Synopses & Reviews
Trust the latest version of this market-leading essentials text to introduce sound statistical methodology in a proven applications setting. ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS, 5e, includes all of the strengths of the longer best-selling Anderson/Sweeney/Williams STATISTICS FOR BUSINESS AND ECONOMICS, with a focus on the most important core topics for a concise presentation that's easy for students to follow. This brief introduction to business statistics offers a wealth of actual business examples, proven methods, and application exercises that clearly demonstrate how statistical results provide insights into business decisions and present solutions to contemporary business problems. High-quality problems, trusted for their unwavering accuracy, and the authors' signature problem-scenario approach clearly show how to apply statistical methods in practical business situations. New case problems as well as methods, applications, and self-test exercises encourage students to master formulas, apply materials, and evaluate their personal understanding. Optional updated appendices highlight the latest Excel 2007 and Minitab 15 popular commercial software, giving you the choice of integrating or omitting computer coverage in your course. This edition's concise approach and comprehensive support package, now including CengageNOW course management system, provides everything you need for an effective statistics course that prepares students for the essentials of statistics success in business today.
Review
"Excellent problem sets and answers in back of book - vivid color (maintains student interest better) - thorough discussion and examples."- Gary Black, University of Southern Indiana
Synopsis
The Sixth Edition of ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS is an introductory statistics book that emphasizes essential statistical concepts and their practical business applications. The discussion and development of each technique are geared toward real-world applications, with the statistical results providing insights for decisions and solutions related to common business problems. The easy-to-follow presentation style and proven problem-scenario approach clearly show how to apply statistical methods in practical business situations. This brief introduction to business statistics provides both a conceptual understanding of statistics and real-world applications of statistical methodology.
About 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 and 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 and Gamble, Federated Department Stores, Kroger, and Cincinnati Gas and 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.
Table of Contents
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. Comparisons Involving Means, Experimental Design, and Analysis of Variance. 11. Comparisons Involving Proportions and a Test of Independence. 12. Simple Linear Regression. 13. Multiple Regression.