Lecture Series ''Testing and Estimating''
- represent the distributions of random variables graphically.
- calculate moments of random variables and interpret them in a given context.
- explain the framework of testing hypotheses and estimating parameters.
- apply basic testing and estimating procedures and generalize the conclusions correctly.
- criticize the assumptions of basic testing and estimating procedures.
- derive the minimal sample size for basic testing and estimating procedures.
Lecture Series ''Multiple Linear Regression''
- apply the ordinary least squares method to derive estimators.
- analyze and compare the statistical properties of estimators.
- explain the classical linear model assumptions.
- run the calculations of a multiple linear regression for toy examples with small data sets by hand.
- interpret the software outputs of multiple linear regression for application examples in the given context.
- use model diagnostics to check the assumptions and to judge the quality of adapted models.
- apply inference procedures in multiple linear regression models.
- compare the advantages and disadvantages of different inference procedures.
- construct testing procedures for multiple linear constraints in multiple linear regression models.
- apply specification techniques to improve the quality of models.
- apply selection techniques to choose appropriate models.
