Statistical Modeling and Data Analysis Session

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📅 2023-08-25 — Session: Statistical Modeling and Data Analysis Session

🕒 20:50–21:10
🏷️ Labels: Statistical Modeling, Data Analysis, Python, Likelihood Estimation, Logistic Regression
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to explore statistical modeling techniques and data analysis methods, focusing on likelihood estimation, data manipulation, and regression analysis.

Key Activities

  • Likelihood Estimation: Implemented a Python example for estimating parameters of a TOM distribution using maximum likelihood estimation with scipy.
  • Data Manipulation: Merged two Pandas DataFrames, demographics and voting_outcomes, to create a combined dataset.
  • Statistical Modeling: Modeled voting outcomes using likelihood functions based on demographic data, involving data selection and transformation.
  • Parameter Inference: Outlined steps for parameter estimation, including convergence assessment, confidence interval calculation, and model fit evaluation.
  • Logistic Regression: Analyzed voting behavior using logistic regression, focusing on feature preparation, model fitting, and prediction.
  • Ecological Inference: Examined King’s ecological inference method for estimating individual voting behaviors from aggregate data using bivariate distributions.

Achievements

  • Successfully implemented maximum likelihood estimation for TOM distribution parameters.
  • Combined demographic and voting outcome data for comprehensive analysis.
  • Developed a framework for modeling voting outcomes and analyzing voting behavior with logistic regression.

Pending Tasks

  • Further exploration of ecological inference methods and their application in real-world datasets.
  • Refinement of logistic regression models for improved prediction accuracy.

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