πŸ“… 2023-08-25 β€” Session: Statistical Modeling of Voting Behavior by Gender

πŸ•’ 21:25–21:40
🏷️ Labels: Voting, Statistics, Beta-Binomial, Ecological Inference, Data Analysis
πŸ“‚ Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to estimate voting behavior by gender using statistical models, specifically the beta-binomial distribution, and to address code execution issues related to this analysis.

Key Activities

  • Estimating Voting Behavior: Utilized a beta-binomial distribution to estimate voting behavior of men and women for a political party, detailing the likelihood function and interpretation of estimated proportions across precincts.
  • Statistical Approach: Addressed code execution issues and outlined a statistical approach using a binomial distribution to estimate voting proportions by gender.
  • Error Correction: Identified and planned to correct an oversight in library imports necessary for calculations.
  • Modeling Vote Percentages: Modeled vote percentages using a beta-binomial distribution, providing a step-by-step explanation and pseudo-code for ecological inference based on King’s method.
  • Ecological Inference Implementation: Implemented King’s Ecological Inference using a beta-binomial likelihood function, including defining the likelihood function, objective function, optimization, and result interpretation.

Achievements

  • Developed a comprehensive statistical model for estimating voting behavior by gender.
  • Identified and planned correction for code execution issues related to library imports.

Pending Tasks

  • Import required libraries and rerun the calculations to ensure accuracy and completeness of the model.
  • Gather more varied data to achieve nuanced results in voting behavior analysis.