MAT-20306 Advanced Statistics


Credits 6.00

Teaching methodContact hours
Independent study0
Course coordinator(s)dr. CGF de Kovel
dr. EJ Bakker
Lecturer(s)dr. LMW Akkermans
dr. JA Hageman
dr. CGF de Kovel
S Gugushvile
V Avagyan
SE Wilson
dr. SK Schnabel
dr. EJ Bakker
dr DJ Brus
ir. SLGE Burgers
prof. dr. FA van Eeuwijk
dr. B Engel
dr. G Gort
dr. ir. E Heuvelink
drs. LCP Keizer
dr. EJH Korendijk
G Korontzis
Examiner(s)dr. CGF de Kovel
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) and/or MAT-22306 Quantitative Research Methodology and Statistics.

This course covers several more advanced statistical models and associated designs, and techniques for statistical inference, as relevant to life science studies. The main topics are categorical data, (multiple) regression, analysis of variance (including multiple comparisons), analysis of covariance, and non-parametric tests. The aims of an analysis, the model assumptions, the properties (and limitations) of the models and associated inferential techniques and the interpretation of results in terms of the practical problem will be discussed. Focus will be upon students gaining an understanding of the model ingredients, an (intuitive) understanding of inferential techniques, insight into data structures and implications for choice of model and analysis. Students will be able to perform analysis of data with statistical software, i.e. with R-Studio.

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 R-Studio);
- interpret the results and form conclusions relevant for the actual problem.


Per week we will have 2 lectures of two hours each, 2 computer practicals of two hours each and one practical where students will do exercises with pen and paper.
- lectures: follow classes;
- study the book and make exercises;
- computer practicals (compulsory): (learn how to) use R-Studio 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 SciencesBSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
BPWPlant SciencesBSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
BEBEconomics and GovernanceBSc6MO
MAMAquaculture and Marine Resource ManagementMSc2MO, 5MO, 6MO
Restricted Optional for: BINInternational Development StudiesBScB: Spec. B - Economics of Development5MO
MBIBiologyMSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
MOAOrganic AgricultureMSc1AF, 1MO, 5MO, 6MO
MFTFood TechnologyMScG: Spec. G - Sensory Science5MO
MESEnvironmental SciencesMSc2AF, 5MO
MPSPlant SciencesMScC: Spec. C - Natural Resource Management1MO, 1AF, 2MO, 2AF, 5MO, 6MO
MPSPlant SciencesMScD: Spec. D - Plant Breeding and Genetic Resources1MO, 1AF, 2MO, 2AF, 5MO, 6MO
MPBPlant BiotechnologyMSc1AF, 1MO, 2AF, 2MO, 5MO, 6MO
MBFBioinformaticsMSc1AF, 2AF, 6MO
MBSBiobased SciencesMScC: Spec. C - Biobased and Circular Economy2AF, 2MO, 5MO, 6MO
MOADDSpec. Agroecology DDMSc1AF, 2MO, 5MO
Restricted Optional for: WUPBRBSc Minor Plant Breeding6MO