MAT-20306 Advanced Statistics


Credits 6.00

Teaching methodContact hours
Independent study0
Course coordinator(s)dr. C Dobre
dr. EJ Bakker
Lecturer(s)dr. LMW Akkermans
dr. EJH Korendijk
dr. WT Kruijer
G Korontzis
dr. ing. M Knotters
dr. C Dobre
dr. ing. MPH Verouden
C Zheng
SE Wilson
dr DJ Brus
dr. SK Schnabel
dr. JK Kampen
dr. JA Hageman
drs. LCP Keizer
dr. B Engel
prof. dr. FA van Eeuwijk
ir. SLGE Burgers
dr. ir. E Heuvelink
dr. EJ Bakker
dr. G Gort
DV Bustos Korts
V Avagyan
S Gugushvile
G Bartzis
JG Velazco
AP Languillaume
Examiner(s)dr. EJ Bakker
dr. G Gort

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.)


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 2 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);
- 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 and PQRS, work on case studies.


- 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. (2010). An Introduction to Statistical Methods and Data Analysis. 6th ed. 1296p.
Lecture notes available in English. (available: WUR-shop).

Compulsory for: BASAnimal SciencesBSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
BPWPlant SciencesBSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
BEBEconomics and GovernanceBSc2MO
MAMAquaculture and Marine Resource ManagementMSc2MO, 5MO, 6MO
Restricted Optional for: BINInternational Development StudiesBScB: Economics of Development5MO
MOAOrganic AgricultureMScC: Double Degree Agroecology1MO, 2MO, 5MO
MOAOrganic AgricultureMSc1AF, 1MO, 5MO, 6MO
MASAnimal SciencesMSc1AF, 1MO, 2MO, 2AF, 5MO, 6MO
MFTFood TechnologyMScG: Sensory Science1MO
MESEnvironmental SciencesMSc2AF, 5MO
MPSPlant SciencesMScD: Plant Breeding and Genetic Resources1MO, 2MO, 5MO, 6MO
MPSPlant SciencesMScC: Natural Resource Management1MO, 2MO, 5MO, 6MO
MPBPlant BiotechnologyMSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
MNHNutrition and HealthMScD: Sensory Science1MO
MBFBioinformaticsMSc1AF, 2AF, 6MO
MIDInternational Development StudiesMScB: Economics of Development1AF
MFQFood Quality ManagementMSc1AF, 6MO