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The Design of Experiments

The Design of Experiments
Statistical Principles for Practical Applications

$108.00 (X)

  • Author: R. Mead, University of Reading
  • Date Published: July 1990
  • availability: Available
  • format: Paperback
  • isbn: 9780521287623

$ 108.00 (X)

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About the Authors
  • 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.

    Reviews & endorsements

    "Simply superlative...concepts and principles are presented with clarity, balance, and thoroughness." Ecology

    "A systematic and comprehensive coverage of statistical principles as applied to the design of scientific experiments...The book is well-written and contains very useful information that is relevant to a variety of scientific disciplines." Choice

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    Product details

    • Date Published: July 1990
    • format: Paperback
    • isbn: 9780521287623
    • length: 636 pages
    • dimensions: 229 x 152 x 36 mm
    • weight: 0.92kg
    • availability: Available
  • Table of Contents

    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. Response surface exploration
    Part IV. Coda:
    19. Designing useful experiments

  • Author

    R. Mead, University of Reading

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