Course detail

Biostatistics

FCH-MCO_BSTAcad. year: 2021/2022

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.

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Meloun M., Militký J.: Statistická analýza experimentálních dat. Academia, Praha 2004. (CS)
Lepš J., Šmilauer P.: Biostatistika. Nakladatelství Jihočeské univerzity, České Budějovice, Česká republika, 2016. (CS)
Meloun M.: Statistická analýza vícerozměrnýcg dat v příkladech, Karolinium, Praha, Česká republika, 2017. (CS)
Meloun M.: Počítačová analýza vícerozměrných dat, Academia, Praha, Česká republika, 2005. (CS)
Ott RL., Longnecker M.: An introduction to statistical methods and data analysis. Cengage learning, Boston, USA, 2016. (EN)
Rosner B.: Fundamentals of biostatistics. Cengage Learning, Boston, USA, 2016. (EN)
Doerffek K., Eckschlager K.: Optimální postup chemické analýzy, SNTL, Praha, Československo, 1988. (CS)

Planned learning activities and teaching methods

Lecture - (2 lessons per 2 weeks): frontal teaching with using demonstration methods (powerpoint).
Seminars (2 lessons per 2 weeks): workshop, individual work, discussion methods.
Electronic Support: The Biostatistics course developed in the Moodle e-learning system, video database of lectures

Assesment methods and criteria linked to learning outcomes

Conditions to obtain of classified credit:
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

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

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 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.

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.

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme NKCP_CHTPO Master's

    specialization BCH , 2. year of study, winter semester, 3 credits, compulsory-optional

  • Programme NPCP_CHTPO Master's

    specialization CHPL , 2. year of study, winter semester, 3 credits, compulsory-optional
    specialization PCH , 2. year of study, winter semester, 3 credits, compulsory-optional

  • Programme NKCP_CHTPO Master's

    specialization CHPL , 2. year of study, winter semester, 3 credits, compulsory-optional
    specialization PCH , 2. year of study, winter semester, 3 credits, compulsory-optional

  • Programme NKCP_CHTP Master's

    branch NKCO_CHTP , 2. year of study, winter semester, 3 credits, compulsory

  • Programme NPCP_CHTP Master's

    branch NPCO_CHTP , 2. year of study, winter semester, 3 credits, compulsory

  • Programme NPCP_CHMA Master's

    specialization CHBL , 2. year of study, winter semester, 3 credits, compulsory-optional
    specialization BF , 2. year of study, winter semester, 3 credits, compulsory-optional

  • Programme NPCP_MA Master's

    branch NPCO_MA , 2. year of study, winter semester, 3 credits, compulsory-optional

Type of course unit

 

Lecture

13 hours, optionally

Teacher / Lecturer

Guided consultation in combined form of studies

26 hours, optionally

Teacher / Lecturer

Exercise

26 hours, compulsory

Teacher / Lecturer

eLearning

eLearning: opened course