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Design and Analysis of Experiments

Design and Analysis of Experiments - 99 edition

ISBN13: 978-0387985619

Cover of Design and Analysis of Experiments 99 (ISBN 978-0387985619)
ISBN13: 978-0387985619
ISBN10: 0387985611
Cover type: Hardback
Edition/Copyright: 99
Publisher: Springer-Verlag New York
Published: 1999
International: No
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Design and Analysis of Experiments - 99 edition

ISBN13: 978-0387985619

Angela M. Dean and Daniel Voss

ISBN13: 978-0387985619
ISBN10: 0387985611
Cover type: Hardback
Edition/Copyright: 99
Publisher: Springer-Verlag New York

Published: 1999
International: No
Summary

The design and analysis of experiments is an essential part of investigation and discovery in science, of process and product improvement in manufacturing, and of comparison of competing protocols or treatments in the applied sciences. This book offers a step by step guide to the experimental planning process and the ensuing analysis of normally distributed data.

Design and Analysis of Experiments emphasizes the practical considerations governing the design of an experiment based on the objectives of the study and a solid statistical foundation for the analysis. Almost all data sets in the book have been obtained from real experiments, either run by students in statistics and the applied sciences, or published in the scientific literature. Details of the planning stage of numerous different experiments are discussed. The statistical analysis of experimental data is based on estimable functions and is developed with some care.

Design and Analysis of Experiments starts with basic principles and techniques of experimental design and analysis of experiments. It provides a checklist for the planning of experiments, and explains the estimation of treatment contrasts and analysis of variance. These basics are then applied in a wide variety of settings. Designs covered include completely randomized designs, complete and incomplete block designs, row-column designs, single replicate designs with confounding, fractional factorial designs, response surface designs, and designs involving nested factors and factors with random effects, including split-plot designs.

The book is accessible to all readers who have a good basic knowledge of expected values, confidence intervals and hypothesis tests. It is ideal for use in the classroom at both the senior undergraduate and the graduate level. A guide to the use of the SAS System computer package is given at the end of each chapter, but the book can equally well be used in conjunction with any statistical package. Exercises based on a large number of different experiments are included in most chapters.

Author Bio

Dean, Angela M. : The Ohio State University Main Campus

Angela Dean is Professor of Statistics at The Ohio State University. She is a Fellow of the American Statistical Association, a Fellow the Royal Statistical Society and an elected member of the International Statistical Institute. Her research focuses on the construction of efficient designs for factorial experiments. She is currently on the editorial board of the Journal of the Royal Statistical Society.

Voss, Daniel : Wright State University Main Campus

Daniel Voss is Associate Professor of Mathematics and Statistics at Wright State University. His research focuses on the analysis of saturated fractional factorial designs, following past work on confounding in factorial experiments as well as robust design. He is a member of the WSU Statistical Consulting Center Advisory Committee and has served as Associate and Interim Director of the center.

Table of Contents

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

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