Essentials of Modern Business Statistics with Microsoft® Office Excel® 7th Edition
David R. Anderson | Dennis J. Sweeney | Thomas A. Williams | Jeffrey D. Camm | James J. Cochran
Essentials of Modern Business Statistics with Microsoft® Excel® 6th Edition
David R. Anderson | Dennis J. Sweeney | Thomas A. Williams
ISBN-13: 9781285867045 | ISBN-10: 1285867041
© 2016 | Published |  784  Pages
Previous Editions: 9780840062383

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Integrating Microsoft Excel® 2013 throughout, ESSENTIALS OF MODERN BUSINESS STATISTICS, 6TH EDITION provides an introduction to business statistics that blends a conceptual understanding of statistics with the real-world application of statistical methodology. Leading the business statistics market for two decades, this author team is renowned for their high-quality problems, unwavering accuracy, and signature problem-scenario approach that clearly illustrates how to apply statistical methods in practical business situations. Step-by-step instructions and screen captures show students how to most effectively use the latest version of Excel in statistical procedures, while numerous exercises give them hands-on experience putting what they learn into practice. In addition, the Sixth Edition is packed with all-new Case Problems, Statistics in Practice applications, and real data examples and exercises.

  • 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. Timer Series Analysis and Forecasting (on Website).
    15. Statistical Methods for Quality Control (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.

    • PROBLEM-SCENARIO APPROACH. With its hands-on approach, the text presents discussion and development of each technique in an applications setting, with the statistical results providing insights to decisions and solutions to problems. The problem scenarios enable students to see how statistics can be applied in real-world business and economics practice--increasing their interest and motivation for learning statistics.
    • STUDENT READABILITY. For more than 30 years, student surveys and instructor feedback have shown that readability is a hallmark of ASW textbooks. In addition, the text is packed with real-world examples, current illustrations, and step-by-step instructions.
    • OUTSTANDING EXERCISES. End-of-section exercises are split into two parts: Methods and Applications. The Methods exercises require students to use formulas and make appropriate computations. The Applications exercises require students to use the chapter material in real-world situations. Many of the Applications exercises are based on recent data obtained from referenced sources. Combining these two methods enables students to focus on the computational "nuts and bolts" before advancing to the subtleties of statistical application and interpretation. Certain exercises are identified as self-test exercises and have completely worked-out-solutions provided in Appendix D.
    • DATA FILES. Data files for case problems and for exercises with large amounts of data are available on the student companion website. These are in Excel 2013, and will save students time and reduce the likelihood of errors in data entry. The WEBfile logo in the margin indicates the existence of a data file; the name of the file appears under the logo.
    • MARGIN ANNOTATIONS AND NOTES AND COMMENTS. Helping ensure thorough student understanding, margin annotations highlight key points and provide additional insights. Many sections end with Notes and Comments designed to give students additional insights about the statistical methodology and its application. Notes and Comments include warnings about or limitations of the methodology, recommendations for application, brief descriptions of additional technical considerations, and more.
    • FREE ACCESS TO STATTOOLS FROM PALISADE CORPORATION. StatTools is an industry-standard Excel add-in that is available to adopters of this text at no charge. StatTools extends the range of statistical and graphical options for Excel users. Its use is completely optional. Instructions for downloading and installing StatTools are in an appendix to Chapter 1, and most chapters include an appendix that shows the steps required to accomplish a statistical procedure using StatTools.
    • SELF-TEST EXERCISES. Helping students maximize their course success, step-by-step solutions for self-test exercises are provided in Appendix D. Students can attempt the Self-Test exercises and immediately check the solution to evaluate their understanding of the concepts presented in each chapter.
    • INTEGRATION OF EXCEL. Immediately following every statistical procedure, a sub-section discusses how to use Excel to perform that procedure. This style enables the use of Excel to be integrated throughout the text while keeping the primary emphasis on the statistical methodology being discussed. In addition, each sub-section uses a consistent framework for applying Excel to help users focus on the statistical methodology without getting bogged down in the details of using Excel.
    • INTEGRATED MICROSOFT EXCEL 2013. Step-by-step instructions and screen captures clearly demonstrate how to use the latest version of Excel 2013 to implement statistical procedures. Excel's new Recommended PivotTables tool and Excel's new Recommended Chart tool are very useful in developing tabular and graphical displays. In addition, the new Appendix E provides a thorough introduction to Excel 2013 and tools for statistical analysis.
    • NEW ILLUSTRATIONS. Chapter 2 "Descriptive Statistics: Tabular and Graphical Presentations" now includes illustrations of new tools available with Excel 2013 and new material on data visualization. The illustrations demonstrate how Excel's new Recommended PivotTables tool is used to construct frequency distributions and how Excel's Recommended Charts tool is used to construct side-by-side and stacked bar charts.
    • EXPANDED WEIGHTED MEAN AND GEOMETRIC MEAN COVERAGE. Chapter 3 "Descriptive Statistics: Numerical Measures" 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, annual percentage rates, etc. 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 and explains how summary statistics can be incorporated to enhance their effectiveness.
    • REVAMPED COVERAGE OF PROBABILITIES. The introductory material in Chapter 5 "Discrete Probability Distributions" has been revised to show how the material on assigning probabilities in Chapter 4 "Introduction to Probability" can be used to develop discrete probability distributions. The empirical discrete probability distribution is developed by using the relative frequency method to assign probabilities.
    • REVISED COMPARISONS COVERAGE. Chapter 11 "Comparisons Involving Proportions and a Test of Independence" has been thoroughly reorganized. While it continues to cover inferences about two population proportions, the chapter also extends the material to hypothesis tests concerning three or more population proportions. The chapter concludes with the Test of Independence.
    • NEW CASE PROBLEMS. Giving students hands-on experience working with chapter concepts, six all-new case problems have been added to the chapters on descriptive statistics and regression analysis.
    • NEW STATISTICS IN PRACTICE APPLICATIONS. Each chapter opens with a Statistics in Practice application, many of which are new to the Sixth Edition. The new Statistics in Practice for Chapter 2 describes the use of data dashboards and data visualization at the Cincinnati Zoo, while the application for Chapter 4 describes how a NASA team used probability to assist in the rescue of 33 Chilean miners trapped by a cave-in.
    • NEW REAL DATA EXAMPLES AND EXERCISES. Illustrating real-world applications of statistics, the Sixth Edition adds approximately 230 new examples and exercises based on real data and recently referenced sources of statistical information. In total, the text is packed with 743 exercises--giving students plenty of hands-on practice putting what they learn into action.
For more information about these supplements, or to obtain them, contact your Learning Consultant

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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.