Multiple Linear Regression - Estimated Regression Equation

%HA1[t] = -6.9679388480478 + 1.74040304895053Leq[t] -0.135483012952622Ldn[t] -0.696089323941329Lmax[t] -0.0639702614716865TNI[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)	-6.9679388480478	13.007851	-0.5357	0.629353	0.314676
Leq	1.74040304895053	0.38872	4.4773	0.020771	0.010386
Ldn	-0.135483012952622	0.145405	-0.9318	0.420191	0.210096
Lmax	-0.696089323941329	0.286314	-2.4312	0.093229	0.046614
TNI	-0.0639702614716865	0.077212	-0.8285	0.468145	0.234072

Multiple Linear Regression - Regression Statistics
Multiple R	0.968051272527081
R-squared	0.9371232662413
Adjusted R-squared	0.8532876212297
F-TEST (value)	11.1781005097728
F-TEST (DF numerator)	4
F-TEST (DF denominator)	3
p-value	0.0379292017093062
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.656232962878103
Sum Squared Residuals	1.29192510470332

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	27.06	27.2036907903388	-0.143690790338843
2	30.64	30.2623456792891	0.377654320710893
3	28.52	27.7311996077521	0.788800392247935
4	26.15	26.2051692884431	-0.0551692884431446
5	27.28	27.7811258793091	-0.501125879309073
6	30.32	30.494829712992	-0.174829712992037
7	30.23	30.076498351043	0.153501648956971
8	29.46	29.9051406908327	-0.445140690832702