Semester:WS 17/18
Type:Lecture
Language:German
Scheduled in semester:2
Semester Hours per Week / Contact Hours:60.0 L / 45.0 h
Self-directed study time:135.0 h
Type:Lecture
Language:German
Scheduled in semester:2
Semester Hours per Week / Contact Hours:60.0 L / 45.0 h
Self-directed study time:135.0 h
Module coordination/Lecturers
- Dipl.-Phys. ETH Jochen Kalser
(Modulleitung)
- Dipl.-Phys. ETH Jochen Kalser
(Interner Dozent)
Curricula
Bachelor's degree programme in Business Administration (01.09.2012)Modules
Qualifications
- Know about the roles of quantiles, variances, standard deviations and correlations to measure risks.
- Know the axioms of a discrete probability space.
- Know the most important distributions and their properties.
- Know the importance of the central limit theorem.
- Can describe univariate and bivariate data according to the level of scale using numerical measures and graphical representations.
- Can explain the content of the axioms of a discrete probability space while modelling a random experiment.
- Use the law of large numbers to interpret a probability as a relative frequency in the long run.
- Can explain why and when a certain distribution is used to model economic situations.
- Can name the basic idea of testing hypotheses referring to the possible types of errors.
- Name the basic ideas of standard testing procedures.
- Calculate the critical values in the decision rules of binomial tests.
- Can explain the meaning of confidence intervals and indicate the duality between confidence intervals and testing hypotheses.
- Use the principle of ordinary least squares to estimate the parameters of a regression model.
- Run simple linear regressions, set up the ANOVA-table and judge the residual plot.
- Calculate probabilities using addition rules, decision trees and combinatorics.
- Can explain the results of Bayes' theorem.
- Use limits theorems to approximate distributions and probabilities.
- Use calculations rules for expectations and variances correctly and can explain their meanings in the context of risk measuring.
- Calculate the critical values of binomial tests and the resulting probability of a type 2 error.
- Evaluate the test statistics of standard procedures, read the corresponding critical values from statistical tables and formulate the conclusion of the testing procedure correctly in the given context.
- Calculate confidence intervals and interpret them correctly in a given context.
- Interpret measures as quantiles, variances, standard deviations, correlations, skewness, curtosis correctly.
- Use the vocabulary introduced to them to describe graphical representations correctly and include the advantages and disadvantages of such representations while interpreting them.
- Judge the certainty or uncertainty of statistical conclusions and formulate their interpretations accordingly.
- Judge the practical relevance of a linear regression in the given context.
- Judge the uncertainty in the conclusions from statistical testing procedures correctly
- Know the central statistical techniques that are often used in business applications.
- Understand the meaning of statistical notions.
- Use the concepts introduced in a purposeful way, interpret the results in the context and formulate their conclusions correctly.
- Use basic commands of the software package R to analyze data graphically and numerically.
- Apply standard learning techniques in abstract contexts so that they get used to working with scientific publications on their own.
- Analyze data to justify decisions in business applications.
- Analyze business cases using methods of probability theory.
Literature
Die Pflichtliteratur des Moduls Statistik umfasst die folgenden Quellen:
- Unterrichtsmaterialen wie Skripten, Folien, Übungs- und Hausaufgaben, welche auf dem Lehrveranstaltungsforum zur Verfügung gestellt werden.
- Wewel, M. C. (2014, 3. Auflage). Statistik im Bachelor-Studium der BWL und VWL. Hallbergmoos: Pearson.
- Fahrmeir, L., Heumann, C., Künstler, R., Pigeot, I. & Tutz, G. (8. Auflage, 2016): Statistik - Der Weg zur Datenanalyse. Berlin: Springer.
Exam Modalities
- written examination (120 min)
Dates
Datum | Zeit | Raum |
12.09.2017 | 15:15 - 16:45 | H2 |
14.09.2017 | 08:30 - 10:00 | H2 |
19.09.2017 | 15:15 - 16:45 | H2 |
21.09.2017 | 08:30 - 10:00 | H2 |
26.09.2017 | 15:15 - 16:45 | H2 |
28.09.2017 | 08:30 - 10:00 | H2 |
03.10.2017 | 15:15 - 16:45 | H2 |
05.10.2017 | 08:30 - 10:00 | H2 |
10.10.2017 | 15:15 - 16:45 | H2 |
12.10.2017 | 08:30 - 10:00 | H2 |
17.10.2017 | 15:15 - 16:45 | H2 |
19.10.2017 | 08:30 - 10:00 | H2 |
24.10.2017 | 15:15 - 16:45 | H2 |
26.10.2017 | 08:30 - 10:00 | H2 |
07.11.2017 | 15:15 - 16:45 | H2 |
09.11.2017 | 08:30 - 10:00 | H2 |
14.11.2017 | 15:15 - 16:45 | H2 |
16.11.2017 | 08:30 - 10:00 | H2 |
21.11.2017 | 15:15 - 16:45 | H2 |
23.11.2017 | 08:30 - 10:00 | H2 |
28.11.2017 | 15:15 - 16:45 | H2 |
30.11.2017 | 08:30 - 10:00 | H2 |
05.12.2017 | 15:15 - 16:45 | H2 |
07.12.2017 | 08:30 - 10:00 | H2 |
12.12.2017 | 15:15 - 16:45 | H2 |
14.12.2017 | 08:30 - 10:00 | H2 |
19.12.2017 | 15:15 - 16:45 | H2 |
21.12.2017 | 08:30 - 10:00 | H2 |
Exams
- PWW-BA-12_Statistik - VO (WS 17/18, bewertet)