Developed statistical models for voting analysis

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Developed statistical models for voting analysis

  • Day: 2023-08-25
  • Time: 20:50 to 21:10
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Statistical Modeling, Voting Analysis, Python, Logistic Regression, Ecological Inference

Description

Session Goal

The session aimed to develop and refine statistical models for analyzing voting behavior using various techniques such as likelihood estimation, logistic regression, and ecological inference.

Key Activities

  • TOM Distribution Likelihood Estimation: Implemented a Python example to estimate parameters of a TOM distribution using maximum likelihood estimation, utilizing scipy for optimization.
  • Data Manipulation with Pandas: Merged demographics and voting_outcomes DataFrames to prepare data for analysis.
  • Voting Outcomes Modeling: Applied likelihood functions to model voting outcomes based on demographic data, including data selection and transformation.
  • Parameter Estimation: Followed a structured approach for parameter estimation, including convergence assessment, confidence interval calculation, and model fit evaluation.
  • Logistic Regression Analysis: Conducted logistic regression to assess the influence of demographic variables on voting behavior, covering feature preparation, model fitting, and significance testing.
  • Ecological Inference Method: Explored King’s ecological inference method for estimating individual voting behaviors from aggregate data using bivariate distributions.

Achievements

  • Successfully implemented and tested multiple statistical models for voting behavior analysis.
  • Gained insights into the application of likelihood functions and logistic regression in voting analysis.

Pending Tasks

  • Further validation and testing of the models developed, particularly the ecological inference method, to ensure robustness and accuracy in predictions.

Evidence

  • source_file=2023-08-25.sessions.jsonl, line_number=1, event_count=0, session_id=e1993b06e6c3e044cd1c19a43a4eef433e3b425fa219630e5952ae51e47dcf2a
  • event_ids: []

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