Statistical methods for industrial process control
Author(s)
Bibliographic Information
Statistical methods for industrial process control
Chapman & Hall, c1997
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Note
Includes bibliographical references (p. 413-416) and index
Description and Table of Contents
Description
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
by "Nielsen BookData"
