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| Følsomhedsanalyse med Central Composite Design× | Fuldt faktordesign× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie≠ | Process / pipeline | Hypothesis test |
| Oprindelsesår≠ | 1951 (CCD); SA integration throughout 1970s–2000s | 1926 |
| Ophavsperson≠ | G. E. P. Box and K. B. Wilson (CCD); sensitivity analysis formalised within RSM by Montgomery and subsequent practitioners | R. A. Fisher |
| Type≠ | Quantitative experimental design with post-hoc sensitivity assessment | Parametric factorial experiment |
| Oprindelig kilde≠ | 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 ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| Aliasser | SA-CCD, CCD sensitivity analysis, RSM sensitivity analysis, response surface sensitivity study | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| Relaterede≠ | 4 | 5 |
| Resumé≠ | Sensitivity analysis with Central Composite Design (CCD) combines a structured, space-filling experimental layout with a systematic examination of how much each input factor drives changes in the response. CCD supports estimation of a full quadratic response surface model; sensitivity analysis then interrogates that model to rank factors by influence, identify interactions, and map the performance landscape — guiding engineers and researchers toward robust operating conditions and efficient optimisation. | 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. |
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