Fundamental Concepts in the Design of Experiments

Rating:
Summary: Statistics by example only - very little theory/concepts
Review: After using this book for a class, we all agreed that this book was not the best for those of us who are just learning the concepts in analyzing variance. The author tends to assume the basics are "hard-coded" into the students head. Fair enough. His next step, however, is not to discuss the theory/concept. Rather, it is to give an example of what he is talking about, hoping you will grasp the concept. It would very helpful to have the concepts explained and then the examples given. In addition, the notation (subscripts) are not always explained, nor are the formulas obvious/straightforward. Finally, the book has editing problems.

A better editor and supplementary manuals (step-by-step explanation of concepts and formulas, SPSS instruction, etc.) would be a big help.

My recommendation would be to find another book from which you will learn statistics.

Rating:
Summary: Fundamental Concepts in the Design of Experiments
Review: This book is very valuable for those actively engaged in the conduct of experiments, either operational or developmental in nature. It does require someone with a background in statistical methods using analysis of variance. The user needs to have a good understanding of statistical inference. There are many good working models of various analytic procedures provided.

Rating:
Summary: Excellent, if you already know theoretical statistics
Review: This book is written for people who already know the theory of statistics and want to do statistic consulting.

The author begins with the basics of design of experiments: experiment, design and analysis. Then a brief (lovely) review of statistical inference follows; including: Estimation, test of hypothesis, power function and some easy applications.

In the following chapters almost all statistical methods are presented; among others: single factor experiments, randomized block and latin square, factorial experiments, nested, experiments of two or more factors, 2^f -, 3^f factorials, split plot design, Taguchi, regression and finally miscellaneous topics including covariance analysis, response-surface experimentation and more.

After each chapter there are problems and answers to odd-numbered problems can be found at the end of the book. Included is a practical summery with all methods presented in one table. Additionally you find a glossary of terms used in statisics, statistical tables and an index.

The examples in the book are analysed using SAS. Knowing that S-Plus is much easier to handle (and knowing that SAS is frequently used in the industry), this is very useful.

The mathematics used is easy, but - as mentioned in the preface - the fundamental concepts of statistical inference must be known.