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Actually, I have a 3 in 3 Chance of winning. A car is nice, but a new furry friend is even nicer.

If revealing goats "transfers" the probability of that door to the remaining doors, why doesn't the probability get transferred to the door we selected? The only difference between them is that we selected one of the doors, how does out selection affect what's behind it? Isn't this literally gambler's fallacy?

your videos are the best explanations I've found anywhere online, keep it up! (Also, requesting a video on ridge/lasso regression)

love your videos❤

You are really good at explaining things simply... there are so few good pure statistics teachers on youtube... thanks a lot for your help!

I think this is the first time I've genuinely grasped this concept. Thank you so much!

Wish you were my prof :/

Hands down the best video on MLE!!!

can you explain why is your intercept -500? the diagram shows that the intercept of the line should be positive. so why is it negative?

I like how you are pretty much taking 'Fundamentals of Mathematical Statistics' but verbalizing and visualizing it... it's very handy and I would love to continue watching every video you make

that's a fantastic visual explanation... you are about to become very popular amongst statistics students worldwide

3:04 I love how he describe the indepence of these samples by talking about the coins coming from '3 sets of 10 flips' ... this ensures that the second sample isn't reliant on the first and the third sample isn't reliant on the second and first and so on... in other words, the samples are independent If the samples were taken from a single set of binomial, the probabilty of success of second flip as well as first flip is dependent on success or fail of first sample

Fantastic video... preparing for IIT JAM MS

Thank you!

As they had no wings the strangers could not fly away, and if they jumped down from such a height they would surely be killed.

man, u managed to explain something that my prof spent 1 whole month explaining in a singular video, a fantastically made video!!!!!

AMAZING explanation!

Fisher information is negative of expected value of double derivative of log L, then why we multiply with 'n' to get it?

Thanks for uploading all this content. I am about to begin my masters in data science soon and I was trying to grasp some math theory which is hard for me coming from a CS Background. Your videos make it so simple to digest all these topics.

The clarity you provide--as in, what the zero or 1 on the x-axis of the normal distribution represent but more importantly what they don't represent, which has been a source of confusion (and a drag) for me, is now more clear and finally validates a hunch/H-sub-A I've held; too many terms in statistics which I've encountered have been near-tautologies and a gigantic obstacle for me. In my humble and quasi-researched opinion about learning, cognitive transfer, linguistics, and abstraction, I postulate that for a new subject, especially those often found as hardly intuitive (clearly as a function of many factors), require the most clarity and for me an exhaustive list of features and areas of overlap, as well as an explicit articulation of the areas or features an idea does not connect with. THANK YOU for the excellent presentation!

Great video!

I have questions about statistical inference. Can you help me solve them?

I love how you put the context of sufficiency in real life chance events. Thank you for this gold video!

this is very helpful, thank you so much ❤

After ages of scrolling through the internet to understand what probability means, I finally reach my destination. I always felt that the analogy of 'Probability of flipping a coin' has different interpretations, one being where we can confirm our beliefs after something has happened for a large frequency of time ( Now I can confirm that it is officially called as the FREQUENTIST APPROACH ) and other one where we just know that it is equally likely. Wrapping my head around these concepts and confirming my beliefs was really a painful one. I finally feel satisfied. Thanks Brian for this video.

I freaking love you

AWESOME VIDEO

I'm glad I found your channel. I have never seen a better explanation of mathematical statistics, nobody else is even close! You are doing an amazing job there

you are a genius

This is a great explanation. I love the visuals showing how they are all related. Thank you.

Nice videos. I'm now preparing for my masters and it will be quite useful; the connection between CRLW and the standard error of the estimates by MLE makes this very nice.

Thank you! You made this make total intuitive sense in less than 2 minutes where other videos were taking 10+ minutes and I still didn't have that intuitive understanding

Well done! Thanks for the vid, however the video is a bit too complicated for me.

Great explanation

insane explanation

wow Thankyou Brian, very clear explaination

Please upload more of these videos so helpful

Ain't no one teaches statistics like you ❤ Thankyou soo much for giving such elaborative explanations.. And your illustrations regarding these inequalities made them very simple to understand

Its giving a hint of Heisenberg uncertainty principle

Thx, brother. Making this sht feel like 5th grade math. Ez PZ. wazzup then edit: no diddy, cute eyes brother. go get em

truly fantastic video, watched this and immediately popped off on my homework question. shoutout!

7:26 would have been easier to use the variance formula for uniform: (b-a)^2/12 and rearrange for E(X^2) = var(X)+E(X)

these are perfect! dumbs it down really well while retaining all the info

Thanks!

Thank you!

Thanks for the straightforward explanation!! Now I can understand why "sufficient" is sufficient!

Send love to U! Your Mic is getting better

What if you don't know what's the data distribution?

Finally understood what it means when we say intervals are random in frequentist paradigm.

By God what an absolutely amazing video.