Statistical Analysis and Learning Repository

This repository contains a comprehensive collection of Jupyter notebooks covering fundamental to advanced statistical concepts, implementations, and analyses. It serves as both a learning resource and a reference for statistical methods in data science.

🎯 Overview

The notebooks in this repository provide hands-on implementations and explanations of statistical concepts, progressing from basic descriptive statistics to advanced topics like hypothesis testing and regression analysis. Each notebook is designed to be self-contained while building upon previous concepts.

📚 Contents

• Descriptive Statistics (abc_01_decriptive_stats.ipynb)

  • Measures of central tendency
  • Variability measures
  • Distribution analysis

• Combinatorics (abc_02_combinatorics.ipynb)

  • Permutations
  • Combinations
  • Probability fundamentals

• Probability Distributions (abc_03_distributions.ipynb)

  • Normal distribution
  • Binomial distribution
  • Other common distributions

• Sampling Methods (abc_04_sampling.ipynb, sampling.ipynb)

  • Random sampling techniques
  • Sample size determination
  • Central Limit Theorem applications

• Hypothesis Testing (abc_05_hypothesis_testing.ipynb)

  • T-tests
  • Chi-square tests
  • ANOVA
  • Multiple testing corrections (FWER, FDR)

• Regression Analysis (abc_06_regression.ipynb)

  • Linear regression
  • Multiple regression
  • Gradient descent implementation

🛠️ Setup and Installation

1. Clone this repository:

git clone https://github.com/macozu/Statistics.git
cd Statistics

2. Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

4. Launch Jupyter Notebook:

jupyter notebook

📋 Prerequisites

• Python 3.8+
• Basic understanding of Python programming
• Familiarity with Jupyter notebooks

🔧 Dependencies

Key libraries used in this project:

• NumPy for numerical computations
• Pandas for data manipulation
• SciPy for statistical functions
• Matplotlib and Seaborn for visualization
• Statsmodels for statistical models

For a complete list of dependencies, see requirements.txt.

🤝 Contributing

Feel free to open issues or submit pull requests if you find any bugs or have suggestions for improvements.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.