InhaltsangabePreface 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 ...

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.

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