Statistical data processing

Teacher(s)

Pircalabelu Eugen;

Language

French

Prerequisites

Mathematical notions.

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.

Main themes

The objective of this course is to give a basic knowledge in the statistical data processing related with the biomedical domain. The course also deals with how computer software, in particular JMP (SAS) can be used to present and analyze data.
The course comprises theoretical lectures and exercise sessions: One- and two-dimensional descriptive statistics. Inferential statistics: populations and samples, probabilities, variables, theoretical distributions, confidence intervals (means, variance, proportion), hypothesis testing based on sample means (Student t-test) and proportions.

Learning outcomes

At the end of this learning unit, the student is able to :
1	This course is designed to introduce the students to the statistical and methodological issues applied to problems in the biomedical sciences and to avoid the common pitfalls in data analysis. At the end of the course the successful student will be able to use the techniques of inferential statistics within the framework of his/her research. The course focuses on the most frequently used statistical methods. The underlying mathematical developments are limited to a strict minimum and replaced by intuitive reasoning and concrete examples, especially via practical exercise sessions. The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled 'Programmes/courses offering this Teaching Unit'.

Content

This course includes lectures and exercises with the JMP software. It contains a summary of the concepts of univariate and bivariate descriptive statistics: produce useful graphical and numerical summaries like mean, median, standard deviation, variance, confidence interval.  It focuses mainly on the basic issues of statistical inference: population and sample, probabilities, random variables, distribution functions, type 1 and type 2 errors, confidence intervals (mean, variance and proportion) and hypothesis tests related to the mean (Student's t) and proportions.

Teaching methods

The class consists of lectures (15h) and exercises sessions (15h).
The classes and the TP are intended to be face to face.
Teaching language: French.

Evaluation methods

Face-to-face, 3-hour exam, closed book with the possibility of using a pocket calculator and a formulary (one double-sided handwritten page; no photocopy). The exam consists of 2 parts: a theoretical part without JMP (for 6/20 of the final grade of the course) and another applied part, on the computer using the JMP software (for 14/20 of the final grade of the course).
The exam is used to test the students' knowledge in terms of general understanding of the course, to answer specific questions in practice, to make calculations, to use the JMP software to do statistical analysis, etc. Students will be questioned on the entire content of the course.
The exact evaluation methods could be adapted according to the constraints linked to the sanitary conditions in force at the time of the exam sessions.

Online resources

Site Moodle du cours : WFARM1247 - Traitement statistique des données.
https://moodleucl.uclouvain.be/course/view.php?id=9327

Bibliography

• Triola, M.M., Triola, M.F. and Roy, J. (2018) Biostatistics for the biological and health sciences, Pearson Education, 2nd edition.
• Rosner B. (2010) Fundamentals of Biostatistics. Duxbury, 7th edition.
• Zar, J.H. (2010) Biostatistical analysis, Pearson Education, 5th edition.
• Forthofer, R.N., Lee, E.S., and Hernandez M. (2007) Biostatistics. A guide to Design, Analysis and Discovery, Elsevier, 2nd edition.
• Senn, S. (2007) Statistical Issues in Drug Development, Wiley, 2nd edition.

Teaching materials

• documents (slides) sur moodle à consulter

Faculty or entity

FARM