Use binomial or
Poisson capability analysis to determine whether your
process meets customer requirements when you have attribute data.

To add output from an attribute capability analysis, go to Add and complete a form.

Use a binomial capability analysis to determine whether the
percentage of defective items meets customer requirements.
Use when each item is classified into one of two categories,
such as pass or fail.

For example, the supervisor for a call center uses a binomial capability analysis to determine whether the rate of unanswered calls that are redirected is stable and below 20%. To see an example, go to Minitab Help: Example of Binomial Capability Analysis.

The data must be counts of defective items. Try to collect at least 25 subgroups. If you do not collect a sufficient amount of data over a long enough period of time, the data may not accurately represent different sources of process variation and the estimates may not indicate the true capability of your process. For details, go to Minitab Help: Data considerations for Binomial Capability Analysis.

Use a
Poisson capability analysis to determine whether
the rate of defects per unit (DPU) meets customer
requirements.
Use this analysis when you count the defects on each item, and
each item can have more than one defect.

For example, a textile manufacturer uses a Poisson capability analysis to determine the number of defects per 100 yards of fabric and to assess whether the defect rate is stable. To see an example, go to Minitab Help: Example of Poisson Capability Analysis.

You must be able to count the number of defects on each item or unit. Try to collect at least 25 subgroups. If you do not collect a sufficient amount of data over a long enough period of time, the data may not accurately represent different sources of process variation and the estimates may not indicate the true capability of your process. For details, go to Minitab Help: Data considerations for Poisson Capability Analysis.