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Probability and inferential statistics


At the end of this module, the student will have understood and be able to explain :
-          The concept of conditional expectation
-          Multidimensional Gaussian variables
-          Markov chains
-          Estimation and hypothesis testing
-          Statistical linear models and model validation
-          Bases of quality management (SPC, DMAIC 6 sigma)  and risk estimation (FMECA)
 The student will be able to :
- Apply the notions that have been introduced in probability to the study of concrete
models (Markov models, random walks, ..)
- Estimate the parameters in a parametric model and to use statistical tests to validate
or invalidate hypotheses.
- Build goodness-of-fit tests for a single distribution or a family of distributions.
- Adjust a linear model for concrete examples.

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...