C-chart
title: "C-chart" type: doc version: 1 created: 2026-02-28 author: "Wikipedia contributors" status: active scope: public tags: ["quality-control-tools", "statistical-charts-and-diagrams"] topic_path: "general/quality-control-tools" source: "https://en.wikipedia.org/wiki/C-chart" license: "CC BY-SA 4.0" wikipedia_page_id: 0 wikipedia_revision_id: 0
::data[format=table title="Infobox control chart"]
| Field | Value |
|---|---|
| name | c-chart |
| proposer | Walter A. Shewhart |
| 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 | \bar c = \frac {\sum_{i=1}^m \sum_{j=1}^n \mbox{no. of defects for } x_{ij |
| :: |
| name = c-chart | proposer = Walter A. Shewhart | 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 = \bar c = \frac {\sum_{i=1}^m \sum_{j=1}^n \mbox{no. of defects for } x_{ij}}{m} | meanlimits = \bar c \pm 3\sqrt{\bar c} | meanstatistic = \bar c_i = \sum_{j=1}^n \mbox{no. of defects for } x_{ij}
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. 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:
- Monitoring the number of voids per inspection unit in injection molding or casting processes
- Monitoring the number of discrete components that must be re-soldered per printed circuit board
- Monitoring the number of product returns per day
The Poisson distribution is the basis for the chart and requires the following assumptions:
- The number of opportunities or potential locations for nonconformities is very large
- The probability of nonconformity at any location is small and constant
- The inspection procedure is same for each sample and is carried out consistently from sample to sample
The control limits for this chart type are \bar c \pm 3\sqrt{\bar c} where \bar c is the estimate of the long-term process mean established during control-chart setup.
References
References
- "Counts Control Charts". [[National Institute of Standards and Technology]].
- Montgomery, Douglas. (2005). "Introduction to Statistical Quality Control". [[John Wiley & Sons]], Inc..
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