MAT-20306 Advanced Statistics
Vak
Studiepunten 6.00
Onderwijstype | Contacturen |
Practical intensively supervised | 24 |
Tutorial | 36 |
Self-study |
Language of instruction:
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:
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 (d) checking model assumptions (e) prediction.
Factorial designs: completely randomized design for 1 and 2 factors, block designs.
One-way and two-way analysis of variance: additive and interaction models, (overparametrization) , F-tests for interaction and/or main effects, t-tests for one mean or a difference of two means, multiple comparisons.
Analysis of covariance and use of a model with a quantitative and a qualitative factor.
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;
- analyse the data (with SPSS);
- interpret the results and form conclusions relevant for the actual problem.
Activities:
- lectures: follow classes;
- study the book and make exercises;
- computer practical's (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).
Opleiding | Fase | Specialisatie | Periode | ||
---|---|---|---|---|---|
Verplicht voor: | BAS | Animal Sciences | BSc | 1MO, 1AF, 2AF, 2MO, 5MO, 6MO | |
BPW | Plant Sciences | BSc | 1MO, 1AF, 2AF, 2MO, 5MO, 6MO | ||
MFQ | Food Quality Management | MSc | 1AF, 6MO | ||
BEB | Economics and Governance | BSc | 2MO | ||
Keuze voor: | MOA | Organic Agriculture | MSc | 1AF | |
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 | 1MO, 1AF | ||
MNH | Nutrition and Health | MSc | D: Sensory Science | 2AF | |
MBF | Bioinformatics | MSc | 1AF, 2AF, 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 |