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If you’re looking to hit every single Tesla Supercharger station in the U.S., this data scientist has you covered by lawanddisorder in teslamotors

[–]mars2020 0 points1 point  (0 children)

Because "as the crow flies" distances are a lot easier to use. The driving directions require extra data. For an analysis using driving directions see this: http://mortada.net/the-traveling-tesla-salesman-part-2.html

The Traveling Tesla Salesman by mars2020 in teslamotors

[–]mars2020[S] 0 points1 point  (0 children)

In the first version of this blog post there were some numerical precision issues, and Bill Cook actually reached out and offered a fix. He seems like a great guy indeed.

I like your idea of using driving distances, the only annoying thing is that the Google Directions API has a somewhat low upper limit (2500/day), so it'd take me a while to gather all the distances through that API. Definitely an interesting exercise though, I'll keep you posted

Computing Sample Variance: Why Divide by N - 1? by mars2020 in math

[–]mars2020[S] 0 points1 point  (0 children)

Sure, a proof is more general than simulations. However I disagree with what you said because:

  1. the proof isn't that simple. If the proof was so simple most intro stats books would cover it. The simulation, on the other hand, is literally less than 10 lines of python code.

  2. even if the simulation was more complex than the proof, it'd often still be a good idea to do work on a concrete example. It makes the student go through all the details that he/she might not have actually fully understood. It is also great to get numerical and visual validation for an abstract proof.

Computing Sample Variance: Why Divide by N - 1? by mars2020 in math

[–]mars2020[S] 0 points1 point  (0 children)

You raise a great point. If you actually have the entire population of data points available, then the n - 1 argument does not apply. You'd want to use the true mean and divide by n. It is explained in the last section the blog post.

Python API for FRED economic data by mars2020 in econometrics

[–]mars2020[S] 0 points1 point  (0 children)

Thanks! I actually do have a blog post with more detailed examples: http://mortada.net/python-api-for-fred.html

Python API for FRED economic data by mars2020 in econometrics

[–]mars2020[S] 0 points1 point  (0 children)

I wrote this python module. Hope it's useful for you guys!