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Interpreting BF10 and BFM in Bayesian linear regression


I'm new to Bayesian statistics and JASP, and have a question about how to interpret the output from a multiple linear regression. Looking at the BF10, the model with openness as the only predictor is the strongest.

What does the BFM represent? There are three models with BFM > 3, so I am wondering whether I need to interpret this, and if so, how to do it. When I add the other predictors to the null model, the model with openness has a BF10 = 225, but the models with q1sum and q2sum each have BF10s < 1.




  • BF_m quantifies the change from prior odds to posterior odds.
    Here I'd select "compare to best model" and then BF_01 for display, and you'll see how many times better the "openness" only model predicts the data compared to other models.

    Thanked by 2SarahA MAgoJ
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