Written by the innovator of the spreadsheet teaching revolution and highly regarded leader in business analytics, Cliff Ragsdale’s new edition of SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS retains the elements and philosophy of past success while now helping your students transition to business analytics.

SPREADSHEET MODELING AND DECISION ANALYSIS, 8E’s updates work seamlessly with Microsoft® Office Excel® 2016. Succinct instruction highlights the most commonly used business analytics techniques and clearly demonstrates how to implement these tools with the most current version of Excel® for Windows. This text focuses on developing both algebraic and spreadsheet modeling skills. This edition now features Analytic Solver and XLMiner Platforms with powerful tools for performing optimization, simulation and decision analysis in Excel, as well as complete tools for performing data mining in Excel and techniques for predictive analytics.

1. Introduction to Modeling and Decision Analysis.
2. Introduction to Optimization and Linear Programming.
3. Modeling and Solving LP Problems in a Spreadsheet.
4. Sensitivity Analysis and the Simplex Method.
5. Network Modeling.
6. Integer Linear Programming.
7. Goal Programming and Multiple Objective Optimization.
8. Nonlinear Programming & Evolutionary Optimization.
9. Regression Analysis.
10. Data Mining.
11. Time Series Forecasting.
12. Introduction to Simulation Using Analytic Solver Platform.
13. Queuing Theory.
14. Decision Analysis.
15. Project Management (Online).

New Mindtap® Digital Learning Solution Helps You Engage Today’S Students. This All-Digital Version Of The Book Enhances Student Learning In Each Chapter With An Engagement Video And Discussion, A Quiz With Rich Feedback, Videos By The Author That Explain Chapter Concepts, And End-Of-Chapter Assignments That Are Tailored To Work Well Digitally.The Latest Version Of Microsoft Excel 2016 Is Featured Throughout. All Screen Shots And Instructions Throughout This Edition Reflect The Latest Version Of Excel To Prepare Your Students With The Contemporary Skills They Will Need.Expanded Discussion Emphasizes Good Decision Making. This Key Discussion In Chapter 1 Clearly Defines Good Decision Making While Updated Examples Highlight Successful Applications Of Business Analytics In Large Organizations.New Coverage Highlights The Line Balancing Problem. New, Key Material In Chapter 6 (Integer Linear Programming) Provides A New Example Of Integer Programming That Is Interesting, Challenging, And Practical.Major Updates Reflect Changes In The Xlminer Platform Software. Coverage In Chapter 10 (Data Mining) Now Also Covers Roc And Auc Graphs As Well As The Concepts Of Precision, Recall, Specificity, And The F1 Score.New And Revised End-Of-Chapter Questions Check Reader Understanding. New Question And Application Opportunities Throughout The Book Ensure Students Understand Key Principles.Updated Content Reflects Microsoft® Office Excel® 2016. This Timely Coverage Provides Students With The Most Current Information For Dealing With Key Business Analytics Decision Making Problems.Algebraic Formulations And Spreadsheets Are Used Side-By-Side To Help Develop Conceptual Thinking Skills. Step-By-Step Instructions And Numerous Annotated Screenshots Make Examples Easy To Follow And Understand.Analytic Solver Platform Provides Hands-On Experience With State-Of-The-Art Business Analytics Software. Powerful Tools In The Analytic Solver Platform Enable Students To Perform Optimization And Simulation And Work With Decision Trees In Excel. This Tool Also Provides The Capabilities For Performing Simulation Optimization And Robust Optimization Methods.Xlminer Platform Offers A Complete Suite Of Tools For Hands-On Experience. This Leading Business Analytics Software Provides A Variety Of Data Mining Tools And Techniques Including Data Import And Cleansing, Data Exploration And Visualization, Feature Selection, Clustering, Affinity Analysis. Students Also Find A Variety Of Techniques For Predictive Analytics Including Discriminant Analysis, Neural Networks, Logistic Regression, Classification And Regression Trees, K-Nearest Neighbor, Naïve Bayes, And Times-Series Analysis.

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