Business Analytics

Professor, University of Cincinnati College of Business

James R. Evans is professor in the Department of Operations, Business Analytics, and Information Systems in the College of Business at the University of Cincinnati. He holds BSIE and MSIE degrees from Purdue and a PhD in Industrial and Systems Engineering from Georgia Tech.

Dr. Evans has published numerous textbooks in a variety of business disciplines, including statistics, decision models, and analytics, simulation and risk analysis, network optimization, operations management, quality management, and creative thinking. He has published over 90 papers in journals such as Management Science, IIE Transactions, Decision Sciences, Interfaces, the Journal of Operations Management, the Quality Management Journal, and many others, and wrote a series of columns in Interfaces on creativity in management science and operations research during the 1990s. He has also served on numerous journal editorial boards and is a past-president and Fellow of the Decision Sciences Institute. In 1996, he was an INFORMS Edelman Award Finalist as part of a project in supply chain optimization with Procter & Gamble that was credited with helping P&G save over $250,000,000 annually in their North American supply chain, and consulted on risk analysis modeling for Cincinnati 2012’s Olympic Games bid proposal.

A recognized international expert on quality management, he served on the Board of Examiners and the Panel of Judges for the Malcolm Baldrige National Quality Award. Much of his current research focuses on organizational performance excellence and measurement practices.

Preface

About the Author

PART 1: Foundations of Business Analytics

1. Introduction to Business Analytics

2. Analytics on Spreadsheets

Part 2: Descriptive Analytics

3. Visualizing and Exploring Data

4. Descriptive Statistical Measures

5. Probability Distributions and Data Modeling

6. Sampling and Estimation

7. Statistical Inference

Part 3: Predictive Analytics

8. Trendlines and Regression Analysis

9. Forecasting Techniques

10. Introduction to Data Mining

11. Spreadsheet Modeling and Analysis

12. Monte Carlo Simulation and Risk Analysis

Part 4: Prescriptive Analytics

13. Linear Optimization

14. Applications of Linear Optimization

15. Integer Optimization

16. Decision Analysis

Supplementary Chapter A (online): Nonlinear and Non-Smooth Optimization

Supplementary Chapter B (online): Optimization Models with Uncertainty

Appendix A

Glossary

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