Tests for differences among nested models

When you are trying to fit a regression o logistic regression model you have to decide the number of parameters to use in your model. Usually you start with a few of them and add new parameters, or you start with all of them and remove parameters. In any case you decide between succesive models which are a kind of russian dolls in respect to the parameters used(nested parameters models).

In these cases, there are two different approaches to decide which model is better:

•  Logistic regression, or models solved using Maximul Likelihood Estimates: In these cases you use one the likelihood ratio, Wald, or Lagrange multiplier (score) tests.

http://www.ats.ucla.edu/stat/mult_pkg/faq/general/nested_tests.htm

• Ordinary regression (OLS): You use Anova, partial-F tests.

Haz clic para acceder a R6.pdf

http://stats.stackexchange.com/questions/16493/difference-between-confidence-intervals-and-prediction-intervals