Course detail

Biostatistics and data processing

FCH-MC_BZDAcad. year: 2024/2025

Biostatistics consists of both theoretical and practical education which is aimed at the statistical field of descriptive data analysis, hypothesis testing, probability theory, correlation and regression analysis and multivariate data analysis. Theoretical knowledge from lectures is transferred to practice through practical lectures on computers. Students will become familiar with advanced statistical software such as Statistica. During the exercises, scientific research problems are solved on model data, but also current datasets of students derived from their diploma theses.

Language of instruction

Czech

Number of ECTS credits

2

Mode of study

Not applicable.

Guarantor

prof. Ing. Adriána Kovalčík, Ph.D.

Department

Institute of Food Science and Biotechnology (ÚCHPBT)

Entry knowledge

Good knowledge of mathematics. Basic skills of work in excel. Ability to analyze and process chemical and biological results.

Rules for evaluation and completion of the course

Conditions to obtain classified credit:
Solve all the given test during the 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). The final exam will consist of a credit test. Furthermore, before 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

Students will get acquainted with basic and advanced statistical methods and their application in the evaluation of biological and chemical-analytical data in the course Biostatistics. The main mission of the course is to pass on knowledge about the method of elaboration of scientific studies with emphasis on the objective presentation of results and conclusions.
Results of the subject study will be:

a) theoretical knowledge of basic statistical apparatus for evaluation of results in the 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

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Lepš J., Šmilauer P.: Biostatistika. Nakladatelství Jihočeské univerzity, České Budějovice, Česká republika, 2016. (EN)
Meloun M., Militký J.: Statistická analýza experimentálních dat. Academia, Praha 2004. (CS)

Recommended reading

Meloun M.: Počítačová analýza vícerozměrných dat, Academia, Praha, Česká republika, 2005. (EN)
Meloun M.: Statistická analýza vícerozměrných dat v příkladech, Karolinium, Praha, Česká republika, 2017. (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme NPCP_CHPL Master's 2 year of study, winter semester, compulsory-optional
  • Programme NKCP_CHPL Master's 2 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

13 hod., optionally

Teacher / Lecturer

Ing. Jaromír Pořízka, Ph.D.

Syllabus

Biostatistics is taught by the combination of theoretical and practical lectures

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 the 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

26 hod., optionally

Teacher / Lecturer

prof. Ing. Adriána Kovalčík, Ph.D.

Exercise

13 hod., compulsory

Teacher / Lecturer

prof. Ing. Adriána Kovalčík, Ph.D.

Elearning

eLearning: currently opened course