Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Christine Baes: Catalogue data in Spring Semester 2015

Name Dr. Christine Baes
Address
Lehre Umweltsystemwissenschaften
ETH Zürich, TAN F 1
Tannenstrasse 1
8092 Zürich
SWITZERLAND
Telephone+41 44 632 33 49
DepartmentEnvironmental Systems Science
RelationshipLecturer

NumberTitleECTSHoursLecturers
751-6212-00LGenetic Evaluation of Lifestock1 credit1GC.  Baes
AbstractMethods for practical genetic evaluation in livestock populations are presentend and applied in assignments using small numerical examples. Applications in practical pig and cattle breeding are dealt with in guest lectures.
ObjectiveThe students know the most important methods used for genetic evaulation in livestock populations. They are able to apply these methods to simple examples.
Content- Selection index and BLUP
- The BLUP Multitrait Animal Model
- Genetic evaulation using maternal effekts
- Random Regression and the test day model
- Guest lectures on practical applications of genetic evaluation in pigs and cattle.
Lecture notesCopies of the slides are available on the net.
LiteratureTo be announced in the lectures.
751-7602-00LApplied Statistical Methods in Animal Science1 credit2VC.  Baes
AbstractRefresh matrix operations and solving of systems of linear equations using the generalised inverse. Introduction to theory and application of linear models: regression, models with fixed effects (one factor, multiple factors, interactions), models with random effects, mixed models. Assignments using the statistics programmes R and SAS.
ObjectiveThe students are familiar with matrix operations and the solving of systems of linear equations. They know the possibilities to solve systems of linearly dependent equations using the generalized inverse. They are able to set up linear models for the analysis of animal science data. They know the difference between fixed and random effects. They are familiar with the application of the statistics programmes R and SAS to solve linear models and to interpret their results.
Content- Matrix algebra, systems of linear equations, generalised inverse
- Linear models with fixed effects:
- Regression: simple linear, multiple, non linear regression
- Models with 1 factor, 2 factors (without and with interaction), generalisation
- Linear models with random effects, mixed linear models
Lecture notesCopies of the slides are available on the net.
LiteratureTo be announced in the lectures.