1.1 📖 About the Textbook

“Data Analysis for Business, Economics, and Policy” teaches:

• How to collect, clean, and explore data
• Regression analysis from basics to advanced techniques
• Machine learning for prediction
• Causal inference and policy evaluation

Target audience: - Undergraduate and graduate students in economics, business, and policy - Data analysts and researchers - Anyone wanting to learn modern data analysis

Approach: - Practical, hands-on learning - Real-world case studies - Emphasis on interpretation and critical thinking - Accessible to students with basic statistics background

1.2 About This Edition

This interactive edition transforms the textbook into an engaging online learning experience:

• 🎨 Modern Design - Beautiful, readable interface with responsive layout
• 💻 Integrated Code - Direct links to GitHub Codespaces for hands-on practice
• 🤖 AI Learning Tasks - Practice problems you can work through with AI assistants
• 📊 Interactive Dashboards - Explore data visualizations (coming soon)
• 📱 Mobile-Friendly - Learn anywhere, on any device
• 🔗 Hyperlinked - Easy navigation between sections and references

1.3 📚 Book Structure

1.3.1 PART I: DATA EXPLORATION

1.3.2 PART II: REGRESSION ANALYSIS

📖 Support the Textbook

This interactive HTML edition is a free companion to the full textbook. For the complete experience with all chapters, case studies, and exercises, please consider purchasing the book.

Buy from Cambridge University Press →

Also available on: Amazon US · Amazon UK · Amazon DE

1.3.3 PART III: PREDICTION

1.3.4 PART IV: CAUSAL ANALYSIS

1.4 🚀 Getting Started

1.4.1 For Students

1. Navigate - Use the sidebar to browse chapters and sections
2. Practice - Work through AI practice tasks for deeper understanding
3. Code - Click “Open in Codespace” to run code examples
4. Explore - Try the interactive dashboards (coming soon)

1.4.2 For Instructors

This interactive edition can: - Supplement your course materials - Provide students with hands-on practice - Enable self-paced learning - Track student progress (coming soon)

1.5 🔗 Additional Resources

1.5.1 Main Resources

• Textbook Website: gabors-data-analysis.com
• Code Repository: GitHub
• Data Repository: OSF
• Dashboards: dashboards.gabors-data-analysis.com (coming soon)

1.5.2 Learning Tools

• R Codebooks: Step-by-step R code for all case studies
• Python Codebooks: Python versions of all analyses
• Stata Codebooks: Stata code for regression-focused analyses
• Datasets: All data cleaned and ready to use

1.6 👥 About the Authors

Gábor Békés is Professor of Economics and Data Science at Central European University. His research focuses on firm dynamics, international trade, and data analysis methods.

Gábor Kézdi (1967-2021) was Professor of Economics at the University of Michigan. He made fundamental contributions to econometrics, labor economics, and education policy.

1.7 📊 Current Status

1.8 📧 Feedback & Contact

We welcome your feedback on this interactive edition!

• Report issues: Use the GitHub repository
• Suggest improvements: Contact via the main website
• Ask questions: Check the Q&A section

1.9 📜 License & Citation

Published by Cambridge University Press (2021), all rights reserved

Copyright: © 2025 Gábor Békés & Gábor Kézdi

Citation:

Békés, G., & Kézdi, G. (2021). Data Analysis for Business, Economics, 
and Policy. Cambridge University Press.

Interactive Edition:

Békés, G., & Kézdi, G. (2025). Data Analysis for Business, Economics, 
and Policy: Interactive HTML Edition. https://gabors-data-analysis.com/book/