Many times measurements are made using measurement increments which are too large for the job. Fortunately this problem is easily detected by ordinary, production-line process behavior charts. No special studies are necessary; no standard parts or batches are needed. You simply need to recognize the telltale signs. It is the purpose of this column to explain these signs of chunky data, to outline the nature of the problem that causes chunky data, and to suggest what can be done about it when it occurs.

All charts for count-based data are charts for individual values. Regardless of whether we are working with a count or a rate, we obtain one value per time period and want to plot a point every time we get a value. This is why four specialty charts for count-based data had been developed before a general approach for charting individual values was discovered. These four charts are the p-chart, the np-chart, the c-chart, and the u-chart.

In part one we found that the skewness and kurtosis parameters characterize the tails of a probability model rather than the central portion, and that because of this, probability models with the same shape parameters will only be similar in overall shape, not identical.

Failure mode and effects analysis (FMEA) is an engineering tool that has been heavily adapted for use in Six Sigma programs where it is commonly used to decide which problem to work on. In this usage a risk priority number (RPN) is computed for each of several problems, and the problem with the largest RPN value is selected.