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 *R*^{2} 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 *F _{0.05}*, indicating a significant regression equation. The average prediction error for the non-linear model was 0.283%. The

Table 3

Model . | R
. | R^{2}
. | Adjusted R^{2}
. | Std. error of the estimate (%) . | F
. | F_{0.05}
. | Sig. . |
---|---|---|---|---|---|---|---|

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 |

Model . | R
. | R^{2}
. | Adjusted R^{2}
. | Std. error of the estimate (%) . | F
. | F_{0.05}
. | Sig. . |
---|---|---|---|---|---|---|---|

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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