Odds and Log(Odds), Clearly Explained!!!

Here's the code for generating the figure at 9:46 https://github.com/StatQuest/odds_and_log_odds_example
NOTE: In statistics, machine learning and many programming languages, like Java, R and Python, the default base for the log function is 'e'. So log(0.17) = "log base e of 0.17" = -1.77. The natural log (log to base 'e') is used because it often simplifies the mathematics that underpins statistics. That said, you can use whatever base you would like as long as you are consistent.
Also, at 10:00, I say that a histogram of the log(odds) has the shape of a normal distribution, but what I should have said is that the shape is similar to a normal distribution and is approximated with a normal distribution.
Lastly, people often ask about how to generate the normal distribution: First, I randomly selected a number between 1 and 100 and I get 32. That means I will use 32 as the numerator for the odds. The denominator is then 100-32 = 68. Then I calculate the log(32 / 68) and get -0.75 (because I use log base 'e', but you can use any log base as long as you are consistent). Then I pick another random number between 1 and 100 and get 54. This means that the numerator for my odds is 54 and the denominator is 100-54 = 46. So I calculate the log(54 / 46)=0.16. Then I just repeat that process a bunch of times (100s) and draw a histogram of the log(odds) values. If I do this, I will get a normal distribution.

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Please, this is from Brazil, send us the code in R for the image 9:52.

Hello! There is one thing that is confusing at the first glance - it might require a different description. I refer to the slide "To show you what the big deal is all about, if I pick pairs of random numbers that add up to 100...". How (from what set) do you pick pairs that add up to 100 so that the histogram is the normal distribution?

Love SQ videos 👍 As an aside . . . 2:11 -- an example where math historically uses similar notation for related but distinct operations. Which is lamentable because many young learners get frustrated and lose interest: www.youtube.com/watch?v=U-ve8Yh4Ro8&t=23m16s

You are the best, mate

I know you need to make money, but gosh is this video unwatchable with all of the adds. absolutely horrible experience

fucking so clear!!!!

You are a fantastic teacher, right now I'm learning about data science and you are helping me a lot

Beautiful

Bro, what on earth!.
I can't believe that I found a gold mine.
I've subscribed, and I'll recommend your channel for anyone who wants to learn statistics and machine learning.

Summarising whatever I have understood -
1. So basically probability batata hai kuch hone ka chances kitna hai. And Odds batata hai kuch hone ka chance COMPARED to kuch nahi hone ka chance.

2. And log of odds bigger numbers to compress and smaller numbers ko stretch karta hai taki values to normalise/standardise/symmetric karke ek scale pe smoothly fit kar sake.

3. And lastly, log of odds manually calculate karke le lie humme e ka power me kitna value lene se odd milega ye pata karna hai. Since I'm a beginner so with my knowledge in this topic (disclaimer**) - assuming power ka value hamesha 1.something hoga. So e power 1 ka value 2.718 toh odd/2.718 will give another power, so if you multiply (odd/2.718) * 2.718, then we should get the log of odds ka value.

Ik we have functions of get log(odds), but it's always good to know how to solve anything without a function.
Lastly, log(odd) ko aur easy and correct way me calculate karne ka agar solution ho toh please let me know by replying to this comment.

Omg this is freaking gooooooooooood