I have watched tons of videos and never understand it , untill I watched yours . You have no idea how much it helps. Your steps are very simple, clear and really easy to understand. Thank you sooooo much
For my high school math class, I am supposed to be teaching exponential distribution to my fellow peers. This video was very helpful! Thank you so much:)
Ok this is fantastic. My prof has the most round about ways of explaining probability densities. This really underlines the basic concepts and makes it much easier to tackle tougher problems, that included exponential distribution as a part of them. Thanks!
Thank you so much Stats. your video has just save me time,unnecessary stress and perfect understanding. God bless you. i enjoyed and learnt from your video.
Good question - Remember that the exponential is a continuous distribution. P(X =k) is considered 0. If the distribution was *discrete* - then the problem you mentioned would arise.
As regards the Interval 1. P(Inside Interval) = 1-P(outside Interval) 2. P(Outside Interval) = P(Too High) + P(Too Low) It is common to all continuous distributions so not discussed here in detail
Stats-Lab Dublin I see! It is important to be aware of those minor things when working with continuous distributions vs. discrete. Thank you for your explanation!
I'm confused.. at 10:00 you say "and that minus there, and that minus there gives a plus".. where are you getting the other minus? I can only see a 1 - (1-e^(-lambda x)) which should give -e^(-lambda x) ??
Ive done all the workings using .10 as Lambda and having the value being in brackets after the lambda, still same result just slightly different method.
I thought because exponential distribution deals with continuous data, that you could not use less/more than or equal to, rather just less than or more than. Because with continuous data the probability of you being exactly on the point of a discrete number such as having exactly 5 litres of fuel in ur tank, would be 0? correct me if I'm wrong
because pdf of a function is the probability per unit if you use it then it will not give you the probability ,instead if one uses the integration of pdf wrt "the unit" then you will get{(probability/unit)xunit} which will give you the probability also known as the cdf.
The failure rate of an electronic component is 2 per 300 hours on average. a) Find the probability that the component will fail before 200 hours of operation? b) Find the probability that the component is still working after 500 hours of operation. i need help with that anyone with an idea pls
"The CDF can be written as the probability of the lifetime being less than some value x"
That helped me soooo much, thanks!
I have watched tons of videos and never understand it , untill I watched yours . You have no idea how much it helps. Your steps are very simple, clear and really easy to understand. Thank you sooooo much
For my high school math class, I am supposed to be teaching exponential distribution to my fellow peers. This video was very helpful! Thank you so much:)
Ok this is fantastic. My prof has the most round about ways of explaining probability densities. This really underlines the basic concepts and makes it much easier to tackle tougher problems, that included exponential distribution as a part of them. Thanks!
Should've explained how you derived the cumulative distribution function from probability density function. Also we can solve P(5
lovely video but much easier on the question C by just integrating f(x) within that limit 5 and 10. Good job man
5:46 E and Var
2:50 PDF
4:10 CDF
Thank you so much Stats. your video has just save me time,unnecessary stress and perfect understanding. God bless you. i enjoyed and learnt from your video.
This was very helpful! Clear and easy to understand
Thank You So Much Sir. It really helped me a lot. Keep on doing this kind of videos
This is a very nice and explicit video... great help. Thanks
I like it for being neatly written making it easy to copy fr future use.
Thankyou,the explanation is very clear.
this video helped so much once i figured out the e is a set number. kinda like pi.
Thank you so much man. This is the only video which helped me so far
Thanks a lot..Earlier i was very much confused in waiting line theory..
Very helpful video. Thank you!
Thanks so much! Loved it even more because of the Irish accent!
finally the educational video I was searching
Very Helpful ..Thank you so much for your upload
Beautiful, thank you! I could not find a detailed explanation anywhere
Thank you! Was very helpful and clear to follow
Thanks for such an informative video.
Super super helpful. Thank you.
For part c), I don't understand how the complement of P(5
Good question - Remember that the exponential is a continuous distribution. P(X =k) is considered 0. If the distribution was *discrete* - then the problem you mentioned would arise.
As regards the Interval
1. P(Inside Interval) = 1-P(outside Interval)
2. P(Outside Interval) = P(Too High) + P(Too Low)
It is common to all continuous distributions so not discussed here in detail
Stats-Lab Dublin I see! It is important to be aware of those minor things when working with continuous distributions vs. discrete. Thank you for your explanation!
Call it Learning. I am sort of build up the library bit by bit - Also can't cram everything into one video
Don't get why you wouldnt just do P(5
Thank you, i understood it perfectly.
I'm confused.. at 10:00 you say "and that minus there, and that minus there gives a plus".. where are you getting the other minus? I can only see a 1 - (1-e^(-lambda x))
which should give -e^(-lambda x) ??
Ive done all the workings using .10 as Lambda and having the value being in brackets after the lambda, still same result just slightly different method.
Thank you!!!!!!!!!!!!
You made my day :)
I can't thank you enough...
I thought because exponential distribution deals with continuous data, that you could not use less/more than or equal to, rather just less than or more than. Because with continuous data the probability of you being exactly on the point of a discrete number such as having exactly 5 litres of fuel in ur tank, would be 0? correct me if I'm wrong
Thank you, this was awesome! :)
thanks
oh my words! thanks so much!
For part B it should be P(x>10) not greater than equal to
Really good presentation. Keep up the good job sir.
THANK YOU!
Thanks bunch.
Thank you so much
Sorry sir. I am not sure why we should use the CDF of exponential distribution in part a and b but not PDF of exponential distribution? Thanks
if you want to use the pdf you have to integrate .. which is the cdf.. less math to do
because pdf of a function is the probability per unit if you use it then it will not give you the probability ,instead if one uses the integration of pdf wrt "the unit" then you will get{(probability/unit)xunit} which will give you the probability also known as the cdf.
How do you find the lambda of an exponential random variable?
Thank you! This was really good :D keep making videos!
muuuuuuuuuuuuuuuito bom!!!
very good : )
good job!
In part A why did you use the CDF instead of the PDF? You pretty much used the CDF in all parts instead of PDF why?
The failure rate of an electronic component is 2 per 300 hours on average.
a) Find the probability that the component will fail before 200 hours of operation?
b) Find the probability that the component is still working after 500 hours of operation. i need help with that anyone with an idea pls
are earthquakes in a region truly independent? one earthquake may give rise to tremors and affect the future
Thanks 😊
Thank you for the video! It is very helpful. (':
shouldnt P(X>=10) be 1-P(X
I liked the video before I saw that part
For discrete variables this would be true, however `1-P(X
Yh
In continuous distributions P(X>=k) = P(X>k). Because doing Integral in a precise point is equal to 0.
Oh my god thank you so much
Thanks a lot this really help. BTW sorry i cant help but kind of laughed at 8:27 "Change it a BI*CH, sorry" haha
am confused in part a
you computed p(x
+Jasmeet Singh for Continuous Distributions they are consider to be the exact same (P(X=k) is considered to be Zero)
Thanks, I looked at the graph again and yes P(X=k) is always zero, Because in Continous random Vars we look for intervals. Thanks
For part a in your example you wrote the question asking for P(x
Mohammad Al-Sweity i think... Because probability of a continuous random variable at particular point is zero...
1:30 that should be "inversely proportional"...and i think you mean "less than 0 seconds" at 2:48
Thank you
Can't find other videos of Probability .
sir when do we use the compliment rule ?
when wallahu fuckbar asks you to
I thought v(x) was e(x squared) - the square of e(x) not just e(x squared)?
thank u so much
thanks
How is P(X>=x)=1 ?
isn't it λe^(-λx)?
That's pdf, for the questions cdf is required
why do you use the complement rule?
in general, it is always a useful thing to consider when tackling probability exercises. ...
Felt more like reading lines than explaining things , so If you explain more pictorially than it would help better
Can lambda be greater than 1?
no
thaaaaaaaaaaaaaaank you
You can't wait less than 5 seconds? 2:46
2 AM in the morning, im now ready for my stats quiz. GG ENGR 371
hahaha 1:57 AM here, ready for my internals tomorrow
@@kumarprateek238 lmao 7 years ago. Dont remeber any of this shit. I prob failed that exam
@@emperorofthegalaxy1 lol
same I failed too
chris o'Dowd teaches stastistics
Many So so so ...
HAND OVER YOUR GOLD LEPRECHAUN
why do you keep saying NOT? the answer is NOT point ....it's confusing. Other than that, good video.
"naught" is simply another name for zero.
oh okay. didn't know that. thanks :)
wasting time too much
THANK YOU!