MAT-20306 Advanced Statistics

Course

Credits 6.00

Teaching methodContact hours
Practical intensively supervised24
Tutorial36
Self-study
Course coordinator(s)dr. EJ Bakker
Lecturer(s)drs. LCP Keizer
dr. EJ Bakker
dr. JA Hageman
dr. M Malosetti
dr. B Engel
dr. EJH Korendijk
ir. PFG Vereijken
dr. ir. E Heuvelink
dr. G Gort
dr. LMW Akkermans
ir. SLGE Burgers
P Haccou
dr. MJ Paulo
dr. C Dobre
Examiner(s)dr. EJ Bakker
dr. G Gort

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).

ProgrammePhaseSpecializationPeriod
Compulsory for: BASAnimal SciencesBSc1MO, 1AF, 2AF, 2MO, 5MO, 6MO
BPWPlant SciencesBSc1MO, 1AF, 2AF, 2MO, 5MO, 6MO
MFQFood Quality ManagementMSc1AF, 6MO
BEBEconomics and GovernanceBSc2MO
Restricted Optional for: MOAOrganic AgricultureMSc1AF
MASAnimal SciencesMSc1MO, 1AF, 5MO, 6MO
MFTFood TechnologyMScG: Sensory Science1MO
MESEnvironmental SciencesMSc2AF, 5MO
MPSPlant SciencesMScC: Natural Resource Management1MO
MPSPlant SciencesMScD: Plant Breeding and Genetic Resources1MO
MPBPlant BiotechnologyMSc1MO, 1AF
MNHNutrition and HealthMScD: Sensory Science2AF
MBFBioinformaticsMSc1AF, 2AF, 6MO
MAMAquaculture and Marine Resource ManagementMScA: Aquaculture5MO
MAMAquaculture and Marine Resource ManagementMScB: Marine Resources and Ecology5MO, 6MO