Statistical Methods for Quality Improvement / Edition 3

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Praise for the Second Edition

"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."

This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement

The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods.

In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include:

  • Updated coverage of control charts, with newly added tools
  • The latest research on the monitoring of linear profiles and other types of profiles
  • Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures
  • New discussions on design of experiments that include conditional effects and fraction of design space plots
  • New material on Lean Six Sigma and Six Sigma programs and training

Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic.

Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

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Editorial Reviews

From the Publisher
"Ryan covers everything you could possibly imagine in a statistical methods book...Those with more advanced statistical experience will get the most from this book, although the reading level is suitable for the average user. This is an excellent reference for any of your quality improvement needs." (Quality Progress, July 2012)
Computational Statistics and Data Analysis
...the book is carefully written, well produced, and contains a wealth of information for the interested reader.
Short Book Reviews
This is a significant update of Professor Ryan's textbook from 1988. The chapters on statistical process contril and process capability have been expanded considerably to include recent research in the field. The chapter on design of experiments is also longer, with the inclusion of robust design issues. The book is very well written.
As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available.
Computational Statistics and Data Analysis
...the book is carefully written, well produced, and contains a wealth of information for the interested reader.
A reference for managers and technicians more than a textbook, though Ryan (statistics, Case Western Reserve U.) has used much of the material in industrial and college courses. He writes in the recognition that quality improvement requires using more than just control charts, and that it would be difficult to keep a particular process characteristic in control without some knowledge of the factors affecting that characteristic. He has updated the 1989 first edition by revising and lengthening the chapters on process capability and multivariate control charts. Annotation c. Book News, Inc., Portland, OR (
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Product Details

Meet the Author

THOMAS P. RYAN, PhD, served on the Editorial Review Board of the Journal of Quality Technology from 1990–2006, including three years as the book review editor. He is an elected Fellow of the American Statistical Association, the American Society for Quality, and the Royal Statistical Society. A former consultant to Cytel Software Corporation, Dr. Ryan currently teaches advanced courses at on the design of experiments, statistical process control, and engineering statistics. He is the author of Modern Experimental Design, Modern Regression Methods, Second Edition, and Modern Engineering Statistics, all published by Wiley.

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Table of Contents

Preface xix

Preface to the Second Edition xxi

Preface to the First Edition xxiii


1 Introduction 3

1.1 Quality and Productivity, 4

1.2 Quality Costs (or Does It?), 5

1.3 The Need for Statistical Methods, 5

1.4 Early Use of Statistical Methods for Improving Quality, 6

1.5 Influential Quality Experts, 7

1.6 Summary, 9

2 Basic Tools for Improving Quality 13

2.1 Histogram, 13

2.2 Pareto Charts, 17

2.3 Scatter Plots, 21

2.4 Control Chart, 24

2.5 Check Sheet, 26

2.6 Cause-and-Effect Diagram, 26

2.7 Defect Concentration Diagram, 28

2.8 The Seven Newer Tools, 28

2.9 Software, 30

2.10 Summary, 31

3 Basic Concepts in Statistics and Probability 33

3.1 Probability, 33

3.2 Sample Versus Population, 35

3.3 Location, 36

3.4 Variation, 38

3.5 Discrete Distributions, 41

3.6 Continuous Distributions, 55

3.7 Choice of Statistical Distribution, 69

3.8 Statistical Inference, 69

3.9 Enumerative Studies Versus Analytic Studies, 81


4 Control Charts for Measurements With Subgrouping (for One Variable) 89

4.1 Basic Control Chart Principles, 89

4.2 Real-Time Control Charting Versus Analysis of Past Data, 92

4.3 Control Charts: When to Use, Where to Use, How Many to Use, 94

4.4 Benefits from the Use of Control Charts, 94

4.5 Rational Subgroups, 95

4.6 Basic Statistical Aspects of Control Charts, 95

4.7 Illustrative Example, 96

4.8 Illustrative Example with Real Data, 114

4.9 Determining the Point of a Parameter Change, 116

4.10 Acceptance Sampling and Acceptance Control Chart, 117

4.11 Modified Limits, 124

4.12 Difference Control Charts, 124

4.13 Other Charts, 126

4.14 Average Run Length (ARL), 127

4.15 Determining the Subgroup Size, 129

4.16 Out-of-Control Action Plans, 131

4.17 Assumptions for the Charts in This Chapter, 132

4.18 Measurement Error, 140

4.19 Software, 142

4.20 Summary, 143

5 Control Charts for Measurements Without Subgrouping (for One Variable) 157

5.2 Transform the Data or Fit a Distribution?, 170

5.3 Moving Average Chart, 171

5.4 Controlling Variability with Individual Observations, 173

5.5 Summary, 175

6 Control Charts for Attributes 181

6.1 Charts for Nonconforming Units, 182

6.2 Charts for Nonconformities, 202

6.3 Summary, 218

7 Process Capability 225

7.1 Data Acquisition for Capability Indices, 225

7.2 Process Capability Indices, 227

7.3 Estimating the Parameters in Process Capability Indices, 232

7.4 Distributional Assumption for Capability Indices, 235

7.5 Confidence Intervals for Process Capability Indices, 236

7.6 Asymmetric Bilateral Tolerances, 243

7.7 Capability Indices That Are a Function of Percent Nonconforming, 245

7.8 Modified k Index, 250

7.9 Other Approaches, 251

7.10 Process Capability Plots, 251

7.11 Process Capability Indices Versus Process Performance Indices, 252

7.12 Process Capability Indices with Autocorrelated Data, 253

7.13 Software for Process Capability Indices, 253

7.14 Summary, 253

8 Alternatives to Shewhart Charts 261

8.1 Introduction, 261

8.2 Cumulative Sum Procedures: Principles and Historical Development, 263

8.3 CUSUM Procedures for Controlling Process Variability, 283

8.4 Applications of CUSUM Procedures, 286

8.5 Generalized Likelihood Ratio Charts: Competitive Alternative to CUSUM Charts, 286

8.6 CUSUM Procedures for Nonconforming Units, 286

8.7 CUSUM Procedures for Nonconformity Data, 290

8.8 Exponentially Weighted Moving Average Charts, 294

8.9 Software, 301

8.10 Summary, 301

9 Multivariate Control Charts for Measurement and Attribute Data 309

9.1 Hotelling's T2 Distribution, 312

9.2 A T2 Control Chart, 313

9.3 Multivariate Chart Versus Individual X-Charts, 326

9.4 Charts for Detecting Variability and Correlation Shifts, 327

9.5 Charts Constructed Using Individual Observations, 330

9.6 When to Use Each Chart, 335

9.7 Actual Alpha Levels for Multiple Points, 336

9.8 Requisite Assumptions, 336

9.9 Effects of Parameter Estimation on ARLs, 337

9.10 Dimension-Reduction and Variable Selection Techniques, 337

9.11 Multivariate CUSUM Charts, 338

9.12 Multivariate EWMA Charts, 339

9.13 Effect of Measurement Error, 343

9.14 Applications of Multivariate Charts, 344

9.15 Multivariate Process Capability Indices, 344

9.16 Summary, 344

10 Miscellaneous Control Chart Topics 353

10.1 Pre-control, 353

10.2 Short-Run SPC, 356

10.3 Charts for Autocorrelated Data, 359

10.4 Charts for Batch Processes, 364

10.5 Charts for Multiple-Stream Processes, 364

10.6 Nonparametric Control Charts, 365

10.7 Bayesian Control Chart Methods, 366

10.8 Control Charts for Variance Components, 367

10.9 Control Charts for Highly Censored Data, 367

10.10 Neural Networks, 367

10.11 Economic Design of Control Charts, 368

10.12 Charts with Variable Sample Size and/or Variable Sampling Interval, 370

10.13 Users of Control Charts, 371

10.14 Software for Control Charting, 374


11 Graphical Methods 387

11.1 Histogram, 388

11.2 Stem-and-Leaf Display, 389

11.3 Dot Diagrams, 390

11.4 Boxplot, 392

11.5 Normal Probability Plot, 396

11.6 Plotting Three Variables, 398

11.7 Displaying More Than Three Variables, 399

11.8 Plots to Aid in Transforming Data, 399

11.9 Summary, 401

12 Linear Regression 407

12.1 Simple Linear Regression, 407

12.2 Worth of the Prediction Equation, 411

12.3 Assumptions, 413

12.4 Checking Assumptions Through Residual Plots, 414

12.5 Confidence Intervals and Hypothesis Test, 415

12.6 Prediction Interval for Y, 416

12.7 Regression Control Chart, 417

12.8 Cause-Selecting Control Charts, 419

12.9 Linear, Nonlinear, and Nonparametric Profiles, 421

12.10 Inverse Regression, 423

12.11 Multiple Linear Regression, 426

12.12 Issues in Multiple Regression, 426

12.13 Software For Regression, 429

12.14 Summary, 429

13 Design of Experiments 435

13.1 A Simple Example of Experimental Design Principles, 435

13.2 Principles of Experimental Design, 437

13.3 Statistical Concepts in Experimental Design, 439

13.4 t-Tests, 441

13.5 Analysis of Variance for One Factor, 445

13.6 Regression Analysis of Data from Designed Experiments, 455

13.7 ANOVA for Two Factors, 460

13.8 The 23 Design, 469

13.9 Assessment of Effects Without a Residual Term, 474

13.10 Residual Plot, 477

13.11 Separate Analyses Using Design Units and Uncoded Units, 479

13.12 Two-Level Designs with More Than Three Factors, 480

13.13 Three-Level Factorial Designs, 482

13.14 Mixed Factorials, 483

13.15 Fractional Factorials, 483

13.16 Other Topics in Experimental Design and Their Applications, 493

13.17 Summary, 500

14 Contributions of Genichi Taguchi and Alternative Approaches 513

14.1 "Taguchi Methods", 513

14.2 Quality Engineering, 514

14.3 Loss Functions, 514

14.4 Distribution Not Centered at the Target, 518

14.5 Loss Functions and Specification Limits, 518

14.6 Asymmetric Loss Functions, 518

14.7 Signal-to-Noise Ratios and Alternatives, 522

14.8 Experimental Designs for Stage One, 524

14.9 Taguchi Methods of Design, 525

14.10 Determining Optimum Conditions, 553

14.11 Summary, 558

15 Evolutionary Operation 565

15.1 EVOP Illustrations, 566

15.2 Three Variables, 576

15.3 Simplex EVOP, 578

15.4 Other EVOP Procedures, 581

15.5 Miscellaneous Uses of EVOP, 581

15.6 Summary, 582

16 Analysis of Means 587

16.1 ANOM for One-Way Classifications, 588

16.2 ANOM for Attribute Data, 591

16.3 ANOM When Standards Are Given, 594

16.4 ANOM for Factorial Designs, 596

16.5 ANOM When at Least One Factor Has More Than Two Levels, 601

16.6 Use of ANOM with Other Designs, 610

16.7 Nonparametric ANOM, 610

16.8 Summary, 611

17 Using Combinations of Quality Improvement Tools 615

17.1 Control Charts and Design of Experiments, 616

17.2 Control Charts and Calibration Experiments, 616

17.3 Six Sigma Programs, 616

17.4 Statistical Process Control and Engineering Process Control, 624

Answers to Selected Exercises 629

Appendix: Statistical Tables 633

Author Index 645

Subject Index 657

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First Chapter



This is a book about using statistical methods to improve quality. It is not a book about Total Quality Management (TQM), Total Quality Assurance (TQA), just-in- time (JIT) manufacturing, benchmarking, QS-9000, or the ISO 9000 series. In other words, the scope of the book is essentially restricted to statistical techniques. Although standards such as QS-9000 and ISO 9000 are potentially useful, they are oriented toward the documentation of quality problems, not the identification or eradication of problems. Furthermore, many people feel that companies tend to believe that all they need to do is acquire ISO 9000 certification, thus satisfying only a minimum requirement.

Statistical techniques, on the other hand, are useful for identifying trouble spots and their causes as well as predicting major problems before they occur. Then it is up to the appropriate personnel to take the proper corrective action. The emphasis is on quality improvement, not quality control. On July 1, 1997, the American Society for Quality Control (ASQC) became simply the American Society for Quality (ASQ). The best choice for a new name is arguable, as some would undoubtedly prefer American Society for Quality Improvement (the choice of the late Bill Hunter, former professor of statistics at the University of Wisconsin). Nevertheless, the name change reflects an appropriate movement away from quality control.

What is quality? How do we know when we have it? Can we have too much quality? The "fitness-for-use" criterion is usually given in defining quality. Specifically, a quality product is defined as a product that meets the needs of the marketplace. Those needs are not likely to be static, however, and will certainly be a function of product quality. For example, if automakers build cars that are free from major repairs for 5 years, the marketplace is likely to accept this as a quality standard. However, if another automaker builds its cars in such a way that they will probably be trouble free for 7 years, the quality standard is likely to shift upward. This is what happened in the Western world some years ago as the marketplace discovered that Japanese products, in particular, are of high quality.

A company will know that it is producing high-quality products if those products satisfy the demands of the marketplace.

We could possibly have too much quality. What if we could build a car that would last for 50 years. Would anyone want to drive the same car for 50 years even if he or she lived long enough to do so? Obviously styles and tastes change. This is particularly true for high-technology products that might be obsolete after a year or two. How long should a personal computer be built to last?

In statistical terms, quality is largely determined by the amount of variability in what is being measured. Assume that the target for producing certain invoices is 15 days, with anything less than, say, 10 days being almost physically impossible. If records for a 6-month period showed that all invoices of this type were processed within 17 days, this invoice-processing operation would seem to be of high quality.

In general, the objective should be to reduce variability and to "hit the target" if target values exist for process characteristics. The latter objective has been in uenced by Genichi Taguchi (see Chapter 14), who has defined quality as the "cost to society."


One impediment to achieving high quality has been the misconception of some managers that there is an inverse relationship between productivity and quality. Specifically, it has been believed (by some) that steps taken to improve quality will simultaneously cause a reduction in productivity.

This issue has been addressed by a number of authors, including Fuller (1986), who related that managers at Hewlett-Packard began to realize many years ago that productivity rose measurably when nonconformities (i.e., product defects) were reduced. This increase was partly attributable to a reduction in rework that resulted from the reduction of nonconformities. Other significant gains resulted from the elimination of problems such as the late delivery of materials. These various problems contribute to what the author terms "complexity" in the workplace, and he discusses ways to eliminate complexity so as to free the worker for productive tasks. Other examples of increased productivity resulting from improved quality can be found in Chapter 1 of Deming (1982).


It is often stated that "quality doesn't cost, it pays." Although Crosby (1979) said that quality is free (the title of his book) and reiterated this in Crosby (1996), companies such as Motorola and General Electric, which have launched massive training programs, would undoubtedly disagree. The large amount of money that GE has committed to a particular training program, Six Sigma, is discussed in, for example, the January 13, 1997 issue of the Wall Street Journal. Wall Street is beginning to recognize Six Sigma companies as companies that, for example, operate efficiently and have greater customer satisfaction. Six Sigma is discussed in detail in Chapter 17.

What is the real cost of a quality improvement program? That cost is impossible to determine precisely, since it would depend in part on the quality costs for a given time period without such a program as well as the costs of the program for the same time period. Obviously we cannot both have a program and not have a program at the same point in time, so the quality costs that would be present if the program were not in effect would have to be estimated from past data.

Such a comparison would not give the complete picture, however. Any view of quality costs that does not include the effect that a quality improvement program will have on sales and customers' perceptions is a myopic view of the subject. Should a supplier consider the cost of a statistical quality control program before deciding whether or not to institute such a program? The supplier may not have much choice if it is to remain a supplier. As a less extreme example, consider an industry that consists of 10 companies. If two of these companies implement a statistical quality improvement program and, as a result, the public soon perceives their products to be of higher quality than their competitors' products, should their competitors consider the cost of such a program before following suit? Definitely not, unless they can adequately predict the amount of lost sales and weigh that against the cost of the program.


Generally, statistical techniques are needed to determine if abnormal variation has occurred in whatever is being monitored, to determine changes in the values of process parameters, and to identify factors that are in uencing process characteristics. Methods for achieving each of these objectives are discussed in subsequent chapters. Statistics is generally comparable to medicine in the sense that there are many subareas in statistics, just as there are many medical specialties. Quality "illnesses" generally can be cured and quality optimized only through the sagacious use of combinations of statistical techniques, as discussed in Chapter 17.


Although statistical methods have been underutilized and underappreciated in quality control/improvement programs for decades, such methods are extremely important. Occasionally their importance may even be overstated. In discussing the potential impact of statistical methods, Hoerl (1994) points out that Ishikawa (1985, pp. 14-15) stated the following: "One might even speculate that the second world war was won by quality control and by the utilization of modern statistics. Certain statistical methods researched and utilized by the Allied powers were so effective that they were classified as military secrets until the surrender of Nazi Germany." Although such a conclusion is clearly arguable, statistical methods did clearly play a role in World War II.

Shortly after the war, the American Society for Quality Control was formed in 1946; it published the journal Industrial Quality Control, the first issue of which had appeared in July 1944. In 1969 the journal was essentially split into two publications-- the Journal of Quality Technology and Quality Progress. The former contains technical articles whereas the latter contains less technical articles and also hasnewsitems. The early issues of Industrial Quality Control contained many interesting articles on how statistical procedures were being used in firms in various industries, whereas articles in the Journal of Quality Technology are oriented more toward the proper use of existing procedures as well as the introduction of new procedures. Publication of Quality Engineering began in 1988, with case studies featured in addition to statistical methodology. The Annual Quality Congress has been held every year since the inception of the ASQC, and the proceedings of the meeting are published as the ASQ Annual Quality Transactions.

Other excellent sources of information include the Fall Technical Conference, which is jointly sponsored by ASQ and the American Statistical Association (ASA), the annual Quality and Productivity Research Conference, and the annual meetings of ASA, which are referred to as the Joint Statistical Meetings (JSM).

There are also various "applied" statistics journals that contain important articles relevant to industry, including Technometrics, published jointly by ASQ and ASA, Quality and Reliability Engineering International, IIE Transactions, Applied Statistics (Journal of The Royal Statistical Society, Series C), andThe Statistician (Journal of the Royal Statistical Society, Series D). The latter two are British publications.

Readers interested in the historical development of statistical quality control in Great Britain are referred to Pearson (1935, 1973). An enlightening look at the early days of quality control practices in the United States, as seen through the eyes of Joseph M. Juran, can be found in Juran (1997). See also Montgomery (1996, pp. 10-11) for a chronology of some important events in the history of quality improvement.


Walter A. Shewhart (1891-1967) came first. As discussed more fully in Chapter 2, he invented the idea of a control chart, with certain standard charts now commonly referred to as "Shewhart charts." Shewhart (1931) is still cited by many writers as an authoritative source on process control. The book was reprinted in 1980 by the ASQC. Shewhart (1939) was Shewhart's other well-known book.

W. Edwards Deming (1900-1993) was such a prominent statistician and quality and productivity consultant that his passing was noted on the front page of leading newspapers. His "14 points for management" for achieving quality have been frequently cited (and also changed somewhat over the years). It has been claimed that there are as many as eight versions. One version is as follows:

  1. Create a constancy of purpose.
  2. Adopt a new philosophy.
  3. Cease dependence on inspection.
  4. Work constantly to improve the system.
  5. Break down barriers between departments.
  6. Do not award business to suppliers solely on the basis of price.
  7. Drive out fear.
  8. Eliminate numerical goals, targets, and slogans.
  9. Eliminate work standards and substitute leadership.
  10. Institute a program of training and education for all employees.
  11. Institute modern training methods.
  12. Remove the barriers that make it difficult for employees to do their jobs.
  13. Institute and practice modern methods of supervision.
  14. Create a management climate that will facilitate the attainment of these objectives.

Although these 14 points are typically applied in industrial settings, they can be slightly modified and applied in other settings. For an application that is certainly far removed from manufacturing, Guenther (1997) gives a closely related list of 14 points for parenting.

There is one point of clarification that should be made. When Deming argued against target values, he was arguing against targets for production quotas, not target values for process characteristics. The use of target values for process characteristics is advocated and illustrated in Chapter 14.

Deming was constantly berating American management, believing that about 90% of quality problems were caused by management. Deming's views on the shortcomings of American management can be found in many places, including Chapter 2 of Deming (1986). In general, Deming claimed that management (1) emphasizes short-term thinking and quarterly profits rather than long-term strategies, (2) is inadequately trained and does not possess an in-depth knowledge of the company, and (3) is looking for quick results.

Deming has also been given credit for the PDCA (plan-do-check-act) cycle, although in his later years his preference was that it be called the PDSA cycle, with 'study' replacing 'check.' This has been termed Deming's wheel, but Deming referred to it as Shewhart's cycle. The cycle consists of planning a study, performing the study, checking or studying the results, and acting in accordance with what was learned from the study. See, for example, Cryer and Miller (1994) for additional information on the PDCA cycle.

Several books have been written about Deming; one of the best-known books was written by Mary Walton, a journalist (Walton, 1986). See also Walton (1990), which is a book of case studies, and Voehl (1995). The latter is an edited volume that contains chapters written by some prominent people in the field of quality improvement.

Joseph M. Juran (1904- ) is another prominent quality figure. He is mentioned only brie y here, however, because his contributions have been to quality management rather than to the use of statistical methods for achieving quality improvement. His quality control handbook, which appropriately enough was renamed Juran's Quality Control Handbook when the fourth edition came out in 1988, does contain a few chapters on statistical techniques, however. The first edition was published in 1951 and has been used as a reference book by countless quality practitioners.

Eugene L. Grant (1897-1996) has not been accorded the status of other quality pioneers, but nevertheless deserves to be mentioned with the others in this section. In Struebing (1996), Juran is quoted as saying, "His contribution to statistical methodology was much greater than (W. Edwards) Deming's. Even though his impact on quality was profound and he was much more instrumental in advancing quality than Deming, the media -- which overstated Deming's contribution -- didn't publicize Grant's contributions." Grant has been described as a quiet worker who did not seek to extol his accomplishments. He was an academic who spent over 30 years on the faculty of Stanford University. In the field of quality improvement he was best known for his classic book Statistical Quality Control, first published in 1946. Recent editions of the book have been co-authored by Richard S. Leavenworth. The seventh edition was published in 1996. A very large number of copies of the book were sold through the various editions, but some observers felt that his teaching of statistical quality control during World War II contributed at least as much to the increase in the use of quality techniques as has his well-known book.

George E. P. Box (1919- ) is not generally listed as a quality leader or "guru," but his contributions to statistical methods for improving quality are well known. His recent book, Box and Luceno (1997), extols the authors' ideas and suggested approaches for improving quality. The primary message of the book is that control charts and engineering process control should be used in tandem. This idea is discussed in Chapter 17. He is the author of several other books, the best known of which is Box, Hunter, and Hunter (1978). Box also had a column entitled George's Corner during the early years of the journal Quality Engineering. He was named an Honorary Member of the ASQ by the ASQ Board of Directors in 1997 in recognition of his contributions to quality improvement.

There are, of course, many other quality leaders, but they won't be listed here for fear of leaving someone out.


Statistical methods should be used to identify unusual variation and to pinpoint the causes of such variation, whether it be for a manufacturing process or for general business. The use of statistical methods should produce improvements in quality, which, in turn, should result in increased productivity. The tools for accomplishing this are presented in Parts II and III.


Box, G. E. P. and A. Luce~ no (1997). Statistical Control by Monitoring and Feedback Adjustment. New York: Wiley.

Box, G. E. P., W. G. Hunter, and J. S. Hunter (1978). Statistics for Experimenters. New York: Wiley.

Crosby, P. (1979). Quality Is Free: The Art of Making Quality Certain. NewYork: McGraw-Hill.

Crosby, P. (1996). Quality Is Still Free: Making Quality Certain in Uncertain Times. New York: McGraw-Hill.

Cryer, J. D. and R. B. Miller (1994). Statistics for Business: Data Analysis and Modeling, 2nd ed. Belmont, CA: Duxbury.

Deming, W. E. (1982). Quality, Productivity, and Competitive Position. Cambridge, MA: Massachusetts Institute of Technology, Center for Advanced Engineering Study.

Deming, W. E. (1986). Out of the Crisis. Cambridge, MA: Massachusetts Institute of Technology, Center for Advanced Engineering Study.

Fuller, F. T. (1986). Eliminating complexity from work: Improving productivity by enhancing quality. Report No. 17, Center for Quality and Productivity Improvement, University of Wisconsin, Madison.

Guenther, M. (1997). Letter to the Editor. Quality Progress 30(10): 12-14. Hoerl, R. (1994). Enhancing the bottom line impact of statistical methods. W. J. Youden Memorial Address given at the 38th Annual Fall Technical Conference. Chemical and Process Industries Division Newsletter, American Society for Quality Control, Winter, pp. 1-9.

Ishikawa, K. (1985). What Is Total Quality Control? The Japanese Way. Englewood Cliffs, NJ: Prentice-Hall.

Juran, J. M. (1997). Early SQC: A historical supplement. Quality Progress 30( 9): 73-81. Juran, J. M., editor-in-chief, and F. M. Gryna, associate editor (1988). Juran's Quality Control Handbook, 4th ed. New York: McGraw-Hill.

Montgomery, D. C. (1996). Introduction to Statistical Quality Control, 3rd ed. New York: Wiley.

Pearson, E. S. (1935). The Application of Statistical Methods to Industrial Standardisation and Quality Control. London: British Standards Association.

Pearson, E. S. (1973). Some historical re ections on the introduction of statistical methods in industry. The Statistician 22(3): 165-179.

Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. New York: Van Nostrand. (Reprinted in 1980 by the American Society for Quality Control.)

Shewhart, W. A. (1939). Statistical Method from the Viewpoint of Quality Control. Washington, DC: Graduate School, Department of Agriculture (editorial assistance by W. Edwards Deming).

Struebing, L. (1996). Eugene L. Grant: 1897-1996. Quality Progress 29(11): 81-83.

Voehl, F., ed. (1995). Deming: The Way We Knew Him. Delray Beach, FL: St. Lucie.

Walton, M. (1986). The Deming Management Method. New York: Dodd and Mead.

Walton, M. (1990). Deming Management at Work. New York: Putnam.

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