| Course label : | Advanced experimental designs and principal component analysis |
|---|---|
| Teaching departement : | CMA / |
| Teaching manager : | Mister CHRISTEL PIERLOT |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | ENSCL_CI_M9_B2_2_1 - Plans exp. avancés & ACP |
Education team
Teachers : Mister CHRISTEL PIERLOT
External contributors (business, research, secondary education): various temporary teachers
Summary
Lesson (5h): -Advanced experimental design -design matrix with factorswith more than 3 levels - Custom matrix, with constraints, construction by exchange algorithm - Principal Component Analysis (PCA) TD (5h): -In classroom Realization of experiment plans with constraints (optimization of a cocktail with fruit juices by sensory evaluation) -On computer: Use of softwares (experiment design and PCA)
Educational goals
To provide the necessary knowledge to design and analyze : -Advanced experience designs (custom matrix, construction by exchange algorithm) -A classic statistical analysis method: Principal Component Analysis (PCA).
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Computer room exam (2h) using 2 softwares (experiment plans and PCA)
Online resources
Techniques de l'Ingï¿œnieur : Analyse des donnï¿œes ou statistique exploratoire multidimensionnelle, Philippe BESSE, Alain BACCINI, AF620 (2011).
Pedagogy
Distribution of a course manuscript, power point presentation
Sequencing / learning methods
| Number of hours - Lectures : | 5 |
|---|---|
| Number of hours - Tutorial : | 5 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Course of classical experiment designs(screening matrices, factorials, fractional, Simplex, Response surfaces) Experiment design course C7.2.3 Classical statistics course (mean, variance, standard deviation, normal distribution, etc.) Applied statistics and Data processing C5.6.3