The Design and Analysis of Factorial Experiments¶
Yates, F. (1937). The Design and Analysis of Factorial Experiments.
Cited by¶
3 citations across 3 artifacts.
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Primes¶
- Blocking (In Experimental Design)
- 5. The nuisance-factor neutralization mechanism — the block structure ensures that each level of the nuisance factor receives all (or a balanced subset of) treatments, preventing confounding of treatment with nuisance-factor level and permitting unbiased comparisons regardless of nuisance-factor effects
This sourceImperial Bureau of Soil Science, Harpenden. Develops factorial and confounded-block analysis ensuring each nuisance-factor level receives a balanced set of treatments.
- 5. The nuisance-factor neutralization mechanism — the block structure ensures that each level of the nuisance factor receives all (or a balanced subset of) treatments, preventing confounding of treatment with nuisance-factor level and permitting unbiased comparisons regardless of nuisance-factor effects
- Factorial Design
- 2. The interaction-effect detection via crossed structure — the factorial crossing of factors permits estimation and statistical testing of interaction terms (e.g., temperature × catalyst, price × channel) that reveal whether a factor's effect depends on another factor's level, an insight invisible to one-factor-at-a-time designs
This sourceThe canonical compendium of factorial designs and their analysis (the Yates algorithm), enabling estimation and testing of interaction effects via the crossed factorial structure.
- 2. The interaction-effect detection via crossed structure — the factorial crossing of factors permits estimation and statistical testing of interaction terms (e.g., temperature × catalyst, price × channel) that reveal whether a factor's effect depends on another factor's level, an insight invisible to one-factor-at-a-time designs
- Randomization
This sourceImperial Bureau of Soil Science. Yates factorial-design analysis randomization confounding-control.
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