This entry describes more advanced statistical methods

• Generalized Linear Models
• Discriminant Function
• Time Series
• Factor Analysis
• Correspondence Analysis
• Multidimensional Scaling
• Cluster Analysis
• Tree-Based Models
• Bootstrapping
• Matrix Algebra 

EXCERT from http://www.statmethods.net/advstats/

Predictive models

Under predictive models, we have generalized linear models (include logistic regression, poisson regression, and survival analysis), discriminant function analysis (both linear and quadratic), and time series modeling.

Latent Variable Models

This includes factor analysis (principal components, exploratory and confirmatory factor analysis), correspondence analysis, and multidimensional scaling (metric and nonmetric).

Partitioning Methods

Cluster Analysis includes partitioning (k-means), hierarchical agglomerative, and model based approaches. Tree-Based methods (which could easily have gone under predictive models!) include classification and regression trees, random forests, and other partitioning methodologies.