Objectives in terms of knowledge (course content) :

• criteria to build the most efficient plan, according to the biological context
• the role of experimental uncertainties in a plan
• a model to build the plan and the plan to build a model
• single and multiple linear regressions in the context of optimization processes
• model validation
• confidence and prediction intervals

• Reminder of the main analytical tools used for experimental design and image processing (jointly presented during the first lectures)
• a unique matrix tool to represent both images and plans: definition and elementary calculations
• projection and dimensionality reduction
• decomposition into simple elements
• solving under-parametrized systems

 

Objectives in terms of acquired skills :
The student will be able to efficiently build basic experimental plans, to perform data analysis in the context of optimization processes, and to understand publications that are using experimental design, specially in the production field.