Advanced Statistical Analysis Using IBM SPSS Statistics (V26) - SPVC

Course Outline

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