Multiple Linear Regression - Estimated Regression Equation

V1[t] = + 130.707 + 1.06171V2[t] -1.38299V3[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)	130.707	27.0943	4.824	0.000270043	0.000135021
V2	1.06171	0.266674	3.981	0.00136522	0.000682608
V3	-1.38299	0.0838143	-16.5	1.43251e-10	7.16255e-11

Multiple Linear Regression - Regression Statistics
Multiple R	0.975337
R-squared	0.951282
Adjusted R-squared	0.944322
F-TEST (value)	136.683
F-TEST (DF numerator)	2
F-TEST (DF denominator)	14
p-value	6.51398e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.56336
Sum Squared Residuals	433.313

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	99.2	93.6924	5.50762
2	99	96.4235	2.57654
3	100	98.579	1.421
4	111.6	116.781	-5.18145
5	122.2	122.452	-0.251685
6	117.6	122.91	-5.31
7	121.1	123.046	-1.94553
8	136	135.425	0.574617
9	154.2	149.804	4.39583
10	153.6	152.057	1.54264
11	158.5	153.905	4.59455
12	140.6	145.557	-4.95709
13	136.2	145.098	-8.89752
14	168	161.584	6.41559
15	154.3	156.861	-2.56142
16	149	156.289	-7.28865
17	165.5	156.135	9.36496