在討論連續製造之產品時所使用的管制圖，通常都假設資料服從常態分配且彼此間相互獨立；但在實際生產過程中，常因機器磨損等因素造成產品之間具有相關性，若還是使用傳統管制圖去分析時，將會導致錯誤的分析結果。本文主要假設相關性資料來自時間序列MA(1)模型時，對於Shewhart管制圖和EWMA管制圖在製程監控能力之探討。另外，由於MA(1)模型中對於誤差項的假設皆為常態分配，但在實際上未必如此，所以本文又針對誤差項做了不同分配之假設。因理論上不容易計算Shewhart管制圖和EWMA管制圖的平均連串值，故使用模擬方法來進行分析；結果發現，在相關性資料下，當製程發生微小變動時，EWMA管制圖的監控能力比Shewhart管制圖要來得好。

The data are usually supposed to be a random sample from normal distribution in the discussion of control chart. But in actual production process, the machine could create the correlative products because of the attrition. If we still use the traditional control chart to analyze them, it will lead to the wrong conclusion. We assume that the correlative data come from the time series model, MA(1), to discuss the monitoring abilities of EWMA control chart and Shewhart control chart. Since it is not true that the white noise is always from the normal distribution in MA(1) model, we also consider other distributions for the white noise in our paper. In theory, it is hard to calculate the ARL values for Shewhart and EWMA control charts, so we compute them by simulation. Finally, the simulation shows that the monitoring ability of EWMA control chart is better than Shewhart control chart for the correlative data.

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