Course detail
Biostatistics
FCH-MC_BSTAcad. year: 2025/2026
Biostatistics consist of both theoretical and practical education which is aimed on the statistical field of descriptive data analysis, hypothesis testing, probability theory, correlation and regression analysis and multivariate data analysis. Theoretical knowledge from lectures are transferred to practice through practical lectures on computers. Student will become familiar with advanced statistical software such as Statistica. During the exercises, scientific-research problems are solved on model data, but also on current datasets of students, derived from theirs doploma theses.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
Solve all the given test during semester.
At the end of the semester, full-time credit test for 50 points, minimum for success: 25 points.
Presentation of essential results from the statistical evaluation of a given research problem
During the semester, students will process applied tasks (full-time form in the seminars, combined form as correspondence tasks). Final exam will consist of credit test. Furhermore, befor the credit week, the student will be given a specific problem, which will have to be solved by using statistical procedures. Substantial results will be publicly presented to other students during the credit.
Aims
Results of the subject study will be:
a) theoretical knowledge of basic statistical apparatus for evaluation of results in chemical, biological and biochemical field,
b) the ability to apply statistical principles to solve practical problems,
c) skills to process data using advanced software Statistica,
d) gain of overview to apply bio-statistics outputs in other subjects of the discipline, science, research and work life,
e) competence to process the final student's work statistically correctly.
Study aids
Prerequisites and corequisites
Basic literature
Lepš J., Šmilauer P.: Biostatistika. Nakladatelství Jihočeské univerzity, České Budějovice, Česká republika, 2016. (CS)
Meloun M., Militký J.: Statistická analýza experimentálních dat. Academia, Praha 2004. (CS)
Meloun M.: Počítačová analýza vícerozměrných dat, Academia, Praha, Česká republika, 2005. (CS)
Meloun M.: Statistická analýza vícerozměrnýcg dat v příkladech, Karolinium, Praha, Česká republika, 2017. (CS)
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
1. Introduction to biostatistics, basic statistical terms and methods
2. Estimation of the mean value, interval estimation of the mean value, assessment of correctness and conformity of results
3. Data distribution analysis, testing for outliers
4. Parametric and nonparametric hypothesis testing - T-Test, U-Test, ANOVA, MANOVA, Kruskal-wallis ANOVA
5. Correlation and regression analysis of data, application of linear regression in biotechnological and chemical practice, polynomial regression, determination of polynomial degree
6. Multivariate data analysis 1 - Cluster analysis, Principal component analysis
7. Multivariate data analysis 2 - Canonical and linear discrimination analysis
All topics are further practically taught in exercises on PC, using software Statistica and Excel.
Guided consultation in combined form of studies
Teacher / Lecturer