How to interpret (and assess!) a GLM in R

Hi! New to stats? Did you just run a GLM and now you have an output that you have no idea how to interpret? Then this video is just for you! In addition to interpreting the output of standard GLM models in R, we also go over diagnosing the suitability/appropriateness of a GLM for your data.

**Our mantra:** Just because it runs, doesn't mean it's right!

Jump around the video:
0:00 Introduction
01:06 Loading Libraries
01:06 **Introduction to Iris Data**
02:34 First GLM table
03:01 Understanding **intercepts**
03:33 Understanding **estimates**
04:28 Changing the levels of comparison in a GLM
05:49 Understanding **standard errors and t-values**
06:59 Understanding **null deviance and residual deviance**
09:09 Understanding **deviance residuals**
09:24 Model quality checks and DHARMa
12:06 **EXAMPLE 2** Diamonds dataset
12:26 Building diamonds GLM
12:52 Knowledge check
13:58 DHARMa analysis for continuous GLM
14:35 Patterns in residuals
15:21 GLM with multiple predictors
15:57 Understanding intercept with multiple predictors
16:40 Are do your data and intercept agree?
17:17 Outro

Disclaimer: I definitely misspeak/misuse some terms throughout this video, but the general concepts are correct. I was just kind of free-balling with no script here, but I still hope you find the content useful! **hugs**

[livinglyrics]

This video is the first video of yours that I’ve come across and I just wanted to say, I absolutely love your teaching and presentation style!! Your enthusiasm and explanation style are so engaging, it’s awesome; and, the way you break things down whilst also simplifying concepts is great, especially because such concepts are generally taught/explained in a much more complex way in university courses, textbooks, and in other YouTube/online tutorials — together, I feel this all really helps with improving understanding of all concepts discussed. I’m a postgrad student and would have loved to have access to this type of content in my earlier years when learning stats - I must say though, I’ve still learnt some new info from this tutorial!! Would love to see more R programming tutorials like this one - if you’re thinking about posting more, please do because you definitely have the gift of making stats engaging and fun (descriptive words that you don’t usually find when people are talking about stats 😅). Thanks for this content!! 🙌

[CharleyDublin]

I am learning mixed effect linear models - could you do a video on how to interpret the outcome of those types of models? I have tons of info on the modeling aspect but not entirely sure how to leverage the output effectively. I appreciate the humor and thoughtfulness in your videos to make them interesting.

[wudaqin]

after finishing this video, I think i never interpret any model before even though I'm working with data for several years! amazing video, you are a good teacher!

[MV-wnkc]

This is exactly what i needed for my university report. Thank you so much!

[samuelderidder]

Thank you so much for these insights! It helped me interpret the data-analysis of my bachelor's thesis!

[emilybrayton]

Thanks so much for this video, I feel like I have some clarity in understanding GLMs and my outputs so much more now. It feels good to have this confidence!!!

[rhodrambles]

This was super useful, not come across the DHARMA package before and its so much simpler than what I was trying to do. Thank you so much!

[fionac]

Hi Chloe, this was a fabulous explanation of how GLM works, clear, concise and helped me no end to get to grips with my GLMM on factors affecting pollinators visiting annual bedding plants! thanks so much, not least for the introduction to DHARMa!! More please, love your friendly style.

[karlaandreoli]

I just arrived here, and I have to say thank you soooo much for this video!
You are very didactic
Hugs from Brazil 🥰

[jsc]

This really helped me fill in some knowledge gaps I had about the GLM, thanks so much 😊

[juanlb]

The model is modelling. that´s meme material there.
Thanks for the video Chloe! finally learned some tricks with GLMs

[isabelvictoriamoralesbelpa]

Thank you very much Chloe, you are the best for explaining this tricky things. Please if you can do a video about GLM including interactions among factors

[yuvalgal-shahaf]

You manage to make statistics fun anc cool! wow. Thank you so much. You are great

[CharleyDublin]

Very good explanation, helpful reminder. And appreciate the tip on the Dharma package.

[martinabautista]

You are incredible! I enjoy every second I watch your video

[paulobarrosbio]

Thank you! Amazing explanation! Really helped me understand key aspects of a GLM. And thanks to the tip on the DHARMa package!

[keniadanielareyesochoa]

OMG, this is pure gold! Thank you so much <3

[icefunkdark]

I love how you present it :) thank you!

[AntoineHavard-gw]

This was really helpfull, clear, and fun to watch ! thank you very much :)

[mattounou]

Merci beaucoup pour les explications claires ! Précieux notamment pour juger la validité du glm et ce joli package DHARMa