bayesian linear regression predicted outcomes

Comments

• vandenman

  November 2021

  Hi Narcilili,

  I'm assuming that you have a single linear regression model (no model-averaging).

  is the predicted weight loss of 3.806 the mean of a normal distribution of predicted values for this flour beetle? If it is, then how is the 95% credible interval calculated?

  That depends a bit on what you want to do. If you want to use the posterior means for b0, b1, and the standard deviation of the residuals, then you have that the predicted weight loss at those observed values is a normal distribution with mean 3.806 and standard deviation equal to that of the residuals. From there on you can use the standard way to compute a uncertainty interval for a normal distribution to obtain the credible interval.

  On the other hand, if you want to look at the posterior predictive distribution, then you need the raw mcmc samples for b0, b1, and the standard deviation of the residuals. Then you can simulate a new value for each mcmc sample. Those samples form a distribution, and then you'd examine the quantiles of that distributions to obtain a credible interval.

  Usually, the posterior predictive approach is more appropriate, although this might be a bit more work.

  
  

  Cheers,

  Don
• narcilili

  November 2021

  hello don,

  thank you very much for responding. can JASP do the two methods you mentioned? let me show you a snippet of the b0 and b1 means

  

  i wonder if you could show me how to do the two methods. thanks.

  cheers