Comparar métodos

	Gráfico de control multirespuesta	Gráfico de Control
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions)	1924 (first use); 1931 (seminal book)
Autor original	Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others	Walter A. Shewhart (Bell Labs)
Tipo	Multivariate statistical process monitoring	Statistical monitoring and control technique
Fuente seminal	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.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.
Alias	multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart	Shewhart chart, process-behavior chart, SPC chart, quality control chart
Relacionados	6	6
Resumen	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.	A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.