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?
@briangreco27186 күн бұрын
The y-intercept is not shown on the graph at all, because the x axis only goes from 60 to 70. X = 0 is way to the left.
@PriyankaRoy-r1g6 күн бұрын
@@briangreco2718 But the regression here is drawn with origin as 0. also the regression line is cutting the Y axis somewhere between 50-100, lets assume 75. so it shows when x=0, y=75, which basically is the intercept. I am a bit confused on this. how is the intercept -500 and the graph shows something else
@briangreco27186 күн бұрын
The graph doesn’t show the x=0, so you are reading the graph incorrectly. The equation is correct and you understand the equation correctly, but you are reading the graph incorrectly. There is no y axis.
@RoyalYoutube_PRO8 күн бұрын
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
@briangreco27188 күн бұрын
Thank you!
@RoyalYoutube_PRO8 күн бұрын
that's a fantastic visual explanation... you are about to become very popular amongst statistics students worldwide
@RoyalYoutube_PRO9 күн бұрын
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
@briangreco27189 күн бұрын
To be clear, we are still assuming all the 30 flips are independent and have the same probability of heads - we are just changing how summarize the data. Whether we talking about each flip individually, 3 sets of 10, or 1 set of 30, all 30 coin flips are independent.
@RoyalYoutube_PRO9 күн бұрын
Fantastic video... preparing for IIT JAM MS
@5romir9 күн бұрын
Thank you!
@Susan_862612 күн бұрын
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.
@brucelam11513 күн бұрын
man, u managed to explain something that my prof spent 1 whole month explaining in a singular video, a fantastically made video!!!!!
@siavashk10018 күн бұрын
AMAZING explanation!
@ridwanwase744419 күн бұрын
Fisher information is negative of expected value of double derivative of log L, then why we multiply with 'n' to get it?
@briangreco271819 күн бұрын
I was assuming the L here is the likelihood of a single data point. In that case, you just multiply by n at the end to get the information of all n observations. If L is the likelihood of all n data points, then the answer will already contain the n and you don't have to multiply at the end. The two methods are equivalent when the data is independent and identically distributed.
@ridwanwase744419 күн бұрын
@@briangreco2718 Thanks for replying so quickly! I have another question, is MLE of population mean always guarantee that it will have the CRLB variance?
@briangreco271819 күн бұрын
Hmm, I don't think this is true in general. At some level, it's certainly not true if we're talking about the CRLB of unbiased estimators, because the MLE is sometimes biased. For example, in a uniform distribution on [0,theta], the MLE is biased, and the Fisher Information is not even defined. My guess is that this applies for some "location families", which the normal, binomial, poisson would all be. For a "scale family" like the exponential distribution, in the parameterization where the mean is 1/lambda, I do not believe the MLE meets the CRLB.
@ligandroyumnam554621 күн бұрын
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.
@TheErgunPascu23 күн бұрын
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!
@briangreco271823 күн бұрын
Thank you, absolutely!
@LayLenChing24 күн бұрын
Great video!
@ninuuh26 күн бұрын
I have questions about statistical inference. Can you help me solve them?
@briangreco271826 күн бұрын
If you have a question related to the video, I may be able to help. If it’s not related to the video, I probably can’t help.
@ninuuh26 күн бұрын
@@briangreco2718 It is about statistical inference, unbiased estimator and sufficient statistic
@ninuuh25 күн бұрын
It is related to statistical inference, adequate statistics and an unbiased estimator@@briangreco2718
@ninuuh25 күн бұрын
It is about statistical inference, unbiased estimator and sufficient statistic@@briangreco2718
@ninuuh25 күн бұрын
@@briangreco2718 Yes, related to the video
@charlesSTATS27 күн бұрын
I love how you put the context of sufficiency in real life chance events. Thank you for this gold video!
@rafaelhadi634228 күн бұрын
this is very helpful, thank you so much ❤
@avadhsavsani114829 күн бұрын
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.
@MoneerGhanemАй бұрын
I freaking love you
@conceptualprogressАй бұрын
AWESOME VIDEO
@ops428Ай бұрын
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
@briangreco2718Ай бұрын
Thanks for the kind words :)
@jwbparkАй бұрын
you are a genius
@sabinaharding1990Ай бұрын
This is a great explanation. I love the visuals showing how they are all related. Thank you.
@santiagodm3483Ай бұрын
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.
@kevinbreckman5321Ай бұрын
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
@briangreco2718Ай бұрын
So glad it helped! I agree, it is usually presented in a way that hides the very simple intuition behind the idea.
@olaoluwaodeyemi4059Ай бұрын
Well done! Thanks for the vid, however the video is a bit too complicated for me.
@phillipmunkhuwa5435Ай бұрын
Great explanation
@severed_toastАй бұрын
insane explanation
@jann4249Ай бұрын
wow Thankyou Brian, very clear explaination
@jwbparkАй бұрын
Please upload more of these videos so helpful
@TousibAhmedBPSO-kq1nkАй бұрын
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
@TousibAhmedBPSO-kq1nkАй бұрын
Its giving a hint of Heisenberg uncertainty principle
@existentialrap521Ай бұрын
Thx, brother. Making this sht feel like 5th grade math. Ez PZ. wazzup then edit: no diddy, cute eyes brother. go get em
@JL-vg5yjАй бұрын
truly fantastic video, watched this and immediately popped off on my homework question. shoutout!
@trolltoll440Ай бұрын
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)
@briangreco2718Ай бұрын
Yeah, that’s what I probably would’ve done myself to save some calculus too - for the video I just wanted to emphasize the idea rather than the most efficient method. Thanks for watching!
@trolltoll440Ай бұрын
these are perfect! dumbs it down really well while retaining all the info
@qkdnrnskfirnsvabk2 ай бұрын
Thanks!
@qkdnrnskfirnsvabk2 ай бұрын
Thank you!
@qkdnrnskfirnsvabk2 ай бұрын
Thanks for the straightforward explanation!! Now I can understand why "sufficient" is sufficient!
@LuksYang2 ай бұрын
Send love to U! Your Mic is getting better
@briangreco27182 ай бұрын
Thanks, I got a new microphone so the only video with the old microphone is the Markov's inequality one :) All other current videos and future ones should have very good audio!
@dariofabian88192 ай бұрын
What if you don't know what's the data distribution?
@briangreco27182 ай бұрын
Maximum likelihood basically requires that you assume something about the distribution, otherwise you get those extreme examples that I mention throughout the video.
@dariofabian88192 ай бұрын
@@briangreco2718 thank you for the answer
@shreyanshchouhan30972 ай бұрын
Finally understood what it means when we say intervals are random in frequentist paradigm.
@ifeanyianene67702 ай бұрын
By God what an absolutely amazing video.
@aldenc.94612 ай бұрын
Really impressed with your videos, keep on making more!
@briangreco27182 ай бұрын
Thank you, many more to come!
@sushantgarudkar1082 ай бұрын
I discovered this channel in youtube search and guess what it burnt all my frustration and I love the way to make people understand topic in this video!
@psycheguy5032 ай бұрын
Thank you so much! this is so intuitive and funny
@SarahR86772 ай бұрын
A bring me the horizon music video led me here in a series of coincidental events LOL 😅😂 enjoyed this video though
@yasamanboroon-zn2lu2 ай бұрын
It was awesome please continue 🔥
@maryziperman44102 ай бұрын
thank you soooooo much. this was so helpful for my college final in mathematical statistics at Texas a&m!!!! you are incredibly gifted!
Пікірлер
Wish you were my prof :/
Hands down the best video on MLE!!!
Thank you!
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?
The y-intercept is not shown on the graph at all, because the x axis only goes from 60 to 70. X = 0 is way to the left.
@@briangreco2718 But the regression here is drawn with origin as 0. also the regression line is cutting the Y axis somewhere between 50-100, lets assume 75. so it shows when x=0, y=75, which basically is the intercept. I am a bit confused on this. how is the intercept -500 and the graph shows something else
The graph doesn’t show the x=0, so you are reading the graph incorrectly. The equation is correct and you understand the equation correctly, but you are reading the graph incorrectly. There is no y axis.
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
Thank you!
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
To be clear, we are still assuming all the 30 flips are independent and have the same probability of heads - we are just changing how summarize the data. Whether we talking about each flip individually, 3 sets of 10, or 1 set of 30, all 30 coin flips are independent.
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?
I was assuming the L here is the likelihood of a single data point. In that case, you just multiply by n at the end to get the information of all n observations. If L is the likelihood of all n data points, then the answer will already contain the n and you don't have to multiply at the end. The two methods are equivalent when the data is independent and identically distributed.
@@briangreco2718 Thanks for replying so quickly! I have another question, is MLE of population mean always guarantee that it will have the CRLB variance?
Hmm, I don't think this is true in general. At some level, it's certainly not true if we're talking about the CRLB of unbiased estimators, because the MLE is sometimes biased. For example, in a uniform distribution on [0,theta], the MLE is biased, and the Fisher Information is not even defined. My guess is that this applies for some "location families", which the normal, binomial, poisson would all be. For a "scale family" like the exponential distribution, in the parameterization where the mean is 1/lambda, I do not believe the MLE meets the CRLB.
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!
Thank you, absolutely!
Great video!
I have questions about statistical inference. Can you help me solve them?
If you have a question related to the video, I may be able to help. If it’s not related to the video, I probably can’t help.
@@briangreco2718 It is about statistical inference, unbiased estimator and sufficient statistic
It is related to statistical inference, adequate statistics and an unbiased estimator@@briangreco2718
It is about statistical inference, unbiased estimator and sufficient statistic@@briangreco2718
@@briangreco2718 Yes, related to the video
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
Thanks for the kind words :)
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
So glad it helped! I agree, it is usually presented in a way that hides the very simple intuition behind the idea.
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)
Yeah, that’s what I probably would’ve done myself to save some calculus too - for the video I just wanted to emphasize the idea rather than the most efficient method. Thanks for watching!
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
Thanks, I got a new microphone so the only video with the old microphone is the Markov's inequality one :) All other current videos and future ones should have very good audio!
What if you don't know what's the data distribution?
Maximum likelihood basically requires that you assume something about the distribution, otherwise you get those extreme examples that I mention throughout the video.
@@briangreco2718 thank you for the answer
Finally understood what it means when we say intervals are random in frequentist paradigm.
By God what an absolutely amazing video.
Really impressed with your videos, keep on making more!
Thank you, many more to come!
I discovered this channel in youtube search and guess what it burnt all my frustration and I love the way to make people understand topic in this video!
Thank you so much! this is so intuitive and funny
A bring me the horizon music video led me here in a series of coincidental events LOL 😅😂 enjoyed this video though
It was awesome please continue 🔥
thank you soooooo much. this was so helpful for my college final in mathematical statistics at Texas a&m!!!! you are incredibly gifted!
Thanks, Mary, I'm glad it helped!
thank u thank u thank u