Optimal Experimental Design with R (Statistical Theory and Methods)

Dieter Rasch; Jürgen Pilz; Rob Verdooren; Albrecht Gebhardt

ISBN 10: 1439816972 / ISBN 13: 9781439816974

Published by CRC Press, 2011

About this Item

Hardcover, xix + 325 pages, NOT ex-library. Printed in the USA. Interior is clean with unmarked text, free of inscriptions and stamps. Boards show regular shelfwear with a few scratches. Cosmetic damage to the rear board (a patch of surface loss, approx. 1.5 cm / 0.6 inch long, exposing the inner cardboard, right on the side edge beneath the upper outer corner). Published 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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Bibliographic Details

• Title: Optimal Experimental Design with R (Statistical Theory and Methods)
• Publisher: CRC Press
• Publication Date: 2011
• Language: English
• ISBN 10: 1439816972
• ISBN 13: 9781439816974
• Binding: Hardcover
• Edition: 1st Edition
• Condition: Good

About this title

Synopsis

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.

About the Author

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 IMS, and author/co-author of 46 books and more than 260 scientific papers.

From 1958- 1990, Dr. Rasch was Head of the Deparment (and Institute) of Biometry at the Research Centre Dummerstorf-Rostock, Germany. Afterwards, Dr. Rasch was professor of Mathematical Statistics at the University of Wageningen, The Netherlands from 1991 to 2000. Since 2000, he has served as a guest professor at the Math. Inst. of the University of Klagenfurt, the University Vienna, and at the Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences (2007 to 2010).

Albrecht Gebhardt: Assistant professor at the Institute of Statistics, University of Klagenfurt since 2004.

Jürgen Pilz: Professor and Chair of Applied Statistics at the University of Klagenfurt (UniKlu), Austria since 1994, and the head of the Department of Statistics at UniKlu since 2007. He has held many guest professorships, including at Purdue University, USA, Charles University, Prague,Czech Republic, the University of Augsburg, Germany, and the University of British Columbia, Vancouver, Canada. He is an Elected Member of the Int. Statist. Institute (ISI), a Fellow of the IMS, and author/co-author of six books and more than 100 scientific papers.

Rob Verdooren: A Consultant Statistician at Danone Research, Centre for Spceialised Nutrition, Wageningen, the Netherlands. He is retired Associate Professor in Experimental Design and Analysis at the Agricultural Uniiversity Wageningen, the Netherlands. Besides Experimental Design, his interests lies in Biostatistics and the design and analysis of breeding trials of Oil Palms in Indonesia.