Syllabus

Title
0327 Statistics I
Instructors
Univ.Prof. Dr. Kurt Hornik
Contact details
kurt.hornik@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/01/23 to 09/22/23
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Statistics I
Dates
Day Date Time Room
Friday 12/01/23 11:00 AM - 02:30 PM TC.1.01 OeNB
Thursday 12/07/23 09:00 AM - 12:30 PM TC.1.01 OeNB
Thursday 12/14/23 09:00 AM - 12:30 PM TC.1.01 OeNB
Thursday 12/21/23 09:00 AM - 12:30 PM TC.1.01 OeNB
Thursday 01/11/24 09:00 AM - 12:30 PM TC.1.01 OeNB
Thursday 01/18/24 09:00 AM - 12:30 PM TC.1.01 OeNB
Thursday 01/25/24 10:00 AM - 12:00 PM TC.0.01
Thursday 02/01/24 09:00 AM - 05:00 PM Online-Einheit

On this page:

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Contents
See the unit description on Learn@WU (lower section).
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Learning outcomes

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

  • design and perform simulation experiments
  • recall the basic tools for exploring univariate and multivariate data sets
  • measure and model key characterics of financial data

Apart from that, the course will contribute to the students' ability to:

  • demonstrate effective team skills in order to contribute appropriately to the production of a group output
  • work, communicate and participate effectively in a team situation and group discussions and to function as a valuable and cooperative team member

Moreover, after completing this course the student will have the ability to:

  • adequately communicate the results of exploring data
  • discuss empirical findings in the light of domain knowledge
  • use the web to access and extract financial data

In addition, the student will be able to:

  • use R for simulation as well as manipulating and exploring data
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    Attendance requirements

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

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    Teaching/learning method(s)

    This course is taught as a lecture combined with homework assignments and a course project.

    In combination with the lecture, the homework assignments will help students to consolidate and expand their knowledge and understanding by developing solutions to theoretical and applied problems, and have to be submitted every week via email to the lecturer.  Selected solutions have to be presented in homework colloquia.

    For the course project teams with up to five members will use R to access and analyze financial data sets.

     

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    Assessment
    • 15% homeworks
    • 25% colloquium
    • 15% final presentations
    • 45% final  

    The assessment of the homework assignments and course project will be based on the correctness of results, the clarity and persuasiveness of each bit of work and the recognizable effort made. This implies an ability to work in teams. For the written exam, the assessment will be based on the ability to describe and apply the key concepts discussed throughout the course and to choose the appropriate analytical techniques to obtain the relevant data.

    To avoid the potential free-rider problem related to group work, the final exam will strongly be related to the problems already discussed in homework assignments and course projects.

    Please note that there will be no opportunity to retake the written final exam.

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    Prerequisites for participation and waiting lists
    • Basic knowledge of probability and statistics (on an undergraduate level)
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    Readings

    Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

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    Availability of lecturer(s)
    kurt.hornik@wu.ac.at
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    Other

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    Unit details
    Unit Date Contents
    1

    Numerical Linear Algebra

    After attending this session, students should recall key matrix operations and decompositions in theory and practice, and how the compositions can be employed for the numerical solution of linear systems.

    Reference: Braun and Murdoch, Chapter 6.
    2

    Random Number Generation and Simulation

    After attending this session, students should know about the principles of random number generation and be able to perform Monte Carlo simulation and integration.

    Reference: Braun and Murdoch, Chapter 5.
    3

    Data Management

    After attending this session, students should be able to use R to access and extract data from a variety of sources and formats, including plain text files with comma or tab separated values, spreadsheets as well as web sites or HTML tables embedded in these. The students should also be able to organize and transform data for subsequent analyses.
    4

    Data Exploration

    After attending this session, students should be able to explore univariate and multivariate data, making use of traditional and modern visualization techniques, including histograms, Q-Q plots, and mosaic plot variants.  

    Reference: Carmona, Chapters 1 and 2
    5

    Graphics and Quantmod

    After attending this session, students should have gained a working knowledge of the R graphics engines and systems and be able to customize high-level graphics via annotation.  They should also be able to use R to obtain financial data from the web.

    Reference: Braun and Murdoch, Chapter 3.
    6

    Heavy Tails and Copulas

    After attending this session, students should recall techniques for investigating and modeling the tails of distributions, classical measures of dependence, and how to use copulas for modeling dependence in financial data.

    Reference: Carmona, Chapters 1 and 2.
    7

    Presentations and Review

    After attending this session students should recall developing, presenting and discussing the results of using R for scientific computing and analyzing financial data.  They should also assess their efficiency for self and group organization and reflect upon the "big picture" of this course.
    8

    Final exam
    Last edited: 2023-06-02



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