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The t-test evaluates the significance of independent variables in equations, whereas the F-test is an evaluation of the overall significance of the entire regression equation (Hajiaghaei et al. 2014). As shown in Table 3, the adjusted R2 for the two models are 0.709 and 0.768, indicating relatively high goodness of fit. The analysis on the model prediction results demonstrates that, apart from individual data, the prediction of the linear model for the overall experiment was relatively good. The average prediction error was 6.141%. The F-value was much greater than F0.05, indicating a significant regression equation. The average prediction error for the non-linear model was 0.283%. The F-value for the model was much greater than F0.05. Nevertheless, the prediction error was about LnRi, which when converted to Ri, had a prediction error of 5.863%, again indicating a significant regression equation. These data demonstrate that it is feasible to use these two models to predict the permeability-reducing effects due to cement infiltration into sandy soil.

Table 3

Model summary

ModelRR2Adjusted R2Std. error of the estimate (%)FF0.05Sig.
Linear 0.859 0.709 0.703 6.141 20.714 2.290 0.0001 
Non-linear 0.876 0.768 0.742 0.283 29.745 2.404 0.0001 
ModelRR2Adjusted R2Std. error of the estimate (%)FF0.05Sig.
Linear 0.859 0.709 0.703 6.141 20.714 2.290 0.0001 
Non-linear 0.876 0.768 0.742 0.283 29.745 2.404 0.0001 

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