Hansen John P
Group Health Cooperative of South Central Wisconsin, Madison, USA.
J Healthc Qual. 2005 Jul-Aug;27(4):32-43. doi: 10.1111/j.1945-1474.2005.tb00566.x.
Statistical process control (SPC) can be thought of as the frequent monitoring of processes using inferential statistics. The feature that distinguishes SPC from the typical use of inferential statistics for analyzing populations is that in the former frequent samples are taken over time, whereas in inferential statistics a single sample is generaLLy taken before and after some intervention or treatment. An x-s control chart is used to monitor a continuous variable that reflects the output of a process. The x-s control chart is a graph that includes serial sample means (x) as the variables of interest, a centerline that represents the grand mean of the samples (x), and upper control limit (UCL) and lower control limit (LCL) that represent three standard errors (SEx) above and below the centerline. An x-s control chart is used to estimate with 99.7% confidence that the population mean of a continuous output variable was within the interval defined by the UCL and LCL during a period of baseline monitoring. It is further assumed that if the process remains stable, future population means wiLL remain between the control Limits for additional process outputs. Control charts allow the evaluation of both common- and special-cause variation. AnaLysis of the common-cause variation aLLows an assessment of the current process performance. Special-cause variation is identified when there is a sample mean that is beyond the UCL or LCL.
统计过程控制（SPC）可以被认为是使用推断统计对过程进行频繁监测。将SPC与用于分析总体的典型推断统计使用方式区分开来的特征在于，在前者中会随着时间推移频繁抽取样本，而在推断统计中通常在某些干预或处理前后抽取单个样本。x-s控制图用于监测反映过程输出的连续变量。x-s控制图是一种图表，它将系列样本均值（x）作为感兴趣的变量，一条代表样本总均值（x）的中心线，以及代表中心线上下三个标准误（SEx）的上控制限（UCL）和下控制限（LCL）。x-s控制图用于以99.7%的置信度估计在基线监测期间连续输出变量的总体均值在由UCL和LCL定义的区间内。进一步假设，如果过程保持稳定，未来的总体均值将在额外过程输出的控制限之间。控制图允许对共同原因变异和特殊原因变异进行评估。对共同原因变异的分析允许评估当前过程性能。当存在超出UCL或LCL的样本均值时，识别出特殊原因变异。
