Statistical analysis of data
CESA-SPSTAcad. year: 2019/2020
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.
Learning outcomes of the course unit
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
Knowledge at the secondary school level of mathematics, PC, work with MS Excel, and Matlab are required.
Recommended optional programme components
Recommended or required reading
MELOUN, M., MILITKÝ, J.: Statistická analýza experimentálních dat. Academia, Praha 2004, ISBN 80-200-1254-0. (CS)
BAŠTINEC, J., FAJMON, B., KOLÁČEK, J.: Pravděpodobnost, statistika a operační výzkum. Skripta VUT, Brno 2014. (CS)
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)
Planned learning activities and teaching methods
Teaching methods include lectures and PC exercises. Course is taking advantage of e-learning system. Students have to write two tests during the course.
Assesment methods and criteria linked to learning outcomes
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.
Language of instruction
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
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.
Specification of controlled education, way of implementation and compensation for absences
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).
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.
Classification of course in study plans
- Programme BPC-STC Bachelor's, 3. year of study, winter semester, 5 credits, compulsory
Type of course unit
26 hours, optionally
Teacher / Lecturer
26 hours, compulsory
Teacher / Lecturer
The other activities
13 hours, optionally
Teacher / Lecturer
eLearning: currently opened course