Parameter | Coefficient | Effect size | P value (one tailed) | P value (two tailed) |
Model 1: event+natal_sex+(age−40)+(age−40)2+nb_predictor | ||||
sex=natal female | 0.12221 | 13.00%*** | 0.0000 | 0.0000 |
(Age−40) | 0.00375 | 0.38 %/y*** | 0.0000 | 0.0000 |
(Age−40)2 | 0.00012 | 0.012 %/y2*** | 0.0000 | 0.0000 |
nb_predictor | −0.00424 | −0.42 % | 0.4071 | 0.8079 |
Model 2: event+gender_id+(age−40)+(age-40)2+nb_predictor | ||||
gender_id=’female’ | 0.12222 | 13.00%*** | 0.0000 | 0.0000 |
gender_id=’non-binary’ | 0.09387 | 9.84%*** | 0.0000 | 0.0000 |
(Age−40) | 0.00375 | 0.376 %/y*** | 0.0000 | 0.0000 |
(Age−40)2 | 0.00012 | 0.012 %/y2*** | 0.0000 | 0.0000 |
nb_predictor | −0.06803 | −6.58%*** | 0.0001 | 0.0001 |
Model 3: event+gender_id+(age−40)+(age−40)2+is_nbm+is_nbf | ||||
sex=natal female | 0.12222 | 13.00%*** | 0.0000 | 0.0000 |
(Age−40) | 0.00375 | 0.376 %/y*** | 0.0000 | 0.0000 |
(Age−40)2 | 0.00012 | 0.012 %/y2 *** | 0.0000 | 0.0000 |
is_nbm | 0.02584 | 2.62% | 0.1324 | 0.2681 |
is_nbf | 0.03969 | 4.05% | 0.0580 | 0.1152 |
Model 4: event+natal_sex+(age−40)+(age−40)2+isNB | ||||
isNB | 0.03225 | 3.278% (.) | 0.0262 | 0.0528 |
natal_sex=natal female | 0.12224 | 13.002 %/y*** | 0.0000 | 0.0000 |
(Age−40) | 0.00375 | 0.376 %/y2*** | 0.0000 | 0.0000 |
(Age−40)2 | 0.00012 | 0.012% *** | 0.0000 | 0.0000 |
Coefficients for different events are ignored. The coefficients are given as a percentage increase in marathon time in the ‘effect size’ column for ease of interpretation. The final two columns contain Monte Carlo estimates for the p values of the coefficients estimated using 100 000 samples.
The symbols (.), *, **, *** indicate statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels using a two-tailed test.