Computational Statistics

Course

Instructors
Dr. Oleg Nenadić (theoretical sessions)
Elisabeth Waldmann (practical sessions)

Date / venue
Tuesday, 12:15 - 13:45 in MZG 7.124.
Wednesday, 12:15 - 13:45 in MZG 6.111.
(1st session on Oct 25)

Topics:
This course covers topics in computational statistics; the freely available statistical software environment R (http://www.r-project.org will be used in this course.
Course overview (tentative schedule):
(i) Introduction:
R basics: Download and installation; data types; R packages; help system; workspace; data management; graphics.
(ii) Statistical distributions and exploratory data analysis:
R syntax for statistical distributions; relationships between distributions; distributional inference; fitting mixture distributions; kernel density estimation.
(iii) Linear models and extensions:
An overview of linear models; the function lm and its methods; simulating the distribution of test statistics in the context of linear models; bootstrapping regression models; extensions to linear models in R.
(iv) Further topics:
Documenting and formatting R code; LaTeX and Sweave; writing R packages.
Weitere Informationen finden Sie im UniVZ.

Literatur

Nenadić, O. & Zucchini, W. (2004), "Statistical Analysis with R - A Quick Start",
http://www.statoek.wiso.uni-goettingen.de/mitarbeiter/ogi/pub/r_workshop.pdf.

Dalgaard, P. (2008), "Introductory Statistics with R", 2nd ed., Springer, Heidelberg.

Ligges, U. (2009), "Programmieren mit R", 3. Aufl., Springer, Heidelberg.

Further literature will be announced in the course.