Multiple Linear Regression - Estimated Regression Equation

%HA2[t] = + 44.7626369978317 + 0.039979709890401Q[t] + 0.346247610570657Qh[t] -1.11991502816352Q2w[t] -0.174160537411628Vs[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)	44.7626369978317	30.250194	1.4797	0.235502	0.117751
Q	0.039979709890401	0.049327	0.8105	0.476982	0.238491
Qh	0.346247610570657	0.422587	0.8194	0.472618	0.236309
Q2w	-1.11991502816352	0.944589	-1.1856	0.321129	0.160565
Vs	-0.174160537411628	0.898725	-0.1938	0.858722	0.429361

Multiple Linear Regression - Regression Statistics
Multiple R	0.709810155327833
R-squared	0.503830456606523
Adjusted R-squared	-0.157728934584781
F-TEST (value)	0.761580083836842
F-TEST (DF numerator)	4
F-TEST (DF denominator)	3
p-value	0.613630253270297
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.8434361755183
Sum Squared Residuals	10.1947707996286

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	27.06	27.7738104627996	-0.713810462799643
2	30.64	28.393786029317	2.24621397068303
3	28.52	28.3515767630259	0.168423236974054
4	26.15	27.9432483487603	-1.79324834876026
5	27.28	27.2866429759578	-0.00664297595776758
6	30.32	31.0818742920383	-0.76187429203825
7	30.23	29.3280659539901	0.901934046009895
8	29.46	29.5009951741111	-0.0409951741110652