Introduction to Business Analytics Using Simulation

Cover -- Title Page -- Copyright Page -- Contents -- 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 - What is simulation? -- 1.4.1 - Why Use Simulation? -- 1.4.2 - Simulation Applications -- 1.5 - Monte Carlo simulation and random variables -- 1.6 - Simulation terminology -- 1.7 - Probability as relative frequency -- 1.8 - Overview of simulation process

1.9 - Random number generation in Excel -- 1.10 - Extra practice -- Chapter 2 - Decision-making and simulation -- Introduction -- 2.1 - Introduction to decision-making -- 2.1.1 - Define the Problem -- 2.1.2 - Identify and Weight the Criteria -- 2.1.3 - Generate Alternatives -- 2.1.4 - Evaluate Each Alternative -- 2.1.5 - Compute the Optimal Decision -- 2.2 - Probability: the measure of uncertainty -- 2.3 - Where do the probabilities come from? -- 2.4 - Elements of probability -- 2.5 - Probability notation -- 2.6 - Examples of simulation and decision-making -- Chapter 3 - Decision Trees

Introduction -- 3.1 - Decision trees and expected value -- 3.2 - Properties of decision trees -- 3.2.1 - Linear Transforms -- 3.3 - Overview of the decision making process -- 3.4 - Sensitivity analysis -- 3.5 - Expected value of perfect information -- 3.6 - Summary of the decision analysis process -- 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 -- Further exercises: common interview questions regarding probability

Chapter 5 - Subjective Probability Distributions -- Introduction -- 5.1 - Subjective probability distributions-probability from experience -- 5.2 - Two-point estimation: uniform distribution -- 5.2.1 - Discrete Uniform Distribution -- 5.3 - Three-point estimation: triangular distribution -- 5.3.1 - Simulating a Symmetric Triangular Distribution -- 5.3.2 - Simulating an Asymmetric Triangular Distribution -- 5.4 - Five-point estimates for subjective probability distributions -- 5.4.1 - Simulating a Five-Point Distribution -- 5.4.2 - Other Estimates for Subjective Probability Distributions

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 -- 7.3.1 - Uniform Distribution -- 7.3.2 - Discrete Uniform Distribution -- 7.3.3 - Continuous Uniform Distribution

7.3.4 - Bernoulli Distribution

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