input: Write a python function to calculate the predicted values using ordinary least squares (OLS) regression.
  assert np.allclose(calculate_predicted_values([2, 3, 5], [5, 7, 10]), [2.0, 3.5, 6.5], atol=0.1)
  assert np.allclose(calculate_predicted_values([1, 2, 3], [2, 4, 5]), [1.67, 3.33, 5.0], atol=0.1)
  assert np.allclose(calculate_predicted_values([3, 4, 6], [7, 8, 11]), [4.0, 5.0, 7.67], atol=0.1)
output: import numpy as np
  
  def calculate_predicted_values(x, y):
      x = np.array(x)
      y = np.array(y)
      X = np.vstack([x, np.ones(len(x))]).T
      beta, alpha = np.linalg.lstsq(X, y, rcond=None)[0]
      return beta * x + alpha