Multicollinearity in Bayesian linear regression?
Hello Team JASP!
I want to do a linear regression analysis along the lines of: age, literacy, years of education predict cognitive functions. Naturally, literacy and years of education correlate highly with one another as is to be expected. This means I shouldn't use them as combined predictors in one regression, right?
If I do use them together as predictors, the Bayesian inclusion probability plot suggests for me to only keep years of education for my first outcome variable. So this sounds to me as if education years and literacy are independent enough in their prediction for their effects to be separated from one another?!
Should I now add literacy to the null model or just remove it since it shouldn't be included? What am I doing about my multicollinearity?