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9:42 Is the log(0.8) meant to be log(1.8)? Nice video btw.

Thank you, but one thing I still don't get about about the Bayesian way of thinking: if one is supposed to constantly update a belief via data, then where did the prior come from? How does one quantify a belief based on zero data?

Great explanation Brian! I have a small question, though. If the response variable has to be normal (in a normal linear regression), why do you think most statistics articles insist that only the residuals have to be normal and not the variable? What tests do you think should be done before a GLM, besides residual plots?

Amazing. Enlightening

Love the video. These concepts are explained very nicely and in a easy to understand manner.

Very clear explanation. Thanks.

great content, keep it up

Is there any reason to have two different ways of solving it? One at 5:43 and another at the end. Which one do you think is better and why ?

you have great videos !! thank you so much <3

perfect and easiest explanation in utube....thanku so much sir it is really helpful

Great Content

Good examples

❤

if I can i will give a 100 likes, best explanation that i have found for this topic so far

Excellent video! It really explains this concept in a simple manner!

I got this question that says let 𝑋 be a discrete random variable where P(𝑋=x) are some function of 𝜃(e.g. 𝜃/2, 1-𝜃) for some number x, and it ask "Find the Method of Moments Estimator of 𝜃 by using 𝑛 copies of 𝑋, where 𝑛 > 1.", what does " using 𝑛 copies of 𝑋" means?

Please, don't stop creating the content! I am so grateful for you! The stats at universities are not properly explained, infortunately, you make difficult things seem easy indeed!

This is an absolutely incredible video. Amazing information. Please keep doing your thing

Universe!!! It had broken my stone, my head. Thank you!

Finally got the point. Not even ChatGPT or Claude explained this topic as easily as you did. Thanks !!

Brooo, i am very thankful someone is choosing the things to explain where on avg every student feel confused and tensed. Thank you very much. It was helpful ❤️

Thank you very much bro. This was really helpful, nicely explained ❤️🍁. Have a bright day🌻

Best and clear cut video. It made sense to me. Thanks, man 👏🏽

Amazing explanation

Could you do a video on the Neyman Pearson lemma ?

You could ask which country you are in 😂 Great video tho

Excellent!

Seriously very clean and simple explanation , keep continuing these lectures

I'm a fan of your videos! So well explained! Keep up the good work :)

Thanks!

Thanks!

I am on my second year of education in uni and these theme was explained awful and I had no idea of what was going on but after watching I am wondering how professor managed to explain such an easy thing in such difficult way thank you so much❤

Exactly what the title says! Thank you

05:30 The probabiliy that X=1 is 1. It's certain since we've already observed it. The question makes no sense simply because it makes no sense to talk about probability for something that already happened. The same holds true even for discrete probability.

thank's Mr. Brian

I don't understand the first "not-complete" example. E[X_1 - X_2] = E[X_1] - E[X_2] = mu - mu = 0, no?

i liked it

So many people say physics does not apply to the stock market, but after watching this video I must disagree.

Great video, sincerely a second year stat major.

Great explanation, thanks.

This content is excellent. Would a better microphone or audio setup be a bad idea?

I round up. I'm not 70.238749327429 inches tall, I'm 71 and I'm sticking to it.

why we use a formula of (predicted weight = -500+10*height)? Why 500 and 10?

Great straightforward intuitive explanation.

Soo good! Didn't get it in class at all

I'm so grateful for all the videos you make that inspire our curiosity!

Constant question, what is the probability of??? Happens with recruiting

Thank You😊

really neat video, could you do one on estimators and LME?

Hi Brian, nice explanation. I was wondering what a more formal approach to deriving the expression at 13:00 might look like? The intuitive "approach" mentioned, which makes sense, implicitly sums over a (latent) variable indicating which X_i is maximal. But I'm not sure how else you could go about it, e.g. the earlier method calculated p(X_1, max(X_i) = t) / p(max(X_i) = t), but that doesn't seem available, e.g. how is p(max(X_i) = t) defined? Many thanks!