Statistical methods for industrial process control


    • Drain, David

Bibliographic Information

Statistical methods for industrial process control

David Drain

Chapman & Hall, c1997

Available at  / 9 libraries

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Includes bibliographical references (p. 413-416) and index

Description and Table of Contents


To practice engineering effectively, engineers must need to have a working knowledge of statistical concepts and methods. What they do not need is a background heavy on statistical theory and number crunching. Statistical Methods for Industrial Process Control provides the practical statistics foundation engineers can immediately apply to the work they do every day, regardless of their industry or specialty. The author illustrates statistical concepts and methods with authentic semiconductor manufacturing process examples-integrated circuit fabrication is an exceedingly rich medium for communicating statistical concepts. However, once learned, these concepts and methods can easily be extended and applied to a variety of other industries. The text emphasizes the application of statistical tools, rather than statistical theory. Modern advances in statistical software have made tedious computations and formula memorization unnecessary. Therefore, the author demonstrates software use throughout the book and supplies MINITAB examples and SAS programs. Review problems at the end of each chapter challenge and deepen readers' understanding of the material. Statistical Methods for Industrial Process Control addresses topics that support the work engineers do, rather than educate them as statisticians, and these topics also reflect modern usage. It effectively introduces novice engineers to a fascinating industry and enables experienced engineers to build upon their existing knowledge and learn new skills.

Table of Contents

Basic Probability and Statistics Introduction Probability Sampling Estimation Hypothesis Testing Summary Linear Regression Analysis Introduction Linear Regression Analysis Interpreting Results Applying Simple Linear Regression Polynomial and Multiple Regression Summary Variance Components and Process Sampling Design Introduction Variance Structures Estimating Nested Variance Components Process Sampling Design Summary Measurement Capability Introduction The Costs of Flawed Measurement Measurement Capability Defined Assessing and Improving Measurement Capability Purchasing and Qualifying Equipment Overcoming Difficult Measurement Problems Summary Introduction to Statistical Process Control Introduction Fundamental Principles of SPC Essential Components of SPC Example Process Control System Benefits and Costs of SPC Statistical Process Control Implementation Introduction Select Key Process Parameters Design a Data Collection System and Collect Data Select Summary Measures and Control Charts Assess Process stability and Capability Develop the Five Working Parts Maintain and Improve the System Disposition Limits Summary Technical Notes Answers to Problems References SAS Appendix

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