商業統計

Essentials of Statistics for Business and Economics 7/e

+作者:

Anderson

+年份:
2015 年7 版
+ISBN:
9781133629658
+書號:
PS0436HC
+規格:
精裝/彩色
+頁數:
752
+出版商:
Cengage
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●Chapter appendices have been updated to cover use of Excel 2013.
●Strong orientation toward visual presentation of data and results.
●Integration of several important business analytics topics not covered by any other single book, including data mining, data visualization, and data dashboards.
●Approximately 200 new examples and exercises based on real data and referenced sources have been added to this edition. Using data from world-class sources like the Wall Street Journal, USA Today, and Barron’s helps students master statistical methods and apply real data to common business problems, while extensive margin notes and comments highlight key points for simple, efficient review.
●Seven new case problems have been added to this edition for a total of 25 cases.

●Comprehensive, Modern Coverage: Demonstrating the myriad uses of statistics in business and economics, the text’s examples and exercises incorporate the most current data, recent studies, and reliable sources of statistical information available, such as the Wall Street Journal, USA Today, Barron’s, and others.
●Unique Problem-Scenario Approach: Students discuss and develop their understanding of each statistical concept by applying techniques to exercises designed to generate a solution (or recommendation), and illustrate the value of statistics in business decision-making.
●Real-World Problems and Application: Methods, Application, and Self-Test Exercises allow students to develop analytical skills by using formulas, making computations, applying chapter material to realistic situations, and evaluating their understanding of key concepts against solutions to exercises in a special appendix.
●Effective Exercises and Examples: Abundant data from world-class sources like the Wall Street Journal, USA Today, and Barron's helps students master statistical methods and apply real data to common business problems, while extensive margin notes and comments highlight key points for simple, efficient review.
●Useful Cumulative Standard Normal Distribution Table: This key graphic prepares students to use computer software in statistics by introducing normal distribution probabilities, and helping first-time users calculate normal probability and p-values for hypothesis testing.
●Optional Support Materials: The CengageNOW™ online course management system helps you plan your course, manage and automatically grade assignments, prepare and teach lessons, create tests, and provide personalized study plans for each student. A Solutions Manual written by the text's authors provides step-by-step solutions for exercises, including detailed explanations of the role of cumulative normal distribution and p-values, to help make lecture preparation effortless.
●Modern, Comprehensive Software Integration: Optional chapter appendices have been updated and expanded to include Excel® 2013, Minitab® 16, and StatTools®.
●Helpful Appendices: A primer appendix introduces Microsoft® Excel® 2013 and its tools for statistical analysis, and includes the Ribbon, basic workbook operations, and functions for statistical analysis, along with instructions for installing the text’s optional Excel® data analysis add-in and optional CengageNOW™ learning tool. Appendix 1 includes steps for downloading StatTools®, an optional, commercial Excel® add-in that students will likely encounter in the workplace. Most chapters also include an appendix outlining statistical procedures using StatTools®.
●Integration of several important business analytics topics that are not covered by any other single book, including data mining, data visualization, and data dashboards.
●Step-by-step instructions on how to use various software to perform the analyses discussed in the book-- Excel® 2013, Minitab® 16, and StatTools®.

David R. Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He 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 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 co-author of 10 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 B.S., M.S., and Ph.D. degrees from Purdue University.

Dennis J. Sweeney is 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 worked in the management science group at Procter & Gamble and served as 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 co-author of 10 textbooks in the areas of statistics, management science, linear programming, and production and operations management. Born in Des Moines, Iowa, he earned a B.S. degree from Drake University, graduating summa cum laude. He received his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow.

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. For seven years, Professor Williams served as a faculty member in the University of Cincinnati College of Business Administration, 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, Dr. Williams has served as a consultant for numerous Fortune 500 companies, working 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 his M.S. and Ph.D. degrees at Rensselaer Polytechnic Institute.

Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Analytics in the School of Business at Wake Forest University. He previously served on the faculty of the University of Cincinnati and as 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 more than 30 papers in the general area of optimization applied to problems in operations management and marketing, and his research has appeared in SCIENCE, MANAGEMENT SCIENCE, OPERATIONS RESEARCH, INTERFACES, and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati, and he received the 2006 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. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University.

James J. Cochran is Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow in the Department of Information Systems, Statistics and Management Science at the University of Alabama. He previously served as Professor of Quantitative Analysis and the Bank of Ruston, Barnes, Thompson, & Thurman Endowed Research Professor at Louisiana Tech University and was a visiting scholar at Stanford University, Universidad de Talca, and the University of South Africa. Professor Cochran has published more than two dozen papers in the development and application of operations research and statistical methods, and his research has appeared 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 received the 2008 INFORMS Prize for the Teaching of Operations Research Practice and the 2010 Mu Sigma Rho Statistical Education Award. Dr. Cochran was elected to the International Statistics Institute in 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, he has organized and chaired teaching effectiveness workshops in Uruguay, South Africa, Colombia, India, Argentina, Kenya, Cameroon, and Croatia. He has served as a statistics and operations research consultant to numerous companies and not-for-profit organizations. He was editor-in-chief of INFORMS TRANSACTIONS ON EDUCATION from 2007 to 2012 and serves on the editorial board of INTERFACES, the JOURNAL OF QUANTITATIVE ANALYSIS IN SPORTS, and ORION. He holds a B.S., M.S., and MBA from Wright State University and a Ph.D. from the University of Cincinnati.

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. 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.
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.
Appendix F. Computing p-Values Using Minitab and Excel.