Global Thriving Analysis: Health & Well-being Study

A comprehensive statistical analysis of global health and well-being indicators across 132 countries, examining the extent to which global citizens are thriving.

🌐 Live Analysis Dashboard

View the interactive analysis: https://hcprieto.github.io/AI-course-analysis/analysis-output-files/

📊 Analysis Overview

This study examines multiple dimensions of global citizen thriving through statistical analysis of health and well-being indicators:

Key Research Question

"To what extent are global citizens thriving? How do these data compare by country?"

Dataset

• 132 countries across 6 continents
• Multiple health indicators from 2018-2020
• Data source: World Data 2.0-Data.csv

🔍 Analysis Features

Health & Well-being Analysis

• Life Satisfaction (Cantril Ladder scale)
• Life Expectancy at birth
• Self-harm Rates per 100,000 people
• Mortality Patterns and leading causes of death

Statistical Methods

• Univariate Analysis: Descriptive statistics, normality testing
• Bivariate Analysis: Correlation analysis, regression
• Regional Analysis: ANOVA tests comparing continents
• Outlier Detection: Z-score methodology
• Mortality Analysis: Global and regional patterns

Key Findings

• Strong correlation (r = 0.72) between life satisfaction and life expectancy
• Significant regional differences across all health indicators
• Outlier countries identified for each indicator
• Statistical significance testing for all relationships

📈 Visualizations

The analysis includes comprehensive visualizations:

• Distribution plots with normality testing
• Regional comparison box plots
• Correlation matrices and scatter plots
• Outlier detection charts
• Mortality pattern analysis

🚀 Two Analysis Levels

Enhanced Analysis (Recommended)

• Interactive charts and visualizations
• Comprehensive statistical testing
• Outlier identification with country annotations
• Regional ANOVA analysis
• Professional presentation

Basic Analysis

• Summary statistics
• Key correlations
• Simplified presentation
• Quick overview format

📁 Repository Structure

AI-course-analysis/
├── analysis-output-files/          # Web-ready analysis reports
│   ├── index.html                  # Main dashboard
│   ├── enhanced_health_analysis.html  # Full statistical analysis
│   ├── health_wellbeing.html       # Basic analysis
│   ├── *.png                       # Generated visualizations
├── code-files/                     # Analysis scripts
│   ├── enhanced_health_analysis.py # Main analysis script
│   ├── simple_health_analysis.py   # Basic analysis
│   ├── data_loader.py              # Data processing
│   └── html_generator.py           # Report generation
├── source-files/                   # Raw data
│   ├── World Data 2.0-Data.csv     # Main dataset
│   └── Analysis plan.csv           # Research framework
├── CLAUDE.md                       # Project documentation
└── README.md                       # This file

🛠 Technical Implementation

Technologies Used

• Python: Data analysis and statistical computing
• Pandas: Data manipulation and analysis
• SciPy: Statistical testing (ANOVA, correlations)
• Matplotlib/Seaborn: Data visualization
• HTML/CSS: Interactive reporting
• GitHub Pages: Web hosting

Analysis Pipeline

1. Data Loading & Cleaning: Handle CSV parsing issues, standardize formats
2. Statistical Analysis: Univariate, bivariate, and multivariate analysis
3. Visualization Generation: Create publication-quality charts
4. Report Generation: Compile results into interactive HTML
5. Web Deployment: Serve via GitHub Pages

📖 How to Use

View Online (Recommended)

Simply visit the GitHub Pages link above to explore the interactive analysis.

Run Locally

1. Clone this repository
2. Install required packages: pip install pandas matplotlib seaborn scipy
3. Run the analysis: python code-files/enhanced_health_analysis.py
4. Open analysis-output-files/index.html in your browser

Reproduce Analysis

All analysis scripts are included and documented. The main analysis can be reproduced by running:

cd code-files
python enhanced_health_analysis.py

🎯 Key Insights

Global Health Patterns

• Life satisfaction varies significantly by continent (p < 0.001)
• Life expectancy shows strong regional clustering
• Self-harm rates identify concerning outlier countries

Statistical Relationships

• Life satisfaction ↔ Life expectancy: Strong positive correlation (r = 0.72)
• Regional differences: All indicators show significant continental variation
• Outlier identification: Afghanistan (life satisfaction), multiple countries (self-harm rates)

Mortality Analysis

• Cardiovascular disease dominates globally
• Regional patterns in leading causes of death
• Distribution analysis across continents

📚 Methodology

The analysis follows a systematic approach:

1. Exploratory Data Analysis: Understand distributions and patterns
2. Statistical Testing: Apply appropriate tests for data types
3. Effect Size Calculation: Measure practical significance
4. Outlier Detection: Multiple methods for robust identification
5. Visualization: Clear, publication-ready charts
6. Interpretation: Context-aware analysis of results

🤝 Contributing

This is an educational project demonstrating statistical analysis techniques. Feel free to:

• Explore the analysis methods
• Suggest improvements to visualizations
• Propose additional research questions
• Report any issues with the code

📄 License

This project is for educational purposes. Data source attributions are maintained in the analysis reports.

🙏 Acknowledgments

• Data analysis techniques adapted from best practices in statistical computing
• Visualization design inspired by modern data science standards
• HTML templates designed for accessibility and user experience

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View the live analysis: GitHub Pages Link

Generated using Claude Code for comprehensive statistical analysis