Number of observations needed per model term in Bayesian regression?
I am trying to determine whether my dataset is appropriate to do regressions on. However, typically the guidelines on the observations needed vary (for a review see e.g., Austin & Steyerberg, 2015). I can't seem to find any references on this topic in regards to the Bayesian regression implemented in JASP: does it depend on the prior distributions used or the sampling method?
I will fit models at the individual level. I have 81 data points with 4 variables. Depending on how I cross these variables I could end up with as many as 15 model terms, but right now I am looking at using 6 model terms.
Thanks for the help,