def dok15_s(k15):
    """
    calculates least-squares matrix for 15 measurements from Jelinek [1976]
    """
#
    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