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. 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.
#Minitab express control charts series
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. Advanced Statistical Analysis and Process Control Techniques with Minitab Program Describe the elements of an SPC Chart and the purposes of SPC Understand how. 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. Eight tests are available with this control chart.
Test 2 detects a possible shift in the process. The first, referred to as a univariate control chart, is a graphical display (chart) of one quality characteristic. Depending on the number of process characteristics to be monitored, there are two basic types of control charts. For example, Test 1 detects a single out-of-control point. Control charts are used to routinely monitor quality. Each of the tests for special causes detects a specific pattern or trend in your data, which reveals a different aspect of process instability. Xbar Xbar-R Xbar-S Individuals I-MR P & U. 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. Graphs, Probability Distributions, Probability Distributions, Hypothesis Tests, ANOVA, etc.