Control charts are graphs that plot your process data in time-ordered sequence. Most control charts include a center line, an upper control limit, and a lower control limit. The center line represents the process mean. The control limits represent the process variation.

Multivariate control charts are a type of variables control chart that shows how correlated, or dependent, variables jointly affect a process or outcome. For example, you can use a multivariate control chart to investigate if temperature and pressure are jointly in control in the production of injection-molded plastic parts.

Minitab offers several attribute control charts that plot defects or defectives. A defect refers to a quality characteristic and a defective unit refers to the overall product. A unit may have many defects, but the unit itself is either defective or nondefective.

How do I specify tests and parameters for a control chart? How do I change the default tests and test parameters? Which tests for special causes are included in Minitab?

A good starting point in investigating special-cause variation is to gather several process experts together. Using the control chart, encourage the process operators, the process engineers, and the quality testers to brainstorm why particular samples were out of control.

Before you interpret the Xbar chart, examine the R chart to determine whether the process variation is in control. If the R chart is not in control, then the control limits on the Xbar chart are not accurate.

Before you interpret the Individual chart (I chart), examine the Moving Range chart (MR chart) to determine whether the process variation is in control. If the MR chart is not in control, then the control limits on the I chart are not accurate.

Use this control chart to monitor process stability over time so that you can identify and correct instabilities in a process. For example, a delivery service manager uses a P chart to monitor the proportion of delivery vehicles that are out of service each day for 2 months.

Choose Stat > Control Charts > Attributes Charts > C. In Variables, enter Defects. Click C Chart Options. On the Tests tab, select 1 point > K standard deviations from center line (Test 1) and K points in a row on same side of center line (Test 2).

Use stages to create a historical control chart that shows how a process changes over specific periods of time. By default, Minitab recalculates the center line and control limits for each stage. For more information, go to Add stages to show how a process changed.