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 | |||
