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Data and Code files to accompany 'Angus, SD, A Statistical Timetable for the sub-2 hour Marathon' (MSSE, 2019)
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.gitattributes
LICENSE
README.md
gender_gap.m
gg_1in10.csv
gg_expected.csv
main.m
out_1in10_female.csv
out_1in10_male.csv
out_expected_female.csv
out_expected_male.csv
wr_female.csv
wr_female_limit-time.pdf
wr_female_main-fig.pdf
wr_male.csv
wr_male_limit-time.pdf
wr_male_main-fig.pdf
wr_male_sub2-inset.pdf

README.md

A Statistical Timetable for the sub-2 hour Marathon

(To appear in MSSE, 2019)

Author: Simon D. Angus

Dept. of Economics, Monash University

Melbourne, Australia.

Requirements

You will need:

  • MATLAB (tested with version r2018b); and
  • Statistics Toolbox (provides fitnlm, predict, and fitgev).

To replicate the paper

Open a MATLAB prompt, navigate to the working folder, and to run for example, the male version of the code run:

main('wr_male.csv', [0.5 0.2 0.10 0.04 0.02 0.01], 1)

with arguments

  • 'wr_male.csv': use the male CSV input file;
  • [0.5 0.2 0.10 0.04 0.02 0.01], the ALPHA values to use for the run;
  • 1: sets ISMALE=1.

To run the female version, one runs equivalently:

main('wr_female.csv', [0.5 0.2 0.10 0.04 0.02 0.01], 0)

When run, main will produce two gendergap files for later comparison with gender_gap. For instance, if ISMALE=1 the two files will be: out_expected_male.csv, and out_1in10_male.csv . After running both male and female variants, one can then conduct a long-run gender gap analysis as follows:

OUTNAME = 'gg_1in10.csv';
gender_gap('out_1in10_male.csv','out_1in10_female.csv',OUTNAME)

Help?

Please get in touch at simon.angus@monash.edu.

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