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Acceptance Sampling by Variables - CompareUser-Defined Plans - Sample Size and Critical Distance |
You can compare other plans by varying the sample size, the critical distance, or both together.
If the sample average is closer to the specification than the critical distance (k), this is evidence that there are more items in the tail of the distribution beyond that specification than our AQL allows. So the critical distance (k) represents the smallest distance from the specification (in Z units) that will produce an acceptable quality level for the lot. It therefore represents a Z-score cut-off for accepting the lot.
Example Output |
Compare User Defined Plan(s)
Sample Critical Defectives Probability Probability Size(n) Distance(k) Per Million Accepting Rejecting AOQ ATI 100 3.44914 100 0.854 0.146 83.0 612.3 100 3.44914 600 0.224 0.776 130.8 2815.1
150 3.44914 100 0.899 0.101 86.1 500.1 150 3.44914 600 0.172 0.828 98.8 3007.3
200 3.44914 100 0.928 0.072 87.6 444.7 200 3.44914 600 0.135 0.865 76.3 3142.0
Sample Critical Maximum At Defectives Size Distance(k) StDev(MSD) AOQL Per Million 100 3.44914 0.0027533 145.6 369.7 150 3.44914 0.0027533 138.5 288.9 200 3.44914 0.0027533 136.7 256.4
Z.LSL = (mean - lower spec)/standard deviation Z.USL = (upper spec - mean)/standard deviation Accept lot if standard deviation ≤ MSD, Z.LSL ≥ k and Z.USL ≥ k; otherwise reject. |
Interpretation |
Using your specifications for lot size, AQL, RQL, producer's risk (alpha), and consumer's risk, Minitab determines that an appropriate sampling plan is to inspect 259 lenses. You want to investigate the risks associated with sampling 100, 150, or 200 lenses, using the same critical distance of 3.44914 at the AQL and RQL levels.