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      1. Ekonomi och Ledarskap
      2. Ledarskapsböcker

      Statistics for Business and Economics

      AvDavid Anderson,Dennis Sweeney

      Häftad, Engelska, 2024

      899 kr

      . Fri frakt över 249 kr.

      Beskrivning

      With the non-mathematician in mind, the sixth edition of Statistics for Business and Economics teaches learners the key concepts of statistics in business, management and economics. The authors blend statistical methodology with applications of data analysis to illustrate the fundamental role of statistics in problem-solving and decision making. Computational methods give students a solid foundation to master statistical application and interpretation. At the end of each section, practical exercises encourage conceptual understanding of real-world problems. New content on big data enriches the learning experience and prepares students for the workplace.

      Produktinformation

      • Utgivningsdatum:2024-01-25
      • Mått:196 x 24 x 258 mm
      • Vikt:1 180 g
      • Format:Häftad
      • Språk:Engelska
      • Antal sidor:656
      • Upplaga:6
      • Förlag:Cengage Learning
      • ISBN:9781473791350

      Utforska kategorier

      • Ledarskapsböcker inom Ekonomi och Ledarskap

      Mer om författaren

      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. 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. 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. 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. Michael J. Fry is Professor of Operations, Business Analytics and Information Systems and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. He earned a BS from Texas A&M University and MSE and PhD degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously Department Head and has been named a Lindner Research Fellow. He 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. Professor Fry has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IISE Transactions, Critical Care Medicine and INFORMS Journal of Applied Analytics (formerly 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 is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. He earned a BS from the University of Nebraska, and MS and PhD degrees from the University of Michigan. He has been at the University of Iowa since 2003. Professor Ohlmann’s research on the modelling and solution of decision-making problems has produced more than two dozen research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, the European Journal of Operational Research and INFORMS Journal of Applied Analytics (formerly Interfaces). He has collaborated with companies 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, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice. 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. 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.

      Innehållsförteckning

      • PrefaceAcknowledgementsAbout the authors1. Data and Statistics2. Descriptive statistics: tabular and graphical presentations3. Descriptive statistics: numerical measures4. Introduction to probability5. Discrete probability distributions6. Continuous probability distributions7. Sampling and sampling distributions8. Interval estimation9. Hypothesis tests10. Statistical inference about means and proportions with two populations11. Inferences about population variances12. Tests of goodness of fit and independence13. Experimental design and analysis of variance14. Simple linear regression15. Multiple regression16. Regression analysis: model building17. Time series analysis and forecasting18. Non-parametric methodsAppendix A References and bibliographyAppendix B TablesGlossaryCreditsIndex
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