In a mixture pattern, the points tend to fall away from the center line and instead fall near the control limits. Test 8: Eight points in a row more than 1σ from center line (either side) Test 8 detects a mixture pattern. Control limits that are too wide are often caused by stratified data, which occur when a systematic source of variation is present within each subgroup. This test detects control limits that are too wide. Test 7: Fifteen points in a row within 1σ of center line (either side) Test 7 detects a pattern of variation that is sometimes mistaken as evidence of good control. Test 6: Four out of five points more than 1σ from center line (same side) Test 6 detects small shifts in the process. True Use Minitab command Column Statistics to calculate a variety of statistics simultaneously. Test 5: Two out of three points more than 2σ from the center line (same side) Test 5 detects small shifts in the process. To create Xbar-R charts, select 'All observations for a chart are in one column' if data is stacked. You want the pattern of variation in a process to be random, but a point that fails Test 4 might indicate that the pattern of variation is predictable. Test 4: Fourteen points in a row, alternating up and down Test 4 detects systematic variation. This test looks for a long series of consecutive points that consistently increase in value or decrease in value. Test 3: Six points in a row, all increasing or all decreasing Test 3 detects trends. If small shifts in the process are of interest, you can use Test 2 to supplement Test 1 in order to create a control chart that has greater sensitivity. Test 2: Nine points in a row on the same side of the center line Test 2 identifies shifts in the process centering or variation. Test 1 is universally recognized as necessary for detecting out-of-control situations. Test 1: One point more than 3σ from center line Test 1 identifies subgroups that are unusual compared to other subgroups. Only Tests 1−4 apply to the R chart portion of this control chart. Test 2 detects a possible shift in the process.Įight tests are available with this control chart. For example, Test 1 detects a single out-of-control point. Each of the tests for special causes detects a specific pattern or trend in your data, which reveals a different aspect of process instability. When you hold the pointer over a red point, you can see more information about the subgroup.Use the tests for special causes to determine which observations you may need to investigate and to identify specific patterns and trends in your data. One point is out of control on the Xbar chart. In these results, the R chart is stable, so it is appropriate to interpret the Xbar chart. The control limits on the Xbar 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 subgroup averages. The center line is the average of all subgroup averages. The Xbar chart plots the average of the measurements within each subgroup. No points are out of control on the R chart (the bottom chart). For more information, go to Specify how to estimate the parameters for Xbar-R Chart. If out-of-control points are due to special causes, then consider omitting these points from the calculations. Out-of-control points can influence the estimates of process parameters and prevent control limits from truly representing your process. If the chart shows out-of-control points, investigate those points. If the same point fails multiple tests, then the point is labeled with the lowest test number to avoid cluttering the graph. Red points indicate subgroups that fail at least one of the tests for special causes and are not in control. The control limits on the R 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 subgroup ranges. If the subgroup sizes differ, then the value of the center line depends on the subgroup size, because larger subgroups tend to have larger ranges. If the subgroup size is constant, then the center line on the R chart is the average of the subgroup ranges. If the R chart is not in control, then the control limits on the Xbar chart are not accurate. Before you interpret the Xbar chart, examine the R chart to determine whether the process variation is in control.
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