Business Statistics: A First Course, 8th edition

Published by Pearson (January 1, 2019) © 2020

  • David M. Levine Baruch College, City University of New York
  • David F. Stephan Two Bridges Instructional Technology
  • Kathryn A. Szabat La Salle University

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For business statistics courses.

A focus on statistical methods to analyze and interpret results and make data-informed decisions

Business Statistics: A First Course shows the role statistics will play in students' careers by providing examples drawn from all functional areas of business. The text is guided by principles set forth by major stat and business science associations (ASA and DSI) while also drawing on the authors' diverse experiences.

The 8th Edition features all new examples, cases and problems. It also continues its focus on the interpretation of results, evaluation of assumptions and discussion of next steps that lead to optimal data-based decision making.

Hallmark features of this title

  • 5 guiding principles ensure students see the relevance of stats in their own careers, focus on interpreting data and analyzing results (vs. on manual calculations), get familiar with data analysis software, and receive clear program instructions.
  • A Define, Collect, Organize, Visualize, and Analyze framework is used to help students solve business problems.
  • A continuing Case throughout the text helps integrate learning across chapters and topics.
  • Digital Cases let students examine interactive documents, sift through various claims, and discover conclusions and claims supported by the data.
  • End-of-chapter problems test students' ability to apply concepts to real business problems.

New and updated features of this title

  • NEW: Online “First Things First” chapter uses real-world examples to show how developments like the increasing use of analytics and big data have made understanding stats that much more critical.
  • 7 NEW and REVISED: Using Statistics business scenarios open each chapter, providing context for the concepts and showing how stats is used in key functional areas of business.
  • UPDATED: Statistical software (JMP, Excel, and Minitab) instructions, guides, and output show students how to use these programs in decision making. Examples were written by the authors, who collectively have 100+ years of experience teaching the application of software to business.
  • UPDATED: Tabular summaries now guide readers to reach conclusions and make decisions based on statistical information (Chs. 9 to 15).
  • NEW: A brand-new Chapter 17, “Business Analytics” makes extensive use of JMP and Minitab to illustrate predictive analytics for prediction, classification, clustering, and association, etc.

Features of MyLab Business Statistics for the 8th Edition

  • NEW: Pearson eText is an easy-to-use digital textbook, available with MyLab, which lets students read, highlight and take notes all in one place, even when offline.
  • The Getting Ready for Statistics Library contains more than 450 exercises to ensure students have mastered the needed developmental math topics before beginning their stats course.
  • Question Help in homework and practice questions help students master the concepts. Learning aids walk them through the problem, giving them assistance when they need it most.
  • The Study Plan gives students personalized recommendations, practice opportunities and learning aids to help them stay on track.
  • NEW: Using proven, field-tested technology, auto-graded Excel Projects let you seamlessly integrate Microsoft® Excel® content into your course without having to manually grade spreadsheets.
  • First Things First
  1. Defining and Collecting Data
  2. Organizing and Visualizing Variables
  3. Numerical Descriptive Measures
  4. Basic Probability
  5. Discrete Probability Distributions
  6. The Normal Distribution
  7. Sampling Distributions
  8. Confidence Interval Estimation
  9. Fundamentals of Hypothesis Testing: One-Sample Tests
  10. Two-Sample Tests
  11. Analysis of Variance
  12. Chi-Square and Nonparametric Tests
  13. Simple Linear Regression
  14. Introduction to Multiple Regression
  15. Multiple Regression Model Building
  16. Time-Series Forecasting
  17. Business Analytics
  18. Getting Ready to Analyze Data in the Future
  19. Statistical Applications in Quality Management (online)
  20. Decision Making (online)

Self-Test Solutions and Answers to Selected Even-Numbered Problems

About our authors

David Levine, Professor Emeritus of Statistics and CIS at Baruch College, CUNY is a nationally recognized innovator in statistics education for more than 3 decades. Levine has coauthored 14 books, including several business statistics textbooks; textbooks and professional titles that explain and explore quality management and the Six Sigma approach; and, with David Stephan, a trade paperback that explains statistical concepts to a general audience. Levine has presented or chaired numerous sessions about business education at leading conferences conducted by the Decision Sciences Institute (DSI) and the American Statistical Association, and he and his coauthors have been active participants in the annual DSI Making Statistics More Effective in Schools and Business (MSMESB) mini-conference.

During his many years teaching at Baruch College, Levine was recognized for his contributions to teaching and curriculum development with the College's highest distinguished teaching honor. He earned BBA and MBA degrees from CCNY, and a PhD in industrial engineering and operations research from New York University.

As Associate Professor and Chair of Business Systems and Analytics at La Salle University, Kathryn Szabat has transformed several business school majors into one interdisciplinary major that better supports careers in new and emerging disciplines of data analysis including analytics. Szabat strives to inspire, stimulate, challenge and motivate students through innovation and curricular enhancements, and shares her coauthors' commitment to teaching excellence and the continual improvement of statistics presentations.

Beyond the classroom she has provided statistical advice to numerous business, nonbusiness, and academic communities, with particular interest in the areas of education, medicine, and nonprofit capacity building. Her research activities have led to journal publications, chapters in scholarly books, and conference presentations. Szabat is a member of the American Statistical Association (ASA), DSI, Institute for Operation Research and Management Sciences (INFORMS), and DSI MSMESB. She received a BS from SUNY-Albany, an MS in statistics from the Wharton School of the University of Pennsylvania, and a PhD degree in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania.

Advances in computing have always shaped David Stephan's professional life. As an undergraduate, he helped professors use statistics software that was considered advanced even though it could compute only several things discussed in Chapter 3, thereby gaining an early appreciation for the benefits of using software to solve problems (and perhaps positively influencing his grades). An early advocate of using computers to support instruction, he developed a prototype of a mainframe-based system that anticipated features found today in Pearson's MathXL and served as special assistant for computing to the Dean and Provost at Baruch College.

In his many years teaching at Baruch, Stephan implemented the first computer-based classroom, helped redevelop the CIS curriculum, and, as part of a FIPSE project team, designed and implemented a multimedia learning environment. He was also nominated for teaching honors. Stephan has presented at the SEDSI conference and the DSI MSMESB mini-conferences, sometimes with his coauthors. Stephan earned a BA from Franklin & Marshall College and an MS from Baruch College, CUNY, and he studied instructional technology at Teachers College, Columbia University.

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