In the previous types of charts, measurement data was the process variable. This data is often continuous, and the charts are based on theory for continuous data. Another type of data is count data or level counts of character data, where the variable of interest is a discrete count of the number of defects or blemishes per subgroup. For discrete count data, attribute charts are applicable, as they are based on binomial and Poisson models. Because the counts are measured per subgroup, it is important when comparing charts to determine whether you have a similar number of items in the subgroups between the charts. Attribute charts, like variables charts, are classified according to the subgroup sample statistic plotted on the chart.
|
Distribution Used to Calculate Sigma |
Statistic Type: Proportion |
Statistic Type: Count |
|---|---|---|
|
Binomial |
P chart |
NP chart |
|
Poisson |
U chart |
C chart |
Control Chart Builder makes some decisions for you based on the variable selected. Once the basic chart is created, you can use the menus and other options to change the type, the statistic, and the format of the chart.
• P charts display the proportion of nonconforming (defective) items in subgroup samples, which can vary in size. Because each subgroup for a P chart consists of Ni items, and an item is judged as either conforming or nonconforming, the maximum number of nonconforming items in a subgroup is Ni.
• NP charts display the number of nonconforming (defective) items in subgroup samples. Because each subgroup for a NP chart consists of Ni items, and an item is judged as either conforming or nonconforming, the maximum number of nonconforming items in subgroup i is Ni.
• C charts display the number of nonconformities (defects) in a subgroup sample that usually, but does not necessarily, consists of one inspection unit.
• U charts display the number of nonconformities (defects) per unit in subgroup samples that can have a varying number of inspection units.