Bootstrap and Covariance Analysis Implementation

Start Time: 21:20
End Time: 23:10

📅 2024-06-23 — Session: Bootstrap and Covariance Analysis Implementation

🕒 21:20–23:10
🏷️ Labels: Bootstrap, Covariance, Python, Data Analysis, Statistical Methods
📂 Project: Dev
⭐ Priority: MEDIUM

The session aimed to implement and refine statistical methods for data analysis, focusing on bootstrap sampling and covariance computation.

Bootstrap Analysis and Covariance Computation: Implemented bootstrap methods for variance analysis and computed covariance of components using Pandas.
Function Development: Developed the calculate_aggregates function in Python to compute statistical aggregates related to sales data, including covariance and variance.
Enhanced Covariance Function: Improved the covariance_and_save function to support quantile and scale options, adding detailed comments for clarity.
Unified Function Implementation: Created a unified function bootstrap_and_simulation to integrate bootstrap sampling and simulation processes, enhancing data processing efficiency.
Algorithm Development: Designed algorithms for distribution analysis and macro moments independence experiments, ensuring robust data generation and result analysis.

Successfully implemented and refined multiple Python functions for statistical analysis.
Enhanced understanding and efficiency of data processing methods.

Further testing and validation of the implemented functions in diverse data scenarios.