The Application of Machine Learning in the Corona Era, With an Emphasis on Economic Concepts and Sustainable Development Goals
Table 23.
Equation estimation
| Dependent Variable: TARGET | | | | Method: Least Squares (Gauss-Newton / Marquardt steps) | | Date: 05/06/22 Time: 08:26 | | | | Sample: 1 537 | | | | | Included observations: 537 | | | | TARGET=C (1) +C (2) *NEW_DEATHS | | | | | | | | | | | | | | | | | Coefficient | Std. Error | t-Statistic | Prob. | | | | | | | | | | | | | | C (1) | 3.957745 | 1.936313 | 2.043960 | 0.0414 | | C (2) | 0.982797 | 0.009191 | 106.9325 | 0.0000 | | | | | | | | | | | | | | R-squared | 0.955303 | Mean dependent var | 176.0205 | | Adjusted R-squared | 0.955220 | S.D. dependent var | 117.9522 | | S.E. of regression | 24.96026 | Akaike info criterion | 9.276164 | | Sum squared resid | 333312.8 | Schwarz criterion | 9.292127 | | Log likelihood | -2488.650 | Hannan-Quinn criter. | 9.282409 | | F-statistic | 11434.56 | Durbin-Watson stat | 2.471785 | | Prob(F-statistic) | 0.000000 | | | | | | | | | | | | | | | |
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