For the best predictor, the best model was selected based on the following criterion: If a) both estimates for the ICF category itself and for the interaction with time were significant, or b) only the interaction with time was significant, then both terms were entered in the model.
For the MMSE score, the best fitting model was the selection model utilizing the non-ignorable missing data assumption with additional predictors for dropout.
The top-ranking GS model indicated that larger GS was explained by polygynous MS and lower annual precipitation (Table 3b); annual mean temperature, although included in the best-fitting model, was not a significant predictor of GS.
After a best model was identified, further procedures tested whether adding IQ variables (WISC Vocabulary, WISC Block Design and Raven) to the regression model changed significant predictors and improved fit.
If p> n, this approach would clearly not work and a different variable selection procedure would have to be used: for example, forward step-wise selection [ 25], which begins with a reduced model containing only one predictor and then progressively keeps on adding predictors until the best model is found.
BSS, in fact, starts with the full model containing all predictors, and then goes backwards removing one predictor at a time, until the best fitting model is obtained.
Our results suggest that the simplest model and the best predictor may be post-treatment pain.
The findings show that the USP IV transfer model was the best predictor of in vivo bioavailability.
The sub-basin regression model was the best predictor of annual NOX-N loads in the watershed.
Insulin resistance calculated by the homeostatic assessment model was the best predictor of MPO.
