WOW: clear explanation, calm background music and being straight to the point. Those ingredients were enough to make me subscribe right away! Looking forward to more videos about statistics!
Now that my hair is all gone from scratching my head because of frustration trying to understand my Professor's lecture, along came this video that basically put a lot of sense into my head in just only about 5 minutes of video, whereas in my Professor's lecture took a whole week to explain to the class. Thanks for the great explanation..
Greeting Dr. Shah, I have a concern that need to share with you, in your video, you proposed about covariance- i.e., Var(xy) = {Exy/n - (Xbar.Ybar)} however it's value is differed by another covariance formula = E{(Xi-Xbar).(Yi-Ybar)}/{n-1}. Request you to suggest.
Agree - the video can calculate the population covariance but not the sample covariance. If we do not subtract it from the 'mean', the division by (n-1) cannot yield the sample covariance.
The variance formula differs from that in wikipedia: The variance of a random variable {\displaystyle X} X is the expected value of the squared deviation from the mean.
Hi Joshua, not sure how you get x1^2 - xbar^2 + x2^2 - xbar^2. using the formula (a-b)(a-b)=a^2-2ab+b^2, i get x^2-x.xbar-x.xbar+xbar^2. not sure whether you can advise me...
Thank you so much! This cleared things up very quickly! One question I have, You calculate the co-variance do you have to really calculate the variance. Can you not just skip to calculating the sum of xy/n - (mean of x)(mean of y) ??
if I'm not mistaken (you may need to verify my answer), you use n-1 as the divisor because many textbooks use unbiased estimator approach, as in, you don't know the value of all population and using the samples instead. this case use n as the divisor because it used maximum likelihood approach, as in, you know the value of all population (we have all value of x and y). CMIIW.
Dear math master, Could you give me an example of finding a covariance matrix from a Gaussian function fit on a Gaussian data? Cheers. You may suggest a book to read about this kid of fitting problems as well.
what you do is distribute the exponent to remove parenthesis x1^2 - xbar^2 + x2^2 - xbar^2 all divided by N Now Xbar^2 can be factored out... it is N * -Xbar^2 .. but remember it is all over N so it cancels the N out. hence Σ(x^2)/N - (xbar)sq * N / N
in my textbook there is a different formula for the variance ( s² = sigma(x-xbar)²/n-1 ) and here he uses a simplified version of it ( var(x) = sigma X²/n - Xbar² ) but i get 2 different answers with the same sample values... Can anyone help me out there?
I just tried calculating it with the formula I posted above and I ended up with 15/2 ( simplified ) giving me 7.5. Your answer gives me 6.67* , i.e. 20/3. Not having a go or anything, just want to know what, most likely, I am doing wrong?
[SUM( Xi- XBar)^2]/n-1 that is the formula that my statistics lecturer taught me to calculate the varience but yours differs in the sense that you don't subtract one from n
Yes, but this is also valid, you just need to keep in mind that it gives you a biased estimate, while n-1 removes the bias introduced by estimating the mean through the same sample.
right? I was trying to figure that one too and I came up with a totally different answer for the sigma of x squared i got 317 for his numbers. I'm lost
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your 7 min of explanation is better then 12 pages of stats lecture notes- amazing!
WOW: clear explanation, calm background music and being straight to the point. Those ingredients were enough to make me subscribe right away! Looking forward to more videos about statistics!
Now that my hair is all gone from scratching my head because of frustration trying to understand my Professor's lecture, along came this video that basically put a lot of sense into my head in just only about 5 minutes of video, whereas in my Professor's lecture took a whole week to explain to the class.
Thanks for the great explanation..
This is by far the best explanation for solving for covariance. Thank you very, very much.
simple and straight to the point, amazing explanation overall!
Thank you prof . I cant even undertand when my lecturer teached us. Its almost 14weeks of lecture and next week we gonna sit for exam . You help me
I just want you to know and be reminded that you have helped someone even in this year 2021. Thank you.
Greeting Dr. Shah,
I have a concern that need to share with you,
in your video, you proposed about covariance- i.e., Var(xy) = {Exy/n - (Xbar.Ybar)}
however it's value is differed by another covariance formula = E{(Xi-Xbar).(Yi-Ybar)}/{n-1}.
Request you to suggest.
Agree - the video can calculate the population covariance but not the sample covariance. If we do not subtract it from the 'mean', the division by (n-1) cannot yield the sample covariance.
So glad I found this video! Thank you for explaining this. I'm taking a summer course and needed to understand covariance quickly!
Thank you so much, Mr. Instructor! This is so much helpful. So easy to understand. Have a wonderful day, and stay safe!
Great explanation, simple and easy to understand. Thank you for the video :)
The variance formula differs from that in wikipedia: The variance of a random variable {\displaystyle X} X is the expected value of the squared deviation from the mean.
Better than khan academy and all other youtubers, thanks bald man
Today I was very afraid that how to calculate this thing and i watch your vedio and clear my all doubt. Thank you sir
This was probably the most concise, clear way anyone has ever explained covariance to me.
OH MY WORD!! This has helped me so much! I haven't been able to understand any other video! Thank you so much!
Holy moly, thank you SOOOO MUCH for this!! How did you make this so easy to understand??
this is still helping me ten years later thank you
Hi Joshua, not sure how you get x1^2 - xbar^2 + x2^2 - xbar^2. using the formula (a-b)(a-b)=a^2-2ab+b^2, i get x^2-x.xbar-x.xbar+xbar^2. not sure whether you can advise me...
YOU ROCK!
OMG!
I WISH YOU WERE MY PROFESSOR!
SHE HAS BEEN TRYING TO TEACH US THAT FOR OVER 5 WEEKS!
AND IT TOOK YOU 7 MIN!
thanks you very much I spent two months with my lecturer who's so suck finally this minutes video made it more easiest .
Thank you so much! This cleared things up very quickly!
One question I have, You calculate the co-variance do you have to really calculate the variance. Can you not just skip to calculating the sum of xy/n - (mean of x)(mean of y) ??
The best video about variance, covariance and correlation coefficient together ever..)
Great video, professor could not teach me this, other videos I look up were not helpful but this video was great.
i learned more in this 7 minute vid than i did sitting in class the past 4 weeks
can u tell me if S.D of x and coefficient of correlation is given and the value of covariance for x & y is given what will be the S.D of Y?
if I'm not mistaken (you may need to verify my answer), you use n-1 as the divisor because many textbooks use unbiased estimator approach, as in, you don't know the value of all population and using the samples instead. this case use n as the divisor because it used maximum likelihood approach, as in, you know the value of all population (we have all value of x and y). CMIIW.
Can we find find the variance of y if we have variance of X and covariance of X and Y
Dear math master, Could you give me an example of finding a covariance matrix from a Gaussian function fit on a Gaussian data? Cheers. You may suggest a book to read about this kid of fitting problems as well.
Wow thank you sooo much! I can't believe that this was what my uni prof was teaching about. I am doubting of the existence of universities nowadays..
Thank you for this, well explained and well understood!!!
Definitely cleared some things up. Hopefully the problems I'm doing allow me to use this method.
Good one. Thanks. Please explain covariance matrix also.
i rarely comment on videos. But this was fantastic, absolutely superb,
what you do is distribute the exponent to remove parenthesis
x1^2 - xbar^2 + x2^2 - xbar^2
all divided by N
Now Xbar^2 can be factored out... it is N * -Xbar^2 .. but remember it is all over N so it cancels the N out. hence Σ(x^2)/N - (xbar)sq * N / N
Fantastic video. Made a difficult concept for me very easy to understand. Thanks!
Isn't the formula a different one for the variance? isnt it something like : s² = Σ(x-xbar)² / n-1 ?
Give this guy all the awards
great concise explanation. thank you
Plz post a video of ancova.....
but what about the degree of freedom (9-1) for the denominator?
So brilliantly explained, thanks so much
best and most intuitive explination
amazing clear cut concept very helpful for my data mining paper
Thank you.. It was really helpful 👍
You explained in such easy terms, thanks !
From which country u are..??
sir, please teach how to calculate Gini index and concentration index
You are brilliant Sir ... Love you Sir ...
in my textbook there is a different formula for the variance ( s² = sigma(x-xbar)²/n-1 ) and here he uses a simplified version of it ( var(x) = sigma X²/n - Xbar² ) but i get 2 different answers with the same sample values... Can anyone help me out there?
did you find the answer?
Thank you for sharing, very helpful
thnx sir your teaching way is very good
Using the variance function on my ti--89: the variance of x= 15/2....
which one is legit?
After scratching off many papers you came to my rescue. thanks @Two-Point-Four
Thank you so much for your excellent explanation. Keep up the great work.
Excellent !!!! Thanks Dr
can I ask what is E[ XY ] ?
Thank you Dr. Shah . I am saved :')
Thank you for making this!! So helpful
I just tried calculating it with the formula I posted above and I ended up with 15/2 ( simplified ) giving me 7.5. Your answer gives me 6.67* , i.e. 20/3.
Not having a go or anything, just want to know what, most likely, I am doing wrong?
Sir I have a doubt
How did you get 20/3 of var.of(x) and 28/3 of var.of(y)
Very skillful in teaching
Thank you so much for sharing!
I knew math wasn't evil! please make more video
Can the covariance be a negative value?
Thank you holy shit. Going to have to rely on you tube videos in order to pass my stats course, God damn my proff is a worthless bad of shit.
Preach brotha
What are you studying buddy?
Really clear explanation..! Thanks!
This explaination is so good
Can u explain ancova sir pls
i always have a formula with (n-1) not over (n) .. so my results are bad or this is just a different method
+Jessica j (n-1) and (n) are used in different scenario, one giving u the sample and the other population.
[SUM( Xi- XBar)^2]/n-1 that is the formula that my statistics lecturer taught me to calculate the varience but yours differs in the sense that you don't subtract one from n
That is for a sample mean and sample variance. To avoid error and bias, you use Xbar and N-1 for the sample, that is the reason behind the formulas
You made my doubt clear thanks ❤️
guys, for the variance he used sample variance. so don't be confused, if you are using estimate of variance then use that (:
how did you get 20/3?
20/3 is 6.666666666667 so its easier to look at it as 20/3 and more correct, when plugging it back into cal.
💞💞💞 great teacher
How did you get 20/3?
Very well explained.
Thank you great all round explanation!
Isn't this a sample? Wouldn't you divide by n-1?
Yes, but this is also valid, you just need to keep in mind that it gives you a biased estimate, while n-1 removes the bias introduced by estimating the mean through the same sample.
Super useful, thank you.
Why there are many different formulas for the same thing? I am very confused
Thanks! This was actually very helpful.
How can solve in three series like x , y& z
well explained.. Thank you
Thank you, great, simple explanation.
Great explanation!
I love this man
thanks from my heart
Whoa this is magic.Thanks.
Thx teacher you are good teacher
Thanks for the great explanation!
right?
I was trying to figure that one too and I came up with a totally different answer for the sigma of x squared i got 317 for his numbers. I'm lost
Very good class🥰
Very well explained. Thanks :)
Thank you soooo much! You are great!
Love you man !
really well done
waoo.. this is an amazing explanation. thank u
THANKYOU SO MUCH!!!
finally a video i understand. thanks for sharing :)