Bi8600 Multivariate Methods

Faculty of Science
Autumn 2013
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 10:00–12:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
At the end of the course the student is able to: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering algorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
  • Enrolment Statistics (Autumn 2013, recent)
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