Mixed Model Notation - A Simple Explanation 

This guy is AMAZING. The best video you can find on the internet on this concept.

GREAT work. Fantastic presentation. You make complicated concepts so understandable. I have been an instructor for 37 years and I admire your abilities and execution. KEEP UP the great work.

Amazing video! at 15:10, when you talk about the "-1", this is to indicate that the intercept should not be random, not that the slope should not be random, isn't it?

These videos are so well done and informative. I took MLM a bit ago and forgot some of the notation, this was very helpful!

Awesome!! This was so complicated for me before this video. You made it so easy. Thanks!

Thank you so much Dustin ! Your videos are a lifesaver.

Is there a certain number of levels that should be present to use a mixed model? Like if we look at gender assigned at birth, there would be 2 levels, would it be appropriate to include that in a mixed model, or just dummy code that?

Dear Prof. For link vdo part 1 is missed, can u fix it? this is the best stat channel that I love :)

Thank you for the video. This is what i need to understand about LMEM

Why I got a different plot at 10:55. There is only one red line, no green line.

As per explanation at 5.15 of video, for intercept : that people who come to respective therapist have already average score of depression is 11 and for slope : where average or general slope is +0.5, but in given group slope is -0.25, which shows efficiency or effectiveness of therapy(for example, may be), which is low compare to average slope across group. am I right? please somebody answer...

I’ve got this model from my professor: Yij=B0j + B1jxij + eij Boj= y00 + u0j B1j= y10 + u1j What does the B1jxij or inserted Y10xij +u1j mean?

You are awesome - thank you!

@0.36 I have a question. We usually have two parts for regression. One is before fitting the model and the second is prediction using the fitted model. What is confusing for me is that (Depression_i = B0_i + B1*Stress_i) seems to be something that we will use during prediction because at prediction we are interested in a single individual. While fitting the model we take all individuals together to fit the regression line so we do not think in terms of single individuals (the ith individual) but all the individuals. So @0.36 seconds, what part of the model are you referring to? If we are fitting the regression line using OLS, how does the subscript I matter while fitting using OLS? Please clarify.

awesome explanation, thank u!

Your video is so helpful! Thank you!

Very good video! Thank you. In the followings models (Alcuse~1+AGE_14+(1|ID ); Alcuse~1+AGE_14+(AGE_14|ID)), does the random intercept (for both models) correspond to the variation of alcohol consumption at Age 14 ? If yes, could you explain why the estimation differs (0.5966 vs 0.6355)?

Thank you for this video! :D :D :D

The background music is distracting.

Hi, very great as usual ! I'm trying to use flexplot to visualize my lmer models however I consistently get a "$ operator not defined for this S4 class" error, even when using your code (without any change) at 10:30. Any idea of what's happening ?

Is the code and dataset available somewherE? tks