| Multiple Linear Regression - Estimated Regression Equation |
| Numeracy[t] = -4.16494 + 3.51314Gebgewicht[t] + e[t] |
| Multiple Linear Regression - Ordinary Least Squares | |||||
| Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
| (Intercept) | -4.165 | 10.04 | -4.1500e-01 | 0.6831 | 0.3415 |
| Gebgewicht | +3.513 | 2.917 | +1.2040e+00 | 0.244 | 0.122 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.2731 |
| R-squared | 0.07459 |
| Adjusted R-squared | 0.02317 |
| F-TEST (value) | 1.451 |
| F-TEST (DF numerator) | 1 |
| F-TEST (DF denominator) | 18 |
| p-value | 0.244 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 4.96 |
| Sum Squared Residuals | 442.9 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 6 | 7.077 | -1.077 |
| 2 | 7 | 7.428 | -0.4284 |
| 3 | 2 | 6.374 | -4.374 |
| 4 | 11 | 8.131 | 2.869 |
| 5 | 13 | 8.834 | 4.166 |
| 6 | 3 | 5.321 | -2.321 |
| 7 | 17 | 8.482 | 8.518 |
| 8 | 10 | 8.131 | 1.869 |
| 9 | 4 | 9.185 | -5.185 |
| 10 | 12 | 7.78 | 4.22 |
| 11 | 7 | 8.834 | -1.834 |
| 12 | 11 | 8.131 | 2.869 |
| 13 | 3 | 5.672 | -2.672 |
| 14 | 5 | 9.185 | -4.185 |
| 15 | 1 | 10.94 | -9.942 |
| 16 | 12 | 7.428 | 4.572 |
| 17 | 18 | 8.482 | 9.518 |
| 18 | 8 | 8.482 | -0.4824 |
| 19 | 6 | 7.428 | -1.428 |
| 20 | 1 | 5.672 | -4.672 |
