Introduction to Business Analytics Using Simulation

2nd Edition - February 6, 2022
Author: Jonathan P. Pinder
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 7 1 7 - 9
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 1 1 7 - 9

Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Preface
Acknowledgments
Chapter 1. Business analytics is making decisions
Introduction
1.1. Business analytics is making decisions subject to uncertainty
1.2. Components of business analytics
1.3. Uncertainty = probability = stochastic
1.4. Example of decision making and the three stages of analytics
1.5. Introduction to decision analysis
1.6. What is simulation?
1.7. Monte Carlo simulation
Chapter 2. Decision trees
Introduction
2.1. Introduction to decision making
2.2. Decision trees and expected value
2.3. Overview of the decision-making process
2.4. Sensitivity analysis
2.5. Expected value of perfect information
2.6. Properties of decision trees
2.7. Probability: the measure of uncertainty
2.8. Where do the probabilities come from?
2.9. Elements of probability
2.10. Probability notation
2.11. Applications of decision analysis and decision trees
2.12. Summary of the decision analysis process
Chapter 3. Decision making and simulation
Introduction
3.1. Simulation to model uncertainty
3.2. Monte Carlo simulation and random variables
3.3. Simulation terminology
3.4. Overview of the simulation process
3.5. Random number generation in Excel
3.6. Examples of simulation and decision making
Chapter 4. Probability: measuring uncertainty
Introduction
4.1. Probability: measuring likelihood
4.2. Probability distributions
4.3. General probability rules
4.4. Conditional probability and Bayes’ theorem
Chapter 5. Subjective probability distributions
Introduction
Chapter 6. Empirical probability distributions
Introduction
6.1. Empirical probability distributions: probability from data
6.2. Discrete empirical probability distributions
6.3. Continuous empirical probability distributions
Chapter 7. Theoretical probability distributions
Introduction
7.1. Theoretical/classical probability
7.2. Review of notation for probability distributions
7.3. Discrete theoretical distributions
7.4. Continuous probability distributions
7.5. Normal approximation of the binomial and Poisson distributions
7.6. Using distributions in decision analysis
7.7. Overview of probability distributions
Chapter 8. Simulation accuracy: central limit theorem and sampling
Introduction
8.1. Introduction to sampling and the margin of error
8.2. Linear properties of probability distributions
8.3. Adding distributions
8.4. Samples
8.5. Central limit theorem
8.6. Confidence intervals and hypothesis testing for proportions
8.7. Confidence intervals and hypothesis testing for means
Chapter 9. Simulation fit and significance: Chi-square and ANOVA
Introduction
Chapter 10. Regression
Introduction
Chapter 11. Forecasting
Introduction
11.1. Overview of forecasting
11.2. Measures of accuracy
11.3. Components of time series data
11.4. Forecasting trend
11.5. Forecasting seasonality
11.6. Aggregating sales
11.7. Review of forecasting with regression
Chapter 12. Constrained linear optimization
Introduction
12.1. Overview of constrained linear optimization
12.2. Components of linear programming
12.3. General layout of a linear programming model in Excel
12.4. Steps for the linear programming modeling
12.5. Network models
12.6. Types of linear programming end conditions
12.7. Sensitivity analysis terms
12.8. Excel Solver messages
Appendix 1. Summary of simulation
Appendix 2. Statistical tables
Index

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Jonathan P. Pinder