text: from statistics import mean
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib import style
  import random
  
  style.use('fivethirtyeight')
  
  #xs = np.array([1,2,3,4,5,6], dtype=np.float64)
  #ys = np.array([5,4,6,5,6,7], dtype=np.float64)
  
  def create_dataset(hm, variance, step=2, correlation=False):
      val = 1
      ys = []
      for i in range (hm):
          y = val + random.randrange(-variance, variance)
          ys.append(y)
          if correlation and correlation == 'pos':
              val += step
          elif correlation and correlation == 'neg':
              val -= step
      xs = [i for i in range(len(ys))]
      return np.array(xs, dtype=np.float64), np.array(ys, dtype=np.float64)
  
  def best_fit_slope_and_intercept(xs,ys):
      m = ( ((mean(xs) * mean(ys)) - mean(xs*ys))/ (mean(xs)**2 - mean(xs**2)))
      b = mean(ys) - m*mean(xs)
      return m, b
  
  def squared_error(ys_orig, ys_line):
      return sum((ys_line-ys_orig)**2)
  
  def coefficient_of_determination(ys_orig,ys_line):
      y_mean_line = [mean(ys_orig) for y in ys_orig]
      squared_error_reg = squared_error(ys_orig, ys_line)
      squared_error_y_mean = squared_error(ys_orig, y_mean_line)
      return 1 - (squared_error_reg / squared_error_y_mean)
  
  xs, ys = create_dataset (40,10,2, correlation='pos')
  #this generates the random data in format (how many points, variance, step (can be pos or neg), and correlation
  m, b = best_fit_slope_and_intercept(xs,ys)
  
  regression_line = [(m*x)+b for x in xs]
  
  predict_x = 8
  predict_y = m*predict_x + b
  
  r_squared = coefficient_of_determination(ys, regression_line)
  print r_squared
  
  
  plt.scatter(xs,ys)
  #plt.scatter(predict_x, predict_y)
  plt.plot(xs, regression_line)
  plt.show()
  