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

Find the code for this video on my GitHub: https://github.com/chloefouilloux/GLM...

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*