How to calculate standard errors/fitting errors of parameters in Linear and Nonlinear Regression?
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Once we estimate the parameters, I read that the standard errors associated with each parameter is given by
,
where Covariance Matrix is given by
, for Multiple Linear Regression and
, for Nonlinear Regression - Levenberg-Marquardt algorithm.
Here n is the number of observations and p is the number of parameters.
I would like to know if the above formulae are correct.
Why aren't the errors associated with the parameters not dependent of the value of alpha (number of standard deviations, eg: 0.5) ?
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