93,55 €*
Versandkostenfrei per Post / DHL
Lieferzeit 1-2 Wochen
-Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota
"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book."
-Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University
"This book is the compelling story of two consultants in dialog as they show their clients how to leave the roads of textbook experimental design and fly the direct route of optimal design as enabled by computer-based methods."
-John Sall, Executive Vice President and Cofounder, SAS Institute
"This book puts cutting-edge optimal design of experiments techniques into the hands of the practitioner. Ten real-world design scenarios, which Goos and Jones present as consulting session conversations with clients, easily engage and absorb the reader. A behind-the-scenes look at various technical treasures accompanies each scenario."
-Marie Gaudard, Professor Emeritus, University of New Hampshire
"Each chapter begins with a realistic experimental situation being informally discussed on site by local engineers and statistical consultants. Next an optimal experimental design is constructed and the data with full detailed analysis provided. Statisticians and para-statisticians alike should enjoy this book. Clearly a new day is dawning in the art and practice of experimental design."
-J. Stuart Hunter, Professor Emeritus, Princeton University
-Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota
"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book."
-Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University
"This book is the compelling story of two consultants in dialog as they show their clients how to leave the roads of textbook experimental design and fly the direct route of optimal design as enabled by computer-based methods."
-John Sall, Executive Vice President and Cofounder, SAS Institute
"This book puts cutting-edge optimal design of experiments techniques into the hands of the practitioner. Ten real-world design scenarios, which Goos and Jones present as consulting session conversations with clients, easily engage and absorb the reader. A behind-the-scenes look at various technical treasures accompanies each scenario."
-Marie Gaudard, Professor Emeritus, University of New Hampshire
"Each chapter begins with a realistic experimental situation being informally discussed on site by local engineers and statistical consultants. Next an optimal experimental design is constructed and the data with full detailed analysis provided. Statisticians and para-statisticians alike should enjoy this book. Clearly a new day is dawning in the art and practice of experimental design."
-J. Stuart Hunter, Professor Emeritus, Princeton University
Bradley Jones, Senior Manager, Statistical Research and Development in the JMP division of SAS, where he leads the development of design of experiments (DOE) capabilities in JMP software. Dr. Jones is widely published on DOE in research journals and the trade press. His current interest areas are design of experiments, PLS, computer aided statistical pedagogy, and graphical user interface design.
Acknowledgments.
1 A simple comparative experiment.
1.1 Key concepts.
1.2 The setup of a comparative experiment.
1.3 Summary.
2 An optimal screening experiment.
2.1 Key concepts.
2.2 Case: an extraction experiment.
2.2.1 Problem and design.
2.2.2 Data analysis.
2.3 Peek into the black box.
2.3.1 Main-effects models.
2.3.2 Models with two-factor interaction effects.
2.3.3 Factor scaling.
2.3.4 Ordinary least squares estimation.
2.3.5 Significance tests and statistical power calculations.
2.3.6 Variance inflation.
2.3.7 Aliasing.
2.3.8 Optimal design.
2.3.9 Generating optimal experimental designs.
2.3.10 The extraction experiment revisited.
2.3.11 Principles of successful screening: sparsity, hierarchy, and heredity.
2.4 Background reading.
2.4.1 Screening.
2.4.2 Algorithms for finding optimal designs.
2.5 Summary.
3 Adding runs to a screening experiment.
3.1 Key concepts.
3.2 Case: an augmented extraction experiment.
3.2.1 Problem and design.
3.2.2 Data analysis.
3.3 Peek into the black box.
3.3.1 Optimal selection of a follow-up design.
3.3.2 Design construction algorithm.
3.3.3 Foldover designs.
3.4 Background reading.
3.5 Summary.
4 A response surface design with a categorical factor.
4.1 Key concepts.
4.2 Case: a robust and optimal process experiment.
4.2.1 Problem and design.
4.2.2 Data analysis.
4.3 Peek into the black box.
4.3.1 Quadratic effects.
4.3.2 Dummy variables for multilevel categorical factors.
4.3.3 Computing D-efficiencies.
4.3.4 Constructing Fraction of Design Space plots.
4.3.5 Calculating the average relative variance of prediction.
4.3.6 Computing I-efficiencies.
4.3.7 Ensuring the validity of inference based on ordinary least squares.
4.3.8 Design regions.
4.4 Background reading.
4.5 Summary.
5 A response surface design in an irregularly shaped design region.
5.1 Key concepts.
5.2 Case: the yield maximization experiment.
5.2.1 Problem and design.
5.2.2 Data analysis.
5.3 Peek into the black box.
5.3.1 Cubic factor effects.
5.3.2 Lack-of-fit test.
5.3.3 Incorporating factor constraints in the design construction algorithm.
5.4 Background reading.
5.5 Summary.
6 A "mixture" experiment with process variables.
6.1 Key concepts.
6.2 Case: the rolling mill experiment.
6.2.1 Problem and design.
6.2.2 Data analysis.
6.3 Peek into the black box.
6.3.1 The mixture constraint.
6.3.2 The effect of the mixture constraint on the model.
6.3.3 Commonly used models for data from mixture experiments.
6.3.4 Optimal designs for mixture experiments.
6.3.5 Design construction algorithms for mixture experiments.
6.4 Background reading.
6.5 Summary.
7 A response surface design in blocks.
7.1 Key concepts.
7.2 Case: the pastry dough experiment.
7.2.1 Problem and design.
7.2.2 Data analysis.
7.3 Peek into the black box.
7.3.1 Model.
7.3.2 Generalized least squares estimation.
7.3.3 Estimation of variance components.
7.3.4 Significance tests.
7.3.5 Optimal design of blocked experiments.
7.3.6 Orthogonal blocking.
7.3.7 Optimal versus orthogonal blocking.
7.4 Background reading.
7.5 Summary.
8 A screening experiment in blocks.
8.1 Key concepts.
8.2 Case: the stability improvement experiment.
8.2.1 Problem and design.
8.2.2 Afterthoughts about the design problem.
8.2.3 Data analysis.
8.3 Peek into the black box.
8.3.1 Models involving block effects.
8.3.2 Fixed block effects.
8.4 Background reading.
8.5 Summary.
9 Experimental design in the presence of covariates.
9.1 Key concepts.
9.2 Case: the polypropylene experiment.
9.2.1 Problem and design.
9.2.2 Data analysis.
9.3 Peek into the black box.
9.3.1 Covariates or concomitant variables.
9.3.2 Models and design criteria in the presence of covariates.
9.3.3 Designs robust to time trends.
9.3.4 Design construction algorithms.
9.3.5 To randomize or not to randomize.
9.3.6 Final thoughts.
9.4 Background reading.
9.5 Summary.
10 A split-plot design.
10.1 Key concepts.
10.2 Case: the wind tunnel experiment.
10.2.1 Problem and design.
10.2.2 Data analysis.
10.3 Peek into the black box.
10.3.1 Split-plot terminology.
10.3.2 Model.
10.3.3 Inference from a split-plot design.
10.3.4 Disguises of a split-plot design.
10.3.5 Required number of whole plots and runs.
10.3.6 Optimal design of split-plot experiments.
10.3.7 A design construction algorithm for optimal split-plot designs.
10.3.8 Difficulties when analyzing data from split-plot experiments.
10.4 Background reading.
10.5 Summary.
11 A two-way split-plot design.
11.1 Key concepts.
11.2 Case: the battery cell experiment.
11.2.1 Problem and design.
11.2.2 Data analysis.
11.3 Peek into the black box.
11.3.1 The two-way split-plot model.
11.3.2 Generalized least squares estimation.
11.3.3 Optimal design of two-way split-plot experiments.
11.3.4 A design construction algorithm for D-optimal two-way split-plot designs.
11.3.5 Extensions and related designs.
11.4 Background reading.
11.5 Summary.
Bibliography.
Index.
| Erscheinungsjahr: | 2011 |
|---|---|
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
XIV
287 S. |
| ISBN-13: | 9780470744611 |
| ISBN-10: | 0470744618 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Goos, Peter
Jones, Bradley |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 235 x 157 x 21 mm |
| Von/Mit: | Peter Goos (u. a.) |
| Erscheinungsdatum: | 15.08.2011 |
| Gewicht: | 0,596 kg |
Bradley Jones, Senior Manager, Statistical Research and Development in the JMP division of SAS, where he leads the development of design of experiments (DOE) capabilities in JMP software. Dr. Jones is widely published on DOE in research journals and the trade press. His current interest areas are design of experiments, PLS, computer aided statistical pedagogy, and graphical user interface design.
Acknowledgments.
1 A simple comparative experiment.
1.1 Key concepts.
1.2 The setup of a comparative experiment.
1.3 Summary.
2 An optimal screening experiment.
2.1 Key concepts.
2.2 Case: an extraction experiment.
2.2.1 Problem and design.
2.2.2 Data analysis.
2.3 Peek into the black box.
2.3.1 Main-effects models.
2.3.2 Models with two-factor interaction effects.
2.3.3 Factor scaling.
2.3.4 Ordinary least squares estimation.
2.3.5 Significance tests and statistical power calculations.
2.3.6 Variance inflation.
2.3.7 Aliasing.
2.3.8 Optimal design.
2.3.9 Generating optimal experimental designs.
2.3.10 The extraction experiment revisited.
2.3.11 Principles of successful screening: sparsity, hierarchy, and heredity.
2.4 Background reading.
2.4.1 Screening.
2.4.2 Algorithms for finding optimal designs.
2.5 Summary.
3 Adding runs to a screening experiment.
3.1 Key concepts.
3.2 Case: an augmented extraction experiment.
3.2.1 Problem and design.
3.2.2 Data analysis.
3.3 Peek into the black box.
3.3.1 Optimal selection of a follow-up design.
3.3.2 Design construction algorithm.
3.3.3 Foldover designs.
3.4 Background reading.
3.5 Summary.
4 A response surface design with a categorical factor.
4.1 Key concepts.
4.2 Case: a robust and optimal process experiment.
4.2.1 Problem and design.
4.2.2 Data analysis.
4.3 Peek into the black box.
4.3.1 Quadratic effects.
4.3.2 Dummy variables for multilevel categorical factors.
4.3.3 Computing D-efficiencies.
4.3.4 Constructing Fraction of Design Space plots.
4.3.5 Calculating the average relative variance of prediction.
4.3.6 Computing I-efficiencies.
4.3.7 Ensuring the validity of inference based on ordinary least squares.
4.3.8 Design regions.
4.4 Background reading.
4.5 Summary.
5 A response surface design in an irregularly shaped design region.
5.1 Key concepts.
5.2 Case: the yield maximization experiment.
5.2.1 Problem and design.
5.2.2 Data analysis.
5.3 Peek into the black box.
5.3.1 Cubic factor effects.
5.3.2 Lack-of-fit test.
5.3.3 Incorporating factor constraints in the design construction algorithm.
5.4 Background reading.
5.5 Summary.
6 A "mixture" experiment with process variables.
6.1 Key concepts.
6.2 Case: the rolling mill experiment.
6.2.1 Problem and design.
6.2.2 Data analysis.
6.3 Peek into the black box.
6.3.1 The mixture constraint.
6.3.2 The effect of the mixture constraint on the model.
6.3.3 Commonly used models for data from mixture experiments.
6.3.4 Optimal designs for mixture experiments.
6.3.5 Design construction algorithms for mixture experiments.
6.4 Background reading.
6.5 Summary.
7 A response surface design in blocks.
7.1 Key concepts.
7.2 Case: the pastry dough experiment.
7.2.1 Problem and design.
7.2.2 Data analysis.
7.3 Peek into the black box.
7.3.1 Model.
7.3.2 Generalized least squares estimation.
7.3.3 Estimation of variance components.
7.3.4 Significance tests.
7.3.5 Optimal design of blocked experiments.
7.3.6 Orthogonal blocking.
7.3.7 Optimal versus orthogonal blocking.
7.4 Background reading.
7.5 Summary.
8 A screening experiment in blocks.
8.1 Key concepts.
8.2 Case: the stability improvement experiment.
8.2.1 Problem and design.
8.2.2 Afterthoughts about the design problem.
8.2.3 Data analysis.
8.3 Peek into the black box.
8.3.1 Models involving block effects.
8.3.2 Fixed block effects.
8.4 Background reading.
8.5 Summary.
9 Experimental design in the presence of covariates.
9.1 Key concepts.
9.2 Case: the polypropylene experiment.
9.2.1 Problem and design.
9.2.2 Data analysis.
9.3 Peek into the black box.
9.3.1 Covariates or concomitant variables.
9.3.2 Models and design criteria in the presence of covariates.
9.3.3 Designs robust to time trends.
9.3.4 Design construction algorithms.
9.3.5 To randomize or not to randomize.
9.3.6 Final thoughts.
9.4 Background reading.
9.5 Summary.
10 A split-plot design.
10.1 Key concepts.
10.2 Case: the wind tunnel experiment.
10.2.1 Problem and design.
10.2.2 Data analysis.
10.3 Peek into the black box.
10.3.1 Split-plot terminology.
10.3.2 Model.
10.3.3 Inference from a split-plot design.
10.3.4 Disguises of a split-plot design.
10.3.5 Required number of whole plots and runs.
10.3.6 Optimal design of split-plot experiments.
10.3.7 A design construction algorithm for optimal split-plot designs.
10.3.8 Difficulties when analyzing data from split-plot experiments.
10.4 Background reading.
10.5 Summary.
11 A two-way split-plot design.
11.1 Key concepts.
11.2 Case: the battery cell experiment.
11.2.1 Problem and design.
11.2.2 Data analysis.
11.3 Peek into the black box.
11.3.1 The two-way split-plot model.
11.3.2 Generalized least squares estimation.
11.3.3 Optimal design of two-way split-plot experiments.
11.3.4 A design construction algorithm for D-optimal two-way split-plot designs.
11.3.5 Extensions and related designs.
11.4 Background reading.
11.5 Summary.
Bibliography.
Index.
| Erscheinungsjahr: | 2011 |
|---|---|
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
XIV
287 S. |
| ISBN-13: | 9780470744611 |
| ISBN-10: | 0470744618 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Goos, Peter
Jones, Bradley |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 235 x 157 x 21 mm |
| Von/Mit: | Peter Goos (u. a.) |
| Erscheinungsdatum: | 15.08.2011 |
| Gewicht: | 0,596 kg |
Ähnliche Produkte
Aktuell nicht verfügbar
Lieferzeit 1-2 Wochen
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Wochen