Multiple Linear Regression - Estimated Regression Equation
Numeracy[t] = -13.6327 + 16.3117Geslacht[t] -3.36748Drugs[t] -0.0252995Fruit[t] + 0.575631Alcohol[t] + 6.71419Gebgewicht[t] -5.03669Inter[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)-13.63 17.57-7.7580e-01 0.4518 0.2259
Geslacht+16.31 23.09+7.0640e-01 0.4924 0.2462
Drugs-3.368 3.22-1.0460e+00 0.3147 0.1574
Fruit-0.0253 2.78-9.1020e-03 0.9929 0.4964
Alcohol+0.5756 2.941+1.9570e-01 0.8479 0.4239
Gebgewicht+6.714 5.386+1.2470e+00 0.2345 0.1173
Inter-5.037 6.769-7.4410e-01 0.47 0.235


Multiple Linear Regression - Regression Statistics
Multiple R 0.4794
R-squared 0.2298
Adjusted R-squared-0.1256
F-TEST (value) 0.6466
F-TEST (DF numerator)6
F-TEST (DF denominator)13
p-value 0.6926
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 5.325
Sum Squared Residuals 368.6


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1 6 4.679 1.321
2 7 9.074-2.074
3 2 8.262-6.262
4 11 10.42 0.5828
5 13 8.86 4.14
6 3 1.128 1.872
7 17 9.268 7.732
8 10 10.42-0.4172
9 4 5.686-1.686
10 12 9.17 2.83
11 7 9.461-2.461
12 11 9.842 1.158
13 3 7.376-4.376
14 5 8.514-3.514
15 1 9.867-8.867
16 12 9.1 2.9
17 18 8.718 9.282
18 8 7.171 0.8291
19 6 4.822 1.178
20 1 5.167-4.167