評估兩組資料集相關性是需要去探討的。其中去觀察兩組資料集相關性的統計方法除了Mantel 檢定，距離共變異數檢定，PROTEST，RV係數，典型相關係數分析等方法，去比較這幾種方法下在不同的資料形態下的好。Mantel檢定與距離共變異數檢定都是通過距離去觀察資料集的相關性，本論文除了使用歐式距離外，也有使用馬氏距離，曼哈頓距離以及明氏距離，並去比較不同距離方法對檢定結果有何影響。我們通過電腦模擬一般多元常態分配以及多變量t分配資料，針對每個模型分配去變更資料變數的變異數，資料的樣本數，資料的維度等，並根據檢定力(power)與檢定力圖來比較每個檢定的結果，最後利用實證資料觀察各檢定的檢定結果。

Across various statistical studies, assessing the correlation between two sets of data is an issue that needs to be discussed in most topics. There are countless statistical methods for observing correlations between two sets of data. The methods used include Mantel test, distance covariance test, PROTEST, RV coefficient, canonical correlation coefficient analysis, then we compare the performance and pros & cons of different data forms under these methods. The Mantel test and the distance covariance test both use distance to observe the correlation of data sets. In addition to using Euclidean distance, this article also uses Mahalanobis distance, Manhattan distance and Minkowski distance to compare the test results of different distance methods. What impact does it have. Then we use computer simulations to simulate general multivariate normal distribution and multivariate t-distribution data, changing the variation of data variables of each model distribution, the number of data samples, the dimensions of the data, etc., and based on the test power and test power diagram to compare the results of each test.

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