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Statistical Principles for the Design of Experiments : Applications to Real Experiments.
Focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis in various disciplines.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge :
Cambridge University Press,
2012.
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| Colección: | Cambridge series on statistical and probabilistic mathematics.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Mead, R. |q (Roger) | |
| 245 | 1 | 0 | |a Statistical Principles for the Design of Experiments : |b Applications to Real Experiments. |
| 260 | |a Cambridge : |b Cambridge University Press, |c 2012. | ||
| 300 | |a 1 online resource (584 pages) | ||
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| 490 | 1 | |a Cambridge Series in Statistical and Probabilistic Mathematics | |
| 588 | 0 | |a Print version record. | |
| 520 | |a Focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis in various disciplines. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 1. Introduction -- 2. Elementary ideas of blocking: the randomised complete block design -- 3. Elementary ideas of treatment structure -- 4. General principles of linear models for the analysis of experimental data -- 5. Experimental units -- 6. Replication -- 7. Blocking and control -- 8. Multiple blocking systems and cross-over designs -- 9. Multiple levels of information -- 10. Randomisation -- 11. Restricted randomisation -- 12. Experimental objectives, treatments and treatment structures -- 13. Factorial structure and particular forms of effects -- 14. Fractional replication -- 15. Incomplete block size for factorial experiments -- 16. Quantitative factors and response functions -- 17. Multifactorial designs for quantitative factors -- 18. Split-unit designs -- 19. Multiple experiments and new variation -- 20. Sequential aspects of experiments and experimental programmes -- 21. Designing useful experiments. | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Experimental design. | |
| 650 | 2 | |a Research Design | |
| 650 | 6 | |a Plan d'expérience. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a REFERENCE |x Research. |2 bisacsh | |
| 650 | 7 | |a Experimental design |2 fast | |
| 700 | 1 | |a Gilmour, S. G. | |
| 700 | 1 | |a Mead, A. |q (Andrew) | |
| 776 | 0 | 8 | |i Print version: |z 9780521862141 |
| 830 | 0 | |a Cambridge series on statistical and probabilistic mathematics. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=480374 |z Texto completo |
| 880 | 0 | |6 505-00/(S |a Cover -- Statistical Principles for the Design of Experiments -- SERIES EDITOR -- Title -- Copyright -- Contents -- Preface -- Part I Overture -- 1 Introduction -- 1.1 Why a statistical theory of design-- 1.2 History, computers and mathematics -- 1.3 The influence of analysis on design -- 1.4 Separate consideration of units and treatments -- 1.5 The resource equation -- 2 Elementary ideas of blocking: the randomised complete block design -- 2.1 Controlling variation between experimental units -- 2.2 The analysis of variance identity -- 2.3 Estimation of variance and the comparison of treatment means -- 2.4 Residuals and the meaning of error -- 2.5 The random allocation of treatments to units -- 2.6 Practical choices of blocking patterns -- 3 Elementary ideas of treatment structure -- 3.1 Choice of treatments -- 3.2 Factorial structure -- 3.3 Models for main effects and interactions -- 3.4 The analysis of variance identity -- 3.5 Interpretation of main effects and interactions -- 3.6 Advantages of factorial structure -- 3.7 Treatment effects and treatment models -- 4 General principles of linear models for the analysis of experimental data -- 4.1 Introduction and some examples -- 4.2 The principle of least squares and least squares estimators -- 4.2.1 Some examples -- 4.3 Properties of least squares estimators -- 4.3.1 The estimation of σ2 -- 4.4 Overparameterisation, constraints and practical solution of least squares equations -- 4.4.1 Practical constraints on parameter values -- 4.4.2 A more complex example of the use of constraints -- 4.5 Subdividing the parameters -- extra SS -- 4.5.1 An example where different orders of fitting produce different SS -- 4.6 Distributional assumptions and inferences -- 4.7 Contrasts, treatment comparisons and component SS -- 4.7.1 Examples of the use of contrasts. | |
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