Table 2.
Summary of statistic results using model selection analysis.
| Models | Coefficients | p-value | AICc | RMSE (95%) | Error factor | |
|---|---|---|---|---|---|---|
| Qs = c0Qac1 | c0 | 89.211 | 5.93E-03*** | 22.49 | 3.27 | 26.31 |
| c1 | 1.004 | 2.13E-06*** | ||||
| Qs = c0Qac1Hc2 | c0 | 30.220 | 8.87E-03*** | 12.93 | 2.55 | 12.80 |
| c1 | 0.505 | 1.05E-02** | ||||
| c2 | 2.249 | 1.31E-03*** | ||||
| Qs = c0Qac(P1)Hc(P3) | c0 | 25.946 | 2.16E-02** | 10.43 | 2.23 | 9.29 |
| c(P1(lo)) | 0.721 | 5.65E-03*** | ||||
| c(P1(hi)) | 0.622 | 2.49E-03*** | ||||
| c(P3(cl)) | 1.950 | 1.80E-03*** | ||||
| c(P3(op)) | 1.396 | 3.17E-02** | ||||
p-values quality is illustrated using asterisk (***Excellent; **very good). The AICc stands for corrected Akaike Information Criterion, the RMSE is given as the natural logarithm of the Root Mean Square Error for a 95% prediction interval and the error factor is calculated as the exponential of the RMSE.