## Course

Credits 6.00

 Teaching method Contact hours Practical intensively supervised 24 Tutorial 36 Self-study
 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

English

### 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.)

### Contents:

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.

### Activities:

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

### Examination:

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

### Literature:

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

Programme Phase Specialization Period Compulsory for: BAS Animal Sciences BSc 1AF, 1MO, 2AF, 2MO, 6MO BPW Plant Sciences BSc 1MO MFT Food Technology MSc G: Sensory Science 2MO BEB Economics and Governance BSc 2MO MOA Organic Agriculture MSc 1AF MAS Animal Sciences MSc 1AF, 1MO, 2MO, 2AF, 6MO MES Environmental Sciences MSc 2AF 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