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040129 VO Statistics 1 (2022S)
Labels
Ausführliche Kursbeschreibung auf Homepagehttp://homepage.univie.ac.at/erhard.reschenhofer/Die Fragestunden (Tutorium) von Anja Bohatschek (anja.bohatschek@univie.ac.at) werden digital online in einem Moodle-Kurs angeboten.
Termin: montags 16:45-18:15 digital
Sie können sich unter dem Link https://moodle.univie.ac.at/course/view.php?id=304354 selbst einschreiben und teilnehmen.
Termin: montags 16:45-18:15 digital
Sie können sich unter dem Link https://moodle.univie.ac.at/course/view.php?id=304354 selbst einschreiben und teilnehmen.
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
Language: German
Examination dates
-
Friday
29.04.2022
15:00 - 16:30
Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß - Thursday 22.09.2022 13:15 - 14:45 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Wednesday 16.11.2022 11:30 - 13:00 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 26.01.2023 11:30 - 13:00 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Lecturers
Classes
Tuesday, 16:45-19:30, 01.03.-05.04.2022, HS 1, OMP 1
Thursday, 13:15-16:00, 03.03.-07.04.2022, HS 1, OMP 1
A possible switch to a digital format (Moodle) will be announced in time.
Information
Aims, contents and method of the course
Assessment and permitted materials
Written exam (15 questions)
Minimum requirements and assessment criteria
14-15: 1, 12-13: 2, 10-11: 3, 8-9: 4, 0-7: 5
Examination topics
Lecture notes and many practice tests on homepage
Reading list
R. J. Larsen and M. L. Marx: Introduction to Mathematical Statistics and its Applications. Pearson Prentice Hall
Association in the course directory
Last modified: Mo 19.09.2022 11:08
To introduce students to statisticsTopics include:
Logic and set theory, events and their probabilities, discrete random variables, continuous random variables, Central Limit Theorem, estimation, testing, linear regression, statistical software ROn successful completion of the course, students should be able to:
Calculate probabilities and conditional probabilities, work with random variables and distribution functions, apply the Central Limit Theorem, find the bias and mean square error of estimators, test statistical hypotheses, analyze data using linear models, display data using graphical methodsTeaching and learning methods:
Lectures