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20729152
Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics)
90% of respondents would recommend this to a friend
Nº de artículo:
20729152
HNL 2791
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This new third edition has been substantially rewritten and updated with new topics and material, new examples and exercises, and to more fully illustrate modern applications of RSM.
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What Stands Out
Comprehensive Guide
This edition offers an extensive overview of Response Surface Methodology, integrating theoretical principles with practical applications, making it an essential resource for practitioners and researchers alike.
Updated Content
The 4th edition includes recent advancements in statistical techniques, ensuring readers access the latest methodologies for optimized process and product design, enhancing reliability in experiments and results.
User-Friendly Structure
Organized for ease of understanding, this book presents complex concepts in a clear manner along with real-world case studies, facilitating effective learning and implementation of response surface methods.
Detalles de producto
- Updated coverage on response surface methodology (RSM) in the Fourth Edition
- Includes classical and modern response surface designs
- Covers topics such as optimal designs, optimization techniques, and robust parameter design
- Integrates examples and experiments with up-to-date software applications
- Ideal textbook for statistics, engineering, and chemical/physical sciences courses
- Useful reference for applied statisticians and engineers in quality, process, and chemistry disciplines
| Publisher | Wiley |
| Publication date | February 1, 2016 |
| Edition | 4th |
| Language | English |
| Print length | 856 pages |
| ISBN-10 | 1118916018 |
| ISBN-13 | 978-1118916018 |
| Item Weight | 3.5 pounds (1.59 kg) |
| Dimensions | 7 x 2 x 10.1 inches (17.8 x 5.1 x 25.7 cm) |
| Part of series | Wiley Series in Probability and Statistics |
Who Should Buy?
-
Statistical Analysts
Ideal for professionals seeking advanced techniques for experimental design and data analysis in various fields.
-
Graduate Students
Perfect for students specializing in statistics or engineering who require a deep understanding of experimental methodologies.
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Industry Researchers
Beneficial for researchers in manufacturing and product development looking to optimize processes using rigorous methodologies.
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Beginner Statisticians
May not be suitable for those with little background in statistics, as it requires some foundational knowledge.
DESCRIPCIÓN DEL PRODUCTO
Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics)
About This Item
Introducing the 4th Edition of "Response Surface Methodology: Process and Product Optimization Using Designed Experiments" from the Wiley Series in Probability and Statistics. This extensively revised edition provides a comprehensive overview of response surface methodology (RSM) and its applications in process and product optimization. With updated coverage and new topics, this edition offers a clear connection between RSM designs and analyses. It includes discussions on optimal designs, robust parameter design, optimization techniques, design evaluation methods, and non-normal responses, among others. The book also explores experiments with computer models, definitive screening designs, and data measured with error.
It features examples and experiments that demonstrate the latest software applications such as JMP®, SAS, and Design-Expert®. Readers will find an extensive references section to stay up-to-date with leading research in the field of RSM. This textbook is an ideal resource for upper-undergraduate and graduate-level courses in statistics, engineering, and chemical/physical sciences. It is also valuable for applied statisticians and engineers in quality, process, and chemistry disciplines looking to enhance their understanding and implementation of RSM. The authors, Raymond H.
Myers and Douglas C. Montgomery, bring their wealth of academic and practical experience to this edition. Dr.
Myers, a Professor Emeritus in the Department of Statistics at Virginia Tech, has over 40 years of expertise in experimental design and analysis. Dr. Montgomery, a Regents' Professor of Industrial Engineering at Arizona State University, has more than 30 years of academic and consulting experience in the design and analysis of experiments. Upgrade your knowledge of response surface methodology with the reliable and comprehensive insights provided in "Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 4th Edition." Order your copy today and take your understanding of RSM to new heights.
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Probability & Statistics Editorial Review
The "Response Surface Methodology: Process and Product Optimization Using Designed Experiments 4th Edition" is a comprehensive guide that provides an in-depth analysis of RSM with a wealth of additional references for deeper exploration of specific topics. Although the book assumes some background knowledge of statistics, it is primarily aimed at advanced undergraduate and graduate students. However, some topics such as the calculation of the critical F-value require additional research. The book also incorporates JMP output in some of the examples. One downside of the book is the poor organizational structure, as the paragraphs do not contain a central idea, leading to a lack of coherence. Overall, the book provides a thorough exploration of RSM, but readers who are not well-versed in statistics may need additional background knowledge to grasp some of the concepts included.
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ventajas
- Thorough coverage of RSM
- Supplemental references included
Contras
- Poor organizational structure
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características y beneficios
- Substantial revision with updated coverage on response surface methodology (RSM)
- Covers classical and modern response surface designs
- Includes topics such as optimal designs, robust parameter design, and multiple response optimization
- Additional coverage on experiments with computer models and data measured with error
- Ideal textbook for upper-undergraduate and graduate-level courses
- Useful reference for applied statisticians and engineers
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