Details

Statistical Methods for Quality Improvement


Statistical Methods for Quality Improvement


Wiley Series in Probability and Statistics, Band 840 3. Aufl.

von: Thomas P. Ryan

120,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 20.09.2011
ISBN/EAN: 9781118058107
Sprache: englisch
Anzahl Seiten: 672

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Beschreibungen

Praise for the Second Edition "As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available." —Technometrics 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.
Preface xix Preface to the Second Edition xxi Preface to the First Edition xxiii PART I FUNDAMENTAL QUALITY IMPROVEMENT AND STATISTICAL CONCEPTS 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 PARTII CONTROL CHARTS AND PROCESS CAPABILITY 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 PART III BEYOND CONTROL CHARTS: GRAPHICAL AND STATISTICAL METHODS 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
"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)
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 statistics.com 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.
Praise for the Second Edition "As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available." —Technometrics 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 cumulative sum (CUSUM) and exponentially weighted moving average (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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