Implemented Bootstrap and Simulation Functions for Data Analysis

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📅 2024-06-23 — Session: Implemented Bootstrap and Simulation Functions for Data Analysis

🕒 21:20–23:05
🏷️ Labels: Python, Data Analysis, Bootstrap, Simulation, Statistics
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to implement and refine various statistical methods and functions for data analysis, focusing on bootstrap sampling, covariance computation, and simulation techniques in Python.

Key Activities

  • Bootstrap Analysis and Covariance Computation: Executed methods for variance analysis using bootstrap techniques, including saving results and computing covariance.
  • Implementation of calculate_aggregates Function: Developed a Python function to compute statistical aggregates related to sales data, with inline comments for clarity.
  • Comparison of Bootstrap Sampling Loops: Analyzed different Python loops for bootstrap sampling and covariance computation to identify structural and functional differences.
  • Enhanced Covariance Function: Improved the covariance_and_save function to include quantile and scale options, with detailed comments for better understanding.
  • Unified Bootstrap and Simulation Function: Created a bootstrap_and_simulation function to integrate sampling and simulation processes, enhancing efficiency.
  • Unified Sales Data Processing: Developed functions for processing sales data and generating distributions, ensuring comprehensive functionality.
  • Algorithm for Macro Moments Independence: Implemented an algorithm to demonstrate the independence of macro moments from size distributions, including data generation and analysis.

Achievements

  • Successfully implemented and refined multiple Python functions for statistical analysis, enhancing data processing capabilities.
  • Developed a cohesive script for data analysis and experimentation, integrating various statistical methods.

Pending Tasks

  • Further testing and validation of the implemented functions to ensure robustness and accuracy in different data scenarios.
  • Exploration of additional statistical methods and their integration into the existing framework.

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