Python Statsmodels testing ratios from a robust linear model based on M-evaluators

I have a linear model that I am trying to correlate with data with a good amount of outliers in the endogenous variable, but not in the exogenous space. I researched that M-based RLMs are good in this situation.

When I enter RLM in my data as follows:

import numpy as np
import statsmodels.formula.api as smf
import statsmodels as sm

modelspec = ('cost ~ np.log(units) + np.log(units):item + item') #where item is a categorical variable
results = smf.rlm(modelspec, data = dataset, M = sm.robust.norms.TukeyBiweight()).fit()
print results.summary()

the final results show statistics z, and, apparently, the criterion of significance is based on this, and not on statistics t. However, the next R manual ( http://www.dst.unive.it/rsr/BelVenTutorial.pdf ) shows the use of t statistics on pages 19-21

Two questions:

• Can someone explain to me conceptually why statsmodels uses the z-test and not the t-test?

• (| z | > 4). 40 . , 21-25 . , RLM ? , , , z-, (.. , , smf.rlm(), 95% ? , t- ...)?

!