Course: Biostatistics

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Course title Biostatistics
Course code KMA/PBST
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech, English
Status of course Compulsory
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Lecturer(s)
  • Picek Jan, prof. RNDr. CSc.
  • Bittner Václav, Mgr. Ph.D.
Course content
Lectures: Introduction to Biostatistics - importance of biostatistics, types of biological data, basic statistical concepts. Descriptive Statistical Methods - basic characteristics of a dataset and their interpretation, graphical representation of data. Theoretical Data Models I - random variable and its distribution, distribution function, density, quantile function, characteristics of a random variable. Theoretical Data Models II - the normal distribution and derived distributions. Foundations of the Mathematical Approach to Statistics I - random sampling, principles of estimation, construction of confidence intervals. Foundations of the Mathematical Approach to Statistics II - basic principles of hypothesis testing: type I and type II errors, test power, t-tests. Goodness-of-Fit Tests. Normality Testing - chi-squared goodness-of-fit test, Shapiro-Wilk test. Analysis of Variance (ANOVA): one-way classification, two-way classification with and without interactions. Nonparametric Approach to Statistical Inference - rank tests. Measuring the Strength of Dependence between Two Quantitative Variables - Pearson and Spearman correlation coefficients, Z-transformation, tests for correlation coefficients. Dependence between Two Quantitative Variables I - linear regression, method of least squares, tests and estimates in regression. Dependence between Two Quantitative Variables II - multiple regression, general regression models, alternative estimators to the least squares method. Analysis of Categorical Data - contingency tables, tests of independence, tests of homogeneity. Classification Methods - cluster analysis, classification trees. Practical Classes: The exercises will practice the material covered in lectures using the Statistica software applied to real biological data.

Learning activities and teaching methods
Lecture, Practicum
Learning outcomes
Prerequisites
unspecified

Assessment methods and criteria
Combined examination

Recommended literature
  • BUDÍKOVÁ, M., KRÁLOVÁ, M., MAROŠ, B. Průvodce základními statistickými metodami. Praha: Grada, 2010. ISBN 978-80-247-3243-5.
  • LEPŠ, J. Biostatistika. České Budějovice: Episteme, 2016. ISBN 978-80-7394-587-9.
  • ZVÁROVÁ, Jana. Základy statistiky pro biomedicínské obory. Biomedicínská statistika I.. Praha: Karolinum, 2016. ISBN 978-80-246-3416-6.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester