How to count Spearman Rank order correlation

In summary, the task is to calculate the rank order correlation between two sets of data using the equation Ʃ( #x_{i} - #y_{i} )^{2}, and then using the Spearman rank order correlation equation to find the final result. The answer is approximately -0.93, which is confirmed by an online calculator, but the last step in the calculation is unclear and may result in a slightly different answer.
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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[itex]_{i}[/itex] - #y[itex]_{i}[/itex] )[itex]^{2}[/itex]

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[itex]_{i}[/itex] - #y[itex]_{i}[/itex] )[itex]^{2}[/itex], I get 2529. Then I use the spearman rank order correlation equation 1 - [itex]\frac{6 * 2529}{20 * (20^{2} - 1)}[/itex] ≈ -0.90
 
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  • #2
Anyone? Exams are just behind the corner and I can not figure this one out. Hate it if they would ask to use this in the test. It could be so simple if i could just understand
 

What is Spearman Rank order correlation?

Spearman Rank order correlation is a statistical measure used to determine the strength and direction of the relationship between two variables. It is a non-parametric test, meaning it does not make assumptions about the underlying distribution of the data.

How is Spearman Rank order correlation calculated?

The calculation for Spearman Rank order correlation involves ranking the data for each variable and then calculating the Pearson correlation coefficient between the ranks. This coefficient ranges from -1 to 1, with a higher absolute value indicating a stronger correlation.

When should I use Spearman Rank order correlation?

Spearman Rank order correlation is typically used when the data is not normally distributed or when there are outliers present. It is also useful when the relationship between the variables is non-linear.

What is the difference between Spearman Rank order correlation and Pearson correlation?

The main difference between Spearman Rank order correlation and Pearson correlation is that Spearman uses ranks instead of actual values when calculating the correlation coefficient. This makes it more robust to outliers and non-linear relationships.

How do I interpret the results of a Spearman Rank order correlation?

The result of a Spearman Rank order correlation is a coefficient between -1 and 1. A coefficient close to 1 indicates a strong positive correlation, while a coefficient close to -1 indicates a strong negative correlation. A coefficient close to 0 suggests a weak or no relationship between the variables.

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