Chapter 1
Introduction to Predictive Analytics1
1.1 Predictive Analytics in Action2
1.2 Analytics Landscape8
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 239
Know Your Data - Data Preparation39
2.1 Classification of Data40
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(TM) 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 35
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 Testing2
3.8 Analysis of Variance (ANOVA)5
3.9 Chi Square6
3. Fit Statistics8
3. Stochastic Models9
3.12 Chapter Summary1
References2
Chapter 4
Predictive Models Using Regression5
4.1 Regression6
4.1.1 Classical assumptions7
4.2 Ordinary Least Squares8
4.3 Simple Linear Regression8
4.3.1 Determining Relationship Between Two Variables9
4.3.2 Line of Best Fit and Simple Linear Regression Equation9
4.4 Multiple Linear Regression1
4.4.1 Metrics to Evaluate the Strength of the Regression Line2
4.3.2 Best-fit model3
4.3.3 Selection of Variables in Regression3
4.5 Principal Component Regression5
4.5.1 Principal Component Analysis Revisited5
4.5.2 Principal Component Regression6
4.6 Partial Least Squares6
4.7 Logistic Regression7
4.7.1 Binary Logistic Regression8
4.7.2 Examination of Coefficients1
4.7.3 Multinomial Logistic Regression3
4.7.4 Ordinal Logistic Regression3
4.8 Implementation of Regression in SAS Enterprise Miner(TM)3
4.8.1 Regression Node Train Properties: Class Targets4
4.8.2 Regression Node Train Properties: Model Options5
4.8.3 Regression Node Train Properties: Model Selection6
4.9 Implementation of Two-Factor Interaction and Polynomial Terms8
4.9.1 Regression Node Train Properties: Equation8 4. DMINE Regression in SAS Enterprise Miner(TM)0
4..1 DMINE Properties0
4..2 DMINE Results2
4. Partial Least Squares Regression in SAS Enterprise Miner(TM)4
4..1 Partial Least Squares Properties4
4..2 Partial Least Squares Results7
4. Least Angles Regression in SAS Enterprise Miner(TM)9
4..1 Least Angle Regression Properties0
4..2 Least Angles Regression Results1
4. Other Forms of Regression4
4. Chapter Summary6
References9 Chapter 5
The Second of the Big Three - Decision Trees1
5.1 What is a Decision Tree?2
5.2 Creating a Decision Tree4
5.3 Data Partitions and Decision Trees6
About the Author:
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 Quinnipiac University. 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.
