Appendix 1—table 6. Results and validation of the linear regression models.
NSP2 was considered as the independent variable and the other viral elements as the dependent variable (see Equation (25)). The slope and standard error for each regression model (Column 2 and 3) were computed in accordance with the Equation (26) and (28) respectively. The t-value of the t-Student distribution function with N-1 degrees of freedom are in Column 4 (see Appendix 1 Section – Linear Regression Model– for details). The p-value for each linear regression model are shows in Column five and were measure as was described in (Equation (29)) and (Equation (30)). Finally, the RSE and the summarize the adjustment of the data through the proposed lineal models. As was advised, the RSE (Column 6) is a fit measure of the linear model to the data (see Equation (27)). The ‘% Error’ is the average error in the prediction and it is computed as: , while the is the percent of the data variance that it is explained by the model (see Equation (32)).
| Model | Slope (β) | Std.error | t-value | p-value | RSE | % Error | R2 |
|---|---|---|---|---|---|---|---|
| 0.8667494 | 0.017947738 | 48.29296 | 2.550154 × 10−50 | 0.07 | 8% | 0.974 | |
| 0.9911517 | 0.006973701 | 142.12708 | 2.271571 × 10−114 | 0.035 | 3.5% | 0.995 | |
| 1.1243931 | 0.014817985 | 75.88030 | 6.242800 × 10−72 | 0.04 | 3.5% | 0.987 | |
| 1.1506138 | 0.019069730 | 60.33718 | 2.238867 × 10−48 | 0.044 | 3.8% | 0.986 | |
| 1.1806335 | 0.011218918 | 105.23595 | 1.087989 × 10−86 | 0.038 | 3.2% | 0.992 | |
| 1.3887083 | 0.024319983 | 57.10153 | 5.757257 × 10−50 | 0.07 | 5% | 0.983 | |
| 1.9407204 | 0.040354225 | 48.09212 | 1.560719 × 10−52 | 0.142 | 7.3% | 0.972 | |
| 1.9416542 | 0.054656327 | 35.52478 | 5.778910 × 10−33 | 0.153 | 7.8% | 0.967 |