STATISTICS FOR BUSINESS AND ECONOMICS brings together more than twenty-five years of author experience, sound statistical methodology, a proven problem-scenario approach, and meaningful applications to demonstrate how statistical information. Discover how the most trusted approach to statistics today is Simply Powerful with the latest market-leading text from respected authors Anderson/Sweeney/Williams.
It introduces sound statistical methodology within a strong applications setting. The authors clearly demonstrate how statistical results provide insights into business decisions and present solutions to contemporary business problems. New cases and more than 350 real business examples and memorable exercises, 150 of which are new in this edition, present the latest statistical data and business information. With this book's comprehensive coverage and unwavering accuracy, you select the topics best for your course, including thorough coverage of the latest versions of MiniTab 15 and Excel 2010, along with StatTools and other leading Excel 2007 statistical add-ins within chapter appendices. Author-written support materials and CengageNOW online course management system provides time-saving, complete support to ensure student understanding.
- TRUSTED TEAM OF EXPERT AUTHORS ENSURES THE MOST ACCURATE, PROVEN PRESENTATION. As prominent, respected leaders and active consultants in business and statistics today, authors David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, now joined by Jeffrey D. Camm and James J. Cochran provide an accurate presentation of statistical concepts you can trust with every edition.
- LEADING PROBLEM-SCENARIO APPROACH HELPS STUDENT UNDERSTAND AND APPLY CONCEPTS. A hallmark strength of this text, this unique approach helps students understand statistical techniques within an applications setting. The statistical results provide insights into business decisions and detail how statistics are used within business today to solve problems.
- SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and learn the use of techniques before moving to statistical application and interpretation of the value of techniques. Methods Exercises at the end of each section stress computation and use of formulas, while Application Exercises require students to use what they know about statistics to address real-world problems.
- CENGAGENOW ONLINE COURSE MANAGEMENT SYSTEM PROVIDES STUDENT RESULTS NOW! This robust, online course management system gives you more control in less time and delivers better student outcomes -- NOW. CengageNOW includes teaching and learning resources organized around lecturing, creating assignments, grading, quizzing, and tracking student progress and performance. Flexible assignments, automatic grading and a gradebook option provide more control while saving you valuable time. A Personalized Study diagnostic tool empowers students to master concepts, prepare for exams, and become more involved in class.
- USE OF CUMULATIVE STANDARD NORMAL DISTRIBUTION TABLE PREPARES STUDENTS TO WORK WITH STATISTICAL SOFTWARE. To more effectively prepare today's students to use computer software in statistics, this book incorporates a normal probability table that is consistent with today's statistical software. This cumulative normal probability table also makes it easier to compute p-values for hypothesis testing.
- SIMPLIFIED INTRODUCTION TO P-VALUES CLARIFIES UNDERSTANDING FOR STUDENTS. As in the previous edition, this edition emphasizes the use of p-values as the preferred approach to hypothesis testing. To further clarify the introduction of this concept for students, 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 LEARN TO USE SOFTWARE TO COMPUTE P-VALUES. For your flexibility, an optional appendix clearly demonstrates how to use MINITAB and Excel to compute p-values associated with z, t, and F test statistics. This coverage is particularly helpful when discussing hypothesis testing (Chs. 9-16).
New to This Edition
- Descriptive Statistics--Chapters 2 and 3. We have significantly revised these chapters to incorporate new material on data visualization, best practices, and much more. Chapter 2 has been reorganized to include new material on side-by-side and stacked bar charts, and a new section has been added on data visualization and best practices I creating effective displays. Chapter 3 now includes coverage of the geometric mean in the section on measures of location. The geometric mean has many applications in the computation of growth rates for financial assets, annual percentage rates, and so on. Chapter 3 also includes a new section on data dashboards and how summary statistics can be incorporated to enhance their effectiveness.
- Discrete Probability Distributions--Chapter 5. The introductory material in this chapter has been revised to better explain the role of probability distributions and to show how the material on assigning probabilities in Chapter 4 can be used to develop discrete probability distributions. We point out that the empirical discrete probability distribution is developed by using the relative frequency method to assign probabilities. At the request of many users, we have added a new Section 5.4 that covers bivariate discrete distributions and financial applications. We show how financial portfolios can be constructed and analyzed using these distributions.
- Chapter 12--Comparing Multiple Proportions, Tests of Independence, and Goodness of Fit. This chapter has undergone a major revision. We have added a new section on testing the equality of three or more population proportions. This section includes a procedure for making multiple comparison tests between all pairs of population proportions. The section on the test of independence has been rewritten to clarify that the test concerns the independence of two categorical variables. Revised appendixes with step-by-step instructions for Minitab, Excel, and StatTools are included.
- New Case Problems. We have added 8 new case problems to this edition; the total number of cases is 31. Three new descriptive statistics cases have been added to chapters 2 and 3. Five new case problems involving regression appear in Chapters 14, 15, and 16. These case problems provide students with the opportunity to analyze larger data sets and prepare managerial reports based on the results of their analysis.
- New Statistics in Practice Applications. Each chapter begins with a Statistics in Practice vignette that describes an application of the statistical methodology to be covered in the chapter. 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 and Botanical Garden. We have also added a Statistics i Practice to Chapter 4 describing how a NASA team used probability to assist in the rescue of 33 Chilean miners trapped by a cave-in.
- New Examples and Exercises based on Real Data. We continue to make a significant effort to update our text examples and exercises with the most current real data and referenced sources of statistical information. In this edition, we have added approximately 180 new examples and exercises based on real data and referenced sources. Using data from sources also used by The Wall Street Journal, USA Today, Barron's, and others, we have drawn from actual studies to develop explanations and to create exercises that demonstrate the many uses of statistics in business and economics. We believe that the use of real data helps generate more student interest in the material and enables the student to learn about both the statistical methodology and its application. The twelfth edition contains over 350 examples and exercises based on real data.
- InfoTrac® Student Collections are specialized databases expertly drawn from the Gale Academic One library. Each InfoTrac® Student Collection enhances the student learning experience in the specific course area related to the product. These specialized databases allow access to hundreds of scholarly and popular publications - all reliable sources - including journals, encyclopedias, and academic reports.
Contents
- 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. Inference 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. Index Numbers.
- 21. Decision Analysis.
- 22. Sample Survey(online).
- 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 2010 and Tools for Statistical Analysis.
- Appendix F. Computing p-Values Using Minitab and Excel.
- Index.
About the Authors
- Dr. David R. Anderson is Professor 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.
- Dr. Dennis J. Sweeney is Professor 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. Thomas A. Williams is Professor 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.
- Jeffrey D. Camm is Professor of Quantitative Analysis, Head of the Department of Operations, Business Analytics, and Information Systems, and College of Business REsearch Fellow int he Carl H. Lindner College of Business at the University of Cincinnati. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University and a Ph.D. from Clemson University. He has been at the University of Cincinnati since 1984, and has been a visiting scholar at Stanford university and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published over 30 papers in the general area of optimization applied to problems in operations management. He has published his research in Science, Management Science, Operations Research, Interfaces, and other professional journals. At the University of Cincinnati, he was named the Dornoff Fellow of Teaching Excellence and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of Interfaces, and is currently on the editorial board of INFORMS Transactions on Education.
- James J. Cochran is the Bank of Ruston Barnes, Thompson, & Thurmon Endowed Research Professor of Quantitative Analysis at Louisiana Tech University. Born in Dayton, Ohio, he holds a B.S., an M.S., and an M.B.A. from Wright State University and a Ph.D. from the University of Cincinnati. He has been at Louisiana Tech University since 2000, and has been a visiting scholar at Stanford University, Universidad de Talca, and the University of South Africa. Professor Cochran has published over two dozen papers in the development and application of operations research and statistical methods. He has published his research in Management Science, The American Statistician, Communications in Statistics--Theory and Methods, European Journal of Operational Research, Journal of Combinatorial Optimization, and other professional journals. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. Professor Cochran was elected to the International Statistics Institute i 2005 and named a Fellow of the American Statistical Association in 2011. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Professor Cochran has organized and chaired teaching effectiveness workshops in Montevideo, Uruguay; Cape Town, South Africa; Cartagena, Colombia; Jaipur, India; Buenos Aires, Argentina; and Nairobi, Kenya. He has served as an operations research consultant to numerous companies and not-for-profit organizations; currently serves as editor-in-chief of INFORMS Transactions on Education; and is on the editorial board of Interfaces, the Journal of the Chilean Institute of Operations Research, and ORiON.
Book Details
- Hardcover: 1120 pages
- Publisher: Cengage Learning; 12 edition (2013)
- Language: English
- ISBN-10: 1133274536
- ISBN-13: 978-1133274537
- Product Dimensions: 10 x 8.3 x 1.7 inches
- List Price: $ 275.95