Calculating a Cumulative Distribution Function (CDF)

MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
View the complete course: ocw.mit.edu/6-041SCF13
Instructor: Jimmy Li
License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
More courses at ocw.mit.edu

[@ThefamousMrcroissant]

Clearly articulated, great voice to listen to and explained every step along the way. Tutorials don't get better than this

[@adrianlee2124]

Oh my god that was an amazing explanation and example. Thanks a bunch, really helped me understand all of it. I hope you make more intermediate statsistics videos.

[@natureshorts6657]

Super clear. Watching this as a refresher, and you totally nailed it. Great video.

[@Frostasy]

This really helped me, clear voice and explanation and I was able to solve my homework problem.

[@windfind3r]

Super concise and well articulated, good refresher for my 700 level class. Thanks.

[@gaoyutailang]

thank you!! really helpful and you offered very clear explanation!

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You literally just saved my arse. I wrote my C.A test without knowing this but I found this video just in time before exam. Now I'm 3x more confident!
From 🇳🇬

[@mishalubich7141]

Well done! Finally someone who knows their stuff! (From CS 70 at UC Berkeley)

[@colanader]

very clear and concise explanation. Thanks!

[@yashii5903]

Good explanation Mr.Lee I finally understood it💜

[@gnomewesely]

Thank you so much, you were amazing, you've helped me a lot!!

[@wangchul]

exam in 12 hours

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mock exam in 14 hours

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Mock exam in 1 hour :x

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exam in 2 hours

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13 minutes

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10 seco

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Brilliant!

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I love you for this. I’m currently doing my test and this is help 🤣😂

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It's 2021 and I'm using this as a source of knowledge

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You are a very good teach, you speak very well in your video. Thanks a lot!!

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your voice is so clear i understand everything. thank you it was helpfull.

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[@navee9470]

Extremely helpful. Thanks so much!

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Thank you so much for this video.Great voice,clear concept. thanks once again. :)

[@k-poplover7484]

Nice voice , It matches the professor personality ,good ,Started to love stastics

[@ethanc.3625]

Very helpful and easy to understand!

[@wissalab2828]

Thank you, that explanation was really helpful 🙏

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ty this is neat and clear even in 2021

[@sudipnext]

Very Clear Explanation, Thank You

[@lachatre82]

Nice, Thanks it was very clear to understand !

[@athena9357]

Thank you sir, very well explained.

[@AJ-et3vf]

Great video sir. Thank you!

[@lauracorrea1126]

He has an amazing voice

[@MrSuperrdad]

That was really awesome!!!

[@lebulle4544]

3:06 - 3:49
This moment is key. Watch it and watch it again until you understand what he means to understand CDF's.

[@confusion3146]

still struggling to understand it:( would you explain why when x>8, F(x)= 1 and not just 0? Thanksss

[@christianaviolari9235]

@@confusion3146 did you figured it out cause I’m wondering the same thing?

[@vaibhavkhobragade9773]

He is the best TA of the MIT

[@ammadurrahman5321]

Thanks.. very much sir,... Complex phenomena in simple way......

[@user-xs1ie1ki4q]

It helps me a lot, THANK U!!!

[@epg3581]

Finally a good video on how to obtain cfg from pdf

[@codemathlab]

Helped me a lot.

[@poojapaulchaudhury7044]

Thank you!!
That really helped

[@stefanosanastasi99]

pooja what is this behaivour?

[@georgesadler7830]

Thank you for the video.

[@user-ck9fd8qm7x]

awesome explanation

[@Inamullahkhan6294]

Thanks for excellent video.
I have one question. If we are given CDF and we are asked to find mean and median. Than what we should do. we should convert into pdf to find mean median formula or is there any way to find directly from CDF it self.Thanks

[@corynllamoza42]

Thank you very, very much!!
ps: loooove your voice☺️

[@dharamkapoor9235]

excellent video, thank you

[@goldfishyzaza5770]

Perrrrfect! Thank you!

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You are a good teacher

[@Stewie_official]

you are the best❤️

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thanks sir, great teaching.

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Excellent !!!

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So helpful!! thank you

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Thank you!

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So helpful!

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Try looking up, I think the joke flew over your head

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idiot

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lol

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i trust him with my future

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Thanks so much bro!!

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great video!!

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THANK YOU!

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Thank you so much! Saved me ! :D

[@danielpapangelis9740]

helps heaps thanks man

[@raichu56k]

good explanation thanks

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Thank you sir,I just got it

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!

[@Pain420]

I wish you were my professor, honestly. Couldn't be clearer.

[@vcv123able]

why is it 1 at z>1?

[@13statistician13]

It's z > 1 at 1 because the CDF is accumulating the probabilities between 0 and 1. Once you reach the point 1, the total area under the curve is 1. Therefore, when you continue beyond 1, the cumulative probability is still 1 since probability is between 0 and 1.

[@user-em9mw9ch3y]

Onur hahahahaha

[@user-em9mw9ch3y]

13stat..thanx a lot.

[@shashankdixit92]

@@13statistician13 that was really helpful buddy...you just made me understand the whole topic in a paragraph

[@13statistician13]

@@shashankdixit92 anytime. Glad I could help.

[@singhman1026]

thanks........... nice work

[@cabq]

Thanks a lot!

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Thanks a lot.

[@ravijaiswal4161]

nice explanation

[@CSoliz-cy4fj]

Thanks!

[@matthewtsang6875]

really helpful thx!

[@iosifpuha6114]

what about when we have more intervals in the PDF?

[@Angelica-tm5jj]

very clear ，thx😀

[@amiryusuf3791]

THAN YOUUUU VERY MUCH

[@MatthijsvandenHoven]

Thanks

[@simplym764]

a teacher i never had

[@dancelifevarsha4109]

thank you sir :)

[@callforpapers_]

rly helps ty

[@darkshinobi2995]

Extremely Thx :)

[@bozhang5482]

How about moving the z=1 situation to the third case? i.e.: 0, if z

[@saugatnepal5956]

Love it

[@pnh505]

how were you able to turn -2

[@TheAugustinePark]

Sam Flatau I think it's from the fact that for a continuous random variable Z, -2

[@thekingprajna]

In the pdf, the interval is -2 < x < 1 and in the cdf it becomes -2 ≤ x ≤ 1. Could you please explain how that happened? So far I know, in a continuous random variable, P(a ≤ X ≤ b) = P ( a < X < b) = P (a < X ≤ b) = P (a ≤ X < b). So if I write -2 < x < 1 in the cdf, would my answer be wrong?

[@kr1sel448]

Hey, three years passed before your question, did you find the answer somwhere? Thanks in advance

[@thekingprajna]

@@kr1sel448 Hello. I found a property-
If X is continuous, then the probability that it will take any particular value is zero.
So, P(a ≤ X ≤ b) = P ( a < X < b) = P (a < X ≤ b) = P (a ≤ X < b)
So the answer I found is - it doesn't matter how I write it 'cause it means the same thing. I should write what is there in the question.

[@kr1sel448]

@@thekingprajna Thanks a lot

[@infiniteunconditionallove1620]

thanks

[@The13mahfuz]

I think CDF does not contained closed limits from both sides in every cases(Discrete and continuous both) explain it.

[@somritasarkar6608]

its great

[@thinktwice4846]

where did that 1/3 come from? it got me confuse. please explain in details.

[@jonesamontgomery113]

The anti derivative of y^2 is y^3/3...he just pulled the fraction out and made it 1/3 y^3

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[@benhickmott7910]

savior