$Y \ = \ X' \beta \ + \ e $
Where $Y = (y_1, ..., y_n)$ and $\beta = (\beta_0,..., \beta_k)$.
Why would Weak Exogenity under random sampling produce Strong Exogenity?
I know that weak exogenity is defined as $ \ E(e_i | x_i) = 0 \hspace{1em} \forall \ i \ $ and strong exogenity is defined as $E(e_i | x_j) = 0 \hspace{1em} \forall \ i,j \in \left\lbrace 1, \cdots, n \right\rbrace $.
And I didn't understand the claim that $ \ \ E(e_i | X) = E(e_i | x_1, \cdots, x_n) = E(e_i | x_i) = 0 \ \ $
I know $ \ \ E(e_i| x_i, x_j) = E(e_i| x_i) \ \ $ if $e_i$ is independent of $x_j$.
And I thought that random sampling is about $e_i$ being independent of $e_j$ for $i \neq j$. (or is it about $x_i$ and $x_j$? I'm confused...)
But why would $e_i$ be independent of $x_j$s due to random sampling?
Thank you so much for your help in advance!