Variable Selection with Demos in R

The standard linear model is commonly used to describe the relationship between a response and a set of variables (predictors). It is often the case that some or many of the variables used in a linear model are in fact not associated with the response. Including such irrelevant variables leads to unnecessary complexity in the resulting model, making it more difficult to interpret. In this workshop, we will cover two types of variable selection approaches, subset section and shrinkage, which can yield better prediction accuracy and model interpretability. Various examples with demos in R will be provided to illustrate a more concrete idea of when and how one should apply each method.