"This book is written focusing on an introductory statistics course aiming to help students in developing the statistical reasoning they need to follow at a later stage a regression analysis or econometrics course. Within its eleven chapters...the emphasis is placed on statistical reasoning, real data, pitfalls in data analysis and modelling issues, as well as on hundreds of extensive examples and real world case studies which demonstrate in an excellent way the power, elegance and beauty of statistical reasoning." --Zentralblatt MATH 2012-1234-62003
"This is an introductory statistics textbook intended for use in either a statistics class that precedes a regression class or a one-term class that encompasses statistics and regression analysis. Complaining of the "fire hose pedagogy" of many introductory statistics courses, Smith (Pomona College) has chosen a focused approach that is intended to provide students with an understanding of the statistical reasoning that is needed for regression analysis. He therefore emphasizes statistical reasoning, real data, pitfalls in data analysis, modeling issues, and word problems." --SciTech Book News
From the Back Cover
Essential Statistics, Regression, and Econometrics is for an introductory statistics course with the aim to help students develop the statistical reasoning they need for econometrics. Too many students mistakenly believe that statistics courses are too abstract, mathematical, and tedious to be useful or interesting. To demonstrate the power, elegance, and even beauty of statistical reasoning, this textbook will provide hundreds of interesting and relevant examples, and discuss not only the uses but also the abuses of statistics. These examples will show how statistical reasoning can be used to answer important questions and also to expose the errors―accidental or intentional―that people often make. The examples will be drawn from many areas to show that statistical reasoning is not an irrelevant abstraction, but an important part of everyday life.
This textbook is intended to give students a deep understanding of the statistical reasoning they need for econometrics. It will be innovative in its focus on econometrics, and in the extended emphasis on statistical reasoning, real data, pitfalls in data analysis, and modeling issues (including functional forms, causality, and instrumental variables). This book will be unusually readable and non-intimidating, with extensive word problems that emphasize intuition and understanding.
The primary audience is undergraduate students taking a statistics course that is focused on preparing them for an econometrics course. By using this textbook, students will have a good understanding of statistical reasoning, the preparation they need for a subsequent econometrics course, and will have learned the tools for doing research papers involving data analysis.
- Extensive examples and real world case studies
- Tailor-made for economists -teach statistics as a prelude to an econometrics class
- A richer introduction to regression analysis
- A single resource to help economics students master a difficult subject
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.
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