We both agree that a real life scenario would have gone through a much fancier scenario such as querying dataįrom a server and even adding conversion at some stage. The idea was basically to create a series of 4 weeks subsets over a 12 months period sort if thing. For the sake of simplicity, I mainly used mock data which I entered Process.capability(q2, spec.limits=c(lsl,usl)) # draw the process capability chart and calculate metrics: # Establish the LSL and USL as set by customer specs, then Which should generate following X-Bar Chart: Q2 <- qcc(my.data, type="xbar", nsigmas=3) # Draw the X-Bar Chart and calculate relevant metrics Then add following to ldraw the X-BAR chart. # Draw the R Chart and calculate relevant metrics # Include those subgroups into a my.data mock list through rbind # Load a mock list of 10 subgroup data manually: So for for simplicity I just added a list of subgroup manually. In a mixture pattern, the points tend to fall away from the center line and instead fall near the control limits.In true life scenario, I'd say the following data would probably be obtained as a result 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. Test 5: Two out of three points more than 2σ from the center line (same side) Test 5 detects small shifts in the process. 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. 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. 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. 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.
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