Introduction to Regression in R (part 2 of 2)
November 16 @ 10:00 am - 12:00 pm
Learn how to apply regression modeling to your research data in this 2-part workshop series on 11/9 and 11/16.
Regression modeling — using input variables to predict or model the value of a response — is widely used by researchers in nearly every domain. While many statistics courses introduce the concept of regression, the practice of actually applying statistical modeling to real datasets can be complex and confusing for new learners. This workshop series covers the fundamentals of using regression modeling in practice through guided case studies. We emphasize best practices and intuition, and introduce only a small amount of math. This workshop is open to all UC Davis researchers. Attendance at both sessions is required as the material builds across sessions. Instruction is in-person and seats are limited. A Zoom link (e.g., broadcast) will be available for those unable to attend who would like to watch live.
After this workshop, learners will be able to:
- – Describe the differences between linear and generalized linear regression models
- – Identify when and how to use fixed and random effects in regression models
- – Explain how continuous and categorical variables are handled differently in regression modeling software
- – List relevant packages for regression modeling in R
- – Write a regression model in R for a case study dataset
- – Interpret regression summary tables
- – Conduct diagnostic checks on their regression models
Participants must have taken DataLab’s “R Basics” workshop series and/or have prior experience using R, be comfortable with basic R syntax, and have the latest version of R and RStudio pre-installed and running on their laptops. We also strongly encourage learners to have either taken DataLab’s Intermediate R Data Visualization workshop, or have general familiarity with how to make plots in R using the ggplot2 package.