Essential Statistics, Regression, and Econometrics: Edition 2
Jun 2015 · Academic Press
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About this ebook
Essential Statistics, Regression, and Econometrics, Second Edition, is innovative in its focus on preparing students for regression/econometrics, and in its extended emphasis on statistical reasoning, real data, pitfalls in data analysis, and modeling issues. This book is uncommonly approachable and easy to use, with extensive word problems that emphasize intuition and understanding. 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 book provides hundreds of new and updated interesting and relevant examples, and discusses not only the uses but also the abuses of statistics. The examples are drawn from many areas to show that statistical reasoning is not an irrelevant abstraction, but an important part of everyday life. - Includes hundreds of updated and new, real-world examples to engage students in the meaning and impact of statistics - Focuses on essential information to enable students to develop their own statistical reasoning - Ideal for one-quarter or one-semester courses taught in economics, business, finance, politics, sociology, and psychology departments, as well as in law and medical schools - Accompanied by an ancillary website with an instructors solutions manual, student solutions manual and supplementing chapters
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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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