
Design and Analysis of Experiments
Description
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 / Votes
"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)
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Persons
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.
Content
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