Product Description

The trusted guide to the statistical methods for quality control.

Quality control and improvement is more than an engineering concern. Quality has become a major business strategy for increasing productivity and gaining competitive advantage. Introduction to Statistical Quality Control, Sixth Edition gives you a sound understanding of the principles of statistical quality control (SQC) and how to apply them in a variety of situations for quality control and improvement.

With this text, you′ll learn how to apply state–of–the–art techniques for statistical process monitoring and control, design experiments for process characterization and optimization, conduct process robustness studies, and implement quality management techniques.

You′ll appreciate the significant updates in the Sixth Edition including:
∗ In–depth attention to DMAIC, the problem–solving strategy of Six Sigma. It will give you an excellent framework to use in conducting quality improvement projects.
∗ New examples that illustrate applications of statistical quality improvement techniques in non–manufacturing settings. Many examples and exercises are based on real data.
∗ New developments in the area of measurement systems analysis
∗ New features of Minitab V15 incorporated into the text
∗ Numerous new examples, exercises, problems, and techniques to enhance your absorption of the material

Product Details

• Amazon Sales Rank: #478018 in Books
• Published on: 2008-09-26
• Original language: English
• Binding: Paperback
• 760 pages

From the Back Cover
The trusted guide to the statistical methods for quality control.
Quality control and improvement is more than an engineering concern. Quality has become a major business strategy for increasing productivity and gaining competitive advantage. Introduction to Statistical Quality Control, Sixth Edition gives you a sound understanding of the principles of statistical quality control (SQC) and how to apply them in a variety of situations for quality control and improvement.
With this text, you′ll learn how to apply state–of–the–art techniques for statistical process monitoring and control, design experiments for process characterization and optimization, conduct process robustness studies, and implement quality management techniques.
You′ll appreciate the significant updates in the Sixth Edition including:
∗ In–depth attention to DMAIC, the problem–solving strategy of Six Sigma. It will give you an excellent framework to use in conducting quality improvement projects.
∗ New examples that illustrate applications of statistical quality improvement techniques in non–manufacturing settings. Many examples and exercises are based on real data.
∗ New developments in the area of measurement systems analysis
∗ New features of Minitab V15 incorporated into the text
∗ Numerous new examples, exercises, problems, and techniques to enhance your absorption of the material

About the Author
Douglas C. Montgomery is Professor of Engineering and Statistics at Arizona State University. He received his B.S., M.S., and Ph.D. degrees from Virginia Polytechnic Institute, all in engineering. The author and coauthor of many technical papers and twelve other books. Dr. Montgomery is a Fellow of the American Society for Quality, the American Statistical Association, the Royal Statistical Society, the Institute of Industrial Engineers, and an Elected member of the International Statistical Institute.

Customer Reviews

A comprehensive,practical guide to SQC
A short while ago we were called upon by a major customer to present detailed test results of work carried out on their behalf.With large amounts of data to handle and analyse I rather quickly decided on a "statistical" approach to the problem.Fortunately,I came upon a reference to Montgomery's textbook and obtained a copy.It has rarely left my deskside since. Written so that anyone with A-level statistics can understand the theory underlying the SQC procedures described it is nevertheless as a superbly PRACTICAL guide to the subject that it really scores,giving clear and comprehensive reasons for the choice of particular statistical techniques as required.Where appropriate the plusses and minuses of these are detailed. The book concludes with two large sections on experimental design and sampling theory both of which further enhance the utility of this excellent book.