📅 2023-08-25 — Session: Developed statistical models for voting analysis
🕒 20:50–21:10
🏷️ Labels: Statistical Modeling, Voting Analysis, Python, Logistic Regression, Ecological Inference
📂 Project: Dev
⭐ Priority: MEDIUM
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
demographicsandvoting_outcomesDataFrames 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.