Description
Book SynopsisThis thoroughly practical and engaging textbook is designed to equip students with the skills needed to undertake sound regression analysis without requiring high-level math.
Regression Analysis covers the concepts needed to design optimal regression models and to properly interpret regressions. It details the most common pitfalls, including three sources of bias not covered in other textbooks. Rather than focusing on equations and proofs, the book develops an understanding of these biases visually and with examples of situations in which such biases could arise. In addition, it describes how âholding other factors constantâ actually works and when it does not work. This second edition features a new chapter on integrity and ethics, and has been updated throughout to include more international examples. Each chapter offers examples, exercises, and clear summaries, all of which are designed to support student learning to help towards producing responsible research.
This is the textbook the author wishes he had learned from, as it would have helped him avoid many research mistakes he made in his career. It is ideal for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand regressions. Additional digital supplements are available at: www.youtube.com/channel/UCenm3BWqQyXA2JRKB_QXGyw.
Table of Contents1. 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
