Exploratory Data Analysis

• Location, Scale, Skewness, kurtosis estimators
  
• Visualisation
  
• Applied Data Analysis using R
  

 Statistical Inference

• Point estimation (ML estimation, Bayesian estimation; Computing estimators in R; Evaluating estimators)
  
• Hypothesis testing (Defining and evaluating tests; p-values)
  
• Interval estimation (Defining and evaluating interval estimators)
  
• Asymptotic evaluations (Consistency and efficiency)
• Properties of Estimators (sufficiency, likelihood principle, Bayesian inference)

 Applications in Statistical Modelling

• Assumptions of Regression, Gauss-Markov theorem
  
• Linear regression 
• Analysis of variance (ANOVA) models

The lectures will be held at campus.  Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most two lectures can be missed.

There is no possibility to compensate for missed lectures. 

The course is taught as a lecture accompanied by practical examples, simulation studies and homework assignments. The lectures are aimed at providing the methodological framework, while the examples, simulation studies, and homework assignments will help students to consolidate and further expand their knowledge of the underlying ideas. Solutions to the home assignments will be discussed in class.  Active participation in class activities is an essential part of the course.

•  25% weekly tutorials/homework exercises
•  40% project in applied statistics
•  35% final exam

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