Equips future data analysts with the skills they need to answer questions in business, economics, and public policy. Covering methods of exploratory, predictive, and causal analysis, it includes case studies that use real-world data and related data exercises supported by code (Stata, R, Python) and data available online.
Equips future data analysts with the skills they need to answer questions in business, economics, and public policy. Covering methods of exploratory, predictive, and causal analysis, it includes case studies that use real-world data and related data exercises supported by code (Stata, R, Python) and data available online.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Gábor Békés is an assistant professor at the Department of Economics and Business of the Central European University, and Director of the Business Analytics Program. He is a senior fellow at KRTK and a research affiliate at the Center for Economic Policy Research (CEPR). He has published in top economics journals on multinational firm activities and productivity, business clusters, and innovation spillovers. He has managed international data collection projects on firm performance and supply chains. He has done policy advising (the European Commission, ECB) as well as private-sector consultancy (in finance, business intelligence, and real estate). He has taught graduate-level data analysis and economic geography courses since 2012.
Inhaltsangabe
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 Bibliography Index.
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; Bibliography; Index.
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 Bibliography Index.
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; Bibliography; Index.
Rezensionen
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