Response surface methodology: process and product optimization using designed experiments
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2009
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| Ausgabe: | 3. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 680 S. Ill., graph. Darst. |
| ISBN: | 9780470174463 |
Internformat
MARC
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| 245 | 1 | 0 | |a Response surface methodology |b process and product optimization using designed experiments |c Raymond H. Myers ; Douglas C. Montgomery ; Christine M. Anderson-Cook |
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| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2009 | |
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| 650 | 7 | |a Surfaces de réponse (statistique) |2 ram | |
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Datensatz im Suchindex
| _version_ | 1804138317624639488 |
|---|---|
| adam_text | Titel: Response surface methodology
Autor: Myers, Raymond H
Jahr: 2009
CONTENTS
Preface xi
1 Introduction 1
1.1 Response Surface Methodology, 1
1.1.1 Approximating Response Functions, 2
1.1.2 The Sequential Nature of RSM, 6
1.1.3 Objectives and Typical Applications of RSM, 8
1.1.4 RSM and the Philosophy of Quality Improvement, 9
1.2 Product Design and Formulation (Mixture Problems), 10
1.3 Robust Design and Process Robustness Studies, 10
1.4 Useful References on RSM, 11
2 Building Empirical Models 13
2.1 Linear Regression Models, 13
2.2 Estimation of the Parameters in Linear Regression Models, 14
2.3 Properties of the Least Squares Estimators and Estimation of a2, 22
2.4 Hypothesis Testing in Multiple Regression, 24
2.4.1 Test for Significance of Regression, 24
2.4.2 Tests on Individual Regression Coefficients and Groups
of Coefficients, 27
2.5 Confidence Intervals in Multiple Regression, 31
2.5.1 Confidence Intervals on the Individual Regression
Coefficients p, 32
2.5.2 A Joint Confidence Region on the Regression Coefficients P, 32
2.5.3 Confidence Interval on the Mean Response, 33
2.6 Prediction of New Response Observations, 35
2.7 Model Adequacy Checking, 36
ii CONTENTS
2.7.1 Residual Analysis, 37
2.7.2 Scaling Residuals, 38
2.7.3 Influence Diagnostics, 42
2.7.4 Testing for Lack of Fit, 44
2.8 Fitting a Second-Order Model, 47
2.9 Qualitative Regressor Variables, 55
2.10 Transformation of the Response Variable, 58
Exercises, 63
3 Two-Level Factorial Designs 73
3.1 Introduction, 73
3.2 The 22 Design, 74
3.3 The 23 Design, 86
3.4 The General 2* Design, 96
3.5 A Single Replicate of the 2k Design, 96
3.6 The Addition of Center Points to the 2k Design, 109
3.7 Blocking in the 2* Factorial Design, 114
3.7.1 Blocking in the Replicated Design, 115
3.7.2 Confounding in the 2k Design, 116
3.8 Split-Plot Designs, 121
Exercises, 124
4 Two-Level Fractional Factorial Designs 135
4.1 Introduction, 135
4.2 The One-Half Fraction of the 2* Design, 136
4.3 The One-Quarter Fraction of the 2k Design, 148
4.4 The General 2k~p Fractional Factorial Design, 154
4.5 Resolution III Designs, 158
4.6 Resolution IV and V Designs, 167
4.7 Fractional Factorial Split-Plot Designs, 168
4.8 Summary, 172
Exercises, 173
5 Process Improvement with Steepest Ascent 181
5.1 Determining the Path of Steepest Ascent, 182
5.1.1 Development of the Procedure, 182
5.1.2 Practical Application of the Method of
Steepest Ascent, 184
5.2 Consideration of Interaction and Curvature, 189
5.2.1 What About a Second Phase?, 191
5.2.2 What Happens Following Steepest Ascent?, 192
5.3 Effect of Scale (Choosing Range of Factors), 193
5.4 Confidence Region for Direction of Steepest Ascent, 195
5.5 Steepest Ascent Subject to a Linear Constraint, 198
5.6 Steepest Ascent in a Split-Plot Experiment, 202
Exercises, 210
CONTENTS vii
6 The Analysis of Second-Order Response Surfaces 219
6.1 Second-Order Response Surface, 219
6.2 Second-Order Approximating Function, 220
6.2.1 The Nature of the Second-Order Function and
Second-Order Surface, 220
6.2.2 Illustration of Second-Order Response Surfaces, 222
6.3 A Formal Analytical Approach to the Second-Order Model, 223
6.3.1 Location of the Stationary Point, 223
6.3.2 Nature of the Stationary Point (Canonical Analysis), 224
6.3.3 Ridge Systems, 228
6.3.4 Role of Contour Plots, 232
6.4 Ridge Analysis of the Response Surface, 235
6.4.1 What is the Value of Ridge Analysis?, 236
6.4.2 Mathematical Development of Ridge Analysis, 237
6.5 Sampling Properties of Response Surface Results, 242
6.5.1 Standard Error of Predicted Response, 243
6.5.2 Confidence Region on the Location of the Stationary Point, 245
6.5.3 Use and Computation of the Confidence Region on the
Location of the Stationary Point, 246
6.5.4 Confidence Intervals on Eigenvalues in Canonical Analysis, 250
6.6 Multiple Response Optimization, 253
6.7 Further Comments Concerning Response Surface Analysis, 264
Exercises, 265
7 Experimental Designs for Fitting Response Surfaces?I 281
7.1 Desirable Properties of Response Surface Designs, 281
7.2 Operability Region, Region of Interest, and Model Inadequacy, 282
7.2.1 Model Inadequacy and Model Bias, 283
7.3 Design of Experiments for First-Order Models, 285
7.3.1 The First-Order Orthogonal Design, 286
7.3.2 Orthogonal Designs for Models Containing Interaction, 288
7.3.3 Other First-Order Orthogonal Designs?The
Simplex Design, 291
7.3.4 Another Variance Property?Prediction Variance, 294
7.4 Designs for Fitting Second-Order Models, 296
7.4.1 The Class of Central Composite Designs, 297
7.4.2 Design Moments and Property of Rotatability, 302
7.4.3 Rotatability and the CCD, 306
7.4.4 More on Prediction Variance?Scaled, Unsealed,
and Estimated, 310
7.4.5 The Cuboidal Region and the Face-Centered Cube, 312
7.4.6 When is the Design Region Spherical?, 315
7.4.7 Summary Statements Regarding CCD, 316
7.4.8 The Box-Behnken Design, 317
7.4.9 Other Spherical RSM Designs; Equiradial Designs, 321
7.4.10 Orthogonal Blocking in Second-Order Designs, 325
Exercises, 336
viii CONTENTS
8 Experimental Designs for Fitting Response Surfaces?II 349
8.1 Designs that Require a Relatively Small Run Size, 350
8.1.1 The Hoke Designs, 350
8.1.2 Koshal Design, 352
8.1.3 Hybrid Designs, 354
8.1.4 The Small Composite Design, 358
8.1.5 Some Saturated or Near-Saturated Cuboidal Designs, 362
8.2 General Criteria for Constructing, Evaluating, and Comparing
Experimental Designs, 362
8.2.1 Practical Design Optimality, 365
8.2.2 Use of Design Efficiencies for Comparison of Standard
Second-Order Designs, 371
8.2.3 Graphical Procedure for Evaluating the Prediction Capability
of an RSM Design, 374
8.3 Computer-Generated Designs in RSM, 386
8.3.1 Important Relationship between Prediction Variance and Design
Augmentation for D-Optimality, 386
8.3.2 Illustrations Involving Computer-Generated Design, 390
8.4 Some Final Comments Concerning Design Optimality and
Computer-Generated Design, 405
Exercises, 406
Advanced Topics in Response Surface Methodology 417
9.1 Effects of Model Bias on the Fitted Model and Design, 417
9.2 A Design Criterion Involving Bias and Variance, 420
9.2.1 The Case of a First-Order Fitted Model and
Cuboidal Region, 423
9.2.2 Minimum Bias Designs for a Spherical Region of Interest, 429
9.2.3 Simultaneous Consideration of Bias and Variance, 430
9.2.4 How Important is Bias?, 431
9.3 Errors in Control of Design Levels, 432
9.4 Experiments with Computer Models, 435
9.5 Minimum Bias Estimation of Response Surface Models, 442
9.6 Neural Networks, 446
9.7 RSM for Non-Normal Responses?Generalized
Linear Models, 449
9.7.1 Model Framework: The Link Function, 449
9.7.2 The Canonical Link Function, 450
9.7.3 Estimation of Model Coefficients, 451
9.7.4 Properties of Model Coefficients, 452
9.7.5 Model Deviance, 453
9.7.6 Overdispersion, 454
9.7.7 Examples, 455
9.7.8 Diagnostic Plots and Other Aspects of the GLM, 462
9.8 Split-Plot Designs for Second-Order Models, 466
Exercises, 476
CONTENTS ix
10 Robust Parameter Design and Process Robustness Studies 483
10.1 Introduction, 483
10.2 What is Parameter Design?, 483
10.2.1 Examples of Noise Variables, 484
10.2.2 An Example of Robust Product Design, 485
10.3 The Taguchi Approach, 486
10.3.1 Crossed Array Designs and Signal-to-Noise Ratios, 486
10.3.2 Analysis Methods, 489
10.3.3 Further Comments, 494
10.4 The Response Surface Approach, 495
10.4.1 The Role of the Control x Noise Interaction, 495
10.4.2 A Model Containing Both Control and Noise Variables, 499
10.4.3 Generalization of Mean and Variance Modeling, 502
10.4.4 Analysis Procedures Associated with the Two
Response Surfaces, 506
10.4.5 Estimation of the Process Variance, 515
10.4.6 Direct Variance Modeling, 519
10.4.7 Use of Generalized Linear Models, 521
10.5 Experimental Designs for RPD and Process Robustness Studies, 525
10.5.1 Combined Array Designs, 525
10.5.2 Second-Order Designs, 527
10.5.3 Other Aspects of Design, 529
10.6 Dispersion Effects in Highly Fractionated Designs, 537
10.6.1 The Use of Residuals, 537
10.6.2 Further Diagnostic Information from Residuals, 538
10.6.3 Further Comments Concerning Variance Modeling, 544
Exercises, 548
11 Experiments with Mixtures 557
11.1 Introduction, 557
11.2 Simplex Designs and Canonical Mixture Polynomials, 560
11.2.1 Simplex Lattice Designs, 560
11.2.2 The Simplex-Centroid Design and Its Associated
Polynomial, 567
11.2.3 Augmentation of Simplex Designs with Axial Runs, 569
11.3 Response Trace Plots, 576
11.4 Reparameterizing Canonical Mixture Models to Contain
a Constant Term (/3o), 577
Exercises, 581
12 Other Mixture Design and Analysis Techniques 589
12.1 Constraints on the Component Proportions, 589
12.1.1 Lower-Bound Constraints on the Component
Proportions, 590
12.1.2 Upper-Bound Constraints on the Component
Proportions, 599
X CONTENTS
12.1.3 Active Upper-and Lower-Bound Constraints, 602
12.1.4 Multicomponent Constraints, 616
12.2 Mixture Experiments Using Ratios of Components, 617
12.3 Process Variables in Mixture Experiments, 621
12.3.1 Mixture-Process Model and Design Basics, 621
12.3.2 Split-Plot Designs for Mixture-Process Experiments, 629
12.3.3 Robust Parameter Designs for Mixture-Process
Experiments, 636 ,,
12.4 Screening Mixture Components, 641
Exercises, 643
Appendix 1 Moment Matrix of a Rotatable Design 655
Appendix 2 Rotatability of a Second-Order Equiradial Design 661
References 665
Index 677
|
| adam_txt |
Titel: Response surface methodology
Autor: Myers, Raymond H
Jahr: 2009
CONTENTS
Preface xi
1 Introduction 1
1.1 Response Surface Methodology, 1
1.1.1 Approximating Response Functions, 2
1.1.2 The Sequential Nature of RSM, 6
1.1.3 Objectives and Typical Applications of RSM, 8
1.1.4 RSM and the Philosophy of Quality Improvement, 9
1.2 Product Design and Formulation (Mixture Problems), 10
1.3 Robust Design and Process Robustness Studies, 10
1.4 Useful References on RSM, 11
2 Building Empirical Models 13
2.1 Linear Regression Models, 13
2.2 Estimation of the Parameters in Linear Regression Models, 14
2.3 Properties of the Least Squares Estimators and Estimation of a2, 22
2.4 Hypothesis Testing in Multiple Regression, 24
2.4.1 Test for Significance of Regression, 24
2.4.2 Tests on Individual Regression Coefficients and Groups
of Coefficients, 27
2.5 Confidence Intervals in Multiple Regression, 31
2.5.1 Confidence Intervals on the Individual Regression
Coefficients p, 32
2.5.2 A Joint Confidence Region on the Regression Coefficients P, 32
2.5.3 Confidence Interval on the Mean Response, 33
2.6 Prediction of New Response Observations, 35
2.7 Model Adequacy Checking, 36
ii CONTENTS
2.7.1 Residual Analysis, 37
2.7.2 Scaling Residuals, 38
2.7.3 Influence Diagnostics, 42
2.7.4 Testing for Lack of Fit, 44
2.8 Fitting a Second-Order Model, 47
2.9 Qualitative Regressor Variables, 55
2.10 Transformation of the Response Variable, 58
Exercises, 63
3 Two-Level Factorial Designs 73
3.1 Introduction, 73
3.2 The 22 Design, 74
3.3 The 23 Design, 86
3.4 The General 2* Design, 96
3.5 A Single Replicate of the 2k Design, 96
3.6 The Addition of Center Points to the 2k Design, 109
3.7 Blocking in the 2* Factorial Design, 114
3.7.1 Blocking in the Replicated Design, 115
3.7.2 Confounding in the 2k Design, 116
3.8 Split-Plot Designs, 121
Exercises, 124
4 Two-Level Fractional Factorial Designs 135
4.1 Introduction, 135
4.2 The One-Half Fraction of the 2* Design, 136
4.3 The One-Quarter Fraction of the 2k Design, 148
4.4 The General 2k~p Fractional Factorial Design, 154
4.5 Resolution III Designs, 158
4.6 Resolution IV and V Designs, 167
4.7 Fractional Factorial Split-Plot Designs, 168
4.8 Summary, 172
Exercises, 173
5 Process Improvement with Steepest Ascent 181
5.1 Determining the Path of Steepest Ascent, 182
5.1.1 Development of the Procedure, 182
5.1.2 Practical Application of the Method of
Steepest Ascent, 184
5.2 Consideration of Interaction and Curvature, 189
5.2.1 What About a Second Phase?, 191
5.2.2 What Happens Following Steepest Ascent?, 192
5.3 Effect of Scale (Choosing Range of Factors), 193
5.4 Confidence Region for Direction of Steepest Ascent, 195
5.5 Steepest Ascent Subject to a Linear Constraint, 198
5.6 Steepest Ascent in a Split-Plot Experiment, 202
Exercises, 210
CONTENTS vii
6 The Analysis of Second-Order Response Surfaces 219
6.1 Second-Order Response Surface, 219
6.2 Second-Order Approximating Function, 220
6.2.1 The Nature of the Second-Order Function and
Second-Order Surface, 220
6.2.2 Illustration of Second-Order Response Surfaces, 222
6.3 A Formal Analytical Approach to the Second-Order Model, 223
6.3.1 Location of the Stationary Point, 223
6.3.2 Nature of the Stationary Point (Canonical Analysis), 224
6.3.3 Ridge Systems, 228
6.3.4 Role of Contour Plots, 232
6.4 Ridge Analysis of the Response Surface, 235
6.4.1 What is the Value of Ridge Analysis?, 236
6.4.2 Mathematical Development of Ridge Analysis, 237
6.5 Sampling Properties of Response Surface Results, 242
6.5.1 Standard Error of Predicted Response, 243
6.5.2 Confidence Region on the Location of the Stationary Point, 245
6.5.3 Use and Computation of the Confidence Region on the
Location of the Stationary Point, 246
6.5.4 Confidence Intervals on Eigenvalues in Canonical Analysis, 250
6.6 Multiple Response Optimization, 253
6.7 Further Comments Concerning Response Surface Analysis, 264
Exercises, 265
7 Experimental Designs for Fitting Response Surfaces?I 281
7.1 Desirable Properties of Response Surface Designs, 281
7.2 Operability Region, Region of Interest, and Model Inadequacy, 282
7.2.1 Model Inadequacy and Model Bias, 283
7.3 Design of Experiments for First-Order Models, 285
7.3.1 The First-Order Orthogonal Design, 286
7.3.2 Orthogonal Designs for Models Containing Interaction, 288
7.3.3 Other First-Order Orthogonal Designs?The
Simplex Design, 291
7.3.4 Another Variance Property?Prediction Variance, 294
7.4 Designs for Fitting Second-Order Models, 296
7.4.1 The Class of Central Composite Designs, 297
7.4.2 Design Moments and Property of Rotatability, 302
7.4.3 Rotatability and the CCD, 306
7.4.4 More on Prediction Variance?Scaled, Unsealed,
and Estimated, 310
7.4.5 The Cuboidal Region and the Face-Centered Cube, 312
7.4.6 When is the Design Region Spherical?, 315
7.4.7 Summary Statements Regarding CCD, 316
7.4.8 The Box-Behnken Design, 317
7.4.9 Other Spherical RSM Designs; Equiradial Designs, 321
7.4.10 Orthogonal Blocking in Second-Order Designs, 325
Exercises, 336
viii CONTENTS
8 Experimental Designs for Fitting Response Surfaces?II 349
8.1 Designs that Require a Relatively Small Run Size, 350
8.1.1 The Hoke Designs, 350
8.1.2 Koshal Design, 352
8.1.3 Hybrid Designs, 354
8.1.4 The Small Composite Design, 358
8.1.5 Some Saturated or Near-Saturated Cuboidal Designs, 362
8.2 General Criteria for Constructing, Evaluating, and Comparing
Experimental Designs, 362
8.2.1 Practical Design Optimality, 365
8.2.2 Use of Design Efficiencies for Comparison of Standard
Second-Order Designs, 371
8.2.3 Graphical Procedure for Evaluating the Prediction Capability
of an RSM Design, 374
8.3 Computer-Generated Designs in RSM, 386
8.3.1 Important Relationship between Prediction Variance and Design
Augmentation for D-Optimality, 386
8.3.2 Illustrations Involving Computer-Generated Design, 390
8.4 Some Final Comments Concerning Design Optimality and
Computer-Generated Design, 405
Exercises, 406
Advanced Topics in Response Surface Methodology 417
9.1 Effects of Model Bias on the Fitted Model and Design, 417
9.2 A Design Criterion Involving Bias and Variance, 420
9.2.1 The Case of a First-Order Fitted Model and
Cuboidal Region, 423
9.2.2 Minimum Bias Designs for a Spherical Region of Interest, 429
9.2.3 Simultaneous Consideration of Bias and Variance, 430
9.2.4 How Important is Bias?, 431
9.3 Errors in Control of Design Levels, 432
9.4 Experiments with Computer Models, 435
9.5 Minimum Bias Estimation of Response Surface Models, 442
9.6 Neural Networks, 446
9.7 RSM for Non-Normal Responses?Generalized
Linear Models, 449
9.7.1 Model Framework: The Link Function, 449
9.7.2 The Canonical Link Function, 450
9.7.3 Estimation of Model Coefficients, 451
9.7.4 Properties of Model Coefficients, 452
9.7.5 Model Deviance, 453
9.7.6 Overdispersion, 454
9.7.7 Examples, 455
9.7.8 Diagnostic Plots and Other Aspects of the GLM, 462
9.8 Split-Plot Designs for Second-Order Models, 466
Exercises, 476
CONTENTS ix
10 Robust Parameter Design and Process Robustness Studies 483
10.1 Introduction, 483
10.2 What is Parameter Design?, 483
10.2.1 Examples of Noise Variables, 484
10.2.2 An Example of Robust Product Design, 485
10.3 The Taguchi Approach, 486
10.3.1 Crossed Array Designs and Signal-to-Noise Ratios, 486
10.3.2 Analysis Methods, 489
10.3.3 Further Comments, 494
10.4 The Response Surface Approach, 495
10.4.1 The Role of the Control x Noise Interaction, 495
10.4.2 A Model Containing Both Control and Noise Variables, 499
10.4.3 Generalization of Mean and Variance Modeling, 502
10.4.4 Analysis Procedures Associated with the Two
Response Surfaces, 506
10.4.5 Estimation of the Process Variance, 515
10.4.6 Direct Variance Modeling, 519
10.4.7 Use of Generalized Linear Models, 521
10.5 Experimental Designs for RPD and Process Robustness Studies, 525
10.5.1 Combined Array Designs, 525
10.5.2 Second-Order Designs, 527
10.5.3 Other Aspects of Design, 529
10.6 Dispersion Effects in Highly Fractionated Designs, 537
10.6.1 The Use of Residuals, 537
10.6.2 Further Diagnostic Information from Residuals, 538
10.6.3 Further Comments Concerning Variance Modeling, 544
Exercises, 548
11 Experiments with Mixtures 557
11.1 Introduction, 557
11.2 Simplex Designs and Canonical Mixture Polynomials, 560
11.2.1 Simplex Lattice Designs, 560
11.2.2 The Simplex-Centroid Design and Its Associated
Polynomial, 567
11.2.3 Augmentation of Simplex Designs with Axial Runs, 569
11.3 Response Trace Plots, 576
11.4 Reparameterizing Canonical Mixture Models to Contain
a Constant Term (/3o), 577
Exercises, 581
12 Other Mixture Design and Analysis Techniques 589
12.1 Constraints on the Component Proportions, 589
12.1.1 Lower-Bound Constraints on the Component
Proportions, 590
12.1.2 Upper-Bound Constraints on the Component
Proportions, 599
X CONTENTS
12.1.3 Active Upper-and Lower-Bound Constraints, 602
12.1.4 Multicomponent Constraints, 616
12.2 Mixture Experiments Using Ratios of Components, 617
12.3 Process Variables in Mixture Experiments, 621
12.3.1 Mixture-Process Model and Design Basics, 621
12.3.2 Split-Plot Designs for Mixture-Process Experiments, 629
12.3.3 Robust Parameter Designs for Mixture-Process
Experiments, 636 ,,
12.4 Screening Mixture Components, 641
Exercises, 643
Appendix 1 Moment Matrix of a Rotatable Design 655
Appendix 2 Rotatability of a Second-Order Equiradial Design 661
References 665
Index 677 |
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| author | Myers, Raymond H. Montgomery, Douglas C. 1943- Anderson-Cook, Christine M. |
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| dewey-search | 519.5/7 |
| dewey-sort | 3519.5 17 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik |
| discipline_str_mv | Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV035156580 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:49:00Z |
| indexdate | 2024-07-09T21:26:16Z |
| institution | BVB |
| isbn | 9780470174463 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016963754 |
| oclc_num | 226389434 |
| open_access_boolean | |
| owner | DE-20 DE-83 DE-858 DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-29T DE-634 |
| owner_facet | DE-20 DE-83 DE-858 DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-29T DE-634 |
| physical | XIII, 680 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Wiley |
| record_format | marc |
| spelling | Myers, Raymond H. Verfasser aut Response surface methodology process and product optimization using designed experiments Raymond H. Myers ; Douglas C. Montgomery ; Christine M. Anderson-Cook 3. ed. Hoboken, NJ Wiley 2009 XIII, 680 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Surfaces de réponse (statistique) ram Experimental design Response surfaces (Statistics) Wirkungsfunktion (DE-588)4190023-6 gnd rswk-swf Empirische Forschung (DE-588)4300400-3 gnd rswk-swf Versuchsplanung (DE-588)4078859-3 gnd rswk-swf Wirkungsfläche (DE-588)4571724-2 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Versuchsplanung (DE-588)4078859-3 s Optimierung (DE-588)4043664-0 s DE-604 Wirkungsfläche (DE-588)4571724-2 s 1\p DE-604 Empirische Forschung (DE-588)4300400-3 s 2\p DE-604 Wirkungsfunktion (DE-588)4190023-6 s 3\p DE-604 Montgomery, Douglas C. 1943- Verfasser (DE-588)12861448X aut Anderson-Cook, Christine M. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016963754&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 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Myers, Raymond H. Montgomery, Douglas C. 1943- Anderson-Cook, Christine M. Response surface methodology process and product optimization using designed experiments Surfaces de réponse (statistique) ram Experimental design Response surfaces (Statistics) Wirkungsfunktion (DE-588)4190023-6 gnd Empirische Forschung (DE-588)4300400-3 gnd Versuchsplanung (DE-588)4078859-3 gnd Wirkungsfläche (DE-588)4571724-2 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4190023-6 (DE-588)4300400-3 (DE-588)4078859-3 (DE-588)4571724-2 (DE-588)4043664-0 |
| title | Response surface methodology process and product optimization using designed experiments |
| title_auth | Response surface methodology process and product optimization using designed experiments |
| title_exact_search | Response surface methodology process and product optimization using designed experiments |
| title_exact_search_txtP | Response surface methodology process and product optimization using designed experiments |
| title_full | Response surface methodology process and product optimization using designed experiments Raymond H. Myers ; Douglas C. Montgomery ; Christine M. Anderson-Cook |
| title_fullStr | Response surface methodology process and product optimization using designed experiments Raymond H. Myers ; Douglas C. Montgomery ; Christine M. Anderson-Cook |
| title_full_unstemmed | Response surface methodology process and product optimization using designed experiments Raymond H. Myers ; Douglas C. Montgomery ; Christine M. Anderson-Cook |
| title_short | Response surface methodology |
| title_sort | response surface methodology process and product optimization using designed experiments |
| title_sub | process and product optimization using designed experiments |
| topic | Surfaces de réponse (statistique) ram Experimental design Response surfaces (Statistics) Wirkungsfunktion (DE-588)4190023-6 gnd Empirische Forschung (DE-588)4300400-3 gnd Versuchsplanung (DE-588)4078859-3 gnd Wirkungsfläche (DE-588)4571724-2 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Surfaces de réponse (statistique) Experimental design Response surfaces (Statistics) Wirkungsfunktion Empirische Forschung Versuchsplanung Wirkungsfläche Optimierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016963754&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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