Timing (each lecture is from 5:00 till 7:45 pm)

• May 5th: 2 Introduction, Motivation and Overview & 1 lecture
• May 6th: 2 lectures & 1 practical
• May 12th: 3 lectures
• May 19th: 2 lectures & 1 practical
• May 20th: 3 lectures
• May 26th: 2 lectures & 1 practical
• May 27th: 3 lectures

Lecturer
Herwig Friedl .

Teaching Aims
To give more emphasis on statistical modelling. Starting from the linear regression framework, results and techniques in the field of generalized linear 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 real 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) Linear Models (LMs): Recap of Results (2) Box-Cox Transformation Family: Extending the LM (3) Generalized Linear Models (GLMs): An Introduction (4) Linear Exponential Family (LEF): Properties and Members (5) GLMs: Parameter Estimates (6) GLMs: glm(.) Function (7) Gamma Regression Models (8) Logistic Regression (Binomial Responses) (9) Loglinear Model (Poisson Responses) (10) Multilevel Models

Download Slides:

• Introduction, Motivation and Overview
• Generalized Linear Models
• Multilevel Models

Download Practicals:

• Practical 1
• Practical 2
• Practical 3

Download Datasets:

• Aimu Data
• Aimu Data Definition File
• Bacteria Data
• Bacteria Data Definition File
• Black Cherry Trees Data
• Insects Data
• Titanic Data

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

• Regression by Simulation
• Analyze Black Cherry Trees Data
• Analyze Insects Data
• Analyze Budworm Data
• Analyze Cancer Remission Data
• Analyze Recurrence Data
• Analyze Micro-Organism Count Data