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
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-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 IndexActualsInterpolation
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