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.
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David R. Anderson
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
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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.
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Thomas A. Williams
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.
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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.
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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.
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James Freeman
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.
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Eddie Shoesmith
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.
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Case Problems, exercises and Statistics in Practice features have been updated throughout to relate statistics to current issues and bring concepts alive.
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The section on data mining in Chapter 1 has been expanded to include a discussion on big data and a new section on analytics has been added. The distinction between observed and experimental data has been highlighted.
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Chapter 5 has been expanded to allow for new material on bivariate distributions, covariance and financial applications.
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Examples have been added to Chapter 12 in particular to point out an alternative computational method for calculating the chi-squared statistic in goodness-of-fit tests.
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Chapter 19 on index numbers (on the online platform) has been updated with current index numbers.
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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.
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Real-life end-of-chapter Case Problems bring theory to life and give students the opportunity to analyse larger data sets and prepare managerial reports.
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Examples are sourced from a wide geographical base to ensure students get insight into statistics at work in different cultures and contexts.
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Self-test problems in each chapter help to guide and consolidate students' learning.
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Data files that can be used with the most common statistics packages accompany exercises and examples. 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.
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A fully updated companion website is available with exercises and solutions, PowerPoint slides, appendices, data sets, online chapters and sample papers as well as software sections, covering basic functions in EXCEL, Minitab, SPSS and R.