R-squared, Clearly Explained!!!
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- Опубліковано 9 лип 2024
- R-squared is one of the most useful metrics in statistics. It can give you a sense of how good your model is.
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NOTE: When I first made this video, I was thinking about how R-squared relates to Linear Regression, which will not fit a line worse than the mean of the y-axis values. This is because if the values along the x-axis are truly useless in terms of predicting y-axis values, then the slope of the line used to make predictions will be 0, and the intercept will equal the mean. However, it is possible to simply draw a line that fits the data worse than the mean and get a negative R^2.
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With enough variables in the data set, it would be easy to create a set of r-squared values so that the cumulative percent "explained by" the different variables goes over 100%. That's why I was never a fan of that terminology. Students think it implies causation when it doesn't. Otherwise, great video.
@@mattkilgore7323 Maybe I should have made it more clear, but if you have a large model with a lot of variables, then you don't add together a bunch of individual R-squared values to find the total R-squared. You calculate a single r-squared value fro the entire model. In other words r-squared refers to the models, not the individual variables.
StatQuest with Josh Starmer If you only consider all unbiased lines, (mean of predicted ys equal mean of real ys), then no negative R^2.
@@mattkilgore7323 Hi Matt can you explain the point you trying to make in a bit more detailed manner
The phrase "explained by" can be deceptive, as students often think it means "caused by." But this is not what it means in the context of r-squared. Does that help?
So glad this channel exists. It's rare that UA-cam videos on stats are this well done
Thanks!
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@@shashankkhare1023 Thank you very much!!! Recommending my channel to your colleagues is the best complement you can give me. :)
you can watch and learn from Dr. Ami Gates. Her videos are great..
You have explained the concept so neatly,clearly ( most importantly in an easier manner ) so that one could get deeper understanding of the concept, a fact that lot many text books / videos / articles failed to do. Keep making such videos !
Thank you so much for making this sooooooo clear, I've struggled to understand the meaning of R2 for a week and you just made it clear to me in 10 min.
Bam! :)
Your videos are so easy to understand, and also explains the intuition behind. I really love the way you start the video, unlike other bouring lectures.
Thank you so much! :)
Your channel is blessing in disguise. Visual aids and the explanations are so smooth and easy to understand. Thank you very much.
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this is the best channel ever that can exist about statistics :D wonderful explanation and illustrations and the music! :) am glad I found this at the right time !
Thank you so much 😀
Are u glad u found it or are u asking us if u are glad??😆😆
I had stats exam coming up and didn't know this particularly well, Thanks for making it much more simpler!
Good luck on the exam! :)
I can't believe the simple relationship between R^2 and R was never made clear to me! Amazing as always!
Awesome!!!! Thank you very much.
I also appreciated his comments on the subject, and him sharing his opinions and intuitions.
Just a quick question?
Added this to my useful tutorials and math playlists.
Thanks StatQuests.
BAM! :)
Josh you are the best!!! Your every video has been helpful to god knows how many times in my studies. Much much love
Thank you very much! :)
Just recently found your channel. These are by FAR the most straight forward explanations I found so far. You sir are a godsend.
Thanks!
Amazing explanation!!Made it very simply for me to understand!! :)
I went through so much content for this..thank you
Hooray! I'm glad the video was helpful.
I read a lot on R square from different books and articles but this was the really different and very intuitive approach. Visualization is the best way to understand statistics and I think most books lack there.
Thanks! :)
cool! you have cleared all the fogs around r2 in my head once for all. appreciate your explanation!
Glad to help!
So all this time I spent sniffing rocks to grow bigger was for nothing???
Ha! You made me laugh. :)
haha
only if you are mouse
I love you hahahaha
You should have made a powder out of rocks, that would speed up your growing. Especially if your powder of white color🤣🤣🤣
god bless, i have been searching high and low for this kind of video. Thank you!!!!
Hooray! :)
love the way you explain things in casual manner
Thank you!
Could not have even imagined such intuitive explanation of this topic before watching this video. Thanks Josh!
Thank you!
Very beautifully explained. Many thanks to the folks of Genetics Department at the University of North Carolina at Chapel Hill.
Just impeccable. I don't think any other better illustration exists other than this. Thank you
Thanks!
I have been looking at a variety of stats videos and these are clearly the best. I am so impressed with StatQuest that I renamed my four dogs, "StatQuest," "StatQuest," "StatQuest," and "John Stamos" because, of course...
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No one could make me understand R Squared in such easy way. Watched many videos. All made it complicated. Thanks.
Hooray!!! I'm glad to hear the video was helpful! :)
a simple concept explained simply. thank you for the straight forward explanation
Thank you very much! :)
The introductions are the cutest thing I have ever seen - the videos are also super duper helpful!
Thank you! :)
I would rather name this video VERY CLEARLY EXPLAINED. Thank you.
Thank you!
These videos are pretty cool. I can always come back and refresh concepts.
Glad you like them!
This channel has become my go-to resource for anything stat related.
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You are a rarity ❤️ really love how you explain statistics! Please tell us more 🙏🏻♥️♥️♥️
Thanks! :)
Great teachers make everything interesting! Thanks Josh
Thank you! :)
watching this again, thank you very much. I jumped into more advanced stuff because of your videos. 🙏🙏
Awesome!
Thank you, you're helping us with these videos.
Thank you very much! :)
Super useful as always. Please continue with the videos (for example, prediction interval vs. confidence interval or maybe p-values vs. randomization tests or logistic regression...)! I liked your explanation because it never occurred to me that R^2 was basically the same as calculating percent change (diff/original)x100.
Wonderful explanation again. I easily understood the concept. I'm grateful.
Thanks! :)
aha! we meet again, and I thank you again!!! wow, I wish you were teaching my class!!
Ha! I'm glad my videos are so helpful. :)
Ah! this is the best video explaining R squared! Thank a lot!
Thank you! :)
Really enjoying your videos. Moreover everything is crystal clear and I am able to understand them. Double BAM
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Thank you very much, your video is very easy way to understand, makes me want to go through the Statistics course again.
You can do it!
Thank you for this precious material
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Thank you!
Working on my MPA stats final and this video has been so helpful
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Was struggling to understand this concept but this video explained everything!
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Thank you, this was a life-giver! Josh Starmer, you just might have become a part of something which will be big
Wow, thanks!
Thanks for the simple explanation. Much appreciated.
Thanks! :)
Thanks for sharing, Sir. It helps me a lot
Glad to hear that!
really boom...I was confused from past 3 days to understand regression value ...now I understand. Thanks
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wow this is the best explanation ive ever seen, thank u!!!!!
Thank you very much! :)
I did not see anyone explain the statistics better than you
God bless you ...
Thank you!
Now I understand the R squared much better! Thank goodness for this video!
Glad it helped!
Thanks, every question/doubt that I had instantly got answered about 10 seconds later.
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Undoubtedly d best and to the point explanation. Thanks a lot
Thank you! :)
totally agree!!
Amazing video! Thank you very much!
Thanks! :)
Beautiful explanation :)
Thank you! :)
This helped me clearly understand R^2. Trying to grasp this from reading a textbook was impossible for me.
bam! :)
Well explained. Thank you!!!
Thanks! :)
这样的创作者请给我来一百个！thank you for your videos!I really appreciate what you have done, and look forward to seeing more of them~
Thank you very much! :)
Very good explanation, thanks!
Thank you! :)
Really good explanation as to why r squared is significant in describing variation in data. Thank you!
Glad you liked it!
Nice explanation. To the point.
This video is absoluted amazing! R^2 and R finaly understood!
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Beutifully explained. Thank you so much.
Thank you!
Thank you Josh, this was really a beautiful explanation :-)
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Thank you! :)
Very clear explanation!
Glad you liked it!
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Hooray! I'm glad you like the videos and my silly jokes. ;)
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After watching your videos, I aced my stats module!
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The best explaination I can find
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"time spent sniffing a rock"! had me cracking😂.... btw thanks josh for putting such great content up... this channel is the my primary source of building my statistics foundations....
Glad you like them!
Dude..this friggin rocks.. THAKS YOU!!!!!
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hats off and thanks a lot you will make me cry. thanks once again.
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Why cry mate???
B - E - A - utiful explanation. Thanks
Thank you!!! :)
best video explaining R and R squared ever!
Thank you!
Gosh, this was SO helpful.
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my dude I understood and I am happy
8-year-old video is this good
liked, subbed and thank you!
My dude! Thank you very much! :)
Nice explanation!
Thanks!
Came here from the Pearson's correlation video. Thank you so much for this
I just wish that you could show in the video:
• how (Var(mean)-Var(line)) / Var(mean) is equal to [Covar(x,y) / (Var(x)^-2)(Var(y)^-2)]^2
• whether (Var(mean)-Var(line)) / Var(mean) using mean and differences from the x-axis also yields the same value
Again, thank you for the video
I'll keep that in mind.
Good explanations!
Thanks!
phew...!!! finally this concept is clear in my head :) thank you sooo much
Glad it helped!
I really love this channel, thank you so much! one question is that is the blue line caculated from the least squared method?
Yes, it is!
This is beautiful!
Thank you! :)
Really awesome lecture
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thanks, good explanation!
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Awesome explanation
Thanks!
Thank You Statquest your video and my knowledge of R^2 have a R^2 of 99.99
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Adding to my previous comment , R2 value can be negative when the variance explained by the line is lesser than the variance explained by mean.
For example var(mean) = 30 and var(line) = 40
Then R2 = -0.3
There exists such models , perhaps that could be worst models.
This is technically correct, but practically speaking, R-squared is always positive because it is used to compare the least squares residuals for the best fitting model to the least squares residuals for the mean, and the best fitting model can't have larger residuals than the mean, otherwise the best fitting model would be the mean. Does that make sense?
Completely agree with you in terms of practicality. It doesn't make sense at all. At the end of day you want a model which performs better than the base model. My point was it can be negative. Nevertheless i really like your videos. That comment of mine was just to clarify my understanding and to reach out to you.
I was thinking more about the negative R-squared and how it could be used in practice. I mean, like you said, even if your model is terrible, worse than the mean, it still might be nice quantify how terrible it is - and that's where the negative R-squared could come in handy. It still has the same meaning, except now you're quantifying how much worse your model is than the mean. Interestingly, it still works out even if var(terrible model) is so bad that the R-squared is less than -1. For example, if var(mean) = 50 and var(terrible model) = 100, then R-squared = (50 - 100) / 50 = -1, so "terrible model" is 100% worse than the mean. If var(terrible model) = 150, then R-squared = (50 - 150) / 50 = -2, and now terrible model is 200% worse.
Right , That's my point. From my own experience , I used to train multiple models on a sample dataset and compute their respected R-squared value to choose the best among those models. There I encountered some models returning negative R-squared value. Those models are practically useless and if you agree that happens when your training data is so huge and the algorithm you are using is so insignificant, like using a multi variant regression for a heavily skewed target variable.That was the motivation behind my comment. I appreciate your time to reply back to my comments. I am glad that it grabbed your attention Mr. Josh.
@@statquest I asked a question about this too and I assumed you meant the best fitting line (even though it was not explicitly stated in the video), or at least one that performed better than the mean line.
this is bizarelly useful for my exam tomorrow
Good luck! :)
Hi Josh,
Is there any videos that explains the Degrees of Freedom? I find it difficult to understand this concept. Pls provide link if there is.
Not yet. It's something I would love to do as soon as possible.