在許多情況下製程或產品的品質特徵是由一個反應變數與一個或多個解釋變數的某種特定關係（函數）所表現，通常我們將此種資料型態稱為輪廓資料。當輪廓資料滿足簡單線性迴歸的模型，且此模型中的解釋變數設定值與實際值的差距大到無法忽略時，我們使用簡單線性Berkson模型來描述此類的輪廓資料。在本論文中，當輪廓資料滿足簡單線性Berkson模型時，我們以Mahmoud、Parker、Woodall和Hawk-ins (2007) 所提出的改變點監控方法為基礎，經由王藝華 (2010) 所建議的製程改變點的估計方法，對簡單線性Berkson輪廓資料做第一階段的監控，並與KMW與Global F兩種監控方法做比較。此外，當改變點的監控方法偵測到製程失控警訊後，我們採用最大化一般概似比的方法來找出製程改變點的估計值。由模擬的結果得知，當輪廓資料滿足簡單線性Berkson模型時，利用本文建議的監控方法與改變點的估計值，無論在製程的監控或是製程改變點的估計上都有不錯的表現。最後我們利用一個例子來說明實際上如何使用所提出的監控方法。

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