Box-Cox 轉換主要應用於將非常態分配資料轉換為近似常態分配,因此轉換係數 $lambda$ 的選取甚為重要. 不過 lambda 的估計量容易受到影響點的干擾, 至今有些許方法探討如何找出這些引影響點,不過多著重於單變量和迴歸議題上. 隨著電腦運算能力的快速與精確,我們可以擴充此議題於多變量問題上. 而且更可以利用圖形來視覺化所謂的影響點, 以方便檢視. 本研究所有的程式與圖示全部利用免費軟體 R 完成.

　The main use of Box-Cox transformation is to transformation data to be approximately normal distributed. While one or a few cases can greatly influence the estimation of transformation coefficient lambda. To detect influential cases, several methods have been suggested. So far, diagnostics for the transformation coefficient lambda is mainly on univariate data and regression problem.With current computing capability, we can replace complicated derivation by fast computing program,which allow us to extend the methodology to multiivariate cases. Among these, graphic presentation provides detailed and further insight through visual examination. All the computing and graphics are carried out by freeware R.

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