|Teaching method||Contact hours|
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.)
The setup of the course will change. 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).
The content will not / only marginally adapted.
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;
- 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:||BAS||Animal Sciences||BSc||1MO, 1AF, 2AF, 2MO, 5MO, 6MO|
|BPW||Plant Sciences||BSc||1MO, 1AF, 2AF, 2MO, 5MO, 6MO|
|BEB||Economics and Governance||BSc||2MO|
|Restricted Optional for:||MOA||Organic Agriculture||MSc||1AF|
|MOA||Organic Agriculture||MSc||C: Double Degree Agroecology||1MO, 2MO, 5MO|
|MAS||Animal Sciences||MSc||1MO, 1AF, 5MO, 6MO|
|MFT||Food Technology||MSc||G: Sensory Science||1MO|
|MES||Environmental Sciences||MSc||2AF, 5MO|
|MPS||Plant Sciences||MSc||C: Natural Resource Management||1MO|
|MPS||Plant Sciences||MSc||D: Plant Breeding and Genetic Resources||1MO|
|MPB||Plant Biotechnology||MSc||1AF, 1MO, 2AF, 2MO, 5MO, 6MO|
|MNH||Nutrition and Health||MSc||D: Sensory Science||1MO|
|MBF||Bioinformatics||MSc||1AF, 2AF, 6MO|
|MFQ||Food Quality Management||MSc||1AF, 6MO|
|MAM||Aquaculture and Marine Resource Management||MSc||A: Aquaculture||5MO|
|MAM||Aquaculture and Marine Resource Management||MSc||B: Marine Resources and Ecology||5MO, 6MO|