Introduction to business analysis using simulation

Chapter 1. Business analytics is making decisions

1.1. Business analytics is making decisions that are 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 analysis

1.5. Introduction to decision analysis

1.7. Monte Carlo simulation

Chapter 2. Decision trees

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 opportunities 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

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

4.1. Probability: measure probability

4.2. Probability distributions

4.3. General Probability Rules

4.4. Conditional probability and Bayes’ theorem

Chapter 5. Subjective probability distributions

Chapter 6. Empirical probability distributions

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

7.1. Theoretical/classical probability

7.2. Revision 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

8.1. Introduction to sampling and the margin of error

8.2. Linear properties of probability distributions

8.3. Add distributions

8.5. Central limit theorem

8.6. Confidence intervals and hypothesis tests for ratios

8.7. Confidence intervals and hypothesis testing for means

Chapter 9. Simulation fit and significance: chi-squared and ANOVA

11.1. Overview of forecasts

11.2. Measures of accuracy

11.3. Components of time series data

11.5. Predict seasonality

11.7. Assessment of forecasts with regression

Chapter 12. Constrained Linear Optimization

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 modeling linear programming

12.6. Types of end conditions for linear programming

12.7. Sensitivity analysis terms

12.8. Excel Solver Messages

Appendix 1. Simulation summary

Appendix 2. Statistical tables