# Today I Learned

Some of the things I've learned every day since Oct 10, 2016

## 29: Variance and Covariance

Where the covariance of 2 random variables $X, Y$ is defined to be

$\textrm{cov}(X, Y) = \textrm{E}(XY) - \textrm{E}(X)\textrm{E}(Y)$

and the variance of a random variable $X$ is defined to be

$\textrm{var}(X) = \textrm{E}(X^2) - (\textrm{E}(X))^2$

it’s easy to see that the variance of a variable is simply the covariance of that variable with itself.