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.
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!
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 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.
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
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?
