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The Seventh Edition of Introduction to StatisticalQuality Control provides a comprehensive treatment of the majoraspects of using statistical methodology for quality control andimprovement. Both traditional and modern methods arepresented, including state-of-the-art techniques for statisticalprocess monitoring and control and statistically designedexperiments for process characterization, optimization, and processrobustness studies. The seventh edition continues to focus onDMAIC (define, measure, analyze, improve, and control--theproblem-solving strategy of six sigma) including a chapter on theimplementation process. Additionally, the text includes newexamples, exercises, problems, and techniques. StatisticalQuality Control is best suited for upper-division studentsin engineering, statistics, business and management science orstudents in graduate courses.
Contents
PART 1 INTRODUCTION 1 3Chapter Overview and Learning Objectives 31.1 The Meaning of Quality and Quality Improvement 41.1.1 Dimensions of Quality 41.1.2 Quality Engineering Terminology 81.2 A Brief History of Quality Control and Improvement 91.3 Statistical Methods for Quality Control and Improvement131.4 Management Aspects of Quality Improvement 161.4.1 Quality Philosophy and Management Strategies 171.4.2 The Link Between Quality and Productivity 351.4.3 Supply Chain Quality Management 361.4.4 Quality Costs 381.4.5 Legal Aspects of Quality 441.4.6 Implementing Quality Improvement 452 THE DMAIC PROCESS 48Chapter Overview and Learning Objectives 482.1 Overview of DMAIC 492.2 The Define Step 522.3 The Measure Step 542.4 The Analyze Step 552.5 The Improve Step 562.6 The Control Step 572.7 Examples of DMAIC 572.7.1 Litigation Documents 572.7.2 Improving On-Time Delivery 592.7.3 Improving Service Quality in a Bank 62IMPROVEMENT 653 MODELING PROCESS QUALITY 67Chapter Overview and Learning Objectives 683.1 Describing Variation 683.1.1 The Stem-and-Leaf Plot 683.1.2 The Histogram 703.1.3 Numerical Summary of Data 733.1.4 The Box Plot 753.1.5 Probability Distributions 763.2 Important Discrete Distributions 803.2.1 The Hypergeometric Distribution 803.2.2 The Binomial Distribution 813.2.3 The Poisson Distribution 833.2.4 The Negative Binomial and Geometric Distributions 863.3 Important Continuous Distributions 883.3.1 The Normal Distribution 883.3.2 The Lognormal Distribution 903.3.3 The Exponential Distribution 923.3.4 The Gamma Distribution 933.3.5 The Weibull Distribution 953.4 Probability Plots 973.4.1 Normal Probability Plots 973.4.2 Other Probability Plots 993.5 Some Useful Approximations 1003.5.1 The Binomial Approximation to the Hypergeometric 1003.5.2 The Poisson Approximation to the Binomial 1003.5.3 The Normal Approximation to the Binomial 1013.5.4 Comments on Approximations 1024 INFERENCES ABOUT PROCESS QUALITY 108Chapter Overview and Learning Objectives 1094.1 Statistics and Sampling Distributions 1104.1.1 Sampling from a Normal Distribution 1114.1.2 Sampling from a Bernoulli Distribution 1134.1.3 Sampling from a Poisson Distribution 1144.2 Point Estimation of Process Parameters 1154.3 Statistical Inference for a Single Sample 1174.3.1 Inference on the Mean of a Population, Variance Known1184.3.2 The Use of P-Values for Hypothesis Testing 1214.3.3 Inference on the Mean of a Normal Distribution, VarianceUnknown 1224.3.4 Inference on the Variance of a Normal Distribution 1264.3.5 Inference on a Population Proportion 1284.3.6 The Probability of Type II Error and Sample Size Decisions1304.4 Statistical Inference for Two Samples 1334.4.1 Inference for a Difference in Means, Variances Known1344.4.2 Inference for a Difference in Means of Two NormalDistributions, Variances Unknown 1364.4.3 Inference on the Variances of Two Normal Distributions1434.4.4 Inference on Two Population Proportions 1454.5 What If There Are More Than Two Populations? The Analysis ofVariance 1464.5.1 An Example 1464.5.2 The Analysis of Variance 1484.5.3 Checking Assumptions: Residual Analysis 1544.6 Linear Regression Models 1564.6.1 Estimation of the Parameters in Linear Regression Models1574.6.2 Hypothesis Testing in Multiple Regression 1634.6.3 Confidance Intervals in Multiple Regression 1694.6.4 Prediction of New Observations 1704.6.5 Regression Model Diagnostics 171CAPABILITY ANALYSIS 185187Chapter Overview and Learning Objectives 1875.1 Introduction 1885.2 Chance and Assignable Causes of Quality Variation 1895.3 Statistical Basis of the Control Chart 1905.3.1 Basic Principles 1905.3.2 Choice of Control Limits 1975.3.3 Sample Size and Sampling Frequency 1995.3.4 Rational Subgroups 2015.3.5 Analysis of Patterns on Control Charts 2035.3.6 Discussion of Sensitizing Rules for Control Charts 2055.3.7 Phase I and Phase II of Control Chart Application 2065.4 The Rest of the Magnificent Seven 2075.5 Implementing SPC in a Quality Improvement Program 2135.6 An Application of SPC 2145.7 Applications of Statistical Process Control and QualityImprovement Tools in Transactional and Service Businesses 2216 CONTROL CHARTS FOR VARIABLES 234Chapter Overview and Learning Objectives 2356.1 Introduction 2356.2 Control Charts for ?x and R 2366.2.1 Statistical Basis of the Charts 2366.2.2 Development and Use of ?x and R Charts 2396.2.3 Charts Based on Standard Values 2506.2.4 Interpretation of ?x and R Charts 2516.2.5 The Effect of Nonnormality on ?x and R Charts2546.2.6 The Operating-Characteristic Function 2546.2.7 The Average Run Length for the ?x Chart 2576.3 Control Charts for ?x and s 2596.3.1 Construction and Operation of ?x and s Charts2596.3.2 The ?x and s Control Charts with Variable SampleSize 2636.3.3 The s2 Control Chart 2676.4 The Shewhart Control Chart for Individual Measurements2676.5 Summary of Procedures for ?x , R, and s Charts 2766.6 Applications of Variables Control Charts 2767 CONTROL CHARTS FOR ATTRIBUTES 297Chapter Overview and Learning Objectives 2977.1 Introduction 2987.2 The Control Chart for Fraction Nonconforming 2997.2.1 Development and Operation of the Control Chart 2997.2.2 Variable Sample Size 3107.2.3 Applications in Transactional and Service Businesses3157.2.4 The Operating-Characteristic Function and Average RunLength Calculations 3157.3 Control Charts for Nonconformities (Defects) 3177.3.1 Procedures with Constant Sample Size 3187.3.2 Procedures with Variable Sample Size 3287.3.3 Demerit Systems 3307.3.4 The Operating-Characteristic Function 3317.3.5 Dealing with Low Defect Levels 3327.3.6 Nonmanufacturing Applications 3357.4 Choice Between Attributes and Variables Control Charts3357.5 Guidelines for Implementing Control Charts 3398 PROCESS AND MEASUREMENT SYSTEM CAPABILITY ANALYSIS355Chapter Overview and Learning Objectives 3568.1 Introduction 3568.2 Process Capability Analysis Using a Histogram or aProbability Plot 3588.2.1 Using the Histogram 3588.2.2 Probability Plotting 3608.3 Process Capability Ratios 3628.3.1 Use and Interpretation of Cp 3628.3.2 Process Capability Ratio for an Off-Center Process 3658.3.3 Normality and the Process Capability Ratio 3678.3.4 More about Process Centering 3688.3.5 Confidence Intervals and Tests on Process CapabilityRatios 3708.4 Process Capability Analysis Using a Control Chart 3758.5 Process Capability Analysis Using Designed Experiments3778.6 Process Capability Analysis with Attribute Data 3788.7 Gauge and Measurement System Capability Studies 3798.7.1 Basic Concepts of Gauge Capability 3798.7.2 The Analysis of Variance Method 3848.7.3 Confidence Intervals in Gauge R & R Studies 3878.7.4 False Defectives and Passed Defectives 3888.7.5 Attribute Gauge Capability 3928.7.6 Comparing Customer and Supplier Measurement Systems3948.8 Setting Specification Limits on Discrete Components 3968.8.1 Linear Combinations 3978.8.2 Nonlinear Combinations 4008.9 Estimating the Natural Tolerance Limits of a Process 4018.9.1 Tolerance Limits Based on the Normal Distribution 4028.9.2 Nonparametric Tolerance Limits 403PART 4 OTHER STATISTICAL PROCESSMONITORING AND CONTROLTECHNIQUES 4119 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGECONTROL CHARTS 413Chapter Overview and Learning Objectives 4149.1 The Cumulative Sum Control Chart 4149.1.1 Basic Principles: The CUSUM Control Chart for Monitoringthe Process Mean 4149.1.2 The Tabular or Algorithmic CUSUM for Monitoring theProcess Mean 4179.1.3 Recommendations for CUSUM Design 4229.1.4 The Standardized CUSUM 4249.1.5 Improving CUSUM Responsiveness for Large Shifts 4249.1.6 The Fast Initial Response or Headstart Feature 4249.1.7 One-Sided CUSUMs 4279.1.8 A CUSUM for Monitoring Process Variability 4279.1.9 Rational Subgroups 4289.1.10 CUSUMs for Other Sample Statistics 4289.1.11 The V-Mask Procedure 4299.1.12 The Self-Starting CUSUM 4319.2 The Exponentially Weighted Moving Average Control Chart4339.2.1 The Exponentially Weighted Moving Average Control Chartfor Monitoring the Process Mean 4339.2.2 Design of an EWMA Control Chart 4369.2.3 Robustness of the EWMA to Nonnormality 4389.2.4 Rational Subgroups 4399.2.5 Extensions of the EWMA 4399.3 The Moving Average Control Chart 44210 OTHER UNIVARIATE STATISTICAL PROCESS-MONITORING ANDCONTROL TECHNIQUES 448Chapter Overview and Learning Objectives 44910.1 Statistical Process Control for Short Production Runs45010.1.1 ?x and R Charts for Short Production Runs 45010.1.2 Attributes Control Charts for Short Production Runs45210.1.3 Other Methods 45210.2 Modified and Acceptance Control Charts 45410.2.1 Modified Control Limits for the ?x Chart 45410.2.2 Acceptance Control Charts 45710.3 Control Charts for Multiple-Stream Processes 45810.3.1 Multiple-Stream Processes 45810.3.2 Group Control Charts 45810.3.3 Other Approaches 46010.4 SPC With Autocorrelated Process Data 46110.4.1 Sources and Effects of Autocorrelation in Process Data46110.4.2 Model-Based Approaches 46510.4.3 A Model-Free Approach 47310.5 Adaptive Sampling Procedures 47710.6 Economic Design of Control Charts 47810.6.1 Designing a Control Chart 47810.6.2 Process Characteristics 47910.6.3 Cost Parameters 47910.6.4 Early Work and Semieconomic Designs 48110.6.5 An Economic Model of the ??x Control Chart48210.6.6 Other Work 48710.7 Cuscore Charts 48810.8 The Changepoint Model for Process Monitoring 49010.9 Profile Monitoring 49110.10 Control Charts in Health Care Monitoring and Public HealthSurveillance 49610.11 Overview of Other Procedures 49710.11.1 Tool Wear 49710.11.2 Control Charts Based on Other Sample Statistics 49810.11.3 Fill Control Problems 49810.11.4 Precontrol 49910.11.5 Tolerance Interval Control Charts 50010.11.6 Monitoring Processes with Censored Data 50110.11.7 Monitoring Bernoulli Processes 50110.11.8 Nonparametric Control Charts 50211 MULTIVARIATE PROCESS MONITORING AND CONTROL 509Chapter Overview and Learning Objectives 50911.1 The Multivariate Quality-Control Problem 51011.2 Description of Multivariate Data 51211.2.1 The Multivariate Normal Distribution 51211.2.2 The Sample Mean Vector and Covariance Matrix 51311.3 The Hotelling T2 Control Chart 51411.3.1 Subgrouped Data 51411.3.2 Individual Observations 52111.4 The Multivariate EWMA Control Chart 52411.5 Regression Adjustment 52811.6 Control Charts for Monitoring Variability 53111.7 Latent Structure Methods 53311.7.1 Principal Components 53311.7.2 Partial Least Squares 53812 ENGINEERING PROCESS CONTROL AND SPC 542Chapter Overview and Learning Objectives 54212.1 Process Monitoring and Process Regulation 54312.2 Process Control by Feedback Adjustment 54412.2.1 A Simple Adjustment Scheme: Integral Control 54412.2.2 The Adjustment Chart 54912.2.3 Variations of the Adjustment Chart 55112.2.4 Other Types of Feedback Controllers 55412.3 Combining SPC and EPC 555PART 5 PROCESS DESIGN AND IMPROVEMENT WITH DESIGNEDEXPERIMENTS 56113 FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESSDESIGN AND IMPROVEMENT 563Chapter Overview and Learning Objectives 56413.1 What is Experimental Design? 56413.2 Examples of Designed Experiments In Process and ProductImprovement 56613.3 Guidelines for Designing Experiments 56813.4 Factorial Experiments 57013.4.1 An Example 57213.4.2 Statistical Analysis 57213.4.3 Residual Analysis 57713.5 The 2k Factorial Design 57813.5.1 The 22 Design 57813.5.2 The 2k Design for k ? 3 Factors 58313.5.3 A Single Replicate of the 2k Design 59313.5.4 Addition of Center Points to the 2k Design 59613.5.5 Blocking and Confounding in the 2k Design 59913.6 Fractional Replication of the 2k Design 60113.6.1 The One-Half Fraction of the 2k Design 60113.6.2 Smaller Fractions: The 2k?p Fractional FactorialDesign 60614 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS 617Chapter Overview and Learning Objectives 61714.1 Response Surface Methods and Designs 61814.1.1 The Method of Steepest Ascent 62014.1.2 Analysis of a Second-Order Response Surface 62214.2 Process Robustness Studies 62614.2.1 Background 62614.2.2 The Response Surface Approach to Process RobustnessStudies 62814.3 Evolutionary Operation 634PART 6 ACCEPTANCE SAMPLING 64715 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES 649Chapter Overview and Learning Objectives 64915.1 The Acceptance-Sampling Problem 65015.1.1 Advantages and Disadvantages of Sampling 65115.1.2 Types of Sampling Plans 65215.1.3 Lot Formation 65315.1.4 Random Sampling 65315.1.5 Guidelines for Using Acceptance Sampling 65415.2 Single-Sampling Plans for Attributes 65515.2.1 Definition of a Single-Sampling Plan 65515.2.2 The OC Curve 65515.2.3 Designing a Single-Sampling Plan with a Specified OCCurve 66015.2.4 Rectifying Inspection 66115.3 Double, Multiple, and Sequential Sampling 66415.3.1 Double-Sampling Plans 66515.3.2 Multiple-Sampling Plans 66915.3.3 Sequential-Sampling Plans 67015.4 Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859) 67315.4.1 Description of the Standard 67315.4.2 Procedure 67515.4.3 Discussion 67915.5 The Dodge?Romig Sampling Plans 68115.5.1 AOQL Plans 68215.5.2 LTPD Plans 68515.5.3 Estimation of Process Average 68516 OTHER ACCEPTANCE-SAMPLING TECHNIQUES 688Chapter Overview and Learning Objectives 68816.1 Acceptance Sampling by Variables 68916.1.1 Advantages and Disadvantages of Variables Sampling68916.1.2 Types of Sampling Plans Available 69016.1.3 Caution in the Use of Variables Sampling 69116.2 Designing a Variables Sampling Plan with a Specified OCCurve 69116.3 MIL STD 414 (ANSI/ASQC Z1.9) 69416.3.1 General Description of the Standard 69416.3.2 Use of the Tables 69516.3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9 69716.4 Other Variables Sampling Procedures 69816.4.1 Sampling by Variables to Give Assurance Regarding the Lotor Process Mean 69816.4.2 Sequential Sampling by Variables 69916.5 Chain Sampling 69916.6 Continuous Sampling 70116.6.1 CSP-1 70116.6.2 Other Continuous-Sampling Plans 70416.7 Skip-Lot Sampling Plans 704APPENDIX 709I. Summary of Common Probability Distributions Often Used inStatistical Quality Control 710II. Cumulative Standard Normal Distribution 711III. Percentage Points of the ?2 Distribution 713IV. Percentage Points of the t Distribution 714V. Percentage Points of the F Distribution 715VI. Factors for Constructing Variables Control Charts 720VII. Factors for Two-Sided Normal Tolerance Limits 721VIII. Factors for One-Sided Normal Tolerance Limits 722BIBLIOGRAPHY 723INDEX 749
