Developed Minimalist OLS Regression Plot in Python

• Day: 2025-03-10
• Time: 12:45 to 13:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Visualization, Regression, Numpy, Matplotlib

Description

Session Goal:

The session aimed to develop a minimalist version of Seaborn’s regplot for Ordinary Least Squares (OLS) regression using Python libraries like NumPy and Matplotlib.

Key Activities:

• Implemented a simplified regplot clone focusing on OLS regression using NumPy.
• Created a scatter plot of x vs y and fitted a linear regression line.
• Enhanced the script with a regress_out() function to remove linear dependencies, improving interpretability.
• Discussed the statistical technique of regressing variable B from A while maintaining A’s mean, providing insights into partial regression and residualization.

Achievements:

• Successfully developed a minimalist OLS regression plot using NumPy and Matplotlib.
• Improved understanding of regression techniques and their statistical implications.

Pending Tasks:

• Further exploration of regression techniques and potential integration with additional statistical methods.

Evidence

• source_file=2025-03-10.sessions.jsonl, line_number=8, event_count=0, session_id=94b5abd744dbfd924d4308c45db841d209f9a2b1546b1c54a39ce9e8ea8fb537
• event_ids: []