Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Monitoring Process Variability for Stationary Process Data



Nien F. Zhang, Adam L. Pintar


Processes that arise naturally, e.g., from manufacturing or the environment, often exhibit complicated autocorrelation structures. When monitoring such a process for changes in variance, accounting for that autocorrelation structure is critical. While charts for monitoring the variance of processes of independent observations and some specific autocorrelated processes have been proposed in the past, the chart presented in this article can handle any general stationary process, which is the major contribution of the work. The performance of the proposed chart was examined through simulations for AR(1) and ARMA(1,1) processes and demonstrated with examples.


Autocorrelated processes, Average run length, Exponentially weighted mean square, Nonstationary processes, Statistical process control, Variance shift.


Zhang, N. and Pintar, A. (2014), Monitoring Process Variability for Stationary Process Data, Technometrics, [online], (Accessed May 24, 2024)


If you have any questions about this publication or are having problems accessing it, please contact

Created June 20, 2014, Updated January 27, 2020