ISBN: 9780367572037 - linear models and the relevant distributions and matrix algebra (chapman & hall/crc texts in statistical science) (11 Ergebnisse)

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Paperback. Zustand: New. Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models, or a resource used (in a course on mathematical statistics) to illustrate various theoretical concepts in the context of a relatively complex setting of great practical importance.Features:Provides coverage of matrix algebra that is extensive and relatively self-contained and does so in a meaningful contexProvides thorough coverage of the relevant statistical distributions, including spherically and elliptically symmetric distributionsIncludes extensive coverage of multiple-comparison procedures (and of simultaneous confidence intervals), including procedures for controlling the k-FWER and the FDRProvides thorough coverage (complete with detailed and highly accessible proofs) of results on the properties of various linear-model procedures, including those of least squares estimators and those of the F test.Features the use of real data sets for illustrative purposesIncludes many exercises.…

Zustand: New. pp. 538.

Zustand: New.

Paperback. Zustand: Brand New. 538 pages. 10.00x7.01x1.10 inches. In Stock.

Taschenbuch. Zustand: Neu. Linear Models and the Relevant Distributions and Matrix Algebra | David A. Harville | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Chapman and Hall/CRC | EAN 9780367572037 | Verantwortliche Person für die EU: Taylor and Francis, Taylor & Francis Verlag Gmbh, Kaufingerstr 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.…

Paperback. Zustand: New. Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models, or a resource used (in a course on mathematical statistics) to illustrate various theoretical concepts in the context of a relatively complex setting of great practical importance.Features:Provides coverage of matrix algebra that is extensive and relatively self-contained and does so in a meaningful contexProvides thorough coverage of the relevant statistical distributions, including spherically and elliptically symmetric distributionsIncludes extensive coverage of multiple-comparison procedures (and of simultaneous confidence intervals), including procedures for controlling the k-FWER and the FDRProvides thorough coverage (complete with detailed and highly accessible proofs) of results on the properties of various linear-model procedures, including those of least squares estimators and those of the F test.Features the use of real data sets for illustrative purposesIncludes many exercises.…