方法对比
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| 多响应控制图× | 统计过程控制× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions) | 1924–1931 |
| 提出者≠ | Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others | Walter A. Shewhart |
| 类型≠ | Multivariate statistical process monitoring | Process monitoring and quality control method |
| 开创性文献≠ | 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 ↗ | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762 |
| 别名 | multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart | SPC, statistical quality control, process control charting, Shewhart control |
| 相关 | 6 | 6 |
| 摘要≠ | 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. | Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers. |
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