The PARETO Procedure

Decide carefully how the bars should be scaled. The default percentage scale is not always the best choice. For example, a count scale might be more appropriate in a comparative Pareto chart where the total count per cell varies widely from cell to cell and where you want to compare Pareto distributions on an absolute scale rather than a relative scale. You can request a count scale by specifying SCALE=COUNT. In other situations, it might be more appropriate to use a weighted percentage scale or a weighted count scale (specify a WEIGHT= variable and either SCALE=PERCENT or SCALE=WEIGHT).

Use a weight variable if the counts are dependent on a factor (such as exposure or opportunity) that varies from one category to another. For example, suppose you are creating a Pareto chart for the number of medical claims that are categorized by the job titles of company employees who submit them. The counts can be weighted to adjust for the fact that there are more individuals in some jobs than in others and for the fact that some jobs might be associated with greater health risks than others.

Use the NOCURVE option to eliminate the cumulative percentage curve in situations where the curve reveals little information about the data. In general, the bars should be more prominent than the curve.

Maximize the space used for the bars by eliminating unnecessary labels and visual clutter. This is particularly important for comparative Pareto charts. The NOCATLABEL, NOFREQLABEL, and NOCUMLABEL options are useful for this purpose. You can also use the NOFREQTICK and NOCUMTICK options to eliminate tick marks and tick mark labels on the frequency and cumulative percentage axes.

Make legends more informative by specifying legend labels.

Avoid filling bars with multiple types of cross-hatched patterns; solid color fills are less distracting. Use color sparingly to emphasize important features (such as the “vital few” categories), and choose bar colors that provide good visual discrimination.

If you are working with a large data set that involves many categories, limit the number of categories that are displayed to achieve visual clarity.

If your application involves classification effects, construct more than one Pareto chart for the data by using various combinations of classification variables. (This approach is illustrated in Example 15.2).

Provide reference lines on comparative Pareto charts to aid visual comparison.