PhD+ Data Science Essentials in R: Linear Modeling

Office of Graduate & Postdoctoral Affairs

The <A HREF="https://www.library.virginia.edu/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">UVA Library’s</A> <A HREF="https://data.library.virginia.edu/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Research Data Services Team</A> is partnering with UVA’s <A HREF="http://phdplus.virginia.edu/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">PhD Plus program</A> to pilot a new module – Data Science Essentials in R – a six-session series to build data analysis, wrangling, and visualization skills.
Linear Modeling in R - The linear model is one of the most commonly-used statistical models. Also called the regression model or the ordinary linear regression, linear modeling is the foundation for more complex general linear models like logit or count models, mixed-effects models, and structural equation models. So it’s a good model to understand. This workshop will cover to use R to fit and analyze linear models. Through hands-on examples, we’ll talk about interpetation of model output and checking model assumptions. We’ll also explore dummy variables, interactions, and variable transformations. While the workshop will assume you’ve met a regression model before, it can serve as a refresher for forgotten statistics! This workshop also assumes basic experience using R and familiarity with the tidyverse libraries, or successful completion of our Intro to R, Data Preparation in R, and Data Visualization in R workshops.

PhD+ Data Science Essentials in R: Linear Modeling

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PhD+ Data Science Essentials in R: Linear Modeling

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