R Programming: Selection of variables

The all-possible-regressions procedure considers all possible subsets of the pool of potential explanatory variables Xi (with i 1, 2, …, m). It then identifies a small group of regression models which are "good" according to a specified criterion. A detailed examination of these models can lead to the selection of the final model. If there are m candidate explanatory variables: 2 m regressions for all possible subsets (e.g. if m 10, then there are 1024 possible regression models) The function leaps() (from package leaps) performs an exhaustive search for the best subsets of the explanatory variables for predicting the response variable in linear regression. This gave us a little idea but still, we are not sure how many parameters to be used.