The Design of Experiments Statistical Principles for Practical Applications [Paperback]

The statistical principles of good experimental design are explained by employing a minimum of mathematics and emphasizing the logical principles of statistical design.The statistical principles of good experimental design are explained by employing a minimum of mathematics and emphasizing the logical principles of statistical design.Describes the statistical principles of good experimental design, explaining that good design of experiments is crucial to the success of research. Emphasizing the logical principles of statistical design, Professor Mead employs a minimum of mathematics. Throughout he assumes that the large-scale analysis of data will be performed by computers and he thus devotes more attention to discussions of how all of the 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 medicine, agriculture, industry, and other disciplines. Numerous exercises are given to help the reader practice techniques and to appreciate the difference that good design of experiments can make to a scientific project.Preface; Part I. Overture: 1. Introduction; 2. Elementary ideas of blocking: the randomised block design; 3. Elementary ideas of treatment structure; 4. General principles of linear models for the analysis of experimental data; 5. Computers for analysing experimental data; Part II. First Subject: 6. Replication; 7. Blocking; 8. Multiple blocking systems and cross-over designs; 9. Randomisation; 10. Covariance - extension of linear models; 11. Model assumptions and more general models; Part III. Second Subject: 12. Experimental objectives, treatments and treatment structures; 13. Factorial structure and particular forms of effects; 14. Split unit designs and repeated measurements; 15. Incomplete bloxk size for factorial experiments; 16. Some mathematical theory for comfounding and fractional replication; 17. Quantitative factors and response functions; 18. Resl³«