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Statistical Modelling


  • Nonparametric statistics : empirical distribution function, Kolmogorov test, normality tests
  • Chi-square goodness-of-fit test, Chi-square independence test
  • Linear models: estimation of the parameters, confidence intervals, prediction intervals, Fisher test for a sub-model, model selection and model validation.
  • Generalized linear model : statistical inference, variable selection .
  • Introduction of the experimental design



At the end of this module, the student will have understood and be able to explain (main concepts):


          The use of statistical tests for goodness-of-fit, independence, populations comparisons

  • The characteristics of a linear model and a generalized linear model, and their use for statistical modelling



At the end of this module, the student should be able to:


  • Use  nonparametric testing procedures  to compare two populations
  • Build goodness-of-fit tests for a single distribution or a family of distributions
  • Estimate the parameters in a linear model and a generalized linear model
  • Use statistical tests to validate or invalidate hypotheses on these linear models.
  • Solve a model selection problem
  • Explain the principle of the experimental design
  • Perform a complete statistical analysis on a real data set with a linear model or a generalized linear model

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...