Business Statistics, Global Edition, 4th Edition

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Business Statistics, Global Edition, 4th Edition

By Norean Sharpe, Richard De Veaux, Paul Velleman
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Business Statistics narrows the gap between theory and practice by focusing on relevant statistical methods, thus empowering business students to make good, data-driven decisions.

Using the latest GAISE (Guidelines for Assessment and Instruction in Statistics Education) report, which included extensive revisions to reflect both the evolution of technology and new wisdom on statistics education, this edition brings a modern edge to teaching business statistics. This includes a focus on the report’s key recommendations: teaching statistical thinking, focusing on conceptual understanding, integrating real data with a context and a purpose, fostering active learning, using technology to explore concepts and analyse data, and using assessments to improve and evaluate student learning. By presenting statistics in the context of real-world businesses and by emphasising analysis and understanding over computation, this book helps students be more analytical, prepares them to make better business decisions, and shows them how to effectively communicate results.

  • Improved organisation A streamlined design and a data-first presentation of information provides students with both the motivation to learn statistics as well as a foundation of real business decisions on which to build their statistical understanding. Chapters 5–7 now cover probability trees and Bayes’ rule. Chapter 21 is a brand-new chapter on data mining and Big Data.
  • Motivating Vignettes Each chapter opens with a vignette, which uses data from or about real-world companies, that helps students relate key statistical concepts to real business events. Companies featured include Visa, H&M, and Whole Foods Market.
  • Case Studies The book provides four cases based on realistically large datasets that challenge students to respond to accompanying open-ended business questions. These cases encourage students to bring together methods they have learned throughout the book.
  • Section Exercises Each chapter provides straightforward exercises targeted at the topics covered in each section and designed to check students’ understanding.
  • Chapter Exercises Each instance of this feature highlights a specific business or area and leads students to draw conclusions about the real world by combining concepts and methods.
New to this edition
  • Recent data. We teach with real data whenever possible, so we’ve updated data throughout the book. New examples reflect current stories in the news and recent economic and business events. When a historical dataset is especially good at illuminating a pedagogical point, we have, from time to time, chosen pedagogy over recency.
  • Chapters 1–4 have been streamlined to cover collecting, displaying, summarising, and understanding data in four chapters.
  • Chapters 5–7 introduce students to randomness and probability models.
  • Chapters 8 and 9 cover data collection by survey and by designed experiments. New discussions here address technology-enabled sampling, online data, and Big Data. Streamlined design. Our goal has always been a readable text. This edition sports a new design that clarifies the purpose of each text element. The major theme of each chapter is linear and easy to follow without distraction. Supporting material is clearly boxed and shaded, so students know where to focus their study efforts.
  • Enhanced Technology Help. We’ve updated Technology Help (now called Tech Support) in almost every chapter.
  • Updated examples to reflect the changing world. Our selection of course content reflects the wisdom of the GAISE 2016 report adopted by the American Statistical Association as a standard for introductory statistics teaching. Our “In Practice” elements have all been re-structured to ref lect real-world business challenges.
  • Increased focus on core material. Statistics in practice means making smart decisions based on data. Students need to know the methods, how to apply them, and the assumptions and conditions that make them work.
Table of contents
  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 (
  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)
  8. Data Sources: Observational Studies and Surveys (Roper Polls)
  9. Data Sources: Experiments (Capital One)
  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)
  16. Inference for Regression (Nambé Mills)
  17. Understanding Residuals (Kellogg’s)
  18. Multiple Regression (
  19. Building Multiple Regression Models (Bolliger and Mabillard)
  20. Time Series Analysis (Whole Foods Market®)
  21. Introduction to Big Data and Data Mining (Paralyzed Veterans of America)
  22. Quality Control (Sony)
  23. Nonparametric Methods (i4cp)
  24. Decision Making and Risk (Data Description, Inc.)
  25. Analysis of Experiments and Observational Studies
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