|
|
|
|
| LEADER |
03135cam a2200397 a 4500 |
| 001 |
99970933004151 |
| 005 |
20241030105406.0 |
| 008 |
210614s2021 enka b 001 0 eng |
| 010 |
|
|
|a 2021024817
|
| 020 |
|
|
|a 9781108483018
|q (hardback)
|
| 020 |
|
|
|a 1108483011
|q (hardback)
|
| 020 |
|
|
|a 9781108716208
|q (paperback)
|
| 020 |
|
|
|a 1108716202
|q (paperback)
|
| 020 |
|
|
|a 9781108591102
|q (ebook)
|
| 020 |
|
|
|a 1108591108
|q (ebook)
|
| 035 |
|
|
|a (OCoLC)1181847769
|
| 035 |
|
|
|a (OCoLC)on1181847769
|
| 040 |
|
|
|a DLC
|c DLC
|d UKMGB
|d OCLCL
|d U@S
|
| 042 |
|
|
|a pcc
|
| 049 |
|
|
|a U@SA
|
| 050 |
0 |
0 |
|a H62
|b .B45 2021
|
| 082 |
0 |
0 |
|a 300.72/3
|2 23
|
| 100 |
1 |
|
|a Békés, Gábor.
|
| 245 |
1 |
0 |
|a Data analysis for business, economics, and policy /
|c Gábor Békés, Gábor Kézdi.
|
| 260 |
|
|
|a Cambridge, UK ;
|a New York :
|b Cambridge University Press,
|c 2021.
|
| 300 |
|
|
|a xxiii, 714 p. :
|b il. ;
|c 26 cm.
|
| 504 |
|
|
|a Incluye referencias bibliográficas e índice.
|
| 505 |
0 |
|
|a I. Data exploration: 1. Origins of data -- 2. Preparing data for analysis -- 3. Exploratory data analysis -- 4. Comparison and correlation -- 5. Generalising from data -- 6. Testing hypothesis -- II. Regression analysis: 7. Simple regression -- 8. Complicated patterns and messy data -- 9. Generalising results of a regression -- 10. Multiple linear regression -- 11. Modelling probabilities -- 12. Regression with time series data -- 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 date -- IV. A framework for casual analysis: 19. Designing and analysing experiments -- 20. Regression and matching with observational data -- 21. Difference-in-differences -- 22. Methods for panel data -- 23. Appropriate control group for panel data.
|
| 520 |
|
|
|a "Data analysis is a process. It starts with formulating a question and collecting appropriate data, or assessing whether the available data can help answer the question. Then comes cleaning and organizing the data, tedious but essential tasks that affect the results of the analysis as much as any other step in the process. Exploratory data analysis gives context to the eventual results and helps deciding the details of the analytical method to be applied. The main analysis consists of choosing and implementing the method to answer the question, with potential robustness checks. Along the way, correct interpretation and effective presentation of the results are crucial. Carefully crafted data visualization help summarize our findings and convey key messages. The final task is to answer the original question, with potential qualifications and directions for future inquiries." --Descripción del editor.
|
| 650 |
|
0 |
|a Social sciences
|x Research
|x Methodology.
|
| 650 |
|
0 |
|a Quantitative research.
|
| 650 |
|
0 |
|a Econometrics.
|
| 650 |
|
7 |
|a Ciencias sociales
|x Investigación
|x Metodología.
|2 UDESA
|
| 650 |
|
7 |
|a Investigación cuantitativa.
|2 UDESA
|
| 650 |
|
7 |
|a Econometría.
|2 UDESA
|
| 700 |
1 |
|
|a Kézdi, Gábor.
|