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Academic & Professional Books  Reference  Data Analysis & Modelling  Data Analysis & Statistics

Statistical Design and Analysis of Experiments

By: Peter WM John(Author)
380 pages
Statistical Design and Analysis of Experiments
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  • Statistical Design and Analysis of Experiments ISBN: 9780898714272 Paperback Jan 1987 Out of stock with supplier: order now to get this when available
    £80.99
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Price: £80.99
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About this book Contents Customer reviews Related titles

About this book

Readers will find this book an invaluable reference on the design of experiments. It contains hard-to-find information on topics such as change-over designs with residual effects and early treatment of analysis of covariance. Other topics include linear models and quadratic forms, experiments with one or more factors, Latin square designs, and fractions of 2n factorial designs. There is also extensive coverage of the analysis of incomplete block designs and of the existence and construction of balanced and partially balanced designs. A new preface (to the classics edition) describes the changes made in experimental design since the book was first published in 1971. It discusses the use of personal computers to analyze data and details the emergence of industrial statistics.

Contents

- Linear Models and Quadratic Forms
- Experiments With a Single Factor
- Experiments With Two Factors
- Experiments With Several Factors
- Latin Square Designs
- Factors With Two or Three Levels
- Fractions of 2n Factorial Designs
- Fractional Factorials With More Than Two Levels
- Response Surfaces
- Incomplete Block Designs
- Partially Balanced Incomplete Block Designs
- The Existence and Construction of Balanced Incomplete Block Designs
- The Existence and Construction of Partially Balanced Designs
- Additional Topics in Partially Balanced Designs
- Appendix - Matrices and Quadratic Forms

Customer Reviews

By: Peter WM John(Author)
380 pages
Media reviews

'Peter John's book is truly a classic but not an outdated one. It was one of the first to blend in the standard matrix notation that almost all linear models books now use. It has topics and discussion that are still valuable today.' Richard F. Gunst, Southern Methodist University

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