code: def expected_h(nvals, fit="RANSAC"):
    
    rsvals = [expected_rs(n) for n in nvals]
    poly = poly_fit(np.log(nvals), np.log(rsvals), 1, fit=fit)
    return poly[0]
docs: Uses expected_rs to calculate the expected value for the Hurst exponent h
    based on the values of n used for the calculation.
  
    Args:
      nvals (iterable of int):
        the values of n used to calculate the individual (R/S)_n
  
    KWargs:
      fit (str):
        the fitting method to use for the line fit, either 'poly' for normal
        least squares polynomial fitting or 'RANSAC' for RANSAC-fitting which
        is more robust to outliers
  
    Returns:
      float:
        expected h for white noise