Multiple Linear Regression - Estimated Regression Equation

Andere[t] = -8.3860019436412 + 0.0319363064426244Droog[t] -0.0680222891507611Regen[t] + 0.187723638751571Mist[t] -0.297792910554755Sneeuw[t] -0.823877344815335Wind[t] + 0.599989723553263t + 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)	-8.3860019436412	40.066033	-0.2093	0.838413	0.419206
Droog	0.0319363064426244	0.027793	1.1491	0.277273	0.138636
Regen	-0.0680222891507611	0.123458	-0.551	0.593745	0.296872
Mist	0.187723638751571	0.300656	0.6244	0.546358	0.273179
Sneeuw	-0.297792910554755	0.350806	-0.8489	0.415805	0.207903
Wind	-0.823877344815335	1.085707	-0.7588	0.465458	0.232729
t	0.599989723553263	1.506165	0.3984	0.69874	0.34937

Multiple Linear Regression - Regression Statistics
Multiple R	0.545224815052182
R-squared	0.297270098948686
Adjusted R-squared	-0.124367841682103
F-TEST (value)	0.705036407548991
F-TEST (DF numerator)	6
F-TEST (DF denominator)	10
p-value	0.653255216451973
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.67391371795085
Sum Squared Residuals	321.932968787508

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8	12.5001580591518	-4.50015805915182
2	10	8.49110667211492	1.50889332788508
3	17	16.2359351985587	0.764064801441313
4	9	12.842992818775	-3.842992818775
5	23	20.0024532816702	2.99754671832984
6	7	11.0407376211081	-4.0407376211081
7	16	15.8345782927654	0.165421707234587
8	19	14.4098188134984	4.59018118650157
9	20	14.6569191796249	5.3430808203751
10	14	12.686509742105	1.31349025789498
11	17	20.2919187866243	-3.29191878662434
12	14	13.814465154401	0.185534845598992
13	25	14.4747313776755	10.5252686223245
14	8	14.9464166941797	-6.94641669417968
15	12	13.1285092956259	-1.12850929562595
16	15	12.9772117786012	2.02278822139882
17	11	16.6655372335199	-5.66553723351993