Quality improvement through statistical methods:
Saved in:
| Format: | Book |
|---|---|
| Language: | German |
| Published: |
Boston [u.a.]
Birkhäuser
1998
|
| Series: | Statistics for industry and technology
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XXXIII, 442 S. graph. Darst. |
| ISBN: | 3764340525 0817640525 |
Staff View
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| 245 | 1 | 0 | |a Quality improvement through statistical methods |c Bovas Abraham, ed. |
| 264 | 1 | |a Boston [u.a.] |b Birkhäuser |c 1998 | |
| 300 | |a XXXIII, 442 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Statistics for industry and technology | |
| 500 | |a Literaturangaben | ||
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Record in the Search Index
| _version_ | 1804126715464646656 |
|---|---|
| adam_text | Contents
Preface xvii
List of Contributors xix
List of Tables xxv
List of Figures xxix
Part I: Statistics and Quality
1. Scientific Learning
G. E. P. Box 3
1.1 Mathematical Statistics or Scientific Statistics?, 3
1.1.1 Inadequacy of the mathematical paradigm, 4
1.1.2 Scientific learning, 5
1.2 Statistics for Discovery, 7
1.3 Can Statistics Departments be Reformed?, 8
1.4 The Role of Computation, 9
1.4.1 Deductive aspects, 9
1.4.2 Inductive aspects, 10
1.4.3 Learning, 10
References, 11
2. Industrial Statistics and Innovation
R. L. Sandland 13
2.1 Introduction, 13
2.2 Innovation, 14
2.3 Industrial Statistics and Quality, 15
2.4 The Current Industrial Statistics Paradigm, 17
2.5 Product Innovation, 17
2.6 Process Innovation, 19
2.7 Link to Technology and Business Strategies, 2Q
References, 21
v
3. Understanding QS 9000 With Its Preventive
and Statistical Focus
Bovas Abraham and G. Dennis Beecroft 23
3.1 Introduction, 23
3.2 Quality Evolution, 24
3.3 ISO 9000 Control Focus, 25
3.4 QS 9000 Prevention Focus, 26
3.5 Statistical Requirements in QS 9000, 30
3.6 Summary and Concluding Remarks, 32
References, 33
4. A Conceptual Framework for Variation Reduction
Shams ur Rahman 35
4.1 Statistical Thinking, 35
4.2 Variation Reduction, 37
4.2.1 Quality by inspection, 38
4.2.2 Quality by process control, 39
4.2.3 Quality by design, 39
4.3 Conceptual Framework, 42
4.4 Conclusion, 43
References, 43
5. The Role of Academic Statisticians in Quality
Improvement: Strategies of Small and Medium Sized
Companies
John F. Brewster, Smiley W. Cheng, Brian D. Macpherson
and Fred A. Spiring 45
5.1 Background, 45
5.2 Workshop Training for Industry, 46
5.3 Workshop Activities, 47
5.4 Workshop Preparation and Delivery, 50
5.5 The Pros and Cons of Our Workshop Involvement, 52
5.6 Statistical Consultation Services, 54
5.7 Quality Resource Centre, 56
5.8 Recent Initiatives, 56
5.9 Conclusion, 57
Part II: Statistical Process Control
6. Developing Optimal Regulation and Monitoring
Strategies to Control a Continuous
Petrochemical Process
A. J. Ferrer, C. Capita, R. Romero and J. Martin 61
6.1 Introduction, 61
6.2 Polymerization Process, 62
6.2.1 Process description, 62
6.2.2 Model formulation and fitting, 63
6.3 EPC Component: Developing the Control Rule, 63
6.3.1 The Minimum Mean Square Error Controller
(MMSEC), 64
6.3.2 The Two Step Ahead Forecasting Controller
(TSAFC), 64
6.3.3 Clarke s Constrained Controller (CCC(r)), 65
6.4 Comparison of Control Strategies, 66
6.5 Robustness Properties of the Controllers, 67
6.6 EPC Component: Performance Under Assignable
Causes, 69
6.7 SPC Component: Developing Monitoring Procedures, 70
6.8 Concluding Remarks, 72
References, 73
Appendix Tables and Figures, 74
7. Capability Indices when Tolerances are Asymmetric
Kerstin Vdnnman 79
7.1 Introduction, 79
7.2 Capability Indices for Asymmetric Tolerances, 81
7.3 The Estimator of Cpa(u,v) and Its Distribution, 87
7.4 The Estimator of Cj»,{u,v) and Its Distribution, 88
7.5 Some Implications, 89
7.6 Concluding Remarks, 93
References, 94
8. Robustness of On line Control Procedures
M. S. Srivastava 97
8.1 Introduction, 97
8.2 Robustness Study When Inspection Cost is Zero, 99
8.2.1 Effect of non normality, 102
8.3 Robustness Study When Inspection Cost is Not Zero, 103
8.3.1 Effect of non normality, 104
8.4 Conclusion, 106
References, 107
9. An Application of Filtering to Statistical Process
Control
A. Thanvaneswaran, B. D. MacPherson and B. Abraham 109
9.1 Introduction, 109
9.2 Control Charts for Autocorrelated Data, 111
9.2.1 Modeling the autocorrelations using ARIMA
(p,d,q) models, 111
9.2.2 An application of the EWMA statistic to
autocorrelated data, 111
9.3 Optimal Filter and Smoother, 112
9.3.1 Optimal filter, 112
9.3.2 Optimal smoother, 114
9.4 Applications, 115
9.4.1 ARMA(0,0,l) process or MA(l) process, 115
9.4.2 ARMA(l.l) process, 117
9.4.3 A numerical example, 117
9.5 Summary and Concluding Remarks, 118
References, 119
10. Statistical Process Control and Its Applications in
Korean Industries
Sung H. Park and Jae J. Kim 121
10.1 Introduction: Definition of SPC, 121
10.2 Flow of an SPC System, 123
10.3 Team Effort for Quality Improvement in SPC, 124
10.4 Computerization of SPC and Its Difficulties, 125
10.5 Case Study, 128
10.6 Summary and Concluding Remarks, 133
References, 133
11. Multivariate Statistical Process Control of a Mineral
Processing Industry
Nihal Yatawara and Jeff Harrison 135
11.1 Introduction, 135
11.2 PC A and PLS Methods, 136
11.2.1 Principal component analysis, 136
11.2.2 Method of partial least squares (PLS), 137
11.3 PLS Monitoring Methods and Diagnostics, 138
11.4 Application of PLS for HPRC Data, 139
11.4.1 The processing plant and data, 139
11.4.2 Application of PLS for crusher performance
monitoring, 140
11.5 Conclusion, 141
References, 142
Appendix Figures, 143
12. Developing Objective Strategies for Monitoring
Multi Input/Single Output Chemical Processes
B. K. Pal and V. Roshan Joseph 151
12.1 Introduction, 151
12.2 General Formulation, 153
12.3 A Case Study, 154
12.4 Conclusions, 162
Reference, 162
13. Properties of the Cpm Index for Seemingly Unstable
Production Processes
Olle Carlsson 163
13.1 Introduction, 163
13.2 Different Models, 165
13.2.1 Random effects model, 165
13.2.2 Fixed effects model, 167
13.3 Confidence Intervals, 168
13.4 Probability of Conforming Items, 170
13.5 Summary and Discussion, 171
References, 171
14. Process Capability Indices for Contaminated Bivariate
Normal Population
Subrahmaniam Kocherlakota and Kathleen Kocherlakota 173
14.1 Introduction, 173
14.2 Contaminated Bivariate Normal Distribution, 175
14.2.1 The distribution of (Sf/(rl,S$/o%), 175
14.2.2 PDF ofiSj2/^, S$/a%), 177
14.3 Behavior of CP , CP i Under Contamination, 178
14.3.1 Distribution functionjDfCPi, CP2, 178
14.3.2 Moments of CP , CP2, 179
14.4 Tests of Hypotheses, 184
References, 186
15. Quality Improvements by Likelihood Ratio Methods
for Surveillance
Marianne Frisen and Peter Wessman 187
15.1 Introduction, 187
15.1.1 Notations and specifications, 188
15.2 Methods, 188
15.3 Results and Conclusions, 189
15.3.1 Robustness with respect to intensity, 189
15.3.2 Delay of an alarm, 190
15.3.3 Predicted value, 191
15.4 Summary and Concluding Remarks, 191
References, 192
16. Properties of the Taguchi Capability Index for
Markov Dependent Quality Characteristics
Erik Wallgren 195
16.1 Introduction, 195
16.2 Earlier Results, 196
16.3 The Taguchi Index for Autocorrelated Variables, 197
16.4 The Model, 197
16.5 Estimation of Cpmr, 198
16.6 A Simulation Study, 199
16.7 Summary and Concluding Remarks, 202
References, 202
Part III: Design and Analysis of Experiments
17. Fast Model Search for Designed Experiments with
Complex Aliasing
Hugh A. Chipman 207
17.1 Introduction, 207
17.2 Priors, 209
17.3 Efficient Stochastic Search, 210
17.4 Automatic Selection of Prior Parameters, 211
17.5 A More Difficult Problem, 215
17.6 Discussion, 219
References, 220
18. Optimal 12 Run Designs
K. Vijayan and K. R. Shah 221
18.1 Introduction, 221
18.2 Notation and Preliminaries, 222
18.3 Optimal Design, 225
18.4 E* optimality (p 9), 226
18.5 D optimality (p 9), 229
18.6 E* and D optimality for p = 6, 7, 8, 232
18.7 Summary and Concluding Remarks, 235
References, 235
19. Robustness of D Optimal Experimental Designs for
Mixture Studies
David W. Bacon and Rodney Lott 237
19.1 Introduction, 237
19.2 Linear Empirical Model Forms, 238
19.3 Experimental Designs for First Order Linear
Mixture Models, 238
19.4 Experimental Designs for Second Order Linear
Mixture Models, 239
19.5 Experimental Design Criteria, 240
19.6 Robustness of D Optimal Mixture Designs, 240
19.6.1 Case 1, 241
19.6.2 Case 2, 242
19.6.3 Case 3, 243
19.6.4 Case 4, 245
19.7 Some General Observations, 246
References, 247
20. A Bivariate Plot Useful in Selecting a Robust Design
Albert Prat and Pere Grima 249
20.1 Introduction, 249
20.2 Background, 250
20.3 Description of the Problem, 250
20.4 Proposed Methodology, 251
20.4.1 Hypothesis about the model for the response, 252
20.4.2 Estimation of the model parameters, 254
20.4.3 Model analysis: Response variance and expected
value, 255
20.4.4 Choosing the optimum values for design factors:
The distance variance plot, 257
20.5 Conclusions, 258
References, 261
21. Process Optimization Through Designed Experiments:
Two Case Studies
A. R. Chowdhury, J. Rajesh and G. K. Prasad 263
21.1 Introduction, 263
21.2 Case Study 1—Frame Soldering Process Improvement, 264
21.2.1 Experimentation, 264
21.2.2 Design and analysis, 265
21.3 Case Study 2—Electro plating Process, 267
21.3.1 Experimentation, 269
21.3.2 Analysis, 270
21.4 Summary and Conclusions, 274
References, 274
22. Technological Aspects of TQM
C. Hirotsu 275
22.1 Introduction, 275
22.2 Use of Nonnumerical Information, 276
22.2.1 Natural orderings in parameters, 276
22.3 Classification of Factors, 277
22.4 A General Theory for Testing Ordered Alternatives, 278
22.5 Testing Ordered Alternatives in Interaction Effects, 280
22.6 Analysis of Generalized Interactions, 281
22.7 Applications, 281
22.7.1 Gum hardness data, 281
22.7.2 Taste testing data, 282
22.7.3 Three way ordinal categorical data, 283
22.8 Concluding Remarks, 285
References, 286
23. On Robust Design for Multiple Quality Characteristics
Jai Hyun Byun and Kwang Jae Kim 289
23.1 Introduction, 289
23.2 Desirability Function Approach, 290
23.3 Robust Design with Multiple Characteristics, 291
23.4 An Illustrative Example, 293
23.5 Summary and Concluding Remarks, 294
References, 296
24. Simultaneous Optimization of Multiple Responses Using
a Weighted Desirability Function
Sung H. Park and Jun 0. Park 299
24.1 Introduction, 299
24.2 Desirability Function Approach, 301
24.2.1 Desirability function, 301
24.2.2 One sided transformation (maximization of ), 301
24.2.3 Two sided transformation, 302
24.3 Weighted Desirability Function Approach, 303
24.4 Example, 304
24.5 Conclusion, 310
References, 310
25. Evaluating Statistical Methods Practiced in Two
Important Areas of Quality Improvement
Subir Ghosh and Luis A. Lopez 313
25.1 Introduction, 313
25.2 Misleading Probability Plots in Factor Screening, 314
25.3 Analysis with Missing Data, 316
25.4 Conclusions and Remarks, 321
References, 323
Part IV: Statistical Methods for Reliability
26. Applications of Generalized Linear Models in
Reliability Studies for Composite Materials
M. V. Ratnaparkhi and W. J. Park 327
26.1 Introduction, 327
26.2 Models for Fatigue Life of a Composite Material, 328
26.2.1 Fatigue failure experiments, 328
26.2.2 Notation and terminology, 329
26.2.3 Deterministic and probability models for the
fatigue data, 329
26.3 Application of GLIM: Estimation of Parameters (k, b, c)
of Graphite/Epoxy Laminates, 331
26.3.1 Fatigue data for graphite/epoxy laminates and
related data analysis problems, 331
26.3.2 Generalized linear models for the analysis of
fatigue data, [McCullagh and Nelder (1989)], 333
26.3.3 Results, 334
26.3.4 Discussion, 335
26.4 Summary and Concluding Remarks, 336
References, 336
27. Bivariate Failure Rates in Discrete Time
G. Asha and N. Unnikrishnan Nair 339
27.1 Introduction, 339
27.2 Scalar Failure Rate, 340
27.3 Vector Failure Rate, 343
27.4 Conditional Failure Rate, 344
27.5 Inter Relationships and Their Implications, 348
27.6 Summary and Concluding Remarks, 349
References, 349
28. A General Approach of Studying Random
Environmental Models
Pushpa L. Gupta and Ramesh C. Gupta 351
28.1 Introduction, 351
28.2 The General Approach Univariate Case, 352
28.3 The General Approach Bivariate Models, 354
28.4 Bivariate Models Under Series System, 356
28.5 Crossings of Survival Curves and Mean Residual
Life Functions, 359
28.6 Summary and Conclusions, 360
References, 360
29. Testing for Change Points Expressed in Terms
of the Mean Residual Life Function
Emad Eldin A. A. Aly 363
29.1 Introduction, 363
29.2 Testing for Change Points, 365
29.3 Proof of Lemma 29.2.1, 367
29.4 Summary and Conclusions, 368
References, 369
30. Empirical Bayes Procedures for Testing the Quality
and Reliability With Respect to Mean Life
Radhey S. Singh 371
30.1 Introduction and Development, 371
30.1.1 Preliminaries, 371
30.1.2 Bayesian approach, 372
30.1.3 Empirical Bayes approach when the prior
distribution is unknown, 373
30.2 Development of Empirical Bayes Procedures, 374
30.3 Asymptotic Optimality of the EB Procedures and
Rates of Convergence, 375
30.4 Extension of the Results and Concluding Remarks, 375
References, 376
Appendix, 377
31. On a Test of Independence in a Multivariate
Exponential Distribution
M. Samanta and A. Thavaneswaran 381
31.1 Introduction, 381
31.2 Likelihood and Sufficiency, 383
31.3 A Test of Independence, 385
31.4 Power Results, 387
31.5 Summary, 388
References, 388
Part V: Statistical Methods for Quality Improvement
32. Random Walk Approximation of Confidence Intervals
D. J. Murdoch 393
32.1 Introduction, 393
32.2 Covering the Region by Gibbs Sampling, 395
32.3 Calculation of Interval Endpoints, 397
32.4 Examples, 399
32.4.1 BOD example, 399
32.4.2 Osborne s Gaussian mixture, 400
32.5 Conclusions, 403
References, 404
33. A Study of Quality Costs in a Multicomponent and
Low Volume Products Automated Manufacturing System
Young Hyun Park 405
33.1 Introduction, 405
33.2 Screening Procedures: A Review, 406
33.2.1 Single screening procedure, 406
33.2.2 Screening procedures in a multi stage system, 407
33.3 Model Development, 408
33.3.1 Multi stage manufacturing system, 408
33.3.2 Quality costs, 409
33.4 Concluding Remarks, 411
33.5 References, 412
34. Estimating Dose Response Curves
Sat N. Gupta and Jacqueline Iannuzzi 415
34.1 Introduction, 415
34.2 Methods, 417
34.3 Discussion, 419
34.3 Conclusion, 421
References, 422
35. On the Quality of Preterm Infants Formula and the
Longitudinal Change in Mineral Contents in
Human Milk
Brajendra C. Sutradhar, Barbara Dawson and James Friel 423
35.1 Introduction, 424
35.2 Analysis of Macrominerals in Human Milk, 425
35.2.1 Longitudinal effects, 425
35.2.2 Gestation effects, 430
35.3 Analysis of Trace Elements in Human Milk, 431
35.3.1 Longitudinal effects, 431
35.3.2 Gestation effects, 433
35.4 Analysis of Ultratrace Minerals in Human Milk, 434
35.4.1 Longitudinal and gestational effects, 434
35.5 Summary and Conclusion, 436
References, 436
Subject Index 439
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| genre | (DE-588)1071861417 Konferenzschrift 1996 Cochin Kerala gnd-content |
| genre_facet | Konferenzschrift 1996 Cochin Kerala |
| id | DE-604.BV012110887 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:21:52Z |
| institution | BVB |
| isbn | 3764340525 0817640525 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008200320 |
| oclc_num | 722672579 |
| open_access_boolean | |
| owner | DE-12 DE-521 |
| owner_facet | DE-12 DE-521 |
| physical | XXXIII, 442 S. graph. Darst. |
| publishDate | 1998 |
| publishDateSearch | 1998 |
| publishDateSort | 1998 |
| publisher | Birkhäuser |
| record_format | marc |
| series2 | Statistics for industry and technology |
| spelling | Quality improvement through statistical methods Bovas Abraham, ed. Boston [u.a.] Birkhäuser 1998 XXXIII, 442 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistics for industry and technology Literaturangaben Quality control -- Statistical methods Statistische Qualitätskontrolle (DE-588)4182962-1 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 1996 Cochin Kerala gnd-content Statistische Qualitätskontrolle (DE-588)4182962-1 s DE-604 Abraham, Bovas Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008200320&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Quality improvement through statistical methods Quality control -- Statistical methods Statistische Qualitätskontrolle (DE-588)4182962-1 gnd |
| subject_GND | (DE-588)4182962-1 (DE-588)1071861417 |
| title | Quality improvement through statistical methods |
| title_auth | Quality improvement through statistical methods |
| title_exact_search | Quality improvement through statistical methods |
| title_full | Quality improvement through statistical methods Bovas Abraham, ed. |
| title_fullStr | Quality improvement through statistical methods Bovas Abraham, ed. |
| title_full_unstemmed | Quality improvement through statistical methods Bovas Abraham, ed. |
| title_short | Quality improvement through statistical methods |
| title_sort | quality improvement through statistical methods |
| topic | Quality control -- Statistical methods Statistische Qualitätskontrolle (DE-588)4182962-1 gnd |
| topic_facet | Quality control -- Statistical methods Statistische Qualitätskontrolle Konferenzschrift 1996 Cochin Kerala |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008200320&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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