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PřF:Bi7541 Data analysis on PC - Course Information

## Bi7541 Data analysis on PC

**Faculty of Science**

Spring 2021

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

Mgr. et Mgr. Jiří Kalina, Ph.D. (seminar tutor)

prof. RNDr. Ladislav Dušek, 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 **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**- The course is basic introduction into practical data analysis for students of biology and clinical study specialisations. The course accompanies theoretical lectures of biostatistics and shows the computation of presented methods on PC using statistical software (descriptive statistics, one sample and two sample tests, categorical data analysis, ANOVA, correlation and regression analysis, data visualisation, basics of experimental design).
**Learning outcomes**- 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 (probably available only in Czech)**- The course can also be completed outside the examination period.

The course is taught annually.

The course is taught: every week.

Note related to how often the course is taught: možno i blokově. **Teacher's information**- http://www.cba.muni.cz/vyuka/

- Enrolment Statistics (recent)

- Permalink: https://is.muni.cz/course/sci/spring2021/Bi7541