OLS Regression Results
Dep. Variable:	Close	R-squared:	0.658
Model:	OLS	Adj. R-squared:	0.657
Method:	Least Squares	F-statistic:	2101.
Date:	Fri, 04 Jun 2021	Prob (F-statistic):	0.00
Time:	10:02:10	Log-Likelihood:	-20513.
No. Observations:	2191	AIC:	4.103e+04
Df Residuals:	2188	BIC:	4.105e+04
Df Model:	2
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
Intercept	-3.128e+06	1.19e+05	-26.286	0.000	-3.36e+06	-2.89e+06
year	1552.3174	59.007	26.307	0.000	1436.601	1668.034
Volume	1.085e-07	7.58e-09	14.312	0.000	9.37e-08	1.23e-07

Omnibus:	729.880	Durbin-Watson:	0.029
Prob(Omnibus):	0.000	Jarque-Bera (JB):	3159.919
Skew:	1.555	Prob(JB):	0.00
Kurtosis:	7.994	Cond. No.	3.25e+13

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 3.25e+13. This might indicate that there are
strong multicollinearity or other numerical problems.