- Knowing and understanding of the basic problems, concepts and methods of regression analysis for cross section data (k1,k2)
- Knowing and understanding of the basic problems, concepts and methods for nonparametric estimation of covariate effects (k1,K2)
- Knowing and understanding of the basic problems, concepts and methods of regression analysis for longitudinal data (k1,k2)
- Fitting, model choice and residual analysis of regression models for different types of responses with statistic software R (k3)
- Implementing and performing simulation studies for different types of regression models (k2,k3)
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- Univariate linear regression
- Heteroscedastic and autocorrelated errors
- Penalized regression (Ridge Regression, LASSO)
- Boosting
- Nonparametric Estimation of covariate effects (Splines estimates, Local Polynomial smoothing and LOESS )
- Generalized linear regression models
- Generalized additive models for location, scale and shape
- Loglinear models
- Multivariate linear models
- Linear mixed effects models
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