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Correlation and Covariance - The Difference
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# Correlation and Covariance - The Difference Between the Two

A common question in statistics is what is the difference between correlation (the Spearman correlation) and covariance analysis.

First, let's discuss the commonality between correlation and covariance. Correlation and Covariance both describe relationships between 2 variables.

The Spearman Correlation is considered to be a standardized form of covariance.

Here are the differences between the Spearman Correlation and Covariance Analysis:

Correlation values fall within the range of -1 to +1. Covariance values can be outside of that range.

In the covariance calculation, covariance values depend on the units of measure of X and Y. Covariance values of data sets using different sets of measure are not comparable. The Spearman correlation coefficient is not influenced by the units of measure when calculating correlation. The Spearman correlation can be used to compare the similarities of multiple data sets that use different units of measure or scale. The Spearman correlation solves the units-of-measure problem by normalizing the covariance to the product of the standard deviations of all variables being compared. The dimensions or units of measure are then taken out of the equation.

The Spearman correlation tells you how how close or far two variables are from being independent from each other. It must be remembered that high Spearman correlation interpretation does not imply causality between the two variables. The covariance calculation and covariance analysis tells you how much two variables tend to change together.

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