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Number of hours
Lectures : 13.0
Tutorials : 4.5
Laboratory works : 15.5
ECTS : 3.0
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.
Contact Jean-Baptiste DURAND
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, supervised classification, linear discriminant analysis, unsupervised classification, K-means. 5. Document and pattern mining on graphs.
Applied Probability 2 (1st year), Statistical Principles and Methods (Semester 2)
Practical exam with R (2 h) and 3 reports on supervised practicals.