bayesian linear regression predicted outcomes
I wonder if someone can help me understand predicted outcomes of Bayesian linear regression models.
i analyzed the effect of relative humidity on weight loss of flour beetles using Bayesian simple linear regression. the results showed an intercept of 6.022 and a slope of -0.052. given the intercept and slope i need to estimate the predicted weight loss for each beetle. the Bayesian student guide gives the prediction equation as y = b0 + b1*x1, where,
y = estimated dependent outcome variable score,
b0 = constant (intercept),
b1 = regression coefficient
x1= score difference for the independent predictor variable (= x – mean x)
the mean relative humidity is 50.389.
if a flour beetle has observed values of 3.72 for weight loss and 93 for relative humidity, then the predicted weight loss = 6.022 + (-0.052*[ 93 – 50.389]) = 3.806
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?