Course detail

Statistical Analysis of Data

CESA-SPSTAcad. year: 2025/2026

The course is focused on data statistical analysis including its theoretical (basic principles for statistical estimates and tests) and practical (design of experiments, one- and two-sample tests, correlation and regression analysis, and analysis of variance) aspects. Theory is discussed in direct connection with practical examples provided via commonly used software tools. This course prepares candidates for the sole use of statistical methods in their scientific or routine work.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Marina Filipenská, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Entry knowledge

Knowledge at the secondary school level of mathematics, PC, work with MS Excel, and Matlab are required.

Rules for evaluation and completion of the course

Requirements for completion of a course are elaborated by the lecturer responsible for the course every year;
- 30 points can be obtained for activity in the PC exercises consisting in solving tasks (at least 15 points are required for further examination),
- 70 points can be obtained for the written exam (at least 35 points are required to pass the exam successfully).
The final exam is focused on understanding of statistical data analysis and various applications from area of interest.
Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Generally:
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.

Aims

The goal of the course is to provide the students with knowledge for data acquisition and to present basic methods of data statistical analysis including the computation procedures, the assumptions, and possible use for solving of real tasks from different areas of biological and sport sciences.
Candidates will get knowledge and skills from statistical data analysis area. He will be competent to apply it to solve the real tasks including study design, selection of appropriate statistical method and interpretation of test results.
During written examination, it is verified, whether the student is able to:
- discuss basic terms from statistical data analysis area and their relations
- describe basic methods in this area
- select and apply appropriate tools to solve the task
- interpret obtained results
- graphically present statistical data
- design study including data acquisition and analysis

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

BAŠTINEC, J., FAJMON, B., KOLÁČEK, J.: Pravděpodobnost, statistika a operační výzkum. Skripta VUT, Brno 2014. (CS)
MELOUN, M., MILITKÝ, J.: Statistická analýza experimentálních dat. Academia, Praha 2004, ISBN 80-200-1254-0. (CS)

Recommended reading

BUDÍKOVÁ, M., KRÁLOVÁ, M., MAROŠ, B.: Průvodce základními statistickými metodami. Grada Publishing, Praha 2010, ISBN 978-80-247-3243-5. (CS)
McDONALD, J.H.: Handbook of biological statistics. 3rd ed. Sparky House Publishing, Baltimore, Maryland 2015. (CS)

Classification of course in study plans

  • Programme SPC-STC Bachelor's 2 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Marina Filipenská, Ph.D.

Syllabus

  • 1. Introduction to statistics. Different types of the statistical tasks (parameters estimation, hypothesis testing, prediction, and classification) – differences and practical use
    2. Statistics and probability. Bayes’ theorem. Sensitivity, specificity, predictive values. ROC curve
    3. Continuous, ordinal and nominal data. Population and sample of data. Descriptive characteristics. Grafical representation of data. Detection of outliers
    4. Random variable. Distribution of continuous and descrete variables. Testing of disctribution type. Normal distribution. Transformation of random variable, its goal
    5. Sample parameters estimation. Central limit theorem, confidence interval and its interpretation. Introduction to hypothesis testing. P-value and its interpretation. Type I and type II error, test power, sample size. Biological vs. statistical significance
    6. One-sample tests. Paired and unpaired data. Parametric and non-parametric methods
    7. Two-sample tests. Paired and unpaired data. Parametric and non-parametric methods
    8. Multiple-sample tests I. Analysis of variance (ANOVA). Goals and assumptions. Generalization
    9. Multiple-sample tests II. Paired and unpaired data. Parametric and non-parametric methods
    10. Binary and ordinary data analysis. Contingency table. One-sample binomial test. Multiple-sample tests: Fisher’s exact test, chi-square test for unpaired data, McNemar test for paired data
    11. Correlation analysis. Parametric and serial correlation (covariance, correlation coefficients, coefficients of similarity). Correlation and covariance matrix
    12. Regression analysis. Linear regression. Method of least squares for estimation of regression coefficients. Residual analysis of regression models
    13. Introduction to design of experiments. Estimation of sample size. Randomization technques. Blinding of the study. Short review of experimental plane types

 

 

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Marina Filipenská, Ph.D.

Syllabus

1. Introduction to statistics. Exploration analysis.

2. Exploratory data analysis II.

3. Statistical testing I.

4. Statistical testing II.

5. ANOVA

6. Non-parametric testing methods.

7. Analysis of categorical data.

8. Correlation analysis.

9. Regression analysis.

10. ROC analysis.