方法对比

	EWMA Chart	属性控制图 (p, np, c, u)	CUSUM 控制图
领域	统计学	统计学	统计学
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1959	1931	1954
提出者	S. W. Roberts	Walter A. Shewhart	E. S. Page
类型	Statistical process control chart for small shifts	Statistical process control charts for count/proportion data	Statistical process control chart for small shifts
开创性文献	Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–250.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company. ISBN: 978-0-87389-076-2	Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1/2), 100–115.
别名	exponentially weighted moving average chart, EWMA control chart, geometric moving average chart, EWMA kontrol kartı	p-chart, np-chart, c-chart, u-chart	cumulative sum chart, CUSUM control chart, Page's CUSUM, kümülatif toplam kontrol kartı
相关	3	4	4
摘要	The exponentially weighted moving average (EWMA) control chart, introduced by S. W. Roberts in 1959, monitors a process using a weighted average that gives the most recent observation the greatest weight while letting older observations fade geometrically. Like CUSUM, this memory makes it highly effective at detecting small, sustained shifts in the process mean, with a single smoothing parameter λ controlling how much past information the chart retains.	Attributes control charts extend Shewhart's framework to count and proportion data — quality characteristics that are classified rather than measured. The p- and np-charts monitor the proportion or number of defective items using the binomial distribution, while the c- and u-charts monitor the number of defects per unit using the Poisson distribution. They are the standard statistical-process-control tools when inspection yields pass/fail or defect counts rather than continuous measurements.	The cumulative sum (CUSUM) control chart, introduced by E. S. Page in 1954, monitors a process by accumulating the deviations of observations from a target value rather than judging each point in isolation. Because small persistent shifts add up over time, the running sum makes them visible far sooner than a Shewhart chart, making CUSUM the tool of choice for detecting small, sustained changes in the process mean.