Multiple Linear Regression - Estimated Regression Equation

FOB[t] = -92.4679 + 0.552586Weight[t] + 226.917Brass[t] + 21.1741Finish[t] + 45.5608Aerator[t] + 15.5384Drain[t] + 89.8235Box[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)	-92.4679	130.772	-0.7071	0.499584	0.249792
Weight	0.552586	0.132528	4.17	0.00312389	0.00156195
Brass	226.917	76.1073	2.982	0.0175591	0.00877957
Finish	21.1741	18.1321	1.168	0.276523	0.138262
Aerator	45.5608	21.4906	2.12	0.0668219	0.033411
Drain	15.5384	36.2576	0.4286	0.679552	0.339776
Box	89.8235	43.5276	2.064	0.0729512	0.0364756

Multiple Linear Regression - Regression Statistics
Multiple R	0.952638
R-squared	0.90752
Adjusted R-squared	0.838159
F-TEST (value)	13.0841
F-TEST (DF numerator)	6
F-TEST (DF denominator)	8
p-value	0.000941112
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	49.4265
Sum Squared Residuals	19543.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	913	896.925	16.075
2	773	738.658	34.3423
3	992	971.49	20.5099
4	977	993.075	-16.075
5	691	745.852	-54.8523
6	686	606.296	79.7039
7	632	662.594	-30.5938
8	683	722.339	-39.3391
9	731	764.687	-33.6873
10	776	792.766	-16.7663
11	820	835.115	-15.1145
12	736	735.85	0.150081
13	827	778.198	48.8018
14	841	809.896	31.1036
15	556	580.259	-24.2585