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Elementary Statistical Quality Control : Statistics: A Series of Textbooks and Monographs - John T. Burr

Elementary Statistical Quality Control

By: John T. Burr (Editor)

Hardcover | 28 December 2004 | Edition Number 2

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Hardcover


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Maintaining the reader-friendly features of its popular predecessor, the Second Edition illustrates fundamental principles and practices in statistical quality control for improved quality, reliability, and productivity in the management of production processes and industrial and business operations - presenting key concepts of statistical quality control in a simple and straightforward manner, this reference will provide a solid foundation in statistical quality control theory, background, and applications.
Reflecting upon classic examples and case studies, as well as recent industrial data, this reference will prove invaluable in the development of solutions to real-world dilemmas in the manufacturing and service industries.
Industry Reviews

"...does a capable job of updating the first edition and is a solid hands-on guide for the next wave of statistical quality control practitioners. ...With detailed examples and a few problems at the end of most chapters, this book gives learning practitioners a good starting point for the key points of statistical quality control, especially in emphasizing the importance of characterizing and adjusting for variability in processes and avoiding common pitfalls when making decisions from observed data. ...it is a worthwhile addition to the reference shelf of quality engineers and practitioners."
-Christine M. Anderson-Cook, Journal of the American Statistical Association

"Its contents are of great interest for engineering, business and industrial professional and students. Good graphical illustrations aid the understanding of the concepts and techniques discussed. The second edition expands the content of the 1976 first edition. I recommend this book for lecturers and students of engineering as a good introductory book on statistical quality control in industry and business."
-Journal of Applied Statistics, June 2006

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