Number of hours
- Lectures 13.5
- Projects -
- Tutorials 1.5
- Internship -
- Laboratory works 18.0
- Written tests -
ECTS
ECTS 3.0
Goal(s)
The aim of this course is to present the statistical approaches for analysing multivariate data. The information age has resulted in masses of multivariate data in many different field: finance, marketing, economy, biology, environmental sciences,...The theoretical and practical aspects of multivariate data analysis are given equal importance. This balance is achieved through practicals involving actual data analysis using the R software.
Responsible(s)
Jean-Baptiste DURAND
Content(s)
1. Multiple linear regression. Least squares, Gaussian linear model, test of linear hypotheses
2 One-way and two-way analysis of variance.
3. Principal Components Analysis (PCA).
4. Classification: logistic / multinomial regression, linear discriminant analysis and Naive Bayes
Applied Probability 2 (1st year), Statistical Principles and Methods (Semester 2)
Test
Practical exam with R (3 h) and reports on supervised practicals.
N1=1/2E1+1/2P
N2=E2
Calendar
The course exists in the following branches:
- Curriculum - Math. Modelling, Image & Simulation - Semester 8
- Curriculum - Information Systems Engineering - Semester 8
- Curriculum - Financial Engineering - Semester 8
Additional Information
Course ID : 4MMASM7
Course language(s): 
The course is attached to the following structures:
You can find this course among all other courses.
Bibliography
CM BISHOP (2006) Pattern recognition and machine Learning. Springer
http://research.microsoft.com/en-us/um/people/cmbishop/prml/
C. CHATFIELD and AJ COLLINS (1980) Introduction to multivariate analysis. Science paperbacks
T HASTIE, R TIBSHIRANI, and J FRIEDMAN (2009). The Elements of Statistical Learning, 2d ed, Springer. http://www-stat.stanford.edu/~tibs/ElemStatLearn/
G. SAPORTA : Probabilités, statistique et analyse des données, Technip, 2006.