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Informatique et Mathématiques appliquées
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Multivariate statistical analysis - 4MMASM7

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  • Number of hours

    • Lectures : 13.5
    • Tutorials : 1.5
    • Laboratory works : 18.0
    ECTS : 3.0

Goals

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

Content

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



Prerequisites

Applied Probability 2 (1st year), Statistical Principles and Methods (Semester 2)

Tests

Practical exam with R (3 h) and reports on supervised practicals.



N1=1/2E1+1/2P
N2=E2

Additional Information

Curriculum->Financial Engineering->Semester 8

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.

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Date of update July 11, 2018

Grenoble INP Institut d'ingénierie Univ. Grenoble Alpes