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North Carolina Crime Analysis

Statistical analysis of NC criminal data using linear regression models to provide evidence-based policy recommendations for crime reduction.

Research UC Berkeley
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Skills

Statistical Inference Linear Regression Policy Analysis Data Cleaning

Tools

R LaTeX ggplot Plotly

A comprehensive examination of criminal statistics from North Carolina (1987) employing linear regression models to provide evidence-based policy recommendations for reducing crime at county and state levels.

Collaborators

Worked with Kevin Kory and Joy First on this capstone project for UC Berkeley’s Statistics for Data Science course (W203, Fall 2019).

Methodology

Applied rigorous statistical methods including linear regression, assumption validation, and overfitting prevention. Addressed critical statistical concepts while maintaining clear stakeholder communication.

Key Achievements

  • Near-perfect assignment score
  • Instrumental in earning A+ semester grade
  • Emphasized practical application of statistical methods to real-world policy problems