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1. |
Record Nr. |
UNINA990004841330403321 |
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Autore |
Prior, Matthew |
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Titolo |
The Literary works of Matthew Prior / edited by H. Bunker Wright and Monroe K. Spears |
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Pubbl/distr/stampa |
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Oxford, : Clarendon press, 1959 |
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Descrizione fisica |
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Locazione |
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Collocazione |
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L 45 (1) |
L 45 (2) |
P.3 BR.C.1355(1) |
P.3 BR.C.1355(2) |
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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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2. |
Record Nr. |
UNINA9910163273503321 |
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Autore |
Kaplan Robert D |
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Titolo |
Earning the rockies : How geography shapes america's role in the world. / / Robert D Kaplan |
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Pubbl/distr/stampa |
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Prince Frederick, : HighBridge Audio, 2017 |
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ISBN |
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Edizione |
[Unabridged.] |
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Descrizione fisica |
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1 online resource (6 audio files) : digital |
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Classificazione |
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HIS036000POL062000TRV025000 |
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Disciplina |
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Soggetti |
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Nonfiction |
History |
Politics |
Travel |
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Lingua di pubblicazione |
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Formato |
Audiolibro |
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Livello bibliografico |
Monografia |
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Note generali |
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Sommario/riassunto |
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As a boy, Robert D. Kaplan listened to his truck-driver father's evocative stories about traveling across America as a young man, travels in which he learned to understand the country from a ground-level perspective. In Earning the Rockies, Kaplan undertakes his own cross-country journey to recapture an appreciation and understanding of American geography that is often lost in the jet age. The history of westward expansion is examined here in a new light-not just a story of genocide and individualism, but also of communalism and a respect for the limits of a water-starved terrain-to understand how settling the West shaped our national character, and how it should shape our foreign policy. In his clear-eyed and moving meditations on the American landscape, Kaplan lays bare the roots of American greatness-the fact that we are a nation, empire, and continent all at once-and how we must reexamine those roots, and understand our geography, in order to confront the challenging, anarchic world that Kaplan describes. Earning the Rockies is a short epic, a story both personal and global in scope. |
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3. |
Record Nr. |
UNINA9910955546603321 |
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Autore |
Goos Peter |
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Titolo |
Optimal design of experiments : a case study approach / / Peter Goos, Bradley Jones |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley, 2011 |
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ISBN |
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Edizione |
[1st ed.] |
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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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Industrial engineering - Experiments - Computer-aided design |
Experimental design - Data processing |
Industrial engineering |
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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 bibliografia |
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Includes bibliographical references and index. |
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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 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 |
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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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"This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities?While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain. The structure of the book is organized around the following chapters: 1) Introduction explaining the concept of tailored DOE. 2) Basics of optimal design. 3) Nine case studies dealing with the above questions using the flow: description → design → analysis → optimization or engineering interpretation. 4) Summary. 5) Technical appendices for the mathematically curious"-- |
"This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"-- |
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