The Covariance Matrix : Data Science Basics

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  • Опубликовано: 23 дек 2024

Комментарии • 239

  • @ebenvia
    @ebenvia 3 года назад +95

    This is one of the most clear, straightforward stats video I've seen in awhile! 👍

  • @joel09878
    @joel09878 3 года назад +4

    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.

  • @exxodas
    @exxodas 3 года назад +2

    I like how you give us little refreshers about concepts we may have forgot.

  • @ahmedalaaeldinmohamed9146
    @ahmedalaaeldinmohamed9146 2 года назад +6

    I can't believe how you make things that easy. Thx for this awesome content.

  • @andreyb7419
    @andreyb7419 3 года назад +5

    You describe things in absolutely clear and simple way, thx for doing this!!!

  • @traich
    @traich 4 года назад +30

    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.

    • @ritvikmath
      @ritvikmath  4 года назад +18

      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!

  • @laureanodiaz4297
    @laureanodiaz4297 3 года назад +1

    To the point, exemplified, condensed and so useful. Thanks for this video!

  • @NugrohoBudianggoro
    @NugrohoBudianggoro 4 года назад +9

    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..

  • @hossanatwino
    @hossanatwino 4 года назад +6

    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.

  • @SarikaKamble-pm2hq
    @SarikaKamble-pm2hq 2 года назад +3

    I was struggling with this concept. You made it very simple and easy to understand. Thank you for this amazing content.

  • @methorogood
    @methorogood Год назад

    This is one best explanations of the concept I have come across. You truly have a gift! Thank you.

  • @rainerwang1712
    @rainerwang1712 3 года назад +2

    Best explain on this topic! Concise and human friendly

  • @alimuqaibel7619
    @alimuqaibel7619 3 года назад +4

    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.

  • @Captain_Rhodes
    @Captain_Rhodes 5 лет назад +23

    thanks. make more videos. you have a talent for keeping thinks understandable

  • @theflippedbit
    @theflippedbit 5 лет назад +2

    your channel deserves way more traction and sub count. keep up the good work. Thanks!!

  • @paramita14
    @paramita14 Год назад +1

    You're an excellent teacher! Wish to see more Stat videos from you! Thank you so much!

  • @lcaslokonon3979
    @lcaslokonon3979 4 года назад +4

    Great real life explanation - extremely helpful. Thank you so much!!

  • @djd829
    @djd829 3 года назад +13

    After giving Khan Academy a shot at explaining this poorly I came across this. Perfect. Thank you!

    • @jakemaxton1714
      @jakemaxton1714 3 года назад

      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!

    • @brodieharley7040
      @brodieharley7040 3 года назад

      @Jake Maxton instablaster =)

  • @liamhan8145
    @liamhan8145 3 года назад +2

    So simple yet so clear. Thank you so much! Subscribed and can't wait to watch your other videos!

  • @DariaEmacs
    @DariaEmacs 4 года назад +2

    Thank you! This is the best explanation in the world! It really helps me! 👍

  • @fatimegashi1595
    @fatimegashi1595 3 года назад +1

    Really good explanations - clear and concise. Thank you.

  • @johningham1880
    @johningham1880 4 года назад +56

    I was following while it was all about apples and bananas, but got lost when you started performing pear-wise operations

    • @ritvikmath
      @ritvikmath  4 года назад +10

      wow ... hilarious!

    • @captincanuckjones1664
      @captincanuckjones1664 4 года назад

      @@ritvikmath That's not nice!

    • @captincanuckjones1664
      @captincanuckjones1664 4 года назад +4

      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;

    • @GinMCS
      @GinMCS 3 года назад +10

      @@captincanuckjones1664 The joke was the 'pear'-wise operations (cause you know... fruits), but it's nice of you for explaining!

  • @debasiskar4662
    @debasiskar4662 4 года назад

    You are covering some very important topics which are generally not available. Please continue doing so.

  • @compsci91
    @compsci91 3 года назад

    I loved this. You are EXCELLENT at explaining mathematics.

  • @elsins9790
    @elsins9790 3 года назад +1

    Besides StatQuest a really good and growing statistics channel. Subbed.

  • @juhokim6149
    @juhokim6149 2 года назад

    Your lecture is so straight that even non-english speaking student can understand easily. That's me. Thank you for the good lecture

  • @HawkEyeLondon
    @HawkEyeLondon 4 года назад +1

    Thank you so much for this very helpful and intuitive video. It really helped me understand specifying mixed models in R!

  • @MYJESUSCHANNEL2022
    @MYJESUSCHANNEL2022 2 года назад

    I want to express my appreciation for tutor. Thank vey much

  • @DistortedV12
    @DistortedV12 4 года назад +13

    Can you do a series leading up to Gaussian process? I like your way of explaining things.

  • @ben6
    @ben6 4 года назад +1

    Thank you for that clear explanation. I don't have time to relearn statistics at the moment.

  • @theonionpirate1076
    @theonionpirate1076 3 года назад +1

    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.

  • @ИльяЯгупов-н4я
    @ИльяЯгупов-н4я Год назад

    Thank you so much for such a clear and detailed explanation, it helped enormously!

  • @gautamsuryawanshi2642
    @gautamsuryawanshi2642 3 года назад +1

    Hi, Ritvik you are creating awesome content. Please do keep creating such beautiful content.

  • @uafiewn
    @uafiewn 3 года назад

    This was an awesome video. Very clear and easy to follow.

  • @Milad_showcase
    @Milad_showcase 4 года назад +1

    Thank you. Very helpful video. Good luck

  • @exxodas
    @exxodas 3 года назад

    Damn where have you been all my life. Thanks dude.

  • @nehdiwmbcuow868
    @nehdiwmbcuow868 2 года назад

    Awesome straightforward explanation thank you

  • @opencast1819
    @opencast1819 3 года назад

    simple and intuitive! great!

  • @aurorasart9458
    @aurorasart9458 3 года назад

    Thank you so much. In 10 minutes you explained it so clearly :D keep on with your videos!!!

  • @Ledozuvys
    @Ledozuvys 4 года назад

    I needed this channel in my life so much.

  • @rajroy2426
    @rajroy2426 4 года назад +1

    Loved the video :D all the best for your channel

    • @ritvikmath
      @ritvikmath  4 года назад +1

      Thank you so much 😁

  • @bluejays440
    @bluejays440 2 года назад

    Excellent explanation. No confusion. No bullshit. Just 100% fruit

  • @MansiMehta-n3v
    @MansiMehta-n3v 6 месяцев назад

    Best video on covariance...thnks man

  • @Mosa-166
    @Mosa-166 27 дней назад

    Talented teacher!

  • @marcozocker1963
    @marcozocker1963 4 года назад +7

    I'm from Germany and I understand you more then every german speaking teacher here

  • @sahirbansal7027
    @sahirbansal7027 4 года назад +2

    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

  • @ananyochatterjee2445
    @ananyochatterjee2445 4 года назад +1

    Learning in quarantine..thanks man!

  • @uvernessomarriba5397
    @uvernessomarriba5397 3 года назад

    Great style of teaching!

  • @jello2233
    @jello2233 4 года назад

    Thanks a lot, when you do your advanced stats class you tend to forget the pure basis elements of stats thank you

  • @necropants7838
    @necropants7838 2 года назад

    This video saved my life.

  • @hillcipher7392
    @hillcipher7392 8 месяцев назад

    Thanks for this amazing explanation!

  • @ZombieTurtle00
    @ZombieTurtle00 5 лет назад +3

    Greatly informative video, thank you! :)

  • @ayikkathilkarthik4312
    @ayikkathilkarthik4312 4 года назад

    You are doing a great work...
    Really appreciate your work, Thanks for the video.

  • @leaboss3223
    @leaboss3223 4 года назад +3

    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

    • @OsamaShehzad1995
      @OsamaShehzad1995 4 года назад

      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.

  • @salvadormarin2276
    @salvadormarin2276 3 года назад

    Helped me so much in econometrics! Thanks!

  • @ОльгаАвдюхина-н4ж
    @ОльгаАвдюхина-н4ж 4 года назад +1

    Thank you great guy!!!!
    how can we calculate correlaion matrix for 3 random variables?

  • @remeajayi4997
    @remeajayi4997 3 года назад

    Great playlist and explanations!! Your camera blurs out at intervals, perhaps you could check that. Thank you for your lessons, they help a bunch!

    • @ritvikmath
      @ritvikmath  3 года назад

      Thank you! I ended up fixing this issue for my newer videos thanks to comments like this.

  • @ahhhwhysocute
    @ahhhwhysocute 3 года назад

    Thank you for making this simple and easy to understand :)

  • @shipper611
    @shipper611 4 года назад +1

    Great video, would be awesome to give a little more intuition on why these numbers are so insightful ;)

  • @HasanAlMasri
    @HasanAlMasri Год назад

    God Bless YOU! you saved me!

  • @geophysicsadvancedseismice7542
    @geophysicsadvancedseismice7542 3 года назад

    Hi, What are Correlation range and exponential correlation orders and how we can compute for variables (are in vector form or arrays)?

  • @shiyi9993
    @shiyi9993 3 года назад

    AMAZING! So clear!!!!

  • @tyson96
    @tyson96 2 года назад

    Very well explained. Thank you

  • @camilamiraglia8077
    @camilamiraglia8077 4 года назад +1

    Great video! Thank you

  • @greenbamboo8979
    @greenbamboo8979 4 года назад

    Thanks so much for your clear explanation.

  • @louishollick8904
    @louishollick8904 4 года назад +2

    Great video!

  • @gesahirsch
    @gesahirsch 3 года назад

    Fantastic explanation, thank you!

  • @SuperReddevil23
    @SuperReddevil23 4 года назад

    Great explanation Ritvik as always. Please can you make a series of Videos on Financial Calculus....?

  • @Arslanqadri
    @Arslanqadri 4 года назад +3

    What does the covariance matrix mean as a whole, i have seen it being used as an entity. Something intuitional as matrix multiplication here?

  • @abtinmeysami6551
    @abtinmeysami6551 2 года назад

    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?

  • @jevz7028
    @jevz7028 3 года назад

    Really appreciate the good work

  • @AmelZulji
    @AmelZulji 3 года назад

    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?

  • @sudeep15
    @sudeep15 4 года назад +1

    Great explanation!!!!

  • @sharris7343
    @sharris7343 3 года назад

    This was so helpful. Thank you!

  • @dante3578
    @dante3578 2 года назад

    so clear, thank you sir!

  • @ewanharris5433
    @ewanharris5433 4 года назад

    Excellent revision

  • @barbaricplayer
    @barbaricplayer 2 года назад

    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

    • @ritvikmath
      @ritvikmath  2 года назад +1

      Good luck! You got this

  • @anuroopnag2718
    @anuroopnag2718 4 года назад

    Really awesome explanation

  • @saraaltamirano
    @saraaltamirano 4 года назад +1

    Wow! So happy to have found this channel, you are a great teacher! Thanks!

  • @tuberliteable
    @tuberliteable 4 года назад

    Solid video. You're good at this

  • @adrianjohn8111
    @adrianjohn8111 7 месяцев назад

    sir
    you are awesome
    thank you
    second video I stumbled across and it was so clear

  • @haiderkamal1106
    @haiderkamal1106 4 года назад

    Thank You sooo MUCH!!!!!! This was a brilliant way to teach!

  • @justinscherzer5593
    @justinscherzer5593 2 года назад

    Thank you, super clear!

  • @oliverwilliams9507
    @oliverwilliams9507 4 года назад +1

    excellent video, thanks very much

  • @ahmedm.alfadhel272
    @ahmedm.alfadhel272 4 года назад +1

    well done

  • @orugantijyoshna7445
    @orugantijyoshna7445 3 года назад

    Understanding very well sir

  • @shashank4277
    @shashank4277 2 года назад

    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?

  • @stephanschaefer155
    @stephanschaefer155 4 года назад

    Don't you divide the sum of squared difference by (n-1) to get the variance? Great video. Thank for explaining so clearly.

  • @AJ-et3vf
    @AJ-et3vf 3 года назад

    Thank you very much sir. Very helpful!

  • @scarlettl2791
    @scarlettl2791 4 года назад

    This is really helpful! Thanks a lot sir!

  • @devendrasharma4033
    @devendrasharma4033 5 лет назад +1

    Thank you! for the video, as always awesome tutorial video.

  • @almonddonut1818
    @almonddonut1818 3 года назад

    Thank you so much for this!

  • @syleshgupta5957
    @syleshgupta5957 4 года назад

    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 ?)

  • @ahold89
    @ahold89 4 года назад

    Very good explanation!

  • @sithius
    @sithius 4 года назад

    Thank you for making this, very useful!

  • @yasirahmad5014
    @yasirahmad5014 4 года назад

    Excellent teaching..

  • @CFATrainer
    @CFATrainer 8 месяцев назад

    excellent stuff bro

  • @tb.adindalaksmana3634
    @tb.adindalaksmana3634 3 года назад

    Nice explanations .. thank you

  • @Nova-Rift
    @Nova-Rift 3 года назад +2

    Don't you need to center the data by subtracting the mean first from all the data?

  • @kevin-johar
    @kevin-johar 2 года назад

    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.