Example of a Variability Chart | JMP 12

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Quality and Process Methods • Variability Gauge Charts • Example of a Variability Chart

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Example of a Variability Chart

Suppose that you have data containing part measurements. Three operators, Cindy, George, and Tom, each took measurements of 10 parts. They measured each part three times, making a total of 90 observations. You want to identify the variation between operators.

1.	Select Help > Sample Data Library and open Variability Data/2 Factors Crossed.jmp.

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

3.	For Chart Type, select Variability.

4.	Select Measurement and click Y, Response.

5.	Select Operator and click X, Grouping.

6.	Select part# and click Part, Sample ID.

7.

Example of a Variability Chart

Looking at the Std Dev chart, you can see that Cindy and George have more variation in their measurements than Tom, who appears to be measuring parts the most consistently. George seems to have the most variation in his measurements, so he might be measuring parts the most inconsistently.

Launch the Variability/Attribute Gauge Chart Platform

Launch the Variability/Attribute Gauge Chart platform by selecting Analyze > Quality and Process > Variability/Attribute Gauge Chart. Set the Chart Type to Variability.

The Variability/Attribute Gauge Chart Launch Window

Choose between a variability gauge analysis (for a continuous response) or an attribute gauge analysis (for a categorical response, usually “pass” or “fail”).

Note: The content in this chapter covers only the Variability chart type. For details about the Attribute chart type, see the Attribute Gauge Charts section.

Choose the model type (Main Effect, Crossed, Nested, and so on). See Statistical Details for Variance Components.

Analysis Settings

Specify the method for computing variance components. See Analysis Settings.

Specify the alpha level used by the platform.

Specify the measurement column. Specifying more than one Y column produces a separate variability chart for each response.

Specify a standard or reference column that contains the “true” or known values for the measured part. Including this column enables the Bias and Linearity Study options. These options perform analysis on the differences between the observed measurement and the reference or standard value. See Bias Report and Linearity Study.

Specify the classification columns that group the measurements. If the factors form a nested hierarchy, specify the higher terms first. If you are doing a gauge study, specify the operator first and then the part.

Identifies the data table column whose values assign a frequency to each row. Can be useful when you have summarized data.

Part, Sample ID

Identifies the part or sample that is being measured.

Identifies a column that creates a report consisting of separate analyses for each level of the variable.

For more information about the launch window, see Using JMP.