title: Multi-variable linear regression with scipy linregress
body: <p>I'm trying to train a very simple linear regression model. </p>
  
  <p>My code is:</p>
  
  <pre><code>from scipy import stats
  
  xs = [[   0,    1,  153]
   [   1,    2,    0]
   [   2,    3,  125]
   [   3,    1,   93]
   [   2,   24, 5851]
   [   3,    1,  524]
   [   4,    1,    0]
   [   2,    3,    0]
   [   2,    1,    0]
   [   5,    1,    0]]
  
  ys = [1, 1, 1, 1, 1, 0, 1, 1, 0, 1]
  
  slope, intercept, r_value, p_value, std_err = stats.linregress(xs, ys)
  </code></pre>
  
  <p>I'm getting the following error:</p>
  
  <pre><code>File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/stats/stats.py", line 3100, in linregress
  ssxm, ssxym, ssyxm, ssym = np.cov(x, y, bias=1).flat
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/lib/function_base.py", line 1747, in cov
  X = concatenate((X, y), axis)
  ValueError: all the input array dimensions except for the concatenation 
  axis must match exactly
  </code></pre>
  
  <p>What's wrong with my input? I've tried changing the structure of <code>ys</code> in several ways but nothing works.</p>