t_test, f_test, model.py for normal instead of t-distribution

[joep]

models.py LikelihoodModelResults

t_test assumes that test statistic is t-distributed. If we only look at the asymptotic normal distribution, then we don't need df_residual.

Currently mainly a thought that needs checking.

model.py provides most of the generic methods that can be used with MLE models that are based on the asymptotic normal distribution.
see also SAS new proc PLM

essentially only params and cov_params are needed for the normal approximation.

I haven't thought yet about the dof in f_test (number of restriction is ok, but I don't know if we need df_resid.

conf_int is already overwritten by some subclasses to use the normal instead of t-distribution