|Teaching method||Contact hours|
|Practical intensively supervised||24|
|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.
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:||BAS||Animal Sciences||BSc||1AF, 1MO, 2AF, 2MO, 6MO|
|MFT||Food Technology||MSc||G: Sensory Science||2MO|
|BEB||Economics and Governance||BSc||2MO|
|Restricted Optional for:||MOA||Organic Agriculture||MSc||1AF|
|MAS||Animal Sciences||MSc||1AF, 1MO, 2MO, 2AF, 6MO|
|MPS||Plant Sciences||MSc||C: Natural Resource Management||1MO|
|MPS||Plant Sciences||MSc||D: Plant Breeding and Genetic Resources||1MO|
|MNH||Nutrition and Health||MSc||D: Sensory Science||2AF|
|MFQ||Food Quality Management||MSc||6MO|
|MAM||Aquaculture and Marine Resource Management||MSc||B: Marine Resources and Ecology||2MO, 2AF|
|MAM||Aquaculture and Marine Resource Management||MSc||A: Aquaculture||2MO, 2AF|