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Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets, 7th Edition
ISBN10: 1260716295 | ISBN13: 9781260716290
© 2023
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Hiller's Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets, 7e in its newest edition introduces both Management Science and Business Analytics in chapter 1 emphasizing the close relationship between them. The new Chapter 2 is an overview of how MS and BA professionals analyze problems and their similarities, chapter 3 is a new chapter on the role of data mining, clustering classification prediction methodology, some algorithms for implementing this methodology, a powerful SW package for data mining and more. Chapter 4 will continue to focus on key techniques of Business Analytics to complete part 1 of the text and the final chapters are focused on Prescriptive and Predictive Analytics based on models based on Linear programming models, Other Certainty Models, and Uncertainty Models.
PART 1 The Essence of Management Science and Business Analytics
1 Introduction
2 Overview of the Analysis Process
PART 2 Models for Predictive Analytics
3 Classification and Prediction Models for Predictive Analytics
4 Predictive Analytics Based on Traditional Forecasting Methods
PART 3 Using Linear Programming to Perform Prescriptive Analytics
5 Linear Programming: Basic Concepts
6 Linear Programming: Formulation and Applications
7 The Art of Modeling with Spreadsheets
8 What-If Analysis for Linear Programming
9 Network Optimization Problems
PART 4 Using Integer or Nonlinear Programming to Perform Prescriptive Analytics
10 Integer Programming
11 Nonlinear Programming
PART 5 Traditional Uncertainty Models for Performing Predictive or Prescriptive Analytics
12 Decision Analysis
13 Queueing Models
14 Computer Simulation: Basic Concepts
15 Computer Simulation with Analytic Solver
APPENDIXES
A Tips for Using Microsoft Excel for Modeling
B Partial Answers to Selected Problems
1 Introduction
2 Overview of the Analysis Process
PART 2 Models for Predictive Analytics
3 Classification and Prediction Models for Predictive Analytics
4 Predictive Analytics Based on Traditional Forecasting Methods
PART 3 Using Linear Programming to Perform Prescriptive Analytics
5 Linear Programming: Basic Concepts
6 Linear Programming: Formulation and Applications
7 The Art of Modeling with Spreadsheets
8 What-If Analysis for Linear Programming
9 Network Optimization Problems
PART 4 Using Integer or Nonlinear Programming to Perform Prescriptive Analytics
10 Integer Programming
11 Nonlinear Programming
PART 5 Traditional Uncertainty Models for Performing Predictive or Prescriptive Analytics
12 Decision Analysis
13 Queueing Models
14 Computer Simulation: Basic Concepts
15 Computer Simulation with Analytic Solver
APPENDIXES
A Tips for Using Microsoft Excel for Modeling
B Partial Answers to Selected Problems
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