Multiple Linear Regression - Estimated Regression Equation

TIMEin[t] = + 1216.47337957194 -1.15627618132896DATE[t] + 2.19776083284445TEMP[t] -15.4677464579001RAIN[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)	1216.47337957194	3.812191	319.1009	0	0
DATE	-1.15627618132896	0.17742	-6.5172	4e-06	2e-06
TEMP	2.19776083284445	0.428965	5.1234	7.1e-05	3.6e-05
RAIN	-15.4677464579001	4.389084	-3.5241	0.002423	0.001212

Multiple Linear Regression - Regression Statistics
Multiple R	0.894341065391886
R-squared	0.799845941246293
Adjusted R-squared	0.766486931454009
F-TEST (value)	23.9769089738175
F-TEST (DF numerator)	3
F-TEST (DF denominator)	18
p-value	1.6449488076109e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.9272603232592
Sum Squared Residuals	863.764840551621

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1192	1201.11547561226	-9.11547561225627
2	1196	1190.87375043161	5.1262495683922
3	1183	1198.47915887175	-15.4791588717511
4	1210	1203.70539992509	6.29460007491424
5	1210	1208.93142120234	1.06857879766291
6	1218	1209.27379813292	8.72620186707523
7	1219	1218.16282739028	0.837172609722057
8	1202	1192.32804617868	9.67195382131707
9	1195	1192.67020333319	2.32979666681268
10	1203	1201.55923259054	1.4407674094595
11	1170	1177.00807219616	-7.00807219615532
12	1189	1187.11817737224	1.88182262776312
13	1199	1198.04421686189	0.955783138105601
14	1196	1200.82852585386	-4.82852585385554
15	1189	1184.07693880266	4.92306119733765
16	1185	1196.97113319462	-11.9711331946218
17	1192	1188.76663802236	3.23336197763935
18	1188	1183.00385513539	4.99614486461028
19	1176	1178.46236794323	-2.46236794323045
20	1166	1163.61617111977	2.38382888022559
21	1176	1177.38928427596	-1.38928427595782
22	1181	1182.61530555321	-1.61530555320915