PřF:Bi7921 Advanced biol. data analysis - Course Information
Bi7921 Advanced statistical methods for analysis of biological dataFaculty of Science
- Extent and Intensity
- 0/2/0. 2 credit(s) (plus 2 credits for an exam). Type of Completion: zk (examination).
- prof. Mgr. Stanislav Pekár, Ph.D. (lecturer)
Ing. Marek Brabec, PhD. (lecturer), prof. Mgr. Stanislav Pekár, Ph.D. (deputy)
Mgr. Petr Šmarda, Ph.D. (lecturer)
- Guaranteed by
- prof. Mgr. Stanislav Pekár, Ph.D.
Department of Botany and Zoology - Biology Section - Faculty of Science
Contact Person: prof. Mgr. Stanislav Pekár, Ph.D.
Supplier department: Department of Botany and Zoology - Biology Section - Faculty of Science
- Bi5040 Biostatistics - basic course && Bi8190 Visualization of biolog. data && Bi7920 Data analysis
Basic biostatistic methods, General Linear Model, Generalised Linear Models.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20
- fields of study / plans the course is directly associated with
- there are 8 fields of study the course is directly associated with, display
- Course objectives
- Aim of this lecture is to show how to analyse data with correlated structure by means of selected advanced methods, univariate or multivariate. Specifically data with temporal, spatial or phzlogeentic dependence (LME, GLS, GEE), as well as models with nonlinear predictor(NLS, GNLS, NLME, GAM).
- Learning outcomes
- Student will know: - how to prepare an exparimental design - use advanced statistical methods
- 1) Correlated data. 2) Experimental design: Nested, Split-plot. Pseudoreplication. 3)Random effects. 4) Models with temporal dependence. 5) Models with spatial dependence. 6) Models with phylogenetic dependence. 7) Mixed-effect models like GLS, LME and GEE. 8) Models with non-linear predictor (NLS, GNLS). 9) Models with random effects and non-linar predictor (NLME). 10) Additive models (GAM).
- Pekár S. & Brabec M. 2012. Moderní analýza biologických dat. 2. Lineární modely s korelacemi v prostředí R. Masaryk University Press, Brno.
- Pinheiro J.C. & Bates D.M. 2000. Mixed-Effects Models in S and S-PLUS. New York: Springer Verlag.
- Teaching methods
- Beside theory, lectures are based on conctere data and ther analysis.
- Assessment methods
- Continuous examination by means of homework. Final exam is oral.
- Language of instruction
- Further Comments
- The course is taught annually.
The course is taught: in blocks.