In Statistical Process Control (SPC) there exists a need to model the run-length distribution of a Q-chart that monitors the process mean when measurements are from an exponential distribution with an unknown parameter. To develop exact expressions for the probabilities of run-lengths the joint distribution of the charting statistics is needed. This gives rise to a new distribution that can be ...

In this paper we describe multivariate statistical visualization techniques designed to improve the quality, accuracy and satisfaction of the statistical data analysis process. We describe techniques for visualizing multivariate data structure, for visualizing multivariate data models, and for visualizing multivariate data analysis sessions. We illustrate these techniques with ViSta, our statis...

Extended Abstract In many situations, the quality of a process can be characterized by a single continuous random variable, which is usually assumed to follow a normal distribution. However, it is increasingly common for processes to be characterized by several, usually correlated, variables. (Kim and Reynolds 2005) Multivariate control charts are widely used to monitor industrial processes (Ma...

The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to ...

Statistical technique used to analyze quality problems and improve process performance is Process Control (SPC). In this study, multivariate control chart the of graduates Statistics Study Program, FMIPA USK, namely using Multivariate Exponentially Weighted Moving Average (MEWMA) average, Variance (MEWMV) variability, analysis Capability assess entire process. Data secondary data, GPA duration ...