How do we decide that distance measure to be minimized for normal distribution in linear regression is:
$||y−Xβ||^2$
from log-likelihood function:
$l(θ) = −\frac{1}{2}nlog(σ^{2})− \frac{1}{2σ^{2}}||y−Xβ||^2$
Is this the reason we use euclidean distance as to compute distances as well?