Essentials of Statistics for Business and Economics / Edition 5

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Overview

This essentials version features selected core topics from the authors' Statistics for Business and Economics text. It provides a brief introduction to business statistics with an approach that balances a conceptual understanding of statistics with the real-world application of statistical methodology. The authors' problem-scenario approach motivates students by using real-world examples to introduce and apply statistical techniques. Examples and exercises throughout this text also focus on ways statistics can contribute to quality improvements.

Edition does not come with a minitab.

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Editorial Reviews

Booknews
Focusing on business and economic applications of statistics, this textbook covers data, descriptive statistics, discrete and continuous probability distributions, sampling, interval estimation, hypothesis testing, comparisons involving means, comparison involving proportions, simple linear regression, and multiple regression. Approximately 100 data sets are included on the companion CD-ROM. The authors teach quantitative analysis and management science at the University of Cincinnati and the Rochester Institute of Technology. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780324653212
  • Publisher: Cengage Learning
  • Publication date: 1/3/2008
  • Edition description: Older Edition
  • Edition number: 5
  • Pages: 672
  • Product dimensions: 8.20 (w) x 10.00 (h) x 1.20 (d)

Meet the Author

Dr. David R. Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He earned his B.S., M.S., and Ph.D. 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 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 honored with numerous nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored 10 leading 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.

Dr. Dennis J. Sweeney is Professor Emeritus of Quantitative Analysis and Founder of the Center for Productivity Improvement at the University of Cincinnati. 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 served as visiting professor at Duke University. Professor Sweeney also has served as Head of the Department of Quantitative Analysis and 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 coauthored ten leading texts in the areas of statistics, management science, linear programming, and production and operations management.

Dr. Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology (RIT). He earned his BS degree at Clarkson University. He completed 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 coauthor of 11 leading 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 of Quantitative Analysis, Head of the Department of Operations, Business Analytics, and Information Systems, and College of Business REsearch Fellow int he Carl H. Lindner College of Business at the University of Cincinnati. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University and a Ph.D. from Clemson University. He has been at the University of Cincinnati since 1984, and has 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 30 papers in the general area of optimization applied to problems in operations management. He has published his research in Science, Management Science, Operations Research, Interfaces, and other professional journals. At the University of Cincinnati, he was named the Dornoff Fellow of Teaching Excellence 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, and is currently on the editorial board of INFORMS Transactions on Education.

James J. Cochran is the Bank of Ruston Endowed Research Professor of Quantitative Analysis at Louisiana Tech University. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. degrees from Wright State University and a Ph.D. from the University of Cincinnati. He has been at Louisiana Tech University since 2000 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 two dozen 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, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, JOURNAL OF COMBINATORIAL OPTIMIZATION, and other professional journals. 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. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Professor Cochran has organized and chaired teaching effectiveness workshops in Montevideo, Uruguay; Cape Town, South Africa; Cartagena, Colombia; Jaipur, India; Buenos Aires, Argentina; Nairobi, Kenya; and Buea, Cameroon. He has served as an operations research consultant to numerous companies and not-for-profit organizations. He has served as editor-in-chief of INFORMS Transactions on Education and is on the editorial board of Interfaces, the Journal of the Chilean Institute of Operations Research, ORiON, and several other academic journals.

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Table of Contents

Preface
About the Authors
Ch. 1 Data and Statistics 1
Ch. 2 Descriptive Statistics: Tabular and Graphical Methods 23
Ch. 3 Descriptive Statistics: Numerical Methods 75
Ch. 4 Introduction to Probability 139
Ch. 5 Discrete Probability Distributions 185
Ch. 6 Continuous Probability Distributions 225
Ch. 7 Sampling and Sampling Distributions 255
Ch. 8 Interval Estimation 296
Ch. 9 Hypothesis Testing 337
Ch. 10 Comparisons Involving Means 387
Ch. 11 Comparisons Involving Proportions and a Test of Independence 446
Ch. 12 Simple Linear Regression 478
Ch. 13 Multiple Regression 547
Appendixes 595
App. A: References and Bibliography 596
App. B: Tables 597
App. C: Summation Notation 618
App. D: Answers to Even-Numbered Exercises 620
App. E: Solutions to Self-Test Exercises 634
Index 651
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