Exploratory Data Analysis
- Location, Scale, Skewness, kurtosis estimators
- Applied Data Analysis using R
- 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
After completing this course the student will have the ability to:
- Describe, explain, and work with the basic concepts and definitions of statistical inference, in particular exploratory data analysis, estimation and hypothesis testing.
- Understand how statistical inferential methods are formulated and evaluated.
- Solve simple real-world problems where skills from statistical modelling and inferential methods are required.
Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.
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.
- 20% weekly tutorials
- 35% project in applied statistics
- 45% final exam
The final exam cannot be retaken
Successful completion of the courses Analysis and Linear Algebra as well as Probability within the Specialization in Business Mathematics