Subject description
1. Review of basic statistical methods and their use for the analysis of data. Statistical testing of assumptions. Methods of studying the dependence of phenomena.
2. Basis of use of the environment for analysis of data »R«. Types of data, preparation and arrangement of data. Entry and extraction of data, exchange of data with other programme environments. Graphic presentation of data. Preparation of own functions. Statistical distribution and simulation of data. Analysis of data with R.
3. Review of methods of multivariate analysis. Basic concepts of linear algebra for use in statistics of multidimensional data. Vector algebra, matrices and matrix calculation, concept of own values and own vectors. Statistical and geometric interpretation of concepts of linear algebra. Method of main components, discrimination analysis, factorial analysis, classifying in groups, visualisation of data.
4. Statistical background to analysis of micronets. Plan of experiment, preparation of data, methods for removing background noise, normalisation of data, analysis of differential expression, graphic presentation and visualisation of results, analysis of networks, linkage with databases amd ontologies on the internet.
5. Selected methods for data analysis. The selection of special methods will be adapted to the orientation and field of work of students.
The subject is taught in programs
Objectives and competences
The student builds on understanding of statistical methods with more demanding methods required in research work. The stress is on conceptual understanding of methods, comparability of methods for various problems and independent analysis of data with the aid of up-to-date software (R).
Teaching and learning methods
– lectures
– work in computer lab
– consultations
– seminar
Expected study results
Knowledge and understanding:
The student is trained for as independent as possible selection of suitable methods and analysis of problems with which he or she is dealing. The achieved knowledge will help him or her in communication with statistical experts and with suitable inclusion of statistical results in reports and scientific articles.
Basic sources and literature
- Weinberg, Harel & Abramovitz. Statistics using R: An integrative approach. Cambridge University Press, 2021. ISBN: 978-1-108-71914-8
- Whitlock & Schluter. The analysis of biological data. Greenwood Village, Colo., Roberts and Co. Publishers, 2009. ISBN: 978-1-319156-71-8
- Dytham. Choosing and using statistics: A biologist's guide. Tretja izdaja. Wiley-Blackwell, 2011. ISBN 978-1-4051-9838-7
- Gotelli & Ellison. A primer of ecological statistics. Druga izdaja. Sinauer Associates Inc., 2013. ISBN 978-1-60535-064-6
- Fowler J, Cohen L, Jarvis P.. Practical statistics for field Biology, 272 pages, John Wiley & Sons; 2 edition (1998), ISBN: 0471982962.
– Krzanowski WJ, Principles of Multivariate Analysis, Oxford Science Publications, 1988.
– Blejec, A: Introduction to R
http://ablejec.nib.si/R/I2R/DOC/I2R.pdf
– različni viri na svetovnem spletu.
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