| Course label : | BME102 Biomedical Statistics |
|---|---|
| Teaching departement : | MSO / Structures, Mechanisms and Construction |
| Teaching manager : | Mister OLIVIER MAYEUR |
| Education language : | |
| Potential ects : | 2 |
| Results grid : | |
| Code and label (hp) : | MR_BME_S1_MSO_BST - BME102 Biomedical Statistics |
Education team
Teachers : Mister OLIVIER MAYEUR
External contributors (business, research, secondary education): various temporary teachers
Summary
Fundamentals statistical approaches with biomedical applications. This module aims to provide an introduction to statistical methods commonly used in biomedical research. Topics covered include descriptive statistics, probability distributions, hypothesis testing, correlation and regression and study design. It covers topics in data presentation sampling, significance tests and clinical trials. The module will also cover the use of statistical software packages such as SPSS and R. Chapter 1: Introduction to Biomedical Statistics, Basic statistical concepts and terminology, Types of data and measurement scales, Sampling techniques Chapter 2: Descriptive Statistics, Measures of central tendency and variability, Frequency distributions and graphical displays Chapter 3: Probability, Normal and Binomial Distributions Chapter 4: Hypothesis Testing, Null and alternative hypotheses, errors and T-tests Chapter 5: Model selection and interpretation for BME application
Educational goals
Upon completion of this module, students should be able to: - Understand basic statistical concepts and terminology used in biomedical research. - Apply appropriate statistical techniques for different types of data in BME. - Interpret and communicate the results of statistical analyses. - Critically evaluate the statistical methods used in published biomedical research studies. - Use statistical software packages to analyze biomedical data.
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments:
Online resources
Free statistical software for data analysis, Online Database,
Pedagogy
The acquisition of knowledge will follow a red thread that will allow the student to assimilate all the different steps of the methodology. Theoretical methods will be confronted with databased in order to verify their adequacy. Following this knowledge acquisition, students will have to solve a problem in project mode.
Sequencing / learning methods
| Number of hours - Lectures : | 0 |
|---|---|
| Number of hours - Tutorial : | 0 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 20 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Basic knowledge in statistic and mathematics