Now that you've got a good sense of how to 'speak' R, let's use it with linear regression to make distinctive predictions.

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

This paper is a discussion in expository form of the use of singular value decomposition in multiple linear regression, with special reference to the problems of collinearity and near collinearity.