We know that if a variable $X$ is iid from a $N(\mu,\sigma^2)$, the distribution of $X+b$ is $N(\mu+b,\sigma^2)$
If we scale the $X$ by a scaling factor $k$, the new distribution will be $N(k\mu+b,k^2\sigma^2)$.
Does the same principle applies for multivariate normal distributions?
What happens if the scaling factor is a matrix?
It's ok if you don't give a full answer but a nice reference would be nice