Statistics for Business and Economics 2e
ISBN-13: 9781408018101 / ISBN-10: 1408018101
This market-leading textbook on business statistics is the definitive introduction for students in Europe, the Middle East and Africa. Recognizing that students succeed best in this demanding subject when engaged, "Statistics in Practice" features open each chapter using high-profile examples such as The Spanish National Lottery, The Economist Newspaper and foreign direct investment in China. Updated coverage of Excel 2007 is matched with equal treatment of SPSS/PASW and Minitab to align student learning with the latest industry software, while a complete set of learning resources for students and lecturers (including data sets on an accompanying CD-Rom, online test banks and much more) make this a 'one stop shop' for all business statistics courses.
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. 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. Index Numbers
19. Non-Parametric Methods
20. Statistical Methods for Quality Control
21. Decision Analysis
22. Sample Surveys (On CD)
"Finally there is an international edition of the highly regarded ‘Statistics for Business and Economics’ by Anderson, Sweeney, Williams. The textbook contains the same broad applications-orientated approach that has made the United States edition a market leader in statistics textbooks while focusing examples and practice problems towards a more international audience." Dr Naomi Feldman, Department of Economics, Ben-Gurion University of the Negev
"This new international edition of the ever-popular statistics text from Anderson, Sweeney, Williams continues in the tradition of the excellent United States text and covers everything that business and economics students require." Martyn Jarvis, Business School, University of Glamorgan
"This edition blends the solid achievements of the original with a European twist and provides some interesting and relevant European examples." Dr John R. Calvert, Loughborough University Business School
"A truly international textbook with explanations, examples and problems for a global audience."Chris Muller, Stellenbosch University
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 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 the 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 nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored eight 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.
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. 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 & 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 & 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 co-authored 10 leading texts 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. Born in Elmira, New York, he earned his B.S. degree at Clarkson University. He did 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 7 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 more advanced courses in regression and decision analysis. Professor Williams is the co-author of nine 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 elementary data analysis to the development of large-scale regression models.