Regression Analysis


Timing
4 hours weekly (3+1) during Summer Terms.

Lecturer
Herwig Friedl .

Teaching Aims
To introduce into the ideas of statistical modelling. Starting from a simple linear regression framework, theoretical results for multiple linear regression models will be developed. To improve the student's ability to apply the theory in exploratory data analysis and further in statistical modelling. To introduce the statistical software package "R".

Learning Objectives
Students should be able to analyse specific data problems by their own. They should be also familiar with the concepts of exploratory data analysis and should find and verify relationships in the data by applying the ideas of statistical modelling.

Contents
(1) Simple Linear Regression Models. (2) Inference in Regresion Models. (3) Diagnostics. (4) Simultaneous Inference (5) Matrix Algebra. (6) Multiple Linear Regression. (7) Extra Sums of Squares. (8) Qualitative Predictors. (9) Diagnostics/Residuals. (10) Nonparametric (Smooth) Regression Models. (11) Variable Selection Techniques.

Prerequisites
Probability, Mathematical Statistics.

Classes

Homework Assignments

Download Datasets:

Download R Macros and R Statements useful to analyze the data:

Download Slides on Linear Regression Analysis


This page last modified March 6th, 2017 by Herwig Friedl (hfriedl@tugraz.at).