Learning outcomes

This course aims to familiarize students with the processes of modeling, rigorous processing, and interpretation of data and results using descriptive statistics and probability methods. Its goal is to provide students with operational skills. That is, to enable them to approach data critically, process it in a coherent and rigorous statistical model, and obtain a precise answer to the object of their investigation.

Content

1) Introduction: defining qualitative and quantitative statistical variables, the usefulness of statistics, the notions of population, sample, individual, and survey.
 
2) The basics of descriptive statistics: defining the concepts of absolute and relative frequency, descriptive tables and graphs.
 
3) Statistics of position, dispersion, shape and concentration (Gini index)
 
4) Bivariate data analysis: distinguishing causality and correlation, statistical independence, marginal and conditional distributions, contingency tables, regression.
 
6) Introduction to probability theory: axiomatics, combinatorial analysis, conditional probability and independence, law of total probabilities and Bayes' theorem.
 
7) Discrete random variables, expectation and variance, parametric distributions, bivariate distributions.

Teaching methods

Lectures and exercise sessions.

Assessment method

The evaluation is carried out in two ways:


1. Formative evaluation (Test):

Written test (multiple-choice questions and/or open-ended questions) halfway through the term.

 

2. Formal assessment (Exam):

Written test (multiple-choice questions and/or open-ended questions); closed book. The exam consists of two parts, one on descriptive statistics, the other on discrete probability theory.

Sources, references and any support material

  • Slides shown during lectures
  • A syllabus of exercises

Language of instruction

French