Synopses & Reviews
With Anderson/Sweeney/Williams' market-leading STATISTICS FOR BUSINESS AND ECONOMICS, 10e, you'll learn much more than simply how to solve statistical equations. You'll discover how statistical results provide insights into business decisions and present solutions to business problems. Numerous actual examples, proven methods, and application exercises provide practical clarity to even complex concepts, while self-testing exercises allow you to assess your personal understanding. Maximize your study time and efficiently complete homework with this edition's innovative CengageNOW online learning system that creates a personalized study plan focusing on the statistical concepts you still need to master. A useful CD-ROM, available at no extra cost with each new text, provides data files to help you master key statistical software for success in today's classroom and tomorrow's business world.
Review
"Engaging examples and the relevant questions at the end of chapters. Clear and understandable. Intiutive explanations."
Review
"The textbook is very user-friendly (easy to read). The text has very good and useful real world problems. The computer applications using Microsoft Excel are a must for any business statistics course."
Synopsis
This market-leading text from well-respected authors Anderson/Sweeney/Williams introduces sound statistical methodology within a strong applications setting. A wealth of real business examples, proven methods, and application exercises within STATISTICS FOR BUSINESS AND ECONOMICS, Revised 10e clearly demonstrate how statistical results provide insights into business decisions and present solutions to contemporary business problems. Comprehensive coverage, trusted for its accuracy, allows you to select the topics best for your course, including coverage of the latest statistical and business software to manage statistical information. This edition's accessible approach is strengthened with the innovative new CengageNOW integrated online course management and learning system that saves you time while using personalized study plans to ensure student understanding.
Synopsis
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About the Author
Dr. David R. Anderson is Professor 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 of Quantitative Analysis and Founder of the Center for Productivity Improvement at the University of Cincinnati. 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. Professor Sweeney has worked in the management science group at Procter and Gamble and has served as visiting professor at Duke University. Professor Sweeney has also 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 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. Professor Sweeney has co-authored 10 leading texts in the areas of statistics, management science, linear programming, and production and operations management. Dr. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology. He earned his B.S. degree at Clarkson University. He completed his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. 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 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.
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 Testing. 10. Statistical Inference about Means and Proportions with Two Populations. 11. Inferences About Population Variances. 12. Test of Goodness of Fit and Independence. 13. Experimental Design and Analysis of Variance. 14. Simple Regression. 15. Multiple Regression. 16. Regression Analysis: Model Building. 17. Index Numbers. 18. Forecasting. 19. Nonparametric Methods. 20. Statistical Methods for Quality Control. 21. Decision Analysis. 22. Sample Survey (on CD). Appendices. A: References and Bibliography. B: Tables. C: Summation Notation. D: Self-Test Solutions and Answers to Even-Numbered Exercises. E: Using Excel Functions. F: Computing p-values.