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Modeling with Data in the Tidyverse
4.9(230)27k studentsINTERMEDIATEEnglish
Korshub's take
Statistical Modeling with R and the Tidyverse
Data modeling helps uncover relationships between outcome variables and explanatory predictors. This intermediate R course teaches you how to construct, interpret, and evaluate linear regression models using tidyverse workflows for both explanatory research and predictive analysis.
Who Is This Course For?
Designed for R users who want to apply linear regression across statistics, causal inference, data analysis, and machine learning contexts.
Key Takeaways
- Explanatory & Predictive Modeling: Distinguish between exploring variable relationships and forecasting future outcomes.
- Tidyverse Integration: Harness tidyverse packages to prepare data and construct readable linear regression models.
- Model Interpretation: Analyze regression coefficients to answer practical questions in fields ranging from real estate pricing to academic evaluations.
About this course
In this course, you will learn to model with data. Models attempt to capture the relationship between an outcome variable of interest and a series of explanatory/predictor variables. Such models can be used for both explanatory purposes, e.g. "Does knowing professors' ages help explain their teaching evaluation scores?", and predictive purposes, e.g., "How well can we predict a house's price based on its size and condition?" You will leverage your tidyverse skills to construct and interpret such models. This course centers around the use of linear regression, one of the most commonly-used and easy to understand approaches to modeling. Such modeling and thinking is used in a wide variety of fields, including statistics, causal inference, machine learning, and artificial intelligence.
Price
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