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| Eksperimen Faktorial Penuh Percontohan× | Metodologi Permukaan Respons (RSM)× | |
|---|---|---|
| Bidang | Desain Eksperimen | Desain Eksperimen |
| Keluarga≠ | Process / pipeline | Hypothesis test |
| Tahun asal≠ | 1920s (Fisher); pilot usage formalised mid-20th century | 1951 |
| Pencetus≠ | R. A. Fisher (full factorial foundations); pilot application codified in applied DOE literature (Box, Hunter & Hunter; Montgomery) | George E. P. Box & K. B. Wilson |
| Tipe≠ | Experimental design (pilot/screening phase) | Second-order polynomial response surface model |
| Sumber perintis≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | 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 ↗ |
| Alias≠ | pilot factorial design, pilot 2^k design, pilot complete factorial experiment, screening factorial pilot | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Terkait≠ | 3 | 7 |
| Ringkasan≠ | A pilot full factorial experiment is a small-scale, complete crossing of all selected factors at all their levels, run before a definitive study to gather preliminary effect estimates, assess variability, and verify experimental logistics. It retains the complete combinatorial structure of a full factorial design — every combination of factor levels is tested — but is intentionally limited in scope (fewer replicates, narrower factor ranges) to conserve resources while maximising learning about factor effects and interactions before committing to a larger investigation. | 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. |
| ScholarGateSet data ↗ |
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