Multiple Linear Regression - Estimated Regression Equation

%HA[t] = + 15.5422591769395 + 1.44393691273766Leq[t] -0.106740029190998Ldn[t] -0.577722661737593Lmax[t] -0.244624854401603TNI[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)	15.5422591769395	26.015762	0.5974	0.592316	0.296158
Leq	1.44393691273766	0.430779	3.3519	0.043996	0.021998
Ldn	-0.106740029190998	0.138857	-0.7687	0.49804	0.24902
Lmax	-0.577722661737593	0.296829	-1.9463	0.146802	0.073401
TNI	-0.244624854401603	0.231211	-1.058	0.367701	0.183851

Multiple Linear Regression - Regression Statistics
Multiple R	0.971458769839381
R-squared	0.943732141497843
Adjusted R-squared	0.868708330161634
F-TEST (value)	12.5791015504216
F-TEST (DF numerator)	4
F-TEST (DF denominator)	3
p-value	0.0322415030317525
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.620787906682815
Sum Squared Residuals	1.15613287525089

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	27.06	27.3090917755298	-0.249091775529796
2	30.64	30.1053650408537	0.534634959146274
3	28.52	27.8334307759387	0.686569224061286
4	26.15	26.1513044153717	-0.0013044153717466
5	27.28	27.6645469378863	-0.38454693788632
6	30.32	30.6557292356661	-0.33572923566609
7	30.23	30.2054342389551	0.0245657610448947
8	29.46	29.7350975797985	-0.275097579798502