現今許多產品的品質好壞或製程是否穩定可藉由是否滿足某個特定函數或曲線關係來決定，這樣的品質資料型態被稱為輪廓資料。
在量測校正的領域中有許多輪廓的資料滿足線性迴歸的模型，然而當此線性迴歸模型中的解釋變數設定值與實際值之間的差異大到無法被忽略時，
則必須將兩者之間的差異納入模型裡，此種考慮解釋變數設定值與實際值不同的模型即為 Berkson 模型。
因此在本論文中，我們對輪廓資料滿足簡單線性 Berkson 模型時，提出一個完整的統計管制監控及診斷方法。
我們比較了多種用來偵測此種輪廓資料函數關係的參數與隨機誤差項的變異數是否發生改變的 EWMA 型態管制圖的效率。
當所使用的管制圖偵測到製程發生失控警訊後，在診斷階段中採用最大化一般概似比的方法來找出製程改變點的估計量，
並証明在簡單線性 Berkson 模型下，此最大化一般概似比方法所得到的改變點估計量具有良好的收斂性質。
同時，在得到製程改變點的估計量後，我們也提出對簡單線性 Berkson 模型下參數的個別診斷檢定統計量，
用以幫助使用者診斷出製程品質關係中的那些參數發生改變，
進而幫助使用者找出製程可能發生改變的原因。當輪廓資料滿足簡單線性 Berkson 模型時，利用本文所建議的管制圖以及相關的診斷方法，
由模擬的結果得知不管是在監控製程的改變,製程改變點的估計或是那些參數改變的診斷上都有令人滿意的表現。
最後利用一個例子來說明實際上如何使用所提出的監控及診斷方法。

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