311.990 (22S) Methodology 2: Statistics
Überblick
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- Lehrende/r
- LV-Titel englisch Methodology 2: Statistics
- LV-Art Vorlesung
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 121
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 03.03.2022
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
More and more data is becoming available in all areas of Business and Economics; one of its use-cases is to build and back hypotheses such as purchase decisions by customers. Transforming data into scientific theories requires tools which are known as statistical methods.
"Methodology 2: Statistics" aims at providing a fundamental knowledge about these tools by analyzing univariate and multivariate data sets. Examples are
- descriptive statistics
- estimation, prediction, and testing of proportions and means
- analysis of variance
- contingency tables
- linear regression
The lecture is accompanied by a KS with the same name, in which exercises and case studies will be solved by the students using the statistical software R.
Lehrmethodik
Lectures with active and passive parts, accompanied by eLearning-material. Individual practice through exercises, case studies, and quizzes.
Inhalt/e
- data, variables, frequencies
- descriptive statistics
- estimating, predicting, and testing of proportions
- estimating, predicting, and testing of means
- comparison of means
- analysis of variance (ANOVA)
- contingency tables and chi-square-test
- linear regression
Erwartete Vorkenntnisse
basic mathematical and statistical knowledge at high-school level, in particular:
- basic arithmetic
- elementary probability calculus and probability distributions (binomial and normal distribution)
- successful completion of "Methodology 1: Mathematics" highly recommended
Curriculare Anmeldevoraussetzungen
Please combine with "KS Methodology 2: Statistics"
Intendierte Lernergebnisse
More and more data is becoming available in all areas of Business and Economics; one of its use-cases is to build and back hypotheses such as purchase decisions by customers. Transforming data into scientific theories requires tools which are known as statistical methods.
"Methodology 2: Statistics" aims at providing a fundamental knowledge about these tools by analyzing univariate and multivariate data sets. Examples are descriptive statistics, estimation, prediction, and testing of proportions and means, analysis of variance, contingency tables, linear regression, and model selection.
The lecture is accompanied by a KS with the same name, in which exercises and case studies will be solved by the students using the statistical software R.
Lehrmethodik
Lectures with active and passive parts, accompanied by eLearning material. Individual practice through exercises, case studies, and quizzes.
Inhalt/e
- data, variables, frequencies
- descriptive statistics
- estimating, predicting, and testing of proportions
- estimating, predicting, and testing of means
- comparison of means
- analysis of variance (ANOVA)
- contingency tables and chi-square-test
- linear regression
Erwartete Vorkenntnisse
basic mathematical and statistical knowledge at high-school level, in particular:
- basic arithmetic
- elementary probability calculus and probability distributions (binomial and normal distribution)
- successful completion of "Methodology 1: Mathematics" highly recommended
Curriculare Anmeldevoraussetzungen
Please combine with "KS Methodology 2: Statistics"
Prüfungsinformationen
Geänderte Prüfungsinformationen (COVID-19 Ausnahmeregelung)
If needed, we switch to an online format via Big Blue Button and Moodle.
Prüfungsmethode/n
Written (digital) exam in English. Exam dates: last day of lecture in June and three further dates throughout the following term (i.e., early October, early December, and after the holiday break in January).
Prüfungsinhalt/e
Content of lectures, quizzes, and supplementary online material.
Beurteilungskriterien/-maßstäbe
Written (online) exam.
Prüfungsmethode/n
Written (digital) exam in English. Exam dates: last day of lecture in June and three further dates throughout the following term.
Prüfungsinhalt/e
Content of the lectures.
Beurteilungskriterien/-maßstäbe
Written (online) exam.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Bachelorstudium Betriebswirtschaft
(SKZ: 518, Version: 18W.1)
-
Fach: Methoden der Sozial- und Wirtschaftswissenschaften
(Pflichtfach)
-
5.2 Methoden II: Statistik (
0.0h VO/ VI / 4.0 ECTS)
- 311.990 Methodology 2: Statistics (2.0h VO / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
5.2 Methoden II: Statistik (
0.0h VO/ VI / 4.0 ECTS)
-
Fach: Methoden der Sozial- und Wirtschaftswissenschaften
(Pflichtfach)
- Bachelorstudium Angewandte Betriebswirtschaft
(SKZ: 518, Version: 14W.3)
-
Fach: Mathematik und Statistik für Wirschaftswissenschaften
(Pflichtfach)
-
§9(1) Statistik für Wirtschaftswissenschaften (
2.0h VO / 4.0 ECTS)
- 311.990 Methodology 2: Statistics (2.0h VO / 4.0 ECTS)
-
§9(1) Statistik für Wirtschaftswissenschaften (
2.0h VO / 4.0 ECTS)
-
Fach: Mathematik und Statistik für Wirschaftswissenschaften
(Pflichtfach)
- Bachelorstudium Informationsmanagement
(SKZ: 522, Version: 17W.1)
-
Fach: Wahlfach Mathematik und Statistik (ABWL)
(Wahlfach)
-
5.1.2 Statistik für Wirtschaftswissenschaften (
0.0h VO / 4.0 ECTS)
- 311.990 Methodology 2: Statistics (2.0h VO / 4.0 ECTS) Absolvierung im 2. Semester empfohlen
-
5.1.2 Statistik für Wirtschaftswissenschaften (
0.0h VO / 4.0 ECTS)
-
Fach: Wahlfach Mathematik und Statistik (ABWL)
(Wahlfach)
- Bachelorstudium Wirtschaftsinformatik
(SKZ: 522, Version: 20W.2)
-
Fach: Mathematik und Statistik (Betriebswirtschaft)
(Wahlfach)
-
7.1.2 Statistik (
0.0h VO, VI / 4.0 ECTS)
- 311.990 Methodology 2: Statistics (2.0h VO / 4.0 ECTS) Absolvierung im 2. Semester empfohlen
-
7.1.2 Statistik (
0.0h VO, VI / 4.0 ECTS)
-
Fach: Mathematik und Statistik (Betriebswirtschaft)
(Wahlfach)
- Bachelorstudium Informationsmanagement
(SKZ: 522, Version: 12W.1)
-
Fach: Wahlfach Mathematik und Statistik (ABWL)
(Wahlfach)
-
1.2.2 Wirtschaftsstatistik (
0.0h VO / 3.0 ECTS)
- 311.990 Methodology 2: Statistics (2.0h VO / 3.0 ECTS)
-
1.2.2 Wirtschaftsstatistik (
0.0h VO / 3.0 ECTS)
-
Fach: Wahlfach Mathematik und Statistik (ABWL)
(Wahlfach)
- Bachelorstudium International Business and Economics
(SKZ: 516, Version: 19W.1)
-
Fach: Methodology in Business and Economics Research
(Pflichtfach)
-
7.2 Methodology 2 (
0.0h VO, VI / 4.0 ECTS)
- 311.990 Methodology 2: Statistics (2.0h VO / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
7.2 Methodology 2 (
0.0h VO, VI / 4.0 ECTS)
-
Fach: Methodology in Business and Economics Research
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Sommersemester 2024
- 311.990 VO Methodology 2: Statistics (2.0h / 4.0ECTS)
-
Wintersemester 2023/24
- 311.970 VO Methoden II: Statistik (2.0h / 4.0ECTS)
-
Sommersemester 2023
- 311.990 VO Methodology 2: Statistics (2.0h / 4.0ECTS)
-
Wintersemester 2022/23
- 311.970 VO Methoden II: Statistik (2.0h / 4.0ECTS)
-
Wintersemester 2021/22
- 311.970 VO Methoden II: Statistik (2.0h / 4.0ECTS)
-
Sommersemester 2021
- 311.970 VO Methoden II: Statistik (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 311.990 VO Methodology 2: Statistics (2.0h / 4.0ECTS)
-
Sommersemester 2020
- 311.970 VO Methoden II: Statistik (2.0h / 4.0ECTS)
-
Wintersemester 2019/20
- 311.990 VO Methodology 2: Statistics (2.0h / 4.0ECTS)
-
Sommersemester 2019
- 311.970 VO Statistik für Wirtschaftswissenschaften (2.0h / 4.0ECTS)
-
Sommersemester 2018
- 311.970 VO Statistik für Wirtschaftswissenschaften (2.0h / 4.0ECTS)
-
Sommersemester 2017
- 311.970 VO Statistik für Wirtschaftswissenschaften (2.0h / 4.0ECTS)
-
Sommersemester 2016
- 311.970 VO Statistik für Wirtschaftswissenschaften (2.0h / 4.0ECTS)
-
Sommersemester 2015
- 311.970 VO Statistik für Wirtschaftswissenschaften (2.0h / 4.0ECTS)
-
Sommersemester 2014
- 310.870 VO Statistik für Wirtschaftswissenschaften (2.0h / 4.0ECTS)
-
Sommersemester 2013
- 310.870 VO Statistik für Wirtschaftswissenschaften (2.0h / 4.0ECTS)