Key Takeaways Linear regression models the relationship between a dependent and independent variable (s). A linear regression essentially estimates a line of best fit among all variables in the model.

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

As in Excel, we can manually remove explanatory variables one-by-one until we have a model in which all the explanatory variables are significant. This is the essence of data-driven (versus theory ...

We propose a multivariate sparse group lasso variable selection and estimation method for data with highdimensional predictors as well as high-dimensional response variables. The method is carried out ...

The statistical literature and folklore contain many methods for handling missing explanatory variable data in multiple linear regression. One such approach is to incorporate into the regression model ...