title: how to add regression line and regression line equation on graph
body: <p>I have the below input file and the code/script to add the regression line on the graph but the code gives this error:<code>ValueError: x and y must have same first dimension</code>. I couldn't figure out that error.</p>
  
  <p>How can I add the regression line and regression line equation on graph?</p>
  
  <p><strong>Input file:</strong></p>
  
  <pre><code>-5.06   -4.27
  -6.69   -7.28
  -3.80   -3.51
  -3.88   -2.79
  -0.90   -0.81
   2.10    2.59
  -1.08    0.28
  -5.00   -3.39
   2.67    2.92
   2.48    2.85
  -5.10   -3.49
   2.88    3.09
   2.30    2.67
  -3.47   -2.20
  -0.90   -0.79
  </code></pre>
  
  <p><strong>Script:</strong></p>
  
  <pre><code>#!/usr/bin/python
  import numpy as np
  import pylab as plot
  import matplotlib.pyplot as plt
  import numpy, scipy, pylab, random
  from matplotlib.ticker import MultipleLocator
  import matplotlib as mpl
  from matplotlib.ticker import MaxNLocator
  from scipy import stats
  
  with open("input.txt", "r") as f:
      x=[]
      y=[]
  
      for line in f:
          if not line.strip() or line.startswith('@') or line.startswith('#'): continue
          row = line.split()
          x.append(float(row[0]))
          y.append(float(row[1]))
  
  fig = plt.figure(figsize=(2.2,2.2), dpi=300)
  ax = plt.subplot(111)
  
  plt.xlim(4, -8)
  plt.ylim(4, -8)
  
  ax.xaxis.set_major_locator(MaxNLocator(6))
  ax.yaxis.set_major_locator(MaxNLocator(6))
  
  ax.xaxis.set_minor_locator(MultipleLocator(1))
  ax.yaxis.set_minor_locator(MultipleLocator(1))
  
  
  #regression part
  slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
  line = slope*x+intercept
  plt.plot(x, line, 'r', label='fitted line')
  #end
  
  plt.scatter(x,y,color=['black','black','black','black','black','black','black','black','black','black','black','black','black','black','black'], s=3.5)
  
  
  plt.savefig("output.png", dpi=300)
  </code></pre>