copyright © 2010, sas institute inc. all rights reserved. applied analytics using sas ® enterprise...

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Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Page 1: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

Copyright © 2010, SAS Institute Inc. All rights reserved.

Applied Analytics Using SAS® Enterprise Miner™

Page 2: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

2

Chapter 1 Introduction1.1 Introduction to SAS Enterprise Miner

1.2 Solutions

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Page 3: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 2 Accessing and Assaying Prepared Data2.1 Introduction

2.2 Creating a SAS Enterprise Miner Project, Library, and Diagram

2.3 Defining a Data Source

2.4 Exploring a Data Source

2.5 Chapter Summary

2.6 Solutions

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Page 4: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 3 Introduction to Predictive Modeling: Decision Trees3.1 Introduction

3.2 Cultivating Decision Trees

3.3 Optimizing the Complexity of Decision Trees

3.4 Understanding Additional Diagnostic Tools (Self-Study)

3.5 Autonomous Tree Growth Options (Self-Study)

3.6 Chapter Summary

3.7 Solutions

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Page 5: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 4 Introduction to Predictive Modeling: Regressions4.1 Introduction

4.2 Selecting Regression Inputs

4.3 Optimizing Regression Complexity

4.4 Interpreting Regression Models

4.5 Transforming Inputs

4.6 Categorical Inputs

4.7 Polynomial Regressions (Self-Study)

4.8 Chapter Summary

4.9 Solutions

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Page 6: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 5 Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools5.1 Introduction

5.2 Input Selection

5.3 Stopped Training

5.4 Other Modeling Tools (Self-Study)

5.5 Chapter Summary

5.6 Solutions

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Page 7: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 6 Model Assessment6.1 Model Fit Statistics

6.2 Statistical Graphics

6.3 Adjusting for Separate Sampling

6.4 Profit Matrices

6.5 Chapter Summary

6.6 Solutions

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Page 8: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 7 Model Implementation7.1 Introduction

7.2 Internally Scored Data Sets

7.3 Score Code Modules

7.4 Chapter Summary

7.5 Solutions to Exercises

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Page 9: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 8 Introduction to Pattern Discovery8.1 Introduction

8.2 Cluster Analysis

8.3 Market Basket Analysis (Self-Study)

8.4 Chapter Summary

8.5 Solutions

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Page 10: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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Chapter 9 Special Topics9.1 Introduction

9.2 Ensemble Models

9.3 Variable Selection

9.4 Categorical Input Consolidation

9.5 Surrogate Models

9.6 SAS Rapid Predictive Modeler

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Page 11: Copyright © 2010, SAS Institute Inc. All rights reserved. Applied Analytics Using SAS ® Enterprise Miner™

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SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2011 SAS Institute Inc. Cary, NC, USA. All rights reserved. Prepared 18OCT2011.