1. Introduction to advanced statistical analysis
- Taxonomy of models
- Overview of supervised models
- Overview of models to create natural groupings
2. 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
3. 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
4. Predicting categorical targets with Nearest Neighbor Analysis
- Nearest Neighbors Analysis basics
- Key issues in Nearest Neighbor Analysis
- Assess model fit
5. Predicting categorical targets with Discriminant Analysis
- Discriminant Analysis basics
- The Discriminant Analysis model
- Assumptions of Discriminant Analysis
- Validate the solution
6. 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
7. Predicting categorical targets with Decision Trees
- Decision Trees basics
- Explore CHAID
- Explore C&RT
- Compare Decision Trees methods
8. Introduction to Survival Analysis
- Survival Analysis basics
- Kaplan-Meier Analysis
- Assumptions of Kaplan-Meier Analysis
- Cox Regression
- Assumptions of Cox Regression
9. Introduction to Generalized Linear Models
- Generalized Linear Models basics
- Available distributions
- Available link functions
10. Introduction to Linear Mixed Models
- Linear Mixed Models basics
- Hierarchical Linear Models
- Modeling strategy
- Assumptions of Linear Mixed Models