Generalized Linear Models (3563)
  
Coordinating lecturer :Prof. dr. Helena GEYS 
  
Co-lecturer :Prof. dr. Marc AERTS 
  
Member of the teaching team :Prof. dr. Ivy JANSEN 
 De heer Pieter GIESEN 

Language of instruction : English
Credits: 3,0
  
Period: semester 2 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Linear Models (3560) 5.0 stptn
 

Prerequisites

Knowledge of basic concepts from probability, statistics and distributions are required, and the student has knowledge of statistical inference.

The students also has knowledge of basic R programming and basic SAS.


Content

At the end of this course, the student should have a profound knowledge of generalized linear models and basic knowledge of some extensions, including

Part I

  • Standard descriptive and inferential methods for multiway contingency t ables (odds ratios, conditional independence, Cochran-Mantel-Haenszel procedures,...)
  • Components of a generalized linear model (GLM)
  • GLM for binary data: logistic regression
  • Building and applying logistic regression models
  • Overdispersion and quasi-likelihood
  • Conditional logistic regression and exact distributions

Part II

  • Extensions to multinomial responses (baseline category, cumulative link, partial odds ratio,...)
  • Extensions to clustered binary (GEE, random effects)
  • Extensions to clustered & multinomial data
  • Loglinear models
  • Models for matched pairs

The student should be able to apply such models and methods using appropriate software (SAS, R).


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Self-study assignment ✔  


Evaluation

Semester 2 (3,00sp)

Evaluation method
Written exam100 %
Open-book ✔
Multiple-choice questions✔
Open questions✔
Use of study material during evaluation✔
Explanation (English)Copy slides, text books, notes, copies

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory textbooks (bookshop)
 

Textbook 1:

Categorical Data Analysis, Agresti, Alan, 3rd edition, Wiley

ISBN: 9780470463635
 

Compulsory course material
 

R and SAS will be used as softwares in this course. 


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art design methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
1st year Master Data Science J
Exchange Programme Statistics J



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