MAT-20306 Advanced Statistics


Credits 6.00

Teaching methodContact hours
Independent study0
Course coordinator(s)dr. EJ Bakker
Lecturer(s)dr. SK Schnabel
SE Wilson
drs. LCP Keizer
dr. LMW Akkermans
dr. EJ Bakker
dr DJ Brus
ir. SLGE Burgers
dr. WT Kruijer
G Korontzis
dr. EJH Korendijk
dr. ir. E Heuvelink
dr. G Gort
dr. B Engel
prof. dr. FA van Eeuwijk
V Avagyan
drs CGF de Kovel
S Gugushvile
dr. JA Hageman
dr ir NA Hartemink
Examiner(s)dr. G Gort
dr. EJ Bakker

Language of instruction:


Assumed knowledge on:

MAT-15303 Statistics 1 + MAT-15403 Statistics 2 or MAT-14303 Basic Statistics or MAT-15403 Statistics 2.
The student should be familiar with 1) The principles of probability calculus and the subjects: estimation, construction of confidence intervals and hypothesis testing from statistical inference 2) Application of these principles to inference about central values (mean or success probability) for the 1-sample and 2-sample situations, in case of Normal observations and binary (0,1) observations 3) Methods of analysis for simple (one explanatory variable) linear regression.
(To refresh this knowledge, (parts of) chapters 1 to 6 and 11 of the book can be studied.)


Note: This course can not be combined in an individual programme with MAT-24306 Advanced Statistics for Nutritionists and/or MAT-25303 Advanced Statistics (DL).

Per week we will have 2 lectures of two hours each (for groups up to 120 students) 2 computer practicals of two hours each (for groups of 20 students with 2 teachers / 1 teacher with one student assistant) and one T16 tutorial (of 3 hours) where students will do exercises with pen and paper (max 32 students supervised by 1 teacher and 1 student assistant).

Learning outcomes:

After successful completion of this course students are expected to (within the limits of the subjects treated) be able to:
- translate a research question into a statistical hypothesis: make a plan (type of design or sampling procedure) for the data collection.
- choose an appropriate model with an understanding of the ingredients of the model in relation to the data;
- analyse the data (with SPSS and R);
- interpret the results and form conclusions relevant for the actual problem.


- lectures: follow classes;
- study the book and make exercises;
- computer practicals (compulsory): (learn how to) use SPSS, R and PQRS, and work on case studies;
- pen and paper practicals: work on exercises and case studies where computer output is given.


- written test with open questions and multiple choice questions, which needs to be passed (contribution to final mark: 100%).
- computer practical (attendance compulsory) has to result in a pass.


R. Lyman Ott; Longnecker, M.T. (2016). An Introduction to Statistical Methods and Data Analysis. 7th ed. 1174p.
Lecture notes available in English. (available at the WUR-shop).

Compulsory for: BASAnimal SciencesBSc1MO, 1AF, 2MO, 2AF, 5MO, 6MO
BPWPlant SciencesBSc1MO, 1AF, 2MO, 2AF, 5MO, 6MO
BEBEconomics and GovernanceBSc6MO
MAMAquaculture and Marine Resource ManagementMSc2MO, 5MO, 6MO
Restricted Optional for: BINInternational Development StudiesBScB: Economics of Development5MO
MBIBiologyMSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
MOAOrganic AgricultureMSc1MO, 1AF, 5MO, 6MO
MOAOrganic AgricultureMScC: Double Degree Agroecology1MO, 2MO, 5MO
MASAnimal SciencesMSc1MO, 1AF, 2MO, 2AF, 5MO, 6MO
MFTFood TechnologyMScG: Sensory Science1MO
MESEnvironmental SciencesMSc2AF, 5MO
MPSPlant SciencesMScC: Natural Resource Management1MO, 2MO, 5MO, 6MO
MPSPlant SciencesMScD: Plant Breeding and Genetic Resources1MO, 2MO, 5MO, 6MO
MPBPlant BiotechnologyMSc1MO, 1AF, 2MO, 2AF, 5MO, 6MO
MNHNutrition and HealthMScA: Nutritional and Public Health Epidemiology1MO, 1AF
MNHNutrition and HealthMScD: Sensory Science1MO, 1AF
MNHNutrition and HealthMScB: Nutritional Physiology and Health Status1MO, 1AF
MNHNutrition and HealthMScF: Food Digestion and Health1MO, 1AF
MNHNutrition and HealthMScC: Molecular Nutrition and Toxicology1MO, 1AF
MBFBioinformaticsMSc1AF, 2AF, 6MO
MIDInternational Development StudiesMScB: Economics of Development1AF
MFQFood Quality ManagementMSc1AF, 6MO
MBSBiobased SciencesMScC: Biobased and Circular Economy2AF, 2MO, 5MO, 6MO