We usually get an estimate of $\beta$ in the logistic regression by finding the $MLE$ of the observed random samples of $X_1,X_2....,X_N$. Then we use Wald's test i.e. ${[\hat \beta / S.E.(\hat \beta)]}^2$ to test whether that variable is significant or not.
From what I have read, this Wald's test is based on two facts (or assumptions, I am not sure).
- $\hat \beta$ follows a normal distribution
- Standard Deviation of this normal distribution is given by the inverse of Fisher's Matrix
Can someone explain the proofs behind these two facts/ (or assumptions). I have read this notes but most of the intuition went over my head.