Based on Jeffrey Luftig’s highly successful training course for quality engineers and managers, this book now offers you the fundamentals of experimental design and thorough guidance on the steps required to carry out experiments that can help your firm improve quality, reduce costs, and increase customer satisfaction. Case studies illustrate every aspect of experimental procedure, including how to define the research question...identify key variables ... select the appropriate model...understand measurement systems...ensure internal and external validity...select the appropriate sampling plan...execute and monitor the experiment...and correctly analyze, report, and standardize the results. Included are pointers on how to avoid financial disasters arising from poorly constructed and performed experiments, as well as a detailed checklist to help optimize the efficiency of even the most complex experiment.
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Book Description
Editorial Reviews
From the Back Cover
Proven techniques and advice for optimizing the design of any experimental experiment. Controlled experiments by engineers are essential to verifying the viability and reliability of materials, products, and processes. No one knows this better than Jeffrey Luftig, whose highly successful training course has helped numerous corporations develop and conduct valid experiments. Based on Lufitg's field-tested program, Design of Experiments in Quality Engineering offers you the fundamentals of experimental design and thorough guidance on the steps required to carry out experiments that can help your firm improve quality, reduce costs, and increase customer satisfaction. Numerous case studies illustrate every aspect of experimental procedure, including how to: Define the research question; Identify key variables; Select the appropriate model; Understand measurement systems; Ensure internal and external validity; Select the appropriate sampling plan execute and monitor the experiment; Correctly analyze, report, and standardize the results.
About the Author
Jeffrey T. Luftig is president of Luftig & Warren International in Southfield, Michigan, which develops and delivers training and consulting services in the Quality Sciences to businesses and industries throughout the U.S., Europe, Asia, South America, and Australia. Clients have included Alcoa, BF Goodrich, Hughes Aircraft, IBM, Ford Motor Company, and Motorola. A prolific author and lecturer, Dr. Luftig is the former Associate Dean of the College of Technology and Director of the Technology Services Center at Eastern Michigan University. He is a past recipient of the Endowed Chair for Research, Development, and Training in the Quality Sciences, funded by Ford Motor Company’s Electrical and Electronics Division. Victoria S. Jordan, a Certified Quality Engineer, is a senior staff associate at Luftig & Warren International, Inc., where she assists service and manufacturing companies in implementing Total Quality, including strategic planning and policy deployment. She also provides management and consulting training to Fortune 500 companies implementing TQM. A former quality assurance manager at General Electric, Mrs. Jordan is a senior member and past section chairperson of the American Society for Quality Control and past chairman of the program committee of the Nashville Quality Forum.
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Most Helpful Customer Reviews
7 of 8 people found the following review helpful:
1.0 out of 5 stars
Skip this one if you want Practical DOE information!!,
By
This review is from: Design of Experiments in Quality Engineering (Hardcover)
If you are looking for a book that delivers countless pages of set-up forms and checklists than maybe this book is for you. It focuses mainly on the project management aspect and lightly skims actual DOE philosophy. If you are looking for a text that delivers real information about designed experiments and analysis of experiments there are many others out there that cover these FAR better.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
Misses the point,
This review is from: Design of Experiments in Quality Engineering (Hardcover)
One of these reviewers misses the point entirely. The worst DOE's are designed by statisticians alone , the best are designed with subject matter experts AND a statistician. It is only those that have suffered the curse of an "expert only" and a "statistician only" that feel the way you do. The technical stuff is trivial, the expert matter is critical, which is what Luftig and Jordan are trying to put together, and to help the academics realize. There IS a process! Get the research question in focus, and go from there. Sounds like you have never actually approached a problem from a realistic point of view in your life (or ran any "real" experiments).Mike ( creator and analyzer of more than more actual experiments than you can ever think of)
4.0 out of 5 stars
Great Practical Resource for Industrial Experimentation,
By Steven Ouellette (Longmont, CO USA) - See all my reviews
This review is from: Design of Experiments in Quality Engineering (Hardcover)
The previous reviewer missed the point...this book is not intended to be a book about what experimental design is. It is intended make it easy for the practitioner of industrial statistics to take all the fancy tools they have learned and use them in a real environment. The "endless checklists" are a redeeming feature, as they make industrial experimentation much easier by noting common pitfalls and doing a lot of the paperwork development for you. In my experience, the most common failure in the industrial application of statistics is not the use of the statistical tools, but in a failure of the logistics of the experiment. This book will help you avoid these issues.This book bridges the gap between knowing the statistical tools and applying them in real life. That said, the first edition of the book contains many egregious editing errors, so it is frustrating at times.
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Inside This Book
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First Sentence:
Figure 1.1 further defines the context and interrelationship of these terms. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
standard steel tooling, average axial load, coolant suppliers, background data base, sheet cleanliness, sufficient statistical evidence, injected molded parts, random sampling distribution, associated test statistic, adherence strength, glue temperature, glue type, experimental design theory, way alias, relational research, percent filler, alias structure, research design process, concordance analysis, confirmation experiment, packaging types, research hypotheses, cutter design, fractional design, appropriate inference Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fixed of Description, Random Levels, Acme Wire, Organization of Treatment, Treatment Method Classification, Warren International, Equality of Means, Ford Motor Company, Mean Square, Number Variable of Cases Mean, Acme Southco Newmark, Designing the Industrial Experiment, Number Level Variable, Source of Variation Sum of Squares, Total Cases, Albion Plant, Developing the Statement of the Problem, Measurement Scale Variable, Bias Agreement Concordance, Experimental Design Primary Dependent Criterion Measure, Experimental Design Secondary Dependent Criterion Measure, Grand Mean, Internet Validity, Operationalizing the Statement of the Problem, Variables Limited At Level Figure New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Figure 1.1 further defines the context and interrelationship of these terms. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
standard steel tooling, average axial load, coolant suppliers, background data base, sheet cleanliness, sufficient statistical evidence, injected molded parts, random sampling distribution, associated test statistic, adherence strength, glue temperature, glue type, experimental design theory, way alias, relational research, percent filler, alias structure, research design process, concordance analysis, confirmation experiment, packaging types, research hypotheses, cutter design, fractional design, appropriate inference Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fixed of Description, Random Levels, Acme Wire, Organization of Treatment, Treatment Method Classification, Warren International, Equality of Means, Ford Motor Company, Mean Square, Number Variable of Cases Mean, Acme Southco Newmark, Designing the Industrial Experiment, Number Level Variable, Source of Variation Sum of Squares, Total Cases, Albion Plant, Developing the Statement of the Problem, Measurement Scale Variable, Bias Agreement Concordance, Experimental Design Primary Dependent Criterion Measure, Experimental Design Secondary Dependent Criterion Measure, Grand Mean, Internet Validity, Operationalizing the Statement of the Problem, Variables Limited At Level Figure 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 21
books:
See all 21 books this book cites
1
book
cites this book:
- Experimental and Quasi-Experimental Designs for Research by Donald T. Campbell on 4 pages
- Experimental Design on page 100, page 169, and Back Matter
- Juran's Quality Control Handbook by J.M. Juran on page 221, page 260, and Back Matter
- Handbook of Research Design and Social Measurement by Delbert C. Miller on page 34, and Back Matter
- Statistics for Research (Wiley Series in Probability and Statistics) by Shirley Dowdy on page 210, and Back Matter
See all 21 books this book cites
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