Multiple Linear Regression - Estimated Regression Equation |
Numeracy[t] = -2.71753 + 22.9636Geslacht[t] -3.0358Drugs[t] -0.0676137Fruit[t] -2.11045Sport[t] + 0.949244Alcohol[t] + 1.03929Gebgew2[t] -6.90318Inter[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) | -2.717 | 10.63 | -2.5570e-01 | 0.8025 | 0.4013 |
Geslacht | +22.96 | 31.72 | +7.2390e-01 | 0.483 | 0.2415 |
Drugs | -3.036 | 3.388 | -8.9620e-01 | 0.3878 | 0.1939 |
Fruit | -0.06761 | 2.96 | -2.2840e-02 | 0.9822 | 0.4911 |
Sport | -2.11 | 3.704 | -5.6990e-01 | 0.5793 | 0.2896 |
Alcohol | +0.9492 | 3.1 | +3.0620e-01 | 0.7647 | 0.3824 |
Gebgew2 | +1.039 | 0.9854 | +1.0550e+00 | 0.3123 | 0.1562 |
Inter | -6.903 | 9.254 | -7.4590e-01 | 0.4701 | 0.235 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.4598 |
R-squared | 0.2114 |
Adjusted R-squared | -0.2486 |
F-TEST (value) | 0.4597 |
F-TEST (DF numerator) | 7 |
F-TEST (DF denominator) | 12 |
p-value | 0.8455 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 5.608 |
Sum Squared Residuals | 377.4 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 6 | 5.762 | 0.2376 |
2 | 7 | 9.482 | -2.482 |
3 | 2 | 7.661 | -5.661 |
4 | 11 | 10.9 | 0.1046 |
5 | 13 | 8.865 | 4.135 |
6 | 3 | 1.823 | 1.177 |
7 | 17 | 7.635 | 9.365 |
8 | 10 | 10.9 | -0.8954 |
9 | 4 | 5.986 | -1.986 |
10 | 12 | 9.229 | 2.771 |
11 | 7 | 9.881 | -2.881 |
12 | 11 | 9.946 | 1.054 |
13 | 3 | 6.955 | -3.955 |
14 | 5 | 7.144 | -2.144 |
15 | 1 | 9.711 | -8.711 |
16 | 12 | 9.55 | 2.45 |
17 | 18 | 8.864 | 9.136 |
18 | 8 | 5.605 | 2.395 |
19 | 6 | 5.68 | 0.32 |
20 | 1 | 5.43 | -4.43 |