Statistics for Business and Economics, Revised (with Printed Access Card)

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9780538481649: Statistics for Business and Economics, Revised (with Printed Access Card)

Simply put, the revised, 11th edition of Statistics for Business and Economics is powerful. The authors bring more than twenty-five years of unmatched experience to this text, along with sound statistical methodology, a proven problem-scenario approach, and meaningful applications that clearly demonstrate how statistical information informs decisions in the business world. Thoroughly updated, the text's more than 350 real business examples, cases, and memorable exercises present the latest statistical data and business information with unwavering accuracy. And, to give you the most relevant text you can get for your course, you select the topics you want, including coverage of popular commercial statistical software programs like Minitab® 16 and Excel® 2010, along with StatTools® and other leading Excel® 2010 statistical add-ins.

About This Edition


New Features

  • Updated and Upsized Excel® Coverage
    Get the latest coverage of Excel® 2010 and related materials throughout the text, including revised, end-of-chapter appendices on Chart Tools, PivotTable Report, and PivotChart Report, as well as spreadsheets, dialog boxes, examples, screen shots, and more. Plus, expanded Excel® coverage is available as optional appendices to the text. Choose Microsoft® Excel® 2010 and Minitab® 16, as well as popular Excel® add-ins PrecisionTree® 2010 and StatTools® 2010.

  • Revised, Rewritten, and Refocused Chapters
    Students will get more out of the statistics course with an enhanced chapter on Nonparametric Methods and their Counterparts, which includes new appendices for use with Minitab®, Excel®, and StatTools®. A clarified chapter on Time Series Analysis and Forecasting helps students select the right forecasting method, while the chapter on Sampling Material now addresses sampling populations and frames, and offers a practical advice section.

  • Emerging Fields, Emerging Issues
    A new introduction to data mining and data warehousing examines the importance of this relatively new field and how it relies on statistics and computer science to create value for businesses. And, as ethical issues become increasingly significant for modern businesses, the text's authors respond with new material that addresses professional responsibility in presenting and interpreting statistical information.

  • Improved Clarity and Ease of Understanding
    Revising with students in mind, the authors have simplified coverage of box plots to emphasize quick and easy comparisons between two or more data sets. Students will also appreciate clarifications to discussions on hypothesis testing, including identifying the null and alternative hypotheses, and helpful changes in terminology to more accurately describe data.

  • Added Case Problems, Features, Examples, and More
    Several new case problems allow students to analyze a variety of data and prepare managerial reports, while "Statistics in Practice" vignettes grab students' attention at the start of each chapter and set the pace for the topics that follow. The more than 350 real data examples and exercises throughout the text--150 of which are new to this edition--come from world-class sources such as The Wall Street Journal, USA Today, and Barron's, and demonstrate the importance of statistical analysis in business and economics today.

Additional Features

  • Useful Cumulative Standard Normal Distribution Table
    This important table prepares students to use computer software in statistics by introducing normal distribution probabilities, and helps first-time users calculate normal probability and p-values for hypothesis testing.

  • Trusted, Expert Authors
    As prominent, respected leaders and active business consultants in statistics, authors David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams provide an accurate, thorough, reliable presentation of the concepts with every edition of the text.

  • Leading Approaches, Proven Methods
    A hallmark strength of the text, the problem-scenario approach shows students the practical side of techniques through the use of applications, providing insights into business decision-making and how statistics influences it. Further, the authors' systematic approach to learning statistics, which involves first developing a computational foundation and techniques before moving on to application and interpretation, helps students better understand the material.

  • Simplified Introduction to P-Values
    Emphasizing the use of p-values as the preferred approach to hypothesis testing, the authors now separate a simplified conceptual definition of p-values from operational definitions that clarify how the p-value is computed for a lower-tail test, an upper-tail test, and a two-tail test. Students also learn how to use software to compute p-values, and an available, optional appendix clearly demonstrates how to use Minitab® and Excel® to compute p-values associated with z, t, and F test statistics.

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About the Author:

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 Professor of Management Science in the College of Business at Rochester Institute of Technology where 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. 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. The co-author of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics, Professor Williams 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. He earned his B.S. degree at Clarkson University and completed his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees.

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