<P> Some authors have criticised the use of average run lengths (ARLs) for comparing control chart performance, because that average usually follows a geometric distribution, which has high variability and difficulties . </P> <P> Some authors have criticized that most control charts focus on numeric data . Nowadays, process data can be much more complex, e.g. non-Gaussian, mix numerical and categorical, or be missing - valued . </P> <Table> <Tr> <Th> Chart </Th> <Th> Process observation </Th> <Th> Process observations relationships </Th> <Th> Process observations type </Th> <Th> Size of shift to detect </Th> </Tr> <Tr> <Td> x _̄ (\ displaystyle (\ bar (x))) and R chart </Td> <Td> Quality characteristic measurement within one subgroup </Td> <Td> Independent </Td> <Td> Variables </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> x _̄ (\ displaystyle (\ bar (x))) and s chart </Td> <Td> Quality characteristic measurement within one subgroup </Td> <Td> Independent </Td> <Td> Variables </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> Shewhart individuals control chart (ImR chart or XmR chart) </Td> <Td> Quality characteristic measurement for one observation </Td> <Td> Independent </Td> <Td> Variables </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> Three - way chart </Td> <Td> Quality characteristic measurement within one subgroup </Td> <Td> Independent </Td> <Td> Variables </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> p - chart </Td> <Td> Fraction nonconforming within one subgroup </Td> <Td> Independent </Td> <Td> Attributes </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> np - chart </Td> <Td> Number nonconforming within one subgroup </Td> <Td> Independent </Td> <Td> Attributes </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> c - chart </Td> <Td> Number of nonconformances within one subgroup </Td> <Td> Independent </Td> <Td> Attributes </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> u-chart </Td> <Td> Nonconformances per unit within one subgroup </Td> <Td> Independent </Td> <Td> Attributes </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> <Tr> <Td> EWMA chart </Td> <Td> Exponentially weighted moving average of quality characteristic measurement within one subgroup </Td> <Td> Independent </Td> <Td> Attributes or variables </Td> <Td> Small (<1.5 σ) </Td> </Tr> <Tr> <Td> CUSUM chart </Td> <Td> Cumulative sum of quality characteristic measurement within one subgroup </Td> <Td> Independent </Td> <Td> Attributes or variables </Td> <Td> Small (<1.5 σ) </Td> </Tr> <Tr> <Td> Time series model </Td> <Td> Quality characteristic measurement within one subgroup </Td> <Td> Autocorrelated </Td> <Td> Attributes or variables </Td> <Td> N / A </Td> </Tr> <Tr> <Td> Regression control chart </Td> <Td> Quality characteristic measurement within one subgroup </Td> <Td> Dependent of process control variables </Td> <Td> Variables </Td> <Td> Large (≥ 1.5 σ) </Td> </Tr> </Table> <Tr> <Th> Chart </Th> <Th> Process observation </Th> <Th> Process observations relationships </Th> <Th> Process observations type </Th> <Th> Size of shift to detect </Th> </Tr>

Types of control charts in total quality management
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