Multiple Linear Regression - Estimated Regression Equation

CONS[t] = + 30.7063274174266 + 0.812402101557397INCOME[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)	30.7063274174266	20.643802	1.4874	0.150482	0.075241
INCOME	0.812402101557397	0.113151	7.1798	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.83155231210972
R-squared	0.691479247775022
Adjusted R-squared	0.67806530202611
F-TEST (value)	51.5492801833571
F-TEST (DF numerator)	1
F-TEST (DF denominator)	23
p-value	2.60483780212262e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	46.1371915351095
Sum Squared Residuals	48958.7301831898

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	52.3	60.2777639141159	-7.97776391411593
2	78.44	68.7267457703128	9.71325422968724
3	88.76	77.1757276265097	11.5842723734903
4	54.08	85.6247094827066	-31.5447094827066
5	111.44	91.0678035631412	20.3721964368588
6	105.2	100.979109202141	4.22089079785857
7	45.73	104.878639289617	-59.148639289617
8	122.35	114.221263457527	8.128736542473
9	142.24	123.726368045749	18.5136319542515
10	86.22	128.925741495716	-42.7057414957159
11	174.5	140.380611127675	34.1193888723248
12	185.2	147.692230041692	37.5077699583082
13	111.8	157.441055260381	-45.6410552603805
14	214.6	171.820572457946	42.7794275420536
15	144.6	178.563509900873	-33.9635099008728
16	174.36	192.53682604766	-18.17682604766
17	215.4	199.685964541365	15.7140354586349
18	286.24	207.647505136628	78.5924948633724
19	188.56	212.034476485038	-23.4744764850376
20	237.2	220.808419181857	16.3915808181425
21	181.8	234.619254908333	-52.8192549083332
22	373	241.93087382235	131.06912617765
23	191.6	265.896735818293	-74.296735818293
24	247.12	271.502310319039	-24.382310319039
25	269.6	284.175783103334	-14.5757831033344