UE Biostatistics, bioinformatics and modeling (part I)

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Degrees incorporating this pedagocial element :


Overview of the principal techniques of statistical data treatment, with an emphasis on practical skills and the use of the statistical software R.

Targeted skills

At the end of the course, the students should be able to:

  1. load, explore and summarize graphically a dataset;
  2. identify a probabilistic model from a practical situation;
  3. compute confidence interval estimates for proportions, means and variances;
  4. formulate hypotheses, compute tests statistics, interpret p-values and make practical decisions for the standard parametric and non-parametric tests for paired or independent samples;
  5. perform analyses of variance (anovas) with one or two factors and understand their outcomes;
  6. adjust simple and multiple linear models, compute predicted values, test goodness-of-fit by an anova;
  7. apply principal component analysis and interpret results.