Відео

Awesome…

Best video to understand SMA regression. Thank you for sharing it.

Hi, why is R studio producing different results even though I am using the same call and data.

Thank you so much for this!

Very interesting شكرا لك

Clear and straightforward. Thank for the explanation.

Good stuff. Well explained.

Thanks! I would ask when I can use the model like lm, glm.. ? Is it instead of ordinary analysis?

Thank you very much

Clear and engaging, thank you!

One example of going from data to conclusion or, say, published paper does not seem likely by half way through. This is description of mixed vs. random. Does not show you what do with it. Maybe gets better second half.

Really great job 👏

is random effect and random parameter model same?

Amazing!

Hello, thanks for this really nice video! Could you please provide the image with the interactions plots and their explanation from min 13:30 and after? The quality of the video is not good enough...

So the evaluation of GLM model is done by comparing AIC values? Do we use R2 or R2 adjusted as well?

Searched for an explanation of "mixed effect models", got something totally badly introduced with very bad analogies. Sorry but you should term this video "for my students.." or alike.. this has no use for random people on youtube.

Could you share the code that you used to make the graphs? particularly the one found at 11:07?

chingon

Great presentation! I wonder if you have a few citations for the use of smatr in non-allometric applications?

Thank you

amazing! this guy needs to post more... what school does he teach at

I don't understand what's the difference between something being a block effect and a random effect.

Thanks'

I finally understand it.

Ur explanations r the best! Thanks a lot

A lecture of around 20 minutes is the perfect length!

I bet those peaks every 4-5 years in web searches correlate with statistics students trying to graduate lol

Wonderful

Sorry, but I don't see any of AIC or AICc explained here. The formulas just pop out of thin air without ever being explained, why the terms are in there and what they really mean and do in the equation. Then there is talk about AICc correcting AIC for sample size "because there is some n in the denominator", but, sorry, that is no explanation at all. What does this term correct and why exactly does it take on this form? I could write down any old formulas with n's all over the place, and contend that they "correct" AIC...

It should be noted that the families can take more links (i.e. you can calculate family=gaussian(link="log"))

This is so helpful, thank you!

Thank you so much for this clear explanation! It is so helpful!!!

I always heard that looking at the base R diagnostic residual plots for generalized linear models isn't useful in the same way it is for general linear models? would like confirmation of the oppisite as it would make my current stats work easier haha

Your videos are so helpful and the way you explain the ideas and methods makes it easy to follow and understand! Thank you!

Brilliant explanation

These videos are great! I'm glad to find an ecology-focussed series on statistics!

Please interpret the result of gamma with log link coefficient results

Thank you, your presentation is very clear and easy to follow :)

Thank you for your clear explanation! Can I ask you a question about your example? I was wondering how the AICc of your quadratic model is lower then you linear model? Because if you look at the CI's of your coefficients in the quadratic model they seem to be bigger then the coefficients of the linear model. Now if i'm not mistaking, this means that the residual sum of squares (used to calculate the CI's for these coefficients) in the quadratic model must be larger then in the linear model? Or does the formula in the AICc utilize a total residual sum of squares? That is still not fully clear to me.... I hope that you can elaborate on this question.

You offer so much irrelevant stuff. Focus on your topic. We know you are clever.

Excellent explanation. Many thanks

Thank you so much! This is so so so helpful!

this is not the kerri chandler you are looking for

This was just awesome...

I really enjoy the analogies you use in your videos, the greenhouse one is really effective

You are pretty much the best teacher of these kind of topics, a perfect balance beetwen simple and difficult key concepts

I have an exam tomorrow... multivariate data? and I swear I'm watching this last-minute because my teacher's messed up. Idk if this is even related, cause I'm confused.. but hope I pass? lol...

The graph at 6:23, where did you get it from? I would like to mention it in my report.

Thanks!