000			03502nam a22001937a 4500
999			_c27007 _d27007
005			20201109161045.0
008			201031b ||||| |||| 00| 0 eng d
020			_a9780128104842
040			_cVITAP
082			_223rd Ed. _a658.472 PIN
100			_910028 _aPinder, Jonathan P.
245			_aIntroduction to Business Analytics Using Simulation / _cJonathan P. Pinder
260			_aLondon _bAcademic Press (An Imprint of Elsevier) _c2017
300			_axiii, 434p. : ill. ; 23cm
500			_aIt includes Appendix and Index Pages. 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
650		0	_910029 _aIndustrial management--Statistical methods; Business--Computer simulation; Business intelligence; Strategic planning; Business planning
856			_uhttps://www.elsevier.com/books/introduction-to-business-analytics-using-simulation/pinder/978-0-12-810484-2
942			_2ddc _cREF _e23rd Ed. _h658.472 PIN