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Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach
| Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Hoboken, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (305 p.) |
| Disciplina | 670.285 |
| Altri autori (Persone) | JonesBradley |
| Soggetto topico |
Experimental design -- Data processing
Industrial engineering -- Case studies Industrial engineering -- Experiments -- Computer-aided design |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-119-97616-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Design of Experiments : A Case Study Approach; Contents; Preface; 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 Aliasing2.3.8 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 reading3.5 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 reading4.5 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 constraint6.3.2 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 |
| Record Nr. | UNINA-9910465896303321 |
Goos Peter
|
||
| Hoboken, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach
| Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Chicester, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (305 p.) |
| Disciplina |
500
620.00420285 |
| Altri autori (Persone) | JonesBradley |
| Soggetto topico |
Experimental design - Data processing
Experimental design -- Data processing Industrial engineering Industrial engineering -- Case studies Industrial engineering - Experiments - Computer-aided design Industrial engineering -- Experiments -- Computer-aided design SCIENCE / Experiments & Projects Engineering & Applied Sciences Applied Mathematics |
| ISBN |
1-283-17783-8
9786613177834 1-119-97401-1 1-119-97400-3 |
| Classificazione | SCI028000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Design of Experiments : A Case Study Approach; Contents; Preface; 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 Aliasing2.3.8 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 reading3.5 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 reading4.5 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 constraint6.3.2 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 |
| Record Nr. | UNINA-9910139627203321 |
|
Goos Peter
|
||
| Chicester, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach
| Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Hoboken, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (305 p.) |
| Disciplina | 670.285 |
| Altri autori (Persone) | JonesBradley |
| Soggetto topico |
Experimental design -- Data processing
Industrial engineering -- Case studies Industrial engineering -- Experiments -- Computer-aided design |
| ISBN | 1-119-97616-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Design of Experiments : A Case Study Approach; Contents; Preface; 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 Aliasing2.3.8 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 reading3.5 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 reading4.5 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 constraint6.3.2 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 |
| Record Nr. | UNINA-9910798565703321 |
|
Goos Peter
|
||
| Hoboken, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach
| Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Hoboken, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (305 p.) |
| Disciplina | 670.285 |
| Altri autori (Persone) | JonesBradley |
| Soggetto topico |
Experimental design -- Data processing
Industrial engineering -- Case studies Industrial engineering -- Experiments -- Computer-aided design |
| ISBN | 1-119-97616-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Design of Experiments : A Case Study Approach; Contents; Preface; 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 Aliasing2.3.8 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 reading3.5 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 reading4.5 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 constraint6.3.2 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 |
| Record Nr. | UNINA-9910807308203321 |
|
Goos Peter
|
||
| Hoboken, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach
| Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Chicester, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (305 p.) |
| Disciplina |
500
620.00420285 |
| Altri autori (Persone) | JonesBradley |
| Soggetto topico |
Experimental design - Data processing
Experimental design -- Data processing Industrial engineering Industrial engineering -- Case studies Industrial engineering - Experiments - Computer-aided design Industrial engineering -- Experiments -- Computer-aided design SCIENCE / Experiments & Projects Engineering & Applied Sciences Applied Mathematics |
| ISBN |
1-283-17783-8
9786613177834 1-119-97401-1 1-119-97400-3 |
| Classificazione | SCI028000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Design of Experiments : A Case Study Approach; Contents; Preface; 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 Aliasing2.3.8 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 reading3.5 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 reading4.5 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 constraint6.3.2 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 |
| Record Nr. | UNINA-9910830036603321 |
|
Goos Peter
|
||
| Chicester, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach
| Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Chicester, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (305 p.) |
| Disciplina |
500
620.00420285 |
| Altri autori (Persone) | JonesBradley |
| Soggetto topico |
Experimental design - Data processing
Experimental design -- Data processing Industrial engineering Industrial engineering -- Case studies Industrial engineering - Experiments - Computer-aided design Industrial engineering -- Experiments -- Computer-aided design SCIENCE / Experiments & Projects Engineering & Applied Sciences Applied Mathematics |
| ISBN |
1-283-17783-8
9786613177834 1-119-97401-1 1-119-97400-3 |
| Classificazione | SCI028000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Design of Experiments : A Case Study Approach; Contents; Preface; 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 Aliasing2.3.8 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 reading3.5 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 reading4.5 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 constraint6.3.2 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 |
| Record Nr. | UNINA-9910841780503321 |
|
Goos Peter
|
||
| Chicester, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics with JMP : hypothesis tests, ANOVA, and regression / / Peter Goos, David Meintrup
| Statistics with JMP : hypothesis tests, ANOVA, and regression / / Peter Goos, David Meintrup |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Chichester, West Sussex : , : John Wiley & Sons, Incorporated, , 2016 |
| Descrizione fisica | 1 online resource (734 p.) |
| Disciplina | 519.50285/53 |
| Soggetto topico |
Probabilities - Data processing
Mathematical statistics - Data processing Regression analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-119-09704-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title page; Copyright; Dedication; Preface; Acknowledgments; Part One Estimators and Tests; 1 Estimating Population Parameters; 1.1 Introduction: Estimators Versus Estimates; 1.2 Estimating a Mean Value; 1.3 Criteria for Estimators; 1.4 Methods for the Calculation of Estimators; 1.5 The Sample Mean; 1.6 The Sample Proportion; 1.7 The Sample Variance; 1.8 The Sample Standard Deviation; 1.9 Applications; Notes; 2 Interval Estimators; 2.1 Point and Interval Estimators; 2.2 Confidence Intervals for a Population Mean with Known Variance
2.3 Confidence Intervals for a Population Mean with Unknown Variance2.4 Confidence Intervals for a Population Proportion; 2.5 Confidence Intervals for a Population Variance; 2.6 More Confidence Intervals in JMP; 2.7 Determining the Sample Size; Notes; 3 Hypothesis Tests; 3.1 Key Concepts; 3.2 Testing Hypotheses About a Population Mean; 3.3 The Probability of a Type II Error and the Power; 3.4 Determination of the Sample Size; 3.5 JMP; 3.6 Some Important Notes Concerning Hypothesis Testing; Notes; Part Two One Population; 4 Hypothesis Tests for a Population Mean, Proportion, or Variance 4.1 Hypothesis Tests for One Population Mean4.2 Hypothesis Tests for a Population Proportion; 4.3 Hypothesis Tests for a Population Variance; 4.4 The Probability of a Type II Error and the Power; Notes; 5 Two Hypothesis Tests for the Median of a Population; 5.1 The Sign Test; 5.2 The Wilcoxon Signed-Rank Test; Notes; 6 Hypothesis Tests for the Distribution of a Population; 6.1 Testing Probability Distributions; 6.2 Testing Probability Densities; 6.3 Discussion; Notes; Part Three Two Populations; 7 Independent Versus Paired Samples 8 Hypothesis Tests for the Means, Proportions, or Variances of Two Independent Samples8.1 Tests for Two Population Means for Independent Samples; 8.2 A Hypothesis Test for Two Population Proportions; 8.3 A Hypothesis Test for Two Population Variances; 8.4 Hypothesis Tests for Two Independent Samples in JMP; Notes; 9 A Nonparametric Hypothesis Test for the Medians of Two Independent Samples; 9.1 The Hypotheses Tested; 9.2 Exact p-Values in the Absence of Ties; 9.3 Exact p-Values in the Presence of Ties; 9.4 Approximate p-Values; Notes; 10 Hypothesis Tests for the Means of Two Paired Samples 10.1 The Hypotheses Tested10.2 The Procedure; 10.3 Examples; 10.4 The Technical Background; 10.5 Generalized Hypothesis Tests; 10.6 A Confidence Interval for a Difference of Two Population Means; Notes; 11 Two Nonparametric Hypothesis Tests for Paired Samples; 11.1 The Sign Test; 11.2 The Wilcoxon Signed-Rank Test; 11.3 Contradictory Results; Notes; Part Four More Than Two Populations; 12 Hypothesis Tests for More Than Two Population Means: One-Way Analysis of Variance; 12.1 One-Way Analysis of Variance; 12.2 The Test; 12.3 One-Way Analysis of Variance in JMP; 12.4 Pairwise Comparisons 12.5 The Relation Between a One-Way Analysis of Variance and a t-Test for Two Population Means |
| Record Nr. | UNINA-9910465876503321 |
|
Goos Peter
|
||
| Chichester, West Sussex : , : John Wiley & Sons, Incorporated, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics with JMP : hypothesis tests, ANOVA, and regression / / Peter Goos, David Meintrup
| Statistics with JMP : hypothesis tests, ANOVA, and regression / / Peter Goos, David Meintrup |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Chichester, West Sussex : , : John Wiley & Sons, Incorporated, , 2016 |
| Descrizione fisica | 1 online resource (734 pages) : illustrations (some color) |
| Disciplina | 519.50285/53 |
| Soggetto topico |
Mathematical statistics - Data processing
Probabilities - Data processing Regression analysis |
| ISBN | 1-119-09704-5 |
| Classificazione |
417
519.50285/53 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910795945403321 |
|
Goos Peter
|
||
| Chichester, West Sussex : , : John Wiley & Sons, Incorporated, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics with JMP : hypothesis tests, ANOVA, and regression / / Peter Goos, David Meintrup
| Statistics with JMP : hypothesis tests, ANOVA, and regression / / Peter Goos, David Meintrup |
| Autore | Goos Peter |
| Pubbl/distr/stampa | Chichester, West Sussex : , : John Wiley & Sons, Incorporated, , 2016 |
| Descrizione fisica | 1 online resource (734 pages) : illustrations (some color) |
| Disciplina | 519.50285/53 |
| Soggetto topico |
Mathematical statistics - Data processing
Probabilities - Data processing Regression analysis |
| ISBN | 1-119-09704-5 |
| Classificazione |
417
519.50285/53 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910821199303321 |
|
Goos Peter
|
||
| Chichester, West Sussex : , : John Wiley & Sons, Incorporated, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics with JMP : graphs, descriptive statistics and probability / / Peter Goos, David Meintrup
| Statistics with JMP : graphs, descriptive statistics and probability / / Peter Goos, David Meintrup |
| Autore | Goos Peter |
| Pubbl/distr/stampa | West Sussex, England : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (368 p.) |
| Disciplina | 519.50285/53 |
| Soggetto topico | Probabilities - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-03575-9
1-119-03574-0 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Acknowledgments; Chapter 1 What is statistics?; 1.1 Why statistics?; 1.2 Definition of statistics; 1.3 Examples; 1.4 The subject of statistics; 1.5 Probability; 1.6 Software; Chapter 2 Data and its representation; 2.1 Types of data and measurement scales; 2.1.1 Categorical or qualitative variables; 2.1.2 Quantitative variables; 2.1.3 Hierarchy of scales; 2.1.4 Measurement scales in JMP; 2.2 The data matrix; 2.3 Representing univariate qualitative variables; 2.4 Representing univariate quantitative variables; 2.4.1 Stem and leaf diagram
2.4.2 Needle charts for univariate discrete quantitative variables2.4.3 Histograms and frequency polygons for continuous variables; 2.4.4 Empirical cumulative distribution functions; 2.5 Representing bivariate data; 2.5.1 Qualitative variables; 2.5.2 Quantitative variables; 2.6 Representing time series; 2.7 The use of maps; 2.8 More graphical capabilities; Chapter 3 Descriptive statistics of sample data; 3.1 Measures of central tendency or location; 3.1.1 Median; 3.1.2 Mode; 3.1.3 Arithmetic mean; 3.1.4 Geometric mean; 3.2 Measures of relative location 3.2.1 Order statistics, quantiles, percentiles, deciles3.2.2 Quartiles; 3.3 Measures of variation or spread; 3.3.1 Range; 3.3.2 Interquartile range; 3.3.3 Mean absolute deviation; 3.3.4 Variance; 3.3.5 Standard deviation; 3.3.6 Coefficient of variation; 3.3.7 Dispersion indices for nominal and ordinal variables; 3.4 Measures of skewness; 3.5 Kurtosis; 3.6 Transformation and standardization of data; 3.7 Box plots; 3.8 Variability charts; 3.9 Bivariate data; 3.9.1 Covariance; 3.9.2 Correlation; 3.9.3 Rank correlation; 3.10 Complementarity of statistics and graphics 3.11 Descriptive statistics using JMPChapter 4 Probability; 4.1 Random experiments; 4.2 Definition of probability; 4.3 Calculation rules; 4.4 Conditional probability; 4.5 Independent and dependent events; 4.6 Total probability and Bayes' rule; 4.7 Simulating random experiments; Chapter 5 Additional aspects of probability theory; 5.1 Combinatorics; 5.1.1 Addition rule; 5.1.2 Multiplication principle; 5.1.3 Permutations; 5.1.4 Combinations; 5.2 Number of possible orders; 5.2.1 Two different objects; 5.2.2 More than two different objects; 5.3 Applications of probability theory 5.3.1 Sequences of independent random experiments5.3.2 Euromillions; Chapter 6 Univariate random variables; 6.1 Random variables and distribution functions; 6.2 Discrete random variables and probability distributions; 6.3 Continuous random variables and probability densities; 6.4 Functions of random variables; 6.4.1 Functions of one discrete random variable; 6.4.2 Functions of one continuous random variable; 6.5 Families of probability distributions and probability densities; 6.6 Simulation of random variables; Chapter 7 Statistics of populations and processes 7.1 Expected value of a random variable |
| Record Nr. | UNINA-9910464101803321 |
|
Goos Peter
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| West Sussex, England : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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