Multiple Linear Regression - Estimated Regression Equation |
Numeracy[t] = -3.2811 -0.715633Geslacht[t] -2.84284Drugs[t] + 0.179305Fruit[t] -0.4232Sport[t] + 1.03283Alcohol[t] + 3.50773Gebgewicht[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) | -3.281 | 11.4 | -2.8780e-01 | 0.7781 | 0.389 |
Geslacht | -0.7156 | 2.534 | -2.8240e-01 | 0.7821 | 0.391 |
Drugs | -2.843 | 3.264 | -8.7100e-01 | 0.3996 | 0.1998 |
Fruit | +0.1793 | 2.824 | +6.3500e-02 | 0.9503 | 0.4752 |
Sport | -0.4232 | 2.908 | -1.4560e-01 | 0.8865 | 0.4433 |
Alcohol | +1.033 | 2.994 | +3.4500e-01 | 0.7356 | 0.3678 |
Gebgewicht | +3.508 | 3.397 | +1.0320e+00 | 0.3207 | 0.1603 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.4453 |
R-squared | 0.1983 |
Adjusted R-squared | -0.1717 |
F-TEST (value) | 0.536 |
F-TEST (DF numerator) | 6 |
F-TEST (DF denominator) | 13 |
p-value | 0.7719 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 5.432 |
Sum Squared Residuals | 383.6 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 6 | 4.385 | 1.615 |
2 | 7 | 9.507 | -2.507 |
3 | 2 | 7.315 | -5.315 |
4 | 11 | 10.21 | 0.7919 |
5 | 13 | 9.161 | 3.839 |
6 | 3 | 3.347 | -0.3469 |
7 | 17 | 9.42 | 7.58 |
8 | 10 | 10.21 | -0.2081 |
9 | 4 | 6.49 | -2.49 |
10 | 12 | 8.824 | 3.176 |
11 | 7 | 10.01 | -3.015 |
12 | 11 | 9.175 | 1.825 |
13 | 3 | 5.402 | -2.402 |
14 | 5 | 6.782 | -1.782 |
15 | 1 | 11.27 | -10.27 |
16 | 12 | 9.327 | 2.673 |
17 | 18 | 8.631 | 9.369 |
18 | 8 | 6.081 | 1.919 |
19 | 6 | 4.915 | 1.085 |
20 | 1 | 6.541 | -5.541 |