title: How to compare the slopes of two regression lines?
body: <p>Assume that you have two numpy arrays: <code>blue_y</code> and <code>light_blue_y</code> such as:</p>
  <pre><code>import numpy as np
  
  x=np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
  blue_y = np.array([0.94732871, 0.85729212, 0.86039587, 0.89169027, 0.90817473, 0.93606619, 0.93890423, 1., 0.97783521, 0.93035495])
  light_blue_y = np.array([0.81346023, 0.72248919, 0.72406021, 0.74823437, 0.77759055, 0.81167983,  0.84050726, 0.90357904, 0.97354455, 1. ])
  
  blue_m, blue_b = np.polyfit(x, blue_y, 1)
  light_blue_m, light_blue_b = np.polyfit(x, light_blue_y, 1)
  </code></pre>
  <p>After fitting linear regression lines to these two numpy arrays I get the following slopes:</p>
  <pre><code>&gt;&gt;&gt; blue_m
  0.009446010787878795
  &gt;&gt;&gt; light_blue_m
  0.028149985151515147
  </code></pre>
  <p><a href="https://i.stack.imgur.com/VOHv4.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VOHv4.png" alt="enter image description here" /></a></p>
  <p>How to compare these two slopes and show that they are statistically different from each other or not?</p>