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1. |
Record Nr. |
UNINA9910458498103321 |
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Autore |
Thayer Robert E |
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Titolo |
The biopsychology of mood and arousal |
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Pubbl/distr/stampa |
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[Place of publication not identified], : Oxford University Press Incorporated, 1990 |
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ISBN |
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9786610440825 |
1-4237-6446-3 |
1-60129-809-9 |
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Descrizione fisica |
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1 online resource (247 pages) |
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Disciplina |
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Soggetti |
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Psychobiology |
Mood (Psychology) - Physiological aspects |
Arousal (Physiology) |
Affective Symptoms |
Psychophysiology |
Physiology |
Psychology |
Arousal |
Biological Science Disciplines |
Behavioral Sciences |
Nervous System Physiological Processes |
Behavioral Symptoms |
Mind-Body Therapies |
Psychological Phenomena and Processes |
Natural Science Disciplines |
Behavioral Disciplines and Activities |
Complementary Therapies |
Nervous System Physiological Phenomena |
Behavior |
Psychiatry and Psychology |
Disciplines and Occupations |
Musculoskeletal and Neural Physiological Phenomena |
Therapeutics |
Behavior and Behavior Mechanisms |
Phenomena and Processes |
Analytical, Diagnostic and Therapeutic Techniques and Equipment |
Neuroscience |
Human Anatomy & Physiology |
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Health & Biological Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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2. |
Record Nr. |
UNINA9910465896303321 |
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Autore |
Goos Peter |
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Titolo |
Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
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Pubbl/distr/stampa |
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ISBN |
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Descrizione fisica |
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1 online resource (305 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Experimental design -- Data processing |
Industrial engineering -- Case studies |
Industrial engineering -- Experiments -- Computer-aided design |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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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 |
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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 |
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Sommario/riassunto |
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<i>""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."" -</i> <b>Douglas C. Montgomery</b>, <b>Regents Professor, Department of Industrial Engineering, Arizona State University</b> <i>""It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion |
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