Multiple Linear Regression - Estimated Regression Equation

X1[t] = + 141.648 + 0.660084X2[t] -1.13957X3[t] + 1.90576M1[t] + 9.46693M2[t] + 4.29389M3[t] + 0.0205019M4[t] + 10.9336M5[t] + 2.29077M6[t] + 5.35801M7[t] + 9.77895M8[t] + 14.8864M9[t] + 10.9705M10[t] + 12.9742M11[t] + 0.600263t + 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)	141.648	371.22	0.3816	0.739502	0.369751
X2	0.660084	3.40146	0.1941	0.864053	0.432027
X3	-1.13957	0.810546	-1.406	0.294973	0.147486
M1	1.90576	12.3464	0.1544	0.891497	0.445749
M2	9.46693	13.6719	0.6924	0.560254	0.280127
M3	4.29389	25.5292	0.1682	0.8819	0.44095
M4	0.0205019	31.1173	0.0006589	0.999534	0.499767
M5	10.9336	35.6377	0.3068	0.787991	0.393996
M6	2.29077	52.4101	0.04371	0.969108	0.484554
M7	5.35801	58.6529	0.09135	0.935539	0.46777
M8	9.77895	61.5339	0.1589	0.88833	0.444165
M9	14.8864	47.8566	0.3111	0.785181	0.39259
M10	10.9705	34.4777	0.3182	0.780493	0.390247
M11	12.9742	23.3391	0.5559	0.634166	0.317083
t	0.600263	2.57155	0.2334	0.837147	0.418574

Multiple Linear Regression - Regression Statistics
Multiple R	0.991571
R-squared	0.983213
Adjusted R-squared	0.865702
F-TEST (value)	8.36702
F-TEST (DF numerator)	14
F-TEST (DF denominator)	2
p-value	0.111755
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.6403
Sum Squared Residuals	149.31

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	99.2	92.8874	6.31258
2	99	102.999	-3.99859
3	100	99.7939	0.20607
4	111.6	110.067	1.5328
5	122.2	126.253	-4.05285
6	117.6	117.6	-5.12611e-16
7	121.1	121.1	-2.90566e-16
8	136	136	1.53523e-16
9	154.2	154.2	3.75568e-16
10	153.6	153.6	1.0417e-15
11	158.5	158.5	-6.85216e-17
12	140.6	140.6	-6.85216e-17
13	136.2	142.513	-6.31258
14	168	164.001	3.99859
15	154.3	154.506	-0.20607
16	149	150.533	-1.5328
17	165.5	161.447	4.05285