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Statistical principles for the design of experiments /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	001.4/34
書名/作者:	Statistical principles for the design of experiments // R. Mead, University of Reading, S.G. Gilmour, University of Southampton, A. Mead, University of Warwick.
作者:	Mead, R.
其他作者:	Gilmour, S. G.,
面頁冊數:	1 online resource (xiv, 572 pages) : : digital, PDF file(s).
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Experimental design.
ISBN:	9781139020879 (ebook)
內容註:	Machine generated contents note: 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 crossover 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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1017/CBO9781139020879

Statistical principles for the design of experiments /
Mead, R.

Statistical principles for the design of experiments /R. Mead, University of Reading, S.G. Gilmour, University of Southampton, A. Mead, University of Warwick. - 1 online resource (xiv, 572 pages) :digital, PDF file(s). - Cambridge series on statistical and probabilistic mathematics ;36. - Cambridge series on statistical and probabilistic mathematics ;36..

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Machine generated contents note: 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 crossover 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.

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.

ISBN: 9781139020879 (ebook)Subjects--Topical Terms:

172416
Experimental design.

LC Class. No.: QA279 / .M38825 2012

Dewey Class. No.: 001.4/34

Statistical principles for the design of experiments /
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520 $a 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.
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