Multiple Linear Regression - Estimated Regression Equation |
Numeracy[t] = -18.6605 + 26.9945Geslacht[t] -3.24798Drugs[t] -0.309166Fruit[t] -2.31672Sport[t] + 0.729851Alcohol[t] + 8.37152Gebgewicht[t] -8.09407Inter[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) | -18.66 | 19.51 | -9.5640e-01 | 0.3577 | 0.1789 |
Geslacht | +26.99 | 28.61 | +9.4360e-01 | 0.364 | 0.182 |
Drugs | -3.248 | 3.297 | -9.8510e-01 | 0.344 | 0.172 |
Fruit | -0.3092 | 2.874 | -1.0760e-01 | 0.9161 | 0.4581 |
Sport | -2.317 | 3.504 | -6.6110e-01 | 0.521 | 0.2605 |
Alcohol | +0.7298 | 3.016 | +2.4200e-01 | 0.8129 | 0.4064 |
Gebgewicht | +8.371 | 6.05 | +1.3840e+00 | 0.1917 | 0.09583 |
Inter | -8.094 | 8.323 | -9.7250e-01 | 0.35 | 0.175 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.5068 |
R-squared | 0.2569 |
Adjusted R-squared | -0.1766 |
F-TEST (value) | 0.5926 |
F-TEST (DF numerator) | 7 |
F-TEST (DF denominator) | 12 |
p-value | 0.7508 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 5.444 |
Sum Squared Residuals | 355.6 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 6 | 5.974 | 0.02618 |
2 | 7 | 9.386 | -2.386 |
3 | 2 | 7.27 | -5.27 |
4 | 11 | 11.06 | -0.06051 |
5 | 13 | 9.051 | 3.949 |
6 | 3 | 0.6946 | 2.305 |
7 | 17 | 7.437 | 9.563 |
8 | 10 | 11.06 | -1.061 |
9 | 4 | 6.14 | -2.14 |
10 | 12 | 9.494 | 2.506 |
11 | 7 | 10.09 | -3.09 |
12 | 11 | 10.33 | 0.6693 |
13 | 3 | 6.794 | -3.794 |
14 | 5 | 7.587 | -2.587 |
15 | 1 | 9.218 | -8.218 |
16 | 12 | 9.695 | 2.305 |
17 | 18 | 9.333 | 8.667 |
18 | 8 | 5.912 | 2.088 |
19 | 6 | 5.692 | 0.3076 |
20 | 1 | 4.78 | -3.78 |