I really hate how the professors go over the simplest examples but then the homework has in depth problems like these. Thank you so much.
@mclovinyousaucin13 күн бұрын
you literally are the reason i’m gonna pass this module, not a single other video on the internet did it like you, exactly the terms and definitions i needed. GOD BLESS YOU ❤️❤️❤️
@Stats4Everyone13 күн бұрын
So happy to hear that you found this content to be helpful! :-D
@leahwilliams32814 жыл бұрын
Seriously. Thank you. My professor didn't explain this very well, but it was totally on the homework. You did a great job explaining.
@kushalmohnot38084 жыл бұрын
I've fallen in love; what an incredibly clear thought process!
@Stats4Everyone4 жыл бұрын
Awesome! Happy to hear that this video was helpful :-)
@wanhope36604 жыл бұрын
explained in simple terms. helped me more than hours of listening in my probability class. Thank you !
@StatsWithJesse2 жыл бұрын
Great video - thank you. I studied applied mathematics a few years back, and I quickly forgot some important things. I needed this video- it was clear and concise.
@movocode Жыл бұрын
Thank you sooo much - you helped me in very last moment of my exam prep - literally seeing this 1 hr before my exam starts. Love from India.
@Stats4Everyone Жыл бұрын
You're very welcome! I'm so happy that this video was helpful :-)
@cleo76633 жыл бұрын
Thank you for saving my life. Seriously.
@baqerghezi1342 Жыл бұрын
Great video thank you. also we can see the answer is 1 from the support (1
@Stats4Everyone Жыл бұрын
Yup. I am just showing the math for that logic. Here is another video where the answer is maybe not so obvious: kzbin.info/www/bejne/eHOzhICClMSbhdE
@kkikkodan10 ай бұрын
thanks so much. my sir did this in short but didn't give reasons for the way things were. so this was very helpful. love from India.
@ahmetkarakartal95632 жыл бұрын
you saved my life
@mirandaatangdithebe38934 жыл бұрын
Could'nt have asked for a clearer video, thank you sm.
@Stats4Everyone4 жыл бұрын
Happy this video helped!
@topstuffspotter78782 жыл бұрын
Great Explanation! and your voice is really sweet.
@Stats4Everyone2 жыл бұрын
Lol. Shucks. Thanks :)
@pppeterrrr47766 жыл бұрын
thanks, its very straightforward and clear
@ihsanerben4 ай бұрын
YA SEN NE BÜYÜK Bİ ADAMSIN BE KARDŞEİM
@malcolmlamya87706 ай бұрын
Thank you, it helps a lot. God bless.
@Stats4Everyone6 ай бұрын
So happy to hear that this video was helpful!
@ghitrifaldiadrian432429 күн бұрын
your’re the best
@Stats4Everyone29 күн бұрын
Thanks for the comment, glad you found this content to be helpful :)
@ghitrifaldiadrian432429 күн бұрын
@@Stats4Everyone yes, it really is, i’m currently struggling a bit on this topic for my mid semester. Then i found your video and all was crystal clear
@rye-bread5236 Жыл бұрын
Jesus. I regret college. I could have been a fantastic electrician and probably make almost as much.
@darcash17384 күн бұрын
I see. so marginal is just integral in respect to the other var, with bounds according to that var. Then for conditional f|y it is f(x, y)/fy, where fy is the marginal prob. Then you plug in whatever the y val is, and do the integral in terms of x, with bounds updated to whatever the y val is.
@izume20324 жыл бұрын
You saved me 😭💙 thank you so much
@bilgegursoy25994 жыл бұрын
you are my savior
@mohitgupta31153 жыл бұрын
thank u so much , i wish my professor learn how to tech like you
@Stats4Everyone Жыл бұрын
Glad you found this video to be helpful :-)
@YokiWong6 жыл бұрын
Thank you so much for the great video!
@tvvt0057 ай бұрын
8:14 hi, if instead of a specific value, if it were given Y
@arnabbanerjee58335 жыл бұрын
Thank you so so much for uploading this vedio... It helped me a lot.!
@ruanvieira25452 жыл бұрын
Great explanation, thanks!
@lynejomaa73656 жыл бұрын
tysm i have my stat final in 4 hours and might pass it thanks to this vid
@Flowerlifts1116 жыл бұрын
did u pass
@lynejomaa73656 жыл бұрын
yes i did!!!! :D
@kasunpathirana94102 жыл бұрын
So understandable
@ottodvalishvili76016 жыл бұрын
great explanation .
@chanakaramanayake84094 жыл бұрын
Thanks a lot. Good explanation. keep it up👍
@rakeshkumar-jw5lb2 жыл бұрын
first u took good example with good explaitions
@rakeshkumar-jw5lb2 жыл бұрын
so it is good to all
@ARCWIZARDАй бұрын
Thx ❤
@vishwajiththippeswamy57144 жыл бұрын
Thank you so much. Examples were very helpful :)
@Stats4Everyone4 жыл бұрын
Glad it was helpful! :-)
@usernameispassword4023 Жыл бұрын
Thank you so much ma'am!
@sheetalkumar45792 жыл бұрын
why is the first part of the integral -> -inf to y for f(x,y)dx = 0 ? Shouldn't it be integrated in that range ?
@ericliu77055 жыл бұрын
Thank you, this was very helpful
@rheabali76916 жыл бұрын
how would we evaluate the conditional probability when y is "less than/equal to" say 1 instead of equalling 1?
@EWB4385 жыл бұрын
P(Y
@Stats4Everyone4 жыл бұрын
I know its been a while since you posted this question, though it is a really good one, so I made a video that might help with this concept: kzbin.info/www/bejne/eHOzhICClMSbhdE .......if this is too late for you, maybe it might help someone else with the same question. thanks for posting this comment!
@sanjaykumarsinha30584 жыл бұрын
The video was very informative! But i don't understand one thing. We know, if the random variable is continuous then probability at a particular point is zero.(The reason is we don't cover any area and integration is simply area under curve). But while calculating conditional pdf we take it as a non zero value. { fy(1)= .5, let's say}.Why is that?
@Stats4Everyone4 жыл бұрын
Hi Sanjay - Good question - the answer to this question has to do with the difference between a discrete and continuous distributions. When y is discrete (say Y = 1 for a Head on a coin, and Y = 0 for a Tail on a coin), the marginal distribution of y evaluated when Y = 1 maybe non-zero. This is because fy(1) is defined to be Pr(Y=1), and if y is discrete, the probability that Y=1 is 0.5 in this example. However, if y is continuous, as in the example in this video, fy(1) = 0 (it does not equal 0.5... it must always be zero when y is continuous). Notice, in this video, I never found the probability Y = 1... in other words, I never evaluated fy(1). Evaluating Pr(Y=1) to find a conditional probability is possible when y is discrete.... though when Y is continuous, we do not find Pr(Y=1), rather we directly find the conditional distribution fx|y by finding the marginal of y and then plugging in the value of y while integrating over x... image we have a two dimensional curve -- the conditional probability is a slice of that two dimensional curve at a particular value of y .
Hello- your videos were very helpful in understanding conditional joint PDF. Can you please share how to solve if the question was something like: P(X>1lY>1)? Thanks
@Stats4Everyone Жыл бұрын
Great question! This video is similar to the example you posted: kzbin.info/www/bejne/eHOzhICClMSbhdE
@gerardoelizondo91823 жыл бұрын
THank you!!
@granthill52632 жыл бұрын
Thank you so much!
@tommyharyanto79353 жыл бұрын
thank you
@dianal60865 жыл бұрын
What would be the answer for P(X>1|Y=1.5)? Would the integral bound for the conditional prob. be between 1.5 and 2 instead of 1 and 2?
@Stats4Everyone4 жыл бұрын
The answer would still be one, since x must be more than y, and you are saying that y now is 1.5. The way the steps would change, is we would plug in y=1.5 instead of y=1. the bounds for the non-zero part of the integral would be from 1.5 to 2 ... as you said.
@wondebest99733 жыл бұрын
my love how are you?
@JeanAlesiagain34 жыл бұрын
You are good. Thank you
@Stats4Everyone4 жыл бұрын
Happy to hear you found this video to be helpful! :-)
@DD27_276 жыл бұрын
Thank you so much
@alinazainab86564 жыл бұрын
Thank you so much ❤️
@Stats4Everyone4 жыл бұрын
You’re welcome 😊 Happy to hear you found this video helpful
@tsunhimwong55204 жыл бұрын
I don't know when should we use integration?
@Stats4Everyone4 жыл бұрын
For all continuous distributions. See how for this distribution, x and y are between 0 and 2 --- so for example, x could be 1.22222 and y could be 0.3333 ... here x and y are continuous, so we use integration. If x and y could only take discrete set of values, then we would use a sum rather than integrate.
@anweshbhattacharyya7763 Жыл бұрын
❤️❤️👌😊👍🔥
@fadikhattar2902 жыл бұрын
Im in love
@kahlanfaiq15105 жыл бұрын
keep up the good work :-)
@yutikasingh54433 ай бұрын
Thank you!!
@danialdunson3 жыл бұрын
that was awesome!
@harshitarathore76183 жыл бұрын
It's helpful ❤️
@Stats4Everyone3 жыл бұрын
Glad you found this video helpful! :-)
@johnsonokeyo5452 жыл бұрын
👍
@sln77366 жыл бұрын
what if (x>1|y>1)? how we find it?
@niveyoga32426 жыл бұрын
Did you watch it at 1.25x too as in the other video ^^
@Stats4Everyone4 жыл бұрын
I know its been a while since you posted this question, though it is a really good one, so I made a video that might help with this concept: kzbin.info/www/bejne/eHOzhICClMSbhdE .......if this is too late for you, maybe it might help someone else with the same question. thanks for posting this comment!
@ActualDayZGod7 жыл бұрын
nice video, thanks
@ackronymm6 жыл бұрын
thank you so much)
@WmsFootball307 жыл бұрын
Good work through, would have been better if the problem wasn't intuitively obvious as to what the answer was though.
@Stats4Everyone4 жыл бұрын
yeah, I agree. sometimes its nice going through the steps and showing that intuition is actually correct.
@albertosafra40036 жыл бұрын
What program is she writing on anyone know?
@Stats4Everyone4 жыл бұрын
I think I used SmoothDraw for this video. I also really like OneNote.
@munyaradzindumeya54442 жыл бұрын
obrigado
@birrawat88567 жыл бұрын
we need definetion of joint probability distribution please give me clear definetion
@ActualDayZGod7 жыл бұрын
In this video, she actually discussed 2 somewhat different mateiral. the first one is the joint probability distribution (the marginal and joint distribution). and the 2nd one is conditional distribution of the joint probability distribution. The joint probabilty distribution (f X,Y (x,y)) is basically a way to express a joint events (2 or more events) which is observed simultaneously in purpose to find their behaviour and relationship. Most times, the random variables are connected, but when they are not connected to each other, we call them independent variable. Which we can say the outcome of an event from the joint events will not affect other events in the joint events. So in short, joint distributions would be useful to describe the probability of 2 or more events happening simultaneously (which they might or might not be independent to one another). Damn I know im not explaining stuffs clear here,(atleast i tried) but at this point i just realized it is just too many things to mention. So probably i will stop trying to explain in detail and I suggest you can search stuffs online. try searching: - joint probability distribution (IMPORTANT please be clear the difference regarding independency, this will help a lot in calculation and an unclear understanding will confuse you a lot) - marginal distributions - Conditional probablity and its properties (like expected value and stuffs) - multivariable integration (this is not neccessary, but might come handy in integrating multivariable integrals. This mostly used to find marginal distributions, etc.), probably what you wanna pay attention to is how to set the lower and upper bound of the integration since it is a bit tricky sometimes. - Last, this is just an optional. If you wanna find out the "relationship" of the random variables, you can learn yourself covariance (Cov(X,Y)) and coefficient of Correlation. Hope this help even if just a bit.. no one be salty please. And sorry if I type or explain anything wrong, im no expert.