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 o
Statistical design and analysis of experiments
✍ Scribed by Peter W. M. John
- Publisher
- Society for Industrial and Applied Mathematics
- Year
- 1998
- Tongue
- English
- Leaves
- 381
- Series
- Classics in applied mathematics 22
- Category
- Library
No coin nor oath required. For personal study only.
✦ Synopsis
This is a reprint of Peter John classic book on experimental designs originally published by MacMillan but now reprinted by the Society for Industrial and Applied Mathematics in their Classics in Applied Mathematics series. The book was written in 1971 and there have been many changes in philosophy for experimental designs since then. Also the revolution in computing has had an influence. In his "Preface to the Classics Edition" John reflects on the state of computing in 1966 when he first contracted to write the book and 1998 when the book was reprinted. He also describes the history of experimental design and extends it to the advances of Taguchi. It was industrial applications that attracted John to do research in experimental designs and he see industry as a rich source for design problems. The text covers all the classical design work with examples from agriculture and industry. It has heavy coverage of incomplete block designs and in Chapter 13 he demonstrates how to construct the various balanced incomplete block designs. There is a lot of theory. Although the title says design and analysis it is primarily a book on designs. Response surface methods and the evolutionary operation approach are also covered....
✦ Subjects
Математика;Теория вероятностей и математическая статистика;Математическая статистика;Планирование эксперимента;
📜 SIMILAR VOLUMES
This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving pre