📅 2023-10-21 — Session: Refinement and Execution of Taxation Data Models

🕒 17:30–19:40
🏷️ Labels: Taxation, Statistical Modeling, Data Visualization, Python, Error Correction
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to refine statistical models for analyzing tax morale and execute data visualization tasks using Python.

Key Activities

  • Reviewed independent variables and methodologies in taxation research, focusing on state services and community capabilities.
  • Outlined ordered logistic model formulas, considering versions with and without city fixed effects.
  • Developed a refined ordered logistic model for tax morale, incorporating controls like satisfaction with tax revenue use and public goods delivery.
  • Suggested regression models for analyzing tax morale based on public goods, community engagement, and political participation.
  • Executed Python code for integrating codebook information into histogram plotting and loading JSON into a Python dictionary.
  • Modified Python code for enhanced data visualization, including legend improvements and Markdown formatting.
  • Addressed errors in dataset loading, including library imports, file path corrections, and dataset reloading.
  • Updated heatmap modifications by rounding numbers and removing black lines.

Achievements

  • Successfully refined statistical models for tax morale analysis.
  • Improved data visualization techniques in Python.
  • Resolved dataset loading errors and confirmed correct file paths.

Pending Tasks

  • Verify the correctness of dataset loading and ensure all necessary libraries are imported.
  • Continue refining data visualization techniques and explore additional model refinements.