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/GLMOutput/tree/main 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**