Probability: Types of Distributions

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 This is superseded by a capital letter depicting the type of the distribution and some characteristics of the dataset in parenthesis.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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[nsenthil]

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.

[Tuffadandem]

This video is pure gold. Thank you so much for sharing this! I'm a Mathematics major, presently undertaking Statistics courses and enjoying them.

[NeidaJayus]

Thanks! I finally understand the concepts behind the formulas that my lecturer has been teaching. what a relief!!

[koshka]

This video did a better job explaining random variable distributions than my prof could do in 10 hours of Lecture.
Very well done!

[rocar]

This video really made the lightbulb light!!! Thank you.

[Manuj_Jha]

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.

[jackaddie]

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!

[viccctv]

Thanks for the content.

Cheers from Bolivia

[humoaz]

Thank you so much. It was amazingly easy to understand. Good work.

[ashokkumar]

Your presentation was great. It will be helpful if you guide how you make them.

[naoletube]

Very good topic, Thank you very much!

[raghuramireddykommerla]

The explanation is simple to understand...Thanks, @365 Data Science Department...Keep Going

[wz]

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..

[np]

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

[mohamedabdilleh]

Way of Explaining and Summarizing concepts and its application of Distributiom is funnay and interesting to the learners watching other vedios.


Thanks for contribution

[sahanashreeg]

Very Talented and skilled one 😮❤ A great teaching 😊 Thank you sir🙏

[ranjanawaraich]

very informative! Can you please tell me which distribution is used in formula equations to find let's say coefficient of friction

[shivasrivastava]

Thankoy
Can you make these videos more descriptive
And how statistics is used in industry!

[rakshyashrestha]

Which distribution should we use if we have to select exactly 2 out of 4 sample having certain probability?

[brightmatthew]

Thank you for sharing this informative video. I’m curious about the distribution pattern in the forex market. Assuming it’s represented graphically, what type of distribution does it exhibit?