Synopses & Reviews
One of the best-selling business statistics books on the market, offering a solid, comprehensive, applications-oriented approach. The success of this title has been threefold: 1) The authors are well-known, proven, and highly regarded in the field. 2) The discussion and development of each technique is presented in an application setting, with the statistical results providing insights into decisions and solutions to problems. 3) The text is proven-- Business professionals and students of business statistics have chosen it for its clarity, examples and exercises!
Synopsis
Esta obra emplea problemas en situaciones de negocios reales para presentar las tecnicas estadisticas. Las secciones de problemas se dividen en metodos, aplicaciones y autoevaluaciones. La seccion "Notas y comentarios" da al lector un conocimiento adicional sobre la metodologia estadistica y presenta tambien los errores estadisticos mas comunes. Existe tambien la version en dos tomos.
About the Author
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.Dennis J. Sweeney is Professor of Quantitative Analysis and Director of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a B.S.B.A. degree from Drake University, graduating summa cum laude. He received his M.B.A. and D.B.A. degrees from Indiana University where he was an NDEA Fellow. During 1978-79, he spent a year working in the management science group at ProcterandGamble; during 1981-82, he was a visiting professor at Duke University. Professor Sweeney served 5 years as Head of the Department of Quantitative Analysis and 4 years as Associate Dean of the College of Business Administration at the University of Cincinnati.
Professor Sweeney has published over 30 articles in the area of management science and statistics. The National Science Foundation, IBM, ProcterandGamble, Federated Department Stores, Kroger, and Cincinnati GasandElectric have funded his research, which has been published in Management Science, Operations Research, athematical Programming, Decision Sciences, and other journals.
Professor Sweeney has coauthored eight textbooks in the areas of statistics, management science, linear programming, and production and operations management.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. Professor Williams is the co-author of twelve 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.
Table of Contents
1. Data and Statistics. 2. Descriptive Statistics I: Tabular and Graphical Methods. 3. Descriptive Statistics II: Numerical Methods. 4. Introduction to Probability. 5. Discrete Probability and 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. Regress Analysis: Model Building. 17. Index Numbers. 18. Forecasting. 19. Nonparametric Methods. 20. Statistical methods for Quality Control. 21. Sample Survey.