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2nd Edition

Elementary Statistical Quality Control

Edited By

John T. Burr




ISBN 9780824790523
Published December 28, 2004 by CRC Press
480 Pages

 
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Book Description

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.

Moving from elementary topics to sampling by variables, sound tolerancing, and relationships between variables, this reference

Table of Contents

Why Statistics?
Statistical Quality Control
Data: Statistics for Action
Patterns of Variation
Wide Applicability
Summary

CHARACTERISTICS OF DATA AND HOW TO DESCRIBE THEM
Two Basic Characteristics of Data
Measuring Average Level and Variability
Condensing Data into a Frequency Table
Sample Data vs. Population
Interpretation of X-Bar and S
Efficient Calculation of X-Bar and S: Coding
Curve Shape
Population vs. Sample Characteristics
Probability as Area, The Normal Curve
Summary
Problems

SIMPLE PROBABILITY AND PROBABILITY DISTRIBUTIONS
Likelihood of an Event Occurring
Occurrence Ratio
Example 1 and Probability Laws
The Binomial Distribution
The Poisson Distribution for Nonconformities
Hypergeometric Distribution for Nonconforming Units
Summary
Problems

CONTROL CHARTS IN GENERAL
Running Record Charts of Performance
Performance Varies
Unusual Performance Calls for Action
What is Unusual
Two Kinds of Causes
Control Charts
Interpretations of Points and Limits
Two Purposes of Control Charts
Process in Statistical Control
Advantages of a Process in Control
Summary
Problems

CONTROL CHARTS FOR ATTRIBUTES
Charts for Nonconforming Pieces
Charts for Nonconformities
Summary
Problems

CONTROL CHARTS FOR MEASUREMENTS: PROCESS CONTROL
Two Characteristics We Desire to Control
An Example, x-Bar, R Charts for Past Data
An Experimental Example, x-Bar and R Charts for Past Data
Some Population Distributions for Sampling Experiments
Control Charts for x-Bar and R, Standards Given
Control Charts for Standard Deviations, s
Comparison of a Control Chart with Specifications
Continuing the Charts
When and How to Set Standard Values
Examples
Some Background of Control Charts
Summary
Problems

PROCESS CAPABILITY
Specification or Tolerance Limits
Manufacturing Tolerances vs. Specification Limits
Natural Machine and Process Tolerances
Measurement Error and its Effect on Decisions Against Tolerances
Comparison of a Process with Specifications
Process Capability Index
Process Capability Index Based on Location of Process Average
Long-Term Process Capability Index-Ppk
Example
Summary
Note
Problems

FURTHER TOPICS IN CONTROL CHARTS AND APPLICATIONS
Types of Sampling
Tool Wear, Slanting Limits
Charts for Individual x's and Moving Ranges
Percent Nonconforming of Bulk Product
Average Run Length for a Point Out
Chart for Demerits, Rating Quality
Some Typical Applications
Problems

ACCEPTANCE SAMPLING FOR ATTRIBUTES
Why Use a Sample for a Decision on a Lot?
Levels of Inspecting or Testing a Lot
The Operating Characteristic of a Plan
Attribute Sampling Inspection
Characteristics of Single Sampling Plans
Double Sampling Plans and their Characteristics
Acceptance Sampling for Nonconformities
Finding a Single Sampling Plan to Match Two Points on the OC Curve
Some Principles and Concepts in Sampling by Attributes
Summary
Problems

SOME STANDARD SAMPLING PLANS FOR ATTRIBUTES
Ansi Asq Z1.4
The Dodge-Romig Sampling Tables
Other Sampling Inspection Plans
Continuous Sampling Plans
Chain Sampling Plan, ChSp-1
Skip-Lot Sampling Plan, SkSp-1
Summary
Problems

SAMPLING BY VARIABLES
Knowledge of Distribution Type
General Aim: To Judge Whether Distribution is Satisfactory
Decisions on Lot Mean, Known s, Normal Distribution
Decisions on Lot by Measurements, s Unknown, Normal Distribution
Single-Sample Test on Variability
Description of Ansi/Asq Z1.9
Checking a Process Setting
Summary
Problems

TOLERANCES FOR MATING PARTS AND ASSEMBLIES
An Example of Bearing and Shaft
An Example of an Additive Combination
General Formulas
Setting Realistic Tolerances
Relations Other than Additive-Subtractive
Summary
Problems

Studying Relationships Between Variables by Linear Correlation and Regression
Two General Problems
First Example-Estimation
Second Example-Correlation
Simplifying the Calculations
Interpretations and Precautions
Some Applications
Problems

A FEW RELIABILITY CONCEPTS
Reliability In General
Definitions of Reliability
Time to First Failure, The Geometric Distribution
Lower Confidence Limit on Reliability
The Exponential Distribution for Length of Life
Reliability of Complex Equipment
Summary
Problems

Appendix
Answers to Odd-Numbered Problems
Index

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Editor(s)

Biography

John T. Burr

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