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 problemsolving skills that you need to perform realworld 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, stepbystep, 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 exampledriven format:
 an addin for Microsoft Excel
 Graph Builder
 dirty data
 visualization
 regression
 ANOVA
 logistic regression
 principal component analysis
 LASSO
 elastic net
 cluster analysis
 decision trees
 knearest 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 realworld, problemsolving 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 ZScore
 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 RealWorld 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
 KMeans Clustering
 KMeans 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: kNearest Neighbors
 Introduction
 kNearest Neighbors Analysis
 kNearest Neighbor for Multiclass Problems
 The kNearest 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
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