Multiple Linear Regression - Estimated Regression Equation

d[t] = + 0.717743972649403 + 0.267095376844774a[t] + 0.235849949421252b[t] + 0.378073882408914c[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)	0.717743972649403	1.463505	0.4904	0.635566	0.317783
a	0.267095376844774	0.162932	1.6393	0.135572	0.067786
b	0.235849949421252	0.114061	2.0678	0.068612	0.034306
c	0.378073882408914	0.663848	0.5695	0.582937	0.291469

Multiple Linear Regression - Regression Statistics
Multiple R	0.73475078302044
R-squared	0.539858713149149
Adjusted R-squared	0.386478284198865
F-TEST (value)	3.5197366237914
F-TEST (DF numerator)	3
F-TEST (DF denominator)	9
p-value	0.0620257294553627
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.35984944866166
Sum Squared Residuals	50.1200047831389

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6	7.6201038923867	-1.6201038923867
2	6	5.29262929392375	0.707370706076251
3	6	3.45420708343385	2.54579291656615
4	6	4.80714672262525	1.19285327737475
5	2	-0.194630929286035	2.19463092928604
6	2	1.86574829233214	0.134251707667861
7	2	3.61054448638844	-1.61054448638844
8	3	3.16986948329551	-0.169869483295509
9	-1	0.245823542132945	-1.24582354213295
10	-4	0.876879331419706	-4.87687933141971
11	4	1.52193832483642	2.47806167516358
12	5	4.61945362641016	0.380546373589835
13	3	3.11028685010116	-0.110286850101157