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This is the 2nd video in a series about Power Laws and Fat Tails. In this video, I describe how to objectively detect Power Laws from real-world data and share a concrete example with social media data.
📹 Series Intro: https://youtu.be/Wcqt49dXtm8
📹 Fat Tails: https://youtu.be/15Kd9OPn7tw
📰 Read more: https://medium.com/towards-data-science/detecting-power-laws-in-real-world-data-with-python-b464190fade6?sk=07960e2c880b7f6f5ac577e6beb843a3
💻 GitHub Repo: https://github.com/ShawhinT/YouTube-Blog/tree/main/power-laws/2-detecting-powerlaws
References
[1] arXiv:0706.1062 [physics.data-an]
[2] arXiv:2001.10488 [stat.OT]
[3] https://en.wikipedia.org/wiki/Likelihood_function
[4] https://en.wikipedia.org/wiki/Pareto_distribution
Intro - 0:00
Power Laws Break STAT 101 - 0:59
Log-Log Approach - 2:21
Maximum Likelihood Approach - 4:54
Example Code: Artificial Data - 8:11
Example Code: Real-world Social Media Data - 16:16
What's Next? - 22:29
