Hardcover New 0324783515 New book with very minor shelf wear. STUDENT US EDITION. VALID PASSCODE INCLUDED. Never used. Nice gift. Best buy. Shipped promptly and packaged ...carefully.Read moreShow Less
From the renowned author team that has been writing market-leading business statistics textbooks for more than 20 years, ESSENTIALS OF MODERN BUSINESS STATISTICS with Microsoft Office Excel, Fourth Edition, provides a brief introduction to business statistics that balances a conceptual understanding of statistics with the real-world application of statistical methodology. The latest version of Microsoft Excel, Microsoft Excel 2007, is integrated throughout the text, showing step-by-step instructions and screen captures to enhance student learning. The fourth edition contains the same student learning features that have made ASW products best-sellers for years, including the problem-scenario approach and real-world examples that introduce statistical techniques.
Dr. David R. Anderson is a textbook author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He 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 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 received numerous honors for excellence in teaching and service to student organizations. He is the coauthor of ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his BS, MS, and PhD degrees from Purdue University.
Dr. Dennis J. Sweeney is a textbook author, Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. He also served five years as head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration. In addition, he has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney 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 is the coauthor of ten textbooks in the areas of statistics, management science, linear programming, and production and operations management. Born in Des Moines, Iowa, he earned a BS degree from Drake University, graduating summa cum laude. He received his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow.
Dr. Thomas A. Williams is a textbook author and Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology, where he was the first chair of the Decision Sciences Department. Before joining RIT, he 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. 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. Born in Elmira, New York, he earned his BS degree at Clarkson University and his MS and PhD at Rensselaer Polytechnic Institute.
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. Comparisons Involving Means, Experimental Design, and Analysis of Variance. 11. Comparisons Involving Proportions and a Test of Independence. 12. Simple Linear Regression. 13. Multiple Regression. 14. Statistics for Quality Control.