| Titre : | Data analysis for business, economics, and policy |
| Auteurs : | Gabor Békes, Auteur ; Gabor Kézdi, Auteur |
| Type de document : | Ouvrages |
| Editeur : | Cambridge University Press, 2021 |
| ISBN/ISSN/EAN : | 978-1-108-48301-8 |
| Format : | 1 vol. (714 p.) |
| Langues: | Anglais |
| Catégories : |
[Eurovoc] ÉCONOMIE > analyse économique > analyse économique > économétrie [Eurovoc] ÉDUCATION ET COMMUNICATION > documentation > documentation > analyse de l'information [Eurovoc] ÉDUCATION ET COMMUNICATION > informatique et traitement des données > traitement des données |
| Tags : | statistical methods ; quantitative analysis |
| Résumé : | 4e de couv : "This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com." |
| Note de contenu : |
Part I. Data Exploration:
1. Origins of data 2. Preparing data for analysis 3. Exploratory data analysis 4. Comparison and correlation 5. Generalizing from data 6. Testing hypotheses Part II. Regression Analysis: 7. Simple regression 8. Complicated patterns and messy data 9. Generalizing results of a regression 10. Multiple linear regression 11. Modeling probabilities 12. Regression with time series data Part III. Prediction: 13. A framework for prediction 14. Model building for prediction 15. Regression trees 16. Random forest and boosting 17. Probability prediction and classification 18. Forecasting from time series data Part IV. Causal Analysis: 19. A framework for causal analysis 20. Designing and analyzing experiments 21. Regression and matching with observational data 22. Difference-in-differences 23. Methods for panel data 24. Appropriate control groups for panel data |
Exemplaires (1)
| Code-barres | Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|---|
| 007478 | X 1008 | Livre | Centre de documentation du CERDI / Ecole d'Economie | Salle de lecture | Disponible |
