Machine Learning - Logistic Regression Details Pt 3: R-squared and p-value

This video follows from where we left off in Part 2 in this series on the details of Logistic Regression. Last time we saw how to fit a squiggly line to the data. This time we'll learn how to evaluate if that squiggly line is worth anything. In short, we'll calculate the R-squared value and it's associated p-value. NOTE: This StatQuest assumes that you are already familiar with Part 1 in this series, Logistic Regression Details Pt1: Coefficients: https://youtu.be/vN5cNN2-HWE For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer Correction: 13:58 The formula at should be 2[(LL(saturated) - LL(overall)) - (LL(saturated) - LL(fit))]. I got the terms flipped. #statquest #logistic