Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 39.3430786860412 -0.125978269097324X1[t] + 16.7449154519559X2[t] + 0.000196453738417245X3[t] + 1.15191714505031e-05X4[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)	39.3430786860412	15.277274	2.5753	0.036725	0.018362
X1	-0.125978269097324	0.191047	-0.6594	0.530719	0.26536
X2	16.7449154519559	24.818183	0.6747	0.521517	0.260759
X3	0.000196453738417245	4.9e-05	3.9752	0.005356	0.002678
X4	1.15191714505031e-05	2.2e-05	0.5305	0.612148	0.306074

Multiple Linear Regression - Regression Statistics
Multiple R	0.94108754452892
R-squared	0.885645766467472
Adjusted R-squared	0.820300490163171
F-TEST (value)	13.5533249923556
F-TEST (DF numerator)	4
F-TEST (DF denominator)	7
p-value	0.00207320056693405
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.583293298399467
Sum Squared Residuals	2.38161750370411

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	58.2	58.9618686755585	-0.761868675558479
2	59.2	59.2975462202483	-0.0975462202483345
3	60	59.6686055386415	0.331394461358544
4	61.1	60.3697949950472	0.730205004952821
5	61.8	61.0087792658014	0.791220734198649
6	61.4	61.6951097074512	-0.295109707451223
7	61.1	61.6885176079853	-0.588517607985296
8	61.4	61.490893900378	-0.0908939003779655
9	61.9	61.745709384739	0.154290615261036
10	62.1	62.0756432737918	0.0243567262082361
11	62.4	62.6347691687998	-0.234769168799803
12	63	62.9627622615582	0.0372377384418165