The Laney P' chart is similar to a traditional P chart. Both charts help you to monitor the proportion of defective items that are produced by your process. The Laney P' chart can be useful in the following situations:

·    You have large subgroups and your data exhibit overdispersion.

Overdispersion can cause the points on a traditional P chart to appear to be out of control when they are not. For the Laney P' chart, the definition of common cause variation includes not only the within-subgroup variation, but also the average variation between subgroups. If there is overdispersion, the control limits on a Laney P' chart are wider than those of a traditional P chart. The wider control limits mean that only important deviations in your process are identified as out of control.

·    Your data exhibit underdispersion.

Underdispersion, which can occur with subgroups of any size, is often caused by a lack of randomness. Underdispersion can result in control limits that are too wide for the data. The Laney P' chart corrects for underdispersion by calculating narrower control limits.

You can use the P Chart Diagnostic to test for overdispersion and underdispersion.

The calculations for the Laney P' chart include Sigma Z, which is an adjustment for overdispersion or underdispersion. A Sigma Z value of 1 indicates that no adjustment is necessary and that the Laney P' chart is exactly the same as a traditional P chart.

The records management staff at a small hospital improved the accuracy and completeness of patient records. To sustain these improvements, the staff tracks the number of incomplete and inaccurate records that are filed each day.

The number of records is high, with an average subgroup size of more than 2500. The data exhibit a large amount of overdispersion (see P Chart Diagnostic). The staff decides to use a Laney P' chart instead of a traditional P chart to monitor patient records.

Data: DefectiveRecords.MTW (available in the Sample Data folder).