random_line:
  Data from random_data.txt involves n = 100 items with dimension d = 2

  Data statistics:
    Min      =  [-0.7722  0.7439]
    Max      =  [2.956 7.678]
    Range    =  [3.7282 6.9341]
    Mean     =  [1.05366843 4.020895  ]
    Variance =  [1.13735492 2.76804376]

  Graphics saved as "random_line.png"

  Computed coefficient vector c:
  1: Normal equations:  [2.44816256 1.49262557]
  2: QR factors:        [2.44816256 1.49262557]
  3: SVD pseudoinverse  [2.44816256 1.49262557]
  4: numpy lstsq()      [2.44816256 1.49262557]
  5: sklearn            [2.44816256 1.49262557]

  MSE (mean square errors)
  1: Normal equations:  0.23409536764217748
  2: QR factors:        0.23409536764217745
  3: SVD pseudoinverse  0.2340953676421775
  4: numpy lstsq()      0.23409536764217748
  5: sklearn            0.2340953676421775

  Graphics saved as "random_line_fit.png"