Business Statistics : 8th Edition - A First Course, Global Edition - David Levine

Business Statistics

8th Edition - A First Course, Global Edition

By: David Levine, Kathryn Szabat, David Stephan

Paperback | 6 January 2020 | Edition Number 8

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For one-semester business statistics courses

Statistics is essential for all business majors, and Business Statistics: A First Course helps students see the role statistics will play in their own careers by providing examples drawn from all functional areas of business. Guided by the principles set forth by major statistical and business science associations (ASA and DSI), plus the authors' diverse experiences, the 8th Edition, Global Edition, continues to innovate and improve the way this course is taught to all students. With new examples, case scenarios, and problems, the text continues its tradition of focusing on the interpretation of results, evaluation of assumptions, and discussion of next steps that lead to data-informed decision making. The authors feel that this approach, rather than a focus on manual calculations, better serves students in their future careers. This brief offering, created to fit the needs of a one-semester course, is part of the established Berenson/Levine series.

Key features
  • Help students see the relevance of statistics in their own careers by using examples from functional areas (e.g., accounting, finance, marketing, etc.) that they may specialise in.
  • Emphasise interpretation and analysis of statistical results over calculation, as these activities are more important to students' futures.
  • Give students ample practice in understanding how to apply statistics to business (e.g., classroom examples and homework exercises involve actual or realistic data, and both small and large data sets).
  • Familiarise students with the use of data analysis software. Microsoft Excel, JMP, and Minitab are integrated into all topics to illustrate how software can help in the business decision-making process.
  • Provide clear data analysis software instructions to students so they understand how to use the programs.
  • A running end-of-chapter Case throughout the text helps integrate learning across chapters and topics.
  • With Digital Cases, students examine interactive documents to sift through various claims and information to discover the conclusions and claims supported by the data. Learners also see how to identify common misuses of statistical information.
  • Visual explorations. The Excel add-in workbook allows students to interactively explore important statistical concepts in descriptive statistics, the normal distribution, sampling distributions, and regression analysis.
  • The DCOVA framework (Define, Collect, Organise, Visualise, and Analyse) is used throughout this text as an integrated approach for applying statistics to help solve business problems.
  • Additional learning opportunities. Student Tips, LearnMore bubbles, and Consider This features extend student-paced learning by reinforcing important points or examining side issues, or answering questions that arise while studying business statistics such as 'What is so 'normal' about the normal distribution?' An Appendix provides additional self-study opportunities via answers to the Self-Test problems and most of the even-numbered problems in the text.
New to this edition
  • A real-life business approach grounds statistics in everyday life, helping students see how the concepts they are learning apply to their future careers.
  • An online First Things First chapter uses real-world examples to illustrate how developments such as the increasing use of business analytics and big data have made knowing and understanding stats that much more critical.
  • Using Statistics business scenarios open each chapter, providing context for the concepts and showing how statistics is used in key functional areas of business. At the end of the chapter, Using Statistics, Revisited, reinforces the statistical methods and applications just discussed. In the 8th Edition, 7 chapters have new or revised Using Statistics scenarios.
  • Pedagogical tools help keep students on track, providing an ideal framework for learning and understanding statistical concepts.
  • Statistical software (JMP, Excel, and Minitab) instructions, guides, and output throughout every chapter familiarise students with how to use these programs in business decision making, and allow learners to focus on interpreting data rather than mathematical computations. Examples were personally written by the authors, who collectively have over 100 years of experience teaching the application of software to business.
  • Tabular summaries now guide readers to reach conclusions and make decisions based on statistical information (Chapters 9 through 15). They help clarify the purpose of the statistical method and better illustrate the role of stats in the business decision-making process.
  • A brand-new Business Analytics Chapter 17 makes extensive use of JMP and Minitab to illustrate predictive analytics for prediction, classification, clustering, and association as well as explaining what text analytics does and how descriptive and prescriptive analytics relate to predictive analytics.
About the Authors

David M. Levine is Professor Emeritus of Statistics and Computer Information Systems at Baruch College (City University of New York). He received BBA and MBA degrees in statistics from City College of New York and a PhD from New York University in industrial engineering and operations research.

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).

As Associate Professor 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.

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