Contents
About This Book
About These Authors
Acknowledgments
Chapter 1 Introduction
Two Questions Organizations Need to Ask
Return on Investment (ROI)
Culture Change
Business Intelligence
Clarification
Book Focus
Introductory Statistics Courses
Practical Statistical Study
Plan Perform, Analyze, Reflect (PPAR) Cycle
References
Chapter 2 Statistics Review
Always Take a Random and Representative Sample
Statistics Is Not an Exact Science
Understand a Z Score
Understand the Central Limit Theorem
Understand One-Sample Hypothesis Testing and p-Values
Many Approaches/Techniques Are Correct, and a Few Are Wrong
Chapter 3 Introduction to Multivariate Data
Multivariate Data and Multivariate Data Analysis
Using Tables to Explore Multivariate Data
Using Graphs to Explore Multivariate Data
Chapter 4 Regression and ANOVA Review
Regression
Simple Regression
Multiple Regression
Regression with Categorical Data
ANOVA
One-way ANOVA
Testing Statistical Assumptions
Testing for Differences
Two-way ANOVA
References
Chapter 5 Logistic Regression
Dependence Technique: Logistic Regression
The Linear Probability Model (LPM)
The Logistic Function
Example: toylogistic.jmp
Odds Ratios in Logistic Regression
A Logistic Regression Statistical Study
References
Exercises
Chapter 6 Principal Components Analysis
Principal Component
Dimension Reduction
Discovering Structure in The Data
Exercises
Chapter 7 Cluster Analysis
Hierarchical Clustering
Using Clusters in Regression
K-means Clustering
K-means versus Hierarchical Clustering
References
Exercises
Chapter 8 Decision Trees
An Example of Classification Trees
An Example of a Regression Tree
References
Exercises
Chapter 9 Neural Networks
Validation Methods
Hidden Layer Structure
Fitting Options
Data Preparation
An Example
Summary
References
Exercises
Chapter 10 Model Comparison
Model Comparison with Continuous Dependent Variable
Model Comparison with Binary Dependent Variable
Model Comparison Using the Lift Chart
Train, Validate, and Test
References
Exercises
Chapter 11 Telling the Statistical Story
From Multivariate Data to the Modeling Process
What Is Data Mining?
A Framework for Predictive Analytics Techniques
The Goal, Tasks, and Phases of Predictive Analytics
References
Appendix Data Sets
Smaller Data Sets
Large Case Data Sets
Index