title: Standard deviation/error of linear regression
body: <p>So I have: </p>
  
  <pre><code>t = [0.0, 3.0, 5.0, 7.2, 10.0, 13.0, 15.0, 20.0, 25.0, 30.0, 35.0]
  U = [12.5, 10.0, 7.6, 6.0, 4.4, 3.1, 2.5, 1.5, 1.0, 0.5, 0.3]
  U_0 = 12.5
  y = []
  for number in U:
      y.append(math.log(number/U_0, math.e))
  (m, b) = np.polyfit(t, y, 1)
  yp = np.polyval([m, b], t)
  plt.plot(t, yp)
  plt.show()
  </code></pre>
  
  <p>So by doing this I get linear regression fit with <code>m=-0.1071</code> and <code>b=0.0347</code>. </p>
  
  <p>How do I get deviation or error for m value?</p>
  
  <p>I would like <code>m = -0.1071*(1+ plus/minus error)</code></p>
  
  <blockquote>
    <p>m is k and b is n in y=kx+n</p>
  </blockquote>