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Academic & Professional Books  Reference  Data Analysis & Modelling  Modelling

Design and Analysis of Experiments

By: Angela Dean and Daniel Voss
740 pages, Figs, tabs
Publisher: Springer-Verlag
Design and Analysis of Experiments
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  • Design and Analysis of Experiments ISBN: 9780387985619 Hardback Dec 1999 Usually dispatched within 1-2 weeks
Selected version: £89.50
About this book Contents Customer reviews Related titles

About this book

Provides a comprehensive introduction to modern experimental design at a level appropriate for advanced undergraduate and beginning graduate students. Includes the design and analysis of many real experiments, and sample SAS programs are included.


Principles and Techniques * Planning Experiments * Designs With One Source of Variation * Inferences for Contrasts and Treatment Means * Checking Model Assumptions * Experiments With Two Crossed Treatment Factors * Several Crossed Treatment Factors * Polynomial Regression * Analysis of Covariance * Complete Block Designs * Incomplete Block Designs * Designs With Two Blocking Factors * Confounded Two-Level Factorial Experiments * Confounding in General Factorial Experiments * Fractional Factorial Experiments * Response Surface Methodology * Random Effects and Variance Components * Nested Models * Split-Plot Designs

Customer Reviews

By: Angela Dean and Daniel Voss
740 pages, Figs, tabs
Publisher: Springer-Verlag
Media reviews
This is a readable book presenting the basic concepts, principles, and techniques of design and analysis of experiments. Written with a view to making it accessible to a wide audience, the authors make concerted efforts to avoid using calculus and linear algebra and, wherever needed, a low mathematical level is used for presentation. Rather than performing exploratory data analysis, the concentration here is on the use of prespecified models and preplanned analyses. Model assumptions are clearly stated, and checked through the use of residual plots rather than formal tests. All analyses are presented by using standard linear models under the assumption of normality. It is the experimentwise control of the error rate and confidence levels on which the presentation is focused as opposed to individual error rates and confidence levels. The popular "Taguchi techniques," used extensively in an industrial set-up, are included and appear throughout several chapters.

The book contains enough material for an instructor to offer a course ranging from one semester to one year. An attractive feature of this book is the inclusion of numerous real experiments which were either run by students or extracted from published articles---thus bringing home to students the practical utility of statistical designs. The authors have done a commendable job in presenting, explaining, and elucidating the fundamental concepts of design and analysis of experiments through illustrative examples. A carefully selected set of exercises is provided at the end of each chapter for students to test their understanding of the material.

--Mathematical Reviews

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