• Semester 4, position 4

The objective of this course is to teach students the basic statistical methods applied in biomedical research and practice. Mastering the selection of meaningful datasets and its analysis allow students to reach conclusions relevant for the research, development and applications in biomedical engineering problems.

Upon successful completion of this course, students shall be able to: •Use R software for statistical data processing •Create and process large sets of statistical data •Identify the limitations of datasets and confidence intervals •Model and explore the relationships between variables •Select and perform statistical analysis according to the problem they have to solve •Adequately interpret results and perform statistical analysis to predict the behavior of observed biomedical systems in the future

Theoretical lectures: Introduction to probability theory. Types of variables and graphical representation of the data. Basics of descriptive statistics. Statistical software and introduction to R software and language of statistical computing. Statistical hypothesis, testing and types of tests. Univariate and multivariate analysis of variance. Cluster analysis. Hierarchical cluster analysis. K-means method. Reduction of dimensionality. Linear regression - univariate and multivariate. Model calibration, prediction of new observations, model validation. Types of validation. Nonlinear models. Basic machine learning algorithms.

Practical work during the course includes assignments that follow theoretical lectures and are solved during practical work in R software package.

None.

Auditory room equipped with computer, video beam, internet connection and accompanied inventory. Computer room with software installed. 1. Written material from lectures (handouts) 2. R Software & R Studio

Total assigned hours: 75

New material: 20
Elaboration and examples (recapitulation): 10

Auditory exercises: 0
Laboratory exercises: 0
Calculation tasks: 30
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 0
Review and grading of the project: 0
Test: 5
Test: 5
Final exam: 5

Activity during lectures: 5
Test/test: 60
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Final exam: 35
Requirement for taking the exam (required number of points): 35

J. Adler, R in a Nutshell, Second Edition, O’Reilly Media, Inc., USA 2012, ISBN: 9781449312084; M. Gardener, Beginning R The Statistical Programming Language, John Wiley& Sons, Inc., USA 2012, ISBN: 9781118164303; T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Second Edition, Springer New York, NY, 2009, ISBN: 978-0-387-84857-0; T. C. Urdan, Statistics in Plain English, Third Edition, Taylor and Francis Group, LLC, USA, 2011, ISBN: 9780415872911; Ј. Gareth, D. Witten, Т. Hastie, An Introduction to Statistical Learning: With Applications in R, Springer, 2017, ISBN: 978-1-4614-7137-0