Spearman相關檢驗是從兩變量X與Y是否具有同步性(例如同時增加)

Spearman相關檢驗是從兩變量X與Y是否具有同步性(例如同時增加) 來檢驗兩變量之間是否存在相關性，對於n對觀察數據(, )(i=1,2,…,n),按照每個１變量的n個數據的大小次序，分別由小到大編上等級(積次)，對重複數據取平均等級，在檢驗兩個變量的等級或積之間是否相關。積相關程度的大小用積相關係數(rank correlation) 表示，為正表示正相關，為負表示負相關，等於零表示零相關(王開軍、黃添強，2010)，將其等級差設為(di)、樣本數量(n)，

結果 (英文) 1:

Spearman correlation test is whether the variable X and Y from the two synchronization (e.g., simultaneously increasing) to test whether there is a correlation between two variables, the observation data for n (,) (i = 1,2, ..., n), 1 according to the magnitude order of each of the n data variables, are compiled on the ascending level (secondary product), averaged level of duplicate data, testing whether the correlation between two variables or product levels. Product related to the degree of the size of the product is represented by the correlation coefficient (rank correlation), is positive is a positive correlation indicates a negative correlation is negative, zero for zero correlation (Wangkai Jun, Huang Tianjiang, 2010), which set level difference (di), sample number (n),

結果 (英文) 2:

The Spearman correlation test is to verify whether there is a correlation between the two variables X and Y (e.g. by increasing at the same time), and for n pairs of observation data (i,2,...,n), in order of size of n data for each 1 variable, from small to large on the hierarchy (product). The average level of duplicate data is taken to test whether the level or product of the two variables is correlated. The size of the product correlation degree is indicated by the factor of the product correlation (paper correlation), which is positive for positive correlation, negative for negative correlation, equal to zero for zero correlation (Wang Kaijun, Huang Tingqiang, 2010), setting its grade difference to (di), sample size (n),

結果 (英文) 3:

Spearman correlation test is to test whether there is a correlation between two variables from whether X and y are synchronous (for example, increasing at the same time). For n pairs of observation data (,) (I = 1,2,..., n), According to the order of N data size of each 1 variable, rank (product times) respectively from small to large, take the average grade for the repeated data, and check whether the grade or product of the two variables are related. The magnitude of the product correlation uses the product correlation coefficient It means that positive means positive correlation, negative means negative correlation, and zero means zero correlation (Wang Kaijun, Huang tianqiang, 2010). Set the grade difference to (DI), sample number (n),