Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пилотный полнофакторный эксперимент× | Дробный факторный эксперимент× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1920s (Fisher); pilot usage formalised mid-20th century | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Автор метода≠ | R. A. Fisher (full factorial foundations); pilot application codified in applied DOE literature (Box, Hunter & Hunter; Montgomery) | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Тип≠ | Experimental design (pilot/screening phase) | Quantitative experimental design |
| Основополагающий источник≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | 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 |
| Другие названия | pilot factorial design, pilot 2^k design, pilot complete factorial experiment, screening factorial pilot | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Связанные≠ | 3 | 4 |
| Сводка≠ | A pilot full factorial experiment is a small-scale, complete crossing of all selected factors at all their levels, run before a definitive study to gather preliminary effect estimates, assess variability, and verify experimental logistics. It retains the complete combinatorial structure of a full factorial design — every combination of factor levels is tested — but is intentionally limited in scope (fewer replicates, narrower factor ranges) to conserve resources while maximising learning about factor effects and interactions before committing to a larger investigation. | 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. |
| ScholarGateНабор данных ↗ |
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