Essentials of Modern Business Statistics with Microsoft®Office Excel® (Book Only),
7th Edition

1. Data and Statistics.
2. Descriptive Statistics: Tabular and Graphical Displays.
3. Descriptive Statistics: Numerical Measures.
4. Introduction to Probability.
5. Discrete Probability Distributions.
6. Continuous Probability Distributions.
7. Sampling and Sampling Distributions.
8. Interval Estimation.
9. Hypothesis Tests.
10. Inferences About Means and Proportions with Two Populations.
11. Inferences About Population Variances.
12. Test of Goodness of Fit, Independence, and Multiple Proportions.
13. Experimental Design and Analysis of Variance.
14. Simple Linear Regression.
15. Multiple Regression.
Appendix A: References and Bibliography.
Appendix B: Tables.
Appendix C: Summation Notation.
Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises (online).
Appendix E: Microsoft Excel 2016 and Tools for Statistical Analysis.

• David R. Anderson

  David R. Anderson is a leading author and professor emeritus of quantitative analysis in the College of Business Administration at the University of Cincinnati. Dr. Anderson has served as head of the Department of Quantitative Analysis and Operations Management and as associate dean of the College of Business Administration. He was also coordinator of the college’s first executive program. In addition to introductory statistics for business students, Dr. Anderson taught graduate-level courses in regression analysis, multivariate analysis and management science. He also taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author of ten well-respected textbooks related to decision sciences, and he actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, Dr. Anderson earned his B.S., M.S. and Ph.D. degrees from Purdue University.

• Dennis J. Sweeney

  Dennis J. Sweeney is professor emeritus of quantitative analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a B.S.B.A. degree from Drake University and his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA fellow. Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. He also served as head of the Department of Quantitative Analysis and served four years as associate dean of the College of Business Administration at the University of Cincinnati. Dr. Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in journals such as Management Science, Operations Research, Mathematical Programming and Decision Sciences. Dr. Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

• Thomas A. Williams

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• Jeffrey D. Camm

  Jeffrey D. Camm is the Inmar Presidential Chair and senior associate dean of business analytics programs in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he served on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in many professional journals, including Science, Management Science, Operations Research and the INFORMS Journal on Applied Analytics. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati, and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, Dr. Camm has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of the INFORMS Journal on Applied Analytics (formerly Interfaces). In 2016 Dr. Camm received the George E. Kimball Medal for service to the operations research profession, and in 2017 he was named an INFORMS fellow.

• James J. Cochran

  James J. Cochran is associate dean for research, a professor of applied statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S. and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has served as a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 50 papers in the development and application of operations research and statistical methods. He has published in numerous journals, including Management Science, The American Statistician, Communications in Statistics-Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal on Applied Analytics, BMJ Global Health and Statistics and Probability Letters. Dr. Cochran received the 2008 INFORMS prize for the Teaching of Operations Research Practice, the 2010 Mu Sigma Rho Statistical Education Award and the 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005 and was named a fellow of the American Statistical Association in 2011 and a fellow of INFORMS in 2017. He also received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In addition, he received the INFORMS President's Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has also served as an operations research or statistics consultant to numerous companies and not-for-profit organizations.

• EXPANDED COVERAGE WITHIN LATER CHAPTERS EXPLORES KEY TOPICS IN DEPTH. The authors have reorganized, streamlined and increased coverage in later chapters of this edition. This edition no longer addresses the advanced topic of Time Series and Quality Control. Instead, deeper coverage in Chapters 10, 11, 12 and 13 includes topics such as inferences of means and proportions for two populations; inferences about population variances; comparing multiple proportions, tests of independence and goodness of fit; experimental design; and regression.

• NEW, ADDITIONAL CASE PROBLEMS PROVIDE MORE PRACTICE ANALYZING AND PREPARING REPORTS. This edition includes 11 new case problems to challenge your students. The 30 case problems in the text provide students with the opportunity to analyze somewhat larger data sets and to prepare managerial reports based on the results of their analyses.

• UPDATED AND IMPROVED END-OF-CHAPTER SOLUTIONS AND SOLUTIONS MANUAL ENSURE ACCURATE, TRUSTED ANSWERS. The authors have carefully reviewed and reworked, when needed, every question and solution in this edition's end-of chapter solutions and solutions manual. The solutions now boast higher accuracy and contain additional details to assist in efficient grading. You'll find improved rounding instructions, expanded explanations with a student-focus, and alternative answers using Excel and a statistical calculator.

• CONTENT SEAMLESSLY INTEGRATES MICROSOFT EXCEL 2016. Immediately following every statistical procedure, a sub-section discusses how to use Excel to perform that procedure. This approach clearly incorporates the use of Excel while keeping the primary emphasis on key statistical methodology. A consistent framework for applying Excel helps users focus on the statistical methodology without getting distracted in the details of using Excel.

• MINDTAP® COMPLETE DIGITAL SOLUTION NOW FEATURES ALL-NEW EXCEL ONLINE INTEGRATION POWERED BY MICROSOFT®. Ideal for your business statistics course, MindTap® takes students from learning basic statistical concepts to actively engaging in critical thinking applications, while learning valuable software skills for their future careers. MindTap® is a customizable digital course solution that includes an interactive eBook and auto-graded, algorithmic exercises from the textbook. All of these materials offer students better access that enable them to truly master the materials in your course.

• CENGAGENOW™ FULLY INTEGRATED ONLINE TEACHING AND LEARNING SYSTEM SAVES TIME WHILE ENSURING STUDENT MASTERY. This innovative course management system combines the best of current technology to help you plan your course, manage and automatically grade extensive homework and student assignments. You teach with the latest built-in technology support and test students using a customized test bank. Personalized study plans for each student help reinforce student comprehension and reduce questions.

• PROVEN PROBLEM-SCENARIO APPROACH EMPHASIZES APPLICATIONS IN BUSINESS TODAY. Using a unique hands-on approach, the authors discuss and develop each technique in an applications setting with statistical results that provide insights into decisions and solutions to problems. The problem scenarios emphasize how to apply statistics in real business and economics practice, which increases student interest and motivation for learning statistics.

• BOOK OFFERS UNMATCHED STUDENT READABILITY. For more than 30 years, student surveys and instructor feedback have shown that readability is a hallmark of this text. In addition, this edition is packed with real-world examples, current illustrations, and step-by-step instructions that clarify and engage readers.

• OUTSTANDING EXERCISES EMPHASIZE METHODS AND APPLICATIONS. Methods exercises require students to use formulas and make appropriate computations. Applications exercises ask students to apply chapter concepts to real situations. Many Applications exercises incorporate recent data from referenced sources. Combining these two methods enables students to focus on the computational "nuts and bolts" before advancing to the subtleties of statistical application and interpretation. All self-test exercises offer worked-out-solutions in Appendix D online.

• ONLINE DATA FILES SAVE TIME AND ENSURE ACCURACY. Data files for case problems and for exercises with large amounts of data are available on the book's student companion website. Data appears in Excel 2016 to both save time and reduce the likelihood of errors in data entry. Helpful margin notes in the text indicate when a data file is available, with the DATAfile logo and the name of the file for reader convenience.

• MARGIN ANNOTATIONS AND NOTES AND COMMENTS. To ensure thorough student understanding, margin annotations highlight key points and provide additional insights. Many sections end with Notes and Comments designed to give students additional insights about the statistical methodology and its application. Notes and Comments include warnings about or limitations of the methodology, recommendations for application, and brief descriptions of additional technical considerations.

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