Using a P Chart
Use a p chart for quality control initiatives when your data is of the discrete attribute type, also known as categorical data. In other words, if your data is obtained by grouping instances into specific categories, you have discrete attribute data. For more information about types of data, check out our article, Six Sigma Data Types.
The p chart plots the proportion of data in a specific category. You do not need to have equal sample sizes for each data point, but if you do you can instead use a variation of the p chart called an np chart. Typically the proportion data is plotted over time, so that business leaders can determine whether process performance is stable or shifting.
For instance, a Six Sigma project team might use a p chart to assess whether the proportion of loan applications approved is decreasing, or whether the proportion of software support calls about program installation is rising. Using a program like Minitab they can identify process shifts and trends, based on the data distribution. Teams conducting DMAIC projects often use a control chart to depict a change in process performance before and after improvements are implemented.
P charts can also be used to analyze data that is arranged in different groups rather than plotted over time. For instance, rather than assessing whether the loan application rate is changing over time, a financial company's leaders can compare application rates for different types of loans, different customer segments, or different processing teams. Because the sample size may differ for each group, the control limits can actually vary for each data point. Each point is thus compared to its own limits to identify outliers, with any point above its upper control limit indicating that group measures higher on that metric than the others, and any point below its own lower control limit representing performance below the other groups.