Go to menu

Information System of Masaryk University

Course Information

IS MU > Course Catalogue

česky | in English

Bi7541 Data analysis on PC

Faculty of Science
spring 2012

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. Jan Mužík, Ph.D. (seminar tutor)
Mgr. Lukáš Kohút (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)

Supervisor

doc. RNDr. Ladislav Dušek, Ph.D.
Research Centre for Toxic Compounds in the Environment (RECETOX) - Chemistry Section - Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Dodavatelské pracoviště: Research Centre for Toxic Compounds in the Environment (RECETOX) - Chemistry Section - Faculty of Science

Timetable

Wed 15:00–16:50 COMPK6,609

Prerequisites

Basic knowledge of MS Windows, MS Office and basic statistisc.

Course Enrollment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

Fields of study the course is directly associated with

there are 6 fields of study the course is directly associated with, display

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. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial 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, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically 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, contingecy 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