Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полный факторный эксперимент× | Дробный факторный эксперимент× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1926 (Fisher's foundational paper); codified by the 1950s–1960s | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Автор метода≠ | Ronald A. Fisher | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Тип≠ | Experimental design | Quantitative experimental design |
| Основополагающий источник | 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 | 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 | full factorial design, complete factorial design, 2^k factorial design, FFD | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Связанные≠ | 6 | 4 |
| Сводка≠ | A full factorial experiment runs every possible combination of all chosen factor levels, making it the gold standard for simultaneously estimating main effects, two-way interactions, and higher-order interactions among multiple independent variables. Introduced through Ronald Fisher's foundational work on factorial designs in the 1920s and systematised by Box, Hunter, and Montgomery, it provides complete information about how factors act individually and in combination on an outcome. | 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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