Teacher coordinatorAlexandrina Rogozan
Teacher(s)Alexandrina Rogozan, Benoît Gaüzère
TeachingLectures : 21h Exercises : 42h
Web site

Aim and objective

Introduction to the basic statistical methods concerning both descriptive statistics and statistical inference. Reasoning with uncertain data that is subject to random variation.

Outcome learning

  • INSA reference data :
    • Obtenir une description statistique d'un ensemble de données [3P]
    • Diagnostiquer des erreurs dans des données [3P]
  • CNISF reference data :
    • J10C [3P]
    • N40T [2I]
1 - Notion, 2 - Concept, 3 - Application, I - fully, P - incomplete

Course description

Descriptive statistics

Univariate analysis to describe the distribution of a single variable

  • Central tendency (mean, median, and mode),
  • Dispersion (range and quantiles of the data-set, and measures of spread such as the variance and standard deviation),
  • Shape of the distribution (skewness and kurtosis),
  • Graphical or tabular format (histogram and stem-and-leaf).

Bivariate analysis to describe the relationship between pairs of variables

  • Cross-tabulations and contingency tables,
  • Graphical representation via scatterplots,
  • Quantitative measures of dependence (correlation and covariance)
  • Regression analysis to reflect the relationship between variables.

Statistical inference

To make statistical propositions about populations using data provided with random sampling.

  • Characterizing an estimate and the Cramer-Rao theorem
  • Building an estimate (maximum likelihood and moment methods)
  • Building a confidence interval
  • Rejection of a hypothesis with parametric and non parametric testing methods


Some backgrounds in probabilities.


  • Probabilités, analyse des données et statistique - Gilbert Saporta, Technip, 1990
  • Méthodes statistiques, Philippe Tassi, Economica, 1992.


  • Oral exam (40%) : Project presentation
  • Written exam (60%) : Max (Mean (Mid exam, Final exam), Final exam)