Statistics

Teacher(s)

Bugli Céline;

Language

French

Prerequisites

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 course comprises theoretical lectures and exercise sessions: 1. Brief recall on one- and two-dimensional descriptive statistics 2. Inferential statistics: populations and samples, probabilities, variables, theoretical distributions, confidence intervals (means, variance, proportion), hypothesis testing based on sample means (Student t-test, analysis of variance, analysis of covariance, multiple comparisons), proportions (chi square, phi, contingency), correlations/regressions (significance, comparison, linearity), adjustment tests (chi square, KS), non-parametric tests (comparison of independant and dependant groups). 3. Application to capacity tests: classification of tests, quality of tests, validity and reproducibility.

Aims

At the end of this learning unit, the student is able to :
1	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.

Content

This course includes lectures and exercises. It contains a brief overview of the concepts of one- and two-dimensional descriptive statistics as seen in thecourse of 11 BAC "Comprehension et traitement de données". It focuses mainly on the basic issues of statistical inference: population and sample probabilities, random variables, distribution theory, confidence intervals (mean, proportion), hypothesis tests related to means (student t, analysis of variance), proportions (1 or 2 proportion test, chi-square test), correlation/regression study (regression straight line calculation, slope test), adjustment tests (chi-square, Shapiro-Wilks), some non-parametric tests (comparison of independent and dependent groups), repeated measurement ANOVA.

Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Lectures and supervised exercises. The modalities foreseen will evolve according to the health situation.

Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Written

Other information

Pre-requisite: Comprehension et traitement de données (LIEPR1003)
Evaluation: Written
Support : power point
Supervision: Titular professors and teaching assistants
Others: Exercise sessions + solutions to problems

Online resources

Site Moodle:https://moodleucl.uclouvain.be/course/view.php?id=9254

Faculty or entity

FSM