自從1930年以來，統計方法在工業製程管制的應用就備受重視。而在這期間，管 制圖扮演著極重要的角色。當處理一個計量值的品質特性時，一般都是用全距管制圖 （Rchart）來監控製程的變異水準，並以平均數管制圖來監控製程的平均水準。而在這二種 管制圖書，通常均使用三倍標準差為管制上下界限。傳統上，一般都假設測量值的母體分配 為常態。這個假設的真實性，則視測量值的偏態和峰態而定。由中央極限定理可知，只要抽 樣數夠大，則樣本平均數的抽樣分配趨近常態分配；然而，即使母體呈常態分配，全距（range） 的抽樣分配亦非對稱，且測量值往往也不服從常態分配。因此，在母體不為常態的情況下， 使用三倍標準差為管制上下界限之全距管制圖，其偵錯之機率可能會變大。本文嘗試利用Burr 所發展之Burr分配，在非常態資料下，選定全距管制圖的管制上下界限，並比較其與傳統 常態假設下之全距管制圖之差異。

　　　　　Control charts have played a key role in industrial process control since the 1930's. When dealing with a quality characteristic of numerical measurements, the X-bar and R charts are generally used to control the process mean and variability, respectively. Traditionally, 3-sigma control limits are constructed in both charts. The numerical measurements are usually assumed to be normally distributed. However, this assumption may not be true. Although the sampling distribution of X-bar approaches normality as the sample size is sufficiently large, the sampling distribution of R is always asymmetrical, no matter what the underlying distribution is. Therefore, if the 3-sigma limits are still used in the R chart, the control ability may be reduced. This paper employs the Burr distribution to construct the control limits for an R chart under non-normality and then, compares the non-normal control limits with the traditional 3-sigma limits based on the average run length of an R chart.