Abstract
Experimenters should be able to choose between competing designs the one which yields the required information clearly and efficiently at the desired precision. One way to achieve this is to allow interaction between design and analysis but few statistical analysis packages include more than rudimentary design facilities. We review some of the theory and tools for design construction with a view to providing the statistician and experimenter with a tool-kit for building the most effective design. Examples in the design process are techniques for determining aliases and patterns of confounding, algorithms for constructing fractional factorial and incomplete block designs and methods of (restricted) randomization. Examples in analysis include algorithms for calculating efficiency factors, for estimating variance components and for assessing general balance.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Franklin, M. F. and Payne, R. W.
(1993).
"TOOLS FOR THE CONSTRUCTION OF EFFECTIVE EXPERIMENTAL DESIGNS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1369
TOOLS FOR THE CONSTRUCTION OF EFFECTIVE EXPERIMENTAL DESIGNS
Experimenters should be able to choose between competing designs the one which yields the required information clearly and efficiently at the desired precision. One way to achieve this is to allow interaction between design and analysis but few statistical analysis packages include more than rudimentary design facilities. We review some of the theory and tools for design construction with a view to providing the statistician and experimenter with a tool-kit for building the most effective design. Examples in the design process are techniques for determining aliases and patterns of confounding, algorithms for constructing fractional factorial and incomplete block designs and methods of (restricted) randomization. Examples in analysis include algorithms for calculating efficiency factors, for estimating variance components and for assessing general balance.
