Study Guide for Anderson/Sweeney/Williams' Modern Business Statistics, 2nd / Edition 2

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More About This Textbook

Overview


The Study Guide prepared by John Loucks of St. Edward's University will provide the student with significant supplementary study materials. For each chapter, it contains key concepts, review materials, example problems worked out in full detail, exercises with answers, and self-test questions with answers.
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Product Details

  • ISBN-13: 9780324233261
  • Publisher: Cengage Learning
  • Publication date: 9/20/2005
  • Edition description: Signed
  • Edition number: 2
  • Pages: 480
  • Product dimensions: 8.40 (w) x 10.80 (h) x 1.00 (d)

Meet the Author


Dr. Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology. He earned his B.S. degree at Clarkson University. He completed 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 seven 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 graduate courses in regression and decision analysis. Professor Williams is the co-author of 11 leading 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 data analysis to the development of large-scale regression models.

Dr. Dennis J. Sweeney is Professor Emeritus 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.

Dr. David R. Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. 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 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 numerous nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored 10 leading 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.

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