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Math refresher
Section 1: Fundamentals of probability theory (McColl,Ch.1)
Section 2 : Random variables (McColl,Ch.2)
Section 3 : Probability distributions
Section 4 : Bivariate random variables (McColl,Ch.4)
Section 5 : Random vectors (McColl,Ch.5)
ECUE Data analyses
ECUE's code : IMEDATA
EUR ELMI
Sciences de gestion et du management
Campus Saint Jean d'Angély
Master 1
Semestre impair
Anglais
PRESENTATION
The course is articulated into two parts :
The first part of the course introduces students to the fundamentals of probability theory and random variables.
The second part of the course introduces students to the most commonly utilized reduction methods used for data analysis.
Course's manager(s)
In class
- 20h of lectures
- 10h of directed studies
PREREQUISITES
Before the start of the course, I must ...
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Undergraduate notions of calculus, probability and statistics are required, as well as basic knowledge of R and Stata.
OBJECTIVES
By the end of this course, I should be able to...
- To derive the moments of random variables.
- To derive the joint, conditional and marginal probability density functions.
- To state the definition and recall the properties of multivariate normal distributions.
- To perform reduction techniques for data dealing with continuous, categorical and mixed variables.
CONTENT
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PART A : Probability theory and random variables
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PART B : Reduction methods
Basic reduction methods for continuous, categorical and mixed data :
- Principal component analysis (PCA)
- Exploratory factor analysis (EFA)
- Multiple correspondence analysis (MCA)
Advanced reduction methods :
- Linear discriminant analysis (LDA)
- PCAIV
- Multiple factor analysis (MFA)
Access to complete Syllabus (Authentification required)
Important
This syllabus has no contractual value. Its content is subject to change throughout this year: be aware to the last updates