CONTEMPORARY BUSINESS STATISTICS

This comprehensive introduction to business statistics text balances a conceptual understanding of statistics with the real-world application of statistical methodology. The latest version of Microsoft® Excel is integrated throughout the text, showing step-by-step instructions and screen captures to enhance student learning. The authors have been writing market-leading business statistics textbooks for over 20 years, and this new edition contains the same student learning features that have made ASW products best sellers for more than two decades.

Complete Integration of Microsoft® Excel Coverage: This text focuses on using Microsoft® Excel to perform statistical analyses and includes step-by-step instructions and screen captures throughout the text.
Problem-Scenario Approach: The discussion and development of each technique is presented in an application setting, with the statistical results providing insights to decisions and solutions to problems.
Fantastic Examples and Exercises: The authors have always been known for their excellent problems, and even more real data examples and exercises have been added to this edition. The use of real data enables students to learn not only about statistical methodology, but also about its application.
Methods and Applications Exercises: The end-of-section exercises are split into these two problem categories. The “Methods” exercises require students to use either formulas or Excel and make the necessary computations. The “Applications” exercises require students to use the chapter material in real-world situations.
Self-Test Exercises: Completely worked-out solutions for certain exercises are provided in an appendix at the end of the book. Students can work through the self-test exercises and immediately check the solution to evaluate their understanding of the concepts presented in each chapter.
Statistics in Practice: Many of the Statistics in Practice chapter openers are new in the new edition. These chapter openers emphasize how real-world companies such as Citibank and Procter & Gamble use statistics in everyday business situations.
Marginal Annotations: The authors’ notes highlight key points for the students.
Authorship: The authors, Anderson, Sweeney, and Williams, are well-known, highly regarded, and respected in the field.
 

David R. Anderson
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. In addition, he was the 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. Professor 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
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. Professor Sweeney has coauthored ten textbooks in the areas of statistics, management science, linear programming, and production and operations management.

Thomas A. Williams
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, 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 first undergraduate program in Information Systems. At RIT, he was the first chair of the Decision Sciences Department. Professor 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 Methods.
3. Descriptive Statistics: Numerical Methods.
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. Tests of Goodness of Fit and Independence.
13. Analysis of Variance and Experimental Design.
14. Simple Linear Regression.
15. Multiple Regression.
16. Regression Analysis: Model Building.
17. Nonparametric Methods.
18. Statistical Methods for Quality Control.
19. Decision Analysis.
20. Sample Survey (located on CD).
APPENDICES.
A. References and Bibliography.
B. Tables.
C. Summation Notation.
D. Self-Test Solutions and Answers to Even-Numbered Exercises.
E. Excel Functions and the Function Wizard.