Multiple Linear Regression - Estimated Regression Equation

A[t] = + 15.200730283204 -49.6747421830253B[t] + 1.84891521232438e-07C[t] -0.186433579145411D[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)	15.200730283204	45.298088	0.3356	0.747027	0.373514
B	-49.6747421830253	21.712561	-2.2878	0.055986	0.027993
C	1.84891521232438e-07	2e-06	0.076	0.941516	0.470758
D	-0.186433579145411	0.233222	-0.7994	0.450338	0.225169

Multiple Linear Regression - Regression Statistics
Multiple R	0.717124663801047
R-squared	0.514267783431764
Adjusted R-squared	0.306096833473949
F-TEST (value)	2.47041089804306
F-TEST (DF numerator)	3
F-TEST (DF denominator)	7
p-value	0.146349062992585
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.603937693117851
Sum Squared Residuals	2.55318516017958

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	7.6	7.15425098166216	0.445749018337844
2	6.9	7.12746364479812	-0.227463644798125
3	6.8	7.59032097476052	-0.79032097476052
4	7.8	7.63399532351769	0.166004676482307
5	7.9	7.66001947972831	0.239980520271687
6	7.9	7.48390026936912	0.416099730630883
7	7.4	7.07947905232963	0.32052094767037
8	6.5	6.27430048167152	0.225699518328479
9	5.9	6.94748064031246	-1.04748064031246
10	6	6.1365164407244	-0.136516440724403
11	7.2	6.81227271112607	0.387727288873934