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Mathematical statistics --- Experimental design --- Plan d'expérience --- Plan d'expérience

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This book is about the statistical principles behind the design of effective experiments and focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis. Emphasising the logical principles of statistical design, rather than mathematical calculation, the authors demonstrate how all available information can be used to extract the clearest answers to many questions. The principles are illustrated with a wide range of examples drawn from real experiments in medicine, industry, agriculture and many experimental disciplines. Numerous exercises are given to help the reader practise techniques and to appreciate the difference that good design can make to an experimental research project. Based on Roger Mead's excellent Design of Experiments, this new edition is thoroughly revised and updated to include modern methods relevant to applications in industry, engineering and modern biology. It also contains seven new chapters on contemporary topics, including restricted randomisation and fractional replication.

Mathematical statistics --- Statistics --- Experimental design. --- Statistique --- Plan d'expérience --- Experimental design --- Mathematics --- Plan d'expérience --- Statistical decision. --- Decision problems --- Game theory --- Operations research --- Management science --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Analysis of means --- Analysis of variance --- Experiments --- Methodology --- Research design

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Operational research. Game theory --- Experimental design --- Plan d'expérience --- 512.54 --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Groups. Group theory --- Experiments --- Methodology --- Experimental design. --- 512.54 Groups. Group theory --- Plan d'expérience --- Statistique mathematique --- Plans d'experience

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A modern and highly innovative guide to industrial experimental designThe past two decades have seen major progress in the use of statistically designed experiments for product and process improvement. In this new work, Jeff Wu and Michael Hamada, two highly recognized researchers in the field, introduce some of the newest discoveries in the design and analysis of experiments as well as their applications to system optimization, robustness, and treatment comparisons in the diverse fields of engineering, technology, agriculture, biology, and medicine.Drawing on examples from their impressive roster of industrial clients (including GM, Ford, AT&T, Lucent Technologies, and Chrysler), Wu and Hamada modernize accepted methodologies, while presenting many cutting-edge topics for the first time in a single, easily accessible source. These include robust parameter design, reliability improvement, analysis of nonnormal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Other features include:Coverage of parameter design for system improvement first introduced by Taguchi in the mid-1980sAn innovative approach to the treatment of design tablesA discussion of new computing techniques, including graphical methods, generalized linear models, and Bayesian computing via Gibbs samplersEach chapter motivated by a real experimentExtensive case studies, including goals, data, and experimental plansMore than 80 data sets as well as hundreds of charts, tables, and figures

Numerical methods of optimisation --- Statistical science --- Mathematical statistics --- Experimental design. --- Plan d'expérience --- 519.242 --- 519.2 --- Experimental design --- #ABIB:astp --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Experimental design. Optimal designs. Block designs --- Probability. Mathematical statistics --- Experiments --- Methodology --- 519.2 Probability. Mathematical statistics --- 519.242 Experimental design. Optimal designs. Block designs --- Plan d'expérience

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This unique book is the first comprehensive guide to the discovery, analysis, and evaluation of natural experiments - an increasingly popular methodology in the social sciences. Thad Dunning provides an introduction to key issues in causal inference, including model specification, and emphasizes the importance of strong research design over complex statistical analysis. Surveying many examples of standard natural experiments, regression-discontinuity designs, and instrumental-variables designs, Dunning highlights both the strengths and potential weaknesses of these methods, aiding researchers in better harnessing the promise of natural experiments while avoiding the pitfalls. Dunning also demonstrates the contribution of qualitative methods to natural experiments and proposes new ways to integrate qualitative and quantitative techniques. Chapters complete with exercises and appendices covering specialized topics such as cluster-randomized natural experiments, make this an ideal teaching tool as well as a valuable book for professional researchers.

Social sciences --- Experimental design --- Experiments --- Research --- Experiments. --- Expériences --- Plan d'expérience --- Expériences --- Methods in social research (general) --- Experimental design. --- Sciences sociales --- Research. --- Recherche --- Experiment. --- Sozialwissenschaften. --- Social science research --- Design of experiments --- Statistical design --- Mathematical optimization --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Methodology --- Science research --- Scientific research --- Information services --- Learning and scholarship --- Research teams --- Design. --- Methodology. --- Social Sciences --- Political Science --- Social sciences - Experiments --- Social sciences - Research