Doctoral candidates and early-stage researchers face a wide variety of professions and career paths open to them. Launching a career and attaining long-term success in these professional fields often demands a wide range of competencies. The Graduate Centre offers training programmes that specifically cater to the needs of early career researchers.
While our information events address cross-disciplinary aspects relevant to early-stage researchers' qualification, our workshops (organized by the Centre for Careers and Competencies) aim at supporting and expanding the skills involved in conducting research.
The foundation of any scientific work at the University of Passau is compliance with the recognised principles of science such as being honest, working lege artis as well as documenting and constantly questioning all results. Scientific misconduct harms the person concerned, the university and science in general. In order to increase awareness of the basic rules and to strengthen the researchers' trust in one another, the Graduate Centre offers an e-learning course free of charge entitled "Good academic practice".
Please contact us if you would like to participate in the e-learning course. You will receive log-in data and further information that will allow you to take part in the e-learning course anytime from your computer.
For further information events, workshops and other events offered by the Graduate Centre in German please refer to the German version of this website.
The faculties pool our researchers' expertise. Although the university does not offer a comprehensive doctoral curriculum, the faculties offer a variety of lectures and seminars that help doctoral researchers deepen their disciplinary knowledge. Each semester, these events are carefully selected to meet doctoral researchers' needs.
35621 Vorlesung: Computational Statistics - Regression in R (WiSe 22/23)
ZeitenDi. 10:00 - 12:00 (wöchentlich)
Erster TerminDi., 18.10.2022 10:00 - 12:00 Uhr
BeschreibungThe course focuses on estimating regression models and evaluating the estimated specifications with the statistical software R. Model evaluation procedures discussed in class range from graphical methods, classic validation techniques and tests to simulation-based approaches. The effects of variables being measured on different scales and variable transformations are discussed. Dealing with different data structures such as cross-sections, time series, and panel data is also covered in class.
HeimateinrichtungLehreinheit für Computergestützte Statistik und Mathematik
VoraussetzungenThe course aims at students with a basic knowledge in statistics and complements some of the topics treated in 'Methods in Econometrics I and II'.
LernorganisationGuided computer tutorials; students are expected to deepen their knowledge by completing self-contained R-exercises and by presenting/explaining code snippets.
LeistungsnachweisFinal exam (60 minutes); R-skills are certified via a certificate when the final exam is passed.
- Kleiber, C. & A. Zeileis (2008), Applied Econometrics with R, Springer.
- Field, A. & Miles, J. & Field, Z. (2012), Discovering Statistics using R, SAGE.
- Wooldridge, J. (2013), Introductory Econometrics, 5Ed., South Western.
- Greene, W.H. (2012), Econometric Analysis, Pearson.
- Ligges, U. (2008), Programmieren mit R, Springer.
SonstigesCourse is taught in English.
For further lectures and seminars offered by the faculties in German please refer to the German version of this website.