Executed statistical analysis and data visualization in Python

• Day: 2023-02-22
• Time: 04:50 to 05:55
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Statistics, Data Visualization, Parameter Sweeping

Description

Session Goal

The primary objective of this session was to perform statistical analysis and [[data visualization]] using Python. The focus was on calculating treatment differences, performing linear regression, and sweeping parameters for Average Treatment Effect (ATE) calculations.

Key Activities

• Implemented a loop to calculate and print the mean and standard deviation of treatment differences (diff1 and diff2).
• Executed linear regression on grouped data using SciPy, including error bars and printing the linear equation.
• Plotted a line using Matplotlib based on the equation y = m*x + b.
• Conducted parameter sweeping for ATE calculation, capturing mean and standard deviation for each parameter value.

Achievements

• Successfully calculated statistical measures for treatment differences.
• Completed linear regression analysis and visualization with error bars.
• Generated plots using Matplotlib for visual representation of data.
• Executed parameter sweeping for ATE calculations, ensuring robust statistical analysis.

Pending Tasks

• Review and optimize the parameter sweeping code for efficiency.
• Explore additional [[data visualization]] techniques to enhance clarity and presentation.

Evidence

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• event_ids: []