Level of study
BAC +5
ECTS
2 credits
Component
Faculty of Science
Description
An experimental design is the ordered sequence of trials of an experiment whose purpose is to test the validity of a hypothesis by reproducing a phenomenon and varying one or more parameters. Each test produces a data and all the data produced during an experiment must be analyzed by rigorous methods to validate or not the hypothesis. This experimental approach makes it possible to acquire new knowledge by confirming a model with a good economy of means (the smallest possible number of tests, for example).
Starting from a simple problem, the module develops methodological and statistical tools that allow to support more and more complex hypotheses in the most optimal way possible. The implementation of these methodologies is done via the statistical language R.
Hourly volumes* :
CM : 15h
Practical work : 5h
Objectives
To provide students with the necessary skills to understand the main concepts of experimental design and the use of inferential statistical tools for the design and analysis of experimental designs.
At the end of this module, students will be able to choose an experimental design adapted to their problem and analyze the results in a complete, rigorous and intellectually mastered way.
Necessary pre-requisites
HAC712X: Chemometrics, statistical data analysis, experimental design
Knowledge control
CC-I
Syllabus
Introduction to the design of experiments: the Hotelling weight problem. Reminder of the notions of response surface, experimental error, modeling, confidence interval and hypothesis testing
Complete experimental designs : presentation. 2p factorial designs, p = 1, 2, ... Multi-level factorial designs : K1* K2 *...* Kp. Notions of interaction and synergy. Statistical analysis: analysis of variance, validation of assumptions.
Fractional experimental designs: presentations, motivations, limitations. Factorial designs 2p-k factorial designs, complete and incomplete block designs, Latin squares, Greco-Latin squares, Youde squares. Statistical analysis: analysis of variance, validation of assumptions.
Response surface: first and second degree models. Estimation and inference. Star designs, D-optimality and D-optimal designs. Mixing designs.
Additional information
Administrative contact(s):
Secretariat Master Chemistry
https://master-chimie.edu.umontpellier.fr/