Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.
040217 KU Data Analysis on Organization and Personell (MA) (2021S)
Prüfungsimmanente Lehrveranstaltung
Labels
DIGITAL
service email address: opim.bda@univie.ac.at
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Do 11.02.2021 09:00 bis Mo 22.02.2021 12:00
- Anmeldung von Do 25.02.2021 09:00 bis Fr 26.02.2021 12:00
- Abmeldung bis Mi 31.03.2021 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine
Digitale Durchführung:
MI 03.03.2021 11.30-14.45 Digital (Bestätigt)
MI 10.03.2021 11.30-14.45 Digital (Bestätigt)
MI 17.03.2021 11.30-14.45 Digital (Bestätigt)
DI 23.03.2021 11.30-14.45 Digital (Bestätigt)
MO 12.04.2021 09.45-20.00 Digital (Bestätigt)
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Your final grade is determined by your performance on the quizzes, assignments, presentations and class participation.
Make-up exams will not be given unless the student has a medical or other serious reason.
Students should hand in homework assignments before class on the day they are due. Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Make-up exams will not be given unless the student has a medical or other serious reason.
Students should hand in homework assignments before class on the day they are due. Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Mindestanforderungen und Beurteilungsmaßstab
Basic knowledge of Business Mathematics and Statistics are required.
Your final grade is determined by your performance on the quizzes, assignments, presentations, final essay and class participation.
Passing grades can generally not be earned by students who miss more than 20% of the total class-time.
Make-up exams will not be given unless the student has a medical or other serious reason.Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Your final grade is determined by your performance on the quizzes, assignments, presentations, final essay and class participation.
Passing grades can generally not be earned by students who miss more than 20% of the total class-time.
Make-up exams will not be given unless the student has a medical or other serious reason.Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Prüfungsstoff
assignments, presentation, final essay, class attendance and participation.
Literatur
Wooldridge „Introductory Econometrics“
– Chapters 1-8, 15, 17
Peter Kennedy „A guide to Econometrics“
– Chapters 1-12, 16, 17
Kohler/Kreuter „Data Analysis using Stata“
– Chapter 8 and 9
– Chapters 1-8, 15, 17
Peter Kennedy „A guide to Econometrics“
– Chapters 1-12, 16, 17
Kohler/Kreuter „Data Analysis using Stata“
– Chapter 8 and 9
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mi 21.04.2021 11:25
Goal: Upon completion of the course, students should be able to undertake regression analysis and inference on a variety of hypotheses involving cross-sectional and time series data.
This course introduces students to regression tools for analyzing data in economics, finance and related disciplines. Extensions include regression with discrete random variables, instrumental variables regression, quasi-experiments, and regression with time series data. The objective of the course is for the student to learn how to conduct – and how to critique – empirical studies in economics, finance and related fields. Accordingly, the emphasis of the course is on empirical applications. The mathematics of econometrics will be introduced only as needed and will not be a central focus.