# Create iid normal random vectors

Le us create N independent and identitcally distributed (iid) normal random vectors with zero mean and specific covariance matrix. Each vector is of size p. Let $\Sigma$ be the specific covariance matrix.

    p = 20;
N = 50;
M = chol($\Sigma$)
Matr =  M' * randn(p, N)


In fact I want to know why in all references, they use chol?. Why they don't simply write Matr = randn(N, p) * ($\Sigma$) ? So what is the advantage of chol (or cholcov) in this case?

I made some results, and I noticed that they significantly differ when I remove the chol !! Even it takes more time for the computation..why??

Any help will be very appreciated!