Number of hours
- Lectures 10.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works 20.0
- Written tests 2.0
ECTS
ECTS 0.4
Goal(s)
Introduction to statistics and data processing
The aim of the course is to make students understand the need for descriptive and inferential statistics. They must understand the notion of measurement variability.
The course is divided into 3 main parts:
- basic descriptive statistics with statistical summaries by quantities and graphs
- inferential statistics with the classic tests for comparing means (Student's t test and analysis of variance) and comparing distributions (chi-square test)
- descriptive statistics for multidimensional data (principal component analysis and linear discriminant analysis).
This course is mainly given in the form of research projects using Python software and various libraries (Numpy, Pandas and Scipy.stats). During these experiments, students work on real data; they have to carry out their own analyses and interpret the data.
Content(s)
1. General introduction to descriptive and inferential statistics
2. Reminders on random variables and probabilities
3. Theory of sampling and estimation
4. Hypothesis testing
5. Analysis of multidimensional data
5.1 Principal component analysis
5.2 Linear discriminant analysis
The Maths course of the 3rd year, in particular the course on probability and random variables and the course on linear algebra (matrix).
The course exists in the following branches:
- Curriculum - IESE - Semester 8
Course ID : KAIE8M05
Course language(s):
You can find this course among all other courses.
- Probabilités, analyse des données et statistique de G. Saporta aux éditions Technip.
- Howell, D. C. (1998). Méthodes statistique en sciences humaines. Ed. De Boeck Université.
- Introduction à l?inférence statistique: Méthodes d?échantillonnage, estimation, tests d?hypothèses, corrélation linéaire, droite de régression et test du khi-deux avec applications diverses de Gérald Baillargeon. Editeur : Smg (5 novembre 1999).