000			03557nam a22004098i 4500
001			CR9781139020879
005			20250919142052.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110217s2012||||enk o ||1 0|eng|d
020			_a9781139020879 (ebook)
020			_z9780521862141 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA279 _b.M38825 2012
082	0	0	_a001.4/34 _223
100	1		_aMead, R. _q(Roger), _eauthor.
245	1	0	_aStatistical principles for the design of experiments / _cR. Mead, University of Reading, S.G. Gilmour, University of Southampton, A. Mead, University of Warwick.
264		1	_aCambridge : _bCambridge University Press, _c2012.
300			_a1 online resource (xiv, 572 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge series on statistical and probabilistic mathematics ; _v36
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aMachine 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.
520			_aThis 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.
650		0	_aExperimental design.
700	1		_aGilmour, S. G., _eauthor.
700	1		_aMead, A. _q(Andrew), _eauthor.
776	0	8	_iPrint version: _z9780521862141
830		0	_aCambridge series on statistical and probabilistic mathematics ; _v36.
856	4	0	_uhttps://doi.org/10.1017/CBO9781139020879
907			_a.b16848123 _b2020-12-22 _c2020-12-22
942			_n0
998			_a1 _b2020-12-22 _cm _da _feng _genk _y0 _z.b16848123
999			_c652155 _d652155