MAT-20306 Advanced Statistics


Credits 6.00

Teaching methodContact hours
Practical intensively supervised24
Course coordinator(s)dr. EJ Bakker
Lecturer(s)dr. EJ Bakker
dr. B Engel
dr. JA Hageman
ir. SLGE Burgers
ir. PFG Vereijken
drs. LCP Keizer
PJ Canas Rodrigues
dr. ir. W van der Werf
NGWM van Strijp-Lockefeer
dr. G Gort
Examiner(s)dr. EJ Bakker
dr. G Gort

Language of instruction:


Assumed knowledge on:

MAT-11806 or MAT-14303 or MAT-15403
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 and one-way ANOVA.
(To refresh this knowledge, (parts of) chapters 1 to 6, 8 and 11 of the book can be studied.)


Brief overview of (a) the principles of inference and (b) inference about means in the 1- and 2-sample situation, including non-parametric procedures. Choosing the sample size required to obtain a given precision in the 1- and 2-sample situations.
Multiple linear regression: 1) model formulation and meaning of model parameters and 2) inference on (a) a single parameter (b) a linear combination of model parameters (c) several model parameters simultaneously.
Factorial designs: completely randomized design for 1 and 2 factors, block designs.
Two-way analysis of variance: additive and interaction models, overparametrization, F-tests for interaction and/or main effects, t-tests for one parameter or a linear combinations of parameters.
Inference (notably Chi-Square tests) for (count) data summarized in a contingency table.

Learning outcomes:

After the course the student should (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;
- analyze the data (with SPSS);
- interpret the results and form conclusions relevant for the actual problem;
- assess and if necessary criticize the sampling procedure, choice of model or analysis of a reported experiment.


1. lectures: follow classes, study the book, make exercises;
2. computer practicals (compulsory): (learn how to) use SPSS and PQRS, work on case studies.


Written exam. The computer practical should result in a pass.


- Statistical Methods and Data Analysis by R. Lyman Ott and Michael Longnecker (ISBN 0495109142: sixth edition);
- lecture notes available in English (Wur Shop).

Compulsory for: BASAnimal SciencesBSc1AF, 1MO, 2AF, 2MO, 6MO
BPWPlant SciencesBSc1MO
MFTFood TechnologyMScG: Sensory Science2MO
BEBEconomics and GovernanceBSc2MO
Restricted Optional for: MOAOrganic AgricultureMSc1AF
MASAnimal SciencesMSc1AF, 1MO, 2MO, 2AF, 6MO
MESEnvironmental SciencesMSc2AF
MPSPlant SciencesMScC: Natural Resource Management1MO
MPSPlant SciencesMScD: Plant Breeding and Genetic Resources1MO
MNHNutrition and HealthMScD: Sensory Science2AF
MFQFood Quality ManagementMSc6MO
MAMAquaculture and Marine Resource ManagementMScB: Marine Resources and Ecology2MO, 2AF
MAMAquaculture and Marine Resource ManagementMScA: Aquaculture2MO, 2AF