This thoroughly practical and engaging textbook conveys the skills needed to responsibly develop, conduct, scrutinize, and interpret statistical analyses, without requiring any high-level math.
Regression Analysis details the most common sources of statistical biases, including some not covered in other textbooks. Rather than focusing on complicated equations, the book describes these biases visually and with examples of situations in which they could arise. As the author argues, just learning how to conduct regressions without learning how to properly assess and interpret regressions can do more harm than good. Other unique features include an innovative approach to describing the elusive concept of ""holding other factors constant"" and proper interpretations of the strength of evidence in light of the Bayesian critique of hypothesis testing. This third edition enhances the emphasis on ethical and responsible research practices and creates more examples demonstrating how the biases and their corrections could affect the regression results.
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 at research organizations and in academia. It is ideal for undergraduate and postgraduate students 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.
By:
Jeremy Arkes
Imprint: Routledge
Country of Publication: United Kingdom
Edition: 3rd edition
Dimensions:
Height: 246mm,
Width: 174mm,
Weight: 969g
ISBN: 9781041002598
ISBN 10: 1041002599
Pages: 500
Publication Date: 12 September 2025
Audience:
College/higher education
,
Primary
Format: Paperback
Publisher's Status: Active
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.
Jeremy Arkes is a retired economics professor from the Graduate School of Business and Public Policy, Naval Postgraduate School, U.S.A.
