Statistical methods for quality improvement:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York [u.a.]
Wiley
2000
|
| Edition: | 2. ed. |
| Series: | Wiley series in probability and statistics
A Wiley interscience publication |
| Subjects: | |
| Online Access: | Publisher description Table of Contents Inhaltsverzeichnis |
| Physical Description: | XXIV, 555 S. graph. Darst. |
| ISBN: | 0471197750 |
Staff View
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| 245 | 1 | 0 | |a Statistical methods for quality improvement |c Thomas P. Ryan |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a New York [u.a.] |b Wiley |c 2000 | |
| 300 | |a XXIV, 555 S. |b graph. Darst. | ||
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| 490 | 0 | |a Wiley series in probability and statistics | |
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| 650 | 4 | |a Statistische Qualitätskontrolle | |
| 650 | 4 | |a Quality control -- Statistical methods | |
| 650 | 4 | |a Process control -- Statistical methods | |
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| adam_text | Contents
Preface xxi
Preface to the First Edition xxiii
PART I FUNDAMENTAL QUALITY IMPROVEMENT
AND STATISTICAL CONCEPTS
CHAPTER 1 Introduction 3
1.1 Quality and Productivity 4
1.2 Quality Costs (or Does It?) 4
1.3 The Need for Statistical Methods 5
1.4 Early Use of Statistical Methods for
Improving Quality 5
1.5 Influential Quality Experts 6
1.6 Summary 9
References 9
CHAPTER 2 Basic Tools for Improving Quality 11
2.1 Histogram 11
2.2 Pareto Charts 15
2.3 Scatter Plots 18
2.3.1 Variations of Scatter Plots 21
2.4 Control Chart 21
2.5 Check Sheet 23
2.6 Cause and Effect Diagram 23
2.7 Defect Concentration Diagram 24
2.8 The Seven New Tools 25
2.8.1 Affinity Diagram 25
2.8.2 Interrelationship Digraph 25
vii
viii CONTENTS
2.8.3 Tree Diagram 25
2.8.4 Prioritization Matrix 26
2.8.5 Matrix Diagram 26
2.8.6 Process Decision Program Chart 26
2.8.7 Activity Network Diagram 26
2.9 Summary 27
References 27
CHAPTER 3 Basic Concepts in Statistics and Probability 28
3.1 Probability 28
3.2 Sample Versus Population 30
3.3 Location 31
3.4 Variation 33
3.5 Discrete Distributions 36
3.5.1 Binomial Distribution 37
3.5.2 Beta Binomial Distribution 44
3.5.3 Poisson Distribution 44
3.5.4 Geometric Distribution 45
3.5.5 Negative Binomial Distribution 46
3.5.6 Hypergeometric Distribution 46
3.6 Continuous Distributions 48
3.6.1 Normal Distribution 48
3.6.2 t Distribution 53
3.6.3 Exponential Distribution 55
3.6.4 Lognormal Distribution 55
3.6.5 Weibull Distribution 57
3.6.6 Gamma Distribution 57
3.6.7 Chi Square Distribution 58
3.6.8 Truncated Normal Distribution 58
3.6.9 Bivariate and Multivariate
Normal Distributions 59
3.6.10 F Distribution 60
3.6.11 Beta Distribution 61
3.6.12 Uniform Distribution 61
3.7 Choice of Statistical Distribution 61
3.8 Statistical Inference 62
3.8.1 Central Limit Theorem 62
3.8.2 Point Estimation 63
3.8.2.1 Maximum Likelihood
Estimation 63
3.8.3 Confidence Intervals 64
3.8.4 Tolerance Intervals 65
CONTENTS ix
3.8.5 Hypothesis Tests 66
3.8.5.1 Probability Plots 66
3.8.5.2 Likelihood Ratio Tests 69
3.8.6 Bonferroni Intervals 69
3.9 Enumerative Studies versus Analytic Studies 70
References 71
Exercises 72
PART II CONTROL CHARTS AND PROCESS CAPABILITY
CHAPTER 4 Control Charts for Measurements
with Subgrouping (for One Variable) 77
4.1 Basic Control Chart Principles 77
4.2 Real Time Control Charting Versus
Analysis of Past Data 79
4.3 Control Charts: When to Use. Where to Use,
How Many to Use 81
4.4 Benefits from the Use of Control Charts 81
4.5 Rational Subgroups 82
4.6 Basic Statistical Aspects of Control Charts 82
4.7 Illustrative Example 83
4.7.1 R Chart 86
4.7.2 R Chart with Probability Limits 88
4.7.3 5 Chart 89
4.7.4 s Chart with Probability Limits 89
4.7.5 ^ Chart 93
4.7.6 X Chart 93
4.7.7 Recomputing Control Limits 96
4.7.8 Applying Control Limits to Future
Production 97
4.7.9 Standards for Control Charts 97
4.7.10 Deleting Points 98
4.7.11 Target Values 98
4.8 Illustrative Example with Real Data 99
4.9 Determining the Time of a Parameter Change 100
4.10 Acceptance Sampling and Acceptance
Control Chart 101
4.10.1 Acceptance Control Chart 102
4.10.1.1 Acceptance Chart
with X Control Limits 104
4.10.1.2 Acceptance Charts
Versus Target Values 105
X CONTENTS
4.11 Modified Limits 106
4.12 Difference Control Charts 107
4.13 Other Charts 108
4.14 Average Run Length 109
4.14.1 Weakness of the ARL Measure 110
4.15 Determining the Subgroup Size 111
4.15.1 Unequal Subgroup Sizes 112
4.16 Out of Control Action Plans 112
4.17 Assumptions for the Charts in This Chapter 113
4.17.1 Normality 114
4.17.2 Independence 118
4.18 Measurement Error 121
4.19 Summary 122
Appendix 123
References 125
Exercises 128
CHAPTER 5 Control Charts for Measurements
without Subgrouping (for One Variable) 133
5.1 Individual Observations Chart 133
5.1.1 Control Limits for the X Chart 134
5.1.2 X Chart Assumptions 135
5.1.3 Illustrative Example: Random Data 136
5.1.4 Example with Particle Counts 138
5.1.5 Illustrative Example: Trended Data 139
5.1.6 Trended Real Data 142
5.2 Moving Average Chart 144
5.3 Controlling Variability with Individual
Observations 145
5.4 Summary 147
Appendix 148
References 148
Exercises 149
CHAPTER 6 Control Charts for Attributes 152
6.1 Charts for Nonconforming Units 153
6.1.1 tip Chart 153
6.1.2 p Chart 155
6.1.3 Stage 1 and Stage 2 Use of p Charts
and np Charts 155
CONTENTS xi
6.1.4 Alternative Approaches 158
6.1.4.1 Arcsin Transformations 158
6.1.4.2 Q Chart for Binomial Data 163
6.1.4.3 Regression Based Limits 163
6.1.4.4 ARL Unbiased Charts 165
6.1.5 Variable Sample Size 166
6.1.6 Charts Based on the Geometric
and Negative Binomial Distributions 167
6.1.7 Overdispersion 168
6.2 Charts for Nonconformities 169
6.2.1 c Chart 169
6.2.2 Transforming Poisson Data 171
6.2.3 Illustrative Example 171
6.2.4 Regression Based Limits 174
6.2.5 u Chart 176
6.2.5.1 Regression Based Limits 177
6.2.6 Overdispersion 177
6.2.7 D Chart 178
6.2.7.1 Probability Type
D Chart Limits 180
6.3 Summary 180
References 181
Exercises 183
CHAPTER 7 Process Capability 186
7.1 Data Acquisition for Capability Indices 186
7.1.1 Selection of Historical Data 187
7.2 Process Capability Indices 187
7.2.1 Cp 188
7.2.2 Cpm 188
7.2.3 Cpk 189
7.2.4 Cpmk 191
7.2.5 Other Capability Indices 191
7.3 Estimating the Parameters in Process
Capability Indices 192
7.3.1 X Chart 193
7.3.2 X Chart 193
7.3.3 Case Study 193
7.4 Distributional Assumption for Capability
Indices 195
xii CONTENTS
7.5 Confidence Intervals for Process
Capability Indices 195
7.5.1 Confidence Interval for C„ 196
7.5.2 Confidence Interval for Cpk 196
7.5.3 Confidence Interval for Cpm 198
7.5.4 Confidence Interval for Cpmk 198
7.5.5 Confidence Intervals Computed Using
Data in Subgroups 198
7.5.6 Nonparametric Capability
Indices and Confidence Limits 199
7.5.6.1 Robust Capability Indices 199
7.5.6.2 Capability Indices Based
on Fitted Distributions 200
7.5.6.3 Data Transformation 201
7.5.6.4 Capability Indices Computed
Using Resampling Methods 201
7.6 Asymmetric Bilateral Tolerances 202
7.6.1 Examples 202
7.7 Capability Indices That Are a Function of
Percent Nonconforming 203
7.7.1 Examples 204
7.8 Modified k Index 208
7.9 Other Approaches 208
7.10 Process Capability Plots 209
7.11 Process Capability Indices Versus Process
Performance Indices 209
7.12 Process Capability Indices with
Autocorrelated Data 210
7.13 Summary 211
References 211
Exercises 214
CHAPTER 8 Alternatives to Shewhart Charts 216
8.1 Introduction 216
8.2 Cumulative Sum Procedures:
Principles and Historical Development 218
8.2.1 CUSUM Procedures Versus X Chart 218
8.2.2 Fast Initial Response CUSUM 224
8.2.3 Combined Shewhart CUSUM Scheme 227
8.2.4 Computation of CUSUM ARLs 229
8.2.5 Robustness of CUSUM Procedures 230
CONTENTS xiii
8.2.6 CUSUM Procedures for
Individual Observations 234
8.3 CUSUM Procedures for Controlling
Process Variability 235
8.4 CUSUM Procedures for Nonconforming Units 237
8.5 CUSUM Procedures for Nonconformity Data 240
8.6 Exponentially Weighted Moving Average Charts 244
8.6.1 EWMA Chart for Subgroup Averages 245
8.6.2 EWMA Misconceptions 247
8.6.3 EWMA Chart for Individual
Observations 247
8.6.4 Shewhart EWMA Chart 248
8.6.5 FIR EWMA 248
8.6.6 EWMA Chart with Variable
Sampling Intervals 248
8.6.7 EWMA Chart for Grouped Data 248
8.6.8 EWMA Chart for Variances 248
8.6.9 EWMA for Attribute Data 248
8.7 Summary 249
References 249
Exercises 251
CHAPTER 9 Multivariate Control Charts for Measurement Data 253
9.1 Hotelling s T2 Distribution 256
9.2 A T2 Control Chart 257
9.2.1 Identifying the Source of the Signal 258
9.2.2 Regression Adjustment 262
9.2.3 Recomputing the UCL 262
9.2.4 Characteristics of Control Charts
Based on T2 263
9.2.5 Determination of a Change in the
Correlation Structure 265
9.2.6 Illustrative Example 265
9.3 Multivariate Chart Versus Individual X Charts 268
9.4 Charts for Detecting Variability and
Correlation Shifts 269
9.4.1 Application to Table 9.2 Data 270
9.5 Charts Constructed Using Individual
Observations 272
9.5.1 Retrospective (Stage 1) Analysis 273
9.5.2 Stage 2 Analysis: Methods for
Decomposing Q 21A
9.5.2.1 Illustrative Example 275
xiv CONTENTS
9.6 When to Use Each Chart 276
9.7 Actual Alpha Levels for Multiple Points 276
9.8 Requisite Assumptions 277
9.9 Effects of Parameter Estimation on ARLs 277
9.10 Dimension Reduction Techniques 278
9.11 Multivariate CUSUM Charts 278
9.12 Multivariate EWMA Charts 279
9.12.1 Design of a MEWMA Chart 280
9.12.2 Searching for Assignable Causes 281
9.13 Applications of Multivariate Charts 281
9.14 Multivariate Process Capability Indices 282
9.15 Summary 282
Appendix 282
References 283
Exercises 286
CHAPTER 10 Miscellaneous Control Chart Topics 289
10.1 Pre Control 289
10.2 Short Run SPC 292
10.3 Charts for Autocorrelated Data 295
10.3.1 Autocorrelated Attribute Data 298
10.4 Charts for Batch Processes 298
10.5 Charts for Multiple Stream Processes 298
10.6 Nonparametric Control Charts 299
10.7 Bayesian Control Chart Methods 300
10.8 Control Charts for Variance Components 301
10.9 Neural Networks 301
10.10 Economic Design of Control Charts 301
10.10.1 Economic Statistical Design 303
10.11 Charts with Variable Sample Size and/or
Variable Sampling Interval 304
10.12 Users of Control Charts 304
10.12.1 Recent Control Chart Nonmanufacturing
Applications 305
10.12.1.1 Healthcare 305
10.12.1.2 Financial 306
10.12.1.3 Environmental 306
10.12.1.4 Clinical Laboratories 306
10.12.1.5 Analytical Laboratories 306
10.12.1.6 Civil Engineering 306
CONTENTS XV
10.12.1.7 Education 306
10.12.1.8 Law Enforcement/
Investigative Work 306
10.12.1.9 Lumber 306
10.12.1.10 Athletic Performance 306
10.13 Software for Control Charting 306
Bibliography 307
Exercises 315
PART III BEYOND CONTROL CHARTS: GRAPHICAL AND
STATISTICAL METHODS
CHAPTER 11 Other Graphical Methods 319
11.1 Stem and Leaf Display 319
11.2 Dot Diagrams 321
11.2.1 DigidotPlot 322
11.3 Boxplot 322
11.4 Normal Probability Plot 325
11.5 Plotting Three Variables 328
11.6 Displaying More Than Three Variables 328
11.7 Multi Vari Chart 329
11.8 Plots to Aid in Transforming Data 330
11.9 Summary 333
References 333
Exercises 334
CHAPTER 12 Linear Regression 337
12.1 Simple Linear Regression 337
12.2 Worth of the Prediction Equation 341
12.3 Assumptions 343
12.4 Checking Assumptions through Residual Plots 343
12.5 Confidence Intervals and Hypothesis Tests 344
12.6 Prediction Interval for Y 345
12.7 Regression Control Chart 346
12.8 Cause Selecting Charts 347
12.9 Inverse Regression 349
12.10 Multiple Linear Regression 351
12.11 Issues in Multiple Regression 352
12.11.1 Variable Selection 352
12.11.2 Extrapolation 353
xvi CONTENTS
12.11.3 Multicollinear Data 353
12.11.4 Residual Plots 353
12.11.5 Regression Diagnostics 354
12.11.6 Transformations 354
12.12 Software for Regression 355
12.13 Summary 355
References 355
Exercises 357
CHAPTER 13 Design of Experiments 359
13.1 A Simple Example of Experimental
Design Principles 359
13.2 Principles of Experimental Design 361
13.3 Statistical Concepts in Experimental Design 362
13.4 t Tests 364
13.4.1 Exact/Test 365
13.4.2 Approximate t Test 367
13.4.3 Confidence Intervals for Differences 367
13.5 Analysis of Variance for One Factor 368
13.5.1 ANOVA for a Single
Factor with More Than Two Levels 370
13.5.2 Multiple Comparison Procedures 374
13.5.3 Sample Size Determination 375
13.5.4 Additional Terms and Concepts
in One Factor ANOVA 376
13.6 Regression Analysis of Data from
Designed Experiments 377
13.7 ANOVA for Two Factors 382
13.7.1 ANOVA with Two Factors:
Factorial Designs 383
13.7.2 Effect Estimates 384
13.7.3 ANOVA Table for Unreplicated
Two Factor Design 386
13.7.4 Yates s Algorithm 388
13.8 The 23 Design 390
13.9 Assessment of Effects without a Residual Term 395
13.10 Residual Plot 397
13.11 Separate Analyses Using Design Units
and Uncoded Units 400
13.12 Two Level Designs with More Than
Three Factors 402
13.13 Three Level Factorial Designs 403
13.14 Mixed Factorials 404
CONTENTS xvii
13.15 Fractional Factorials 405
13.15.1 2* Designs 405
13.15.2 2*~2 Designs 410
13.15.3 More Highly Fractionated
Two Level Designs 412
13.15.4 Fractions of Three Level Factorials 412
13.15.5 Incomplete Mixed Factorials 412
13.15.6 Cautions 413
13.16 Other Topics in Experimental Design
and Their Applications 413
13.16.1 Hard to Change Factors 413
13.16.2 Split Lot Designs 413
13.16.3 Mixture Designs 413
13.16.4 Response Surface Designs 414
13.16.5 Designs for Measurement System
Evaluation 415
13.16.6 Computer Aided Design and
Expert Systems 416
13.16.7 Sequential Experimentation 416
13.16.8 Supersaturated Designs and Analyses 417
13.16.9 Multiple Responses 417
13.17 Summary 419
References 419
Exercises 424
CHAPTER 14 Contributions of Genichi Taguchi and
Alternative Approaches 427
14.1 Taguchi Methods 427
14.2 Quality Engineering 428
14.3 Loss Functions 428
14.4 Distribution Not Centered at the Target 431
14.5 Loss Functions and Specification Limits 431
14.6 Asymmetric Loss Functions 432
14.7 Signal to Noise Ratios and Alternatives 435
14.8 Experimental Designs for Stage 1 437
14.9 Taguchi Methods of Design 438
14.9.1 Inner Arrays and Outer Arrays 439
14.9.2 Orthogonal Arrays as
Fractional Factorials 439
14.9.3 Other Orthogonal Arrays Versus
Fractional Factorials 442
14.9.4 Product Arrays Versus Combined
Arrays 447
xviii CONTENTS
14.9.5 Application of Product Array 453
14.9.5.1 Cautions 463
14.9.6 Desirable Robust Designs and Analyses 463
14.9.6.1 Designs 463
14.9.6.2 Analyses 463
14.10 Determining Optimum Conditions 464
14.11 Summary 468
References 469
Exercises 471
CHAPTER 15 Evolutionary Operation 474
15.1 EVOP Illustrations 475
15.2 Three Variables 484
15.3 Simplex EVOP 486
15.4 Other EVOP Procedures 489
15.5 Miscellaneous Uses of EVOP 489
15.6 Summary 490
Appendix 490
References 491
Exercises 492
CHAPTER 16 Analysis of Means 494
16.1 ANOM for One Way Classifications 495
16.2 ANOM for Attribute Data 498
16.2.1 Proportions 498
16.2.2 Count Data 500
16.3 ANOM When Standards Are Given 501
16.3.1 Nonconforming Units 501
16.3.2 Nonconformities 501
16.3.3 Measurement Data 502
16.4 ANOM for Factorial Designs 502
16.4.1 Assumptions 505
16.4.2 Alternative Way of Displaying
Interaction Effects 506
16.5 ANOM When at Least One Factor
Has More Than Two Levels 507
16.5.1 Main Effects 507
16.5.2 Interaction Effects 511
16.6 Use of ANOM with Other Designs 515
16.7 Nonparametric ANOM 515
CONTENTS XIX
16.8 Summary 516
Appendix 516
References 516
Exercises 517
CHAPTER 17 Using Combinations of Quality Improvement Tools 519
17.1 Control Charts and Design of Experiments 520
17.2 Control Charts and Calibration Experiments 520
17.3 Six Sigma Programs 520
17.3.1 Components of a Six Sigma Program 524
17.3.2 Six Sigma Applications and Programs 524
17.3.3 Six Sigma Concept for Customer
Satisfaction 525
17.3.4 Six Sigma Training 525
17.3.5 Related Programs/Other Companies 526
17.3.5.1 SEMATECH s Qual Plan 526
17.3.5.2 AlliedSignal s Operational
Excellence Program 526
17.4 Statistical Process Control and Engineering
Process Control 527
References 528
Answers to Selected Exercises 530
Appendix: Statistical Tables 532
Author Index 545
Subject Index 551
|
| any_adam_object | 1 |
| author | Ryan, Thomas P. |
| author_facet | Ryan, Thomas P. |
| author_role | aut |
| author_sort | Ryan, Thomas P. |
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| bvnumber | BV013029199 |
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| callnumber-sort | TS 3156 R9 42000 |
| callnumber-subject | TS - Manufactures |
| classification_rvk | QH 251 SK 850 |
| ctrlnum | (OCoLC)246585330 (DE-599)BVBBV013029199 |
| dewey-full | 658.5/6221 658.562 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.5/62 21 658.562 |
| dewey-search | 658.5/62 21 658.562 |
| dewey-sort | 3658.5 262 221 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV013029199 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:37:55Z |
| institution | BVB |
| isbn | 0471197750 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008876452 |
| oclc_num | 246585330 |
| open_access_boolean | |
| owner | DE-703 DE-739 DE-521 DE-83 |
| owner_facet | DE-703 DE-739 DE-521 DE-83 |
| physical | XXIV, 555 S. graph. Darst. |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics A Wiley interscience publication |
| spelling | Ryan, Thomas P. Verfasser aut Statistical methods for quality improvement Thomas P. Ryan 2. ed. New York [u.a.] Wiley 2000 XXIV, 555 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics A Wiley interscience publication Statistische Qualitätskontrolle Quality control -- Statistical methods Process control -- Statistical methods Statistik (DE-588)4056995-0 gnd rswk-swf Qualitätskontrolle (DE-588)4047968-7 gnd rswk-swf Statistische Qualitätskontrolle (DE-588)4182962-1 gnd rswk-swf Qualitätskontrolle (DE-588)4047968-7 s Statistik (DE-588)4056995-0 s Statistische Qualitätskontrolle (DE-588)4182962-1 s 1\p DE-604 http://www.loc.gov/catdir/description/wiley033/99015784.html Publisher description http://www.loc.gov/catdir/toc/onix04/99015784.html Table of Contents HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008876452&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Ryan, Thomas P. Statistical methods for quality improvement Statistische Qualitätskontrolle Quality control -- Statistical methods Process control -- Statistical methods Statistik (DE-588)4056995-0 gnd Qualitätskontrolle (DE-588)4047968-7 gnd Statistische Qualitätskontrolle (DE-588)4182962-1 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4047968-7 (DE-588)4182962-1 |
| title | Statistical methods for quality improvement |
| title_auth | Statistical methods for quality improvement |
| title_exact_search | Statistical methods for quality improvement |
| title_full | Statistical methods for quality improvement Thomas P. Ryan |
| title_fullStr | Statistical methods for quality improvement Thomas P. Ryan |
| title_full_unstemmed | Statistical methods for quality improvement Thomas P. Ryan |
| title_short | Statistical methods for quality improvement |
| title_sort | statistical methods for quality improvement |
| topic | Statistische Qualitätskontrolle Quality control -- Statistical methods Process control -- Statistical methods Statistik (DE-588)4056995-0 gnd Qualitätskontrolle (DE-588)4047968-7 gnd Statistische Qualitätskontrolle (DE-588)4182962-1 gnd |
| topic_facet | Statistische Qualitätskontrolle Quality control -- Statistical methods Process control -- Statistical methods Statistik Qualitätskontrolle |
| url | http://www.loc.gov/catdir/description/wiley033/99015784.html http://www.loc.gov/catdir/toc/onix04/99015784.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008876452&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT ryanthomasp statisticalmethodsforqualityimprovement |