E7541 Data analysis on PC

Faculty of Science
Autumn 2023
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)
RNDr. Denisa Krejčí, Ph.D. (seminar tutor)
Mgr. et Mgr. Jiří Kalina, Ph.D. (seminar tutor)
Zbyněk Cincibus (assistant)
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
Timetable
Mon 14:00–15:50 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
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 or R software 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
Theoretical and practical training using computers
Assessment methods
Test based on a solution of four data analytical tasks with real data.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
https://portal.matematickabiologie.cz/index.php?pg=aplikovana-analyza-klinickych-a-biologickych-dat--biostatistika-pro-matematickou-biologii
English version is available as a course E0410 Fundamentals of Statistics for Scientific Data Using R.
The course is also listed under the following terms Autumn 2022, Autumn 2024.
  • Enrolment Statistics (Autumn 2023, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2023/E7541