Introduction to Statistical Quality Control, 4th Edition,9780471316480

Introduction to Statistical Quality Control, 4th Edition

by
Edition: 4th
ISBN13: 9780471316480
ISBN10: 0471316482
Format: Hardcover
Pub. Date: 2001-01-01
Publisher(s): Wiley
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Summary

This book is about the use of modern statistical methods for quality control and improvement. It provides comprehensive coverage of the subject from basic principles to state-of-art concepts and applications. The objective is to give the reader a sound understanding of the principles and the basis for applying them in a variety of both product and nonproduct situations. While statistical techniques are emphasized throughout, the book has a strong engineering and management orientation.

Author Biography

Douglas C. Montgomery: Arizona State University

Table of Contents

Quality Improvement in the Modern Business Environment
1(36)
The Meaning of Quality and Quality Improvement
2(6)
Dimensions of Quality
2(4)
Quality Engineering Terminology
6(2)
A Brief History of Quality Control and Improvement
8(4)
Statistical Methods for Quality Control and Improvement
12(5)
Other Aspects of Quality Control and Improvement
17(20)
Quality Philosophy and Management Strategies
18(7)
The Link between Quality and Productivity
25(1)
Quality Costs
26(6)
Legal Aspects of Quality
32(2)
Implementing Quality Improvement
34(3)
PART I Statistical Methods Useful in Quality Improvement 37(114)
Modeling Process Quality
39(43)
Describing Variation
40(16)
The Stem-and-Leaf Plot
40(3)
The Frequency Distribution and Histogram
43(2)
Numerical Summary of Data
45(3)
The Box Plot
48(1)
Sample Computer Output
49(2)
Probability Distributions
51(5)
Important Discrete Distributions
56(6)
The Hypergeometric Distribution
56(2)
The Binomial Distribution
58(2)
The Poisson Distribution
60(1)
The Pascal and Related Distributions
61(1)
Important Continuous Distributions
62(12)
The Normal Distribution
63(6)
The Exponential Distribution
69(2)
The Gamma Distribution
71(2)
The Weibull Distribution
73(1)
Some Useful Approximations
74(3)
The Binomial Approximation to the Hypergeometric
75(1)
The Poisson Approximation to the Binomial
75(1)
The Normal Approximation to the Binomial
75(1)
Comments on Approximations
76(1)
Exercises
77(5)
Inferences about Process Quality
82(69)
Statistics and Sampling Distributions
83(7)
Sampling from a Normal Distribution
85(4)
Sampling from a Bernoulli Distribution
89(1)
Sampling from a Poisson Distribution
89(1)
Point Estimation of Process Parameters
90(3)
Statistical Inference for a Single Sample
93(21)
Inference on the Mean of a Population, Variance Known
94(4)
The Use of P-Values for Hypothesis Testing
98(1)
Inference on the Mean of a Normal Distribution, Variance Unknown
99(4)
Inference on the Variance of a Normal Distribution
103(2)
Inference on a Population Proportion
105(2)
The Probability of Type II Error
107(3)
Probability Plotting
110(4)
Statistical Inference for Two Samples
114(16)
Inference for a Difference in Means, Variances Known
114(3)
Inference for a Difference in Means of Two Normal Distributions, Variances Unknown
117(10)
Inference on the Variances of Two Normal Distributions
127(1)
Inference on Two Population Proportions
128(2)
What If There Are More Than Two Populations? The Analysis of Variance
130(12)
An Example
131(2)
The Analysis of Variance
133(7)
Checking Assumptions: Residual Analysis
140(2)
Exercises
142(9)
PART II Basic Methods of Statistical Process Control and Capability Analysis 151(252)
Methods and Philosophy of Statistical Process Control
153(53)
Introduction
154(1)
Chance and Assignable Causes of Quality Variation
154(2)
Statistical Basis of the Control Chart
156(21)
Basic Principles
156(8)
Choice of Control Limits
164(2)
Sample Size and Sampling Frequency
166(4)
Rational Subgroups
170(2)
Analysis of Patterns on Control Charts
172(3)
Discussion of Sensitizing Rules for Control Charts
175(2)
The Rest of the ``Magnificent Seven''
177(7)
Implementing SPC
184(2)
An Application of SPC
186(7)
Nonmanufacturing Applications of Statistical Process Control
193(8)
Exercises
201(5)
Control Charts for Variables
206(77)
Introduction
207(1)
Control Charts for x and R
207(32)
Statistical Basis of the Charts
207(5)
Development and Use of x and R Charts
212(16)
Charts Based on Standard Values
228(1)
Interpretation of x and R Charts
229(3)
The Effect of Nonnormality on x and R Charts
232(1)
The Operating-Characteristic Function
233(3)
The Average Run Length for the x Chart
236(3)
Control Charts for x and S
239(10)
Construction and Operation of x and S Charts
239(5)
The x and S Control Charts with Variable Sample Size
244(4)
The S2 Control Chart
248(1)
The Shewhart Control Chart for Individual Measurements
249(11)
Summary of Procedures for x, R, and S Charts
260(1)
Applications of Variables Control Charts
260(5)
Exercises
265(18)
Control Charts for Attributes
283(66)
Introduction
284(1)
The Control Chart for Fraction Nonconforming
284(24)
Development and Operation of the Control Chart
286(12)
Variable Sample Size
298(5)
Nonmanufacturing Applications
303(2)
The Operating-Characteristic Function and Average Run Length Calculations
305(3)
Control Charts for Nonconformities (Defects)
308(21)
Procedures with Constant Sample Size
308(11)
Procedures with Variable Sample Size
319(3)
Demerit Systems
322(2)
The Operating-Characteristic Function
324(1)
Dealing with Low Defect Levels
325(3)
Nonmanufacturing Applications
328(1)
Choice between Attributes and Variables Control Charts
329(4)
Guidelines for Implementing Control Charts
333(6)
Exercises
339(10)
Process and Measurement System Capability Analysis
349(54)
Introduction
350(2)
Process Capability Analysis Using a Histogram or a Probability Plot
352(5)
Using the Histogram
352(3)
Probability Plotting
355(2)
Process Capability Ratios
357(16)
Use and Interpretation of Cp
357(5)
Process Capability Ratio for an Off-Center Process
362(2)
Normality and the Process Capability Ratio
364(1)
More about Process Centering
365(2)
Confidence Intervals and Tests on Process Capability Ratios
367(6)
Process Capability Analysis Using a Control Chart
373(3)
Process Capability Analysis Using Designed Experiments
376(1)
Gage and Measurement System Capability Studies
377(11)
Control Charts and Tabular Methods
377(7)
Methods Based on Analysis of Variance
384(4)
Setting Specification Limits on Discrete Components
388(6)
Linear Combinations
388(4)
Nonlinear Combinations
392(2)
Estimating the Natural Tolerance Limits of a Process
394(3)
Tolerance Limits Based on the Normal Distribution
395(1)
Nonparametric Tolerance Limits
396(1)
Exercises
397(6)
PART III Other Statistical Process Monitoring and Control Techniques 403(166)
Cumulative Sum and Exponentially Weighted Moving Average Control Charts
405(38)
The Cumulative Sum Control Chart
406(19)
Basic Principles: The Cusum Control Chart for Monitoring the Process Mean
406(4)
The Tabular or Algorithmic Cusum for Monitoring the Process Mean
410(5)
Recommendations for Cusum Design
415(2)
The Standardized Cusum
417(1)
Rational Subgroups
418(1)
Improving Cusum Responsiveness for Large Shifts
418(1)
The Fast Initial Response or Headstart Feature
419(2)
One-Sided Cusums
421(1)
A Cusum for Monitoring Process Variability
421(1)
Cusums for Other Sample Statistics
422(1)
The V-Mask Procedure
423(2)
The Exponentially Weighted Moving Average Control Chart
425(12)
The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean
426(5)
Design of an EWMA Control Chart
431(1)
Rational Subgroups
432(1)
Robustness of the EWMA to Nonnormality
432(1)
Extensions of the EWMA
433(4)
The Moving Average Control Chart
437(3)
Exercises
440(3)
Other Univariate Statistical Process Monitoring and Control Techniques
443(64)
Statistical Process Control for Short Production Runs
444(5)
x and R Charts for Short Production Runs
444(3)
Attributes Control Charts for Short Production Runs
447(1)
Other Methods
448(1)
Modified and Acceptance Control Charts
449(5)
Modified Control Limits for the x Chart
449(4)
Acceptance Control Charts
453(1)
Control Charts for Multiple-Stream Processes
454(4)
Multiple-Stream Processes
454(1)
Group Control Charts
455(2)
Other Approaches
457(1)
SPC with Autocorrelated Process Data
458(20)
Sources and Effects of Autocorrelation in Process Data
458(4)
Model-Based Approaches
462(11)
A Model-Free Approach
473(5)
Adaptive Sampling Procedures
478(1)
Economic Design of Control Charts
479(15)
Designing a Control Chart
479(1)
Process Characteristics
480(1)
Cost Parameters
481(1)
Early Work and Semieconomic Designs
482(2)
An Economic Model of the x Control Chart
484(9)
Other Work
493(1)
Overview of Other Procedures
494(5)
Tool Wear
494(1)
Control Charts Based on Other Sample Statistics
495(1)
Fill Control Problems
496(1)
Precontrol
497(2)
Exercises
499(8)
Multivariate Process Monitoring and Control
507(39)
The Multivariate Quality Control Problem
508(2)
Description of Multivariate Data
510(2)
The Multivariate Normal Distribution
510(1)
The Sample Mean Vector and Covariance Matrix
511(1)
The Hotelling T2 Control Chart
512(14)
Subgrouped Data
512(10)
Individual Observations
522(4)
The Multivariate EWMA Control Chart
526(4)
Regression Adjustment
530(2)
Control Charts for Monitoring Variability
532(3)
Latent Structure Methods
535(7)
Principal Components
535(7)
Partial Least Squares
542(1)
Exercises
542(4)
Engineering Process Control and SPC
546(23)
Process Monitoring and Process Regulation
547(1)
Process Control by Feedback Adjustment
548(14)
A Simple Adjustment Scheme: Integral Control
548(7)
The Adjustment Chart
555(2)
Variations of the Adjustment Chart
557(4)
Other Types of Feedback Controllers
561(1)
Combining SPC and EPC
562(4)
Exercises
566(3)
PART IV Process Design and Improvement with Designed Experiments 569(104)
Factorial and Fractional Factorial Experiments for Process Design and Improvement
571(68)
What Is Experimental Design?
572(1)
Examples of Designed Experiments in Process Improvement
573(4)
Guidelines for Designing Experiments
577(2)
Factorial Experiments
579(12)
An Example
582(1)
Statistical Analysis
583(5)
Residual Analysis
588(3)
The 2k Factorial Design
591(31)
The 22 Design
591(7)
The 2k Design for k ≥ 3 Factors
598(13)
A Single Replicate of the 2k Design
611(4)
Addition of Center Points to the 2k Design
615(5)
Blocking and Confounding in the 2k Design
620(2)
Fractional Replication of the 2k Design
622(13)
The One-Half Fraction of the 2k Design
622(6)
Smaller Fractions: The 2k-p Fractional Factorial Design
628(7)
Exercises
635(4)
Process Optimization with Designed Experiments
639(34)
Response Surface Methods and Designs
640(11)
The Method of Steepest Ascent
642(3)
Analysis of a Second-Order Response Surface
645(6)
Process Robustness Studies
651(11)
Background
651(2)
The Response Surface Approach to Process Robustness Studies
653(9)
Evolutionary Operation
662(7)
Exercises
669(4)
PART V Acceptance Sampling 673(76)
Lot-by-Lot Acceptance Sampling for Attributes
675(47)
The Acceptance-Sampling Problem
676(6)
Advantages and Disadvantages of Sampling
677(1)
Types of Sampling Plans
678(1)
Lot Formation
679(1)
Random Sampling
680(1)
Guidelines for Using Acceptance Sampling
680(2)
Single-Sampling Plans for Attributes
682(12)
Definition of a Single-Sampling Plan
682(1)
The OC Curve
682(7)
Designing a Single-Sampling Plan with a Specified OC Curve
689(1)
Rectifying Inspection
690(4)
Double, Multiple, and Sequential Sampling
694(11)
Double-Sampling Plans
694(7)
Multiple-Sampling Plans
701(1)
Sequential-Sampling Plans
701(4)
Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859)
705(9)
Description of the Standard
705(2)
Procedure
707(5)
Discussion
712(2)
The Dodge-Romig Sampling Plans
714(5)
AOQL Plans
716(1)
LTPD Plans
716(3)
Estimation of Process Average
719(1)
Exercises
719(3)
Other Acceptance-Sampling Techniques
722(27)
Acceptance Sampling by Variables
723(3)
Advantages and Disadvantages of Variables Sampling
723(1)
Types of Sampling Plans Available
724(1)
Caution in the Use of Variables Sampling
725(1)
Designing a Variables Sampling Plan with a Specified OC Curve
726(3)
MIL STD 414 (ANSI/ASQC Z1.9)
729(5)
General Description of the Standard
729(1)
Use of the Tables
730(3)
Discussion of MIL STD 414 and ANSI/ASQC Z1.9
733(1)
Other Variables Sampling Procedures
734(1)
Sampling by Variables to Give Assurance Regarding the Lot or Process Mean
734(1)
Sequential Sampling by Variables
735(1)
Chain Sampling
735(2)
Continuous Sampling
737(4)
CSP-1
738(2)
Other Continuous-Sampling Plans
740(1)
Skip-Lot Sampling Plans
741(4)
Exercises
745(4)
Appendix 749(16)
I. Summary of Common Probability Distributions Often Used in Statistical Quality Control
751(1)
II. Cumulative Standard Normal Distribution
752(2)
III. Percentage Points of the X2 Distribution
754(1)
IV. Percentage Points of the t Distribution
755(1)
V. Percentage Points of the F Distribution
756(5)
VI. Factors for Constructing Variables Control Charts
761(1)
VII. Factors for Two-Sided Normal Tolerance Limits
762(1)
VIII. Factors for One-Sided Normal Tolerance Limits
763(1)
IX. Random Numbers
764(1)
Bibliography 765(12)
Answers to Selected Exercises 777(14)
Index 791

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