14. The backward elimination of stepwise regression:
1-runs all possible models and then chooses the best one.
2-adds predictors one at a time starting with the best single predictor.
3-sometimes misses the best model for a given number of predictors.
4-requires nonlinear estimation using maximum likelihood.