ISBN: 9781439816974 - optimal experimental design with r (18 risultati)

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Hardcover. Condizione: Good. 1st Edition. Hardcover, xix + 325 pages, NOT ex-library. Printed in the USA. Interior is clean and bright throughout with unmarked text, free of inscriptions and stamps. Cosmetic damage to the rear board: a patch of surface loss, approx. 1.5 cm long, exposing the inner cardboard, right on the side edge beneath the upper outer corner. Issued without a dust jacket. -- Experimental design is often overlooked in the literature of applied and mathematical statistics: statistics is taught and understood as merely a collection of methods for analyzing data. Consequently, experimenters seldom think about optimal design, including prerequisites such as the necessary sample size needed for a precise answer for an experimental question. Providing a concise introduction to experimental design theory, Optimal Experimental Design with R: - Introduces the philosophy of experimental design - Provides an easy process for constructing experimental designs and calculating necessary sample size using R programs - Teaches by example using a custom made R program package: OPDOE - Consisting of detailed, data-rich examples, this book introduces experimenters to the philosophy of experimentation, experimental design, and data collection. It gives researchers and statisticians guidance in the construction of optimum experimental designs using R programs, including sample size calculations, hypothesis testing, and confidence estimation. A final chapter of in-depth theoretical details is included for interested mathematical statisticians. -- Contents: 1 Introduction; I Determining the Minimal Size of an Experiment for Given Precision: - 2 Sample Size Determination in Completely Randomised Designs [Confidence estimation; Selection procedures; Testing hypotheses; Summary of sample size formulae] 3 Size of Experiments in Analysis of Variance Models [One-way layout; Two-way layout; Three-way layout] 4 Sample Size Determination in Model II of Regression Analysis [Confidence intervals; Hypothesis testing; Selection procedures] 5 Sequential Designs [Wald's sequential likelihood ratio test (SLRT) for one-parametric exponential families; Test about means for unknown variances; Triangular designs; A sequential selection procedure]; II Construction of Optimal Designs: -- 6 Constructing Balanced Incomplete Block Designs [Basic definitions; Construction of BIBD] 7 Constructing Fractional Factorial Designs [Introduction and basic notations; Factorial designs: basic definitions; Fractional factorial designs with two levels of each factor (2p−k -designs) & three levels of each factor (3p−m -designs)] 8 Exact Optimal Designs and Sample Sizes in Model I of Regression Analysis [Exact [phi]-optimal designs; Determining the size of an experiment]; III Special Designs: -- 9 Second Order Designs [Central composite designs; Doehlert designs; D-optimum and G-optimum second order designs; Comparing the determinant criterion for some examples] 10 Mixture Designs [The simplex lattice designs; Simplex centroid designs; Extreme vertice designs; Augmented designs; Constructing optimal mixture designs with R; An example]; A Theoretical Background [Non-central distributions; Groups, fields and finite geometries; Difference sets; Hadamard matrices; Existence and non-existence of non-trivial BIBD; Conference matrices]; References; Index.…

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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dieter Rasch: Currently Senior Consultant at the Centre of Experimental Design: University of Natural Resources and Life Sciences, Vienna, Dr. Rasch is an Elected Member of the International Statistical Institute (ISI), a Fellow of the I.…

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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optimal Experimental Design with R introduces experimenters to the philosophy of experimentation, and the need for good design and data collection for experiments. It gives experimenters and statisticians guidance on how to construct optimum experimental designs and calculate the sample size needed using R programs. The book contains many detailed real-world examples that show how the R programs should be used. The authors discuss variations of the regression model, including linear and nonlinear regression models. They also provide a final chapter of theoretical details for interested mathematical statisticians.…