I am using a linear regression model to determine the effect of diet score at baseline as predictor of weight at follow up where:
lm(weight_followup ~ Diet_Score*weight_base)
When I center my predictor variables I get the following:
From clinical knowledge, the second set of results (uncentered) makes more sense, as Diet Score (aka quality) weight at follow up goes down.
Not sure what to use, as some posts suggest that if using continous predictors where 0 is not in the data range than predictors should centered.
All suggestions welcome. Its a large dataframe of N = 3000.