Multiple Linear Regression - Estimated Regression Equation

%HA2[t] = + 47.7730138619401 + 0.0115425588063096Q[t] + 0.0321461508342218Qh[t] -0.75215973556757Q2w[t] + 0.124103364166012Vs[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)	47.7730138619401	32.759532	1.4583	0.240837	0.120418
Q	0.0115425588063096	0.029524	0.391	0.721938	0.360969
Qh	0.0321461508342218	0.530982	0.0605	0.955532	0.477766
Q2w	-0.75215973556757	0.650989	-1.1554	0.331591	0.165796
Vs	0.124103364166012	0.860535	0.1442	0.894473	0.447236

Multiple Linear Regression - Regression Statistics
Multiple R	0.704087180302158
R-squared	0.495738757465844
Adjusted R-squared	-0.176609565913031
F-TEST (value)	0.737324300854232
F-TEST (DF numerator)	4
F-TEST (DF denominator)	3
p-value	0.624355973717992
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.85840707213527
Sum Squared Residuals	10.3610305372872

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	27.06	28.075057443813	-1.01505744381304
2	30.64	28.2893293984169	2.35067060158307
3	28.52	28.3652013813776	0.154798618622395
4	26.15	27.5723517052717	-1.42235170527171
5	27.28	27.6301919869756	-0.350191986975597
6	30.32	31.2362623449219	-0.916262344921891
7	30.23	29.4336428514941	0.796357148505929
8	29.46	29.0579628877292	0.402037112270849