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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 a textbook author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He has served as head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. He was also coordinator of the College's first Executive Program. In addition to introductory statistics for business students, 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, D.C. Professor Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the coauthor of ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his BS, MS, and PhD degrees from Purdue University.

Dr. Dennis J. Sweeney is a textbook author, Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. He also served five years as head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration. In addition, he has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney has published more than 30 articles 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. Dr. Sweeney is the coauthor of ten textbooks in the areas of statistics, management science, linear programming, and production and operations management. Born in Des Moines, Iowa, he earned a BS degree from Drake University, graduating summa cum laude. He received his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow.

Dr. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology where 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. 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. The co-author of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics, Professor Williams 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. He earned his B.S. degree at Clarkson University and completed his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees.

Dr. Jeffrey D. Camm is Professor of Quantitative Analysis and head of the Department of Quantitative Analysis and Operations Management at the University of Cincinnati, where he has been since 1984. He also has served as 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 30 papers in the general area of optimization applied to problems in operations management, and his research has been funded by the Air Force Office of Scientific Research, The Office of Naval Research, and the U.S. Department of Energy. Among his honors, he was named the Dornoff Fellow of Teaching Excellence and received the 2006 INFORMS Prize for the Teaching of Operations Research Practice. Dr. Camm currently serves as editor-in-chief of INTERFACES and is on the editorial board of INFORMS TRANSACTIONS ON EDUCATION. He received his PhD in Management Science from Clemson University.

James J. Cochran is Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow in the Department of Information Systems, Statistics and Management Science at the University of Alabama. Prior to joining the University of Alabama faculty, Dr. Cochran was Professor of Quantitative Analysis and the Bank of Ruston, Barnes, Thompson, & Thurman Endowed Research Professor at Louisiana Tech University. He has been a visiting scholar at Stanford University, Universidad de Talca, and the University of South Africa. 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 received the 2008 INFORMS Prize for the Teaching of Operations Research Practice and the 2010 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, he has organized and chaired teaching effectiveness workshops in Uruguay, South Africa, Colombia, India, Argentina, Kenya, Cameroon, and Croatia. He has served as a statistics and operations research consultant to numerous companies and not-for-profit organizations. He served as editor-in-chief of INFORMS TRANSACTIONS ON EDUCATION from 2007 to 2012, and is on the editorial board of INTERFACES, the JOURNAL OF QUANTITATIVE ANALYSIS IN SPORTS, and ORION. He holds a BS, MS, and MBA from Wright State University and a PhD from the University of Cincinnati.

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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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