Module 31-M-ISDA Introduction to Statistical Data Analysis

Faculty

Person responsible for module

Regular cycle (beginning)

Every winter semester

Credit points and duration

8 Credit points

For information on the duration of the modul, refer to the courses of study in which the module is used.

Competencies

In both courses included in the module students internalize statistical thinking and learn to deal with typical problems involved in working with real life data. Basic methods of the multivariate analysis of data set are presented focusing on the modeling of the interaction between different variables, in particular in the context of the classical linear regression model. Students will learn to master established methods for the analysis of (economic) data sets including an assessment of limits of the methods. Moreover students learn to address research questions by using appropriate data sets and methods.

Content of teaching

Both courses in this module extend previously acquired skills in statistical modeling, both theoretically as well as in hands-on applications. In this module the foundations of econometrical single equation models (ordinary and generalized least squares) are discussed: specification, parameter estimation, statistical tests, confidence intervals and prediction. Both the theoretical basis as well as applications are discussed. Furthermore the statistical research methodology is extended using probability theoretical concepts, in order to provide a complete spectrum of scientific tools for data analysis.

Recommended previous knowledge

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Necessary requirements

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Explanation regarding the elements of the module

Module structure: 1 bPr 1

Courses

Multivariate Methods
Type lecture o. lecture with exercises
Regular cycle WiSe
Workload5 120 h (30 + 90)
LP 4
Regression Analysis
Type lecture o. lecture with exercises
Regular cycle WiSe
Workload5 120 h (30 + 90)
LP 4

Examinations

written examination o. e-oral examination o. oral examination
Allocated examiner Person responsible for module examines or determines examiner
Weighting 1
Workload -
LP2 -

1,5 hour written examination or oral (e-) examination of 15 to 20 minutes.
The person responsible for the module designates one or more persons authorized to take the module examination as examiners.

The module is used in these degree programmes:

Degree programme Profile Recom­mended start 3 Duration Manda­tory option 4
Data Science / Master of Science [FsB vom 01.04.2026] Variante 2 1. 1 semes­ter Obli­gation
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 2 1. 1 semes­ter Obli­gation

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Legend

1
The module structure displays the required number of study requirements and examinations.
2
LP is the short form for credit points.
3
The figures in this column are the specialist semesters in which it is recommended to start the module. Depending on the individual study schedule, entirely different courses of study are possible and advisable.
4
Explanations on mandatory option: "Obligation" means: This module is mandatory for the course of the studies; "Optional obligation" means: This module belongs to a number of modules available for selection under certain circumstances. This is more precisely regulated by the "Subject-related regulations" (see navigation).
5
Workload (contact time + self-study)
SoSe
Summer semester
WiSe
Winter semester
SL
study requirement
Pr
Examination
bPr
Number of examinations with grades
uPr
Number of examinations without grades
This academic achievement can be reported and recognised.
Non-official translation of the module descriptions. Only the German version is legally binding.