Multiple Linear Regression - Estimated Regression Equation

Droog[t] = + 309.477522597241 + 2.7835051479818Regen[t] + 5.0325040141591Mist[t] + 7.81605840346455Sneeuw[t] + 15.5771727299243Wind[t] + 3.93954991138877Andere[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)	309.477522597241	77.212137	4.0081	0.002057	0.001029
Regen	2.7835051479818	0.644011	4.3221	0.00121	0.000605
Mist	5.0325040141591	2.533271	1.9866	0.072452	0.036226
Sneeuw	7.81605840346455	3.01298	2.5941	0.024954	0.012477
Wind	15.5771727299243	11.878214	1.3114	0.216433	0.108217
Andere	3.93954991138877	3.468696	1.1357	0.280204	0.140102

Multiple Linear Regression - Regression Statistics
Multiple R	0.953642272784482
R-squared	0.909433584441553
Adjusted R-squared	0.868267031914986
F-TEST (value)	22.0915653273285
F-TEST (DF numerator)	5
F-TEST (DF denominator)	11
p-value	2.14280637196307e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	66.3049371089457
Sum Squared Residuals	48359.7915352337

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1019	1098.99826804745	-79.9982680474476
2	1093	1086.41704493852	6.58295506147671
3	1119	1131.83692536587	-12.8369253658708
4	1015	966.490429976363	48.509570023637
5	988	964.148807935286	23.8511920647145
6	900	881.242670162331	18.7573298376691
7	937	900.440693295768	36.5593067042316
8	907	889.546865235683	17.4531347643171
9	839	836.802292942155	2.19770705784486
10	830	851.914338478872	-21.9143384788723
11	909	774.99280683352	134.00719316648
12	696	719.596015073647	-23.5960150736475
13	649	705.12185042712	-56.1218504271202
14	637	628.507731000096	8.49226899990385
15	614	647.474152404075	-33.4741524040753
16	583	691.903087594603	-108.903087594603
17	576	535.566020288638	40.4339797113624