juliasilge / juliasilge.com

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https://juliasilge.com/

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Handling model coefficients for #TidyTuesday collegiate sports | Julia Silge #64

Open utterances-bot opened 2 years ago

utterances-bot commented 2 years ago

Handling model coefficients for #TidyTuesday collegiate sports | Julia Silge

A data science blog

https://juliasilge.com/blog/college-sports/

nickschizas commented 2 years ago

Your screencasts are SO helpful! What theme are you using on Rstudio appearrence?

juliasilge commented 2 years ago

It's one of the ones available in rsthemes, I believe "Oceanic Plus".

jrosell commented 2 years ago

Awesome @juliasilge. I guess it's not the goal of this post, but, for completeness, I think I miss a final conclusion of the decision of including or not including sport variable in the final model for predicting expenditures. Thanks for your videos and posts!

juliasilge commented 2 years ago

@jrosell This sounds like a cop-out but I do honestly think it depends.

If we want to know whether women athletes are treated fairly (in terms of 💵 spent on their programs) in any given sport, we would for sure want to include that variable and focus on that model.
If we wanted to know whether women athletes are treated fairly in a more general way, I would probably want to use both models to explain the situation holistically.

jrosell commented 2 years ago

It make sense to me. Thank you!

somilasthana commented 1 year ago

Amazing screencast @juliasilge, pls keep casting... I was wondering the participant vs gender difference could be attributed to Simpson Paradox :)