Variance: It is the expectation of the squared deviation of a random variable from its mean. In other words, it measures how far a set of (random) numbers are spread out from their average value. Mathematically,
Uses: Variance analysis, also described as analysis of variance or ANOVA, involves assessing the difference between two figures.
Covariance: It provides the measure of the strength of the correlation between two or more sets of random variates. Mathematically,
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Correlation: The most familiar measure of dependence between two quantities is the Pearson product-moment correlation coefficient, or “Pearson’s correlation coefficient”, commonly called simply “the correlation coefficient”. It is obtained by dividing the covariance of the two variables by the product of their standard deviations.
![\rho _{X,Y}=\mathrm {corr} (X,Y)={\mathrm {cov} (X,Y) \over \sigma _{X}\sigma _{Y}}={E[(X-\mu _{X})(Y-\mu _{Y})] \over \sigma _{X}\sigma _{Y}},](https://wikimedia.org/api/rest_v1/media/math/render/svg/bb20ca021c7e440a88d006f541d2dde73e23d4aa)