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Statistics 2

Module name (EN):
Name of module in study programme. It should be precise and clear.
Statistics 2
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Business Administration, Bachelor, ASPO 01.10.2020
Module code: BBWL-2020-450
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P420-0144
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
6V (6 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 4
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam (90 min. / can be repeated semesterly)

[updated 02.01.2019]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

BBWL-450 (P420-0144, P620-0576) Business Administration, Bachelor, ASPO 01.10.2012 , semester 4, mandatory course
BBWL-450 (P420-0144, P620-0576) Business Administration, Bachelor, ASPO 01.10.2016 , semester 4, mandatory course
BBWL-2020-450 (P420-0144) Business Administration, Bachelor, ASPO 01.10.2020 , semester 4, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
90 class hours (= 67.5 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 82.5 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
BBWL-2020-240 Mathematical Economics 2 and Statistics 1


[updated 17.12.2019]
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Teresa Melo
Lecturer: Prof. Dr. Teresa Melo

[updated 01.10.2016]
Learning outcomes:
After successfully completing this module, students will be able to:
 
- describe and model random phenomena using concepts from the probability theory,
- describe basic methods from the probability theory and apply them to exemplary economic situations,
 
- develop and interpret statements of probability,
 
- apply the most important discrete and continuous probability distributions (e. g. binomial and normal distributions),
- explain basic procedures of inferential statistics such as the principle of point and interval estimators and the testing of hypotheses,
 
- solve business practice problems with the help of adequate statistical methods and interpret the results obtained,
 
- describe the structure and procedures of non-parametric methods and use them to analyze economic data sets,
 
 
- Students will know and be able to critically discuss the limits of the statistical methodology used.
 
Probability calculation:


[updated 02.01.2019]
Module content:
 
- Combinatorics
- Basic principles of set theory
- Probability terms: Laplace distribution, statistical
  probability, Kolmogorov´s probability theory
- Elementary computation rules, total probability theorem, Bayesian theorem
- Discrete and continuous random variables
- Special distribution models (e.g. binomial and normal distribution)
  
Inferential statistics:
- Properties and construction of estimators
- Estimation of parameters (point and interval estimation)
- Formulation of statistical hypotheses
- Test procedure for expected values, proportional values and variances
- Nonparametric methods: Goodness-of-Fit, independence and
  homogeneity tests
- Other nonparametric methods: distribution-free methods


[updated 02.01.2019]
Teaching methods/Media:
Lecture and discussion in a large group using transparencies (projector) and the blackboard (theory and example computations).
The lecture will be supplemented by exercises and tutorials. A large number of exercise sheets covering the wide range topics in this module will be provided. Afterwards, the solutions will be discussed with the students.
Both the lecture notes and the exercise sheets will be available to students in electronic form.


[updated 02.01.2019]
Recommended or required reading:
- Bamberg, Baur, Krapp: Statistik, 14. Auflage, Oldenbourg, 2008
- Bamberg, Baur, Krapp: Statistik - Arbeitsbuch, 8. überarb. Auflage,
  Oldenbourg, München, 2008
- Caputo, Fahrmeir, Künstler, Lang, Pigeot-Kübler, Tutz: Arbeitsbuch Statistik,
  5. Auflage, Springer, Berlin, 2009
- Fahrmeir, Künstler, Pigeot, Tutz: Statistik: Der Weg zur Datenanalyse, 6.
  überarbeitete Auflage, Springer-Verlag, Berlin, Heidelberg, 2007
- Mosler, Schmid: Wahrscheinlichkeitsrechnung und schließende Statistik, 2.
  Auflage, Springer-Verlag, Berlin, Heidelberg, 2006
- Schira: Statistische Methoden der VWL und BWL: Theorie und Praxis, 3.,
  aktualisierte Auflage, Pearson Studium, 2009
- Schwarze: Grundlagen der Statistik, Band 2: Wahrscheinlichkeitsrechnung und
  induktive Statistik, 9. vollständig über. Auflage, Reihe: NWB Studium
  Betriebswirtschaft, nwb Verlag, 2009
- Toutenburg, Heumann: Induktive Statistik: Eine Einführung mit R und SPSS, 4.
  überab. und erw. Auflage, Springer-Verlag, Berlin, Heidelberg, 2008
- Toutenburg, Heumann: Arbeitsbuch zur deskriptiven und induktiven Statistik, 2.
  Auflage, Springer, 2009


[updated 02.01.2019]
 
[Thu Dec 26 18:14:22 CET 2024, CKEY=bs2a, BKEY=bbw3, CID=BBWL-2020-450, LANGUAGE=en, DATE=26.12.2024]