title: scipy linregress function erroneous standard error return?
body: <p>I have a weird situation with scipy.stats.linregress seems to be returning an incorrect standard error:</p>
  
  <pre><code>from scipy import stats
  x = [5.05, 6.75, 3.21, 2.66]
  y = [1.65, 26.5, -5.93, 7.96]
  gradient, intercept, r_value, p_value, std_err = stats.linregress(x,y)
  &gt;&gt;&gt; gradient
  5.3935773611970186
  &gt;&gt;&gt; intercept
  -16.281127993087829
  &gt;&gt;&gt; r_value
  0.72443514211849758
  &gt;&gt;&gt; r_value**2
  0.52480627513624778
  &gt;&gt;&gt; std_err
  3.6290901222878866
  </code></pre>
  
  <p>Whereas Excel returns the following:</p>
  
  <pre><code> slope: 5.394
  
   intercept: -16.281
  
   rsq: 0.525
  
   steyX: 11.696
  </code></pre>
  
  <p>steyX is excel's standard error function, returning 11.696 versus scipy's 3.63. Anybody know what's going on here? Any alternative way of getting the standard error of a regression in python, <em>without going to Rpy</em>?</p>