title: Least Squares method in practice
body: <p>Very simple regression task. I have three variables <code>x1, x2, x3</code> with some random noise. And I know target equation: <code>y = q1*x1 + q2*x2 + q3*x3</code>. Now I want to find target coefs: <code>q1, q2, q3</code> evaluate the
  performance using the mean Relative Squared Error (RSE) <code>(Prediction/Real - 1)^2</code> to evaluate the performance of our prediction methods.</p>
  
  <p>In the research, I see that <em>this is ordinary Least Squares Problem</em>. But I can't get from examples on the internet how to solve this particular problem in Python. Let say I have data:</p>
  
  <pre><code>import numpy as np
  
  sourceData = np.random.rand(1000, 3)
  koefs = np.array([1, 2, 3])
  target = np.dot(sourceData, koefs)
  </code></pre>
  
  <p>(In real life that data are noisy, with not normal distribution.) How to find this koefs using Least Squares approach in python? <em>Any lib usage.</em></p>