Multiple Linear Regression - Estimated Regression Equation

Numeracy[t] = -257.794 -44.4619Geslacht[t] -4.8654Drugs[t] -0.217231Fruit[t] + 3.22869Sport[t] -2.92673Alcohol[t] + 163.222Gebgewicht[t] + 13.1261Inter[t] -24.6204Gebgew2[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)	-257.8	57.75	-4.4640e+00	0.0009567	0.0004784
Geslacht	-44.46	24.95	-1.7820e+00	0.1023	0.05116
Drugs	-4.865	2.155	-2.2580e+00	0.04529	0.02264
Fruit	-0.2172	1.849	-1.1750e-01	0.9086	0.4543
Sport	+3.229	2.606	+1.2390e+00	0.2412	0.1206
Alcohol	-2.927	2.123	-1.3780e+00	0.1955	0.09773
Gebgewicht	+163.2	36.71	+4.4460e+00	0.0009849	0.0004925
Inter	+13.13	7.328	+1.7910e+00	0.1008	0.05038
Gebgew2	-24.62	5.804	-4.2420e+00	0.001384	0.0006921

Multiple Linear Regression - Regression Statistics
Multiple R	0.8474
R-squared	0.7181
Adjusted R-squared	0.5131
F-TEST (value)	3.502
F-TEST (DF numerator)	8
F-TEST (DF denominator)	11
p-value	0.02904
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.502
Sum Squared Residuals	134.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6	5.08	0.9198
2	7	9.579	-2.579
3	2	5.29	-3.29
4	11	8.74	2.26
5	13	12.96	0.03781
6	3	-1.442	4.442
7	17	13.6	3.398
8	10	8.74	1.26
9	4	7.484	-3.484
10	12	12.33	-0.3324
11	7	10.25	-3.253
12	11	11.67	-0.6666
13	3	1.724	1.276
14	5	5.295	-0.2951
15	1	0.5933	0.4067
16	12	9.796	2.204
17	18	13.52	4.483
18	8	9.089	-1.089
19	6	6.495	-0.4945
20	1	6.204	-5.204