Acknowledgments
by B. D. McCullough, Ron Klimberg
Fundamentals of Predictive Analytics with JMP, Second Edition
- About This Book
- About These Authors
- Acknowledgments
- Chapter 1: Introduction
- Historical Perspective
- Two Questions Organizations Need to Ask
- Business Intelligence and Business Analytics
- Introductory Statistics Courses
- Practical Statistical Study
- Framework and Chapter Sequence
- Chapter 2: Statistics Review
- Introduction
- Fundamental Concepts 1 and 2
- Fundamental Concept 3: Understand a Z-Score
- Fundamental Concept 4
- Fundamental Concept 5
- Fundamental Concept 6:
- Chapter 3: Dirty Data
- Introduction
- Data Set
- Error Detection
- Outlier Detection
- General First Steps on Receipt of a Data Set
- Exercises
- Chapter 4: Data Discovery with Multivariate Data
- Introduction
- Use Tables to Explore Multivariate Data
- Use Graphs to Explore Multivariate Data
- Explore a Larger Data Set
- Explore a Real-World Data Set
- Chapter 5: Regression and ANOVA
- Introduction
- Regression
- Analysis of Variance
- Exercises
- Chapter 6: Logistic Regression
- Introduction
- A Straightforward Example Using JMP
- A Realistic Logistic Regression Statistical Study
- Exercises
- Chapter 7: Principal Components Analysis
- Introduction
- Basic Steps in JMP
- Dimension Reduction
- Discovery of Structure in the Data
- Exercises
- Chapter 8: Least Absolute Shrinkage and Selection Operator and Elastic Net
- Introduction
- Least Absolute Shrinkage and Selection Operator
- Elastic Net
- Exercises
- Chapter 9: Cluster Analysis
- Introduction
- Hierarchical Clustering
- Understand the Dendrogram
- Understand the Methods for Calculating Distance between Clusters
- Perform a Hierarchal Clustering with Complete Linkage
- Examine the Results
- Consider a Scree Plot to Discern the Best Number of Clusters
- Apply the Principles to a Small but Rich Data Set
- Consider Adding Clusters in a Regression Analysis
- K-Means Clustering
- K-Means Clustering versus Hierarchical Clustering
- Exercises
- Chapter 10: Decision Trees
- Introduction
- Classification Trees
- Begin Tree and Observe Results
- Use JMP to Choose the Split That Maximizes the LogWorth Statistic
- Split the Root Node According to Rank of Variables
- Split Second Node According to the College Variable
- Examine Results and Predict the Variable for a Third Split
- Examine Results and Predict the Variable for a Fourth Split
- Examine Results and Continue Splitting to Gain Actionable Insights
- Prune to Simplify Overgrown Trees
- Examine Receiver Operator Characteristic and Lift Curves
- Regression Trees
- Exercises
- Chapter 11: k-Nearest Neighbors
- Introduction
- k-Nearest Neighbors Analysis
- k-Nearest Neighbor for Multiclass Problems
- The k-Nearest Neighbor Regression Models
- Limitations and Drawbacks of the Technique
- Exercises
- Chapter 12: Neural Networks
- Introduction
- Understand Validation Methods
- Understand the Hidden Layer Structure
- Understand Options for Improving the Fit of a Model
- Complete the Data Preparation
- Use JMP on an Example Data Set
- Exercises
- Chapter 13: Bootstrap Forests and Boosted Trees
- Introduction
- Bootstrap Forests
- Boosted Trees
- Exercises
- Chapter 14: Model Comparison
- Introduction
- Perform a Model Comparison with Continuous Dependent Variable
- Perform a Model Comparison with Binary Dependent Variable
- Perform a Model Comparison Using the Lift Chart
- Train, Validate, and Test
- Exercises
- Chapter 15: Text Mining
- Introduction
- Developing the Document Term Matrix
- Developing the Document Term Matrix with a Larger Data Set
- Using Multivariate Techniques
- Using Predictive Techniques
- Exercises
- Chapter 16: Market Basket Analysis
- Introduction
- Understand Support, Confidence, and Lift
- Use JMP to Calculate Confidence and Lift
- Analyze a Real Data Set
- Exercises
- Chapter 17: Statistical Storytelling
- The Path from Multivariate Data to the Modeling Process
- Definitions of Data Mining
- A Framework for Predictive Analytics Techniques
- The Goal, Tasks, and Phases of Predictive Analytics
- References
- Index
Acknowledgments
We would like to thank Jenny Jennings Foerst and Julie McAlpine Platt of SAS Press for providing editorial project support from start to finish. We would also like to thank Kathy Underwood of SAS Publishing for her copyediting assistance on the final manuscript.
Regarding technical reviews of the manuscript, we want to thank Mia Stephens, Dan Obermiller, Adam Morris, Sue Walsh, and Sarah Mikol of SAS Institute, as well as Russell Lavery, Majid Nabavi, and Donald N. Stengel, for their detailed reviews, which improved the final product. We also would like to thank Daniel Valente and Christopher Gotwalt of SAS Institute for their guidance and insight in writing the text mining chapter.
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