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.

Learning outcomes

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

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

Evaluation methods

The evaluation is carried out by means of a written exam consisting of a part A with MCQ questions (with 6 suggested answers and one expected correct answer) and a part B with open questions. In the final grade, part A is worth 10/20 and part B is worth 10/20.

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