code: def dok15_s(k15):
      
  #
      A, B = design(15)  # get design matrix for 15 measurements
      sbar = np.dot(B, k15)  # get mean s
      t = (sbar[0] + sbar[1] + sbar[2])  # trace
      bulk = old_div(t, 3.)  # bulk susceptibility
      Kbar = np.dot(A, sbar)  # get best fit values for K
      dels = k15 - Kbar  # get deltas
      dels, sbar = old_div(dels, t), old_div(sbar, t)  # normalize by trace
      So = sum(dels**2)
      sigma = np.sqrt(old_div(So, 9.))  # standard deviation
      return sbar, sigma, bulk
docs: calculates least-squares matrix for 15 measurements from Jelinek [1976]