Statistical Modelling
Faculteit  Science and Engineering 
Jaar  2022/23 
Vakcode  WBMA02805 
Vaknaam  Statistical Modelling 
Niveau(s)  bachelor 
Voertaal  Engels 
Periode  semester I b 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Statistical Modelling  
Leerdoelen  At the end of the course, the student is able to: 1. state and apply a taxonomy of Generalized Linear Models (GLM) in terms of measurement levels of the response, link function, design of (repeated) data collection related to the exponential family of distributions; 2. mathematically derive and computationally implement maximum likelihood estimation of a GLM by iterative weighted least squares using the R programming environment; 3. integrate the testing of medical/economic hypotheses to appropriate statistical model fitting as well as their selection by likelihood ratio or the Akaike Information Criterion; 4. assess the quality of model estimation in various applied scientific fields. 

Omschrijving  Taking the exponential family of distributions as a starting point, various generalized linear models are developed to model nonGaussian response data. The aim is to describe this response in terms of a number of explanatory variables. Estimated models are evaluated and interpreted in order to test hypotheses from various scientific fields. In this course we will introduce estimation principles underlying generalized linear modeling. This course covers the following themes:  Exponential family  Maximum likelihood, Deviance, likelihood ratio  Iterative weighted least squares  Model Design  Goodness of fit, residuals  Linear, logistic (multinomial), Poisson regression, Loglinear models, Survival Analysis 

Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Opdracht (ASM), Werkcollege (T)
(4 hours lectures, 2 hours tutorials/computer lab) 

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE)
(There are three homework assignment grades (H1, H2, H3) and a written exam grade (E). The final grade (F) is computed via: If E >=5.0 then F = 0.1 x H1 + 0.1 x H2 + 0.1 x H3 + 0.7 x E If E < 5.0 then F = ET, The course is passed if final grade F>=5.5 For the resit we ignore H1, H2 and H3 and apply: F = resit exam grade) 

Vaksoort  bachelor  
Coördinator  prof. dr. M.A. Grzegorczyk  
Docent(en)  prof. dr. M.A. Grzegorczyk ,dr. W.P. Krijnen  
Verplichte literatuur 


Entreevoorwaarden  This course builds on Statistics in the second year. Prior knowledge assumed: Students have a background in the courses Probability Theory, Statistics, and Statistical Reasoning.  
Opmerkingen  This course was registered last year with course code WISM08  
Opgenomen in 
