Intervals.Multiple Regression: Model Validation and Diagnostics.Multiple Regression: Randomx's.Analysis of Variance Models.One-Way Analysis of Variance: Balanced Case.Two-Way Analysis of Variance: Balanced Case.Analysis of Variance: Unbalanced Data.Analysis of Covariance.Random Effects Models and Mixed Effects Models.Additional Models.Answers and Hints to Selected Problems.Data Sets and SAS Files.Bibliography.
    Synopsis
    Linear models made easy with this unique introduction Linear Models in Statistics discusses classical linear models from a matrix algebra perspective, making the subject easily accessible to readers encountering linear models for the first time. It provides a solid foundation from which to explore the literature and interpret correctly the output of computer packages, and brings together a number of approaches to regression and analysis of variance that more experienced practitioners will also benefit from. With an emphasis on broad coverage of essential topics, Linear Models in Statistics carefully develops the basic theory of regression and analysis of variance, illustrating it with examples from a wide range of disciplines. Other features of this remarkable work include: * Easy-to-read proofs and clear explanations of concepts and procedures * Special topics such as multiple regression with random x's and the effect of each variable on R¯2 * Advanced topics such as mixed and generalized linear models as well as logistic and nonlinear regression * The use of real data sets in examples, with all data sets available over the Internet * Numerous theoretical and applied problems, with answers in an appendix * A thorough review of the requisite matrix algebra * Graphs, charts, and tables as well as extensive references, This unique book takes a look at linear models from a matrix perspective. With an emphasis on the theory of regression and analysis of variance, well-known author, Alvin Rencher, carefully develops the theory, lavishly illustrating it with numerically applied examples., Linear models made easy with this unique introduction Linear Models in Statistics discusses classical linear models from a matrix algebra perspective, making the subject easily accessible to readers encountering linear models for the first time. It provides a solid foundation from which to explore the literature and interpret correctly the output of computer packages, and brings together a number of approaches to regression and analysis of variance that more experienced practitioners will also benefit from. With an emphasis on broad coverage of essential topics, Linear Models in Statistics carefully develops the basic theory of regression and analysis of variance, illustrating it with examples from a wide range of disciplines. Other features of this remarkable work include:Easy-to-read proofs and clear explanations of concepts and proceduresSpecial topics such as multiple regression with random x's and the effect of each variable on R2Advanced topics such as mixed and generalized linear models as well as logistic and nonlinear regressionThe use of real data sets in examples, with all data sets available over the InternetNumerous theoretical and applied problems, with answers in an appendixA thorough review of the requisite matrix algebraGraphs, charts, and tables as well as extensive references
    LC Classification Number
    QA276.R425 2000

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