Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Pilotní frakcionální faktorový experiment× | Metodologie ploch odezvy (RSM)× | |
|---|---|---|
| Obor | Plánování experimentů | Plánování experimentů |
| Rodina≠ | Process / pipeline | Hypothesis test |
| Rok vzniku≠ | 1950s–1960s (fractional factorial foundation); pilot study integration formalized in 20th century DOE practice | 1951 |
| Tvůrce≠ | Box, Hunter & Hunter (fractional factorial); pilot study concept developed broadly in industrial and clinical experimentation | George E. P. Box & K. B. Wilson |
| Typ≠ | Experimental screening design (pilot phase) | Second-order polynomial response surface model |
| Původní zdroj≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45. link ↗ |
| Další názvy≠ | pilot FFE, screening pilot design, pilot fractional factorial, pilot FF screening study | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Příbuzné≠ | 5 | 7 |
| Shrnutí≠ | A pilot fractional factorial experiment is a small-scale preliminary study that uses a fractional factorial design — testing only a subset of all possible factor combinations — to screen multiple factors simultaneously before committing to a full-scale investigation. It provides early estimates of effect sizes, variance, and feasibility at substantially reduced cost and participant burden compared to a full factorial pilot or a full-scale trial. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
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