Multiple Linear Regression - Estimated Regression Equation

Aluminium[t] = + 133.474642619835 + 0.019762720459104Staal[t] + 0.61356554835313Koper[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)	133.474642619835	179.502362	0.7436	0.498433	0.249217
Staal	0.019762720459104	0.76603	0.0258	0.980654	0.490327
Koper	0.61356554835313	1.108312	0.5536	0.609337	0.304668

Multiple Linear Regression - Regression Statistics
Multiple R	0.66430534299375
R-squared	0.441301588730044
Adjusted R-squared	0.161952383095066
F-TEST (value)	1.57974885851899
F-TEST (DF numerator)	2
F-TEST (DF denominator)	4
p-value	0.312143914755573
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	339.866825651803
Sum Squared Residuals	462037.836714531

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2.7	139.089525219759	-136.389525219759
2	658	828.301907391	-170.301907391
3	960	372.475631393436	587.524368606564
4	385	263.405751328639	121.594248671361
5	220	370.606463876909	-150.606463876909
6	24	143.835626813171	-119.835626813171
7	2.75	134.735093977086	-131.985093977086