0.0 0.2 0.4 0.6 0.8 GLYPH<Minus> 2 GLYPH<Minus> 1 0 1 2 3 z w de 0.0 0.2 0.4 0.6 0.8 1.0 GLYPH<Minus> 4 GLYPH<Minus> 2 0 2 4 z w de 0.0 0.2 0.4 0.6 0.8 GLYPH<Minus> 1.0 GLYPH<Minus> 0.5 0.0 0.5 1.0 z q 0.0 0.2 0.4 0.6 0.8 1.0 GLYPH<Minus> 3 GLYPH<Minus> 2 GLYPH<Minus> 1 0 1 2 z q w$_{L}$ GLYPH<Equal> $_{GLYPH<Minus>}$1 w$_{L}$ GLYPH<Equal> $_{GLYPH<Minus>}$1 w$_{L}$ unfixed w$_{L}$ unfixed uncorrelated uncorrelated FIG. 2: 1 σ and 2 σ errors of w$_{de}$ in Model II. Left panels are for the model with w$_{L}$ = − 1 and right panels are with w$_{L}$ floating, top panels are for correlated parameters in w$_{de}$ ( z ) and the bottom panels are for uncorrelated ones. more explicit deviations from − 1 around z = 0 and z = 0 . 45, however, there is still a 2 σ deviation from − 1 around z = 0 . 9. One may suspect that the explicit derivations are caused due to the fact that we have fixed the value of w$_{de}$ as w$_{L}$ = − 1 in the third bin. To check this, we consider the case with a floating w$_{L}$ . Two right panels of Fig. 2 show the correlated and uncorrelated results for the case with the floating w$_{L}$ . We see that in this case, there is even larger deviation from − 1 around z = 0 . 9. We have also used the CPL parametrization to replace the linear expansion in each bin, and found that the errors are almost the same as those in the case of the linear expansion and there is still a deviation of w$_{de}$ from − 1 around z = 0 . 9. We will extend our discussion of this result in the last section. In Fig. 3 we plot the likelihoods and weight functions of the uncorrelated parameters q$_{i}$ . The weight functions are constructed from the transformation matrix U, which show how uncorrelated parameters q$_{i}$ are determined. It is shown that the parameters of w$_{de}$ are less correlated than that of the cubic-spline case [16]. We have also divided z ∈ (0 , 0 . 9) into three bins, to see whether there exist two turning 11