Introduction to Business Analytics Using Simulation

Jon Pinder
Additional contact information
Jon Pinder: School of Management, Wake Forest University, Winston-Salem NC, USA

in Elsevier Monographs from Elsevier, currently edited by Candice Janco

Abstract: 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. 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

Keywords: Analysis of Variance (ANOVA); autoregression; Bayes' theorem; Bernoulli distribution; Binomial distribution; Business Analytics; centered moving average; Central Limit Theorem; Chi-square test; conditional probability; confidence intervals; continuous probability distribution; continuous random variables; correlation; decision analysis; decision tree; Descriptive Analytics; discrete probability distribution; discrete random variables; empirical probability; expected value; five-point estimation; forecasting; heteroscedasticity; hypothesis testing; indicator variable; interaction term; joint probability; Law of Large Numbers; Likert scale; linear regression; MAD; MAPE; margin of error; marginal probability; Mincer-Zarnowitz; Monte Carlo simulation; MSE; multicollinearity; multiple regression; newsvendor problem; nonlinear regression; Normal distribution; optimization; p-value; Poisson distribution; polynomial regression; Predictive Analytics; Prescriptive Analytics; probability; probability distributions; ratio-to-moving-average (RTMA); RMSE; sampling; seasonal index; seasonality; simulation; spurious correlation; standard deviation; standard error; stochastic optimization; Student's t-distribution; subjective probability; triangular distribution; uniform distribution; value of perfect information; variance (search for similar items in EconPapers)
Date: 2016 Originally published 2016-08-31.
Edition: 1
ISBN: 978-0-12-810484-2
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/book/9780128104842
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:monogr:9780128104842

Access Statistics for this book

More books in Elsevier Monographs from Elsevier
Bibliographic data for series maintained by ().