The third edition of Business Statistics: Communicating with Numbers provides a unique, innovative, and engaging learning experience for students studying Business Statistics. It is an intellectually stimulating, practical, and visually attractive textbook, from which students can learn and instructors can teach. Throughout the book, the authors have presented the material in an accessible way by using timely business applications to which students can relate. Although the text is application-oriented, it is also mathematically sound and uses notation that is generally accepted for the topic being covered.

New Features
1. Business Statistics Coupled with Connect:Sanjiv Jaggia and Alison Kelly both utilize connect in their courses making the technology component seamless with the text itself. They have reviewed every Connect exercise, and during this process, have painstakingly evaluated rounding rules and revised tolerance levels. In addition, every LearnSmart probe has been reviewed. Instructors who teach in an online or hybrid environment will especially appreciate the extent of the author updates within connect.
2. Integration of R: R is a powerful software that merges the convenience of statistical packages with the power of coding. There are several good reasons for learning R in a Business Statistics class. First, R is open source as well as cross-platform compatible. This means that there is zero cost to download R, and it can be run on Windows, Mac OS X, or Linux. Second, with the availability of several data analysis tools for conventional and modern statistical models, R is easy to use. Third, R is wildly popular and quickly becoming one of the most powerful programming languages for data analytics.
3. Focus on the p-Value Approach: In the earlier editions of Business Statistics, Authors stressed both the p-value approach and the critical value approach in all chapters related to hypothesis testing. Students often get confused with the mechanics of implementing a hypothesis test with both approaches. While the critical value approach is attractive when a computer is unavailable and all calculations must be done by hand, most researchers and practitioners favor the p-value approach since virtually every statistical software package reports p-values.