Abstract
Blocked two-level fractional factorial designs are a very useful tool for efficient data collection in agricultural and other scientific research. In most experiments, in addition to the main effects, some two-factor interactions are also meaningful and need to be estimated. We propose a method for efficiently selecting blocked two-level fractional factorial designs when some of the two-factor interactions are non-negligible. We then present some results for a design with only 8 or 16 runs to illustrate how to use this method.
Keywords
blocking factor; defining contrast subgroup; defining words; minimum aberration; resolution; word-length pattern; confounding pattern
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Ke, Weiming; Ren, Cuirong; and Lu, Huitian
(2007).
"SELECTION OF BLOCKED TWO-LEVEL FRACTIONAL FACTORIAL DESIGNS FOR AGRICULTURAL EXPERIMENTS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1109
SELECTION OF BLOCKED TWO-LEVEL FRACTIONAL FACTORIAL DESIGNS FOR AGRICULTURAL EXPERIMENTS
Blocked two-level fractional factorial designs are a very useful tool for efficient data collection in agricultural and other scientific research. In most experiments, in addition to the main effects, some two-factor interactions are also meaningful and need to be estimated. We propose a method for efficiently selecting blocked two-level fractional factorial designs when some of the two-factor interactions are non-negligible. We then present some results for a design with only 8 or 16 runs to illustrate how to use this method.
