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