Modern Business Statistics with Microsoft® Office Excel® (with XLSTAT Education Edition Printed Access Card) 6th Edition
David R. Anderson | Dennis J. Sweeney | Thomas A. Williams | Jeffrey D. Camm | James J. Cochran
Modern Business Statistics with Microsoft® Excel® 5th Edition
David R. Anderson | Dennis J. Sweeney | Thomas A. Williams
ISBN-13: 9781285433301 | ISBN-10: 1285433300
© 2015 | Published |  1056  Pages
Previous Editions: 9780538479752

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US $321.95
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Give students a balanced, conceptual understanding of statistics with a focus on real-world applications and the integrated strengths of Microsoft® Excel® 2013. The authoritative leader in modern business statistics, this best-selling, comprehensive text carefully discusses and clearly develops each statistical technique in an application setting.

Microsoft® Excel® 2013 instruction, which is integrated in each chapter, plays an integral part in strengthening this edition's applications orientation. Immediately after each easy-to-follow presentation of a statistical procedure, a subsection discusses how to use Excel® to perform the procedure. This integrated approach emphasizes the applications of Excel® while focusing on the statistical methodology. Step-by-step instructions and screen captures further clarify student learning.

A wealth of timely business examples, proven methods, and additional exercises throughout this edition demonstrate how statistical results provide insights into business decisions and present solutions to contemporary business problems. High-quality problems noted for their unwavering accuracy and the authors' signature problem-scenario approach clearly show how to apply statistical methods to practical business situations. New case problems and self-tests allow students to challenge their personal understanding. This edition's comprehensive support package, including the CengageNOW course management system, provides everything you and your students need for an understanding of business statistics. Anderson/Sweeney/Williams' MODERN BUSINESS STATISTICS, 5e, is simply excellent!



  • 1. Data and Statistics.
    2. Descriptive Statistics: Tabular and Graphical Displays.
    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 Inferences About Means and Proportions with Two Populations.
    11. Inferences About Population Variances.
    12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit.
    13. Experimental Design and Analysis of Variance.
    14. Simple Linear Regression.
    15. Multiple Regression.
    16. Regression Analysis: Model Building.
    17. Time Series Analysis and Forecasting.
    18. Nonparametric Methods.
    19. Statistical Methods for Quality Control.
    20. Decision Analysis on Website.
    21. Sample Survey on Website.
    Appendix A: References and Bibliography.
    Appendix B: Tables.
    Appendix C: Summation Notation.
    Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises.
    Appendix E: Microsoft Excel 2013 and Tools for Statistical Analysis.

    • Revised Chapter 2: Chapter 2 now includes illustrations of new tools available with Excel® 2013 and new material on data visualization. Excel's new Recommended PivotTables tool is used to construct frequency distributions for categorical variables, and Excel's Recommended Charts tool is used to construct side-by-side and stacked bar charts.
    • Revised Chapter 3: Chapter 3 now includes coverage of the weighted mean and geometric mean in the section on measures of location. The geometric mean has many financial applications in the computation of asset growth rates and annual percentage rates, for example. Anderson, Sweeney, and Williams have revised their procedure for computing percentiles and quartiles to be consistent with Excel's new PERCENTILE.EXC function. Chapter 3 also includes a new section on data dashboards, discussing how summary statistics can be incorporated to enhance their effectiveness.
    • Revised Chapter 5: The introductory material has been revised to show how the material on assigning probabilities in Chapter 4 can be used to develop discrete probability distributions. The empirical discrete probability distribution is developed using the relative frequency method to assign probabilities. At the request of many users, Anderson, Sweeney, and Williams have added a new section (Section 5.4) that covers bivariate discrete distributions and financial applications such as portfolio development.
    • Chapter 12: Sections 12.1 and 12.2 have been rewritten to better explain how the goodness of fit test and the test of independence are related methods for the analysis of categorical data. Section 12.3 (Testing for Equality of Three or More Population Proportions) is a new section that shows how methods for categorical data analysis can be used in tests for multiple proportions. For the hypothesis tests in each section, Excel's CHISQ.TEST function is used to compute the p-value corresponding to the chi-square test statistic.
    • CengageNOW Fully Integrated Online Teaching and Learning System: This innovative course management system combines the best of current technology to help you plan your course, manage and automatically grade extensive homework and student assignments, teach with the latest built-in technology support, and test students using a customized test bank. Personalized study plans for each student help reinforce student comprehension and reduce questions.
    • Powerful Examples and Exercises: Always a well-known strength from this author team, the exceptional exercises and examples throughout this text are stronger with more real data from sources such as the Census Bureau and the Wall Street Journal. Exercises drawn from real-world events encourage students to learn the statistical methodology and the application of real data to problems. Nine new cases and approximately 225 new examples and exercises have been added to the core material. There are 983 exercises in MODERN BUSINESS STATISTICS WITH MICROSOFT® EXCEL®, 5E.
    • Proven Self-Test Exercises to Ensure Student Understanding: Completely worked-out solutions for specific exercises appear in an appendix at the end of the book. Students can complete the self-test exercises and immediately check their solutions to evaluate their understanding of the concepts presented in the chapter.
    • Statistics in Practice Chapter Openers: Students immediately see the practical value of the information they are learning as Statistics in Practice chapter openers highlight intriguing scenarios from companies such as Citibank and Procter & Gamble using statistics in everyday business situations. These high-interest openers draw students into the information that follows. New to this edition is a Statistics in Practice for Chapter 2, describing the use of data dashboards and data visualization at the Cincinnati Zoo. A new Statistics in Practice has also been added to Chapter 4, describing how a NASA team used probability to assist the rescue of 33 Chilean miners trapped by a cave-in.
    • Equal Emphasis on Methods and Applications: Methods Exercises at the end of each section require students to use formulas and make necessary computations, whereas Application Exercises ask students to apply the chapter material to address real-world problems.
    • Trusted Team of Expert Authors: As respected leaders and active consultants in the fields of business and statistics, Anderson, Sweeney, and Williams provide an accurate presentation of statistical concepts you can trust with every edition as they use a cohesive, student-friendly writing approach. To ensure accuracy, the authors triple-check all problems and examples themselves.
    • Integrated Microsoft® Excel® 2013: Step-by-step instructions and screen captures clearly demonstrate how to use the latest version of Excel® to implement statistical procedures. Excel's new recommended PivotTables tool and new Recommended Chart tool are very useful in developing tabular and graphical displays.
    • Excel® 2013 Primer: "Microsoft Excel 2013 Tools for Statistical Analysis." This new section of material on basic Excel® operations discusses how to open and save workbooks, copy and paste, and enter formulas. It appears as an appendix at the end of the book.
    • Microsoft® Excel® Add-ins Optional Appendices: New chapter appendices give you the choice of covering StatTools, an industry-standard add-in that students will use throughout their business careers. Appendices highlight how this Microsoft® Excel® add-in provides capabilities not available in Excel® 2010, such as producing a box plot. This optional coverage allows you to choose to present Excel® with or without StatTools.
    • Integrated Microsoft® Excel® 2013: Step-by-step instructions and screen captures clearly demonstrate how to use the latest version of Excel® to implement statistical procedures. Excel's new recommended PivotTables tool and new Recommended Chart tool are very useful in developing tabular and graphical displays.
    • Excel® 2013 Primer: "Microsoft Excel 2013 Tools for Statistical Analysis." This new section of material on basic Excel® operations discusses how to open and save workbooks, copy and paste, and enter formulas. It appears as an appendix at the end of the book.
    • Microsoft® Excel® Add-ins Optional Appendices: New chapter appendices give you the choice of covering StatTools, an industry-standard add-in that students will use throughout their business careers. Appendices highlight how this Microsoft® Excel® add-in provides capabilities not available in Excel® 2010, such as producing a box plot. This optional coverage allows you to choose to present Excel® with or without StatTools.
    • Revised Chapter 2: Chapter 2 now includes illustrations of new tools available with Excel® 2013 and new material on data visualization. Excel's new Recommended PivotTables tool is used to construct frequency distributions for categorical variables, and Excel's Recommended Charts tool is used to construct side-by-side and stacked bar charts.
    • Revised Chapter 3: Chapter 3 now includes coverage of the weighted mean and geometric mean in the section on measures of location. The geometric mean has many financial applications in the computation of asset growth rates and annual percentage rates, for example. Anderson, Sweeney, and Williams have revised their procedure for computing percentiles and quartiles to be consistent with Excel's new PERCENTILE.EXC function. Chapter 3 also includes a new section on data dashboards, discussing how summary statistics can be incorporated to enhance their effectiveness.
    • Revised Chapter 5: The introductory material has been revised to show how the material on assigning probabilities in Chapter 4 can be used to develop discrete probability distributions. The empirical discrete probability distribution is developed using the relative frequency method to assign probabilities. At the request of many users, Anderson, Sweeney, and Williams have added a new section (Section 5.4) that covers bivariate discrete distributions and financial applications such as portfolio development.
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  • 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.