Experimental Design Lecture 7 - Bayesian analysis of covariance (ANCOVA)

Lecture 7 - Bayesian Analysis of covariance
Building on Lecture 6, we talk about Bayesian ANCOVA in JASP and hopefully remove some of the mystery around the different output tables.
Some links:
- Lecture 6 video: • Experimental Design Le...
- my JASP blog post: jasp-stats.org/2020/11/26/how...
Course:
PSYC 5316: Advanced Quantitative Methods & Experimental Design
Dr. Tom Faulkenberry (Tarleton State University)

[@user-cw5ln4lg5f]

This was enormously helpful and beautifully clear. Very many thanks.

[@YUNYIZHANG-ow2cb]

It was so helpful. Thank you!

[@bhinapatria]

Thank you! this was a great help in conducting Bayesian Ancova.

[@ernstpaulswens]

Thank you! I found your explanation very clear and insightful :)

[@leviwilkins9351]

Best professor

[@f2harrell]

Excellent presentation and choice of statistical methods. One suggestion for future coverage: When multiple models are entertained and the analyst selects the one that is best in a certain sense, not all uncertainties are carried forward. When instead you stick with a single model that has parameters for all the things you don't know (e.g., a parameter for a variable that you might have otherwise dropped), the posterior distributions are a little bit wider, and I feel this is appropriate because this will carry along the caution at the point of final effect assessment.

[@BirtanTR]

Thank you very much.