Book description
Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis.First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP .
Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format:
- an add-in for Microsoft Excel
- Graph Builder
- dirty data
- visualization
- regression
- ANOVA
- logistic regression
- principal component analysis
- LASSO
- elastic net
- cluster analysis
- decision trees
- k-nearest neighbors
- neural networks
- bootstrap forests
- boosted trees
- text mining
- association rules
- model comparison
With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis.
This book is part of the SAS Press program.
Table of contents
- 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
Product information
- Title: Fundamentals of Predictive Analytics with JMP, Second Edition
- Author(s):
- Release date: December 2017
- Publisher(s): SAS Institute
- ISBN: 9781629608013
You might also like
book
Fundamentals of Predictive Analytics with JMP
Fundamentals of Predictive Analytics with JMP bridges the gap between courses on basic statistics, which focus …
book
Data Mining and Predictive Analytics, 2nd Edition
Learn methods of data analysis and their application to real-world data sets This updated second edition …
book
Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science
Master predictive analytics, from start to finish Start with strategy and management Master methods and build …
book
Predictive Analytics and Data Mining
Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy …