Essentials of Modern Business Statistics with Microsoft®Office Excel® (Book Only)

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 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 10 textbooks in the areas of statistics, management science, linear programming and production and operations management. Jeffrey D. Camm is professor and Inmar Presidential Chair in analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a BS from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he was 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 over 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, INFORMS Journal on Applied Analytics and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the recipient of the 2006 INFORMS Prize for the Teaching of Operations Research Practice. He is a recipient of the George E. Kimball Medal for service to the operations research profession. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010, he served as editor-in-chief of INFORMS Journal on Applied Analytics. In 2017, he was named an INFORMS Fellow. In 2021, Professor Camm was named an Academic Data Leader by Chief Data Officer (CDO) Magazine. James J. Cochran is professor of applied statistics and the Mike and Kathy Moroun Research Chair at the University of Alabama. Born in Dayton, Ohio, he earned his BS, MS and MBA degrees 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 been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Professor Cochran has published over 60 papers in the development and application of operations research and statistical methods. He has published his research in 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, Statistics and Probability Letters and other professional Journals. Professor Cochran's research has been funded by the Department of Justice, National Science Foundation and other agencies. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. Professor Cochran was elected to the International Statistics Institute in 2005 and named a Fellow of the American Statistical Association in 2011. He received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In 2017, he received the American Statistical Association's Waller Distinguished Teaching Career Award and was named a Fellow of INFORMS; in 2018, he received the INFORMS President's Award; in 2024, he received the William G. Hunter Award from the American Society for Quality and in 2025, he was named a Fellow of the African Academy of Sciences. He has been recognized as a finalist for the Innovative Applications in Analytics Award three times.

• 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.