Multiple Linear Regression - Estimated Regression Equation

OQ120y[t] = + 0.33116 + 0.705004OQ107x[t] -0.002781Sex[t] + 0.00800751Lihue[t] + 0.0292538Kane[t] -0.0171184CPRL[t] -0.00791902Mili[t] + 0.00107758Visits[t] + 0.0355727Cauc[t] + 0.00284969HawPac[t] + 0.00612137Asian[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)	0.33116	0.0402108	8.236	3.97603e-16	1.98802e-16
OQ107x	0.705004	0.0349722	20.16	8.92391e-80	4.46195e-80
Sex	-0.002781	0.00521882	-0.5329	0.594201	0.2971
Lihue	0.00800751	0.00911703	0.8783	0.379926	0.189963
Kane	0.0292538	0.0105225	2.78	0.00550493	0.00275246
CPRL	-0.0171184	0.0110914	-1.543	0.122956	0.0614778
Mili	-0.00791902	0.0101731	-0.7784	0.436445	0.218223
Visits	0.00107758	0.0010665	1.01	0.31248	0.15624
Cauc	0.0355727	0.00832807	4.271	2.07006e-05	1.03503e-05
HawPac	0.00284969	0.00887835	0.321	0.748279	0.37414
Asian	0.00612137	0.00757936	0.8076	0.419434	0.209717

Multiple Linear Regression - Regression Statistics
Multiple R	0.487626
R-squared	0.237779
Adjusted R-squared	0.232468
F-TEST (value)	44.7657
F-TEST (DF numerator)	10
F-TEST (DF denominator)	1435
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0985538
Sum Squared Residuals	13.9379