Business Analytics

Wayne L. Winston is Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University and is now Professor of Decision and Information Sciences at the Bauer College at the University of Houston. Dr. Winston has received more than 45 teaching awards and is a six-time recipient of the school-wide M.B.A. award. His current interest focuses on showing how to use spreadsheet models to solve business problems in all disciplines, particularly in finance, sports and marketing. In addition to publishing more than 20 articles in leading journals, Dr. Winston has written such successful textbooks, including OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS; MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS; SIMULATION MODELING WITH @RISK; DATA ANALYSIS FOR MANAGERS; SPREADSHEET MODELING AND APPLICATIONS; MATHLETICS, DATA ANALYSIS AND BUSINESS MODELING WITH EXCEL 2016; MARKETING ANALYTICS; and FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. Dr. Winston received his B.S. degree in mathematics from MIT and his Ph.D. in operations research from Yale. S. Christian Albright received both his B.S. degree in mathematics and his Ph.D. in operations research from Stanford. He then taught in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University until his retirement in 2011. He taught courses in management science, computer simulation and statistics to all levels of business students, including undergraduate, M.B.A. and Ph.D. students. He has published more than 20 articles in leading operations research journals in applied probability. After retiring, he worked for several years for the Palisade software company. Now living in Hilton Head, SC, he continues to revise several successful textbooks, including this edition, PRACTICAL MANAGEMENT SCIENCE and VBA FOR MODELERS.

1. Introduction to Business Analytics.
Part 1: DATA ANALYSIS.
2. Describing the Distribution of a Variable.
3. Finding Relationships among Variables.
4. Business Intelligence (BI) Tools for Data Analysis
Part 2: PROBABILITY AND DECISION MAKING UNDER UNCERTAINTY.
5. Probability and Probability Distributions.
6. Decision Making under Uncertainty.
Part 3: STATISTICAL INFERENCE.
7. Sampling and Sampling Distributions.
8. Confidence Interval Estimation.
9. Hypothesis Testing.
Part 4: REGRESSION ANALYSIS AND TIME SERIES FORECASTING.
10. Regression Analysis: Estimating Relationships.
11. Regression Analysis: Statistical Inference.
12. Time Series Analysis and Forecasting.
Part 5: OPTIMIZATION AND SIMULATION MODELING.
13. Introduction to Optimization Modeling.
14. Optimization Models.
15. Introduction to Simulation Modeling.
16. Simulation Models.
Part 6: ADVANCED DATA ANALYSIS.
17. Data Mining.
Part 7: BONUS ONLINE MATERIAL.
18. Analysis of Variance and Experimental Design.
19. Statistical Process Control.
Appendix A: Statistical Reporting.