linregress and polyfit

[Krishnan]

I created an xy pair

y = slope*x + intercept

then I added some noise to "y" using

numpy.random.normal - call it z 

I could recover the slope, intercept from (x,y) using linregress
BUT cannot calculate the slope, intercept from (x, z)

What is puzzling is that for both pairs (x,y) and (x,z) the
polyfit (order 1) works just fine (gives me the slope, intercept)
----------------------------------------------------------------------
import numpy as np
import scipy
# create a straight line, y= slope*x + intercept 
x = np.linspace(0.0,10.0,21)  
slope = 1.0  
intercept = 3.0   
y = []  
for i in range(len(x)):  
    y.append(slope*x[i] + intercept)  
# now create a data file with noise
z= []  
for i in range(len(x)):  
    z.append(y[i] + 0.1*np.random.normal(0.0,1.0,1))  
# now calculate the slope, intercept using linregress
from scipy import stats  
# No error here this is OK, works for x, y
cslope, cintercept, r_value, p_value, std_err = stats.linregress(x,y)
print cslope, cintercept
# I get an error here
#ValueError: array dimensions must agree except for d_0
nslope, nintercept, nr_value, np_value, nstd_err = stats.linregress(x,z)  
print nslope, nintercept
# BUT polyfit works fine, polynomial or order 1 with both data sets
ncoeffs = scipy.polyfit(x,z,1)  
print ncoeffs
coeffs = scipy.polyfit(x,y,1)  
print coeffs