How to count Spearman Rank order correlation

AI Thread Summary
The discussion focuses on calculating the Spearman Rank Order Correlation for two sets of data. The user attempts to follow the method outlined on VassarStats, which involves ranking the data, subtracting corresponding ranks, squaring the differences, and summing them. They report achieving a correlation of approximately -0.93 using the online tool but struggle to replicate this result manually, obtaining around -0.90 instead. The user expresses frustration over the lack of detailed guidance on the calculation process and the impending exams. Understanding the calculation method is crucial for exam preparation.
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Homework Statement


calculate the rank order correlation between the following data:

6, 5, 4, 2, 3, 3, 8, 3, 7, 6, 7, 5, 5, 4, 2, 7, 6, 2, 4, 6

4, 3, 6, 7, 6, 7, 1, 9, 1, 2, 3, 4, 5, 5, 7, 1, 2, 9, 5, 4


Homework Equations



Following the output from http://www.vassarstats.net/corr_rank.html, given the ranks, then from each individual rank subtract the equivalent opposing / matching rank. Lastly, raise every subtraction to the power of two and sum up results:

Ʃ( #x_{i} - #y_{i} )^{2}

The Attempt at a Solution



I have used the aforementioned online site to help my calculations, and have used the ranks given there, from the appropriate variables, to perform the previously described actions.

The answer is ≈ -0.93. This is confirmed by vassarstat.net. However, vassarstat does not give any explanation how to perform the rest of the problem.

I can only get ≈-0.90. After Ʃ( #x_{i} - #y_{i} )^{2}, I get 2529. Then I use the spearman rank order correlation equation 1 - \frac{6 * 2529}{20 * (20^{2} - 1)} ≈ -0.90
 
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