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| Adaptivt fuldt faktorielt eksperiment× | Fraktioneret Faktoriel Eksperiment× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1950s (factorial foundations); adaptive extensions prominent from 1990s onward | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Ophavsperson≠ | Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Type≠ | Experimental design | Quantitative experimental design |
| Oprindelig kilde≠ | Atkinson, A., Donev, A., & Tobias, R. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606 | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 |
| Aliasser | adaptive full-factorial design, sequential full factorial experiment, adaptive complete factorial design, dynamic full factorial trial | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Relaterede≠ | 5 | 4 |
| Resumé≠ | An adaptive full factorial experiment is an experimental design that starts with a complete crossing of all factors and all their levels, then uses interim data to modify subsequent runs — dropping unpromising factor levels, adding new ones, or re-allocating replication — while preserving the full factorial structure within each phase. This integration of full factorial coverage with adaptive decision rules allows researchers to explore all main effects and interactions without committing to a fixed, inefficient run plan before any data are observed. | A fractional factorial experiment is a resource-efficient experimental design that tests only a carefully chosen fraction of all possible factor-level combinations. By exploiting the principle that high-order interactions are usually negligible, it identifies the main effects and low-order interactions of k factors using far fewer runs than a full factorial design — making it the workhorse of industrial and engineering screening experiments. |
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