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Appendix B Main Menu 443
The Graph Menu
Variability/Gauge Chart
In a variability analysis, a number of parts assumed to be identical are taken from a production line.
Each one is measured several times by a number of operators using different measuring instruments.
You want to know the magnitudes of the variation due to operators, parts, and instruments. In the
same way that a Shewhart control chart can identify processes which are going out of control over time,
a variability chart can help identify operators, parts, and instruments.
Variability or Continuous Gauge charts are for responses whose values can be measured on a
continuous scale. For example, the width of a washer might be measured as 2.3 mm.
Attribute Gauge charts are for responses whose values are binary or categorical. For example, a circuit
might be measured as pass/fail. Because different reports are generated on the raters, raters each need to
be in different columns. The chapter “Variability Charts” of JMP Statistics and Graphics Guidedescribes
the Variability/Gauge Chart command in detail.
B The Main Menu
Pareto Plot
The Pareto Plot command creates a bar chart (Pareto chart) that displays the severity (frequency) of
problems in a quality-related process or operation. Pareto plots compare quality-related measures or
counts in a process or operation. The defining characteristic of Pareto plots is that the bars are in
descending order of values, which visually emphasizes the most important measures or frequencies.
Pareto Plot uses a single y variable, called a process variable, and gives:
• A simple Pareto plot when you do not specify an x (classification) variable
• A one-way comparative Pareto plot when you specify a single x variable
• A two-way comparative plot when there are two x variables
The Pareto Plot command does not distinguish between numeric and character variables or between
modeling types. All values are treated as discrete, and bars represent either counts or percentages.
The chapter “Pareto Plots” of JMP Statistics and Graphics Guide describes the Pareto Plot command in
detail.
Capability
Capability analysis, used in quality control, measures the conformance of a process to given
specification limits. Using these limits, you can compare a current process to specific tolerances and
maintain consistency in production. Graphical tools such as the goal plot and box plot give you quick
visual ways of observing within-spec behaviors. For details, see the JMP Statistics and Graphics Guide.
Profiler
The Profiler is available for tables with columns whose values are computed from model prediction
formulas. Usually, a profiler plot results when you do a Standard Least Squares analysis and then
request it. However, if you save the prediction equation from the analysis, you can access the prediction
profile later from the Graph menu and look at the model using the response column with the saved
prediction formula.
The prediction profiler displays prediction traces for each x variable. A prediction trace is the predicted
response as one variable is changed while the others are held constant at the current values. The
prediction profiler is a way of changing one variable at a time and looking at the effect on the predicted