Interpret the key results for T Chart

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

Step 1: Determine whether the occurrence of rare events is stable and in control

The T chart plots the time between events so that you can easily detect when events occur more or less frequently than usual. The center line is the 50th percentile of the distribution. The control limits indicates the amount of expected variation in the process.

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 T Chart.

In these results, one point fails Test 2, which indicates that 9 points are in a row on the upper side of the center line. When you hold the pointer over a red point, you can get more information about this point.

Step 2: Identify which points failed each test

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.

TEST 2. 9 points in a row on same side of center line. Test Failed at points: 39 * WARNING * If graph is updated with new data, the results above may no longer be correct.

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.