简介
Summary:
Publisher Summary 1
Additions to this fourth edition of a text/CD-ROM package include discussion of dot plots as an alternative to histograms, material on Six Sigma methodology, emphasis on the role that acceptance control charts play in controlling risks, and scatter lot matrices as applied to datasets of higher dimensions. There are new chapters on process capability and improvement and on measurement studies. The CD-ROM contains papers on ANOM and ANOME published in the Journal of Quality Technology , plus an Excel add-in for performing these analyses and freeware versions of graphing and statistical utilities. Ott was affiliated with Rutgers University. Annotation 漏2005 Book News, Inc., Portland, OR (booknews.com)
目录
Table Of Contents:
List of Figures and Tables xiii
Case Histories xxvii
Preface to the Fourth Edition xxix
Preface to the Third Edition xxxiii
Preface to the Second Edition xxxvii
Preface to the First Edition xxxix
Part 1 Basics of Interpretation of Data
Chapter 1 Variables Data: An Introduction 3(48)
1.1 Introduction: An Experience with Data 3(3)
1.2 Variability 6(2)
1.3 Organizing Data 8(1)
1.4 Grouping Data When n Is Large 9(3)
1.5 The Arithmetic Average or Mean鈥擟entral Value 12(1)
1.6 Measures of Variation 13(5)
1.7 Normal Probability Plots 18(2)
1.8 Predictions Regarding Sampling Variation: The Normal Curve 20(8)
1.9 Series of Small Samples from a Production Process 28(1)
1.10 Change in Sample Size: Predictions about Χ and σ 29(2)
1.11 How Large a Sample Is Needed to Estimate a Process Average? 31(1)
1.12 Sampling and a Second Method of Computing σ 32(3)
1.13 Some Important Remarks about the Two Estimates 35(2)
1.14 Stem-and-Leaf 37(1)
1.15 Box Plots 38(3)
1.16 Dot Plot 41(1)
1.17 Tolerance Intervals for Populations 42(2)
1.18 A Note on Notation 44(2)
1.19 Summary 46(1)
1.20 Practice Exercises 47(4)
Chapter 2 Ideas from Time Sequences of Observations 51(48)
2.1 Introduction 51(3)
2.2 Data from a Scientific or Production Process 54(1)
2.3 Signals and Risks 55(1)
2.4 Run Criteria 56(6)
2.5 Shewhart Control Charts for Variables 62(9)
2.6 Probabilities Associated with an Χ Control Chart: Operating-Characteristic Curves 71(17)
2.7 Control Charts for Trends 88(7)
2.8 Digidot Plot 95(1)
2.9 Practice Exercises 96(3)
Chapter 3 Ideas from Outliers-Variables Data 99(10)
3.1 Introduction 99(4)
3.2 Other Objective Tests for Outliers 103(3)
3.3 Two Suspected Outliers on the Same End of a Sample of n (Optional) 106(2)
3.4 Practice Exercises 108(1)
Chapter 4 Variability-Estimating and Comparing 109(18)
4.1 Introduction 109(1)
4.2 Statistical Efficiency and Bias in Variability Estimates 109(2)
4.3 Estimating σ and σ虏 from Data: One Sample of Size n 111(1)
4.4 Data from n Observations Consisting of kappa Subsets of ng = r: Two Procedures 112(2)
4.5 Comparing Variabilities of Two Populations 114(9)
4.6 Summary 123(2)
4.7 Practice Exercises 125(2)
Chapter 5 Attributes or Go/No-Go Data 127(26)
5.1 Introduction 127(1)
5.2 Three Important Problems 127(11)
5.3 On How to Sample 138(1)
5.4 Attributes Data That Approximate a Poisson Distribution 139(7)
5.5 Notes on Control Charts 146(3)
5.6 Practice Exercises 149(4)
Part 2 Statistical Process Control
Chapter 6 Sampling and Narrow-Limit Gauging 153(42)
6.1 Introduction 153(1)
6.2 Scientific Sampling Plans 154(1)
6.3 A Simple Probability 155(1)
6.4 Operating-Characteristic Curves of a Single Sampling Plan 156(1)
6.5 But Is It a Good Plan? 157(1)
6.6 Average Outgoing Quality (AOQ) and Its Maximum Limit (AOQL) 158(2)
6.7 Computing the Average Outgoing Quality (AOQ) of Lots from a Process Producing P Percent Defective 160(1)
6.8 Other Important Concepts Associated with Sampling Plans 161(1)
6.9 Risks 162(1)
6.10 Tabulated Sampling Plans 162(1)
6.11 Feedback of Information 163(3)
6.12 Where Should Feedback Begin? 166(14)
6.13 Narrow-Limit Gauging 180(1)
6.14 Outline of an NL-Gauging Plan 181(1)
6.15 Selection of a Simple NL-Gauging Sampling Plan 182(5)
6.16 OC Curves of NL-Gauge Plans 187(4)
6.17 Hazards 191(1)
6.18 Selection of an NL-Gauging Plan 192(1)
6.19 Optimal Narrow-Limit Plans 192(1)
6.20 Practice Exercises 193(2)
Chapter 7 Principles and Applications of Control Charts 195(54)
7.1 Introduction 195(1)
7.2 Key Aspects of Process Quality Control 196(1)
7.3 Process Control 197(2)
7.4 Uses of Control Charts 199(1)
7.5 Rational Subgroups 199(1)
7.6 Special Control Charts 200(1)
7.7 Median Chart 200(3)
7.8 Standard Deviation Chart 203(1)
7.9 Acceptance Control Chart 204(7)
7.10 Modified Control Limits 211(1)
7.11 Arithmetic and Exponentially Weighted Moving Average Charts 212(4)
7.12 Cumulative Sum Charts 216(13)
7.13 Precontrol 229(3)
7.14 Narrow-Limit Control Charts 232(1)
7.15 Other Control Charts 232(9)
7.16 How to Apply Control Charts 241(4)
7.17 Practice Exercises 245(4)
Chapter 8 Process Capability, Performance, and Improvement 249(20)
8.1 Process Capability 249(1)
8.2 Process Optimization Studies 250(1)
8.3 Capability and Specifications 251(6)
8.4 Narrow-Limit Gauging for Process Capability 257(2)
8.5 Process Performance 259(2)
8.6 Process Improvement 261(1)
8.7 Process Change 262(1)
8.8 Problem Identification 263(1)
8.9 Prioritization 263(2)
8.10 Summary 265(1)
8.11 Practice Exercises 265(4)
Part 3 Troubleshooting and Process Improvement
Chapter 9 Some Basic Ideas and Methods of Troubleshooting and Problem Solving 269(18)
9.1 Introduction 269(1)
9.2 Some Types of Independent and Dependent Variables 270(2)
9.3 Some Strategies in Problem Finding, Problem Solving, and Troubleshooting 272(4)
9.4 Bicking's Checklist 276(2)
9.5 Problem Solving Skills 278(2)
9.6 Six Sigma Methodology 280(6)
9.7 Practice Exercises 286(1)
Chapter 10 Some Concepts of Statistical Design of Experiments 287(28)
10.1 Introduction 287(1)
10.2 Effects 288(3)
10.3 Sums of Squares 291(2)
10.4 Yates Method 293(5)
10.5 Blocking 298(1)
10.6 Fractional Factorials 299(3)
10.7 Graphical Analysis of 2p Designs 302(5)
10.8 Conclusion 307(4)
10.9 Practice Exercises 311(4)
Chapter 11 Troubleshooting with Attributes Data 315(64)
11.1 Introduction 315(1)
11.2 Ideas from Sequences of Observations over Time 316(1)
11.3 Decision Lines Applicable to kappa Points Simultaneously 317(7)
11.4 Analysis of Means for Proportions When n is Constant 324(1)
11.5 Analysis of Means for Proportions When n Varies 325(4)
11.6 Analysis of Means for Count Data 329(1)
11.7 Introduction to Case Histories 330(1)
11.8 One Independent Variable with kappa Levels 331(10)
11.9 Two Independent Variables 341(14)
11.10 Three Independent Variables 355(12)
11.11 A Very Important Experimental Design: 1/2 x 2鲁 367(5)
11.12 Case History Problems 372(4)
11.13 Practice Exercises 376(3)
Chapter 12 Special Strategies in Troubleshooting 379(34)
12.1 Ideas from Patterns of Data 379(4)
12.2 Disassembly and Reassembly 383(4)
12.3 A Special Screening Program for Many Treatments 387(6)
12.4 Other Screening Strategies 393(1)
12.5 Relationship of One Variable to Another 394(3)
12.6 Mechanics of Measuring the Degree of a Relationship 397(5)
12.7 Scatter Plot Matrix 402(1)
12.8 Use of Transformations and ANOM 403(9)
12.9 Practice Exercises 412(1)
Chapter 13 Comparing Two Process Averages 413(18)
13.1 Introduction 413(1)
13.2 Tukey's Two-Sample Test to Duckworth's Specifications 413(2)
13.3 Analysis of Means, kappa = 2, ng = r1 = r2 = r 415(2)
13.4 Student's t and F test Comparison of Two Stable Processes 417(3)
13.5 Magnitude of the Difference between Two Means 420(9)
13.6 Practice Exercises 429(2)
Chapter 14 Troubleshooting with Variables Data 431(30)
14.1 Introduction 431(1)
14.2 Suggestions in Planning Investigations-Primarily Reminders 432(1)
14.3 A Statistical Tool for Process Change 433(1)
14.4 Analysis of Means for Measurement Data 434(1)
14.5 Example-Measurement Data 435(1)
14.6 Analysis of Means: A 2虏 Factorial Design 436(8)
14.7 Three Independent Variables: A 2鲁 Factorial Design 444(5)
14.8 Computational Details for Two-Factor Interactions in a 2鲁 Factorial Design 449(1)
14.9 A Very Important Experimental Design: 1/2 x 23 450(6)
14.10 General ANOM Analysis of 2p and 2p-1 Designs 456(3)
14.11 Practice Exercises 459(2)
Chapter 15 More Than Two Levels of an Independent Variable 461(64)
15.1 Introduction 461(1)
15.2 An Analysis of kappa Independent Samples-Standard Given, One Independent Variable 462(1)
15.3 An Analysis of kappa Independent Samples-No Standard Given, One Independent Variable 463(6)
15.4 Analysis of Means-No Standard Given, More Than One Independent Variable 469(1)
15.5 Analysis of Two-Factor Crossed Designs 470(7)
15.6 The Relation of Analysis of Means to Analysis of Variance (Optional) 477(2)
15.7 Analysis of Fully Nested Designs (Optional) 479(5)
15.8 Analysis of Means for Crossed Experiments鈥擬ultiple Factors 484(13)
15.9 Nested Factorial Experiments (Optional) 497(1)
15.10 Multifactor Experiments with Attributes Data 498(7)
15.11 Analysis of Means When the Sample Sizes are Unequal 505(1)
15.12 Comparing Variabilities 506(6)
15.13 Nonrandom Uniformity 512(3)
15.14 Calculation of ANOM Limits for 2p Experiments 515(1)
15.15 Development of Analysis of Means 516(2)
15.16 Practice Exercises 518(7)
Chapter 16 Assessing Measurements as a Process 525(30)
16.1 Introduction 525(1)
16.2 Measurement as a Process 525(2)
16.3 What Can Affect the Measurement Process? 527(3)
16.4 Crossed vs. Nested Designs 530(1)
16.5 Gauge Repeatability and Reproducibility Studies 530(23)
16.6 Practice Exercises 553(2)
Chapter 17 What's on the CD-ROM 555(12)
Chapter 18 Epilogue 567(8)
18.1 Practice Exercises 573(2)
Appendix Tables 575(42)
Index 617
List of Figures and Tables xiii
Case Histories xxvii
Preface to the Fourth Edition xxix
Preface to the Third Edition xxxiii
Preface to the Second Edition xxxvii
Preface to the First Edition xxxix
Part 1 Basics of Interpretation of Data
Chapter 1 Variables Data: An Introduction 3(48)
1.1 Introduction: An Experience with Data 3(3)
1.2 Variability 6(2)
1.3 Organizing Data 8(1)
1.4 Grouping Data When n Is Large 9(3)
1.5 The Arithmetic Average or Mean鈥擟entral Value 12(1)
1.6 Measures of Variation 13(5)
1.7 Normal Probability Plots 18(2)
1.8 Predictions Regarding Sampling Variation: The Normal Curve 20(8)
1.9 Series of Small Samples from a Production Process 28(1)
1.10 Change in Sample Size: Predictions about Χ and σ 29(2)
1.11 How Large a Sample Is Needed to Estimate a Process Average? 31(1)
1.12 Sampling and a Second Method of Computing σ 32(3)
1.13 Some Important Remarks about the Two Estimates 35(2)
1.14 Stem-and-Leaf 37(1)
1.15 Box Plots 38(3)
1.16 Dot Plot 41(1)
1.17 Tolerance Intervals for Populations 42(2)
1.18 A Note on Notation 44(2)
1.19 Summary 46(1)
1.20 Practice Exercises 47(4)
Chapter 2 Ideas from Time Sequences of Observations 51(48)
2.1 Introduction 51(3)
2.2 Data from a Scientific or Production Process 54(1)
2.3 Signals and Risks 55(1)
2.4 Run Criteria 56(6)
2.5 Shewhart Control Charts for Variables 62(9)
2.6 Probabilities Associated with an Χ Control Chart: Operating-Characteristic Curves 71(17)
2.7 Control Charts for Trends 88(7)
2.8 Digidot Plot 95(1)
2.9 Practice Exercises 96(3)
Chapter 3 Ideas from Outliers-Variables Data 99(10)
3.1 Introduction 99(4)
3.2 Other Objective Tests for Outliers 103(3)
3.3 Two Suspected Outliers on the Same End of a Sample of n (Optional) 106(2)
3.4 Practice Exercises 108(1)
Chapter 4 Variability-Estimating and Comparing 109(18)
4.1 Introduction 109(1)
4.2 Statistical Efficiency and Bias in Variability Estimates 109(2)
4.3 Estimating σ and σ虏 from Data: One Sample of Size n 111(1)
4.4 Data from n Observations Consisting of kappa Subsets of ng = r: Two Procedures 112(2)
4.5 Comparing Variabilities of Two Populations 114(9)
4.6 Summary 123(2)
4.7 Practice Exercises 125(2)
Chapter 5 Attributes or Go/No-Go Data 127(26)
5.1 Introduction 127(1)
5.2 Three Important Problems 127(11)
5.3 On How to Sample 138(1)
5.4 Attributes Data That Approximate a Poisson Distribution 139(7)
5.5 Notes on Control Charts 146(3)
5.6 Practice Exercises 149(4)
Part 2 Statistical Process Control
Chapter 6 Sampling and Narrow-Limit Gauging 153(42)
6.1 Introduction 153(1)
6.2 Scientific Sampling Plans 154(1)
6.3 A Simple Probability 155(1)
6.4 Operating-Characteristic Curves of a Single Sampling Plan 156(1)
6.5 But Is It a Good Plan? 157(1)
6.6 Average Outgoing Quality (AOQ) and Its Maximum Limit (AOQL) 158(2)
6.7 Computing the Average Outgoing Quality (AOQ) of Lots from a Process Producing P Percent Defective 160(1)
6.8 Other Important Concepts Associated with Sampling Plans 161(1)
6.9 Risks 162(1)
6.10 Tabulated Sampling Plans 162(1)
6.11 Feedback of Information 163(3)
6.12 Where Should Feedback Begin? 166(14)
6.13 Narrow-Limit Gauging 180(1)
6.14 Outline of an NL-Gauging Plan 181(1)
6.15 Selection of a Simple NL-Gauging Sampling Plan 182(5)
6.16 OC Curves of NL-Gauge Plans 187(4)
6.17 Hazards 191(1)
6.18 Selection of an NL-Gauging Plan 192(1)
6.19 Optimal Narrow-Limit Plans 192(1)
6.20 Practice Exercises 193(2)
Chapter 7 Principles and Applications of Control Charts 195(54)
7.1 Introduction 195(1)
7.2 Key Aspects of Process Quality Control 196(1)
7.3 Process Control 197(2)
7.4 Uses of Control Charts 199(1)
7.5 Rational Subgroups 199(1)
7.6 Special Control Charts 200(1)
7.7 Median Chart 200(3)
7.8 Standard Deviation Chart 203(1)
7.9 Acceptance Control Chart 204(7)
7.10 Modified Control Limits 211(1)
7.11 Arithmetic and Exponentially Weighted Moving Average Charts 212(4)
7.12 Cumulative Sum Charts 216(13)
7.13 Precontrol 229(3)
7.14 Narrow-Limit Control Charts 232(1)
7.15 Other Control Charts 232(9)
7.16 How to Apply Control Charts 241(4)
7.17 Practice Exercises 245(4)
Chapter 8 Process Capability, Performance, and Improvement 249(20)
8.1 Process Capability 249(1)
8.2 Process Optimization Studies 250(1)
8.3 Capability and Specifications 251(6)
8.4 Narrow-Limit Gauging for Process Capability 257(2)
8.5 Process Performance 259(2)
8.6 Process Improvement 261(1)
8.7 Process Change 262(1)
8.8 Problem Identification 263(1)
8.9 Prioritization 263(2)
8.10 Summary 265(1)
8.11 Practice Exercises 265(4)
Part 3 Troubleshooting and Process Improvement
Chapter 9 Some Basic Ideas and Methods of Troubleshooting and Problem Solving 269(18)
9.1 Introduction 269(1)
9.2 Some Types of Independent and Dependent Variables 270(2)
9.3 Some Strategies in Problem Finding, Problem Solving, and Troubleshooting 272(4)
9.4 Bicking's Checklist 276(2)
9.5 Problem Solving Skills 278(2)
9.6 Six Sigma Methodology 280(6)
9.7 Practice Exercises 286(1)
Chapter 10 Some Concepts of Statistical Design of Experiments 287(28)
10.1 Introduction 287(1)
10.2 Effects 288(3)
10.3 Sums of Squares 291(2)
10.4 Yates Method 293(5)
10.5 Blocking 298(1)
10.6 Fractional Factorials 299(3)
10.7 Graphical Analysis of 2p Designs 302(5)
10.8 Conclusion 307(4)
10.9 Practice Exercises 311(4)
Chapter 11 Troubleshooting with Attributes Data 315(64)
11.1 Introduction 315(1)
11.2 Ideas from Sequences of Observations over Time 316(1)
11.3 Decision Lines Applicable to kappa Points Simultaneously 317(7)
11.4 Analysis of Means for Proportions When n is Constant 324(1)
11.5 Analysis of Means for Proportions When n Varies 325(4)
11.6 Analysis of Means for Count Data 329(1)
11.7 Introduction to Case Histories 330(1)
11.8 One Independent Variable with kappa Levels 331(10)
11.9 Two Independent Variables 341(14)
11.10 Three Independent Variables 355(12)
11.11 A Very Important Experimental Design: 1/2 x 2鲁 367(5)
11.12 Case History Problems 372(4)
11.13 Practice Exercises 376(3)
Chapter 12 Special Strategies in Troubleshooting 379(34)
12.1 Ideas from Patterns of Data 379(4)
12.2 Disassembly and Reassembly 383(4)
12.3 A Special Screening Program for Many Treatments 387(6)
12.4 Other Screening Strategies 393(1)
12.5 Relationship of One Variable to Another 394(3)
12.6 Mechanics of Measuring the Degree of a Relationship 397(5)
12.7 Scatter Plot Matrix 402(1)
12.8 Use of Transformations and ANOM 403(9)
12.9 Practice Exercises 412(1)
Chapter 13 Comparing Two Process Averages 413(18)
13.1 Introduction 413(1)
13.2 Tukey's Two-Sample Test to Duckworth's Specifications 413(2)
13.3 Analysis of Means, kappa = 2, ng = r1 = r2 = r 415(2)
13.4 Student's t and F test Comparison of Two Stable Processes 417(3)
13.5 Magnitude of the Difference between Two Means 420(9)
13.6 Practice Exercises 429(2)
Chapter 14 Troubleshooting with Variables Data 431(30)
14.1 Introduction 431(1)
14.2 Suggestions in Planning Investigations-Primarily Reminders 432(1)
14.3 A Statistical Tool for Process Change 433(1)
14.4 Analysis of Means for Measurement Data 434(1)
14.5 Example-Measurement Data 435(1)
14.6 Analysis of Means: A 2虏 Factorial Design 436(8)
14.7 Three Independent Variables: A 2鲁 Factorial Design 444(5)
14.8 Computational Details for Two-Factor Interactions in a 2鲁 Factorial Design 449(1)
14.9 A Very Important Experimental Design: 1/2 x 23 450(6)
14.10 General ANOM Analysis of 2p and 2p-1 Designs 456(3)
14.11 Practice Exercises 459(2)
Chapter 15 More Than Two Levels of an Independent Variable 461(64)
15.1 Introduction 461(1)
15.2 An Analysis of kappa Independent Samples-Standard Given, One Independent Variable 462(1)
15.3 An Analysis of kappa Independent Samples-No Standard Given, One Independent Variable 463(6)
15.4 Analysis of Means-No Standard Given, More Than One Independent Variable 469(1)
15.5 Analysis of Two-Factor Crossed Designs 470(7)
15.6 The Relation of Analysis of Means to Analysis of Variance (Optional) 477(2)
15.7 Analysis of Fully Nested Designs (Optional) 479(5)
15.8 Analysis of Means for Crossed Experiments鈥擬ultiple Factors 484(13)
15.9 Nested Factorial Experiments (Optional) 497(1)
15.10 Multifactor Experiments with Attributes Data 498(7)
15.11 Analysis of Means When the Sample Sizes are Unequal 505(1)
15.12 Comparing Variabilities 506(6)
15.13 Nonrandom Uniformity 512(3)
15.14 Calculation of ANOM Limits for 2p Experiments 515(1)
15.15 Development of Analysis of Means 516(2)
15.16 Practice Exercises 518(7)
Chapter 16 Assessing Measurements as a Process 525(30)
16.1 Introduction 525(1)
16.2 Measurement as a Process 525(2)
16.3 What Can Affect the Measurement Process? 527(3)
16.4 Crossed vs. Nested Designs 530(1)
16.5 Gauge Repeatability and Reproducibility Studies 530(23)
16.6 Practice Exercises 553(2)
Chapter 17 What's on the CD-ROM 555(12)
Chapter 18 Epilogue 567(8)
18.1 Practice Exercises 573(2)
Appendix Tables 575(42)
Index 617
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