Design of Agricultural Experiments (1781)
  
Coordinating lecturer :Prof. dr. Niel HENS 
  
Member of the teaching team :dr. Jade MEMBREBE 

Language of instruction : English
Credits: 4,0
  
Period: semester 2 (4sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Content

Designing of Agricultural Experiments:

- General aspects and considerations

- Completely Randomized Design (CRD)

- Randomized Complete Block Design (RCBD)

- Latin squares designs (LS)

- Incomplete block design (IBD)

- Split plot design (SPD)

- Statistical analysis of experimental designs

- Factorial and Nested designs

- On farm trials

- Inter-cropping

- Research methods complexities

This course is organised in cooperation with lecturers from our South partners.


Organisational and teaching methods
Organisational methods  
Lecture ✔  


Evaluation

Semester 2 (4,00sp)

Evaluation method
Written exam100 %
Closed-book ✔
Open-book ✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

Handouts by the instructor.


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.

  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

  •  EC 

    • The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them.

  •  EC 

    • The student knows the societal relevance of statistics and data science.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
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1st year Master Bioinformatics - icp J
1st year Master Biostatistics J
1st year Master Biostatistics - icp J
1st year Master Data Science J
1st year Master Quantitative Epidemiology - icp J
1st year Quantitative Epidemiology J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.