Ph.D. course Statistics III: Regression analysis

Prior knowledge

Statistics I: Basic Statistics or equivalent

Objective, including learning outcomes

The objective of the course is to give an overview of linear, nonlinear and nonparametric regression and general linear models. On completion of the course, the student will be able to: 
• describe regression models and general linear models including conditions and assumptions
• select an appropriate regression model for a given problem
• carry out a regression analysis
• interpret and evaluate results correctly and draw reasonable conclusions
• clearly and concisely communicate results and conclusions
• critically assess published results from regression analysis
• use statistical software for analysis

Content

The course will cover the following topics:
• Simple linear regression.
• Multiple linear regression.
• Nonlinear models. 
• Nonparametric regression.
• Regression with autocorrelated errors.
• General linear models (GLM).
• Generalized additive models (GAM).
• Analysis of residuals.