| Multiple Linear Regression - Estimated Regression Equation |
| Numeracy[t] = -183.352 + 110.088Gebgewicht[t] -15.6489Gebgew2[t] + e[t] |
| Warning: you did not specify the column number of the endogenous series! The first column was selected by default. |
| Multiple Linear Regression - Ordinary Least Squares | |||||
| Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
| (Intercept) | -183.3 | 47.55 | -3.8560e+00 | 0.001268 | 0.0006338 |
| Gebgewicht | +110.1 | 28.01 | +3.9310e+00 | 0.001078 | 0.0005388 |
| Gebgew2 | -15.65 | 4.1 | -3.8170e+00 | 0.001379 | 0.0006895 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.7083 |
| R-squared | 0.5017 |
| Adjusted R-squared | 0.443 |
| F-TEST (value) | 8.557 |
| F-TEST (DF numerator) | 2 |
| F-TEST (DF denominator) | 17 |
| p-value | 0.002685 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 3.745 |
| Sum Squared Residuals | 238.5 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 6 | 8.685 | -2.685 |
| 2 | 7 | 9.522 | -2.522 |
| 3 | 2 | 6.072 | -4.072 |
| 4 | 11 | 10.26 | 0.7434 |
| 5 | 13 | 9.74 | 3.26 |
| 6 | 3 | -0.1952 | 3.195 |
| 7 | 17 | 10.15 | 6.845 |
| 8 | 10 | 10.26 | -0.2566 |
| 9 | 4 | 9.012 | -5.012 |
| 10 | 12 | 10.05 | 1.954 |
| 11 | 7 | 9.74 | -2.74 |
| 12 | 11 | 10.26 | 0.7434 |
| 13 | 3 | 2.207 | 0.7933 |
| 14 | 5 | 9.012 | -4.012 |
| 15 | 1 | 0.6779 | 0.3221 |
| 16 | 12 | 9.522 | 2.478 |
| 17 | 18 | 10.15 | 7.845 |
| 18 | 8 | 10.15 | -2.155 |
| 19 | 6 | 9.522 | -3.522 |
| 20 | 1 | 2.207 | -1.207 |
