I am performing a least squares regression as below (univariate). I would like to express the significance of the result in terms of R^2. Numpy returns a value of unscaled residual, what would be a sensible way of normalizing this. field_clean,back_clean = rid_zeros(backscatter,field_data) num_vals = len(field_clean) x = field_clean[:,row:row+1] y = 10*log10(back_clean) A = hstack([x, ones((num_vals,1))]) soln = lstsq(A, y ) m, c = soln [0] residues = soln [1] print residues