Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Планирование эксперимента для смесей× | Полный факторный экспериментальный план× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Hypothesis test | Hypothesis test |
| Год появления≠ | 1958 | 1926 |
| Автор метода≠ | Henry Scheffé | R. A. Fisher |
| Тип≠ | Constrained mixture experiment | Parametric factorial experiment |
| Основополагающий источник≠ | Scheffé, H. (1958). Experiments with Mixtures. Journal of the Royal Statistical Society, Series B, 20(2), 344–360. DOI ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| Другие названия≠ | mixture experiment, simplex-lattice design, simplex-centroid design, Scheffé mixture design | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Mixture experiment design is a class of constrained experimental design in which the factors are the proportions of components in a blend, subject to the constraint that all proportions sum to one. The framework was formalised by Henry Scheffé in 1958 and covers simplex-lattice, simplex-centroid, and D-optimal mixture designs widely used in pharmaceutical formulation, food science, and materials research. | A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured. |
| ScholarGateНабор данных ↗ |
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