| c-chart | |||||||||||||||||||
| Subgroupsize: | n > 1 | ||||||||||||||||||
| Measurementtype: | Number of nonconformances in a sample | ||||||||||||||||||
| Qualitycharacteristictype: | Attributes data | ||||||||||||||||||
| Distribution: | Poisson distribution | ||||||||||||||||||
| Sizeofshift: | ≥ 1.5σ | ||||||||||||||||||
| Meanchart: | C control chart.svg | ||||||||||||||||||
| Meancenter: | \barc=
| ||||||||||||||||||
| Meanlimits: | \barc\pm3\sqrt{\barc} | ||||||||||||||||||
| Meanstatistic: | \barci=
no.ofdefectsforxij | ||||||||||||||||||
In statistical quality control, the c-chart is a type of control chart used to monitor "count"-type data, typically total number of nonconformities per unit.[1] It is also occasionally used to monitor the total number of events occurring in a given unit of time.
The c-chart differs from the p-chart in that it accounts for the possibility of more than one nonconformity per inspection unit, and that (unlike the p-chart and u-chart) it requires a fixed sample size. The p-chart models "pass"/"fail"-type inspection only, while the c-chart (and u-chart) give the ability to distinguish between (for example) 2 items which fail inspection because of one fault each and the same two items failing inspection with 5 faults each; in the former case, the p-chart will show two non-conformant items, while the c-chart will show 10 faults.
Nonconformities may also be tracked by type or location which can prove helpful in tracking down assignable causes.
Examples of processes suitable for monitoring with a c-chart include:
The Poisson distribution is the basis for the chart and requires the following assumptions:[2]
The control limits for this chart type are
\barc\pm3\sqrt{\barc}
\barc