Introduction to Business Analytics Using Simulation

Introduction to Business Analytics Using Simulation

2nd Edition - February 6, 2022

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  • Author: Jonathan Pinder
  • Paperback ISBN: 9780323917179
  • eBook ISBN: 9780323991179

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Description

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.

Key Features

  • Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making
  • Explains the processes needed to develop, report and analyze business data
  • Describes how to use and apply business analytics software
  • Offers expanded coverage on the value and application of prescriptive analytics
  • Includes a wealth of illustrative exercises that are newly organized by difficulty level
  • Winner of the 2017 Textbook and Academic Authors Association's (TAA) Most Promising New Textbook Award in the prior edition

Readership

Upper-level undergraduates and graduate students in business decision-making and business analytics; Professionals working in finance and business globally. Secondary audience: Professionals working in finance and business globally

Table of Contents

  • 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

Product details

  • No. of pages: 512
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 6, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323917179
  • eBook ISBN: 9780323991179

About the Author

Jonathan Pinder

Dr. Pinder's research has been published in Decision Sciences, the Journal of Operations Management, the Journal of Forecasting, the Journal of Economics and Business, Managerial and Decision Economics, the Journal of the Operational Research Society, Decision Sciences Journal of Innovative Education, and Decision Economics, among others. Dr. Pinder has received numerous teaching awards. He is a member of the Decision Sciences Institute and the Institute for Operations Research and Management Science.

Affiliations and Expertise

School of Management, Wake Forest University, Winston-Salem NC, USA

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