STAT 204 - Intro to Statistical Data Analysis
π Course Brief
Focus: the focus of this course is to learn tools for exploratory data analysis (EDA) and statistical modeling in R
How: hands-on learning. This means that we will have a workshop style class every week, with active learning and a clear roadmap to apply these tools to real data in a project starting on week 4.
π― Learning Objectives
By the end of the course, students should be able to:
- Comfortably load, manipulate, and analyze data within the R environment, building proficiency from no prior R knowledge to advanced statistical computing
- Apply numerical and graphical methods to explore datasets, understand data structures, and create meaningful visualizations using modern R packages
- Fit, diagnose, and interpret common linear models including linear regression, ANOVA, and logistic regression, with emphasis on model assumptions and validation
- Implement principal components analysis (PCA), clustering methods, and regularized regression techniques to handle complex, high-dimensional datasets
- Grasp the mathematical foundations underlying linear regression, ANOVA, logistic regression, and PCA, including key assumptions, theoretical properties, and when each method is appropriate to use
- Integrate exploratory analysis with statistical modeling to draw meaningful conclusions from real-world datasets, culminating in a comprehensive final project
Acknowledgements
Iβm grateful to several talented people who helped make this course webpage possible. Danielle Navarro created the beautiful aRt featured in our banner and throughout various slides. Allison Horst designed the fun webpage icon and other images youβll see in our presentations. Jon Cardoso developed the elegant template that powers our course webpage, and Markus Eger generously provided guidance in adapting Jonβs template for our needs. Last but not least, Paul Parker shared foundational materials that helped shape the content for STAT 204.
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