Statistics
- Fakult?t
Wirtschafts- und Sozialwissenschaften
- Version
Version 18.0 vom 30.01.2023
- Modulkennung
22B0753
- Modulname (englisch)
Statistics
- Studieng?nge mit diesem Modul
- Betriebswirtschaft und Management - WiSo (B.A.)
- Internationale Betriebswirtschaft und Management (B.A.)
- International Management (B.A.)
- Betriebliches Informationsmanagement (B.Sc.)
- Volkswirtschaftslehre (B.A.)
- Niveaustufe
2
- Lehrinhalte
- Principles
1.1 Data classification
1.2 Data collection - One-dimensional features
2.1 Distributions and their graphic representation
2.2 Key figures
2.3 Economic applications - Two-dimensional features & regression analysis
3.1 Contingency tables
3.2 Association dimensions
3.3 Regression analysis
3.4 Economic applications - Measurements and index values
4.1 Measurements
4.2 Index values
4.3 Economic applications - Elementary time series analysis
5.1 Trend determination
5.2 Estimation of components
5.3 Economic applications - Random variables and distributions
- Estimation and testing procedures
7.1 Point and interval estimations
7.2 Testing procedures
7.3 Economic applications - Analysis of economic data using statistics software
8.1 Introduction to statistics software
8.2 Computer-aided graphic representation of data
8.3 Computer-aided statistical computation
- Principles
- Lernergebnisse / Kompetenzziele
Wissensverbreiterung
The students know the different methods to prepare and to present static data. They are able to understand and to interpret diagrams, tables, frequency distributions, statistical measures und indexes. The students know the differences between one-dimensional and two-dimensional features.
Wissensvertiefung
The students are able to carry out independently a statistical study in a company. They can prepare the results graphically and in table form and interpret it comprehensively. Finally they can analyse the basic material and can transform the results into understandable reports. They can verify hypotheses.
K?nnen - instrumentale Kompetenz
The students:
- carry out data collections
- can differentiate characteristics by the scale
- know how the absolute and the relative frequencies are defined and can
draw up frequency tables
- can calculate statistical measures and indexes
- can carry out a simple regression analysis
- can calculate key figures
- can recognise a time serie and calculate the most important parameters
- can verify hypothesis with statistical methods of testing
- can estimate parameters
- can calculate simple key figures by means of statistic software
K?nnen - kommunikative Kompetenz
The students learn how to use data. They can evaluate data and they can interpret and communicate the results. They are able to verify hypotheses and to estimate parameters.
K?nnen - systemische Kompetenz
The students are able to justify their decisions by means of statistical methods and analysis.
- Lehr-/Lernmethoden
Lectures, exercises, case studies, self-study, e-Learning
- Empfohlene Vorkenntnisse
Arithmetic
- Modulpromotor
Markovic-Bredthauer, Danijela
- Lehrende
- Faatz, Andreas
- Neumann, Ludger
- Hübner, Ursula Hertha
- Markovic-Bredthauer, Danijela
- Leistungspunkte
5
- Lehr-/Lernkonzept
Workload Dozentengebunden Std. Workload Lehrtyp 30 Vorlesungen 30 ?bungen Workload Dozentenungebunden Std. Workload Lehrtyp 30 Veranstaltungsvor-/-nachbereitung 20 Hausarbeiten 20 Literaturstudium 20 Prüfungsvorbereitung
- Literatur
Chapman, C. N. (2015). R for Marketing Research and Analytics (2015th ed.). New York, NY: Springer.
Field, A. (2013). Discovering Statistics Using SPSS (4th Edition.). Los Angeles: Sage Publications Ltd.
Field, A., & Miles, J. (2012). Discovering Statistics Using R. London?; Thousand Oaks, Calif: Sage Publications Ltd.
McClave, J. T., Benson, P. G., & Sincich, T. L. (2013). Statistics for Business and Economics: Pearson New International Edition (12th ed.). Pearson.
- Prüfungsleistung
Klausur 2-stündig
- Bemerkung zur Prüfungsform
none
- Dauer
1 Semester
- Angebotsfrequenz
Wintersemester und Sommersemester
- Lehrsprache
Englisch