Module: Management-Methodology II (5301-440)

Persons:

• Prof. Dr. Alfonso Sousa-Poza (verantwortlich)

Degree Program:

• Agribusiness (Master, since 01.10.2014)
  3. Semester, elective
• Agribusiness (Master, since 01.04.2019)
  3. Semester, elective
• Bioeconomy (Master, since 01.10.2014)
  3. Semester, elective
• Bioeconomy (Master, since 01.10.2014)
  1. Semester, elective
• Bioeconomy (starting WS 16/17) (Master, since 01.04.2017)
  3. Semester, elective
• Bioeconomy (starting WS 16/17) (Master, since 01.04.2017)
  1. Semester, elective
• Education for Business and Economics (Master, since 01.10.2009)
  1. Semester, compulsory
• Management (WS 16/17) (Master, since 01.10.2016)
  1. Semester, compulsory
• Management (WS 17/18) (Master, since 01.10.2016)
  1. Semester, compulsory
• Education for Business and Economics (Master, since 01.10.2016)
  1. Semester, compulsory

Relation to other Modules:

Quantitative Methoden 1, Quantitative Methoden 2, Quantitative Methoden 3 (B.Sc.)

Prerequisites for Attendance:

Ansätze der deskriptiven Datenanalyse, Grundlagen der Wahrscheinlichkeitstheorie, das Konzept von Zufallsvariablen und ihrer Verteilung, inferenzstatistische Schätz- und Testansätze sind bekannt.

Sprache:

English

ECTS:

4 credits

Frequency:

every winter semester

Length of the Module:

1 semester

Final examination:

written exam

Length of the examination:

60 minutes

Workload:

120 Stunden: 42 Stunden Präsenzstudium 78 Stunden Selbststudium

Professional competences:

Students are already acquainted with the information they need to understand and apply key techniques of multivariate data analysis. Building upon the know-how obtained during their undergraduate/Bachelor‘s studies, students are able to understand and make use of the results of specific statistical techniques. It is an applications-oriented introduction to multivariate analysis, thereby reducing heavy statistical research into fundamental concepts and placing more emphasis on the application of methods than in their formal properties. They are in a good position to properly apply statistical and econometric key techniques such as multiple regression analysis, factor analysis, and logistic regression .

Comments:

Vorlesungen werden auf Englisch stattfinden.

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