Spearman's Rank Correlation is a non-parametric method used in order to make statistical studies of relationship between variables. In this case, the factual degree of parallelism between two numeric sequences will be detected.
The practical calculation of Spearman's Rank Correlation includes the following stages:
1) pair each indication with its number (rank) and rank them from the highest to the lowest or vice versa;
2) subtract the two sets of ranks of each pair of values to be compared;
3) square each difference and add the obtained values;
4) calculate the rank correlation from the following formula:
where 
is the sum of squares of rank differences, 
is the number of paired observations.
When employing the rank correlation, one conditionally estimates the correlation ratio between indications considering the values being equal to or below 0.3 to be the indications of low correlation ratio, whereas the values between 0.4 and 0.7 are considered to indicate a moderate correlation ratio, values above 0.7 - to indicate a high correlation ratio.
Spearman's Rank Correlation is a little less powerful than the Parametric Correlation.
It is reasonable to use the rank correlation when there is just a small amount of observations. This method can be used for both numerical data and in the cases, when the registered values are detected by attributes of various intensity.
Download
SpearmanRankCorr.mq4
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