OLS Regression Results

Dep. Variable:	temperature_norm	R-squared:	0.000
Model:	OLS	Adj. R-squared:	-0.005
Method:	Least Squares	F-statistic:	0.04253
Date:	Sun, 09 Feb 2025	Prob (F-statistic):	0.837
Time:	12:31:53	Log-Likelihood:	-283.77
No. Observations:	200	AIC:	571.5
Df Residuals:	198	BIC:	578.1
Df Model:	1
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
const	-1.271e-16	0.071	-1.79e-15	1.000	-0.140	0.140
longitude_norm	-0.0147	0.071	-0.206	0.837	-0.155	0.125

Omnibus:	1423.927	Durbin-Watson:	0.037
Prob(Omnibus):	0.000	Jarque-Bera (JB):	25.855
Skew:	-0.118	Prob(JB):	2.43e-06
Kurtosis:	1.254	Cond. No.	1.00

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.