Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/ Corrections: 13:05 When the residual is negative, the pink circle should be on the left side of the y-axis. And when the residual is positive, the pink circle should be on the right side.
I have started my machine learning journey a month ago and I stumbled onto a myriad of resources that explain linear models using the RSS function but no one, and I mean no one, managed to explain it with as much clarity and elegance as you have in just under 20 minutes. You sir are a boon to the world.
As someone who is doing medical research and needs to learn little-by-little about statistics, neural networks and machine learning as my project goes on, your channel is a literal life-saver! It has been so hard to try to keep my M.D. stuff together with my PhD research all the while learning statistics, programming and neural network structures and machine learning. Trying to arrange courses from my uni to fit in with all the other stuff is simply impossible, so I've been left to my own devices and find a way to gain knowledge about said subjects and your channel has done just that. Your teaching is great and down-to-earth enough to be easily grasped, but you also delve deep into the subject after the initial baby steps, so the person watching isn't just left with "nice to know"-infobits. Love it! Keep up the great work!
Over the past three years, I have been studying neural networks and delving into the world of coding. However, despite my best efforts, I struggled to grasp the true essence of this complex subject. That is until I stumbled upon your enlightening video. I cannot emphasize enough how much your video has helped me. It has shed light on the intricate aspects of neural networks, allowing me to comprehend the subject matter with greater clarity and depth. The way you presented the material was truly remarkable, and it made a profound impact on my understanding. What astounds me even more is that you provide such valuable content for free. It is a testament to your passion for educating and empowering individuals like myself. Your dedication to spreading knowledge and fostering learning is truly commendable. Thanks to your channel, I have been able to unlock the true essence of mathematics and its relationship with neural networks. The confidence and clarity I now have in this subject are invaluable to my personal and professional growth. Your video has been a game-changer for me, and I am grateful beyond words. Please continue your fantastic work and know that your efforts are deeply appreciated.
BY FAR the best explanation of the chain rule I have ever seen (and trust me - I've seen A LOT) You, sir, just earned yourself yet another well-deserved subscriber. F'n brilliant!!!
Take my words Josh you are the best teacher on the internet who teaches Statistics........ and the chain rule made me crazy.......... by your explanation.
We could have had a "dreaded terminology alert" : "decomposition of functions". But even without it: this was a perfect explanation of the chain rule , with great practical examples. Bravo, Josh!
Awesome!! None of my math teachers in high school or collage never explained to me WHY chain rule works this way. but you explained it with a very simple example. I'm certain that from now on I'll never forget the chain rule formula. Thanks a million. 👌✔
@@statquestI know this isn't related to this video, i just want you to help me because you replied to this comment. With gradeint descent, how am i supposed to get the derivative for each weight and bias in a loss function dynamically? because surely for networks with more than 100 neurons there would be a way, i know there is i just don't know. When i am calculating the derivative for one varaible in the loss function, to optimize it, i get some overly complicated function, but i see some papers on it and it isn't complicated.
Josh you are a master in teaching, you make difficult topics so easy to understand which is really amazing. My mother language is not English but you explain so well and clear that I can understand everything. Congratulations Sir, please keep doing this job.
I love StatQuest! I got my SQ mug in the morning and just got the Illustrated Guide to Machine Learning. Super excited to start! Thank you for all the great content!
I would insert a BAM at 5:25. :) ...also, I realized the thing I like about your videos is you explain things, not only in a clear way, but in a different way. It adds to the depth of our understanding. Thank you!
Best reference for learning statistics. Btw, would just like to point out that in 6:16, there appears to be a minor mistake. Actually for every 1 unit increase in Weight, there is a 2 unit increase in Shoe Size, because the equation would be Size = (1/2)*Weight, or 2*Size = 1*Weight
This video is actually correct. For every one unit increase in weight, there is only a 1/2 unit increase in Shoe Size. What your equation shows is that for every unit increase in Size, there is a 2 unit increase Weight. That's not the same thing as "for every unit increase in Weight, there is a 2 unit increase in Size".
Simply the best explanation of chain rule! Now I understand CR better to teach my kid when she needs it... Thank you!!! Do you publish a book on calculus I would love to buy it!
13:15 Is the residual(squared) graph mirrored? Since residual=(observed - predicted), wouldn't that mean that when on the original graph the intercept is zero, the residual would be positive(2-1=1), so the position on the residual(squared) graph should be on the positive x-axis(x=1), as opposed to the negative side on the video, and vice versa?
Hi, I think I found a mistake. (?) The pink ball in the graph from 13:08 should be on the other side of the Y axis. It doesn't change the educational value of the whole video but it caught my eye.
YES!!! This is the first video in my series on Neural Nets!!!!!!! The next one should be out soon (hopefully late July, but I always run behind so maybe early August).
13:27 When the residual is negative, the pink circle is shown to be on the right side of the y-Axis, but shouldn't it be on the left side? Aside from that, great content! Cheers from Germany
Hey, can someone help me understand why at 14:55 we Observed and Weight to 0 because they do not contain the intercept? I thought I understood until this point. Now I'm a bit confused and discouraged! Thank you!
When we change the value for the intercept, the Observed values do not change (because they are what we observed, they don't every change). Since there is 0 change in the observed values when we change the intercept, the derivative of the observed values with respect to the intercept is 0.
I have a couple questions... At 6:54, what's the time^2 + 1/2 formula supposed to be representing? 🤔 and is that 1/2 supposed to be the intercept? why do we plug it in, is that just a set formula you've gotta learn?
The formula is for the curve that fits our data. What you do is you get some data and then fit a line (or curve in this case) to it - so the line, and the equation for it, depend on the data.
Is 13:06 a slight error? The residual-intercept graph shows the point in the negative part of the residual's axis (negative y), yet the residual-sq-residual graph shows the point on the positive side of the residual's axis on that graph (positive x)
At 6:54 you said that you fit an exponential line to the graph and got hunger = time^2 + 1/2. I have a few questions about that. 1. I've never heard the phrase 'exponential line' before. Do you just mean an exponential 'line' of best fit? 2. You said that the equation is exponential, but that looks quadratic to me. Am I missing something? I really like the way you explained this. Once you think about problems in the 'real world' like this it really starts to make sense how changing one function affects and changes the other and then why you need the chain rule to find the rate of change.
1. I just mean that we fit a curve defined by the function hunger = time^2 + 1/2 2. I should have said quadratic instead of exponential. I apologize for any confusion that this may have caused.
@@statquest Thanks for replying so quickly on an older video like this! I'm making some math videos of my own right now and I can't believe how easy it is to misspeak or write something wrong. You've done an amazing job with all your videos. This is the only video I've found that attempts to explain the chain rule in an intuitive way without using the limit definition.
In the 1st example both initial relationships(hight to weight and shoe size to hight) are given as linear. Thist the deriviative multiplication gives me not only the deriviative but the slope of themodel predicting the shoe size by weight(final model) What I am missing are 2 things: 1) but in some non linear final model what is the use of knowing the slope equation? It is not a model equation, so can not be used for predictions... what am I missing? 2) another thing confuses me is that here ,at least in ghe shoesize example you use the chainrool to get the final model. But further in backpropagation in each iteration the use of it is different itis kinda to predict weights using them get the "final model" Could you please formulate this difference better then I am trying to? Thank you so much.
I'm not sure I understand your questions. The idea is that we want to establish a relationship among variables - and how much one changes when we change another. This works for linear and non-linear equations. It also sounds like you are interested in how derivatives are used for backpropagation. For details, see: ua-cam.com/video/sDv4f4s2SB8/v-deo.html and ua-cam.com/video/IN2XmBhILt4/v-deo.html
13:39 how come the slope of at a point on squared residual curve can be written in terms of derivate of squared residual with respect to intercept and not derivate of squared residual with respect to residual? Why do we set the derivate of squared residual with respect to intercept to zero when the slope of the squared residual curve should be written in terms with respect to residual? Shouldn’t we set the latter to zero and solve for intercept? Is it because residual is a function of intercept itself?
I'm not sure I understand your questions because they all seem to be answered immediately following that time point in the video. The goal is to find the optimal intercept for the graph of "weight vs height". So we use the chain rule to tell us the derivative of the residual^2 with respect to the intercept. This derivative has two parts, the residual^2 with respect to the residual and the residual with respect to the intercept.
Despite how good you are at explaining, I'm still having a hard time with it all. My confidence isn't exactly helped by the fact that all the other people in the comments seem to somehow be doing PhDs and stuff, but okay... How can I try to understand it even better?
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
Corrections:
13:05 When the residual is negative, the pink circle should be on the left side of the y-axis. And when the residual is positive, the pink circle should be on the right side.
This is an amazing explanation!!! Thanks
@@adolfocarrillo248 Thank you very much! :)
Got my copy of The StatQuest Illustrated Guide to Machine Learning today! Quadruple BAM!!!!
@@anushreesaran Hooray! Thank you very much! :)
@@statquestwhat do mean by the last term not containing the intercept?
I have started my machine learning journey a month ago and I stumbled onto a myriad of resources that explain linear models using the RSS function but no one, and I mean no one, managed to explain it with as much clarity and elegance as you have in just under 20 minutes. You sir are a boon to the world.
Thank you!
Did I just UNDERSTAND the CHAIN RULE ? SURREAL, thank you!
:)
Man you are amazing. You should get a Nobel prize!
Thank you! :)
Agree!
more than a nobel! book bought
Yes Yes Yes
Or a Grammy!
Amazing pedagogy. Slow pace , short setences , visuals consistent with the talk. great job ;-) Thanks
Glad you liked it!
As someone who is doing medical research and needs to learn little-by-little about statistics, neural networks and machine learning as my project goes on, your channel is a literal life-saver! It has been so hard to try to keep my M.D. stuff together with my PhD research all the while learning statistics, programming and neural network structures and machine learning. Trying to arrange courses from my uni to fit in with all the other stuff is simply impossible, so I've been left to my own devices and find a way to gain knowledge about said subjects and your channel has done just that.
Your teaching is great and down-to-earth enough to be easily grasped, but you also delve deep into the subject after the initial baby steps, so the person watching isn't just left with "nice to know"-infobits. Love it! Keep up the great work!
Thank you!
I am Biostatistician, proclaiming that you are really a good teacher.
Thank you very much!
Over the past three years, I have been studying neural networks and delving into the world of coding. However, despite my best efforts, I struggled to grasp the true essence of this complex subject. That is until I stumbled upon your enlightening video.
I cannot emphasize enough how much your video has helped me. It has shed light on the intricate aspects of neural networks, allowing me to comprehend the subject matter with greater clarity and depth. The way you presented the material was truly remarkable, and it made a profound impact on my understanding.
What astounds me even more is that you provide such valuable content for free. It is a testament to your passion for educating and empowering individuals like myself. Your dedication to spreading knowledge and fostering learning is truly commendable.
Thanks to your channel, I have been able to unlock the true essence of mathematics and its relationship with neural networks. The confidence and clarity I now have in this subject are invaluable to my personal and professional growth.
Your video has been a game-changer for me, and I am grateful beyond words. Please continue your fantastic work and know that your efforts are deeply appreciated.
Thank you very much! BAM! :)
The way you link equations to visuals and show how everything is working along with the math at the SAME time. Beautiful, elegant, easy to follow.
Wow, thank you!
Your videos are fantastic, even without the sound effects... but the sound effects really bring them over the top.
Thank you! And thank yo so much for supporting StatQuest!!! BAM! :)
BY FAR the best explanation of the chain rule I have ever seen (and trust me - I've seen A LOT)
You, sir, just earned yourself yet another well-deserved subscriber.
F'n brilliant!!!
Thank you very much!!! BAM! :)
If I watched your videos during my college, my career trajectory would be totally different. BIG BAM!!!!
Thanks!
Take my words Josh you are the best teacher on the internet who teaches Statistics........ and the chain rule made me crazy.......... by your explanation.
Wow, thanks!
@@statquest ❤️
We could have had a "dreaded terminology alert" : "decomposition of functions". But even without it: this was a perfect explanation of the chain rule , with great practical examples. Bravo, Josh!
Thank you!
Best chain rule explanation i have ever seen.
Thank you!
this channel was suggested by my professor, and i always watch the videos while doing a machine learning tasks. Big appreciate to you :D
Cool, thanks!
Awesome!! None of my math teachers in high school or collage never explained to me WHY chain rule works this way. but you explained it with a very simple example. I'm certain that from now on I'll never forget the chain rule formula. Thanks a million. 👌✔
BAM! :)
Nobody:
The demon in my room at 3am: 7:56
Dang! :)
jesus, this was funny xD
Bro your the only tutorial that actually helped me grasp this concept, thank you so much.
Glad it helped!
@@statquestI know this isn't related to this video, i just want you to help me because you replied to this comment.
With gradeint descent, how am i supposed to get the derivative for each weight and bias in a loss function dynamically? because surely for networks with more than 100 neurons there would be a way, i know there is i just don't know.
When i am calculating the derivative for one varaible in the loss function, to optimize it, i get some overly complicated function, but i see some papers on it and it isn't complicated.
@@mr.shroom4280 See: ua-cam.com/video/IN2XmBhILt4/v-deo.html ua-cam.com/video/iyn2zdALii8/v-deo.html and ua-cam.com/video/GKZoOHXGcLo/v-deo.html
@@statquest thankyou so much, i watched those but i totally forgot about the chain rule lol
dear @stat quest you must have come from heaven to save students from suffering's
just unbeliable explanation
Thank you! :)
Josh you are a master in teaching, you make difficult topics so easy to understand which is really amazing. My mother language is not English but you explain so well and clear that I can understand everything. Congratulations Sir, please keep doing this job.
Thank you very much! :)
Explaining u-substitution besides chain rule is brilliant
Thanks!
You are a genius at this I can't believe I hadn't heard of this channel before.
Thanks!
Guess I will not be afraid of the ***THE CHAAAAAINNNN RULE***
Thank you, Josh! Always Waiting for your videos!
Bam! :)
I’ve watched videos like this for work, yours is the best, I fully grasp what a derivative is!
Glad you liked it!
As always, clear and in simple language. Thank you !
Glad it was helpful!
These seriously are some of my favorite videos on youtube!
Thanks!
This is probably the best video about on the internet!! Thank you so much for taking the time to do it!!
Glad it was helpful!
This dude explains things clearly. Huge thanks!
Thanks!
One of the best video i have ever watched. Thank yoy guys for providing such a wonderful content for free.
Thanks!
I love StatQuest! I got my SQ mug in the morning and just got the Illustrated Guide to Machine Learning. Super excited to start! Thank you for all the great content!
That is awesome! TRIPLE BAM!!!! :)
This one outdoes all the best videos on the topic .
Thank you!
The best video in the internet about the Chain Rule!
Thank you!
you have great videos that help explain a lot of concepts very clearly, step by step. You have help a lot of students for sure.
Thank you very much! :)
I would insert a BAM at 5:25. :) ...also, I realized the thing I like about your videos is you explain things, not only in a clear way, but in a different way. It adds to the depth of our understanding. Thank you!
That is definitely a BAM moment! And thank you. One of my goals is to always explain things in a different way, so I'm glad you noticed! :)
You had made my machine learning path easy!
Glad to hear that!
Genius serious sincere
I’m a mathematician and am convinced you are a born sage
Thanks!
Dear Josh Starmer, Thank you so much. May God bless with you more knowledge so that you can energize learners like me. ❤. Thank you again.
Thank you very much!
i'm so moved to finally understand this, thank you!
bam! :)
Teaching is an art. thank you StatQuest
Thank you!
Best reference for learning statistics. Btw, would just like to point out that in 6:16, there appears to be a minor mistake. Actually for every 1 unit increase in Weight, there is a 2 unit increase in Shoe Size, because the equation would be Size = (1/2)*Weight, or 2*Size = 1*Weight
This video is actually correct. For every one unit increase in weight, there is only a 1/2 unit increase in Shoe Size. What your equation shows is that for every unit increase in Size, there is a 2 unit increase Weight. That's not the same thing as "for every unit increase in Weight, there is a 2 unit increase in Size".
@@statquest I calculated through the equation, and you are correct. Thanks for the verification!
Very clear explanation. I saw different people explaining this topic but you are the best.
Thank you so much.
Thank you!
Top notch visualization.
Thank you! :)
Awesome Explanation Mr. Starmer! I wish your videos existed back when I was taking Calculus in the university!!! ( which was a long time ago =) )
Wow, thanks!
Simply the best explanation of chain rule!
Now I understand CR better to teach my kid when she needs it...
Thank you!!!
Do you publish a book on calculus I would love to buy it!
Thanks! I don't have a book on calculus, but I have on on machine learning: statquest.org/statquest-store/
Now I can't read "the chain rule" without hearing your voice !
:)
this is epic, simple, and applicable chain rule in real life too - we need more videos like this damn
Thank you! :)
Such a beautiful intuition that weight height then height shoe size example was just commendable
Thanks!
13:15 Is the residual(squared) graph mirrored? Since residual=(observed - predicted), wouldn't that mean that when on the original graph the intercept is zero, the residual would be positive(2-1=1), so the position on the residual(squared) graph should be on the positive x-axis(x=1), as opposed to the negative side on the video, and vice versa?
Yes! You are correct. Oops!
Thank you Sir for the amazing Tutorial.
Thanks!
such a clean and simple explanation! can't wait for more Math and Statistic videos. You are the awesomeness in UA-cam!
Thank you! :)
I graduated with stats degrees from college 10+ years ago and never touched it. Now I feel I re-learned everything overnight!!!!!
BAM! :)
After this awesome statquest, I will hear 'The Chain Rule' with the echo playing in my head
bam! :)
thanks for clearing up the confusions i had with chain rule!
bam!
Another concept well explained ❤
Thanks a lot 😊!
An epically clear explanation. Thank you so much!
Thank you! :)
Hi, I think I found a mistake. (?) The pink ball in the graph from 13:08 should be on the other side of the Y axis. It doesn't change the educational value of the whole video but it caught my eye.
Oh, I see someone already brought this up.
yep
Your are an amazing teacher !
Thank you! 😃
Great teaching Josh Starmer!
Thank you kindly!
Reading abour Loss in Neural Network and optimization from 20+ sources and could not understand it until watching this video. Big BAM!
Hooray! Thank you!
I think you must be an alien! This is the best, most simplistic and complete explanation I have seen -ever. Fantastic job you did ❤️ thanks
Thank you!
you deserve Nobel prize Nobel man
Thank you!
This be the first time I am laughing learning stats🤣 Thanks alot!
Hooray! :)
Oh boy that's a teaser for neural net. Been looking forward to this!!
YES!!! This is the first video in my series on Neural Nets!!!!!!! The next one should be out soon (hopefully late July, but I always run behind so maybe early August).
I would like to thank you from bottom of my heart for such wonderful videos.
Such difficult topic made simple, you are awesome man , keep rocking!!!!
And Triple BAM!!!!
Thank you very much! :)
BAM! best explanation so far
Thank you! :)
Your explanation is awesome. Make more videos.
Thank you!
Please add more adds so we can watch them and actually give back to you
Ha! I wish I could remove all the ads. But even then, UA-cam will add them.
your videos are fantastic
Glad you like them!
Thank you so much for your videos! I got a StatQuest Shirt for my Birthday... hurray! :)
BAM! :)
Thanks a lot Sir Josh. Jzakallah. 😊Emotional
Thank you very much! :)
13:27 When the residual is negative, the pink circle is shown to be on the right side of the y-Axis, but shouldn't it be on the left side?
Aside from that, great content! Cheers from Germany
Yep. Thanks for catching that! I've added a correction to the pinned comment.
Awesome Statquest...
Initially played Song and concept too!!😎😎😎
Thanks! :)
Awesome Tutorial...
Thank you 🙂!
hanks for all your amazing videos. I'm still learning from you :)
Thank you!
Thanks for informative video.
Thanks!
Amazing video! Back to basics 😄👍
Thanks!
Amazing video thanks!
Thanks!
I'm getting strong MST3K and Star Control II vibes from this guy and that's pretty cool
bam!
6:52 that's not an exponential line (2^x), it's just a parabola (x^2). Anyhow, you're awesome! BAM! Just subscribed!
Thanks for catching that. :)
Hey, can someone help me understand why at 14:55 we Observed and Weight to 0 because they do not contain the intercept? I thought I understood until this point. Now I'm a bit confused and discouraged! Thank you!
When we change the value for the intercept, the Observed values do not change (because they are what we observed, they don't every change). Since there is 0 change in the observed values when we change the intercept, the derivative of the observed values with respect to the intercept is 0.
@@statquest Thank you!!!😄
I have a couple questions...
At 6:54, what's the time^2 + 1/2 formula supposed to be representing? 🤔 and is that 1/2 supposed to be the intercept? why do we plug it in, is that just a set formula you've gotta learn?
The formula is for the curve that fits our data. What you do is you get some data and then fit a line (or curve in this case) to it - so the line, and the equation for it, depend on the data.
A video also on probability chain rule would be awesome
Noted! :)
This video is not just the explanation of "The Chain Rule" instead explained the intuition behind the various loss functions.
That's right. The chain rule is used a lot in machine learning so I tried to explain it from that perspective.
@@statquest Thanks for all of the videos, they all really help alot
@@dhruvsharma7992 Thanks!
LMAO, the song at the beginning xD, just for that I'm giving it a like.
BAM! :)
Is 13:06 a slight error? The residual-intercept graph shows the point in the negative part of the residual's axis (negative y), yet the residual-sq-residual graph shows the point on the positive side of the residual's axis on that graph (positive x)
You are correct! The x-axis on the Residual vs Residual^2 graph is backwards.
@@statquest thanks for clarifying - and amazing video again, looking forward to your illustrated guide!
Simply beautiful. you are the best.
Wow, thank you!
謝謝！
Hooray!!! Thank you so much for supporting StatQuest!!! BAM! :)
Beautiful Just Beautiful
Thank you! 😊
At 6:54 you said that you fit an exponential line to the graph and got hunger = time^2 + 1/2. I have a few questions about that.
1. I've never heard the phrase 'exponential line' before. Do you just mean an exponential 'line' of best fit?
2. You said that the equation is exponential, but that looks quadratic to me. Am I missing something?
I really like the way you explained this. Once you think about problems in the 'real world' like this it really starts to make sense how changing one function affects and changes the other and then why you need the chain rule to find the rate of change.
1. I just mean that we fit a curve defined by the function hunger = time^2 + 1/2
2. I should have said quadratic instead of exponential. I apologize for any confusion that this may have caused.
@@statquest Thanks for replying so quickly on an older video like this! I'm making some math videos of my own right now and I can't believe how easy it is to misspeak or write something wrong. You've done an amazing job with all your videos. This is the only video I've found that attempts to explain the chain rule in an intuitive way without using the limit definition.
Awesomeness = like statquest squared 😆 🤣
Thank you!
awsome work man!!!! you have created the best content...... I wish that you should be teaching us at our college🥺
Thank you so much 😀
Bam! You are awesome. Thanks a lot.
bam! :)
Спасибо, вы молодец!
bam! :)
Awesome. You made my day!
Hooray! :)
The best Chain Role tutorial! Do you have any for Relu? Thank you!!
Coming soon!
In the 1st example both initial relationships(hight to weight and shoe size to hight) are given as linear. Thist the deriviative multiplication gives me not only the deriviative but the slope of themodel predicting the shoe size by weight(final model)
What I am missing are 2 things:
1) but in some non linear final model what is the use of
knowing the slope equation? It is not a model equation, so can not be used for predictions... what am I missing?
2) another thing confuses me is that here ,at least in ghe shoesize example you use the chainrool to get the final model. But further in backpropagation in each iteration the use of it is different itis kinda to predict weights using them get the "final model"
Could you please formulate this difference better then I am trying to?
Thank you so much.
I'm not sure I understand your questions. The idea is that we want to establish a relationship among variables - and how much one changes when we change another. This works for linear and non-linear equations. It also sounds like you are interested in how derivatives are used for backpropagation. For details, see: ua-cam.com/video/sDv4f4s2SB8/v-deo.html and ua-cam.com/video/IN2XmBhILt4/v-deo.html
13:39 how come the slope of at a point on squared residual curve can be written in terms of derivate of squared residual with respect to intercept and not derivate of squared residual with respect to residual? Why do we set the derivate of squared residual with respect to intercept to zero when the slope of the squared residual curve should be written in terms with respect to residual? Shouldn’t we set the latter to zero and solve for intercept? Is it because residual is a function of intercept itself?
I'm not sure I understand your questions because they all seem to be answered immediately following that time point in the video. The goal is to find the optimal intercept for the graph of "weight vs height". So we use the chain rule to tell us the derivative of the residual^2 with respect to the intercept. This derivative has two parts, the residual^2 with respect to the residual and the residual with respect to the intercept.
Despite how good you are at explaining, I'm still having a hard time with it all. My confidence isn't exactly helped by the fact that all the other people in the comments seem to somehow be doing PhDs and stuff, but okay...
How can I try to understand it even better?
Can you tell me what time point, minutes and seconds, you first got confused?
Thank you ❤❤❤❤
Any time!