Timing from 5.30 pm till 8.00 pm on
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 statistical software packages like 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
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Download R Macros and R Statements useful to analyze the data: