Jeremy Arkes

Regression Analysis (eBook, ePUB)

A Practical Introduction

• Format: ePub

Produktbeschreibung

This 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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 412
• Erscheinungstermin: 19. Januar 2023
• Englisch
• ISBN-13: 9781000825565
• Artikelnr.: 67055632

Autorenporträt

Jeremy Arkes is a retired economics professor from the Graduate School of Business and Public Policy, Naval Postgraduate School, U.S.A. He is currently writing books on economics, nature, and basketball.

Inhaltsangabe

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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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.