Thanks for making an effort to explain things at a slow pace. I love the way you don't use technical terms to explain things immediately, but then you do give us the technical term once it's explained. Much appreciated and subscribed.
You're an amazing instructor and I really enjoy your videos. Great content. Can I make a small suggestion regarding a technicality - the camera seems to be fishing for focus every time you move in closer to it. If you manually focus and fix the focal distance so that the board is in focus, whenever you move closer only you will go out of focus for a brief moment ( not necessarily, if there is sufficient light you can use a small aperture that will allow for a greater focal distance ) and avoid the pitfalls of the slow autofocus.
Thank you so much for the suggestion! I had a couple videos around this time where the focus went in and out and I apologize for that. In my more recent videos, fortunately I did exactly what you suggested so they are easier to watch. Thanks!
huge thanks for the explanation. i was reading a book about this but i couldn't get my head around it. your explanation clear things up. best of success to you, bro..
Really simple and great explanation of the covariance matrix. It would be great if at the end you tell us what the covariance matrix means in terms of whether there was a relationship between eating a banana and apple - in this case, that yes, there is a positive relationship.
Excellent presentation but at 2:21 .... confused correlation with covariance with correlation coefficient. Correlation is not bounded between -1 and +1 that is rather the correlation coefficient Correlation coefficient is the one that is bounded. Also the explanation given ... when one is positive and the other is negative ... (that is the definition of correlation) Covariance has to be defined relative to the mean. Please double check in any Standard Statistics Book including Peebles or Papoulis... The presentation style and clarity is excellent. Keep up the good work.
i dont mean to be so offtopic but does any of you know of a trick to get back into an instagram account..? I was dumb forgot my login password. I would love any help you can give me!
I am assuming that you got lost when he said that the Expectation of A * Expectation of B cancel out to zero. By that he meant A= 1* 3 * -1= -3, B= 1*0*1=0 So, the Expectation of A = -3, and the Expectation of B=0, now, multiply A(-3) * B(0) = 0;
There's actually an important difference between covariance and correlation. Yes, for both, in general you want that the larger one variable gets the larrger the other gets, and vice-versa. However, for covariance, if the value of one variable were fixed, you will always get a larger covariance if you make the other variable of greater magnitude, with the same sign as first variable. So for instance, if there were values for apple enjoyment of -3, -2, -1, 0, 1, 2, and 3, and they were fixed, you'd increase the covariance by choosing the values of banana enjoyment to be as negative as possible for the negative apple values and as positive as possible for the positive apple values (the 0 one wouldn't matter). On the other hand, (linear) correlation measures the degree to which the variables fall on a line. So, with the same example as above, we'd maximize correlation by choosing values of banana that were, say, equal to each for apple, or any set of values that make a straight line. This clearly means we would NOT want to just choose the largest magnitude, with appropriate sign, banana values that we can.
hey, can we subtract mean from each term to make each column zero mean before calculating covariance matrix. also some texts divide by n-1 instead of n. why is that? Thanks
Hi! Shouldn't one devide by N-1 instead of N ? Because we compute the means from the samples. Should Cov(A,B) then not be 2/(3-1) instead of 2/3? Thanks
He is taking the covariance of entire population i.e. all 3 people therefore, he divides by N. Had he taken a sample out of this population, he would have divided by N-1.
Thank you very much your explanation was great, the only question is that what is the relation between the curve you plotted at the first of the video and the calculated matrix?
Very often one can see a formulation like variance-covariance matrix. Is it the same as covariance matrix and are being used interchangeably, or variance-covariance matrix should denote something else?
I’ve never taken a stats class in my life and now I have to construct covariant models for NASA.... thank you so much! Now I just gotta apply this to MatLab, can’t be too hard lol
I understood the concept very well. One question I have (i.e. what type of insights we can achieve by calculating covariance between two elements helps in real life ?)
The equation he used for covariance seems to give the same results as another formula I see being used as the standard online, I just can't figure out how to view them as equal. The equation I see online is E[(x_i-E(x)) (y_i-E(y))] Would love some clarification.
This is one of the most clear, straightforward stats video I've seen in awhile! 👍
Thanks for making an effort to explain things at a slow pace. I love the way you don't use technical terms to explain things immediately, but then you do give us the technical term once it's explained. Much appreciated and subscribed.
You're very welcome!
I like how you give us little refreshers about concepts we may have forgot.
I can't believe how you make things that easy. Thx for this awesome content.
You describe things in absolutely clear and simple way, thx for doing this!!!
You're an amazing instructor and I really enjoy your videos. Great content.
Can I make a small suggestion regarding a technicality - the camera seems to be fishing for focus every time you move in closer to it. If you manually focus and fix the focal distance so that the board is in focus, whenever you move closer only you will go out of focus for a brief moment ( not necessarily, if there is sufficient light you can use a small aperture that will allow for a greater focal distance ) and avoid the pitfalls of the slow autofocus.
Thank you so much for the suggestion! I had a couple videos around this time where the focus went in and out and I apologize for that. In my more recent videos, fortunately I did exactly what you suggested so they are easier to watch. Thanks!
To the point, exemplified, condensed and so useful. Thanks for this video!
huge thanks for the explanation. i was reading a book about this but i couldn't get my head around it. your explanation clear things up. best of success to you, bro..
Really simple and great explanation of the covariance matrix. It would be great if at the end you tell us what the covariance matrix means in terms of whether there was a relationship between eating a banana and apple - in this case, that yes, there is a positive relationship.
I was struggling with this concept. You made it very simple and easy to understand. Thank you for this amazing content.
This is one best explanations of the concept I have come across. You truly have a gift! Thank you.
Glad it was helpful!
Best explain on this topic! Concise and human friendly
thanks!
Excellent presentation but at 2:21 .... confused correlation with covariance with correlation coefficient. Correlation is not bounded between -1 and +1 that is rather the correlation coefficient Correlation coefficient is the one that is bounded. Also the explanation given ... when one is positive and the other is negative ... (that is the definition of correlation) Covariance has to be defined relative to the mean. Please double check in any Standard Statistics Book including Peebles or Papoulis... The presentation style and clarity is excellent. Keep up the good work.
thanks. make more videos. you have a talent for keeping thinks understandable
Thank you!
your channel deserves way more traction and sub count. keep up the good work. Thanks!!
agreed
Thank you!
You're an excellent teacher! Wish to see more Stat videos from you! Thank you so much!
Great real life explanation - extremely helpful. Thank you so much!!
After giving Khan Academy a shot at explaining this poorly I came across this. Perfect. Thank you!
i dont mean to be so offtopic but does any of you know of a trick to get back into an instagram account..?
I was dumb forgot my login password. I would love any help you can give me!
@Jake Maxton instablaster =)
So simple yet so clear. Thank you so much! Subscribed and can't wait to watch your other videos!
Thank you! This is the best explanation in the world! It really helps me! 👍
Really good explanations - clear and concise. Thank you.
Glad it was helpful!
I was following while it was all about apples and bananas, but got lost when you started performing pear-wise operations
wow ... hilarious!
@@ritvikmath That's not nice!
I am assuming that you got lost when he said that the Expectation of A * Expectation of B cancel out to zero. By that he meant A= 1* 3 * -1= -3, B= 1*0*1=0 So, the Expectation of A = -3, and the Expectation of B=0, now, multiply A(-3) * B(0) = 0;
@@captincanuckjones1664 The joke was the 'pear'-wise operations (cause you know... fruits), but it's nice of you for explaining!
You are covering some very important topics which are generally not available. Please continue doing so.
I loved this. You are EXCELLENT at explaining mathematics.
Besides StatQuest a really good and growing statistics channel. Subbed.
Thanks!
Your lecture is so straight that even non-english speaking student can understand easily. That's me. Thank you for the good lecture
Thank you so much for this very helpful and intuitive video. It really helped me understand specifying mixed models in R!
I want to express my appreciation for tutor. Thank vey much
Can you do a series leading up to Gaussian process? I like your way of explaining things.
plus one from my side!
Thank you for that clear explanation. I don't have time to relearn statistics at the moment.
There's actually an important difference between covariance and correlation. Yes, for both, in general you want that the larger one variable gets the larrger the other gets, and vice-versa. However, for covariance, if the value of one variable were fixed, you will always get a larger covariance if you make the other variable of greater magnitude, with the same sign as first variable. So for instance, if there were values for apple enjoyment of -3, -2, -1, 0, 1, 2, and 3, and they were fixed, you'd increase the covariance by choosing the values of banana enjoyment to be as negative as possible for the negative apple values and as positive as possible for the positive apple values (the 0 one wouldn't matter).
On the other hand, (linear) correlation measures the degree to which the variables fall on a line. So, with the same example as above, we'd maximize correlation by choosing values of banana that were, say, equal to each for apple, or any set of values that make a straight line. This clearly means we would NOT want to just choose the largest magnitude, with appropriate sign, banana values that we can.
Thank you so much for such a clear and detailed explanation, it helped enormously!
Hi, Ritvik you are creating awesome content. Please do keep creating such beautiful content.
Thank you so much 🙂
This was an awesome video. Very clear and easy to follow.
Thank you. Very helpful video. Good luck
Damn where have you been all my life. Thanks dude.
Awesome straightforward explanation thank you
simple and intuitive! great!
Thank you so much. In 10 minutes you explained it so clearly :D keep on with your videos!!!
I needed this channel in my life so much.
Loved the video :D all the best for your channel
Thank you so much 😁
Excellent explanation. No confusion. No bullshit. Just 100% fruit
Best video on covariance...thnks man
Talented teacher!
I'm from Germany and I understand you more then every german speaking teacher here
hey, can we subtract mean from each term to make each column zero mean before calculating covariance matrix. also some texts divide by n-1 instead of n. why is that? Thanks
Learning in quarantine..thanks man!
Great style of teaching!
Thanks a lot, when you do your advanced stats class you tend to forget the pure basis elements of stats thank you
This video saved my life.
Thanks for this amazing explanation!
Greatly informative video, thank you! :)
Thanks!
You are doing a great work...
Really appreciate your work, Thanks for the video.
Hi! Shouldn't one devide by N-1 instead of N ? Because we compute the means from the samples. Should Cov(A,B) then not be 2/(3-1) instead of 2/3? Thanks
He is taking the covariance of entire population i.e. all 3 people therefore, he divides by N. Had he taken a sample out of this population, he would have divided by N-1.
Helped me so much in econometrics! Thanks!
Thank you great guy!!!!
how can we calculate correlaion matrix for 3 random variables?
Great playlist and explanations!! Your camera blurs out at intervals, perhaps you could check that. Thank you for your lessons, they help a bunch!
Thank you! I ended up fixing this issue for my newer videos thanks to comments like this.
Thank you for making this simple and easy to understand :)
Great video, would be awesome to give a little more intuition on why these numbers are so insightful ;)
God Bless YOU! you saved me!
Hi, What are Correlation range and exponential correlation orders and how we can compute for variables (are in vector form or arrays)?
AMAZING! So clear!!!!
Very well explained. Thank you
Great video! Thank you
Glad you liked it!
Thanks so much for your clear explanation.
Great video!
Thanks!
Fantastic explanation, thank you!
Great explanation Ritvik as always. Please can you make a series of Videos on Financial Calculus....?
What does the covariance matrix mean as a whole, i have seen it being used as an entity. Something intuitional as matrix multiplication here?
Thank you very much your explanation was great, the only question is that what is the relation between the curve you plotted at the first of the video and the calculated matrix?
Really appreciate the good work
Very often one can see a formulation like variance-covariance matrix. Is it the same as covariance matrix and are being used interchangeably, or variance-covariance matrix should denote something else?
Great explanation!!!!
Glad it was helpful!
This was so helpful. Thank you!
so clear, thank you sir!
Excellent revision
I’ve never taken a stats class in my life and now I have to construct covariant models for NASA.... thank you so much! Now I just gotta apply this to MatLab, can’t be too hard lol
Good luck! You got this
Really awesome explanation
Wow! So happy to have found this channel, you are a great teacher! Thanks!
Welcome!
Solid video. You're good at this
sir
you are awesome
thank you
second video I stumbled across and it was so clear
Thank You sooo MUCH!!!!!! This was a brilliant way to teach!
Thank you, super clear!
excellent video, thanks very much
You are welcome!
well done
Understanding very well sir
hi..can you please tell at the last when you have derived the matrix value what does it symbolize ,i mean what does matrix should be interpreted?
Don't you divide the sum of squared difference by (n-1) to get the variance? Great video. Thank for explaining so clearly.
Thank you very much sir. Very helpful!
This is really helpful! Thanks a lot sir!
Thank you! for the video, as always awesome tutorial video.
Thank you so much for this!
I understood the concept very well. One question I have (i.e. what type of insights we can achieve by calculating covariance between two elements helps in real life ?)
Very good explanation!
Thank you for making this, very useful!
Excellent teaching..
excellent stuff bro
Nice explanations .. thank you
Don't you need to center the data by subtracting the mean first from all the data?
The equation he used for covariance seems to give the same results as another formula I see being used as the standard online, I just can't figure out how to view them as equal.
The equation I see online is E[(x_i-E(x)) (y_i-E(y))]
Would love some clarification.