Data considerations for NP Chart

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

Items must be classified into one of two categories, such as pass or fail
A defective item has one or more defects that make it not acceptable. If you can determine only whether an item is defective or nondefective, use this chart. If you can count the number of defects on each item, use U Chart, Laney U' Chart, or C Chart to plot the number of defects per unit.
If your data exhibit overdispersion or underdispersion, a traditional attributes chart may be misleading
If your data exhibit overdispersion or underdispersion, Laney P' Chart may more accurately distinguish between common-cause variation and special-cause variation. Overdispersion can cause an NP chart to show an increased number of points outside the control limits. Underdispersion can cause an NP chart to show too few points outside of the control limits. The Laney P' chart adjusts for these conditions. You can test your data for overdispersion and underdispersion with P Chart Diagnostic. For more information, go to Overdispersion and underdispersion.
The data should be in time order

Because control charts detect changes over time, the order of the data is important. You should enter the data in the order it was collected, with the oldest data at the top of the worksheet.

The data should be collected at appropriate time intervals

Collect data at equally spaced time intervals, such as every hour, every shift, or every day. Select a time interval that is short enough that you can identify changes to the process soon after the changes occur.

Collect data in subgroups

A subgroup is a sample of similar items from the process that you want to evaluate. The items in each subgroup should be collected under the same process conditions, such as personnel, equipment, suppliers, or environment.

If the subgroup is a collection of units, they should be collected under the same process conditions, such as personnel, equipment, suppliers, or environment. If you don't collect data in subgroups where items are subject to the same process conditions, you may not be able to distinguish between common-cause and special-cause variation.

The subgroups must be large enough

If the subgroup sizes are not large enough, the control limits may not be accurate when they are estimated from the data. The required subgroup size () depends on the average proportion of defective items (). Use the following formula to determine whether your subgroups are large enough, . For example, if the average proportion of defective items is 0.06, then all subgroups must have at least 9 items: , rounded up to the nearest whole number = 9.

The data must include enough subgroups to obtain precise control limits

If you don't have enough subgroups, you can still use the control chart, but you should consider the results preliminary because the control limits may not be precise. If you are using the chart on an ongoing basis, re-estimate the control limits after you have collected enough subgroups.

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