পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| বক্স-বেঙ্কেন ডিজাইন সহ সংবেদনশীলতা বিশ্লেষণ× | কেন্দ্রীয় যৌগিক নকশা× | |
|---|---|---|
| ক্ষেত্র | পরীক্ষামূলক নকশা | পরীক্ষামূলক নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1960 (BBD); sensitivity integration formalized 2000s–2010s | 1951 |
| প্রবর্তক≠ | Box & Behnken (design, 1960); Saltelli et al. (sensitivity framework, 2000s) | George E. P. Box and K. B. Wilson |
| ধরন≠ | Integrated experimental-design and sensitivity-analysis technique | Response surface experimental design |
| মৌলিক উৎস≠ | Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475. DOI ↗ | 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. DOI ↗ |
| অপর নাম | SA-BBD, Box-Behnken sensitivity analysis, BBD with sensitivity analysis, sensitivity-augmented Box-Behnken design | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| সম্পর্কিত≠ | 5 | 3 |
| সারসংক্ষেপ≠ | Sensitivity analysis with Box-Behnken design combines a resource-efficient three-level response surface experiment with a systematic assessment of how much each input factor drives variation in the response. The Box-Behnken design (BBD) fits a second-order polynomial model using fewer runs than a full central composite design, while the overlaid sensitivity analysis quantifies each factor's relative influence — helping engineers and researchers distinguish the vital few drivers from the inconsequential many. | Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing. |
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