Bi7541 Data analysis on PC

Faculty of Science
autumn 2017
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: z (credit). Other types of completion: graded credit.
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
RNDr. Denisa Krejčí (seminar tutor)
Mgr. et Mgr. Jiří Kalina, Ph.D. (seminar tutor)
Zbyněk Cincibus (assistant)
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
Mon 18. 9. to Fri 15. 12. Mon 14:00–15:50 D29/347-RCX2
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
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
  • 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.
  • 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.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data.
Language of instruction
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
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Spring 2011, Autumn 2011, Spring 2012, Autumn 2011 - acreditation, spring 2012 - acreditation, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, spring 2018, Autumn 2018, Spring 2019, Autumn 2019, Spring 2020, Autumn 2020.
  • Enrolment Statistics (autumn 2017, recent)
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