Subject description
Modern graphics for data presentation.
Analysis of correlation and dependence: correlation analysis, simple regression, multiple regression.
Data exploratory analyses: cluster analysis, multidimensional scaling.
Methods for lowering the dimension of space:
principal component analysis, correspondence analysis.
Methods for analyses of groups: discriminant analysis, factor analysis.
The subject is taught in programs
Objectives and competences
The main objective is an overview of concepts and statistical methods for analysis of multivariate data.
Teaching and learning methods
Lectures in computer room; modern software is used. Home work.
Expected study results
Knowledge and understanding: students upgrade basic knowledge of statistics with modern statistical and computing approaches. The focus is on the choice of appropriate methods, on the interpretation of the results and of the use of modern tools for statistical computing.
Basic sources and literature
FERLIGOJ, Anuška. Razvrščanje v skupine : teorija in uporaba v družboslovju, (Zbirka Metodološki zvezki, 4). Ljubljana: Fakulteta za sociologijo, politične vede in novinarstvo, Raziskovalni inštitut, 1989. 182 str. http://dk.fdv.uni-lj.si/metodoloskizvezki/Pdfs/Mz_4Ferligoj.pdf. [COBISS.SI-ID 13947648]
Kastelec D. in Košmelj K.: Interna študijska gradiva (pdf datoteke)
Johnson R. A., Wichern D. W. (2002): Applied multivariate statistical analysis, Prentice Hall, New Jersy, 767 str.
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL https://www.R-project.org/..
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