Complete the following steps to interpret a C chart. Key output includes the C chart and the test results.

The C chart plots the number of defects (also called nonconformities) per subgroup when subgroup sizes are equal. It is possible for an item to have one or more defects but still be acceptable in function and performance. The center line represents the mean number of defects per subgroup. The control limits, which are set at a distance of 3 standard deviations above and below the center line, show the amount of variation that is expected in the defect rate.

Red points indicate subgroups that fail at least one of the tests for special causes and are not in control. If the same point fails multiple tests, then the point is labeled with the lowest test number to avoid cluttering the graph. If the chart shows out-of-control points, investigate those points.

Out-of-control points can influence the estimates of process parameters and prevent control limits from truly representing your process. If out-of-control points are due to special causes, then consider omitting these points from the calculations. For more information, go to Specify subgroups to estimate parameters for C Chart.

In these results, on average, technicians find 10 dead pixels in each sample. Sample 17 is out of control. The technicians should identify any special causes that contribute to the unusually high number of dead pixels. When you hold the pointer over a red point, you can get more information about the subgroup.

Investigate any subgroups that fail the tests for special causes. By default, Minitab conducts only Test 1, which detects points that fall outside of the control limits. However, if you conduct additional tests, then points can fail multiple tests. The output shows exactly which points failed each test, as shown here.

These results show that subgroup 17 failed Test 1.

TEST 1. One point more than 3.00 standard deviations from center line.
Test Failed at points: 17

When you use several tests at the same time, the sensitivity of the chart increases. However, the false alarm rate also increases, which can make you react to the test results unnecessarily.

For more information on each of the tests and when to use them, go to Using tests for special causes in control charts