The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.
Chapter 1.- Introduction to Predictive Analytics.- 1.1 Predictive
Analytics in Action.- 1.2 Analytics Landscape.- 1.3 Analytics.- 1.3.2
Predictive Analytics.- 1.4 Regression Analysis.- 1.5 Machine Learning
Techniques.- 1.6 Predictive Analytics Model.- 1.7 Opportunities in
Analytics.- 1.8 Introduction to the Automobile Insurance Claim Fraud
Example.- 1.9
Chapter Summary.- References.
Chapter 2.- Know Your Data
Data Preparation.- 2.1 Classification of Data.- 2.1.1 Qualitative versus
Quantitative.- 2.1.2 Scales of Measurement.- 2.2. Data Preparation Methods..-
2.2.1 Inconsistent Formats.- 2.2.2 Missing Data.- 2.2.3 Outliers.- 2.2.4
Other Data Cleansing Considerations.- 2.3 Data Sets and Data Partitioning.-
2.4 SAS Enterprise Miner Model Components.- 2.4.1 Step
1. Create Three of
the Model Components.- 2.4.2 Step
2. Import an Excel File and Save as a SAS
File.- 2.4.3 Step 3. Create the Data Source.- 2.4.4 Step
4. Partition the
Data Source.- 2.4.5 Step 5 Data Exploration.-2.4.6 Step 6 Missing Data.-
2.4.7 Step
7. Handling Outliers.- 2.4.8 Step
8. Categorical Variables with
Too Many Levels.- 2.5
Chapter Summary.- References.
Chapter 3.- What do
Descriptive Statistics Tell Us.- 3.1 Descriptive Analytics.- 3.2 The Role of
the Mean, Median and Mode.- 3.3 Variance and Distribution.- 3.4 The Shape of
the Distribution.- 3.4.2 Kurtosis.- 3.5 Covariance and Correlation.- 3.6
Variable Reduction.- 3.6.1 Variable Clustering.- 3.6.2 Principal Component
Analysis.- 3.7 Hypothesis Testing.- 3.8 Analysis of Variance (ANOVA).- 3.9
Chi Square.-
3. Fit Statistics.-
3. Stochastic Models.- 3.12
Chapter
Summary.- References.
Chapter 4.- Predictive Models Using Regression.- 4.1
Regression.- 4.1.1 Classical assumptions.- 4.2 Ordinary Least Squares.- 4.3
Simple Linear Regression.- 4.3.1 Determining Relationship Between Two
Variables.- 4.3.2 Line of Best Fit and Simple Linear Regression Equation.-
4.4 Multiple Linear Regression.- 4.4.1 Metrics to Evaluate the Strength of
the Regression Line.- 4.3.2 Best-fit model.- 4.3.3 Selection of Variables in
Regression.- 4.5 Principal Component Regression.- 4.5.1 Principal Component
Analysis Revisited.- 4.5.2 Principal Component Regression.- 4.6 Partial Least
Squares.- 4.7 Logistic Regression.- 4.7.1 Binary Logistic Regression.- 4.7.2
Examination of Coefficients.- 4.7.3 Multinomial Logistic Regression.- 4.7.4
Ordinal Logistic Regression.- 4.8 Implementation of Regression in SAS
Enterprise Miner.- 4.8.1 Regression Node Train Properties: Class Targets.-
4.8.2 Regression Node Train Properties: Model Options.- 4.8.3 Regression Node
Train Properties: Model Selection.- 4.9 Implementation of Two-Factor
Interaction and Polynomial Terms.- 4.9.1 Regression Node Train Properties:
Equation.-
4. DMINE Regression in SAS Enterprise Miner.- 4..1 DMINE
Properties.- 4..2 DMINE Results.-
4. Partial Least Squares Regression in SAS
Enterprise Miner.- 4..1 Partial Least Squares Properties.- 4..2 Partial
Least Squares Results.-
4. Least Angles Regression in SAS Enterprise Miner.-
4..1 Least Angle Regression Properties.- 4..2 Least Angles Regression
Results.-
4. Other Forms of Regression.-
4.
Chapter Summary.- References.-
Chapter 5.- The Second of the Big Three Decision Trees.- 5.1 What is a
Decision Tree?.- 5.2 Creating a Decision Tree.- 5.3 Data Partitions and
Decision Trees.- 5.4 Creating a Decision Tree Using SAS Enterprise Miner.-
The key properties include:.- Subtree Properties.- 5.4.1 Overfitting.- 5.5
Creating an Interactive Decision Tree using SAS Enterprise Miner .- 5.6
Creating a Maximal Decision Tree using SAS Enterprise Miner .- 5.7
Chapter
Summary.- References.
Chapter 6.- The Third of the Big Three - Neural
Networks.- 6.1 What is a Neural Network?.- 6.2 History of Neural Networks.-
6.3 Components of a Neural Network.- 6.4 Neural Network Architectures.- 6.5
Training a Neural Network.- 6.6 Radial Basis Function Neural Networks.- 6.7
Creating a Neural Network using SAS Enterprise MinerÔ.- 6.8 Using SAS
Enterprise MinerÔ to Automatically Generate a Neural Network.- 6.9 Explaining
a Neural Network.-
6.
Chapter Summary.- References.
Chapter 7.- Model
Comparisons and Scoring.- 7.1 Beyond the Big.- 7.2 Gradient Boosting.- 7.3
Ensemble Models.- 7.4 Random Forests.- 7.6 Two-Stage Model.- 7.7 Comparing
Predictive Models.- 7.7.1 Evaluating Fit Statistics Which Model Do We
Use?.- 7.8 Using Historical Data to Predict the Future Scoring.- 7.8.1
Analyzing and Reporting Results.- 7.8.2 Save Data Node.- 7.8.3 Reporter
Node.- 7.9 The Importance of Predictive Analytics.- 7.9.1 What Should We
Expect for Predictive Analytics in the Future?.-
7.
Chapter Summary.-
References.
Chapter 8.- finding Associations in Data through Cluster
Analysis.- 8.1 Applications and Uses of Cluster Analysis.- 8.2 Types of
Clustering Techniques.- 8.3 Hierarchical Clustering.- 8.3.1 Agglomerative
Clustering.- 8.3.2 Divisive Clustering.- 8.3.3 Agglomerative vs Divisive
Clustering.- 8.4 Non-hierarchical clustering.- 8.4.1 K-means Clustering.-
8.4.2 Initial Centroid Selection.- 8.4.3 Determining the Number of Clusters.-
8.4.4 Evaluating your clusters.- 8.5 Hierarchical vs Nonhierarchical.- 8.6
Cluster Analysis using SAS Enterprise Miner.- 8.6.1 Cluster Node.- 8.6.2
Additional Key Properties of the Cluster Node.- 8.7 Applying Cluster Analysis
to the Insurance Claim Fraud Data Set.- 8.8
Chapter Summary.- References.- .-
Chapter 9.- 9.1 What is Text Analytics?.- 9.2 Information Retrieval.- 9.3
Text Parsing.- 9.4 Zipfs Law.- 9.5 Text Filter.- 9.6 Text Cluster.- 9.7 Text
Topic.- 9.8 Text Rule Builder.- 9.9 Text Profile.-
9.
Chapter Summary.-
Discussion Questions.- References.- Appendix A.- Data Dictionary for the
Automobile Insurance Claim Fraud Data Example.- Appendix B.- Can you Predict
the Money Laundering Cases?.- B.1 Introduction.- B.2. Business Problem.- B.3.
Analyze Data.- B.4. Development and Optimization of a Best Fit Model.- B.5.
Final Report.- References.
Richard V. McCarthy (DBA, Nova Southeastern University, MBA, Western New England College) is a professor of Computer Information Systems at the School of Business, Quinnipiac University. Prior to this, Dr. McCarthy was an associate professor of management information systems at Central Connecticut State University. He has twenty years of experience within the insurance industry and has held a Charter Property Casualty Underwriter (CPCU) designation since 1991. He has authored numerous research articles and contributed to several textbooks. He has served as the associate dean of the School of Business, the MBA director, and the director of the Master of Science in Business Analytics program. In 2019, he was awarded the Computer Educator of the Year from the International Association for Computer Information Systems.
Wendy Ceccucci (PhD and MBA, Virginia Polytechnic University) is a Professor and Chair of Computer Information Systems at QuinnipiacUniversity. Her teaching areas include business analytics and programming. She is the past president of the Education Special Interest Group (EDSIG) of the Association for Information Technology Professionals (AITP) and past Associate Editor of the Information Systems Education Journal (ISEDJ). Her research interests include Information Systems Pedagogy.
Mary McCarthy (DBA, Nova Southeastern University, MBA, University of Connecticut) is a professor and chair of Accounting, Central Connecticut State University. She has twenty years of financial reporting experience and has served as the controller for a Fortune 50 industry organization. She holds a CPA and CFA designation. She has authored numerous research articles.
