Syllabus

Titel
4879 Statistics
LV-Leiter/innen
ao.Univ.Prof. Dr. Klaus Pötzelberger
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
22.04.2019 bis 02.05.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
Wochentag Datum Uhrzeit Raum
Dienstag 07.05.2019 10:00 - 12:00 TC.4.01
Mittwoch 08.05.2019 10:30 - 12:30 TC.3.01
Dienstag 14.05.2019 10:00 - 12:00 TC.2.01
Mittwoch 15.05.2019 10:00 - 12:00 D5.0.001
Dienstag 21.05.2019 10:00 - 12:00 TC.5.15
Mittwoch 22.05.2019 10:30 - 12:30 TC.5.01
Dienstag 28.05.2019 10:00 - 12:00 TC.4.03
Mittwoch 29.05.2019 10:00 - 12:00 D3.0.225
Dienstag 04.06.2019 10:00 - 12:00 D4.0.022
Mittwoch 12.06.2019 10:00 - 12:00 TC.5.27
Mittwoch 12.06.2019 12:00 - 14:00 TC.5.27
Dienstag 18.06.2019 10:00 - 12:00 D4.0.022

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

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

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

  •  20% weekly tutorials
  •  35% project in applied statistics
  •  45% final exam

      The final exam cannot be retaken

Literatur

1 Autor/in: Venables, W. and Ripley, B.
Titel: Modern Applied Statistics with S

Verlag: Springer
Auflage: 2nd Edition
Jahr: 2002
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Successful completion of the courses Analysis and Linear Algebra as well as Probability within the Specialization in Business Mathematics
(Spezialisierung Wirtschaftsmathematik)

Empfohlene inhaltliche Vorkenntnisse


Erreichbarkeit des/der Vortragenden

klaus.poetzelberger@wu.ac.at
Zuletzt bearbeitet: 07.11.2018



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