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PřF:Bi7541 Data analysis on PC - Course Information

## Bi7541 Data analysis on PC

**Faculty of Science**

Autumn 2013

**Extent and Intensity**- 0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
**Teacher(s)**- RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)

RNDr. Denisa Krejčí (seminar tutor)

Mgr. et Mgr. Jiří Kalina, Ph.D. (seminar tutor) **Guaranteed by**- prof. RNDr. Ladislav Dušek, Ph.D.

RECETOX - Faculty of Science

Contact Person: RNDr. Jiří Jarkovský, Ph.D.

Supplier department: RECETOX - Faculty of Science **Timetable**- Mon 12:00–13:50 D29/347-RCX2
**Prerequisites**- Basic knowledge of MS Windows, MS Office and basic statistisc.
**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**- Environmental Chemistry (programme PřF, N-CH)
- Special Biology (programme PřF, N-EXB)
- Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

**Course objectives**- In the end of the course student should be able to apply basic principles of biostatistical analysis and utilize them in his/her research work:

Using MS Excel for data preprocessing

Using Statistica for Windows for data analysis

Application of charts in MS Office and Statistica software for data visualisation

Application of descriptive statistics in Statistica for Windows

Application of statistical tests in Statistica for Windows **Syllabus**- 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Graphical features of statistical softwares - graphical presentation of continuous and categorical data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogeneity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and nonparametric correlation analysis. 9. Binomially distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingency tables.

**Literature**- Petrie, A., Watson, P. (2006) Statistics for Veterinary and Animal Science, Wiley-Blackwell; 2nd ed
- Sokal, R.R., Rohlf, F.J. (1994) Biometry, W. H. Freeman, 3th ed.
- Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
- http://www.statsoft.com/textbook/stathome.html

**Teaching methods**- Practical training using computers
**Assessment methods**- Individual projects on correct application of statistical methods on example data.
**Language of instruction**- Czech
**Follow-Up Courses****Further Comments**- Study Materials

The course can also be completed outside the examination period.

The course is taught annually. **Teacher's information**- http://www.cba.muni.cz/vyuka/

- Enrolment Statistics (Autumn 2013, recent)
- Permalink: https://is.muni.cz/course/sci/autumn2013/Bi7541