## Course

Credits 6.00

 Teaching method Contact hours Lecture 24 Tutorial 14 Practical 24 Independent study
 Course coordinator(s) dr. C Dobre dr. EJ Bakker Lecturer(s) drs. LCP Keizer dr. EJ Bakker dr. JA Hageman dr. M Malosetti dr. SK Schnabel dr. B Engel dr. EJH Korendijk dr. G Gort dr. LMW Akkermans ir. SLGE Burgers RRH Rincent dr. C Dobre dr. ir. E Heuvelink dr. ing. MPH Verouden Examiner(s) dr. EJ Bakker dr. G Gort

English

### 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.)

### Contents:

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.

### 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;
- analyse the data (with SPSS);
- interpret the results and form conclusions relevant for the actual problem.

### Activities:

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

### Examination:

- 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.

### Literature:

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).

Programme Phase Specialization Period 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 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