Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
3 |
1 |
8 |
15 weeks |
120 hours |
Instructor
Clarice Garcia Borges Demetrio
Cristian Marcelo Villegas Lobos
Renata Alcarde Sermarini
Objective
Enable the student to use the mixed model methodology to analyse data from studies that include
random effects.
Content
To define mixed models, to estimate the parameters, to predict the random effects, to calculate the
expectation of mean squares or to use the Hasse diagrams to get them and to estimate the components
of variance. Enable the student to make applications of variance components, using R software.
Contents:1. Mixed model definition.
2. Parameter estimation and prediction of random effects
3. Expectation of mean squares
3.1 Completely randomized designs
3.2 Randomized complete block designs
3.3 Latin saquare designs
3.4 Factorial designs
3.5 Split-plot designs
3.6 Hierarchical designs
3.7 Series of experiments
3.8 Hasse Diagram
4. Estimation methods for variance components
4.1 Moment method
4.2 Maximum likelihood
4.3 Restricted maximum likelihood
5. Hypotheses testing
6. Confidence intervals for variance components
7. Aplications in sampling and breeding
Bibliography
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