Course Outline
1. Introduction to advanced statistical analysis
- Taxonomy
of models
- Overview
of supervised models
- Overview
of models to create natural groupings
2. Group variables: Factor Analysis and Principal Components
Analysis
- Factor
Analysis basics
- Principal
Components basics
- Assumptions
of Factor Analysis
- Key
issues in Factor Analysis
- Improve
the interpretability
- Use
Factor and component scores
3. Group similar cases: 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. Predict categorical targets with Nearest Neighbor Analysis
- Nearest
Neighbor Analysis basics
- Key
issues in Nearest Neighbor Analysis
- Assess
model fit
5. Predict categorical targets with Discriminant Analysis
- Discriminant
Analysis basics
- The
Discriminant Analysis model
- Core
concepts of Discriminant Analysis
- Classification
of cases
- Assumptions
of Discriminant Analysis
- Validate
the solution
6. Predict categorical targets with Logistic Regression
- Binary
Logistic Regression basics
- The
Binary Logistic Regression model
- Multinomial
Logistic Regression basics
- Assumptions
of Logistic Regression procedures
- Testing
hypotheses
7. Predict categorical targets with Decision Trees
- Decision
Trees basics
- Validate
the solution
- Explore
CHAID
- Explore
CRT
- Comparing
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
- Hierachical
Linear Models
- Modeling
strategy
- Assumptions
of Linear Mixed Models