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Relevant statistical methods that empower individuals to make effective, data-informed business decisions

Business Statistics, 4th Edition , by Sharpe, De Veaux, and Velleman, narrows the gap between theory and practice, by covering relevant and real-life statistical methods that help business students make good, data-driven decisions. With their unique blend of teaching, consulting, and entrepreneurial experiences, this dynamic author team brings a modern edge to teaching statistics to business students. Focusing on stats in the context of real business issues, with an emphasis on analysis and understanding over computation, the text helps students to be analytical, prepares them to make better business decisions, and shows them how to effectively communicate results.

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Norean R. Sharpe, PhD, is Dean and the Joseph H. and Maria C. Schwartz Distinguished Chair at The Peter J. Tobin College of Business at St. John’s University. As the chief academic officer of the Tobin College of Business, she is responsible for the curriculum for 2500 undergraduate business majors and 600 graduate students in one of seven MS/MBA programs, all supported by more than 150 faculty and staff on the Manhattan; Queens; Staten Island; and Rome, Italy campuses. Within the Tobin College is the Center for Enterprise Risk Management, the Applied Finance Institute, and the Global Business Stewardship Center, as well as the acclaimed School of Risk Management, Insurance, and Actuarial Science.

Dr. Sharpe is an accomplished scholar, with 30 years of teaching experience at Yale University, Bowdoin College, Babson College, and Georgetown University -- and with more than 30 scholarly publications in analytics and statistics education. Her research interests include time series analysis, forecasting, analytics, and women’s roles in entrepreneurship in the Middle East. Dr. Sharpe earned her BA from Mt. Holyoke College, her MS from the University of North Carolina, and her PhD in Systems Engineering from the University of Virginia.

Richard D. De Veaux (PhD Stanford University) is an internationally known educator, consultant, and lecturer. Dick has taught statistics at a business school (Wharton), an engineering school (Princeton), and a liberal arts college (Williams). While at Princeton, he won a Lifetime Award for Dedication and Excellence in Teaching. Since 1994, he has taught at Williams College, although he returned to Princeton for the academic year 2006—2007 as the William R. Kenan Jr. Visiting Professor of Distinguished Teaching. He is currently the C. Carlisle and Margaret Tippit Professor of Statistics at Williams College. Dick holds degrees from Princeton University in Civil Engineering and Mathematics and from Stanford University in Dance Education and Statistics, where he studied with Persi Diaconis. His research focuses on the analysis of large datasets and data mining in science and industry. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is an elected member of the International Statistics Institute (ISI) and a Fellow of the American Statistical Association (ASA). Dick is also well known in industry, having consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. He was named Statistician of the Year for 2008 by the Boston Chapter of the American Statistical Association. In his spare time he is an avid cyclist and swimmer. He also is the founder and bass for the doo-wop group the Diminished Faculty and is a frequent singer and soloist with various local choirs, including the Choeur Vittoria of Paris, France. Dick is the father of four children.

Paul F. Velleman (PhD Princeton University) has an international reputation for innovative statistics education. He designed the Data Desk^® software package and is also the author and designer of the award-winning ActivStats^® multimedia software, for which he received the EDUCOM Medal for innovative uses of computers in teaching statistics and the ICTCM Award for Innovation in Using Technology in College Mathematics. He is the founder and CEO of Data Description, Inc. (www.datadesk.com), which supports both of these programs. Data Description also developed and maintains the Internet site Data and Story Library (DASL; dasl.datadescription.com), which provides all of the datasets used in this text as well as many others useful for teaching statistics, and the statistics conceptual tools at astools.datadesk.com. Paul coauthored (with David Hoaglin) the book ABCs of Exploratory Data Analysis. Paul is Emeritus Professor of Statistical Sciences, where he was at Cornell University awarded the MacIntyre Prize for Exemplary Teaching. Paul earned his MS and PhD from Princeton University, where he studied with John Tukey. His research often focuses on statistical graphics and data analysis methods. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul was a member of the working group that developed the GAISE 2016 guidelines for teaching statistics. Paul’s experience as a professor, entrepreneur, and business leader brings a unique perspective to the book.

Richard De Veaux and Paul Velleman have authored successful books in the introductory college and AP High School market with David Bock, including Intro Stats, 5th Edition (Pearson, 2018); Stats: Modeling the World, 5th Edition (Pearson, 2019); and Stats: Data and Models, 4th Edition (Pearson, 2016).

PART I: EXPLORING AND COLLECTING DATA

1. Data and Decisions (H&M)

2. Visualizing and Describing Categorical Data (Dalia Research)

3. Describing, Displaying, and Visualizing Quantitative Data (AIG)

4. Correlation and Linear Regression (Zillow.com)

PART II: MODELING AND PROBABILITY

5. Randomness and Probability (Credit Reports, the Fair Isaacs Corporation, and Equifax)

6. Random Variables and Probability Models (Metropolitan Life Insurance Company)

7. The Normal and Other Continuous Distributions (The NYSE)

PART III: GATHERING DATA

8. Data Sources: Observational Studies and Surveys (Roper Polls)

9. Data Sources: Experiments (Capital One)

PART IV: INFERENCE FOR DECISION MAKING

10. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)

11. Confidence Intervals for Means (Guinness & Co.)

12. Testing Hypotheses (Casting Ingots)

13. More About Tests and Intervals (Traveler’s Insurance)

14. Comparing Two Means (Visa Global Organization)

15. Inference for Counts: Chi-Square Tests (SAC Capital)

PART V: MODELS FOR DECISION MAKING

16. Inference for Regression (Nambé Mills)

17. Understanding Residuals (Kellogg’s)

18. Multiple Regression (Zillow.com)

19. Building Multiple Regression Models (Bolliger and Mabillard)

20. Time Series Analysis (Whole Foods Market^®)

PART VI: ANALYTICS

21. Introduction to Big Data and Data Mining (Paralyzed Veterans of America)

PART VII: ONLINE TOPICS

22. Quality Control (Sony)

23. Nonparametric Methods (i4cp)

24. Decision Making and Risk (Data Description, Inc.)

25. Analysis of Experiments and Observational Studies

Appendix A. Answers

Appendix B. Tables and Selected Formulas

Appendix C. Credits

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