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<b>Preface xix</b> <p><b>Preface to the Second Edition xxi</b></p> <p><b>Preface to the First Edition xxiii</b></p> <p><b>PART I FUNDAMENTAL QUALITY IMPROVEMENT AND STATISTICAL CONCEPTS</b></p> <p><b>1 Introduction 3</b></p> <p>1.1 Quality and Productivity, 4</p> <p>1.2 Quality Costs (or Does It?), 5</p> <p>1.3 The Need for Statistical Methods, 5</p> <p>1.4 Early Use of Statistical Methods for Improving Quality, 6</p> <p>1.5 Influential Quality Experts, 7</p> <p>1.6 Summary, 9</p> <p><b>2 Basic Tools for Improving Quality 13</b></p> <p>2.1 Histogram, 13</p> <p>2.2 Pareto Charts, 17</p> <p>2.3 Scatter Plots, 21</p> <p>2.4 Control Chart, 24</p> <p>2.5 Check Sheet, 26</p> <p>2.6 Cause-and-Effect Diagram, 26</p> <p>2.7 Defect Concentration Diagram, 28</p> <p>2.8 The Seven Newer Tools, 28</p> <p>2.9 Software, 30</p> <p>2.10 Summary, 31</p> <p><b>3 Basic Concepts in Statistics and Probability 33</b></p> <p>3.1 Probability, 33</p> <p>3.2 Sample Versus Population, 35</p> <p>3.3 Location, 36</p> <p>3.4 Variation, 38</p> <p>3.5 Discrete Distributions, 41</p> <p>3.6 Continuous Distributions, 55</p> <p>3.7 Choice of Statistical Distribution, 69</p> <p>3.8 Statistical Inference, 69</p> <p>3.9 Enumerative Studies Versus Analytic Studies, 81</p> <p><b>PARTII CONTROL CHARTS AND PROCESS CAPABILITY</b></p> <p><b>4 Control Charts for Measurements With Subgrouping (for One Variable) 89</b></p> <p>4.1 Basic Control Chart Principles, 89</p> <p>4.2 Real-Time Control Charting Versus Analysis of Past Data, 92</p> <p>4.3 Control Charts: When to Use, Where to Use, How Many to Use, 94</p> <p>4.4 Benefits from the Use of Control Charts, 94</p> <p>4.5 Rational Subgroups, 95</p> <p>4.6 Basic Statistical Aspects of Control Charts, 95</p> <p>4.7 Illustrative Example, 96</p> <p>4.8 Illustrative Example with Real Data, 114</p> <p>4.9 Determining the Point of a Parameter Change, 116</p> <p>4.10 Acceptance Sampling and Acceptance Control Chart, 117</p> <p>4.11 Modified Limits, 124</p> <p>4.12 Difference Control Charts, 124</p> <p>4.13 Other Charts, 126</p> <p>4.14 Average Run Length (ARL), 127</p> <p>4.15 Determining the Subgroup Size, 129</p> <p>4.16 Out-of-Control Action Plans, 131</p> <p>4.17 Assumptions for the Charts in This Chapter, 132</p> <p>4.18 Measurement Error, 140</p> <p>4.19 Software, 142</p> <p>4.20 Summary, 143</p> <p><b>5 Control Charts for Measurements Without Subgrouping (for One Variable) 157</b></p> <p>5.2 Transform the Data or Fit a Distribution?, 170</p> <p>5.3 Moving Average Chart, 171</p> <p>5.4 Controlling Variability with Individual Observations, 173</p> <p>5.5 Summary, 175</p> <p><b>6 Control Charts for Attributes 181</b></p> <p>6.1 Charts for Nonconforming Units, 182</p> <p>6.2 Charts for Nonconformities, 202</p> <p>6.3 Summary, 218</p> <p><b>7 Process Capability 225</b></p> <p>7.1 Data Acquisition for Capability Indices, 225</p> <p>7.2 Process Capability Indices, 227</p> <p>7.3 Estimating the Parameters in Process Capability Indices, 232</p> <p>7.4 Distributional Assumption for Capability Indices, 235</p> <p>7.5 Confidence Intervals for Process Capability Indices, 236</p> <p>7.6 Asymmetric Bilateral Tolerances, 243</p> <p>7.7 Capability Indices That Are a Function of Percent Nonconforming, 245</p> <p>7.8 Modified <i>k</i> Index, 250</p> <p>7.9 Other Approaches, 251</p> <p>7.10 Process Capability Plots, 251</p> <p>7.11 Process Capability Indices Versus Process Performance Indices, 252</p> <p>7.12 Process Capability Indices with Autocorrelated Data, 253</p> <p>7.13 Software for Process Capability Indices, 253</p> <p>7.14 Summary, 253</p> <p><b>8 Alternatives to Shewhart Charts 261</b></p> <p>8.1 Introduction, 261</p> <p>8.2 Cumulative Sum Procedures: Principles and Historical Development, 263</p> <p>8.3 CUSUM Procedures for Controlling Process Variability, 283</p> <p>8.4 Applications of CUSUM Procedures, 286</p> <p>8.5 Generalized Likelihood Ratio Charts: Competitive Alternative to CUSUM Charts, 286</p> <p>8.6 CUSUM Procedures for Nonconforming Units, 286</p> <p>8.7 CUSUM Procedures for Nonconformity Data, 290</p> <p>8.8 Exponentially Weighted Moving Average Charts, 294</p> <p>8.9 Software, 301</p> <p>8.10 Summary, 301</p> <p><b>9 Multivariate Control Charts for Measurement and Attribute Data 309</b></p> <p>9.1 Hotelling's <i>T</i>2 Distribution, 312</p> <p>9.2 A <i>T</i>2 Control Chart, 313</p> <p>9.3 Multivariate Chart Versus Individual <i>X</i>-Charts, 326</p> <p>9.4 Charts for Detecting Variability and Correlation Shifts, 327</p> <p>9.5 Charts Constructed Using Individual Observations, 330</p> <p>9.6 When to Use Each Chart, 335</p> <p>9.7 Actual Alpha Levels for Multiple Points, 336</p> <p>9.8 Requisite Assumptions, 336</p> <p>9.9 Effects of Parameter Estimation on ARLs, 337</p> <p>9.10 Dimension-Reduction and Variable Selection Techniques, 337</p> <p>9.11 Multivariate CUSUM Charts, 338</p> <p>9.12 Multivariate EWMA Charts, 339</p> <p>9.13 Effect of Measurement Error, 343</p> <p>9.14 Applications of Multivariate Charts, 344</p> <p>9.15 Multivariate Process Capability Indices, 344</p> <p>9.16 Summary, 344</p> <p><b>10 Miscellaneous Control Chart Topics 353</b></p> <p>10.1 Pre-control, 353</p> <p>10.2 Short-Run SPC, 356</p> <p>10.3 Charts for Autocorrelated Data, 359</p> <p>10.4 Charts for Batch Processes, 364</p> <p>10.5 Charts for Multiple-Stream Processes, 364</p> <p>10.6 Nonparametric Control Charts, 365</p> <p>10.7 Bayesian Control Chart Methods, 366</p> <p>10.8 Control Charts for Variance Components, 367</p> <p>10.9 Control Charts for Highly Censored Data, 367</p> <p>10.10 Neural Networks, 367</p> <p>10.11 Economic Design of Control Charts, 368</p> <p>10.12 Charts with Variable Sample Size and/or Variable Sampling Interval, 370</p> <p>10.13 Users of Control Charts, 371</p> <p>10.14 Software for Control Charting, 374</p> <p><b>PART III BEYOND CONTROL CHARTS: GRAPHICAL AND STATISTICAL METHODS</b></p> <p><b>11 Graphical Methods 387</b></p> <p>11.1 Histogram, 388</p> <p>11.2 Stem-and-Leaf Display, 389</p> <p>11.3 Dot Diagrams, 390</p> <p>11.4 Boxplot, 392</p> <p>11.5 Normal Probability Plot, 396</p> <p>11.6 Plotting Three Variables, 398</p> <p>11.7 Displaying More Than Three Variables, 399</p> <p>11.8 Plots to Aid in Transforming Data, 399</p> <p>11.9 Summary, 401</p> <p><b>12 Linear Regression 407</b></p> <p>12.1 Simple Linear Regression, 407</p> <p>12.2 Worth of the Prediction Equation, 411</p> <p>12.3 Assumptions, 413</p> <p>12.4 Checking Assumptions Through Residual Plots, 414</p> <p>12.5 Confidence Intervals and Hypothesis Test, 415</p> <p>12.6 Prediction Interval for <i>Y</i>, 416</p> <p>12.7 Regression Control Chart, 417</p> <p>12.8 Cause-Selecting Control Charts, 419</p> <p>12.9 Linear, Nonlinear, and Nonparametric Profiles, 421</p> <p>12.10 Inverse Regression, 423</p> <p>12.11 Multiple Linear Regression, 426</p> <p>12.12 Issues in Multiple Regression, 426</p> <p>12.13 Software For Regression, 429</p> <p>12.14 Summary, 429</p> <p><b>13 Design of Experiments 435</b></p> <p>13.1 A Simple Example of Experimental Design Principles, 435</p> <p>13.2 Principles of Experimental Design, 437</p> <p>13.3 Statistical Concepts in Experimental Design, 439</p> <p>13.4 <i>t</i>-Tests, 441</p> <p>13.5 Analysis of Variance for One Factor, 445</p> <p>13.6 Regression Analysis of Data from Designed Experiments, 455</p> <p>13.7 ANOVA for Two Factors, 460</p> <p>13.8 The 23 Design, 469</p> <p>13.9 Assessment of Effects Without a Residual Term, 474</p> <p>13.10 Residual Plot, 477</p> <p>13.11 Separate Analyses Using Design Units and Uncoded Units, 479</p> <p>13.12 Two-Level Designs with More Than Three Factors, 480</p> <p>13.13 Three-Level Factorial Designs, 482</p> <p>13.14 Mixed Factorials, 483</p> <p>13.15 Fractional Factorials, 483</p> <p>13.16 Other Topics in Experimental Design and Their Applications, 493</p> <p>13.17 Summary, 500</p> <p><b>14 Contributions of Genichi Taguchi and Alternative Approaches 513</b></p> <p>14.1 "Taguchi Methods", 513</p> <p>14.2 Quality Engineering, 514</p> <p>14.3 Loss Functions, 514</p> <p>14.4 Distribution Not Centered at the Target, 518</p> <p>14.5 Loss Functions and Specification Limits, 518</p> <p>14.6 Asymmetric Loss Functions, 518</p> <p>14.7 Signal-to-Noise Ratios and Alternatives, 522</p> <p>14.8 Experimental Designs for Stage One, 524</p> <p>14.9 Taguchi Methods of Design, 525</p> <p>14.10 Determining Optimum Conditions, 553</p> <p>14.11 Summary, 558</p> <p><b>15 Evolutionary Operation 565</b></p> <p>15.1 EVOP Illustrations, 566</p> <p>15.2 Three Variables, 576</p> <p>15.3 Simplex EVOP, 578</p> <p>15.4 Other EVOP Procedures, 581</p> <p>15.5 Miscellaneous Uses of EVOP, 581</p> <p>15.6 Summary, 582</p> <p><b>16 Analysis of Means 587</b></p> <p>16.1 ANOM for One-Way Classifications, 588</p> <p>16.2 ANOM for Attribute Data, 591</p> <p>16.3 ANOM When Standards Are Given, 594</p> <p>16.4 ANOM for Factorial Designs, 596</p> <p>16.5 ANOM When at Least One Factor Has More Than Two Levels, 601</p> <p>16.6 Use of ANOM with Other Designs, 610</p> <p>16.7 Nonparametric ANOM, 610</p> <p>16.8 Summary, 611</p> <p><b>17 Using Combinations of Quality Improvement Tools 615</b></p> <p>17.1 Control Charts and Design of Experiments, 616</p> <p>17.2 Control Charts and Calibration Experiments, 616</p> <p>17.3 Six Sigma Programs, 616</p> <p>17.4 Statistical Process Control and Engineering Process Control, 624</p> <p><b>Answers to Selected Exercises 629</b></p> <p><b>Appendix: Statistical Tables 633</b></p> <p><b>Author Index 645</b></p> <p><b>Subject Index 657</b></p>

<p>"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." (<i>Quality Progress</i>, July 2012)</p>

<b>THOMAS P. RYAN</b>, 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 statistics.com on the design of experiments, statistical process control, and engineering statistics. He is the author of <i>Modern Experimental Design</i>, <i>Modern Regression Methods</i>, Second Edition, and <i>Modern Engineering Statistics</i>, all published by Wiley.

<b>Praise for the <i>Second Edition</i></b> <p>"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."<br /> —<b><i>Technometrics</i></b></p> <p><b>This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement</b></p> <p>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. <i>Statistical Methods for Quality Improvement</i>, 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.</p> <p>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:</p> <ul> <li> <p>Updated coverage of control charts, with newly added tools</p> </li> <li> <p>The latest research on the monitoring of linear profiles and other types of profiles</p> </li> <li> <p>Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) procedures</p> </li> <li> <p>New discussions on design of experiments that include conditional effects and fraction of design space plots</p> </li> <li> <p>New material on Lean Six Sigma and Six Sigma programs and training</p> </li> </ul> <p>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.</p> <p><i>Statistical Methods for Quality Improvement</i>, 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.</p>

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