Presents a novel approach to the statistical design of experiments, offering a simple way to specify and evaluate all possible designs without restrictions to classes of named designs. The work also presents a scientific design method from the recognition stage to implementation and summarization.
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Editorial Reviews
Review
Professors Anderson and McLean have done a noteworthy job of writing a text dealing with experiments. . . .a valuable contribution to the experimental design literature and it is strongly recommended for your statistical library.
---Journal of Qualtiy Technology
The authors employ a wide range of examples to illustrate the theory and practice of experimental design. . .For the teacher, the examples analyzed fully and others outlined as problems for the student will prove useful source of ideas. . . .refreshingly different from other texts.
---Nature
The material in this textbook has been thoroughly tested in the classroom by both of the authors. The reviews enthusiastically recommends it for use as a text. . .This book will also serve as valuable reference work for practitioners.
---Mathematical Review --This text refers to the Hardcover edition.
---Journal of Qualtiy Technology
The authors employ a wide range of examples to illustrate the theory and practice of experimental design. . .For the teacher, the examples analyzed fully and others outlined as problems for the student will prove useful source of ideas. . . .refreshingly different from other texts.
---Nature
The material in this textbook has been thoroughly tested in the classroom by both of the authors. The reviews enthusiastically recommends it for use as a text. . .This book will also serve as valuable reference work for practitioners.
---Mathematical Review --This text refers to the Hardcover edition.
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Most Helpful Customer Reviews
3 of 3 people found the following review helpful:
4.0 out of 5 stars
Great book for real experimenters with stat knowledge,
By
This review is from: Design of Experiments (Statistics: A Series of Textbooks and Monographs) (Hardcover)
I use this text to teach an applied, but advanced, course in Design of Experiment for (mostly) engineers and physical scientists. If you want an introductory text on DOE, don't buy this book. If you run experiments on a regular basis and have a reasonable comfort level with statistics, this book will help you design and analyze better experiments. The "no-name" in the title will likely be appreciated by experimentalists--most textbooks in DOE provide basic good designs, ones that have names (factorial, Latin square, RCBD,...), but most interesting experiments require designs that are too complex to be named. These authors show general design principles that are used to build designs of any complexity within a certain class (complete balanced designs). They also show how to generate designs outside of this class, such as fractional factorial designs. This covers a very broad spectrum of designs for industrial experiments. If you have been surprised that the designs in the standard texts don't seem to be quite right for your problem, this book is for you. I would suggest the uninitiated begin with a simpler text, and buy this one next.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
A Rare Gem,
By dgs "dgs" (CA) - See all my reviews
This review is from: Design of Experiments (Statistics: A Series of Textbooks and Monographs) (Hardcover)
Most books and articles on experiment design spend very few, if any, pages on experiment design. Instead, they describe a myriad of existing experiments and leave the reader to select from some sort of catalogue. After that, they move directly into the analysis of experimental data. Well, no amount of analysis is going to salvage data collected from an inappropriate design, and a more frequent problem, analyzing data with techniques that don't reflect how the experiment was actually conducted can lead to misleading, if not irreproducible results. With Dr. Lorenzen's approach, the practitioner can create an initial design, review its utility as well as short-comings and then proceed through enough iterations to converge at the best set of trade-offs for their situation. As one example, classic texts discuss "Completely Randomized Designs" in one chapter and then "Split Plots" in another, without addressing why one might choose one over the other. Without this guidance, people tend to overwhelm true experimental signals with controllable errors or trade off diagnostic ease for difficulties in changing configurations in real-world settings like manufacturing plants and test facilities.You're better off understanding the building blocks of variable types, nesting, restrictions on randomization and so forth as described in this text. After that, you're well equipped to both design and accurately analyze the data you've taken. Now that's efficiency!
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Inside This Book
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First Sentence:
Experiments are performed by people in nearly all walks of life. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
orthogonal main effect design, basic factor technique, confounded string, inferential experimental unit, significant only tests, complete confounding pattern, table including source, ith treatment level, pseudo factors, restriction error, completely randomized fashion, identity generator, mixed level designs, specific fuel economy, redesigning the experiment, inference space, main effect designs, factor level combinations, control factor combinations, pooling rules, sliding factors, minimal detectable difference, two factor interactions, four injectors, eight injectors Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, John Wiley, Marcel Dekker, Journal of the American Statistical Association, Realistic Approach, Annals of Mathematical Statistics, Fundamental Concepts, Iowa State University Press, Kraus International Publications, Model Building, White Plains, Applied Statistics, Biometrical Approach, Journal of the Royal Statistical Society, Statistical Principles, Applications of Statistics, Quality Engineering, Repeat Problem, Englewood Cliffs, Foam Base, General Motors Research Laboratories, Latex Glue, Mathematics Department, National Bureau of Standards, Research Publication New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Experiments are performed by people in nearly all walks of life. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
orthogonal main effect design, basic factor technique, confounded string, inferential experimental unit, significant only tests, complete confounding pattern, table including source, ith treatment level, pseudo factors, restriction error, completely randomized fashion, identity generator, mixed level designs, specific fuel economy, redesigning the experiment, inference space, main effect designs, factor level combinations, control factor combinations, pooling rules, sliding factors, minimal detectable difference, two factor interactions, four injectors, eight injectors Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, John Wiley, Marcel Dekker, Journal of the American Statistical Association, Realistic Approach, Annals of Mathematical Statistics, Fundamental Concepts, Iowa State University Press, Kraus International Publications, Model Building, White Plains, Applied Statistics, Biometrical Approach, Journal of the Royal Statistical Society, Statistical Principles, Applications of Statistics, Quality Engineering, Repeat Problem, Englewood Cliffs, Foam Base, General Motors Research Laboratories, Latex Glue, Mathematics Department, National Bureau of Standards, Research Publication 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 100
books:
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100
books
cite this book:
See all 100 books citing this book
- Design of Experiments (Statistics: A Series of Textbooks and Monographs) by Virgil L. Anderson on 14 pages
- Design and Analysis of Experiments by Douglas C. Montgomery on 8 pages
- Fundamental Concepts in the Design of Experiments by Charles R. Hicks on 8 pages
- Statistical Design and Analysis of Experiments (Classics in Applied Mathematics No 22. ) by Peter W. M. John on 7 pages
- Statistical Design and Analysis of Industrial Experiments (Statistics: A Series of Textbooks and Monographs) by Ghosh on 7 pages
See all 100 books this book cites
- Design of Experiments (Statistics: A Series of Textbooks and Monographs) by Virgil L. Anderson on 9 pages
- Fundamental Concepts in the Design of Experiments by Charles R. Hicks on 8 pages
- Analysis of Variance: Fixed, Random and Mixed Models by Hardeo Sahai in Back Matter (1), Back Matter (2), and Back Matter (3)
- Item Response Theory: Parameter Estimation Techniques, Second Edition (Statistics: A Series of Textbooks and Monographs) by Frank B. Baker in Front Matter (1), Front Matter (2), and Front Matter (3)
- Sense and Nonsense of Statistical Inference (Popular Statistics) by Charmont Wang on page 17, page 23, and page 136
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