Multiple Linear Regression - Estimated Regression Equation

Brood400[t] = + 0.405066975654454 + 0.431394519710709Brood800[t] -0.0554140776204353Bruin800[t] + 0.500229194254866meergraan800[t] -0.131582968320259kramiek[t] -0.0568656412345975broodje[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)	0.405066975654454	0.282782	1.4324	0.195124	0.097562
Brood800	0.431394519710709	0.189322	2.2786	0.056751	0.028376
Bruin800	-0.0554140776204353	0.179595	-0.3085	0.766644	0.383322
meergraan800	0.500229194254866	0.180896	2.7653	0.027883	0.013942
kramiek	-0.131582968320259	0.097608	-1.3481	0.219626	0.109813
broodje	-0.0568656412345975	0.053805	-1.0569	0.325665	0.162832

Multiple Linear Regression - Regression Statistics
Multiple R	0.956225922742958
R-squared	0.914368015325622
Adjusted R-squared	0.85320231198678
F-TEST (value)	14.949031326596
F-TEST (DF numerator)	5
F-TEST (DF denominator)	7
p-value	0.00128608488194948
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00241542275548296
Sum Squared Residuals	4.08398696139344e-05

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.34	1.34010644799837	-0.000106447998370173
2	1.34	1.33882881735765	0.00117118264235506
3	1.34	1.34077212997861	-0.000772129978608494
4	1.34	1.34002495670775	-2.49567077518305e-05
5	1.34	1.33822298054395	0.00177701945604552
6	1.33	1.33079122949985	-0.000791229499847144
7	1.33	1.33135988591219	-0.00135988591219311
8	1.33	1.33361999592544	-0.0036199959254354
9	1.33	1.32918636039523	0.00081363960476719
10	1.33	1.32881990424773	0.00118009575226649
11	1.33	1.32995721707243	4.27829275745145e-05
12	1.33	1.32627075481308	0.00372924518692073
13	1.32	1.32203931954772	-0.00203931954772336