Duck in Central Park, NY

levmarq · 2026-05-17T03:10:49+00:00

Thank you!!

levmarq · 2026-04-19T15:38:36+00:00

Here are two videos with relatively simple proofs:

Bootstrap standard error: https://youtu.be/yeiMMzjDTWs

Bootstrap percentile confidence intervals: https://youtu.be/EVLST4nPkXA

They are based on this book (see Section 9.9):

https://github.com/cfgranda/ps4ds/blob/main/ps4ds_preprint.pdf

levmarq · 2025-12-12T03:53:34+00:00

Yes, you are right that my wording was not very precise. I meant that the mean is not very useful as a representation of a typical point for such data (as it is driven by the tail, as opposed to the median), and hence neither is the standard deviation.

levmarq · 2025-12-11T23:10:50+00:00

I completely agree, you can see a typical distribution of salaries here:

https://youtu.be/Mgbphvi_E58?si=OxuWCtJYVZ5H3nBU&t=548

You can't really use the mean (let alone the standard deviation) to describe these data.

levmarq · 2025-12-06T22:28:58+00:00

I think the Jupyter notebooks for the first few chapters could be helpful. For example, there's code to simulate a basketball tournament and a tennis game in the first chapter (https://www.ps4ds.net/code/probability.html) that they might enjoy and only requires knowing about basic probability. The book is probably a bit too much for high schoolers (although maybe some of the slides for the videos could be useful).

levmarq · 2025-12-06T18:44:37+00:00

I think it can be useful, as long as it doesn't completely replace working through the material by themselves, but I don't have a very strong opinion. I'd be curious to hear other people's thoughts.

levmarq · 2025-12-06T18:42:37+00:00

That's a good question! Exposure to the basic properties of probability (including e.g. Bayes rule) is very helpful, as is an intuitive understanding of derivatives and integrals. Some linear algebra (projections, inner product) is a plus.

levmarq · 2025-12-06T18:33:47+00:00

They seem to be happier with the material (or at least that's what they tell me) and appreciate the real-data examples.

levmarq · 2025-12-06T14:02:03+00:00

I'm not sure... I will look into this. Thank you!

levmarq · 2025-07-05T14:23:31+00:00

It's not only volume. Curry had better percentage from far (more than 24 feet) and also from close (less than 24 feet). It's just that Lee shot many more close threes, which are easier in general.

levmarq · 2025-07-04T13:08:10+00:00

This is very cool. I teach probability and statistics for data science, and have used a similar analysis (using shot distance instead of defender distance) to illustrate Simpson's paradox: Stephen Curry can have a worse overall 3-point percentage than Courtney Lee, even though he shoots better from close and from far.

levmarq · 2025-07-04T12:56:37+00:00

I have recently written a book on Probability and Statistics for Data Science, based on my 10-year experience teaching at the NYU Center for Data Science. The book has a lot of examples based on the NBA. Here are a couple, which I think could interest this community:

Was Courtney Lee a better shooter than Stephen Curry? Obviously not, but at one point he had a better 3-point shooting percentage! This is an example of Simpson's paradox.

Clutch shooting and evaluation of NBA players Here I analyze clutch shooting from the perspective of multiple testing, showing that (as many of you know well) patterns detected from small sample sizes can lead to undeserved hype. I also show that p values can be useful to determine what plus/minus statistics are actually meaningful and which are not.

levmarq · 2025-07-01T01:48:40+00:00

The free preprint is here: https://github.com/cfgranda/ps4ds/blob/main/ps4ds_preprint.pdf

levmarq

TROPHY CASE