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.

Linear regression can be used for two closely related, but slightly different purposes. You can use linear regression to predict the value of a single numeric variable (called the dependent variable) ...