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With its application-oriented approach, the fifth EMEA edition of Statistics for Business and Economics teaches students the core concepts of statistics in the fields of business, management and economics, with the needs of the non-mathematician in mind. The authors interweave statistical methodology with applications of data analysis to enrich students’ understanding of how statistics underpin problem-solving and decision-making. Students develop a computational foundation and learn to use various techniques before moving on to statistical application and interpretation. At the end of each section, exercises focus on computation and use of formulas, while application exercises require students to apply what they have learnt to real-world problems. WebAssign is available with this title, a powerful digital solution designed by educators to enrich the teaching and learning experience. WebAssign provides extensive content, instant assessment and superior support.
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
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.
University of Cincinnati
David R. Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, 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. In addition, he was the coordinator of the college’s first executive programme. At the University of Cincinnati, Dr Anderson has taught graduate-level courses in regression analysis, multivariate analysis and management science. He also has taught statistical courses at the Department of Labor in Washington, DC. Professor Anderson has been honoured with nominations and awards for excellence in teaching and excellence in service to student organizations. He has co-authored ten 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
Dennis J. Sweeney
University of Cincinnati
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 BSBA degree from Drake University and his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow. Professor Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney served as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. Professor 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 Management Science, Operations Research, Mathematical Programming, Decision Sciences and other journals. Professor Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.
Rochester Institute of Technology
Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology (RIT). 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 first undergraduate programme in Information Systems. At RIT he was the first chair 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 elementary data analysis to the development of large-scale regression models.
Jeffrey D. Camm
Wake Forest University
Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics 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 served as a visiting scholar at Stanford University and as a visiting Professor of Business Administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 40 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in numerous professional journals, including Science, Management Science, Operations Research and Interfaces. 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, 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 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
University of Alabama
James J. Cochran is Associate Dean for Research, 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 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 40 papers in the development and application of operations research and statistical methods. He has published in several 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, Interfaces and Statistics and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005, was named a Fellow of the American Statistical Association in 2011 and was named a Fellow of INFORMS in 2017. He received the Founders Award in 2014, the Karl E. Peace Award in 2015 from the American Statistical Association and 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 served as operations research consultant to numerous companies and not-for-profit organizations.
Manchester Business School, UK
Jim Freeman was formerly Senior Lecturer in Statistics and Operational Research at Alliance Manchester Business School (AMBS), United Kingdom. He was born in Tewkesbury, Gloucestershire. 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 master's 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. More recently he received significant government (‘KTP’) funding for research in the area of risk management. Between July 2008 and December 2014 he was Editor of the Operational Research Society’s OR Insight journal and is currently 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.
University of Buckingham, UK
Eddie Shoesmith is a Fellow of the University of Buckingham, UK, where he was formerly Senior Lecturer in Statistics and Programme Director for undergraduate business and management programmes in the School of Business. He was born in Barnsley, 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 taking an academic post at Buckingham, he worked for the UK Government Statistical Service (now the UK Office for National Statistics), in the Cabinet Office, for the London Borough of Hammersmith and for the London Borough of Haringey. At Buckingham, before joining the School of Business, he held posts as Dean of Sciences and Head of Psychology. 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..
"A rigorous, but student-friendly introduction to business statistics that uses real-world applications to put theory to practice. A very well-written book indeed."
"Teaching statistics to business economics students is one of the more challenging tasks in higher education. Statistics for Business and Economics is without a doubt the book for this journey of discovery. It is accessible and suits all levels, from foundation to more advanced levels. It is designed to make statistics more fun to learn, with applications, data and examples. The authors have used their extensive experience in the field to produce a very solid text that explains and illustrates abstract statistical notions and concepts intuitively."