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  
Included in these programmesTolerance3
1st year Master Bioinformatics Y
1st year Master Bioinformatics - icp N
1st year Master Biostatistics Y
1st year Master Biostatistics - icp Y
1st year Master Data Science Y
1st year Master Quantitative Epidemiology - icp Y
1st year Quantitative Epidemiology Y
Exchange Programme Statistics Y



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.