Executed statistical analysis and data visualization in Python

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📅 2023-02-22 — Session: Executed statistical analysis and data visualization in Python

🕒 04:50–05:55
🏷️ Labels: Python, Statistics, Data Visualization, Parameter Sweeping
📂 Project: Dev

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.

Graph View

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