Basics

$X, Y$ random variables or vectors  
$X_{1}, X_{2}, \ldots, X_{p}$ random variables  
$X=(X_{1},\ldots, X_{p})^\top$ random vector  
$X \sim \cdot$ $X$ has distribution $\cdot$  
$\data{A}, \data{B}$ matrices [*]
$\Gamma, \Delta$ matrices [*]
$\data{X}, \data{Y}$ data matrices [*]
$\Sigma$ covariance matrix [*]
$1_{n}$ vector of ones $(\underbrace{1,\ldots,1}_
{n\textrm{\scriptsize -times}})^\top$ [*]
$0_{n}$ vector of zeros $(\underbrace{0,\ldots,0}_
{n\textrm{\scriptsize -times}})^\top$ [*]
${\boldsymbol{I}}(.)$ indicator function, i.e. for a set $M$ is ${\boldsymbol{I}}=1$
on $M$, ${\boldsymbol{I}}=0$ otherwise
 
$\textrm{\bf i}$ $\sqrt{-1}$  
$\Rightarrow$ implication  
$\Leftrightarrow$ equivalence  
$\approx$ approximately equal  
$\otimes$ Kronecker product  
iff if and only if, equivalence