Probability: Types of Distributions
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- Опубліковано 28 тра 2024
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In this lecture we are going to talk about various types of probability distributions and what kind of events they can be used to describe. Certain distributions share features, so we group them into types. Some, like rolling a die or picking a card, have a finite number of outcomes. They follow discrete distributions. Others, like recording time and distance in track & field, have infinitely many outcomes. They follow continuous distributions.
Video Timestamps:
1:29 Discrete Distributions
3:42 Continuous Distributions
We are going to examine the characteristics of some of the most common distributions. For each one we will focus on an important aspect of it or when it is used. Before we get into the specifics, you need to know the proper notation we implement when defining distributions. We start off by writing down the variable name for our set of values, followed by the “tilde” sign. This is superseded by a capital letter depicting the type of the distribution and some characteristics of the dataset in parenthesis. The characteristics are usually, mean and variance but they may vary depending on the type of the distribution. Alright! Let us start by talking about the discrete
ones. We will get an overview of them and then we will devote a separate lecture to each one. So, we looked at problems relating to drawing cards from a deck or flipping a coin. Both examples show events where all outcomes are equally likely. Such outcomes are called equiprobable and
these sorts of events follow a discrete Uniform Distribution. Then there are events with only two possible outcomes - true or false. They follow a Bernoulli Distribution, regardless of whether one outcome is more likely to occur. Any event with two outcomes can be transformed into a Bernoulli event. We simply assign one of them to be “true” and the other one to be “false”. Imagine we are required to elect a captain for our college sports team. The team consists of 7 native students and
3 international students. We assign the captain being domestic to be “true” and the captain being an international as “false”. Since the outcome can now only be “true” or “false”, we have a Bernoulli distribution. Now, if we carry out a similar experiment several times in a row we are dealing with
a Binomial Distribution.
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This video is pure gold. Thank you so much for sharing this! I'm a Mathematics major, presently undertaking Statistics courses and enjoying them.
The best part of this series is the clear English used without any accents. As an international person this helps me a lot in understanding the content. I look forward to watching all 365 Data Science videos. Also thank you very much for no advertisement breaks.
Its a very subjective statement. Whats wrong with having accents? I dont ususally face problems understanding videos where speakers have accents lol
@@TawhidShahrior subjective to only you and a small minority.
too strong of any accent is hard to understand and is far away from the normal phonetics.
think about the way english sounds in typical pop music. Even singers from the U.K or other parts of the world drop their accents and speak "clearly."
Yes, "clearly" as in easiest to understand.
No such thing as no accent
Even if you speak native English, you still have an accent-the accent of the native speaker.
Everyone has an accent. Why does your comment feel backhanded
This video really made the lightbulb light!!! Thank you.
At 5:54, you said Chi squared distribution is skewed to the left. But this distribution is typically skewed to the right. The shape of this distribution depends solely upon degrees of freedom. As degree of freedom approaches 90, the chi squared distribution resembles the Normal Distribution.
Thanks! I finally understand the concepts behind the formulas that my lecturer has been teaching. what a relief!!
This video did a better job explaining random variable distributions than my prof could do in 10 hours of Lecture.
Very well done!
Thanks for the content.
Cheers from Bolivia
absolutely great video! I have been trying to figure out what my lecturer has been talking about for the past few days and you guys have saved me!
Thank you so much. It was amazingly easy to understand. Good work.
absolutely great video!!
Very good topic, Thank you very much!
thanks a lot for great explanation !
The explanation is simple to understand...Thanks, @365 Data Science Department...Keep Going
Outstanding didactics!! Congrats
Your presentation was great. It will be helpful if you guide how you make them.
so helpful, thank you
Me: Ok, time to stop playing dota and get serious this semester, probability is no easy subject
365 data science: For example, take a competitive esport, like dota 2
Thanks 365, this really helps my addiction
plot twist, playing Dota helps with learning probability
Way of Explaining and Summarizing concepts and its application of Distributiom is funnay and interesting to the learners watching other vedios.
Thanks for contribution
Very Talented and skilled one 😮❤ A great teaching 😊 Thank you sir🙏
clear and great explanation
Very helpful...thank you
HELPFUL THANKS
Really good content... Great work
Thanks this video helped a lot
Simply outstanding
What a good channel!!! 5 Stars.
Thank you so much
I like how in this video it briefly touches on all the distributions, thanks a lot! But one thing, you didn't really explain normal distri except saying that many real life examples use it..
Great value for time
Pretty sure the chi-square distribution is typically right-skewed not left-skewed. 6:00
Tq so much ♥️
Thankyou sir.
This channel is so underrated
Great video 👍🏻
spectacular!
very informative! Can you please tell me which distribution is used in formula equations to find let's say coefficient of friction
thank you
thank your for this clear and informatives vedios, one question please, what about gaussian distributian,where can we classified it ?
thanks guys
perfect♥️
Thankoy
Can you make these videos more descriptive
And how statistics is used in industry!
+100 yess please sir
Awesome ...
Nice video... made things so easy to understand. At around 6 mins in the video, you mention that the Chi-squared distribution graph is skewed towards left. Should it not be skewed to the right? Please confirm. Thanks
I had also the same question. I checked again and as you said that it should be mentioned right-skewed.
U r so kind😜
You look so good 😍 what a voice amazing skills ❤️
Great
Which distribution should we use if we have to select exactly 2 out of 4 sample having certain probability?
How is the chi-square you are saying skewed to the left? In your picture it clearly looks right skewed
Informative. See u around.
Super
Uniform Distribution, Bernoulli, BInomial, Poisson.
Normal, T-Distribution, Chi-Square, Exponential, Logistics.
If I have the race time/speed of runners, what sort of distribution would fit such data? Chi squared distribution skewed to the left and non negative. What is the distribution for data skew to the right?
Thaaaaaanks
is this full course available on udemy?
When you work on stats for hours and found that video where he starts making an example with Lebron James, thank you man. + 1 like and + 1 follow
The chi-square distribution is right-skewed. Not left skewed.
Which Distribution should we use to predict General Assembly Elections of a country? For winning a single candidate or which party will gain majority?
Great stuff, although you left out a lot of continuous distributions!
I can assure you that my teammates in dota doesn't give a fck about the win probability because they are fckn around during the first 10 mins 😂
Wow 🤩 Ure so cute
While calculating probability do we care about order of distribution?
The simulated distribution array (list of values) itself might not be sorted, but when you histogram it, it of course would have an order: y axis the count, x the measure binned
@@oberlinio tx for clearance
Is flipping a coin an example of uniform, or binomial distribution?
If more than once then binomial
5:59 Isn't this graph right-skewed?
Yes
Great Video ,
now how do i boost my mmr?
I just now completed my school.I am planning of studying Data Science.
Typically skewed to the right?*
Is chi square skewed to left or skewed to right?
He says in the video it is skewed to the left, where as I think it is skewed to the right ???
@@johandegroot7791 yeah. I checked. It is skewed to the right.
Geometric distribution ??
Isn't chi-squared right skewed?
Yep
Introduction to Probability Language
ua-cam.com/video/eqxozuUebaM/v-deo.html
2:12
consider flipping a coin. both heads and tails are equally likely. why are not they then falling in the category of discrete distributions?
I always forget about probability
i love u
how would you do this
Say you started a UA-cam channel about a year ago. You’ve done quite well so far and have collected some data. You want to know the probability of at least x visitors to your channel given some time period. The obvious choice in distributions is the Poisson distribution which depends only on one parameter, λ, which is the average number of occurrences per interval. We want to estimate this parameter using Maximum Likelihood Estimation.
Simulate 100 visits to your youtube channel, assuming that they will a Poisson distribution with a mean of 10 visits per minute. Plot the arrival time vs visitor index.
I think you just told us how
Oh my god!! The asymmetric Chi-squared distribution does not mirror real life events 🤦♂️🤦♂️🤦♂️🤦♂️🤦♂️ 5:59 😮😮😮😮it is what really matter. It should be the only thing to focus on in real life!!! 5:59
Lol yall didn't have to do Lebron's hairline like that tho
Great work! But why is the probability of an international student becoming a captain is false? Why such pretexts?
You have explained everything instead of showing actual distributions!
ua-cam.com/video/M6014opxMo0/v-deo.html
URV gang
puwasungh
pls use common examples not games as not everyone knows every game
I still don't understand what us meant to "distribute" a price...I can't advance until I can fully explain what us meant by distribute
That's not correct. In nature what matters is the outliers. The normal distribution is ok for the weight of the bears. Do not say in nature. Say the weight of the bears can be normally distributed 4:09
I was 23 year old when I knew Poisson is pronounced as plason🤥