Basic Statistics
Module MA2402
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2020
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | |||
|---|---|---|---|
| SS 2021 | SS 2020 | SS 2012 | WS 2011/2 |
Basic Information
MA2402 is a semester module in German language at Bachelor’s level which is offered in summer semester.
This module description is valid to SS 2021.
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 150 h | 45 h | 5 CP |
Content, Learning Outcome and Preconditions
Content
The module covers the basic concepts of statistical inference. Besides point estimation techniques like maximum likelihood or method of moment estimation, also different interval estimators are being analysed. After the introduction of the main ideas of hypothesis testing, we derive a number of properties for a range of specific statistical tests. As an example for a statistical model we consider the simple linear regression model.
Most of the topics will be illustrated using the statistical programming environment R.
Most of the topics will be illustrated using the statistical programming environment R.
Learning Outcome
At the end of the module students are able to understand the basic statistical concepts, models and methods.
Further they are able to perform basic statistical analyses using R.
Further they are able to perform basic statistical analyses using R.
Preconditions
MA1001 - Analysis 1
MA1002 - Analysis 2
MA1101 - Linear Algebra and Discrete Structures 1
MA1102 - Linear Algebra and Discrete Structures 2
MA1401 - Introduction to Probability Theory
MA8505 - Lab course: Programming with R
MA1002 - Analysis 2
MA1101 - Linear Algebra and Discrete Structures 1
MA1102 - Linear Algebra and Discrete Structures 2
MA1401 - Introduction to Probability Theory
MA8505 - Lab course: Programming with R
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Statistics: Basics [MA2402] | Dettling, P. Drton, M. |
Tue, 10:15–11:45, virtuell |
eLearning |
| UE | 1 | Statistics: Basics (Exercise Session) [MA2402] | Dettling, P. Drton, M. | dates in groups |
Learning and Teaching Methods
The module is offered as lectures with accompanying practice sessions. In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should animate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress. At the beginning of the module, the practice sessions will be offered under guidance, but during the term the sessions will become more independent, and intensify learning individually as well as in small groups.
Media
blackboard
Literature
Casella and Berger (2002). Statistical Inference, Duxbury.
Fahrmeir, Künstler, Pigeot, Tutz (2010). Statistik - Der Weg zur Datenanalyse, Springer.
Georgii, H.-O. (2007). Stochastik, De Gruyter.(begleitend)
Young und Smith (2010). Essentials of Statistical Inference, Cambridge University Press.
Fahrmeir, Künstler, Pigeot, Tutz (2010). Statistik - Der Weg zur Datenanalyse, Springer.
Georgii, H.-O. (2007). Stochastik, De Gruyter.(begleitend)
Young und Smith (2010). Essentials of Statistical Inference, Cambridge University Press.
Module Exam
Description of exams and course work
The module examination is based on a written exam (60 minutes). Students have to know basic terms and methods of statistics and reflect different parametric distribution models. They are able to discuss adequately properties of statistical hypothesis testing.
Exam Repetition
The exam may be repeated at the end of the semester.