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.

See Local Data Filter, Redo Menus, and Save Script Menusin the Using JMP book for more information about the following options: