Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics.
Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Preface - p. iii 7. Acknowledgments - p. iv 8. Chapter 1. Business analytics is making decisions - p. 1 9. Chapter 2. Decision trees - p. 19 10. Chapter 3. Decision making and simulation - p. 57 11. Chapter 4. Probability: measuring uncertainty - p. 85 12. Chapter 5. Subjective probability distributions - p. 101 13. Chapter 6. Empirical probability distributions - p. 131 14. Chapter 7. Theoretical probability distributions - p. 163 15. Chapter 8. Simulation accuracy: central limit theorem and sampling - p. 209 16. Chapter 9. Simulation fit and significance: Chi-square and ANOVA - p. 273 17. Chapter 10. Regression - p. 327 18. Chapter 11. Forecasting - p. 389 19. Chapter 12. Constrained linear optimization - p. 439 20. Appendix 1. Summary of simulation - p. 477 21. Appendix 2. Statistical tables - p. 483 22. Index - p. 487
Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics.
Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Preface - p. iii 7. Acknowledgments - p. iv 8. Chapter 1. Business analytics is making decisions - p. 1 9. Chapter 2. Decision trees - p. 19 10. Chapter 3. Decision making and simulation - p. 57 11. Chapter 4. Probability: measuring uncertainty - p. 85 12. Chapter 5. Subjective probability distributions - p. 101 13. Chapter 6. Empirical probability distributions - p. 131 14. Chapter 7. Theoretical probability distributions - p. 163 15. Chapter 8. Simulation accuracy: central limit theorem and sampling - p. 209 16. Chapter 9. Simulation fit and significance: Chi-square and ANOVA - p. 273 17. Chapter 10. Regression - p. 327 18. Chapter 11. Forecasting - p. 389 19. Chapter 12. Constrained linear optimization - p. 439 20. Appendix 1. Summary of simulation - p. 477 21. Appendix 2. Statistical tables - p. 483 22. Index - p. 487
