Complete the following steps to interpret a nonnormal capability sixpack. Key output includes the control charts, probability plot, and capability indices.

Your process should be stable and the process data should follow the nonnormal distribution that you selected for the analysis. The control charts and probability distribution plot allow you to evaluate whether these requirements are met.

Control charts help you monitor the stability of your process by identifying out-of-control points and patterns and trends in your data.

Red points indicate subgroups that fail at least one of the tests for special causes and are not in control. Out-of-control points indicate that the process may not be stable and that the results of a capability analysis may not be reliable. You should identify the cause of out-of-control points and eliminate special-cause variation before you analyze process capability.

The type of control chart that Minitab displays depends on the size of the subgroups in your data:

- If the subgroup size is 1, Minitab displays an I chart with an MR chart
- If the subgroup size is greater than 1, Minitab displays an Xbar chart with either an R chart (when the subgroup is from 2 to 8) or an S chart (when the subgroup size is 9 or more).

Use the probability plot to assess the fit of the nonnormal distribution used for the analysis.

If the distribution is a good fit for the data, the points should form an approximately straight line. Departures from this straight line indicate that the fit is unacceptable. If the p-value is greater than 0.05, you can assume that the data follow the nonnormal distribution used in the analysis.

If the p-value is less than 0.05, your data do not follow the selected distribution and the capability analysis results may not be accurate. Use Individual Distribution Identification to determine which nonnormal distribution or data transformation is more effective for your data.

Use the capability histogram to visually examine the sample observations in relation to the process requirements.

Visually examine the data in the histogram in relation to the lower and upper specification limits. Ideally, the spread of the data is narrower than the specification spread, and all the data are inside the specification limits. Data that are outside the specification limits represent nonconforming items.

To determine the actual number of nonconforming items in your process, use the results for PPM. For more information, go to Capability statistics for Nonnormal Capability Sixpack and click "PPM Total for Expected Overall Performance".

Evaluate whether the process is centered between the specification limits or at the target value, if you have one. The peak of the distribution curve shows where most of the data is located.

Use Ppk to evaluate the overall capability of your process based on both the process location and the process spread. Overall capability indicates the actual performance of your process that your customer experiences over time.

Generally, higher Ppk values indicate a more capable process. Lower Ppk values indicate that your process may need improvement.

Compare Ppk to a benchmark value that represents the minimum value that is acceptable for your process. Many industries use a benchmark value of 1.33. If Ppk is lower than your benchmark, consider ways to improve your process.

The Ppk index measures the capability of the process in relation only to the specification limit that is closest to the process mean. Therefore, it represents only one side of the process curve, and does not measure how the process performs on the other side of the process curve. If your process has nonconforming items that fall outside both specification limits, use additional capability measures to more fully evaluate process performance.