Unbiased Estimators (Why n-1 ???) : Data Science Basics

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Title	:	Unbiased Estimators (Why n-1 ???) : Data Science Basics
Lasting	:	8.35
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I am reading a book on Jim Simons, who ran the Medallion fund I’ve gone down the rabbit hole of Markov chains and this is an excellent tutorial Thank you
Comment from : Matthew

been trying to understand this for weeks now, this video cleared it all up THANK YOU :))
Comment from : Cadence Goveas

Why n-1we could adjust even better by doing n-2
Comment from : Asif Shikari

good one
Comment from : EkShunya

Thank you for great content!!!❤❤❤
Comment from : Nguyễn Kim Quang

Thanks!! I love the way of saying "boost the variance"
Comment from : June Chu

You are awesome
Comment from : Pranav Jain

you are GREAT
Comment from : Nelson K

you're hired!
Comment from : Jeff Bezos

Best Data Science Teacher on youtube Appreciate both the intuitive explanation and the math
Comment from : Sam Willis

I guess second approach for n-1 explanation will be right when both population and sample will follow same distribution which is very rare case
Comment from : Chinmay Bhalerao

Now it makes total sense Thank you 👏👍
Comment from : Yass theta

just wow!
Comment from : Soumik Dey

Great explanation! Love your videos
Comment from : Martin W

Thank you for the video, can you help me how to prove that is unbiased in this question? Question: Compare the average height of employees in Google with the average height in the United States, do you think it is an unbiased estimate? If not, how to prove it is not mathced?
Comment from : Pltt J

Please do one lesson on the concept of ESTIMATORs It would be good if the basics of these ESTIMATORs is understood before getting into the concept of being BIASED or not Anyways, you are doing extremely good and you way of explanation is simply superb clap clap
Comment from : DSK Chakravarthy

Great video, thanks!
Comment from : Abdusattor Sattarov

Thanks for the explaination from this perspective Can u talk more about why 'n-1'? I remember there is something with the degree of freedom but I never fully understand that when I was learning it
Comment from : Jingsi Xu

this is how we can understand stats not by just throwing some number to students
Comment from : miss ghani

good stuff!
Comment from : Syed Zain Zaidi

The lucidity of this explanation is commendable
Comment from : stelun56

tks, great explanation
Comment from : Matthaeus Muniz

Great video but still not convinced on the intuition How do you know that the adjustment compensates for missing tail in sampling? And if so, why not n-2, etc? I guess, if anywhere there would be missing data, it would be in the tail
Comment from : C

Exactly what I have been looking for
Comment from : Abrar Ahmed

well explained very clear to understand
Comment from : Richard Chabu

Best explanation I've seen on YouTube Excellent!
Comment from : Physicsnerd1

Never understood why "data science" and not "statistics"
Comment from : Gianluca LEpiscopia

<3
Comment from : Alex Combei

Great explanation!! brFYI derivation of the 3 steps for expected values wwwjbstatisticscom/proof-that-the-sample-variance-is-an-unbiased-estimator-of-the-population-variance/
Comment from : Mengguo Jing

Bias is not the factor that is used to deside the best estimatesits Mean Squares Errorn-1 is used because error is low not because its unbiased
Comment from : Too irRational

In order to be even more practical, I would simply say that:br- Mean: You only need 1 value to estimate it (Mean is the value itself)brbr- Variance: You need at least 2 values to estimate it Indeed the variance estimates the propagation between values (the more variance, the more spreaded around the mean it is) It is impossible to get this propagation with only one valuebrbrFor me it is sufficient to explain practicaly why it is n for mean and n-1 for variance
Comment from : David Szmul

I believe this is the best channel I have discovered in a long time Thanks man
Comment from : Ashish Kumar

Thank you Could you please do a clip on Expected value and it's rules and how to derive some results
Comment from : !____!___

Quality video , keep it up !
Comment from : Loafclotnit

is that because of we lose 1 degree of freedom when we used the estimated mean to calculate the estimated variance?
Comment from : YITONG CHEN

Try explaining the above ideas using the degrees of freedom
Comment from : Christos Koutkos

I watch all your vids in my free time Thanks for sharing!
Comment from : DistortedV12

You still are not clear why we use n-1 instead n in the sample variance, intuitively
Comment from : Thomas Kim

What about n-2 or n-p, howcome more estimators we have the more we adjust? How does it exactly transfer intro calculation and ehat is the logic behind it?
Comment from : Machiavelli