Use the multivariate control chart to quickly identify shifts in your process and to monitor your process for special cause indications.
Follow the instructions in Example of a Multivariate Control Chart to produce the results shown in Multivariate Control Chart.
The multivariate control chart plots Hotelling’s T2 statistic. The calculation for the control limit differs based on whether targets have been specified. To understand how the T2 statistic and the UCL (Upper Control Limit) are calculated, see Statistical Details for Multivariate Control Charts. For more details about control limits, see Tracy, et al., 1992.
In this example, the Principal Components reports for both data sets indicate that the first eigenvalue, corresponding to the first principal component, explains about 95% of the total variation in the variables. The values in both Eigenvectors tables indicate that the first principal component is driven primarily by the variables Fuel and Steam Flow. You can use this information to construct a potentially more sensitive control chart based only on this first component. For more details about the Principal Components reports, see Principal Components.