Skip To Content
Model Selection in R is a Course

Model Selection in R

Nov 17 - Nov 17, 2020
2.0 credits

Spots remaining: 0

Enrollment is closed
Add yourself to the wait list and you'll be auto enrolled when a spot opens

Add to Wait List

Full course description

Term: Fall 2020

Date: November 17th, 2020

Time: 4:30 p.m. - 6:30p.m

Location: ONLINE ONLY

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

Presented By:  Statistical Application and Innovations Group (SAIG) and Professional Development Network (PDN).

 

Description:

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: https://www.r-project.org/ https://www.rstudio.com/products/rstudio/download/ 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).