OLS Regression Results
Dep. Variable:	y	R-squared:	0.741
Model:	OLS	Adj. R-squared:	0.734
Method:	Least Squares	F-statistic:	108.1
Date:	Thu, 24 Jun 2021	Prob (F-statistic):	6.72e-135
Time:	04:25:20	Log-Likelihood:	-1498.8
No. Observations:	506	AIC:	3026.
Df Residuals:	492	BIC:	3085.
Df Model:	13
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	36.4595	5.103	7.144	0.000	26.432	46.487
x1	-0.1080	0.033	-3.287	0.001	-0.173	-0.043
x2	0.0464	0.014	3.382	0.001	0.019	0.073
x3	0.0206	0.061	0.334	0.738	-0.100	0.141
x4	2.6867	0.862	3.118	0.002	0.994	4.380
x5	-17.7666	3.820	-4.651	0.000	-25.272	-10.262
x6	3.8099	0.418	9.116	0.000	2.989	4.631
x7	0.0007	0.013	0.052	0.958	-0.025	0.027
x8	-1.4756	0.199	-7.398	0.000	-1.867	-1.084
x9	0.3060	0.066	4.613	0.000	0.176	0.436
x10	-0.0123	0.004	-3.280	0.001	-0.020	-0.005
x11	-0.9527	0.131	-7.283	0.000	-1.210	-0.696
x12	0.0093	0.003	3.467	0.001	0.004	0.015
x13	-0.5248	0.051	-10.347	0.000	-0.624	-0.425

Omnibus:	178.041	Durbin-Watson:	1.078
Prob(Omnibus):	0.000	Jarque-Bera (JB):	783.126
Skew:	1.521	Prob(JB):	8.84e-171
Kurtosis:	8.281	Cond. No.	1.51e+04

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.51e+04. This might indicate that there are
strong multicollinearity or other numerical problems.