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In the regression process of the linear model, SPSS performed eight steps, and the coefficients of the regression equation are given in Table 1. As shown in Table 1, the six factors of PT, DD, WPH, QAC, SS, and CCS were included in the linear regression equation, and their t-values all exceeded t0.05/2 (2.009). In the column of significance analysis, all of the Sig. values were lower than 0.05, indicating that the significance levels evaluated by the t-test were relatively high. In collinearity diagnostics, all of the variance inflation factor (VIF) values were less than 5, suggesting that there was no linear correlation between any two residual independent variables. Therefore, these factors could be applied in prediction simulation. The independent variables TDS and CC were eliminated because of their lower t-values.

Table 1

Coefficients of stepwise regression equations of the linear model

Linear model (Dependent variable Ri)Unstandardized coefficients
Standardized coefficienttSig.Collinearity statistics
BStd. Err.BetaToleranceVIF
Step 8 
 (Constant) 114.859 23.607  4.865 0.000   
PT 0.673 0.080 0.680 8.445 0.000 0.915 1.093 
DD −77.908 16.053 −0.381 −4.853 0.000 0.964 1.038 
WPH 0.199 0.052 0.294 3.794 0.000 0.990 1.010 
QAC 3.428 1.116 0.252 3.071 0.004 0.883 1.132 
SS −0.178 0.071 −0.197 −2.497 0.016 0.956 1.046 
CCS −0.504 0.130 −0.305 −3.866 0.000 0.956 1.046 
Linear model (Dependent variable Ri)Unstandardized coefficients
Standardized coefficienttSig.Collinearity statistics
BStd. Err.BetaToleranceVIF
Step 8 
 (Constant) 114.859 23.607  4.865 0.000   
PT 0.673 0.080 0.680 8.445 0.000 0.915 1.093 
DD −77.908 16.053 −0.381 −4.853 0.000 0.964 1.038 
WPH 0.199 0.052 0.294 3.794 0.000 0.990 1.010 
QAC 3.428 1.116 0.252 3.071 0.004 0.883 1.132 
SS −0.178 0.071 −0.197 −2.497 0.016 0.956 1.046 
CCS −0.504 0.130 −0.305 −3.866 0.000 0.956 1.046 

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