Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полный факторный эксперимент в промышленности× | Статистическое управление процессами× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1926 (foundational); industrially systematized by Box, Hunter & Hunter ~1950s–1978 | 1924–1931 |
| Автор метода≠ | Ronald A. Fisher | Walter A. Shewhart |
| Тип≠ | Experimental design / factorial experiment | Process monitoring and quality control method |
| Основополагающий источник≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762 |
| Другие названия | industrial FFD, full factorial experiment, complete factorial design, 2^k factorial design | SPC, statistical quality control, process control charting, Shewhart control |
| Связанные≠ | 3 | 6 |
| Сводка≠ | Full factorial design (FFD) applied in industrial settings is a structured experimental methodology in which every combination of factor levels is tested, enabling engineers to quantify main effects and all interaction effects among process or product variables. Widely used in manufacturing, chemical processing, materials science, and quality engineering, it provides a complete picture of how input factors jointly influence a response variable such as yield, strength, or defect rate. | Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers. |
| ScholarGateНабор данных ↗ |
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