This thoroughly practical and engaging textbook conveys the skills needed to responsibly develop, conduct, scrutinize, and interpret statistical analyses without requiring high-level math. Rather than focusing on complicated equations, the book describes these biases visually and with examples of situations in which they could arise.
This thoroughly practical and engaging textbook conveys the skills needed to responsibly develop, conduct, scrutinize, and interpret statistical analyses without requiring high-level math. Rather than focusing on complicated equations, the book describes these biases visually and with examples of situations in which they could arise.
Jeremy Arkes is a retired economics professor from the Graduate School of Business and Public Policy, Naval Postgraduate School, U.S.A.
Inhaltsangabe
1. Introduction 2. Regression analysis basics 3. Essential tools for regression analysis 4. What does "holding other factors constant" mean? 5. Imprecision, standard errors, hypothesis tests, p-values, and aliens 6. What could go wrong when estimating causal effects? 7. Strategies for other regression objectives 8. Methods to address biases 9. Other methods besides Ordinary Least Squares 10. Time-series models 11. Some really interesting research 12. How to conduct a research project 13. The ethics of regression analysis 14. Summarizing thoughts. Appendix A: Background statistical tools. Appendix B: Data licenses for temperature_gdp dataset in exercises. Glossary.
1. Introduction
2. Regression analysis basics
3. Essential tools for regression analysis
4. What does "holding other factors constant" mean?
5. Standard errors, hypothesis tests, p-values, and aliens
6. What could go wrong when estimating causal effects?
1. Introduction 2. Regression analysis basics 3. Essential tools for regression analysis 4. What does "holding other factors constant" mean? 5. Imprecision, standard errors, hypothesis tests, p-values, and aliens 6. What could go wrong when estimating causal effects? 7. Strategies for other regression objectives 8. Methods to address biases 9. Other methods besides Ordinary Least Squares 10. Time-series models 11. Some really interesting research 12. How to conduct a research project 13. The ethics of regression analysis 14. Summarizing thoughts. Appendix A: Background statistical tools. Appendix B: Data licenses for temperature_gdp dataset in exercises. Glossary.
1. Introduction
2. Regression analysis basics
3. Essential tools for regression analysis
4. What does "holding other factors constant" mean?
5. Standard errors, hypothesis tests, p-values, and aliens
6. What could go wrong when estimating causal effects?
7. Strategies for other regression objectives
8. Methods to address biases
9. Other methods besides Ordinary Least Squares
10. Time-series models
11. Some really interesting research
12. How to conduct a research project
13. The ethics of regression analysis
14. Summarizing thoughts
Appendix of background statistical tools
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