# Essentials of Modern Business Statistics with Microsoft® Excel®,9th Edition

#### Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, Dennis J. Sweeney, Thomas A. Williams

ISBN-13: 9780357930045
816 pages | List Price: USD \$312.95

Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' ESSENTIALS OF MODERN BUSINESS STATISTICS WITH MICROSOFT® EXCEL®, 9th Edition, balances real-world applications with an integrated focus on the latest version of Microsoft® Excel®. Learn to master statistical methodology with an easy-to-follow presentation of a statistical procedure followed by a discussion on how to use Excel®. Step-by-step instructions and images ensure understanding. Over 70 new business examples, proven methods and application exercises show how statistics provide insights into today's business decisions and problems. A unique problem-scenario approach and new case problems demonstrate how to apply statistical methods to practical business situations. MindTap digital resources provide tools to help you master Excel®, Excel® Online and R as well as gain an understanding of business statistics.

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.
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: Microsoft Excel and Tools for Statistical Analysis.
Appendix E: Microsoft Excel Online and Tools for Statistical Analysis. (Cengage eBook)
Appendix F: Solutions to Even-Numbered Exercises. (Cengage eBook)

• 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.

• Michael J. Fry

Michael J. Fry is a professor of operations, business analytics and information systems as well as academic director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, Dr. Fry earned his B.S. from Texas A&M University and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department head. He has also been named a Lindner Research Fellow. Dr. Fry has also been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at Cornell University and the Sauder School of Business at the University of British Columbia.He has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IIE Transactions, Critical Care Medicine and Interfaces. His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics and public-policy operations. He has worked with many different organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals and the Cincinnati Zoo & Botanical Garden. He was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati.

• Jeffrey W. Ohlmann

Jeffrey W. Ohlmann is associate professor of business analytics and a Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska and M.S. and Ph.D. degrees from the University of Michigan. Dr. Ohlmann has been at the University of Iowa since 2003. His research on the modeling and solution of decision-making problems has produced more than two dozen research papers published in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science and the European Journal of Operational Research. He has collaborated with organizations such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections and three National Football League franchises. Because of the relevance of his work to industry, Dr. Ohlmann received the George B. Dantzig Dissertation Award, and he was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

• David R. Anderson

• 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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• EXPANDER COVERAGE OF DATA VISUALIZATION. Chapter 2 of the textbook features expanded coverage of data visualization concepts and tools. A new section covering the visualization of geospatial data using maps has also been added. Introduce both choropleth maps and cartograms and explain how to create choropleth maps in Excel. Existing coverage of best practices for data visualization has been expanded with a discussion of the use of data-ink ratio and decluttering to create effective visualizations.

• NEW EXAMPLES AND EXERCISES BASED ON REAL DATA. This edition brings over 70 new examples and exercises based on real data and referenced sources. This text uses data from sources used by The Wall Street Journal, USA Today, The Financial Times, Forbes and others. Actual studies and applications allow for developed explanations and exercises demonstrating the many uses of statistics in business and economics. The use of real data from relevant problems generates greater student interest in the material, enabling students to effectively learn about both statistical methodology and its application.

• NEW CASE PROBLEMS. Three new case problems have been added to this edition. The 38 case problems in the text provide students with the opportunity to analyze somewhat larger data sets and prepare managerial reports based on the results of their analysis.

• LEARNING OBJECTIVES. Learning Objectives have been added to the beginning of each chapter. These explain the key concepts that are covered in each chapter. The objectives are also mapped on to each problem so instructors can easily identify which objectives are covered by each problem.

• CONTENT SEAMLESSLY INTEGRATES COVERAGE OF THE LATEST VERSION OF MICROSOFT EXCEL. 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 becoming distracted by the details of using Excel.

• MINDTAP FEATURES EXCEL ONLINE INTEGRATION POWERED BY MICROSOFT®. MindTap takes students from basic statistical concepts to actively engaging in critical thinking applications while learning valuable software skills. MindTap's online learning platform includes an interactive eBook and auto-graded, algorithmic exercises from the latest edition of the printed text. With over 80 unique Excel Online activities, students learn how to apply statistical problem-solving with real, live application as they would on the job with step-by-step problem walk-through videos and personalized Excel solutions.

• AUTHORS' SIGNATURE 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 while clearly demonstrating how statistical results 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 proven learning solution. In addition to clear explanations, this edition is packed with real-world examples, current illustrations and step-by-step instructions that clarify and engage readers.

• OUTSTANDING EXERCISES EMPHASIZE BOTH METHODS AND APPLICATIONS. Methods Exercises at the end of each section require students to use formulas and make necessary computations, while practical Application Exercises ask students to apply chapter material to address real-world problems. Many Applications Exercises incorporate recent data from referenced sources. This approach enables students to focus on computational "nuts and bolts" before advancing to the subtleties of statistical application and interpretation. Solutions for even-numbered exercises appear online in the eBook Appendix D.

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

• MARGIN ANNOTATIONS AND NOTES AND COMMENTS ENSURE STUDENT UNDERSTANDING. This edition's margin annotations highlight key points and provide additional insights. Many sections end with Notes and Comments designed to give students more information 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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