#### STAT 536

This course trains students in using statistical methods for modeling a response variable as a function of explanatory variables. Stat 535 (a prerequisite) covered linear models, and this course attempts to cover the complement set. At a minimum you will learn the derivation, computation, and application of the different methods on data.

#### Linear Regression

Review Linear Regression Models

#### Weighted Least Squares

Review Weighted Least Squares, Mixed Models

#### Bayesian

Bayesian Linear Regression

#### Measurement

Measurement Error Models

#### Linear Models

Generalized Linear Models (logistic)

#### Model Assessment

Model Assessment and Selection

#### Shrinkage Methods

Shrinkage Methods, Bias-Variance Tradeoff, Subset Selection

#### Local Regression

Local Regression (splines, smoothers)

#### GAM

Generalized Additive Models (GAM)

#### Tree-based Models

Tree-based Models, Random Forests

#### Boosting

Boosting, Bayesian Adaptive Regression Trees

#### p >> n

p >> n