Optimal Experimental Design with R (Statistical Theory and Methods)

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. …