Essential Statistics, Regression, and Econometrics
- 2nd Edition - June 30, 2015
- Author: Gary Smith
- Language: English
Essential Statistics, Regression, and Econometrics, Second Edition, is innovative in its focus on preparing students for regression/econometrics, and in its extended emphas… Read more
- 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
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. Modeling
"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
- Edition: 2
- Published: June 30, 2015
- Language: English
GS
Gary Smith
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.