Note: These data comes from the Automotive Industry Action Group (AIAG) (2002), Measurement Systems Analysis Reference Manual, 3rd edition, 94.
 1 Select Help > Sample Data Library and open Variability Data/MSALinearity.jmp.
 2 Select Analyze > Quality and Process > Variability / Attribute Gauge Chart.
 3 Select Response and click Y, Response.
 4 Select Standard and click Standard.
 5 Select Part and click X, Grouping.
 6 Click OK.
 7 From the red triangle menu, select Gauge Studies > Bias Report.
Measurement 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 t-test 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 p-value is greater than 0.05, which also shows that the Average Bias is not significantly different from 0.
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.
 1 Select Help > Sample Data Library and open Variability Data/MSALinearity.jmp.
 2 Select Analyze > Quality and Process > Variability / Attribute Gauge Chart.
 3 Select Response and click Y, Response.
 4 Select Standard and click Standard.
 5 Select Part and click X, Grouping.
 6 Click OK.
 7 From the red triangle menu, select Gauge Studies > Linearity Study.
 8 In the window that prompts you to Specify Process Variation, type 16.5368.
Linearity Study
 • The slope is -0.131667. This value appears as part of the equation below the graph, and also in the third table.
 • The p-value 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 p-value 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.

Help created on 9/19/2017