Eliminating variables from the model - adjusted R-squared selection

Now you will create a new model, where you will drop the variable that when dropped yields the highest improvement in the adjusted $R^2$.

Create a new model, m1, where you remove rank from the list of explanatory variables. Check out the adjusted $R^2$ of this new model and compare it to the adjusted $R^2$ of the full model.
If you don't want to view the entire model output, but just the adjusted R-squared, use summary(m1)$adj.r.squared.
Create another new model, m2, where you remove ethnicity from the list of explanatory variables. Check out the adjusted $R^2$ of this new model and compare it to the adjusted $R^2$ of the full model.
Repeat until you have tried removing each variable from the full model m_full at a time, and determine the removal of which variable yields the highest improvement in the adjusted $R^2$.
Make note of this variable (you will be asked about it in the next question).