Mastering Linear and Mixed Models for Data Analysis in the R Programming Language
In short: This session provides a practical guide to implementing linear and linear mixed models using R. It is designed for those looking to understand the theoretical foundations of these models and the technical skills required to interpret and store their results effectively.
Part of the MIT RES.1-002 course from Fall 2023, this instruction led by Helena Vallicrosa focuses on the application of statistical modeling. The curriculum covers the essential logic behind choosing between standard linear models and mixed models, providing context on when each approach is appropriate for data analysis.
Beyond the theory, the material emphasizes the practical workflow of handling model outputs. Students learn how to extract and manage results within the R environment, ensuring that complex statistical findings are stored and accessible for further research or reporting.
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Sources
- Summary by Intellect · source: Introduction to R, Part III: Linear and Mixed Models in R
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