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The covariance gives the degree to which two variables vary together.
Covariance is not considered hindering interpretability in higher dimensions.
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Equation
<MATH> \begin{array}{rrl} \text{Covariance of X and Y} & = & \frac{\displaystyle \sum_{i=1}^{N}{(X_i-\bar{X}).(Y_i-\bar{Y})}}{N} \\ & = & \frac{\displaystyle\sum_{i=1}^{N}{\href{cross_product}{\text{Cross Product of X & Y}}}}{N} \\ & = & \frac{\displaystyle \href{cross_product#SP}{\text{Sum of Cross Product (SP)}}}{N} \end{array} </MATH>
where:
- <math>\bar{X}</math> and <math>\bar{Y}</math> are the mean
- N is the sample size
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