Multiple Linear Regression - Estimated Regression Equation

Temp[t] = + 62.2438478832116 + 1.22097777297407Time[t] + 3.84164081023246ElNino[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)	62.2438478832116	5.296795	11.7512	0	0
Time	1.22097777297407	0.554658	2.2013	0.046385	0.023193
ElNino	3.84164081023246	4.323528	0.8885	0.390394	0.195197

Multiple Linear Regression - Regression Statistics
Multiple R	0.531177910557785
R-squared	0.282149972664534
Adjusted R-squared	0.171711506920617
F-TEST (value)	2.55481612103139
F-TEST (DF numerator)	2
F-TEST (DF denominator)	13
p-value	0.115936233800515
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.0716299964697
Sum Squared Residuals	1318.69050021525

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	53	67.7866715676972	-14.7866715676972
2	82	64.4937213886482	17.5062786113518
3	57	61.3288259031455	-4.32882590314546
4	55	63.7022959191893	-8.70229591918927
5	67	67.2602718519775	-0.260271851977535
6	78	72.0347673744166	5.96523262558337
7	76	72.103252904321	3.89674709567903
8	67	73.7083947583183	-6.70839475831829
9	85	73.5527845740363	11.4472154259637
10	75	74.9338307142314	0.0661692857685598
11	84	73.9458650213219	10.0541349786781
12	63	74.2384462650284	-11.2384462650284
13	77	79.5251605624212	-2.52516056242123
14	90	78.1210171148137	11.8789828851863
15	75	77.8053385636948	-2.80533856369477
16	72	81.4593555167388	-9.45935551673885