這篇論文的目的在應用拔靴法建構在95%信心水準下多注頭製程能力指標MSpk與MSpk,s信賴區間的研究成果。分別以兩種資料型態:單樣本資料與次樣本資料為資料來源並應用標準拔靴法、百分位拔靴法、修正偏度百分位拔靴法以及修正偏度加速度百分位拔靴法，此四種拔靴法信賴區間分別建構製程能力指標MSpk與MSpk,s之信賴區間，此外，模擬研究中針對樣本數、注頭數量、MSpk與MSpk,s真實值與拔靴法信賴區間做變異數分析之研究，以期找出各因子對信賴區間的影響。
最後則是以覆蓋率，信賴區間平均長度及信賴區間長度標準差三項評量標準找出最佳之拔靴法信賴區間。在研究中發現，無論樣本資料是單樣本或次樣本資料，樣本數越大時會有較優之區間長度，且由模擬研究與實例結果可知，在95%的信心水準下百分位拔靴法為最佳的拔靴法信賴區間。

The paper aims to present the results of the four 95 percent bootstrap confidence intervals, i.e. standard bootstrap (SB), percentile bootstrap (PB), biased-correction percentile bootstrap (BCPB) and biased-correction and accelerated bootstrap (BCa) for estimating two capability indices MSpk for one single sample and MSpk,s for subsamples which are from multiple streams process. Additionally, an analysis of variance (ANOVA) table is made to study the effects of the bootstrap methods, the true value of MSpk and MSpk,s,the number of streams and the sample size on the bootstrap confidence intervals. Finally, we use the three assessment criteria, that is, coverage percentage, the average length of bootstrap confidence interval and the standard deviation of length interval to get the best result within the four 95 percent bootstrap confidence intervals.
For multiple process streams, whether the data is the single or subsamples, the greater number of the samples will be better in the average length of bootstrap confidence interval. Percentile bootstrap is the best method of those bootstrap methods in the 95 percent confidence level.

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