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Select Analyze > Quality and Process > Variability / Attribute Gauge Chart.

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Click OK.

8.

From the red triangle menu, select Heterogeneity of Variance Tests.

9.

Select Crossed.

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Click OK.

Note: This data comes from the Automotive Industry Action Group (AIAG) (2002), Measurement Systems Analysis Reference Manual, 3rd edition, 94.
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Select Analyze > Quality and Process > Variability / Attribute Gauge Chart.

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Click OK.

8.

From the red triangle menu, select Gauge Studies > Bias Report.

The bias (Response minus Standard) is calculated for every measurement. The Overall Measurement Bias Report shows a histogram of the bias and a ttest to see whether the average bias is equal to 0. You can see that the Average Bias is not zero, it is 0.0533. However, zero is contained within the confidence interval (0.1152,0.0085), which means that the Average Bias is not significantly different from 0. Using a significance level of 0.05, you can see that the pvalue is greater than 0.05, which also shows that the Average Bias is not significantly different from 0.
Tip: To see confidence intervals for the bias, rightclick in the table and select Columns > Lower 95% and Upper 95%.
Using the same data and scenario as the Bias Report option, you can now examine the linearity to determine whether there is a significant relationship between the size of the parts and the operator’s ability to measure them.
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Select Analyze > Quality and Process > Variability / Attribute Gauge Chart.

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Click OK.

8.

From the red triangle menu, select Gauge Studies > Linearity Study.

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The pvalue associated with the test on the slope is quite small, <.0001. The t test for the slope is testing whether the bias changes with the standard value.

Because the pvalue is small, you can conclude that there is a significant linear relationship between the size of the parts and the operator’s ability to measure them. You can also see this in the graph. If the part or standard value is small, the bias is high, and vice versa.