Interpreting robust linear regression results in Python

Keywords: python python-3.x linear-regression

Question: 

Trying to understand the results from robust linear regression. I'm trying the following code:

xx = np.random.rand(100, 1 )*50
yy = np.random.rand(100, 1)
rlm_res_fake = sm.RLM(xx, yy, M=sm.robust.norms.TrimmedMean()).fit()
plt.scatter(xx,yy)
plt.plot(xx, rlm_res_fake.params*xx)

print ('pvalue')
print (rlm_res_fake.pvalues)
print ('params')
print (rlm_res_fake.summary())

Why do I always get a significant p-value even for randomized data that is clearly not correlated ? Is that not what the p-value mean - that we reject the hypothesis of zero slope ?

Answers: