# Linear Regression Assumptions vs. Gauss Markov Theorem

I am wondering what is the difference between the Gauss Markov theorem and the assumptions of linear regression found here or here? For example, the third link says that the distribution of residuals should be normal, while this is not an assumption of Gauss-Markov.

So if I wanted to consider a list of all assumptions of linear regression, what is the ground truth?

• the expectation of each error is $$0;$$