September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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第七章：可否請壞資料站出來？
範例 1：製造業減少毀損
一家製造公司開發了一個相當聰明的方法，來減少寄送到客戶的毀損產品數量，並且不
會有額外的成本。基本的概念是，使用一個量來當作物品品質的指標，這個量本來就已
經被用來測量每個新製造的產品上（讓我們將其稱為「尺寸」—它的名稱不是這個，但
無所謂）。若尺寸是「off」，那麼該物品可能將無法正常運作。對於製造者來說，這個
間接方法的關鍵好處就是低成本。尺寸已經是在製造過程中會被測量的量，所以它並不
會產生額外的負擔（「確保品質」的關鍵總是在於它必須要是便宜的）。
他們在適當的地方放了一個系統來標籤可能是「off」的物品，再來使用人工檢查。問題
是：這個系統運作得如何？它實際偵測損毀物品的準確率好嗎？這不是很簡單能說的，
因為整體的損毀率很低：大約 10,000 個物品中有 1 個離開產品線後被發現是真的損毀
了。這個新的標記系統能夠偵測到的損毀比率為何，以及有多少正常的物品會被錯誤的
標記為損毀的？（這是關鍵的問題。因為所有被標籤的物品會送去作人工測試，大量的
誤判將會造成成本急速上升。記住，最初的概念是希望整體的程序是便宜的！）
這是一個習題。
你
會如何做？
在這整個方法背後的中心假設是，每個產品的尺寸都會有一個「典型」的值，而發現
（量測）的值將會在某個區間之內。只有在這些假設都被滿足的狀況下，說某個特定的
產品為「off」才有道理，代表它超出了典型的範圍。並且，因為我們嘗試從 10,000 個
產品之中偵測 1 個，我們需要瞭解在尾端的分佈。
你沒有辦法從一個統計直方圖中找到 ...
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