Intermediate Data Analysis (Math 439)

Math 439 focuses on building a deeper understanding of statistical modeling, with an emphasis on regression, model diagnostics, ANOVA, and modern computational techniques. The course covers the mathematical foundations of least squares, simple and multiple regression, variable selection, model checking, bootstrapping, and introductory Bayesian treatments of regression models. Throughout the semester, we applied these methods to real datasets using R and developed practical skills in evaluating and improving statistical models.

Projects

Car Prices Project

In this project, I used regression modeling to predict used car prices based on dealership data. I compared multiple predictors, assessed model fit, and evaluated which factors most strongly influenced hypothetical sale prices.
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COVID Lung Capacity Project

This analysis investigated whether individuals who were vaccinated had higher post-COVID lung capacity compared to unvaccinated individuals. Using bootstrapping and model diagnostics, I examined differences between groups and evaluated the strength of the evidence.
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Earthquake Project

This project analyzed mainshock–aftershock pairs from the Atacama Fault Zone using regression techniques. I applied transformations and model diagnostics to address issues such as nonlinearity while studying how the magnitude of a mainshock influences its aftershock.
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