Essential Statistics, Regression, and Econometrics

by Gary Smith

Overview

Essential Statistics, Regression, and Econometrics, Third Edition, is intended to help students in an introductory statistics course develop their statistical reasoning and critical thinking skills. To demonstrate the power, elegance, and even beauty of statistical reasoning, this book provides hundreds of new and updated examples, and discusses not only the uses but also the abuses of statistics. The examples are drawn from many real, contemporary areas to show that statistical reasoning is not an irrelevant abstraction, but an important part of everyday life. Innovative in its extended emphasis on statistical reasoning, real data, pitfalls in statistical analysis, the perils of p-hacking and data mining, and modeling issues, including functional forms and causality, the book is readable and non-intimidating, with extensive word problems that emphasize intuition, understanding, and practical applications. Now in its Third Edition, this popular resource highlights recent exciting discoveries and provides a thorough foundation for students, instructors, and researchers alike, all approaching the field from a variety of backgrounds.

Product Details

ISBN-13:	9780443448072
Publisher:	Elsevier Science
Publication date:	06/09/2026
Edition description:	3rd ed.
Pages:	400
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

Gary Smith received his Ph.D. in Economics from Yale University and was an Assistant Professor there for seven years. He has won two teaching awards and written (or co-authored) more than 100 academic papers and 20 books. His Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics (Overlook/Duckworth, 2015) was a London Times Book of the Week and has been translated into Chinese, Japanese, Korean, and Turkish. The AI Delusion (Oxford University Press, 2018) argues that, in this age of Big Data, the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions they should not be trusted to make. The 9 Pitfalls of Data Science (Oxford University Press, 2019, co-authored with Jay Cordes), won the PROSE award for Excellence in Popular Science & Popular Mathematics. His statistical and financial research has been featured in various media, including The New York Times, Wall Street Journal, Wired, NPR Tech Nation, NBC Bay Area, CNBC, WYNC, WBBR Bloomberg Radio, NBC Think, Silicon Valley Insider, Motley Fool, Scientific American, Forbes, MarketWatch, MoneyCentral.msn, NewsWeek, Fast Company, The Economist, MindMatters, OZY, Slate, and BusinessWeek.

Table of Contents

1. Data, Data, Data
2. Displaying Data
3. Descriptive Statistics
4. Probability
5. Sampling
6. Estimation
7. Hypothesis Testing
8. Simple Regression
9. The Art of Regression Analysis
10. Multiple Regression
11. Replication Crisis

What People are Saying About This

From the Publisher

Key textbook focused on the application of statistics and econometrics principles

Editorial Reviews

"It is very well written, and covers the basics clearly and understandably to someone with only high school algebra — nothing more advanced is needed." —MAA Reviews

"I can wholeheartedly recommend this book as an introductory book for a statistics course taught to early undergraduate economics students. It is clear and well-written and contains a wealth of interesting examples, exercises, and historical anecdotes. The author has done an excellent job..." —The Gazette

"I can wholeheartedly recommend this book as an introductory book for a statistics course taught to early undergraduate economics students. It is clear and well-written and contains a wealth of interesting examples, exercises, and historical anecdotes. The author has done an excellent job in elucidating statistical methods in an econometric context." —Gazette of the Australian Mathematical Society

From the Publisher