পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মাল্টি-রেসপন্স কন্ট্রোল চার্ট× | বহু-প্রতিক্রিয়া পরীক্ষণ নকশা× | |
|---|---|---|
| ক্ষেত্র | পরীক্ষামূলক নকশা | পরীক্ষামূলক নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions) | 1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s |
| প্রবর্তক≠ | Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others | Derringer & Suich (desirability function); Montgomery (systematic DoE integration) |
| ধরন≠ | Multivariate statistical process monitoring | Experimental optimization methodology |
| মৌলিক উৎস≠ | Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill. link ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| অপর নাম | multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart | Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE |
| সম্পর্কিত≠ | 6 | 4 |
| সারসংক্ষেপ≠ | A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs. | Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once. |
| ScholarGateডেটাসেট ↗ |
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