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, 11e, International Edition 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.

David R. Anderson
Dr. David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, 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. He was also coordinator of the College's first Executive Program. In addition to teaching 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. Dr. Anderson has been honored with nominations and awards for excellence in teaching and excellence in service to student organizations. He has coauthored ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods.

Dennis J. Sweeney
Dr. Dennis J. Sweeney is Professor of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned BS and BA degrees from Drake University, graduating summa cum laude. He received his MBA and DBA 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. Professor Sweeney served five years as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. He 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 has coauthored ten textbooks in the areas of statistics, management science, linear programming, and production and operations management.

Thomas A. Williams
Dr. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology (RIT). Born in Elmira, New York, 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, Dr. Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the first undergraduate program in Information Systems. At RIT he was the first chair of the Decision Sciences Department. Dr. Williams is the coauthor 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 in areas ranging from the use of elementary data analysis to the development of large-scale regression models.

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. 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 Linear 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 Using Minitab and Excel.