【2019版】Business Analytics, 3rd Edition-经管之家官网！

Business Analytics, 3rd Edition
by Jeffrey D. Camm (Author), James J. Cochran (Author), Michael J. Fry (Author), Jeffrey W. Ohlmann (Author), David R. Anderson (Author), Dennis J. Sweeney (Author), Thomas A. Williams (Author)
About the Author
Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he was on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College.
James J. Cochran is Associate Dean for Research, Professor of Applied Statistics, and the Rogers-Spivey Faculty Fellow at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. degrees from Wright State University and a Ph.D. from the University of Cincinnati. He has been at the University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa, and Pole Universitaire Leonard de Vinci.
Michael J. Fry is Professor and Head of the Department of Operations, Business Analytics, and Information Systems in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University, and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he has been named a Lindner Research Fellow and has served as Assistant Director and Interim Director of the Center for Business Analytics. He has also been a visiting professor at Cornell University and at the University of British Columbia.
Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska, and M.S. and Ph.D. degrees from the University of Michigan. He has been at the University of Iowa since 2003.
David R. Anderson is Professor Emeritus of Quantitative Analysis in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Grand Forks, North Dakota, 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.
Dennis J. Sweeney is Professor Emeritus of Quantitative Analysis and Founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, 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. During 1978–1979, Professor Sweeney worked in the management science group at Procter & Gamble; during 1981–1982, he was a visiting professor at Duke University. Professor Sweeney served as Head of the Department of Quantitative Analysis and as Associate Dean of the College of Business Administration at the University of Cincinnati.
Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology. Born in Elmira, New York, he earned his B.S. degree at Clarkson University. He did his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees.
About this book
Build valuable skills that are in high demand in today's businesses with BUSINESS ANALYTICS, 3E. You master the full range of analytics as you strengthen your descriptive, predictive and prescriptive analytic skills. Real-world examples and visuals help illustrate data and results for each topic. Clear, step-by-step instructions for various software programs, including Microsoft Excel, Analytic Solver, and JMP Pro, teach you how to perform the analyses discussed. Practical, relevant problems at all levels of difficulty further help you apply what you've learned to succeed in your course.
This textbook contains one of the first collections of materials that are essential to the growing field of business analytics. In Chapter 1 the book presents an overview of business analytics and our approach to the material in this textbook. In simple terms, business analytics helps business professionals make better decisions based on data. The book discusses models for summarizing, visualizing, and understanding useful information from historical data in Chapters 2 through 6. Chapters 7 through 9 introduce methods for both gaining insights from historical data and predicting possible future outcomes. Chapter 10 covers the use of spreadsheets for examining data and building decision models. In Chapter 11, the book demonstrates how to explicitly introduce uncertainty into spreadsheet models through the use of Monte Carlo simulation. In Chapters 12 through 14 the book discusses optimization models to help decision makers choose the best decision based on the available data. Chapter 15 is an overview of decision analysis approaches for incorporating a decision maker’s views about risk into decision making. In Appendix A the book presents optional material for students who need to learn the basics of using Microsoft Excel. The use of databases and manipulating data in Microsoft Access is discussed in Appendix B.
This textbook can be used by students who have previously taken a course on basic statistical methods as well as students who have not had a prior course in statistics. Business Analytics 3E is also amenable to a two-course sequence in business statistics and analytics. All statistical concepts contained in this textbook are presented from a business analytics perspective using practical business examples. Chapters 2, 5, 6, and 7 provide an introduction to basic statistical concepts that form the foundation for more advanced analytics methods. Chapters 3, 4, and 9 cover additional topics of data visualization and data mining that are not traditionally part of most introductory business statistics courses, but they are exceedingly important and commonly used in current business environments. Chapter 10 and Appendix A provide the foundational knowledge students need to use Microsoft Excel for analytics applications. Chapters 11 through 15 build upon this spreadsheet knowledge to present additional topics that are used by many organizations that are leaders in the use of prescriptive analytics to improve decision making.
Brief Contents
CHAPTER 1 Introduction 2
CHAPTER 2 Descriptive Statistics 18
CHAPTER 3 Data Visualization 82
CHAPTER 4 Descriptive Data Mining 138
CHAPTER 5 Probability: An Introduction to Modeling Uncertainty 166
CHAPTER 6 Statistical Inference 220
CHAPTER 7 Linear Regression 294
CHAPTER 8 Time Series Analysis and Forecasting 372
CHAPTER 9 Predictive Data Mining 422
CHAPTER 10 Spreadsheet Models 464
CHAPTER 11 Monte Carlo Simulation 500
CHAPTER 12 Linear Optimization Models 556
CHAPTER 13 Integer Linear Optimization Models 606
CHAPTER 14 Nonlinear Optimization Models 646
CHAPTER 15 Decision Analysis 678
APPENDIX A Basics of Excel 724
APPENDIX B Database Basics with Microsoft Access 736
APPENDIX C Solutions to Even-Numbered Questions (MindTap Reader)
REFERENCES 774
INDEX 776
Length: 818 pages
Publisher: Cengage Learning; 3rd edition (2019)
Language: English
ISBN-10: 1-337-40642-2
ISBN-13: 978-1-337-40642-0