Control Chart- Control charts can either be univariate when they monitor a single CTQ characteristic of a product or service or be multivariate when they monitor more than one CTQ.
A typical control chart plots sample statistics and is made up of a minimum of four lines of, a vertical line to measure the levels of the sample’s means, the two out-most horizontal lines for the UCL and the LCL; and the centerline, which represents the mean of the process.
Attribute Data univariate chart – It’s characteristics resemble binary data — they can only take one of two given forms like conforming or not conforming, good or bad, etc
- p–chart – The p-chart is used when dealing with ratios, proportions, or percentages of conforming or nonconforming parts in a given sample.
- np-chart – The np-chart is one of the easiest to build. While the p-chart tracks the proportion of nonconformities per sample, the np-chart plots the number of nonconforming items per sample.
- c-chart – The c-chart monitors the process variations due to the fluctuations of defects per item or group of items.
- u-chart – One of the premises for a c-chart is that the sample sizes had to be the same.
Variable control charts – Control charts monitor not only the means of the samples for CTQ characteristics but also the variability of those characteristics.
- X charts and R-charts – It is similar to attribute control charts but, quantitative measurements are considered for the CTQ characteristics instead of qualitative attributes.
- Standard error-based X-chart – It is based on the Central Limit Theorem, the standard deviation used for the control limits is nothing but the standard deviation of the process divided by the square root of the sample’s size
- Mean range- based X-chart – With sample sizes n ≤ 10, the variations are also small, so the range can be used against the standard deviation when constructing a control chart.
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