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Business Statistics, Canadian Edition, 4th edition

Published by Pearson Canada (March 24, 2020) © 2021

  • Norean R. Sharpe Georgetown University
  • Richard D. De Veaux Williams College
  • Paul F. Velleman Cornell University
  • David Wright University of Ottawa

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For two-semester business statistics courses.

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

Business Statistics 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.

Hallmark features of this title

  • Motivating Vignettes. Each chapter opens with a motivating vignette, often taken from the authors' consulting experiences. These descriptions of companies—such as Bell Canada, Sport Chek, Rogers, Intact Financial Corp., Ipsos Reid, PotashCorp of Saskatch- ewan, Canada's Wonderland, and Loblaw—enhance and illustrate the story of each chapter and show how and why statistical thinking is so vital to modern business decision making. We analyze data from or about the companies in the motivating vignettes throughout the chapter.
  • For Examples. Nearly every section of every chapter includes a focused example that illustrates and applies the concepts or methods of that section. The best way to understand and remember a new theoretical concept or method is to see it applied in a real-world business context right away. That's what these examples do throughout the book.
  • Step-by-Step Guided Examples. The answer to a statistical question is almost never just a number. Statistics is about understanding the world and making better decisions with data. To that end, some examples in each chapter are presented as Guided Examples. A thorough solution is modelled in the right column while commentary appears in the left column. The overall analysis follows our innovative Plan, Do, Report template. 
  • What Can Go Wrong? Each chapter contains an innovative section called “What Can Go Wrong?” which highlights the most common statistical errors and the misconceptions about Statistics. The most common mistakes for the new user of Statistics involve misusing a method—not miscalculating a statistic. 

New and updated features of this title

  • Conceptual Question Library: In addition to algorithmically regenerated questions that are aligned with your textbook, there is a library of 1,000 Conceptual Questions available in the assessment manager that require students to apply their statistical understanding.
  • The Getting Ready for Statistics Library contains more than 450 exercises to ensure students have mastered the required developmental math topics, before beginning their statistics course. These are key for students who may need extra prerequisite support and can be added to any assignment.

Important Digital Assets in MyLab Statistics

  • Technology Help. At the end of each chapter, we summarize what students can find in the most common software, often with annotated output. We then offer specific guidance for Excel, Minitab, SPSS, and JMP, formatted in easy-to-read sections. This advice is intended not to replace the documentation that accompanies the software, but rather to point the way and provide startup assistance.
  • Mini Case Studies. Each chapter includes Mini Case Studies that ask students to conduct an analysis based on a real business situation. Students define the objective, plan the process, complete the analysis, and report a conclusion. An ideal way for students to write up their work is the “Plan/Do/Report” format described above and used in each chapter. Data for the Mini Case Studies are available on the MyLab Statistics site and are formatted for use with various technologies.
  • Case Studies. Parts 1, 2, and 3 of the book have a Comprehensive Case Study on MyLab Statistics. Students are given realistically large data sets (also on the MyLab Statistics site) and challenged to respond to open-ended business questions using the data. Students have the opportunity to bring together methods they have learned in the chapters included in that part (and indeed, throughout the book) to address the issues raised. Students will be required to use a computer to manipulate the large data sets that accompany these Case Studies.
  • Section Exercises. The Exercises for each chapter begin with a series of straightforward exercises targeted at the topics in each chapter section. This is the place to check understanding of specific topics. Because the exercises are labelled by section, turning back to the right part of the chapter to clarify a concept or review a method is easy. 
  • Chapter Exercises. These exercises are designed to be more realistic than the Section Exercises and to lead to conclusions about practical management situations. The Chapter Exercises may combine concepts and methods from different sections. We've worked hard to make sure that they contain relevant, modern, and realistic business situations. Whenever possible, the data are on the MyLab Statistics site (always in a variety of formats) so they can be explored further. Often, we pair the exercises so that each odd-numbered exercise (with answers that appear at the end of the book) is followed by an even-numbered exercise on the same Statistics topic 
  • Data and Sources. Most of the data used in examples and exercises stem from real-world sources. Whenever possible, we present the original data as we collected it. Sometimes, due to concerns about confidentiality or privacy, we had to change the values of the data or the names of the variables slightly, always being careful to keep the context as realistic and true to life as possible. Whenever we can, we include references to internet data sources. As internet users know well, URLs often break as websites evolve. To minimize the impact of such changes, we point as high in the address tree as is practical, so it may be necessary to search down into a site to find the data. Moreover, the data online may change as more recent values become available. The data we use are usually posted on the MyLab Statistics site.
  1. An Introduction to Statistics
  2. Data
  3. Surveys and Sampling
  4. Displaying and Describing Categorical Data
  5. Displaying and Describing Quantitative Data
  6. Scatterplots, Association, and Correlation
  7. Introduction to Linear Regression
  8. Randomness and Probability
  9. Random Variables and Probability Distributions
  10. Sampling Distributions
  11. Confidence Intervals for Proportions
  12. Testing Hypotheses About Proportions
  13. Confidence Intervals and Hypothesis Tests for Means
  14. Comparing Two Means
  15. Design of Experiments and Analysis of Variance (ANOVA)
  16. Inference for Counts: Chi-Square Tests
  17. Nonparametric Methods
  18. Inference for Regression
  19. Understanding Regression Residuals
  20. Multiple Regression
  21. Building Multiple Regression Models
  22. Time Series Analysis
  23. Decision Making and Risk
  24. Quality Control
  25. Introduction to Data Mining (Online)

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 is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a “Lifetime Award for Dedication and Excellence in Teaching.” He is the C. Carlisle and M. Tippit Professor of Statistics at Williams College, where he has taught since 1994. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA. Dick is also well known in industry, where for more than 30 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart on his book Planet Drum, he has also sometimes been called the “Official Statistician for the Grateful Dead.” His real-world experiences and anecdotes illustrate many of this book's chapters.

Paul F. Velleman has an international reputation for innovative Statistics education. He is the author and designer of the multimedia Statistics program ActivStats, for which he was awarded the EDUCOM Medal for innovative uses of computers in teaching statistics, and the ICTCM Award for Innovation in Using Technology in College Mathematics. He also developed the award-winning statistics program Data Desk, and the Internet site Data and Story Library (DASL) (ASL.datadesk.com), which provides data sets for teaching Statistics. Paul's understanding of using and teaching with technology informs much of this book's approach.

David Wright combines an Engineering Ph.D. from Cambridge University, UK, with his current position as Full Professor at the University of Ottawa's Telfer School of Management to provide a business perspective on renewable energy. Dr. Wright has taught in universities in North America, Europe, and Africa and has experience in government and in industry. In government, he has developed statistical models to evaluate the impact of industrial society on natural resource depletion. In industry, he has worked with solar power developers on the economic viability and risk assessment of solar power projects. He also has experience in the telecommunications industry on the impact of new technology on business. His university teaching and research includes the economics of solar energy, business statistics, and the smart electricity grid. In his research he collaborates with professors in engineering and with industrial partners. He is cited in Who's Who in the World, Who's Who in Canadian Business, and Who's Who in Science and Engineering.

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