Basic Probability: The Multiplication Rule
HTML-код
- Опубликовано: 1 окт 2024
- An introduction to the multiplication rule. (I assume the viewer has an understanding of conditional probability and independence, but I do a very brief review of those concepts.) I introduce the multiplication rule for two events, work through a simple example, then discuss the multiplication rule for 3 events and how it generalizes to any number of events. I end with an example using the multiplication rule to find the probability of a 3 way intersection.
Canadian demographic information is based on Statistics Canada numbers found here:
www150.statcan...
0:18 Brief review of conditional probability and independence.
1:39 Multiplication rule for two events
2:49 Simple urn example
4:32 Cautionary example
6:43 Multiplication rule for 3 events
7:35 Example involving Canadian demographics
Seeing you after one year thank you for uploading again.
One of the best stats channels on youtube!
I hope there could be 3 hard questions for each video bro ❤️🔥
Glad seeing you back ! You got me into and through college. Keep up the great work !
Do you have a video covering Bayes' Theorem?
Your presentations are very easy to follow - Thanks.
Wow, amazing! Thank you for the video. Absolutely clear.
You're very welcome!
For the algorithm, and to encourage more vids
I am the 11,000th viewer. lol
Come back for the 111,000!
Make a video on Bayes Rule please.
Amazing! explanation thank you.
Hi. I need your help, can you help me, please?
I watched your videos about linear regression, but these formulas are not working for me, let me explain. I am translating one python library to standard python. So when I tried to get t-value, from standard error of beta. I have zero intercept ordinary least square, and SE(b) should be s/sqrt(sum(x^2)), where s=sqrt( sum( resud^2 ) / (n-1) ) ( not -2 because of zero intercept )
x = 1, 2, 3
y = 7, 3, 5
beta = 2
s = 5.217
sqrt(SSxx) = 374.165
SE(b) = 1.394
what I should get is (library's solution)
Se(b) = 0.98
Help me, please
Hi
Can you please upload code in R( without use of boot())
for non parametric bootstrap confidence Interval for Binomial distribution to conduct inference on p. We could use phat= y/n
For the bootstrap methods, use B = 200 resamples.
N=1500 and p varies from 0.01 to 0.99 by 0.01
Hello JB statistics. Thank you for your videos. I am a beginner student of statistics and find your videos really helpful. Is there an order for them from the start? if I'm to go through them to really get a grasp of it.PLEASE HELP
I'm quite certain that this incorrect. Given that these probabilities are not independent.
Quite certain what is incorrect? And even without knowing what you're talking about, I'm very, very confident you are simply wrong.
Damn bro your videos are lit🔥🔥🔥 thank you for this I'm really happy I found a gold like yours❤️.
Can u please discuss caushys distribution, log normal distribution etc.... U r really amazing ❤
This is just what I needed... someone to slowly and carefully explain this looping situation, and that it can't be solved with just those two probabilities.
Thanks for the video! very well understood....
Can you make an organise playlist of statistics videos for those who are interested in learning statistics in a uniform, organised and progressive way?
wow still uploading after so many years, keep it up!
Can you make a video on jointly distributed random variables in a continuous setting? I am struggling to understand this, and I would guess that is also true for many other people. I love your videos, and I hope you continue to make them!
I would like know if you give online classes? if you do, I would like to know more info
Thank you for all the videos. I find these quite digestible! Do you have any suggested order for watching your videos if one wants to learn probability comprehensively? Thanks in advance.
nothing excites me more than your teaching skills🤗
thank you so much. quite demystifying
Perfection. I want also to see the how you derive the addition rule.
I like your comment about not JAMMING things into the nearest formula. Than you for the occasional comic relief throughout your videos. Urn vs goblet… was totally a goblet.
Like I said, it's an urn and I'm sticking to it :)
Which software was used to create the presentation? Looks neat and minimal.
The background is a Latex/Beamer presentation. It was captured and edited in Screenflow. I'm glad you like the look! I do too!
Software used?
He answered this in another comment on this video
Thank you for giving a more challenging example, instead of keeping it all at the trivial level. This was great. :)
Thanks! And you are very welcome. I wanted to keep it all pretty straightforward, as it is aimed at students in an applied intro statistics class, but I also wanted to go a little beyond the basic urn and deck of cards problems.
La probabilité peut-elle être négative ?
Comment est-ce possible?
Y a-t-il des événements tels que la probabilité est négative ?
@@AryssaRiyasat Pas selon les définitions standards de la probabilité.
4:58 you got me there , again... :)