Use the probability plots to assess the fit of the nonnormal distribution for each variable.
If the distribution is a good fit for the data, the points should form an approximately straight line. Departures from this straight line indicate that the fit is unacceptable. If the p-value is greater than 0.05, you can assume that the data follow the nonnormal distribution used in the analysis.
If the distributions differ for multiple variables, you should perform a separate capability analysis for each variable.
Use the capability histogram to view your sample data in relation to the distribution fit and the specification limits.
For each variable, compare the distribution curve to the bars of the histogram to assess whether your data seem to follow the distribution that you chose for the analysis. If the bars vary greatly from the curve, your data may not follow your chosen distribution and the capability estimates may not be reliable for your process. If you are unsure which distribution best fits your data, use Individual Distribution Identification to identify an appropriate distribution or transformation.
The histograms provide only a rough indication of the distribution fit. To more definitively assess the distribution fit, use the results on the probability plots. If the distributions differ for multiple variables, you should perform a separate capability analysis for each variable.
To determine the actual number of nonconforming items in your process, use the results for PPM < LSL, PPM > USL, and PPM Total. For more information, go to All statistics and graphs.
For each variable, evaluate whether the process is centered between the specification limits or at the target value, if you have one. The peak of the distribution curve shows where most of the data are located.