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wonderful

thanks man! this was great help!!

Good job in explaining.

presentation could be polished (rehearsed). t comes across a little bumpy.

X values ranges from 0 in geometric

Thanks a lot for such a comprehensive, simple, and detailed explanation!

Excellent explanation. Just what I needed!

Conceptual❤

this what we call a math teacher!

Sweet

Ueeesshole.disgusting lecture. Leaen first, then teach.

Hi Boer, really appreciate the work you're doing! Know that you probably get it a lot from the commentors, but you really do teach better than my lecturers! Cheers Boer :)

nice thanks for explanation

where have you been hiding

I really appreciate your video and that you chose show us how to derive both parameters of the Gamma distribution. Especially deriving alpha. I have a stat question that involved finding the MLE of the Gamma distribution. Thanks.

U have the best explanation here on utube

Thanks a million 💙💙

Amazing explanation, many thanks.

Using "x" as multiply by sign when using x also as variable was a little bit confusing. xD

Thank you very much.

awesome!

Thank you

Good explanation!

Yes, my boer brother! You give us a good name. Well educated

What about var of x²

Thank you sir. Please do for exponential distribution 🙏🏽🙏🏽🙏🏽

this video is so good at explaining😁

you simply solved all of my problems regarding this section, good job! Keep going. thank you

Thank you so much

Awesome 👍

Please mean of uniform distribution, using mgf

None of my university statistics teachers explained these fundamental equations and how they are derived. Thanks a lot.

Wow! I have been searching this lessons! I just found it and understand your easy way of explanation. 🙏 I keep flowing your channel.

Great explanation. Thank you for this tutorial.

baie dankie my vriend

So the mean of negative binomial distribution is r/p?

Best explanation

why do you remove the identity matrix when calculating Var(Beta hat)? It goes from sigma squared * I to just sigma squared

very helpful, thanks

I was looking for this breakdown for a long time. Thanks a lot mate

Your steps are too difficult to understand. Thanks for the video.

don't have the same result with this command for likelihood for negative exponential distribution: L1=dgamma(theta1,shape=n,rate=sum(x)) L.NEXP=function(x,theta){ n=length(x) s=sum(x) L=(theta^n)*exp(-theta*s) return(L) }

Honestly I've gained a lot Thanks man

Great explanation, thanks 🙏

Sir which book is this please tell us the book name

amazing video🎉

Your works are too rough bro

I really appreciate you, that what I was searching about. That helped me a lot. Thanks..

Thank you. Too many videos on you tube don't derive the variance.

Good