New blog post! Seven (7!) new tidyexplain-esque animations showing how {dplyr}'s mutate(), summarize(), group_by(), and ungroup() all work together #rstats #statsodon https://www.andrewheiss.com/blog/2024/04/04/group_by-summarize-ungroup-animations/

New blog post! Have you (like me!) wondered what the ATT means in causal inference and how it's different from average treatment effects (ATE)? I use #rstats to explore why we care about the ATE, ATT, and ATU and show how to calculate them with observational data! https://www.andrewheiss.com/blog/2024/03/21/demystifying-ate-att-atu/ #statsodon

This paper by @nickchk (https://doi.org/10.1080/1350178X.2022.2088085 ; ungated here: https: //ftp.cs.ucla.edu/pub/stat_ser/huntington-klein-jem-june2022.pdf) is the best, most accessible introduction and explanation of how DAGs can be useful for causal inference for people more familiar with potential outcomes and econometrics-style approaches #statsodon #CausalInference

It's DAG day in class today and I *think* figured out a way to animatedly demonstrate collider bias (at least the selection bias version of it) #CausalInference #Statsodon

New post! Have you (like me) been confused/intimidated by Dirichlet distributions? Here's a basic, visual-heavy, intuition-heavy guide to the Dirichlet distribution. It's just a Beta distribution, but ~fancy~! #rstats #statsodon https://www.andrewheiss.com/blog/2023/09/18/understanding-dirichlet-beta-intuition/

yessssss this brms bayesian model for a conjoint survey experiment took 3 hours to fit, estimated nearly 40,000 unique parameters, takes up 4 GB of space, and is most definitely way overkill, but IT CONVERGED AND WORKS GLORIOUSLY #rstats #bayesian #statsodon

Also, in the course of adding DOIs to past posts, I updated my big ol' guide to different flavors of marginal effects to use {marginaleffects}'s newer slopes(), predictions(), and comparisons() functions https://www.andrewheiss.com/blog/2022/05/20/marginalia/ #rstats #statsodon

Check out this new ultimate guide to multilevel/hierarchical multinomial conjoint analysis with #rstats and {brms}, including how to find both marketing-style predicted market shares *and* polisci-style causal effects *across individual covariates* #bayesian #statsodon

https://www.andrewheiss.com/blog/2023/08/12/conjoint-multilevel-multinomial-guide/

Here it is! The ultimate practical guide to Bayesian and frequentist conjoint data analysis with #rstats and {brms} and {marginaleffects}, including how to distinguish between marginal effects and marginal means + work with subgroups! #statsodon https://www.andrewheiss.com/blog/2023/07/25/conjoint-bayesian-frequentist-guide/

OLS Strikes Back
#academia #statsodon #SAG
ht @WeedenKim

New blog post! Here's a guide to calculating the differences between categorical proportions in a principled, #bayesian way with #rstats, #mcmcstan, and {brms}, including fancy things like mosaic plots (with {ggmosaic} and striped fills (with {ggpattern}) https://www.andrewheiss.com/blog/2023/05/15/fancy-bayes-diffs-props/ #statsodon

The 8th iteration of my #ProgramEvaluation and #CausalInference course is up and live at https://evalsp23.classes.andrewheiss.com/ !

It covers basic econometrics and DAGs, all with #rstats, and it's mostly asynchronous with dozens of hours of videos, and the whole thing is Creative Commons-licensed, so do whatever you want with it! #epitwitter #EconTwitter #statsodon

Finally using my recent blog post on conditional vs. marginal effects in multilevel models (https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/) with some real data in a long-running project I'm working on and it's SO NEAT #rstats #statsodon

New post! If you think of "marginal effects" as slopes, the terms "marginal effects" and "conditional effects" aren't quite the same thing in the world of multilevel models, which is *so confusing*.

I recreate a post by @kristoffer to show the differences between the two kinds of effects using @vincentab 's phenomenal {marginaleffects} package

https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/

working on a new blog post about "fixed effects" and how they're, like, conceptual opposites depending on the kind of model you're using or the discipline you're working with #statsodon

Hello Mathstodon! I am Amy Hogan, NYC high school teacher. Currently teaching AP Statistics, Math Analysis (sophomore math team), and Algebra 2. I am involved with stats education at the K-12 level with the ASA and NCTM, and serving on the committee for USCOTS 2023.

What else should I include in this introduction?

Edited to add hashtags: #APStats #statistics #statsodon #MTBOS #iteachmath #statsed #USCOTS23

Can folks help me think through smtg:
I’ve got a 5-point Likert response with 1 binary predictor. If I dichotomize it into Agree/Str Agree vs other 3 groups and model as binomial, that is equivalent to a constrained multinomial model. What are those constraints? #Stats #Statsodon #brms

More *Bayes Rules!* notes + #brms #rstats translation: logistic and negative binomial models, which are super tricky to work with!

I added extra stuff on marginal effects, including the (new!) ability to integrate out random effects with {marginaleffects}

https://bayesf22-notebook.classes.andrewheiss.com/bayes-rules/18-chapter.html