This book focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis in a variety of disciplines, including biological and physical sciences, engineering, medicine and agriculture. It contains numerous exercises and a wide range of examples drawn from real experiments.
This book focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis in a variety of disciplines, including biological and physical sciences, engineering, medicine and agriculture. It contains numerous exercises and a wide range of examples drawn from real experiments.
Produktdetails
Produktdetails
Cambridge Series in Statistical and Probabilistic Mathematics
R. Mead is Emeritus Professor of Applied Statistics at the University of Reading.
Inhaltsangabe
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.
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.
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