Note: Variability Gauge Chart illustrates some of these options.
Tip: To set the default behavior of these options, select File > Preferences > Platforms > Variability Chart.
Shows or hides the separator lines between levels of the X, Grouping variables.
(Available only if you have specified a Standard variable) Shows or hides the bias line in the main variability chart.
(Available only if you have specified a Standard variable) Shows or hides the mean of the standard column.
Estimates the variance components for a specific model. Variance components are computed for these models: main effects, crossed, nested, crossed then nested (three factors only), and nested then crossed (three factors only). See Variance Components.
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Gauge RR interprets the first factors as grouping columns and the last factor as Part, and creates a gauge R&R report using the estimated variance components. (Note that there is also a Part field in the launch window). See Gauge RR Option.

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Discrimination Ratio characterizes the relative usefulness of a given measurement for a specific product. It compares the total variance of the measurement with the variance of the measurement error. See Discrimination Ratio.

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Misclassification Probabilities show probabilities for rejecting good parts and accepting bad parts. See Misclassification Probabilities.

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Bias Report shows the average difference between the observed values and the standard. A graph of the average biases and a summary table appears. This option is available only when you specify a Standard variable in the launch window. See Bias Report.

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Linearity Study performs a regression using the standard values as the X variable and the bias as the Y variable. This analysis examines the relationship between bias and the size of the part. Ideally, you want the slope to equal 0. A nonzero slope indicates that your gauge performs differently with different sized parts. This option is available only when you specify a Standard variable in the launch window. See Linearity Study.

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Gauge RR Plots shows or hides Mean Plots (the mean response by each main effect in the model) and Std Dev plots. If the model is purely nested, the graphs appear with a nesting structure. If the model is purely crossed, interaction graphs appear. Otherwise, the graphs plot independently at each effect. For the standard deviation plots, the red lines connect for each effect.

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AIAG Labels enables you to specify that quality statistics should be labeled in accordance with the AIAG standard, which is used extensively in automotive analyses.

The Heterogeneity of Variance Tests option performs a test for comparing variances across groups. The test is an Analysis of Means for Variances (ANOMV) based method. This method indicates whether any of the group standard deviations are different from the square root of the average group variance.
The Variance Components option models the variation from measurement to measurement. The response is assumed to be a constant mean plus random effects associated with various levels of the classification.
Note: Once you select the Variance Components option, if you did not select the Model Type in the launch window (if you selected Decide Later), you are prompted to select the model type. For more information about model types, see Launch the Variability/Attribute Gauge Chart Platform.
The Variance Components report shows the estimates themselves. See Statistical Details for Variance Components.
From the launch window, click Analysis Settings to choose the method for computing variance components.
Chooses the best analysis from EMS or REML, using the same logic as the Choose best analysis (EMS, REML, or Bayesian) option. However, this option never uses the Bayesian method, even for negative variance components. The bounded REML method is used and any negative variance component is forced to be 0.
Tip: Alternatively, you can use the EMP method to assess your measurement system. See the Measurement Systems Analysis section.
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The process variation, from one part to another. This is the ultimate variation that you want to be studying if your measurements are reliable.

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The variability inherent in making multiple measurements, that is, repeatability. In Definition of Terms and Sums in Gauge R&R Analysis, this is called the within variation.

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The Gauge RR option shows measures of variation interpreted for a gauge study of operators and parts.
Once you select the Gauge RR option, if you have not already selected the model type, you are prompted to do so. Then, modify the Gauge R&R specifications.
Select Tolerance Interval to enter the tolerance directly, where tolerance = USL – LSL.
Select LSL and/or USL to enter the specification limits and then have JMP calculate the tolerance.
K is a constant value that you choose to multiply with sigma. For example, you might type 6 so that you are looking at 6*sigma or a 6 sigma process.
Note: To generate the reduced Gauge R&R report, select File > Preferences > Platforms > Variability Chart > Reduced Gauge RR Report.
In this example, the values in the Variation column are the square roots of sums of variance components scaled by the value of k (6 in this example).
Acceptable Percent Measurement Variation shows guidelines for measurement variation, as suggested by Barrentine (1991).
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If you have provided a Tolerance Interval in the Enter/Verify Gauge R&R Specifications window, a % of Tolerance column appears in the Gauge R&R report. This column is computed as 100*(Variation/Tolerance). Also, a PrecisiontoTolerance ratio appears at the bottom of the report. This ratio represents the proportion of the tolerance or capability interval that is lost due to gauge variability.

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If you have provided a Historical Sigma in the Enter/Verify Gauge R&R Specifications window, a % Process column appears in the Gauge R&R report. This column is defined as follows: 100*(Variation/(K*Historical Sigma)).

See Statistical Details for the Discrimination Ratio for more information.
Due to measurement variation, good parts can be rejected and bad parts can be accepted. This is called misclassification. Once you select the Misclassification Probabilities option, if you have not already done so, you are prompted to select the model type and enter specification limits.
The Bias Report shows a graph for Overall Measurement Bias with a summary table and a graph for Measurement Bias by Standard with a summary table. The average bias, or the differences between the observed values and the standard values, appears for each level of the X variable. A t test for the bias is also given.
The Linearity Study performs a regression analysis using the standard variable as the X variable and the bias as the Y variable. This analysis examines the relationship between bias and the size of the part. Ideally, you want to find a slope of zero. If the slope is significantly different from zero, you can conclude that there is a significant relationship between the size of the part or variable measured as a standard and the ability to measure.
The Linearity Study option is available only when a Standard variable is provided in the launch window.
Produces separate linearity plots for each level of the X, Grouping variables that you specified in the launch window.