Interpret the key results for Individuals Chart

Complete the following steps to interpret an Individuals chart. Key output includes the Individuals chart and test results.

Step 1: Determine whether the process mean is in control

The individuals chart (I chart) plots individual observations. The center line is an estimate of the process average. The control limits on the I chart, 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 individual sample values.

Red points indicate observations 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 how to estimate the parameters for Individuals Chart.

In these results, 1 observation is out of control. The process is not stable over time. 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 observations that failed 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.

I Chart of Weight

Test Results for I Chart of Weight

TEST 1. One point more than 3.00 standard deviations from center line. Test Failed at points: 23 * 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