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Model Selection in R (Self-Paced)

Started Jan 1, 2021
2 credits


Full course description

Term: Spring 2021

Date & Time: Self-Paced


Instructors: Frances McCarty, Jennifer Van Mullekom, & Katherine Miller

Presented By: Statistical Applications & Innovations Group (SAIG)



Regression is one of the most commonly used statistical methods – but things can quickly get complicated when you have multiple predictors. In the SAIG Short Course Model Selection in R, we will cover different methods to evaluate different regression models with different sets of predictors.

The following topics will be covered:

• Running Multiple Linear Regression in R

• Checking MLR assumptions with plots and tests • Applying model selection methods like stepwise regression & LASSO

• Criterion used to assess models (AIC, BIC, etc.) Attend this course on [date] from [time] in [location].

No previous coding experience is required! Bring your laptop with R and RStudio installed on your machine. You can download them from the following links: If you already have R and RStudio on your laptop, make sure they are the most up to date versions. This course is jointly sponsored by the Statistical Application and Innovations Group (SAIG) and Professional Development Network (PDN).

Sign up for this course today!