
Introduction to Business Analytics Using Simulation
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Introduction to Business Analytics Using Simulation employs an innovative strategy to teach business analytics. It 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 the uncertainty and variability of business, this comprehensive book provides a better foundation for business analytics than standard introductory business analytics books. 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
- Winner of the 2017 Textbook and Academic Authors Association (TAA) Most Promising New Textbook Award
- 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
Readership
Upper-division undergraduates and graduate students worldwide working on business decision-making. Prerequisite: statistics
Table of Contents
- Preface
- Acknowledgments
- Chapter 1: Business analytics is making decisions
- Abstract
- Introduction
- Chapter 2: Decision-making and simulation
- Abstract
- Introduction
- Chapter 3: Decision Trees
- Abstract
- Introduction
- Chapter 4: Probability: measuring uncertainty
- Abstract
- Introduction
- Chapter 5: Subjective Probability Distributions
- Abstract
- Introduction
- Chapter 6: Empirical probability distributions
- Abstract
- Introduction
- Chapter 7: Theoretical probability distributions
- Abstract
- Introduction
- Chapter 8: Simulation accuracy: central limit theorem and sampling
- Abstract
- Introduction
- Chapter 9: Simulation fit and significance: chi-square and ANOVA
- Abstract
- Introduction
- Chapter 10: Regression
- Abstract
- Introduction
- Chapter 11: Forecasting
- Abstract
- Introduction
- Appendix 1: Summary of simulation
- Appendix 2: Statistical tables
- Index
Product details
- No. of pages: 448
- Language: English
- Copyright: © Academic Press 2016
- Published: August 31, 2016
- Imprint: Academic Press
- eBook ISBN: 9780128104866
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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