Gives a systematic account of the theory underlying experiment design, focusing on key concepts rather than on methods of data analysis
Present illustrations from a range of applications
Includes extensive bibliographic notes covering historical development and current research
Offers a detailed account of the statistical software package S-Plus
Why study the theory of experiment design? Although it can be useful to know about special designs for specific purposes, experience suggests that a particular design can rarely be used directly. It needs adaptation to accommodate the circumstances of the experiment. Successful designs depend upon adapting general theoretical principles to the special constraints of individual applications.
Written for a general audience of researchers across the range of experimental disciplines, The Theory of the Design of Experiments presents the major topics associated with experiment design, focusing on the key concepts and the statistical structure of those concepts. The authors keep the level of mathematics elementary, for the most part, and downplay methods of data analysis. Their emphasis is firmly on design, but appendices offer self-contained reviews of algebra and some standard methods of analysis.
From their development in association with agricultural field trials, through their adaptation to the physical sciences, industry, and medicine, the statistical aspects of the design of experiments have become well refined. In statistics courses of study, however, the design of experiments very often receives much less emphasis than methods of analysis. The Theory of the Design of Experiments fills this potential gap in the education of practicing statisticians, statistics students, and researchers in all fields.
SOME GENERAL CONCEPTS
Types of Investigation
Observational Studies
Some Key Terms
Requirements in Design
Interplay between Design and Analysis
Key Steps in Design
A Simplified Model
A Broader View
AVOIDANCE OF BIAS
General Remarks
Randomization
Retrospective Adjustment for Bias
Some More on Randomization
More on Causality
CONTROL OF HAPHAZARD VARIATION
General Remarks
Precision Improvement by Blocking
Matched Pairs
Randomized Block Design
Partitioning Sums of Squares
Retrospective Adjustment for Improving Precision
Special Models of Error Variation
SPECIALIZED BLOCKING TECHNIQUES
Latin Squares
Incomplete Block Designs
Cross-Over Designs
FACTORIAL EXPERIMENTS: BASIC IDEAS
General Remarks
Example
Main Effects and Interactions
Example: Continued
Two-Level Factorial Systems
Fractional Factorials
Example
FACTORIAL EXPERIMENTS: FURTHER DEVELOPMENTS
General Remarks
Confounding in 2k Designs
Other Factorial Systems
Split Plot Designs
Nonspecific Factors
Designs for Quantitative Factors
Taguchi Methods
Conclusion
OPTIMAL DESIGN
General Remarks
Some Simple Examples
Some General Theory
Other Optimality Criteria
Algorithms for Design Construction
Nonlinear Design
Space-Filling Designs
Bayesian Design
Optimality of Traditional Designs
SOME ADDITIONAL TOPICS
Scale of Effort
Adaptive Designs
Sequential Regression Design
Designs for One-Dimensional Error Structure
Spatial Designs
APPENDIX A: Statistical Analysis
APPENDIX B: Some Algebra
APPENDIX C: Computational Issues
Each chapter also contains Bibliographic Notes plus Further Results and Exercises
