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Design and Analysis of Experiments

  • Textbook
  • © 2017

Overview

Authors:
  1. Angela Dean

    1. Ohio State University, Columbus, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Daniel Voss

    1. Wright State University, Dayton, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  3. Danel Draguljić

    1. Lancaster, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Second edition includes new material on screening experiments and analysis of mixed models, a new chapter on computer experiments, added “Using R” sections, updated SAS output, and use of SAS Proc Mixed
  • Presents a step-by-step guide to design, including a planning checklist that emphasizes practical considerations
  • Explains all the basics of analysis: estimation of treatment contrasts and analysis of variance, while also applying these in a wide variety of settings
  • Utilizes data drawn from real experiments
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Texts in Statistics (STS)

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xxv
    Download chapter PDF

  2. Principles and Techniques

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 1-5

  3. Planning Experiments

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 7-30

  4. Designs with One Source of Variation

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 31-68

  5. Inferences for Contrasts and Treatment Means

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 69-102

  6. Checking Model Assumptions

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 103-137

  7. Experiments with Two Crossed Treatment Factors

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 139-200

  8. Several Crossed Treatment Factors

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 201-247

  9. Polynomial Regression

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 249-284

  10. Analysis of Covariance

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 285-304

  11. Complete Block Designs

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 305-347

  12. Incomplete Block Designs

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 349-397

  13. Designs with Two Blocking Factors

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 399-431

  14. Confounded Two-Level Factorial Experiments

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 433-472

  15. Confounding in General Factorial Experiments

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 473-493

  16. Fractional Factorial Experiments

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 495-564

  17. Response Surface Methodology

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 565-614

  18. Random Effects and Variance Components

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 615-669

  19. Nested Models

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 671-701

  20. Split-Plot Designs

    • Angela Dean, Daniel Voss, Danel Draguljić
    Pages 703-764
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Keywords

About this book

This textbook takes a strategic approach to the broad-reaching subject of experimental design by identifying the objectives behind an experiment and teaching practical considerations that govern design and implementation, concepts that serve as the basis for the analytical techniques covered. Rather than a collection of miscellaneous approaches, chapters build on the planning, running, and analyzing of simple experiments in an approach that results from decades of teaching the subject. In most experiments, the procedures can be reproduced by readers, thus giving them a broad exposure to experiments that are simple enough to be followed through their entire course. Outlines of student and published experiments appear throughout the text and as exercises at the end of the chapters. The authors develop the theory of estimable functions and analysis of variance with detail, but at a mathematical level that is simultaneously approachable. Throughout the book, statistical aspects of analysiscomplement practical aspects of design. 

This new, second edition includes

  • an additional chapter on computer experiments
  • additional "Using R” sections at the end of each chapter to illustrate R code and output 
  • updated output for all SAS programs and use of SAS Proc Mixed
  • new material on screening experiments and analysis of mixed models



Reviews

“The textbook provides a practically oriented version of design and analysis of experiments. The corresponding methods are illustrated by means of numerous simple experiments. Thus, the models and methods are equipped with many examples, exercises, numerical results and related tables and figures. ... The present volume can be recommended as textbook for lectures on models and methods of experimental design as well as handbook for use in practice.” (Kurt Marti, zbMATH 1383.62001, 2018)

Authors and Affiliations

  • Ohio State University, Columbus, USA

    Angela Dean

  • Wright State University, Dayton, USA

    Daniel Voss

  • Lancaster, USA

    Danel Draguljić

About the authors

Angela Dean, PhD, is Professor Emeritus of Statistics and a member of the Emeritus Academy at The Ohio State University, Columbus, Ohio. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a former chair of the Section on Physical and Engineering Sciences of the American Statistical Association. Her research interests include design of screening and computer experiments.

Daniel Voss, PhD,  is Professor Emeritus of Mathematics and Statistics at Wright State University, Dayton, Ohio. He is a former Interim Dean of the College of Science and Mathematics and Interim Director of the Statistical Consulting Center at WSU. His research interests include the analysis of saturated fractional factorial experiments, and the equivalence of hypothesis testing and confidence interval estimation.


Danel Draguljic, PhD, is Assistant Professor of Mathematics at Franklin & Marshall College, Lancaster, Pennsylvania. His research interests include design of screening experiments, design of computer experiments, and statistics education.

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