Statistics for Business and Economics

Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology. Born in Elmira, New York, he earned his BS degree at Clarkson University. He did his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the undergraduate program in Information Systems and then served as its coordinator. At RIT he was the first chairman of the Decision Sciences Department. He teaches courses in management science and statistics, as well as graduate courses in regression and decision analysis. Professor Williams is the co-author of 11 textbooks in the areas of management science, statistics, production and operations management and mathematics. He has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of data analysis to the development of large-scale regression models. David R. Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He earned his BS, MS and PhD degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration at the University of Cincinnati. In addition, he was the coordinator of the College�s first Executive Program. At the University of Cincinnati, Professor Anderson has taught introductory statistics for business students as well as graduate-level courses in regression analysis, multivariate analysis and management science. He has also taught statistical courses at the Department of Labor in Washington, D.C. He has been honoured with nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored 10 textbooks in the areas of statistics, management science, linear programming and production and operations management. He is an active consultant in the field of sampling and statistical methods. James J. Cochran is Professor of Applied Statistics, the Mike and Cathy Mouron Research Chair and Associate Dean for Faculty and Research at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S. and M.B.A. degrees from Wright State University and his Ph.D. from the University of Cincinnati. Dr. Cochran has served 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. Dr. Cochran has published more than 50 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, BMJ Global Health and Statistics and Probability Letters. 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. 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 in 2018 he received the INFORMS President�s Award. Dr. Cochran is an elected member of the International Statistics Institute, a fellow of the American Statistical Association and a fellow of INFORMS. A strong advocate for effective statistics and operations research education as a means of improving the quality of applications to real problems, Dr. Cochran has organized and chaired teaching workshops throughout the world. Eddie Shoesmith is a Fellow of the University of Buckingham, UK, where he was formerly Senior Lecturer in Statistics. Born and brought up in the West Riding of Yorkshire, he was awarded an MA (Natural Sciences) at the University of Cambridge and a BPhil (Economics and Statistics) at the University of York. Prior to his 35 years at Buckingham, Eddie worked as a statistician and researcher for the UK Government Statistical Service and for the London Boroughs of Hammersmith and Haringey. During his Buckingham career, he held posts, at various times, as Dean of Sciences, as Head of Psychology and as Programme Director for undergraduate business and management programmes. He has taught introductory and intermediate-level applied statistics courses to undergraduate and postgraduate student groups in a wide range of disciplines: business and management, economics, accounting, psychology, biology and social sciences. He has also taught statistics to social and political sciences undergraduates at the University of Cambridge and has held external examiner posts at the Universities of Cranfield and Hertfordshire. Now retired from full-time academic life, Eddie contributes as an Associate Lecturer in the School of Leadership & Management, the School of Computing & IT and the School of Digital Finance at the University of Arden. 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. James Freeman is formerly Senior Lecturer in Statistics and Operational Research at Alliance Manchester Business School (AMBS), UK. After taking a first degree in Pure Mathematics at UCW Aberystwyth, he went on to receive MSc and PhD degrees in Applied Statistics from Bath and Salford Universities, respectively. In 1992/3 he was visiting professor at the University of Alberta. Before joining AMBS, he was Statistician at the Distributive Industries Training Board � and prior to that � the Universities Central Council on Admissions. He has taught undergraduate and postgraduate courses in business statistics and operational research courses to students from a wide range of management and engineering backgrounds. Until 2017, he taught the statistical core course on AMBS�s Business Analytics masters programme � since rated top in Europe and sixth in the world. For many years he was also responsible for providing introductory statistics courses to staff and research students at the University of Manchester�s Staff Teaching Workshop. Through his gaming and simulation interests, he has been involved in a significant number of external consultancy and grant-aided projects. This culminated in his receiving significant government (�KTP�) funding for research in the area of risk management in 2012. Between July 2008 and December 2014, he was Editor of the Operational Research Society�s OR Insight journal and between 2018 and 2020 was Editor of the Tewkesbury Historical Society Bulletin. In November 2012, he received the Outstanding Achievement Award at the Decision Sciences Institutes 43rd Annual Meeting in San Francisco. In 2018 he was awarded an Honorary Fellowship by the University of Manchester. Jeffrey D. Camm is the Inmar Presidential Chair of Analytics and Senior Associate Dean for Faculty 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, Dr. Camm 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 Science, Management Science, Operations Research, The 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. A firm believer in practicing what he preaches, he has served as a consultant to numerous companies and government agencies. Dr. Camm served as editor-in-chief of INFORMS Journal on Applied Analytics and is an INFORMS fellow.

Book contents
Preface
Acknowledgements
About the authors
1 Data and statistics
2 Descriptive statistics: tabular and graphical presentations
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 Statistical inference about means and proportions with two populations
11 Inferences about population variances
12 Tests of goodness of fit and independence
13 Experimental design and analysis of variance
14 Simple linear regression
15 Multiple regression
16 Regression analysis: model building
17 Time series analysis and forecasting
18 Non-parametric methods
Online contents
19 Index numbers
20 Statistical methods for quality control
21 Decision analysis
22 Sample surveys
�Chapter Software Sections for EXCEL, MINITAB, SPSS and R
�Appendix A: References and bibliography
�Appendix B: Tables
�Appendix C: Summation Notation
�Appendix D: Answers to even-numbered exercises and fully worked solutions to exercises flagged with the SOLUTIONS icon.