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
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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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0 of 1 people found the following review helpful:
5.0 out of 5 stars
Great book,
By
This review is from: Design and Analysis of Experiments (Springer Texts in Statistics) (Hardcover)
I thought the book was awesome and organized very well. I liked the SAS code at the end of each chapter and the was the examples were worked through.
4 of 9 people found the following review helpful:
5.0 out of 5 stars
Design and analysis of experiments,
By cloudy (GA usa) - See all my reviews
This review is from: Design and Analysis of Experiments (Springer Texts in Statistics) (Hardcover)
This book is one of the best Exp.Design ever!It demonstrates the sas code in each design thus makes it easy to go through.
0 of 8 people found the following review helpful:
5.0 out of 5 stars
Design and Analysis of Experiments,
This review is from: Design and Analysis of Experiments (Springer Texts in Statistics) (Hardcover)
detail and specific.
It is appropriate for graduate students.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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log sample variance, coded factor levels, confounding equations, gage bar setting, ith treatment combination, varnish experiment, kth randomly, twelve treatment combinations, aliasing scheme, four treatment factors, twelfth gear, ice cream experiment, hth block, ammunition experiment, factorial center points, design factor combination, bleach experiment, noise factor combinations, two treatment factors, trout experiment, minimum significant difference, simultaneous confidence level, group divisible design, combination ijk, detergent experiment Key Phrases - Capitalized Phrases (CAPs): (learn more)
Squares Square, Corrected Total, Sum of Mean Source, Software Exercises, Blackwell Publishers, John Wiley, Std Error of Parameter Estimate Parameter, Denominator Denominator, Age Sex Ord, Applied Statistics, Exercises Table, Limit Means Limit, Simultaneous Simultaneous Lower Difference Upper, Block Treatment Combinations, Design Issues, Software Table, Estimation of Parameters, International Biometric Society, New York, Real Experiment-Cotton-Spinning Experiment, University of Wisconsin, Least Squares Estimation, Real Experiment-Popcorn-Microwave Experiment, Addison Wesley Longman Ltd, Analysis of Polynomial Regression Models New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
log sample variance, coded factor levels, confounding equations, gage bar setting, ith treatment combination, varnish experiment, kth randomly, twelve treatment combinations, aliasing scheme, four treatment factors, twelfth gear, ice cream experiment, hth block, ammunition experiment, factorial center points, design factor combination, bleach experiment, noise factor combinations, two treatment factors, trout experiment, minimum significant difference, simultaneous confidence level, group divisible design, combination ijk, detergent experiment Key Phrases - Capitalized Phrases (CAPs): (learn more)
Squares Square, Corrected Total, Sum of Mean Source, Software Exercises, Blackwell Publishers, John Wiley, Std Error of Parameter Estimate Parameter, Denominator Denominator, Age Sex Ord, Applied Statistics, Exercises Table, Limit Means Limit, Simultaneous Simultaneous Lower Difference Upper, Block Treatment Combinations, Design Issues, Software Table, Estimation of Parameters, International Biometric Society, New York, Real Experiment-Cotton-Spinning Experiment, University of Wisconsin, Least Squares Estimation, Real Experiment-Popcorn-Microwave Experiment, Addison Wesley Longman Ltd, Analysis of Polynomial Regression Models New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 72
books:
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59
books
cite this book:
See all 59 books citing this book
- Multiple Comparisons: Theory and Methods by Jason Hsu on 7 pages
- Experimental Designs by William Gemmel Cochran on 5 pages
- Incomplete Block Designs (Lecture Notes in Statistics) by Peter William Meredith John on page 348, and Back Matter
- Fundamental Concepts in the Design of Experiments by Charles R. Hicks on page 446, and Back Matter
- Applied Regression Analysis (Wiley Series in Probability and Statistics) by Norman R. Draper in Back Matter (1), and Back Matter (2)
See all 72 books this book cites
- Statistical Analysis of Designed Experiments by Helge Toutenburg in Front Matter, Back Matter (1), and Back Matter (2)
- A Calculus for Factorial Arrangements (Lecture Notes in Statistics) by Sudhir Gupta in Back Matter (1), and Back Matter (2)
- Applied Multivariate Analysis by Neil H. Timm in Front Matter, and Back Matter
- Modeling Longitudinal Data (Springer Texts in Statistics) by Robert E. Weiss in Front Matter
- Statistical Analysis of Financial Data in S-PLUS by Rene A. Carmona in Front Matter
See all 59 books citing this book
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