Chi-square distribution introduction | Probability and Statistics | Khan Academy
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- Опубліковано 23 лип 2024
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Chi-Square Distribution Introduction
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9 years later students are still being saved by this. Super grateful!
I swear
it's true...
Make it 10 years later!
@@dryrain2 make it 11
12 years now.
My stats teacher just throws a bunch of formulae on the board, and then when you have to do stuff I have no idea where the distributions come from or what they are used for, how to use them, or what they mean.
Lol
Holy crap You wrote this message 9 years ago when and I have to say, things don't change...
I am curious what are you doing now after 9 years.!
I can't agree more ROFL
this is exactly what my teacher does, things dont really change even after 11 years 🤷🏻♀️
dude, your math videos are the best ones I've found. My prof is awful, I'd be lost without these.
This is such a great video. It's amazing how much great information is conveyed in such a simple and succinct way.
12 years later and still useful!! Thank you so much
Thanks! Never had a class for this subject but now I understand it all!
Thank you so much! This is THE video that really taught me the concept of Chi squared distribution.
I love your videos Sal, the one concept I still dont understand is degrees of freedom. i understand what they do but not what they are. I think you explained them at one point but i couldnt find the video looking back through the playlist. maybe dedicating a video to df would be helpful for other statistic students as well. thanks!!!
Great video! Actually all your videos are really helpful and make things understandable and even intuitive in a way :) Thank you!
A very nice video, I have a statistics course now, this was really helpful! Thank you!
Thank you so much. My university lecturers aren't great, and I just can't learn maths from a textbook. It helps so much that you have explained it in such a simple, clear way.
How's your maths now?
Awesome Explanation Sir!!! Thanks for the valuable knowledge !
I'd be lost without these videos man many thanks!!
Thanks for visually explaining what this distribution means!
I am too stupid for this.
my thoughts everytime I study something math-related lol
😂😂😂😂😂😂😂 really
really great videos!! keep up the good work!
You are a magician man, thank you!
At 04:45 please explain how a probability distribution sample has a probability of greater than 1 in the chi square curve for k=1?
This guy's classroom probably has more A's than an Energizer factory.
~Reus Vult Ave Sumia, Pegasus Breeder and Root Beer Connoisseur
Unlikely, for most colleges, there is an implied rule that most students will not get an A.
Coming back after 10 years. Thanks Sal!
I can't believe this. How come I understand, all this time I only understand now. But how.
Thanks Khan
lol its called ki-square ..and I have been calling it Chi-square (like chili)
Same here bruh😂😂😂..you can't imagine the embarassment i felt after finding out
Actually it should be pronounce chee, not ki. The letter is chi, it is greek and that is how it is correctly pronounced. Same with pi, it should be pronounced pee
our teacher usually says "guy square" :D
it's useful, thanks so much.
thank you, that helped alot for my exams
Thank you for the nice video :D This video is really helpful!!
I'm lost right from the beginning. I'm using the Pearson book for stats class and I think it takes a completely different approach to the chi-distribution. It isn't close to being clear to me yet.
Great explanation!
it will be better if the origin of these terms (motivation of creation of these terms) are explained in advance of these tutorials. anyway, these tutorials are great :)
Excellent, very instructive
I am currently in my Statistics class, waiting for my Professor to finish his lecture on Chi-test (which I don't understand by the way and feeling dizzy) so I can come back here to get the real lecture
i can pass my exams because of you..thank you so much!
I love this guy.
You're the best!
When the probability is .3, that gives us the value at 2.41 … but aren’t we looking for values greater than 2.41? I think I’m misunderstanding. I would think the answer would be .3 if the question asked P(Q2 ≥ 2.41) … but since we’re strictly looking for values greater than 2.41 I would think we would move up one box … can anyone explain?
Thanks for the videos, what software is he using? Excel?
Khan is my hero.
Actually this introduction vid seems to be the only of your chi-square vids I´m having a hard time to understand haha. I guess I shouldn´t have skipped the basics.
So, I'm studying for Actuarial Exam P and in a sample exam i'm taking, there is a time when I have to just somehow know that the sum of two squared standard normal random variables is exponentially distributed. Well, more precisely, I"m asked to find the moment generating function of (X^2 + Y^2) / 2 where X and Y are distributed N(0, 1), but in the solution they just throw out there that "Obviously" X^2 + Y^2 is exponentially distributed with hazard rate 0.5 and mean 2. I just don't know how they know that. Wouldn't it be easier to use that this would be Chi-squared?
you can prove by joint pdf of X and Y, and switch to polar coordinates that sum of squared standard normal random variable is exponential distributed. It all depends on how the questions are phrased though
Great video!!! just wanted to understand why do we square X1 and X2?
Could you record a video about degrees of freedom?
Thanks for this video
Still watching it in 2023. He helped everyone
I’m from 2024
great stuff as always
So standard normal distribution is normal distribution's z-scores distribution?
The key in teaching stats is to use examples and not just terms so instead of saying variable X it would be better if u did indeed use a variable like weight, height, or anything else so we can follow
I'm a bit lost.. is there a preliminary video to this? I don't know the language.
You're the bessst
Thank you
I am pretty new to Statistics, what is the use of a chi-square distribution, based on what I have seen a question could be: What is the chance that it is under this value or is this chi-square distributed.
Thanks a lot
13 years later and here I am, finally my turn to watch this for an exam 🎉
ur awesome mate
His handwriting is great with the mouse, he must be awesome at shooters, HEADSHOT HEADSHOT
aldezmail he most likely uses a digital pen.
@@vivekmittal7893 Probably right, but I trip out the whole time I'm watching him thinking what if he was writing with a mouse. The thought distracts me the whole time.
You forgot to normalize the new chi-squared distributions. You need to make sure the multiple integral ("volume") under the multivariate probability distribution is equal to 1.
Sal's VOICE gives me CONFIDENCE.
Good
Khan is so smart.
I am currently taking a biology class, and one our EXTREEEEMELY DIFFICULT assignments is 'Chi Square Test and Corn Genetics Lab'.. I am sooooooo lost!!! I have absolutely NOOOOOOOOOO idea what to do! PLEASE HELP!!!!!!!!!!!!
ur a hero
Im not sure I understood much from this explanation. I would have prefered a more practical application. Could you indicate if you have another video. Also as it relates to the degree of freedom I was a little confused as I thought it was n-1 but you seem to suggest its = to n
For degrees of freedom, why are we not applying the rule of n-1. E.g. If you take a sample of 1, you are saying the df is 1 but should it not be (1-1) 0?
the rule of n-1 is applied supposingly when X ~ N(mu, sigma^2) where mu is unknown and you use the sample mean to estimate. Because mu is being estimated it takes away 1 degree of freedom when doing chi square test.
By the way in this case X has to be converted to standard normal first because it is not.
Maseno University Kenya super supportive
Why do chi-squared distribution and chi-squared test have different formulas? Please answer, I'm going to have a presentation tomorrow 🙏🏿
I am really confused with the degree of freedom . I know that formula is : Number of independent variables- Number of constraints. Is degree of freedom 1 when we consider 1 variable because it is an independent variable and we are not doing analysis involving a constraint?
How is sigma squared = sigma??? I thought (and been taught always) that standard deviation (sigma) was the square root of the variance (sigma squared)
Does chi-squared distribution formula is E(X)=k, V(X)=2k.? Correct me if this wrong.
How does this distribution approach standard normal distribution (mean = 0) as df increases if the mean is increasing? Isn't it just approaching a normal distribution (not standard normal)?
yes, it won't be standard.
when n tends to infinity, by central limit theorem, it approaches standard normal.
amen
My professor is all good...but I'm here since I was dumb enough not to listen in class
how do you know the degrees of freedom?
number of parameters you are estimating
it should be n-1 and not just n as he makes it appear in this video
it depends on number of X you have, and number of unknowns you are estimating.
Can you explain what exactly X and Q are in real experiment.. may be with some example!
X is just a random variable sampled from N ~ (0,1), while Q is X^2 in which X is randomly sampled standard normal random variable
@@eXcelMathS Why the degree for freedom from first sample distribution is one and so on?
why is it that P of Q2 greater than 2,41 and not SMALLER than 2,41??? I do not get how he got to that conclusion
What's the degree of freedom?
Damn it.. my x's looked like chi's to begin with!
yeah, i thought he was writing with a mouse too. but from the writing style, i think the mouse is very likely to be made in pen shaped.
😂😂
I love Sal
Not all heroes wear capes
When I first see the of chi-square in English I thought was something chinese pphrase
so wat the hell is it used for?
Try to draw one of those graphs. Khan can't do that.
👏
After 11 year
so that's where the square coming from??????
sdks
Although derived from one another, standard deviation isn't the same as variance. It's true that variance is the measure of spread of the data around the mean, but it by itself can't be interpreted. If we take the square root of the variance, we obtain the standard deviation, which is what we see when we look at spread around the mean in the normal distribution. In the case of N(0, 1), the population mean is zero and its variance is one; the square root of one is one, so our standard deviation becomes one. If we have N (0, 2), however, then we have a mean of zero and a standard deviation of ~1.41.
I think he was just talking about when the SD is 1.
😭
Chi-ote XDDDDD
k-1
This teacher is really confused