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