## Course

Credits 6.00

 Teaching method Contact hours Lecture 24 Tutorial 18 Practical 24 Independent study 0
 Course coordinator(s) dr. EJ Bakker dr. CGF de Kovel Lecturer(s) dr. EJ Bakker dr. G Gort dr. ir. E Heuvelink dr. CGF de Kovel dr. SK Schnabel dr DJ Brus ir. SLGE Burgers prof. dr. FA van Eeuwijk V Avagyan dr. B Engel drs. LCP Keizer dr. JA Hageman dr. LMW Akkermans dr. EJH Korendijk G Korontzis S Gugushvile SE Wilson Examiner(s) dr. CGF de Kovel dr. EJ Bakker dr. G Gort

EN

### 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:

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.

### Activities:

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.
- 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.

### 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. (2016). An Introduction to Statistical Methods and Data Analysis. 7th ed. 1174p.
Lecture notes available in English. (available at the WUR-shop).

Programme Phase Specialization Period Compulsory for: BAS Animal Sciences BSc 1AF, 1MO, 2AF, 2MO, 5MO, 6MO BPW Plant Sciences BSc 1AF, 1MO, 2AF, 2MO, 5MO, 6MO BEB Economics and Governance BSc 6MO MAM Aquaculture and Marine Resource Management MSc 2MO, 5MO, 6MO BIN International Development Studies BSc B: Spec. B - Economics of Development 5MO MBI Biology MSc 1AF, 1MO, 2AF, 2MO, 5MO, 6MO MOA Organic Agriculture MSc 1AF, 1MO, 5MO, 6MO MFT Food Technology MSc G: Spec. G - Sensory Science 5MO MES Environmental Sciences MSc 2AF, 5MO MPS Plant Sciences MSc C: Spec. C - Natural Resource Management 1MO, 1AF, 2MO, 2AF, 5MO, 6MO MPS Plant Sciences MSc D: Spec. D - Plant Breeding and Genetic Resources 1MO, 1AF, 2MO, 2AF, 5MO, 6MO MPB Plant Biotechnology MSc 1AF, 1MO, 2AF, 2MO, 5MO, 6MO MBF Bioinformatics MSc 1AF, 2AF, 6MO MBS Biobased Sciences MSc C: Spec. C - Biobased and Circular Economy 2AF, 2MO, 5MO, 6MO MOADD Master Spec. Agroecology DD (2020) MSc 1AF, 2MO, 5MO
MinorPeriod
Restricted Optional for: WUPBRBSc Minor Plant Breeding6MO