Calculating the closeness coefficient of each alternative using Eq. (8) and selecting the alternative with the highest closeness coefficient: begin{aligned} C_i =frac{mathop sum nolimits _{j=1}^m w_j (1-v_{ij} )}{mathop sum nolimits _{j=1}^m w_j (1+pi _{ij} )}, end{aligned} (8) where (i=1,2,ldots,n) and (w_j ) is the weight of the jth attribute.
Using Eq. (8), the closeness coefficient of each alternative is calculated (Table 3).
The relative closeness coefficient of each alternative is calculated as follows: C_{a} = frac{{d_{a}^{{d_{a}^ + d_{a}^ .
The closeness coefficient of each alternative (C i ) is calculated as follows: C_{i} = frac{{S_{i}^{{S_{i}^ + S_{i}^,i,,i = 1,,2,, ldots,mm (24).
Calculate the closeness coefficient of each alternative: begin{aligned}&quad ; r(x_{i})=frac{d_{i}(x_{i},tilde{alpha }^{d_{i}(x_{i},tilde{alpha } ^)+d_{i}(x_{i},tilde{alpha }^, quad i=1,2,ldots,m. nonumber end{aligned} (5.3).
The linearity coefficient of the alternative equation are not changed by Gaussian selection.
The Pearson's correlation coefficient of those alternative scores with the DAS28 at week 22 was 0.982 for the DAS28-3, 0.952 for the DAS28-CRP, 0.928 for the DAS28-CRP-3, 0.914 for the SDAI and 0.893 for the CDAI.
Each MWTP is calculated by the ratio of the coefficient of each alternative-specific constant (coefficient of "TOU," "CPP," "RTP," "DLC" and "DLCCPP" in Table 2) to that of the electricity fee per month (coefficient of "month" in Table 2).
It contains n rows, and for each row, there are 0 or more columns of coefficients representing alternative ways of producing the output of the row sector.
