Obtaining SE from the hessian matrix

 So, what is the _right_ way for obtatining SE? Why two those formulas above
 differ?

If you are maximising a likelihood then the covariance matrix of the
estimates is (asymptotically) the inverse of the negative of the Hessian.

The standard errors are the square roots of the diagonal elements of the
covariance.

So if you have the Hessian you need to invert it, if you have the
covariance matrix, you don't.

Yes, the covariance matrix is inverse of the Hessian, that's clear.
But my queston is, why in the first example:

    > sqrt(diag(2*out$minimum/(length(y) - 2) * solve(out$hessian)))
	      
    The 2 in the line above represents the number of parameters. A 95%
    confidence interval would be the parameter estimate +/- 1.96 SE. We
    can superimpose the least squares fit on a new plot:

- we don _not_ use simply 'sqrt(diag(solve(out$hessian)))', how in the
second example, but also include in some way "number of parameters" == 2?
What does '2*out$minimum/(length(y) - 2)' multiplier mean?

Thanks!

--
WBR,
Timur.