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442 Main Menu Appendix B<br />

The Graph Menu<br />

can specify that different variables be shown on the sides and bottom of the matrix, giving maximum<br />

flexibility for comparisons. For details, see the JMP Statistics and Graphics <strong>Guide</strong>.<br />

Ternary Plot<br />

The Ternary Plot command constructs a plot using triangular coordinates. The ternary platform uses<br />

the same options as the contour platform for building and filling contours. In addition it a specialized<br />

crosshair tool that lets you read the triangular axes values.<br />

The chapter “Ternary Plots” of JMP Statistics and Graphics <strong>Guide</strong> describes the Ternary Plot command<br />

in detail and shows ternary plot examples.<br />

Diagram<br />

The Diagram platform is used to construct Ishikawa charts, also called fishbone charts, or<br />

cause-and-effect diagrams. These charts are useful when organizing the sources (causes) of a problem<br />

(effect), perhaps for brainstorming, or as a preliminary analysis to identify variables in preparation for<br />

further experimentation. See the JMP Statistics and Graphics <strong>Guide</strong> for examples of Ishikawa charts.<br />

Control Chart<br />

The Control Chart menu has a sub-menu that creates dynamic plots of sample subgroups as they are<br />

received and recorded. Control charts are a graphical analytic tool used for statistical quality<br />

improvement. Control charts can be broadly classified according to the type of data analyzed:<br />

• Control charts for variables are used when the quality characteristic to be analyzed is measured on a<br />

continuous scale.<br />

• Control charts for attributes are used when the quality characteristic is measured by counting the<br />

number of nonconformities (defects) in an item or by counting the number of nonconforming<br />

(defective) items in a sample.<br />

The concepts underlying the control chart are that the natural variability in any process can be<br />

quantified with a set of control limits, and that variation exceeding these limits signals a special cause of<br />

variation. In industry, control charts are commonly used for studying the variation in output from a<br />

manufacturing process. They are typically used to distinguish variation due to special causes from<br />

variation due to common causes.<br />

The control chart platform offers the following types of charts:<br />

• Mean, range, and standard deviation<br />

• Individual measurement and moving range (run chart, XBar Chart, and IR)<br />

• P-chart, NP-chart, C-chart, and U-chart<br />

• UWMA and EWMA<br />

• CUSUM<br />

• Presummarized<br />

• Levey-Jennings<br />

• Multivariate Control Charts<br />

The “Statistical Control Charts” of JMP Statistics and Graphics <strong>Guide</strong> describes the Control Charts<br />

command in detail.

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