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Nr. LV-Typ(en) LV-Titel
4541 PI Statistics Präsenz-Modus
Anmeldung über LPIS
vom 29.04.2024 14:00 bis 12.05.2024 23:59

LV-Leiter/in Tomas Masak, Ph.D.
Planpunkte Bachelor Course IV - Business Mathematics
Kurs IV - Wirtschaftsmathematik
Semesterstunden 2
Unterrichtssprache Englisch

Termine
Di, 14.05.2024 16:00-18:30 Uhr TC.4.02 (Lageplan)
Do, 16.05.2024 12:00-14:30 Uhr TC.4.04 (Lageplan)
Di, 21.05.2024 16:00-18:30 Uhr D5.1.004 (Lageplan)
Do, 23.05.2024 12:00-14:30 Uhr TC.4.04 (Lageplan)
Di, 28.05.2024 13:00-15:30 Uhr TC.5.12 (Lageplan)
Di, 04.06.2024 13:00-15:30 Uhr TC.5.12 (Lageplan)
Do, 06.06.2024 12:00-14:30 Uhr TC.3.10 (Lageplan)
Di, 11.06.2024 13:00-15:30 Uhr D5.1.004 (Lageplan)
Do, 13.06.2024 12:00-14:30 Uhr TC.5.18 (Lageplan)
Di, 18.06.2024 13:00-15:30 Uhr TC.5.12 (Lageplan)
Do, 20.06.2024 12:00-14:30 Uhr P TC.2.03 (Lageplan)
Termindownload (ical) | Termine abonnieren

Weitere Informationen https://learn.wu.ac.at/vvz/24s/4541

Kontakt:
wimath@wu.ac.at; tomas.masak@wu.ac.at
Inhalte der LV:

 Exploratory Data Analysis

  • Location, Scale, Skewness, kurtosis estimators
  • Visualisation
  • Applied Data Analysis using R

 Statistical Inference

  • Point estimation (ML estimation, Bayesian estimation; Computing estimators in R; Evaluating estimators)
  • Hypothesis testing (Defining and evaluating tests; p-values)
  • Interval estimation (Defining and evaluating interval estimators)
  • Asymptotic evaluations (Consistency and efficiency)
  • Properties of Estimators (sufficiency, likelihood principle, Bayesian inference)

 Applications in Statistical Modelling

  • Assumptions of Regression, Gauss-Markov theorem
  • Linear regression 
  • Analysis of variance (ANOVA) models
Lernergebnisse (Learning Outcomes):

After completing this course the student will have the ability to:

  • Describe, explain, and work with the basic concepts and definitions of statistical inference, in particular exploratory data analysis, estimation and hypothesis testing.
  • Understand how statistical inferential methods are formulated and evaluated.
  • Solve simple real-world problems where skills from statistical modelling and inferential methods are required.
Regelung zur Anwesenheit:

The lectures will be held at campus.  Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most two lectures can be missed.

There is no possibility to compensate for missed lectures. 
Lehr-/Lerndesign:

The course is taught as a lecture accompanied by practical examples, simulation studies and homework assignments. The lectures are aimed at providing the methodological framework, while the examples, simulation studies, and homework assignments will help students to consolidate and further expand their knowledge of the underlying ideas. Solutions to the home assignments will be discussed in class.  Active participation in class activities is an essential part of the course.
Leistung(en) für eine Beurteilung:
  •  25% weekly assignments
  •  30% project
  •  45% final exam

     
Teilnahmevoraussetzung(en):
Successful completion of the courses Analysis and Linear Algebra as well as Probability within the Specialization in Business Mathematics
(Spezialisierung Wirtschaftsmathematik)
Zuletzt bearbeitet: 29.02.2024 13:55
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