De elektronische studiegids voor het academiejaar 2025 - 2026 is onder voorbehoud.





Principles of Statistical Inference (3768)
  
Coordinating lecturer :Prof. dr. Anneleen VERHASSELT 
  
Member of the teaching team :De heer Pieter GIESEN 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (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
    Concepts of Probability and Statistics (1798) 5.0 stptn
 

Prerequisites

The student needs to understand and to be able to apply/calculate the basic concepts in probability theory and statistics: random variable, continous/discrete distributions, expectation, variance, multivariate/marginal distribution, independence of random variables, conditional probability/density, central limit theorem, multivariate normal distribution.



Content

This course deals with the theoretical basis of parametric statistical inference. Key concepts of estimation, confidence regions and hypothesis testing are introduced and applied to several parametric statistical models.


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Response lecture ✔  


Evaluation

Period 1    Credits 3,00

Evaluation method
Written exam100 %
Closed-book ✔
Open questions✔
Use of study material during evaluation✔
Explanation (English)Formularium is provided.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Previously purchased compulsory textbooks
  Mathematical Statistics and Data Analysis,John Rice,Brooks/Cole,9780495118688
 

Compulsory course material
 

The R software will be used in this course.

 

Recommended reading
  Statistical Inference,G. Casella; R.L. Berger,second,Brooks/Cole Cengage Learning,9780534243128

An introduction to mathematical statistics,Bijma, Jonker and Van Der Vaart,Amsterdam Univeristy Press,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/de tail.action?docID=5046611&pq-origsite=summon


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
Master of Teaching in Sciences and Technology
  •  EC 

    • 5.4. The master of education is a domain expert SCIENCES: the EM has advanced knowledge and understanding of the domain disciplines relevant to the specific subject doctrine(s).

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
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