📅 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.