Table 2.

Multivariate ordinary least squares (OLS) regression analysis of marathon average speed at each split distance as a function of the weather factors.

OLS regression results
Dep. variable	Y	R-squared	0.035
Model	OLS	Adj. R-squared	0.035
Method	Least squares	F-statistic	5101
Date	Sat, 01 Oct 2022	Prob (F-statistic)	0.00
Time	18:38:05	Log-likelihood	-1.1319e + 06
No. observations	560,731	AIC	2.264e + 06
Df residuals	560,726	BIC	2.264e + 06
Df model	4
Covariance type	Nonrobust

	Coef	Std err	t	P >|t|	[0.025	0.975]
Const	31.1370	0.470	66.200	0	30.215	32.059
Temperature (°C)	−0.1131	0.001	−120.649	0	−0.115	−0.111
Pressure (hPa)	−0.0234	0.000	−50.246	0	−0.024	−0.022
Humidity (%)	0.0534	0.000	125.803	0	0.053	0.054
Sunshine (min)	−0.0011	0.000	−8.262	0	−0.001	−0.001
Omnibus	33,194.095		Durbin–Watson	0.809
Prob (omnibus)	0.000		Jarque–Bera (JB)	41,714.737
Skew	0.581		Prob (JB)	0.00
Kurtosis	3.660		Cond. No	1.98e + 05 s