Introduction to advanced statistical analysis
• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupings
Grouping variables with Factor Analysis and Principal Components Analysis
• Factor Analysis basics
• Principal Components basics
• Assumptions of Factor Analysis
• Key issues in Factor Analysis
• Use Factor and component scores
Grouping cases with Cluster Analysis
• Cluster Analysis basics
• Key issues in Cluster Analysis
• K-Means Cluster Analysis
• Assumptions of K-Means Cluster Analysis
• TwoStep Cluster Analysis
• Assumptions of TwoStep Cluster Analysis
Predicting categorical targets with Nearest Neighbor Analysis
• Nearest Neighbors Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fit
Predicting categorical targets with Discriminant Analysis
• Discriminant Analysis basics
• The Discriminant Analysis model
• Assumptions of Discriminant Analysis
• Validate the solution
Predicting categorical targets with Logistic Regression
• Binary Logistic Regression basics
• The Binary Logistic Regression model
• Multinomial Logistic Regression basics
• Assumptions of Logistic Regression procedures
• Test hypotheses
• ROC curves
Predicting categorical targets with Decision Trees
• Decision Trees basics
• Explore CHAID
• Explore C&RT
• Compare Decision Trees methods
Introduction to Survival Analysis
• Survival Analysis basics
• Kaplan-Meier Analysis
• Assumptions of Kaplan-Meier Analysis
• Cox Regression
• Assumptions of Cox Regression
Introduction to Generalized Linear Models
• Generalized Linear Models basics
• Available distributions
• Available link functions
Introduction to Linear Mixed Models
• Linear Mixed Models basics
• Hierarchical Linear Models
• Modeling strategy
• Assumptions of Linear Mixed Models
