Multiple Linear Regression - Estimated Regression Equation

c[t] = -0.519620914522577 -0.0870056115138375a[t] + 0.211943793425693b[t] -0.0491629467416928d[t] + 0.720998820259653e[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)	-0.519620914522577	0.671136	-0.7742	0.461055	0.230527
a	-0.0870056115138375	0.216207	-0.4024	0.697915	0.348958
b	0.211943793425693	0.156081	1.3579	0.211548	0.105774
d	-0.0491629467416928	0.181073	-0.2715	0.792874	0.396437
e	0.720998820259653	0.627914	1.1482	0.284041	0.142021

Multiple Linear Regression - Regression Statistics
Multiple R	0.799726366631694
R-squared	0.639562261485931
Adjusted R-squared	0.459343392228897
F-TEST (value)	3.54880853554666
F-TEST (DF numerator)	4
F-TEST (DF denominator)	8
p-value	0.0600561332084104
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.10136806745033
Sum Squared Residuals	9.70409295999416

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2	2.09240100115334	-0.0924010011533431
2	0	0.873487624777531	-0.873487624777531
3	0	0.757556360625366	-0.757556360625366
4	3	1.25392812663711	1.74607187336289
5	-2	-0.565197423827618	-1.43480257617238
6	0	0.0197227438023783	-0.0197227438023783
7	1	0.727742921812967	0.272257078187033
8	-1	-0.286780694418302	-0.713219305581698
9	-1	-1.13686923233093	0.13686923233093
10	-1	-2.07784107197602	1.07784107197602
11	-1	-1.23741445575342	0.237414455753425
12	1	-0.0297314557212875	1.02973145572129
13	-2	-1.39100444478111	-0.608995555218888