Multiple Linear Regression - Estimated Regression Equation

Inc[t] = -34.8942759378569 + 0.49528783625183Cons[t] + 0.868235231892937Price[t] + 0.555659857747887t + 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)	-34.8942759378569	33.921478	-1.0287	0.322391	0.161196
Cons	0.49528783625183	0.125802	3.937	0.001703	0.000851
Price	0.868235231892937	0.232109	3.7406	0.002471	0.001236
t	0.555659857747887	0.561028	0.9904	0.34004	0.17002

Multiple Linear Regression - Regression Statistics
Multiple R	0.760168409348884
R-squared	0.577856010572012
Adjusted R-squared	0.480438166857861
F-TEST (value)	5.93172655584115
F-TEST (DF numerator)	3
F-TEST (DF denominator)	13
p-value	0.00889256497552537
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.82097374023926
Sum Squared Residuals	189.797924206774

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	96.7	102.485695697259	-5.78569569725907
2	98.1	102.160886279053	-4.06088627905297
3	100	103.125010449863	-3.1250104498634
4	104.9	101.264598028339	3.6354019716611
5	104.9	103.510544499595	1.38945550040485
6	109.5	104.566233052642	4.93376694735798
7	110.8	107.636812045975	3.16318795402505
8	112.3	108.80002585511	3.4999741448898
9	109.3	107.343336887601	1.95666311239891
10	105.3	103.521118453701	1.77888154629892
11	101.7	102.943924258322	-1.24392425832189
12	95.4	95.6758141254335	-0.275814125433541
13	96.4	95.0072662587556	1.39273374124439
14	97.6	101.762491758489	-4.16249175848939
15	102.4	101.697178406027	0.702821593972941
16	101.6	99.4541656852617	2.14583431473834
17	103.8	109.744898258572	-5.94489825857202