The c chart is similar to the np chart, in that it requires equal sample sizes for each data point. For example, in evaluating errors on loan applications, you would use this chart if you sampled the same number of applications each week. But instead of plotting the proportion of data in a certain category, as does the np chart, the c chart plots count data, such as number of errors. As with the other control charts, special cause tests check for outliers and process shifts.
The u chart is a more general version of the c chart for use when the data points do not come from equal-sized samples. For instance, if you review all loan applications each week, and the number submitted differs on a weekly basis, you could still count errors and plot the number of errors by week over time. Because of the difference in sample sizes, the control limits will not be constant for each data point. Thus while the same special cause tests apply as for other charts, the outlier test checks specifically for whether a given data point is outside its own control limits.
Special use of p charts
Most control charts plot data over time. The p chart has an additional use, however, for data that is being compared across conditions rather than over time. For instance, a project team might be analyzing resolution rates for different technical support teams or different types of support problems. In such a situation, a p chart calculates control limits separately for each data point, and any data point lying outside its own control limits represents special cause. The project team would then investigate why performance for that specific category was higher or lower than expected based on typical process performance.
Read more about it
We have additional charts explained in More Types of Control Charts available on Bright Hub PM.