دانلود کتاب Fundamentals of Predictive Analytics with JMP, 3rd Edition

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About This Book
About These Authors
Acknowledgments
Chapter 1: Introduction
Historical Perspective
Two Questions Organizations Need to Ask
Return on Investment
Cultural Change
Business Intelligence and Business Analytics
Introductory Statistics Courses
The Problem of Dirty Data
Added Complexities in Multivariate Analysis
Practical Statistical Study
Obtaining and Cleaning the Data
Understanding the Statistical Study as a Story
The Plan-Perform-Analyze-Reflect Cycle
Using Powerful Software
Framework and Chapter Sequence
Chapter 2: Statistics Review
Introduction
Fundamental Concepts 1 and 2
FC1: Always Take a Random and Representative Sample
FC2: Remember That Statistics Is Not an Exact Science
Fundamental Concept 3: Understand a Z-Score
Fundamental Concept 4
FC4: Understand the Central Limit Theorem
Learn from an Example
Fundamental Concept 5
Understand One-Sample Hypothesis Testing
Consider p-Values
Fundamental Concept 6:
Understand That Few Approaches/Techniques Are Correct—Many Are Wrong
Three Possible Outcomes When You Choose a Technique
Chapter 3: Dirty Data
Introduction
Data Set
Error Detection
Outlier Detection
Approach 1
Approach 2
Missing Values
Statistical Assumptions of Patterns of Missing
Conventional Correction Methods
The JMP Approach
Example Using JMP
General First Steps on Receipt of a Data Set
Exercises
Chapter 4: Data Discovery with Multivariate Data
Introduction
Use Tables to Explore Multivariate Data
PivotTables
Tabulate in JMP
Use Graphs to Explore Multivariate Data
Graph Builder
Scatterplot
Explore a Larger Data Set
Trellis Chart
Bubble Plot
Explore a Real-World Data Set
Use Graph Builder to Examine Results of Analyses
Generate a Trellis Chart and Examine Results
Use Dynamic Linking to Explore Comparisons in a Small Data Subset
Return to Graph Builder to Sort and Visualize a Larger Data Set
Chapter 5: Regression and ANOVA
Introduction
Regression
Perform a Simple Regression and Examine Results
Understand and Perform Multiple Regression
Understand and Perform Regression with Categorical Data
Analysis of Variance
Perform a One-Way ANOVA
Evaluate the Model
Perform a Two-Way ANOVA
Exercises
Chapter 6: Logistic Regression
Introduction
Dependence Technique
The Linear Probability Model
The Logistic Function
A Straightforward Example Using JMP
Create a Dummy Variable
Use a Contingency Table to Determine the Odds Ratio
Calculate the Odds Ratio
A Realistic Logistic Regression Statistical Study
Understand the Model-Building Approach
Run Bivariate Analyses
Run the Initial Regression and Examine the Results
Convert a Continuous Variable to Discrete Variables
Produce Interaction Variables
Validate and Use the Model
Exercises
Chapter 7: Principal Components Analysis
Introduction
Basic Steps in JMP
Produce the Correlations and Scatterplot Matrix
Create the Principal Components
Run a Regression of y on Prin1 and Excluding Prin2
Understand Eigenvalue Analysis
Conduct the Eigenvalue Analysis and the Bartlett Test
Verify Lack of Correlation
Dimension Reduction
Produce the Correlations and Scatterplot Matrix
Conduct the Principal Component Analysis
Determine the Number of Principal Components to Select
Compare Methods for Determining the Number of Components
Discovery of Structure in the Data
A Straightforward Example
An Example with Less Well Defined Data
Exercises
Chapter 8: Least Absolute Shrinkage and Selection Operator and Elastic Net
Introduction
The Importance of the Bias-Variance Tradeoff
Ridge Regression
Least Absolute Shrinkage and Selection Operator
Perform the Technique
Examine the Results
Refine the Results
Elastic Net
Perform the Technique
Examine the Results
Compare with LASSO
Exercises
Chapter 9: Cluster Analysis
Introduction
Example Applications
An Example from the Credit Card Industry
The Need to Understand Statistics and the Business Problem
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
Understand the Benefits and Drawbacks of the Method
Choose k and Determine the Clusters
Perform k-Means Clustering
Change the Number of Clusters
Create a Profile of the Clusters with Parallel Coordinate Plots
Perform Iterative Clustering
Score New Observations
K-Means Clustering versus Hierarchical Clustering
Exercises
Chapter 10: Decision Trees
Introduction
Benefits and Drawbacks
Definitions and an Example
Theoretical Questions
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
Understand How Regression Trees Work
Restart a Regression Driven by Practical Questions
Use Column Contributions and Leaf Reports for Large Data Sets
Exercises
Chapter 11: k-Nearest Neighbors
Introduction
Example—Age and Income as Correlates of Purchase
The Way That JMP Resolves Ties
The Need to Standardize Units of Measurement
k-Nearest Neighbors Analysis
Perform the Analysis
Make Predictions for New Data
k-Nearest Neighbor for Multiclass Problems
Understand the Variables
Perform the Analysis and Examine Results
The k-Nearest Neighbor Regression Models
Perform a Linear Regression as a Basis for Comparison
Apply the k-Nearest Neighbors Technique
Compare the Two Methods
Make Predictions for New Data
Limitations and Drawbacks of the Technique
Exercises
Chapter 12: Neural Networks
Introduction
Drawbacks and Benefits
A Simplified Representation
A More Realistic Representation
Understand Validation Methods
Holdback Validation
k-fold Cross-Validation
Understand the Hidden Layer Structure
A Few Guidelines for Determining Number of Nodes
Practical Strategies for Determining Number of Nodes
The Method of Boosting
Understand Options for Improving the Fit of a Model
Complete the Data Preparation
Use JMP on an Example Data Set
Perform a Linear Regression as a Baseline
Perform the Neural Network Ten Times to Assess Default Performance
Boost the Default Model
Compare Transformation of Variables and Methods of Validation
Exercises
Chapter 13: Bootstrap Forests and Boosted Trees
Introduction
Bootstrap Forests
Understand Bagged Trees
Perform a Bootstrap Forest
Perform a Bootstrap Forest for Regression Trees
Boosted Trees
Understand Boosting
Perform Boosting
Perform a Boosted Tree for Regression Trees
Use Validation and Training Samples
Exercises
Chapter 14: Model Comparison
Introduction
Perform a Model Comparison with Continuous Dependent Variable
Understand Absolute Measures
Understand Relative Measures
Understand Correlation between Variable and Prediction
Explore the Uses of the Different Measures
Perform a Model Comparison with Binary Dependent Variable
Understand the Confusion Matrix and Its Limitations
Understand True Positive Rate and False Positive Rate
Interpret Receiving Operator Characteristic Curves
Compare Two Example Models Predicting Churn
Perform a Model Comparison Using the Lift Chart
Train, Validate, and Test
Perform Stepwise Regression
Examine the Results of Stepwise Regression
Compute the MSE, MAE, and Correlation
Examine the Results for MSE, MAE, and Correlation
Understand Overfitting from a Coin-Flip Example
Use the Model Comparison Platform
Exercises
Chapter 15: Text Mining
Introduction
Historical Perspective
Unstructured Data
Developing the Document Term Matrix
Understand the Tokenizing Stage
Understand the Phrasing Stage
Understand the Terming Stage
Observe the Order of Operations
Developing the Document Term Matrix with a Larger Data Set
Generate a Word Cloud and Examine the Text
Examine and Group Terms
Add Frequent Phrases to List of Terms
Parse the List of Terms
Using Multivariate Techniques
Perform Latent Semantic Analysis
Perform Topic Analysis
Perform Cluster Analysis
Using Predictive Techniques
Perform Primary Analysis
Perform Logistic Regressions
Exercises
Chapter 16: Market Basket Analysis
Introduction
Association Analyses
Examples
Understand Support, Confidence, and Lift
Association Rules
Support
Confidence
Lift
Use JMP to Calculate Confidence and Lift
Use the A Priori Algorithm for More Complex Data Sets
Form Rules and Calculate Confidence and Lift
Analyze a Real Data Set
Perform Association Analysis with Default Settings
Reduce the Number of Rules and Sort Them
Examine Results
Target Results to Take Business Actions
Exercises
Chapter 17: Statistical Storytelling
The Path from Multivariate Data to the Modeling Process
Early Applications of Data Mining
Numerous JMP Customer Stories of Modern Applications
Definitions of Data Mining
Data Mining
Predictive Analytics
A Framework for Predictive Analytics Techniques
The Goal, Tasks, and Phases of Predictive Analytics
The Difference between Statistics and Data Mining
SEMMA
References
Index