Graduate course in statistics
(for students of the third year or higher)
This course is designed for students working already on their master or PhD thesis.
The course deals with matrix algebra, Monte Carlo and ‘basic’ multivariate techniques and continues the first level course in statistics. During 15 sessions (in total 45 hours) we discuss the following statistical techniques: the general linear model, analysis of variance, regression and path analysis, factor analysis, cluster analysis, discriminant analysis, multidimensional scaling and certain non-standard techniques of data analysis. Emphasis is laid on the discriminate power of these tests and on error types occurring during analyses. A major part of the course will also deal with the application of matrix algebra in statistics.
Students have to perform own analyses using the STATISTICA and PC-ORD software packages.
Zar J. H. 1984 – Biostatistical analysis – Prentice Hall (Englewood Cliffs), 2.nd. Ed.
Sokal R. R., Rohlf F. J. 1995 – Biometry – 3. Ed., Freeman & Co., New York
Bortz J 1999 – Statistik für Sozialwissenschaftler – Springer, Heidelberg 5th Ed.
Introductory statistics and multivariate statistics. http://www.psychstat.smsu.edu/multibook/mlt00.htm
Hyperstat http://davidmlane.com/hyperstat/ and http://www.davidmlane.com/hyperstat/glossary.html