Statistics
Information
Teacher coordinator | Alexandrina Rogozan |
Teacher(s) | Alexandrina Rogozan, Benoît Gaüzère |
Language | French |
Credits | 4.5 |
Teaching | Lectures : 21h Exercises : 42h |
Web site | https://moodle.insa-rouen.fr/course/view.php?id=93 |
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]
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
Prerequisites
Some backgrounds in probabilities.
Bibliography
- Probabilités, analyse des données et statistique - Gilbert Saporta, Technip, 1990
- Méthodes statistiques, Philippe Tassi, Economica, 1992.
Assessment
- Oral exam (40%) : Project presentation
- Written exam (60%) : Max (Mean (Mid exam, Final exam), Final exam)