# Statistical Methods for Industrial Process Control

## 1st Edition

Chapman and Hall/CRC

476 pages

Hardback: 9780412085116
pub: 1997-02-01
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### 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.

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

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

References

SAS Appendix