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Statistics 📊

A collection of statistical functions and tools implemented in Python. This project is part of my personal journey to better understand mathematical statistics and data analysis from the ground up.

About the Project

This repository contains custom implementations of essential statistical concepts, such as point estimators, hypothesis tests, probability distributions, and numerical integration methods (e.g., Simpson's Rule). The main goal is to internalize the logic behind statistical procedures rather than rely solely on black-box functions from external libraries like scipy or statsmodels.

The project aims to:

  • Deepen my understanding of mathematical statistics through hands-on coding.
  • Build a small, educational library of core statistical tools.
  • Experiment with data (e.g., sports data from FIFA) using hypothesis testing without relying on linear regression or high-level ML libraries.

Project Structure

The project is organized into several thematic modules, including:

  • descriptive_statistics.py – basic statistics: mean, variance, standard deviation
  • distributions.py – probability distributions and their cumulative distribution functions
  • hypothesis_testing.py – statistical tests (e.g., t-test, p-value calculation)
  • univariate_calculus.py – utility functions like numerical integration and gamma function
  • benjamini_hochberg.py – implementation of multiple testing correction

Requirements

To use the library, you need:

  • Python 3.12+
  • Libraries listed in requirements.txt

Environment setup:

git clone https://github.com/DanielFaltynowski/statistics.git  
cd statistics  
pip install -r requirements.txt