Syllabus

Title
4595 Statistics
Instructors
ao.Univ.Prof. Dr. Klaus Pötzelberger
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
05/02/23 to 05/12/23
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Tuesday 05/16/23 12:00 PM - 02:00 PM TC.4.12
Wednesday 05/17/23 12:00 PM - 02:00 PM D4.0.039
Tuesday 05/23/23 12:00 PM - 02:00 PM TC.4.12
Wednesday 05/24/23 12:00 PM - 02:00 PM D4.0.039
Tuesday 05/30/23 12:00 PM - 02:00 PM TC.4.12
Wednesday 05/31/23 12:00 PM - 02:00 PM D4.0.039
Tuesday 06/06/23 12:00 PM - 02:00 PM TC.4.12
Wednesday 06/07/23 12:00 PM - 02:00 PM D4.0.039
Tuesday 06/13/23 12:00 PM - 02:00 PM TC.4.12
Wednesday 06/14/23 12:00 PM - 02:00 PM D4.0.039
Tuesday 06/20/23 12:00 PM - 02:00 PM TC.4.12
Wednesday 06/21/23 12:00 PM - 02:00 PM D4.0.039

Contents

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
Learning outcomes

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.

Attendance requirements

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.

Teaching/learning method(s)

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.

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

Prerequisites for participation and waiting lists
Successful completion of the courses Analysis and Linear Algebra as well as Probability within the Specialization in Business Mathematics
(Spezialisierung Wirtschaftsmathematik)