Control Charts Overview
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You can use control charts to track process statistics over time and to detect the presence of special causes.

Minitab plots a process statistic, such as a subgroup mean, individual observation, weighted statistic, or number of defects, versus sample number or time. Minitab draws the:

·    Center line at the average of the statistic by default

·    Upper control limit, 3s above the center line by default

·    Lower control limit, 3s below the center line by default

Structure of a control chart

Special causes result in variation that can be detected and controlled. Examples include differences in supplier, shift, or day of the week. Common cause variation, on the other hand, is inherent in the process. A process is in control when only common causes - not special causes - affect the process output.

A process is in control when points fall within the bounds of the control limits, and the points do not display any nonrandom patterns. Use the tests for special causes offered with Minitab's control charts to detect nonrandom patterns in your data.

You can also perform a Box-Cox transformation on non-normal data.

When a process is in control, you can use control charts to estimate process parameters needed to determine capability.

Minitab offers a variety of options for customizing your control charts:

·    See Control Chart Options for options specific to control charts, such as using stages and tests for special causes.

·    See Control Chart Display Options for options that are available before graph creation, such as subsetting your data and changing the control chart's title.

·    See Graph Editing Overview for options that are available after graph creation, such as changing font size and figure location.

See Control Charts for the types of control charts available.